diff --git a/Assignment 1/Assignment1_AryaKishor/AryaKishor_Assignment1_Matplotlib.ipynb b/Assignment 1/Assignment1_AryaKishor/AryaKishor_Assignment1_Matplotlib.ipynb
new file mode 100644
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+++ b/Assignment 1/Assignment1_AryaKishor/AryaKishor_Assignment1_Matplotlib.ipynb	
@@ -0,0 +1,561 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_sfWZU_eST2O"
+      },
+      "source": [
+        "**NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP.**\n",
+        "\n",
+        "# Exercises\n",
+        "\n",
+        "Follow the instructions to recreate the plots using this data:\n",
+        "\n",
+        "## Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "l0eZ10dLST2X"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "x = np.arange(0,100)\n",
+        "y = x*2\n",
+        "z = x**2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "veY4-lsGST2c"
+      },
+      "source": [
+        "**Import matplotlib.pyplot as plt**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tAOmEcNKST2e"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5j_39gsVST2f"
+      },
+      "source": [
+        "## Exercise 1\n",
+        "\n",
+        "**Follow along with these steps**\n",
+        "* Create a figure object called fig using plt.figure()\n",
+        "* Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax.\n",
+        "* Plot (x,y) on that axes and set the labels and titles to match the plot below:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "03Ts4r5SST2g",
+        "outputId": "f7cff796-c07f-44b8-e0e6-fa6348746189",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 669
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.axis.YTick at 0x7ddd4deab080>,\n",
+              " <matplotlib.axis.YTick at 0x7ddd589ba0f0>,\n",
+              " <matplotlib.axis.YTick at 0x7ddd4dea9e20>,\n",
+              " <matplotlib.axis.YTick at 0x7ddd4dee2e10>,\n",
+              " <matplotlib.axis.YTick at 0x7ddd4dee1ee0>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig= plt.figure()\n",
+        "ax = fig.add_axes([0, 0, 1, 1])\n",
+        "ax.plot(x,y, color='b')\n",
+        "ax.set_xlabel(\"x\")\n",
+        "ax.set_ylabel(\"y\")\n",
+        "ax.set_title(\"title\")\n",
+        "ax.set_yticks([0,50,100,150,200])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I8mFrIGwST2i"
+      },
+      "source": [
+        "## Exercise 2\n",
+        "**Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D4nUfU26ST2k",
+        "outputId": "02bae384-4dd2-4539-eb2e-365633810ecb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 650
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.axis.YTick at 0x7ddd582e3b30>,\n",
+              " <matplotlib.axis.YTick at 0x7ddd583113d0>,\n",
+              " <matplotlib.axis.YTick at 0x7ddd582e1e20>,\n",
+              " <matplotlib.axis.YTick at 0x7ddd58354620>,\n",
+              " <matplotlib.axis.YTick at 0x7ddd5833dee0>,\n",
+              " <matplotlib.axis.YTick at 0x7ddd58355070>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig2 = plt.figure()\n",
+        "\n",
+        "ax1 = fig2.add_axes([0, 0, 1, 1])\n",
+        "ax2 = fig2.add_axes([0.2, 0.5, 0.2, 0.2])\n",
+        "ax2.set_xticks([0.0,0.2,0.4,0.6,0.8,1.0])\n",
+        "ax2.set_yticks([0.0,0.2,0.4,0.6,0.8,1.0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SK8p_A2NST2l"
+      },
+      "source": [
+        "**Now plot (x,y) on both axes. And call your figure object to show it.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig2 = plt.figure()\n",
+        "\n",
+        "ax1 = fig2.add_axes([0, 0, 1, 1])\n",
+        "ax2 = fig2.add_axes([0.2, 0.5, 0.2, 0.2])\n",
+        "ax1.plot(x, y,color='r')\n",
+        "ax2.plot(x, y,color='r')\n",
+        "ax2.set_xticks([0,20,40,60,80,100])\n",
+        "ax2.set_yticks([0,50,100,150,200])\n",
+        "ax1.set_xticks([0,20,40,60,80,100])\n",
+        "ax1.set_yticks([0,50,100,150,200])\n",
+        "plt.show"
+      ],
+      "metadata": {
+        "id": "m_2gVMryST2m",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 710
+        },
+        "outputId": "200944f0-b6ad-4df5-bfd3-941e8ffd039c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<function matplotlib.pyplot.show(close=None, block=None)>"
+            ],
+            "text/html": [
+              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
+              "      pre.function-repr-contents {\n",
+              "        overflow-x: auto;\n",
+              "        padding: 8px 12px;\n",
+              "        max-height: 500px;\n",
+              "      }\n",
+              "\n",
+              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
+              "        cursor: pointer;\n",
+              "        max-height: 100px;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <pre style=\"white-space: initial; background:\n",
+              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
+              "         border-bottom: 1px solid var(--colab-border-color);\"><b>matplotlib.pyplot.show</b><br/>def show(*args, **kwargs) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.12/dist-packages/matplotlib/pyplot.py</a>Display all open figures.\n",
+              "\n",
+              "Parameters\n",
+              "----------\n",
+              "block : bool, optional\n",
+              "    Whether to wait for all figures to be closed before returning.\n",
+              "\n",
+              "    If `True` block and run the GUI main loop until all figure windows\n",
+              "    are closed.\n",
+              "\n",
+              "    If `False` ensure that all figure windows are displayed and return\n",
+              "    immediately.  In this case, you are responsible for ensuring\n",
+              "    that the event loop is running to have responsive figures.\n",
+              "\n",
+              "    Defaults to True in non-interactive mode and to False in interactive\n",
+              "    mode (see `.pyplot.isinteractive`).\n",
+              "\n",
+              "See Also\n",
+              "--------\n",
+              "ion : Enable interactive mode, which shows / updates the figure after\n",
+              "      every plotting command, so that calling ``show()`` is not necessary.\n",
+              "ioff : Disable interactive mode.\n",
+              "savefig : Save the figure to an image file instead of showing it on screen.\n",
+              "\n",
+              "Notes\n",
+              "-----\n",
+              "**Saving figures to file and showing a window at the same time**\n",
+              "\n",
+              "If you want an image file as well as a user interface window, use\n",
+              "`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n",
+              "``show()`` the figure is closed and thus unregistered from pyplot. Calling\n",
+              "`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n",
+              "limitation of command order does not apply if the show is non-blocking or\n",
+              "if you keep a reference to the figure and use `.Figure.savefig`.\n",
+              "\n",
+              "**Auto-show in jupyter notebooks**\n",
+              "\n",
+              "The jupyter backends (activated via ``%matplotlib inline``,\n",
+              "``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n",
+              "the end of every cell by default. Thus, you usually don&#x27;t have to call it\n",
+              "explicitly there.</pre>\n",
+              "      <script>\n",
+              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
+              "        for (const element of document.querySelectorAll('.filepath')) {\n",
+              "          element.style.display = 'block'\n",
+              "          element.onclick = (event) => {\n",
+              "            event.preventDefault();\n",
+              "            event.stopPropagation();\n",
+              "            google.colab.files.view(element.textContent, 569);\n",
+              "          };\n",
+              "        }\n",
+              "      }\n",
+              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
+              "        element.onclick = (event) => {\n",
+              "          event.preventDefault();\n",
+              "          event.stopPropagation();\n",
+              "          element.classList.toggle('function-repr-contents-collapsed');\n",
+              "        };\n",
+              "      }\n",
+              "      </script>\n",
+              "      </div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hYaQZfbFST2n"
+      },
+      "source": [
+        "## Exercise 3\n",
+        "\n",
+        "**Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BuiYx0xbST2o",
+        "outputId": "a5d7d7f1-ebf9-49f2-e54a-65a2b0a84c35",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 546
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig3=plt.figure()\n",
+        "a1=fig3.add_axes([0,0,1,1])\n",
+        "a2=fig3.add_axes([0.2,0.5,0.4,0.4])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SeRSiZDGST2o"
+      },
+      "source": [
+        "**Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jhy7t5AKST2p",
+        "outputId": "b2338630-5de7-464f-f48e-9c3aff2a7621",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 565
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig3=plt.figure()\n",
+        "a1=fig3.add_axes([0,0,1,1])\n",
+        "a2=fig3.add_axes([0.2,0.5,0.4,0.4])\n",
+        "a1.plot(x,z,color='b')\n",
+        "a2.plot(x,y,color='b')\n",
+        "a2.set_title(\"zoom\")\n",
+        "a1.set_xlabel(\"X\")\n",
+        "a1.set_ylabel(\"Z\")\n",
+        "a2.set_xlabel(\"X\")\n",
+        "a2.set_ylabel(\"Y\")\n",
+        "a2.set_xlim(20,22)\n",
+        "a2.set_ylim(30,50)\n",
+        "a1.set_ylim(0,10000)\n",
+        "a1.set_xlim(0,100)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TM6XOILJST2q"
+      },
+      "source": [
+        "## Exercise 4\n",
+        "\n",
+        "**Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "osk0Jco2ST2r",
+        "outputId": "7c1ea5a3-6625-495a-cb95-1b854a7d8ddd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig4, axes= plt.subplots(nrows=1, ncols=2, figsize=(8,4))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QWwItNZVST2r"
+      },
+      "source": [
+        "**Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZoEMYyLOST2r",
+        "outputId": "b23346a5-d1b4-4edf-dceb-7eb2fce6af98",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 385
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7ddd4d0d3a40>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig4, axes= plt.subplots(nrows=1, ncols=2, figsize=(8,4))\n",
+        "axes[0].plot(x,y,linewidth=2,linestyle=':',color='b')\n",
+        "axes[1].plot(x,z,linewidth=3,linestyle='-', color='g')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hV08IzIvST2s"
+      },
+      "source": [
+        "**See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0pq5vAFZST2s",
+        "outputId": "b83d9146-b94b-4b7f-837a-37f08f2e4448",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 350
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7ddd6b5cc7d0>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 4000x1000 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig4, axes= plt.subplots(nrows=1, ncols=2, figsize=(40,10))\n",
+        "axes[0].plot(x,y,linewidth=2,linestyle='-',color='b')\n",
+        "axes[1].plot(x,z,linewidth=3,linestyle='--', color='r')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-ONbbrWEST2t"
+      },
+      "source": [
+        "# Great Job!"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.12"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Assignment 1/Assignment1_AryaKishor/AryaKishor_Assignment1_Numpy_.ipynb b/Assignment 1/Assignment1_AryaKishor/AryaKishor_Assignment1_Numpy_.ipynb
new file mode 100644
index 00000000..6be09c24
--- /dev/null
+++ b/Assignment 1/Assignment1_AryaKishor/AryaKishor_Assignment1_Numpy_.ipynb	
@@ -0,0 +1,667 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HfJB9JnS9xlK"
+      },
+      "source": [
+        "# NumPy Exercises\n",
+        "\n",
+        "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u9POEVg29xlM"
+      },
+      "source": [
+        "#### Import NumPy as np"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 81,
+      "metadata": {
+        "id": "yyS-PuO_9xlM"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ehlpKUPc9xlM"
+      },
+      "source": [
+        "#### Create an array of 10 zeros"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "arr = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0],dtype=float)\n",
+        "arr"
+      ],
+      "metadata": {
+        "id": "aEQkK-Dw9xlN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1966ad05-c1d5-465d-f346-ff18506a25e5"
+      },
+      "execution_count": 82,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 82
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6QHp05Ei9xlN"
+      },
+      "source": [
+        "#### Create an array of 10 ones"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 83,
+      "metadata": {
+        "scrolled": true,
+        "id": "ebe9xrC29xlN",
+        "outputId": "5ad38bc0-2385-410c-b04e-644ee160e036",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 83
+        }
+      ],
+      "source": [
+        "arr = np.array([1,1,1,1,1,1,1,1,1,1],dtype=float)\n",
+        "arr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ziS-l3xp9xlO"
+      },
+      "source": [
+        "#### Create an array of 10 fives"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 84,
+      "metadata": {
+        "id": "fFC7bqLM9xlO",
+        "outputId": "7a1cbe4b-95cb-4175-90fb-c7d0a1f73471",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 84
+        }
+      ],
+      "source": [
+        "arr= np.array([5]*10,dtype=float)\n",
+        "arr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kBugMXlC9xlO"
+      },
+      "source": [
+        "#### Create an array of the integers from 10 to 50"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {
+        "id": "JsO6qS9R9xlO",
+        "outputId": "d90fb589-1ae7-4681-b47f-f1abb4d5870e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
+              "       27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
+              "       44, 45, 46, 47, 48, 49, 50])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 85
+        }
+      ],
+      "source": [
+        "arr=np.arange(10,51)\n",
+        "arr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dMw4V2L79xlO"
+      },
+      "source": [
+        "#### Create an array of all the even integers from 10 to 50"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 86,
+      "metadata": {
+        "id": "4lK-5SQV9xlO",
+        "outputId": "b6f71a43-fd73-49f4-cea3-f0617b5916c6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
+              "       44, 46, 48, 50])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 86
+        }
+      ],
+      "source": [
+        "arr=np.arange(10,51,2)\n",
+        "arr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g__F0rB39xlP"
+      },
+      "source": [
+        "#### Create a 3x3 matrix with values ranging from 0 to 8"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 87,
+      "metadata": {
+        "id": "MmVXCn0K9xlP",
+        "outputId": "ee51db38-f8b3-4b0a-e555-9b15dc201c07",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0, 1, 2],\n",
+              "       [3, 4, 5],\n",
+              "       [6, 7, 8]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 87
+        }
+      ],
+      "source": [
+        "arr= np.arange(9).reshape(3,3)\n",
+        "arr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4YfT0aLo9xlP"
+      },
+      "source": [
+        "#### Create a 3x3 identity matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 88,
+      "metadata": {
+        "id": "UHa42UpQ9xlP",
+        "outputId": "62e4f54b-4e6d-4a26-82d5-6a14177c1d49",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[1., 0., 0.],\n",
+              "       [0., 1., 0.],\n",
+              "       [0., 0., 1.]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 88
+        }
+      ],
+      "source": [
+        "arr=np.array([1,0,0,0,1,0,0,0,1],dtype=float).reshape(3,3)\n",
+        "arr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bfwDbjhI9xlP"
+      },
+      "source": [
+        "#### Use NumPy to generate a random number between 0 and 1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 89,
+      "metadata": {
+        "id": "Z0OroZxW9xlP",
+        "outputId": "ce51f131-bbde-4a0b-c4bb-bdb1cadcbf51",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.1032329270205824"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 89
+        }
+      ],
+      "source": [
+        "np.random.rand()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rLW0Jjzp9xlP"
+      },
+      "source": [
+        "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 90,
+      "metadata": {
+        "id": "szluy14n9xlP",
+        "outputId": "e135434b-d86c-4dd2-bee9-1a4570ea1d21",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ 0.69161571,  1.9581831 ,  1.73926461, -0.47098155, -0.95261901,\n",
+              "       -0.43739865, -1.28750383,  0.75042703,  0.01295087,  0.79151157,\n",
+              "        0.26804685,  1.93969358, -0.35493288, -0.10770177,  0.35194899,\n",
+              "        1.102204  , -1.00032705,  0.9718835 ,  1.0263854 , -1.04624079,\n",
+              "        0.21780494,  0.07559835,  0.65915801, -0.52256627, -0.09605253])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 90
+        }
+      ],
+      "source": [
+        "np.random.randn(25)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_GhI8LYn9xlP"
+      },
+      "source": [
+        "#### Create the following matrix:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 91,
+      "metadata": {
+        "id": "wS1ZBddV9xlP",
+        "outputId": "11fccdc1-7806-4a6e-a9e5-676b44a5074f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
+              "       [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
+              "       [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
+              "       [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
+              "       [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
+              "       [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
+              "       [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
+              "       [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
+              "       [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
+              "       [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.  ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 91
+        }
+      ],
+      "source": [
+        "np.arange(0.01,1.01,0.01).reshape(10,10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3OqA-QtL9xlQ"
+      },
+      "source": [
+        "#### Create an array of 20 linearly spaced points between 0 and 1:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 92,
+      "metadata": {
+        "id": "FNXTugQ29xlQ",
+        "outputId": "b6fe1598-6005-40e9-b784-eb7f2c2e2ea1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
+              "       0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
+              "       0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
+              "       0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 92
+        }
+      ],
+      "source": [
+        "np.linspace(0,1,20)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "elx0EaxE9xlQ"
+      },
+      "source": [
+        "## Numpy Indexing and Selection\n",
+        "\n",
+        "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2Tbm9kVf9xlQ"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 93,
+      "metadata": {
+        "id": "Ft8P8e249xlQ",
+        "outputId": "125ca25e-61e5-4ce6-d0b5-af129ee85884",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 1,  2,  3,  4,  5],\n",
+              "       [ 6,  7,  8,  9, 10],\n",
+              "       [11, 12, 13, 14, 15],\n",
+              "       [16, 17, 18, 19, 20],\n",
+              "       [21, 22, 23, 24, 25]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 93
+        }
+      ],
+      "source": [
+        "np.arange(1,26).reshape(5,5)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 94,
+      "metadata": {
+        "id": "WrcFkpGL9xlQ",
+        "outputId": "e23adff1-f925-4cae-d376-578f640dbddb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[12, 13, 14, 15],\n",
+              "       [17, 18, 19, 20],\n",
+              "       [22, 23, 24, 25]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 94
+        }
+      ],
+      "source": [
+        "arr=np.arange(12,26)\n",
+        "arr2= np.delete(arr,(4,9)).reshape(3,4)\n",
+        "arr2\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 95,
+      "metadata": {
+        "id": "rnUSntEa9xlQ",
+        "outputId": "41f86caf-edbf-4599-b841-cad284030b20",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "20\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(20)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 96,
+      "metadata": {
+        "id": "3DMBC_wp9xlQ",
+        "outputId": "edcba7dd-a9e9-4d1f-b410-06598a171457",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 2],\n",
+              "       [ 7],\n",
+              "       [12]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 96
+        }
+      ],
+      "source": [
+        "np.array([2,7,12]).reshape(3,1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 97,
+      "metadata": {
+        "id": "pGjD4HUK9xlQ",
+        "outputId": "2211517f-00f6-4b25-cbf8-e9ec1c2b2be5",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([21, 22, 23, 24, 25])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 97
+        }
+      ],
+      "source": [
+        "np.arange(21,26)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 98,
+      "metadata": {
+        "id": "dqsdpxUo9xlR",
+        "outputId": "3d432dbc-58c2-4f29-be7f-2fd9cdc8ae3d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[16, 17, 18, 19, 20],\n",
+              "       [21, 22, 23, 24, 25]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 98
+        }
+      ],
+      "source": [
+        "np.arange(16,26).reshape(2,5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "6vtHuVJU9xlf"
+      },
+      "source": [
+        "# Great Job!"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.12"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Assignment 1/Assignment1_AryaKishor/AryaKishor_Assignment1_Pandas.ipynb b/Assignment 1/Assignment1_AryaKishor/AryaKishor_Assignment1_Pandas.ipynb
new file mode 100644
index 00000000..585f53ea
--- /dev/null
+++ b/Assignment 1/Assignment1_AryaKishor/AryaKishor_Assignment1_Pandas.ipynb	
@@ -0,0 +1,3131 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## General Instructions for all Assignments"
+      ],
+      "metadata": {
+        "id": "AWDeauE3Bdzs"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1. Fork the repository and make a copy of this notebook and rename it as your name_Assignment1_Pandas\n",
+        "2. Generate a pull request. The pull request message should also be yourname_Assignment1"
+      ],
+      "metadata": {
+        "id": "Xaf9uMvXBJEC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "1. This code notebook is composed of cells. Each cell is either text or Python code.\n",
+        "2. To run a cell of code, click the \"play button\" icon to the left of the cell or click on the cell and press \"Shift+Enter\" on your keyboard. This will execute the Python code contained in the cell. Executing a cell that defines a variable is important before executing or authoring a cell that depends on that previously created variable assignment.\n"
+      ],
+      "metadata": {
+        "id": "4_6uxHVGB_XT"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "b68xNjkKCBIF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "BiD4gl6_cAYs"
+      },
+      "outputs": [],
+      "source": [
+        "#import pandas library\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "JUtm9KricAYu"
+      },
+      "outputs": [],
+      "source": [
+        "df = pd.read_csv(\"/content/Iris.csv\") #enter path for the Iris csv in the brackets\n",
+        "#reads the csv file and stores it in a data frame(define in the pandas library)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "qzUnSkfRcAYu"
+      },
+      "outputs": [],
+      "source": [
+        "dictionary = {'col':[4,2,3], 'col2':[4,7,6], 'col3':[7,10,9]}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "ACswu4BXcAYu"
+      },
+      "outputs": [],
+      "source": [
+        "dictionarydf = pd.DataFrame.from_dict(dictionary)\n",
+        "#it creats a new datafram which stores the dictionary made above"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()\n",
+        "#shows top 5 rows (row indexing starts from 0)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "MvYNz8E2dp3K",
+        "outputId": "6cc36d6c-9f77-42df-8632-6990b5f19a7c"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
+              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
+              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
+              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
+              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
+              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0999178c-7b49-42f8-ac08-13a889f977e9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Id</th>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "      <th>Species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0999178c-7b49-42f8-ac08-13a889f977e9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0999178c-7b49-42f8-ac08-13a889f977e9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0999178c-7b49-42f8-ac08-13a889f977e9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-365ec366-5a54-4a1f-b5a4-cd1e1fedf909\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-365ec366-5a54-4a1f-b5a4-cd1e1fedf909')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-365ec366-5a54-4a1f-b5a4-cd1e1fedf909 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"#shows top 5 rows (row indexing starts from 0)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2073644135332772,\n        \"min\": 4.6,\n        \"max\": 5.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          5.0,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2588435821108957,\n        \"min\": 3.0,\n        \"max\": 3.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          3.6,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865474,\n        \"min\": 1.3,\n        \"max\": 1.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.4,\n          1.3,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "iAx3B-TacAYu",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "outputId": "dd0a3d7b-1515-4345-e72d-382c9b7ad6d6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
+              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
+              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
+              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
+              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
+              "4   5            5.0           3.6            1.4           0.2  Iris-setosa\n",
+              "5   6            5.4           3.9            1.7           0.4  Iris-setosa\n",
+              "6   7            4.6           3.4            1.4           0.3  Iris-setosa\n",
+              "7   8            5.0           3.4            1.5           0.2  Iris-setosa\n",
+              "8   9            4.4           2.9            1.4           0.2  Iris-setosa\n",
+              "9  10            4.9           3.1            1.5           0.1  Iris-setosa"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-847a3b93-b232-414f-8501-5fab68da3800\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Id</th>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "      <th>Species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.4</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>8</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-847a3b93-b232-414f-8501-5fab68da3800')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-847a3b93-b232-414f-8501-5fab68da3800 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-847a3b93-b232-414f-8501-5fab68da3800');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-bd34a013-415f-4705-88ca-d141968a10f7\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd34a013-415f-4705-88ca-d141968a10f7')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-bd34a013-415f-4705-88ca-d141968a10f7 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n          19,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "#Similiar to above, show 10 rows from the top\n",
+        "df.head(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "P9RtjFLQcAYv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 520
+        },
+        "outputId": "e1987b36-1fe3-42d9-9719-6f9ae973d1d0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
+              "135  136            7.7           3.0            6.1           2.3   \n",
+              "136  137            6.3           3.4            5.6           2.4   \n",
+              "137  138            6.4           3.1            5.5           1.8   \n",
+              "138  139            6.0           3.0            4.8           1.8   \n",
+              "139  140            6.9           3.1            5.4           2.1   \n",
+              "140  141            6.7           3.1            5.6           2.4   \n",
+              "141  142            6.9           3.1            5.1           2.3   \n",
+              "142  143            5.8           2.7            5.1           1.9   \n",
+              "143  144            6.8           3.2            5.9           2.3   \n",
+              "144  145            6.7           3.3            5.7           2.5   \n",
+              "145  146            6.7           3.0            5.2           2.3   \n",
+              "146  147            6.3           2.5            5.0           1.9   \n",
+              "147  148            6.5           3.0            5.2           2.0   \n",
+              "148  149            6.2           3.4            5.4           2.3   \n",
+              "149  150            5.9           3.0            5.1           1.8   \n",
+              "\n",
+              "            Species  \n",
+              "135  Iris-virginica  \n",
+              "136  Iris-virginica  \n",
+              "137  Iris-virginica  \n",
+              "138  Iris-virginica  \n",
+              "139  Iris-virginica  \n",
+              "140  Iris-virginica  \n",
+              "141  Iris-virginica  \n",
+              "142  Iris-virginica  \n",
+              "143  Iris-virginica  \n",
+              "144  Iris-virginica  \n",
+              "145  Iris-virginica  \n",
+              "146  Iris-virginica  \n",
+              "147  Iris-virginica  \n",
+              "148  Iris-virginica  \n",
+              "149  Iris-virginica  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cd871073-2069-46ce-a15f-6d440ef20ec2\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Id</th>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "      <th>Species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>135</th>\n",
+              "      <td>136</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>6.1</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>136</th>\n",
+              "      <td>137</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>137</th>\n",
+              "      <td>138</td>\n",
+              "      <td>6.4</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>138</th>\n",
+              "      <td>139</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>139</th>\n",
+              "      <td>140</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>140</th>\n",
+              "      <td>141</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>141</th>\n",
+              "      <td>142</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>142</th>\n",
+              "      <td>143</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>143</th>\n",
+              "      <td>144</td>\n",
+              "      <td>6.8</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>144</th>\n",
+              "      <td>145</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>3.3</td>\n",
+              "      <td>5.7</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>145</th>\n",
+              "      <td>146</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>146</th>\n",
+              "      <td>147</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>147</th>\n",
+              "      <td>148</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>148</th>\n",
+              "      <td>149</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>149</th>\n",
+              "      <td>150</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd871073-2069-46ce-a15f-6d440ef20ec2')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-cd871073-2069-46ce-a15f-6d440ef20ec2 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cd871073-2069-46ce-a15f-6d440ef20ec2');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f90d0eee-1fd9-49c1-b303-b7e1a02911de\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f90d0eee-1fd9-49c1-b303-b7e1a02911de')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f90d0eee-1fd9-49c1-b303-b7e1a02911de button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 136,\n        \"max\": 150,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          145,\n          147,\n          136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48136710078097966,\n        \"min\": 5.8,\n        \"max\": 7.7,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6.7,\n          7.7,\n          6.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23543273227945902,\n        \"min\": 2.5,\n        \"max\": 3.4,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3.0,\n          3.4,\n          3.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35697138740881107,\n        \"min\": 4.8,\n        \"max\": 6.1,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          5.2,\n          5.6,\n          5.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25014281634983754,\n        \"min\": 1.8,\n        \"max\": 2.5,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2.3,\n          2.4,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ],
+      "source": [
+        "#Show the last 15 rows using tail function\n",
+        "df.tail(15)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "QrzERa4qcAYv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "af6ebcfb-8113-4085-8c29-cd1da9c33ecd"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['Id',\n",
+              " 'SepalLengthCm',\n",
+              " 'SepalWidthCm',\n",
+              " 'PetalLengthCm',\n",
+              " 'PetalWidthCm',\n",
+              " 'Species']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "#list all the columns of the dataset\n",
+        "list(df.columns)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "5yPReLqxcAYv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "outputId": "5f20cc0d-ed05-4fde-8f8f-44dcbea324c7"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Id                 int64\n",
+              "SepalLengthCm    float64\n",
+              "SepalWidthCm     float64\n",
+              "PetalLengthCm    float64\n",
+              "PetalWidthCm     float64\n",
+              "Species           object\n",
+              "dtype: object"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Id</th>\n",
+              "      <td>int64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <td>float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <td>float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <td>float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "      <td>float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Species</th>\n",
+              "      <td>object</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> object</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ],
+      "source": [
+        "#show the data types of each feature or column name\n",
+        "df.dtypes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "lT3OEUNDcAYv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "outputId": "494a05ea-bffb-40fd-c551-adbf943ebde2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
+              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
+              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
+              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
+              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
+              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
+              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
+              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
+              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c3eb8703-102a-4ca0-8bd0-8546a6b09c08\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Id</th>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>150.000000</td>\n",
+              "      <td>150.000000</td>\n",
+              "      <td>150.000000</td>\n",
+              "      <td>150.000000</td>\n",
+              "      <td>150.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>75.500000</td>\n",
+              "      <td>5.843333</td>\n",
+              "      <td>3.054000</td>\n",
+              "      <td>3.758667</td>\n",
+              "      <td>1.198667</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>43.445368</td>\n",
+              "      <td>0.828066</td>\n",
+              "      <td>0.433594</td>\n",
+              "      <td>1.764420</td>\n",
+              "      <td>0.763161</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>4.300000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.100000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>38.250000</td>\n",
+              "      <td>5.100000</td>\n",
+              "      <td>2.800000</td>\n",
+              "      <td>1.600000</td>\n",
+              "      <td>0.300000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>75.500000</td>\n",
+              "      <td>5.800000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>4.350000</td>\n",
+              "      <td>1.300000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>112.750000</td>\n",
+              "      <td>6.400000</td>\n",
+              "      <td>3.300000</td>\n",
+              "      <td>5.100000</td>\n",
+              "      <td>1.800000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>150.000000</td>\n",
+              "      <td>7.900000</td>\n",
+              "      <td>4.400000</td>\n",
+              "      <td>6.900000</td>\n",
+              "      <td>2.500000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3eb8703-102a-4ca0-8bd0-8546a6b09c08')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c3eb8703-102a-4ca0-8bd0-8546a6b09c08 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c3eb8703-102a-4ca0-8bd0-8546a6b09c08');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6cf7cbda-210a-4b26-9ca8-2a6258ac7341\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6cf7cbda-210a-4b26-9ca8-2a6258ac7341')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6cf7cbda-210a-4b26-9ca8-2a6258ac7341 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.756293020494844,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          150.0,\n          75.5,\n          112.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08647211421483,\n        \"min\": 0.4335943113621737,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0540000000000003,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.835227940958106,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.758666666666666,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.63663424340991,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1986666666666668,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ],
+      "source": [
+        "#describe the data through statistics\n",
+        "df.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "Z_52ct0BcAYw",
+        "outputId": "048796ec-85e9-4b40-d1a2-609cb068736f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     SepalLengthCm  SepalWidthCm\n",
+              "0              5.1           3.5\n",
+              "1              4.9           3.0\n",
+              "2              4.7           3.2\n",
+              "3              4.6           3.1\n",
+              "4              5.0           3.6\n",
+              "..             ...           ...\n",
+              "145            6.7           3.0\n",
+              "146            6.3           2.5\n",
+              "147            6.5           3.0\n",
+              "148            6.2           3.4\n",
+              "149            5.9           3.0\n",
+              "\n",
+              "[150 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-43a7cb4b-b995-44f5-a199-a8c490a8ac93\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>5.1</td>\n",
+              "      <td>3.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>145</th>\n",
+              "      <td>6.7</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>146</th>\n",
+              "      <td>6.3</td>\n",
+              "      <td>2.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>147</th>\n",
+              "      <td>6.5</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>148</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>3.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>149</th>\n",
+              "      <td>5.9</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>150 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43a7cb4b-b995-44f5-a199-a8c490a8ac93')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-43a7cb4b-b995-44f5-a199-a8c490a8ac93 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-43a7cb4b-b995-44f5-a199-a8c490a8ac93');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-b47bc632-9a5e-4441-8d15-33a37b4f4794\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b47bc632-9a5e-4441-8d15-33a37b4f4794')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-b47bc632-9a5e-4441-8d15-33a37b4f4794 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"#From taking multiple columns from the dataset\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ],
+      "source": [
+        "df[['SepalLengthCm', 'SepalWidthCm']]\n",
+        "#From taking multiple columns from the dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JWYXoLl0cAYw",
+        "outputId": "1a5c9917-7373-46de-8048-31618e623f62"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.4, 4.8, 4.3, 5.8, 5.7, 5.2, 5.5,\n",
+              "       4.5, 5.3, 7. , 6.4, 6.9, 6.5, 6.3, 6.6, 5.9, 6. , 6.1, 5.6, 6.7,\n",
+              "       6.2, 6.8, 7.1, 7.6, 7.3, 7.2, 7.7, 7.4, 7.9])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "df.SepalLengthCm.unique()\n",
+        "#gives the unique values of Sepal Length present in that particular column"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "MiOVp1WacAYw",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "outputId": "8e0fbb71-4aae-47c9-b59a-68bc177b5d6a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Id                        11\n",
+              "SepalLengthCm            5.4\n",
+              "SepalWidthCm             3.7\n",
+              "PetalLengthCm            1.5\n",
+              "PetalWidthCm             0.2\n",
+              "Species          Iris-setosa\n",
+              "Name: 10, dtype: object"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>10</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Id</th>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <td>5.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <td>3.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Species</th>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> object</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ],
+      "source": [
+        "#Get the 11th row from the dataset\n",
+        "#Hint: for taking 11th row do we use 11 or 10 index?\n",
+        "df.iloc[10]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.iloc[10,-1]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "TF03jh_MovET",
+        "outputId": "a7f5ee3b-169c-41fe-90a9-dc53d9b58db8"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'Iris-setosa'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Display the second last entry of the 10th row\n",
+        "print(df.iloc[10,-2])"
+      ],
+      "metadata": {
+        "id": "gV-h3rK-g5lZ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "69d61baf-e031-4905-83b6-dd12e80342fb"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "KXWCH34ecAYx",
+        "outputId": "f1b3d529-032f-4716-d7be-1d95bb3d5e6c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
+              "22  23            4.6           3.6            1.0           0.2  Iris-setosa\n",
+              "23  24            5.1           3.3            1.7           0.5  Iris-setosa\n",
+              "24  25            4.8           3.4            1.9           0.2  Iris-setosa\n",
+              "25  26            5.0           3.0            1.6           0.2  Iris-setosa\n",
+              "26  27            5.0           3.4            1.6           0.4  Iris-setosa\n",
+              "27  28            5.2           3.5            1.5           0.2  Iris-setosa\n",
+              "28  29            5.2           3.4            1.4           0.2  Iris-setosa\n",
+              "29  30            4.7           3.2            1.6           0.2  Iris-setosa\n",
+              "30  31            4.8           3.1            1.6           0.2  Iris-setosa\n",
+              "31  32            5.4           3.4            1.5           0.4  Iris-setosa\n",
+              "32  33            5.2           4.1            1.5           0.1  Iris-setosa"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-305f6de9-1d3c-4bc3-9245-caf1d24cafbc\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Id</th>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "      <th>Species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>23</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>24</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>3.3</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>25</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>26</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>27</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.4</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>28</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>29</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>30</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>31</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>32</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.4</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>33</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-305f6de9-1d3c-4bc3-9245-caf1d24cafbc')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-305f6de9-1d3c-4bc3-9245-caf1d24cafbc button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-305f6de9-1d3c-4bc3-9245-caf1d24cafbc');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-fc2bf0ae-83dd-456f-b22e-b2dec799eebb\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fc2bf0ae-83dd-456f-b22e-b2dec799eebb')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-fc2bf0ae-83dd-456f-b22e-b2dec799eebb button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"#takes everything from row 23(included) and 34(not included)\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 23,\n        \"max\": 33,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          28,\n          23,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2489979919597748,\n        \"min\": 4.6,\n        \"max\": 5.4,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4.6,\n          5.1,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28982753492378865,\n        \"min\": 3.0,\n        \"max\": 4.1,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.3,\n          3.2,\n          3.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22033033051637385,\n        \"min\": 1.0,\n        \"max\": 1.9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          1.7,\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12135597524338358,\n        \"min\": 0.1,\n        \"max\": 0.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.5,\n          0.1,\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ],
+      "source": [
+        "df.iloc[22:33]\n",
+        "#takes everything from row 23(included) and 34(not included)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1=df.copy()\n",
+        "df1.dropna(axis=0, inplace=False) #drops any values that are null or na"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "quhmLFx-q6vS",
+        "outputId": "0d91aa58-92e0-4e03-8f7f-880597f0e1d3"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
+              "0      1            5.1           3.5            1.4           0.2   \n",
+              "1      2            4.9           3.0            1.4           0.2   \n",
+              "2      3            4.7           3.2            1.3           0.2   \n",
+              "3      4            4.6           3.1            1.5           0.2   \n",
+              "4      5            5.0           3.6            1.4           0.2   \n",
+              "..   ...            ...           ...            ...           ...   \n",
+              "145  146            6.7           3.0            5.2           2.3   \n",
+              "146  147            6.3           2.5            5.0           1.9   \n",
+              "147  148            6.5           3.0            5.2           2.0   \n",
+              "148  149            6.2           3.4            5.4           2.3   \n",
+              "149  150            5.9           3.0            5.1           1.8   \n",
+              "\n",
+              "            Species  \n",
+              "0       Iris-setosa  \n",
+              "1       Iris-setosa  \n",
+              "2       Iris-setosa  \n",
+              "3       Iris-setosa  \n",
+              "4       Iris-setosa  \n",
+              "..              ...  \n",
+              "145  Iris-virginica  \n",
+              "146  Iris-virginica  \n",
+              "147  Iris-virginica  \n",
+              "148  Iris-virginica  \n",
+              "149  Iris-virginica  \n",
+              "\n",
+              "[150 rows x 6 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fe000f3b-17d6-41f4-90bc-c9867d2c0d08\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Id</th>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "      <th>Species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>145</th>\n",
+              "      <td>146</td>\n",
+              "      <td>6.7</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>146</th>\n",
+              "      <td>147</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>147</th>\n",
+              "      <td>148</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>148</th>\n",
+              "      <td>149</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>149</th>\n",
+              "      <td>150</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>150 rows × 6 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe000f3b-17d6-41f4-90bc-c9867d2c0d08')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fe000f3b-17d6-41f4-90bc-c9867d2c0d08 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fe000f3b-17d6-41f4-90bc-c9867d2c0d08');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c8335f65-b6bc-4fd6-8673-24a27154ae33\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c8335f65-b6bc-4fd6-8673-24a27154ae33')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c8335f65-b6bc-4fd6-8673-24a27154ae33 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n          19,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "kZg8z57fcAY2"
+      },
+      "outputs": [],
+      "source": [
+        "df.dropna(axis=0, inplace=True)\n",
+        "#drops all the rows with missing values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "id": "FRWcDJA2cAY2",
+        "outputId": "40570956-5b14-4897-c37c-6adf64078cd2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Id               0\n",
+              "SepalLengthCm    0\n",
+              "SepalWidthCm     0\n",
+              "PetalLengthCm    0\n",
+              "PetalWidthCm     0\n",
+              "Species          0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Id</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Species</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ],
+      "source": [
+        "df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "JioW0K_pcAY3"
+      },
+      "outputs": [],
+      "source": [
+        "df.to_csv('cleandata_iris.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "asToKeGJcAY4"
+      },
+      "outputs": [],
+      "source": [
+        "del df"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.4"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Assignment 2/Assignment2_AryaKishor/Assignment2_AryaKishor.ipynb b/Assignment 2/Assignment2_AryaKishor/Assignment2_AryaKishor.ipynb
new file mode 100644
index 00000000..8afdc3d0
--- /dev/null
+++ b/Assignment 2/Assignment2_AryaKishor/Assignment2_AryaKishor.ipynb	
@@ -0,0 +1,198 @@
+{
+  "cells": [
+    {
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-12-15T12:32:16.103248Z",
+          "start_time": "2025-12-15T12:32:16.089920Z"
+        },
+        "id": "fbc121e30a2defb3"
+      },
+      "cell_type": "code",
+      "source": [
+        "import yfinance as yf\n",
+        "import plotly.graph_objects as go"
+      ],
+      "id": "fbc121e30a2defb3",
+      "outputs": [],
+      "execution_count": 2
+    },
+    {
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-12-15T12:32:16.619010Z",
+          "start_time": "2025-12-15T12:32:16.121426Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "66bba02bb9aeb106",
+        "outputId": "bde8629c-8a7a-4b8f-9c4b-6151b5918f50"
+      },
+      "cell_type": "code",
+      "source": [
+        "ticker_symbol= \"MSFT\"\n",
+        "ticker= yf.Ticker(ticker_symbol)\n",
+        "historical_data= ticker.history(period=\"6mo\", interval=\"1d\")\n",
+        "print(historical_data)"
+      ],
+      "id": "66bba02bb9aeb106",
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                                 Open        High         Low       Close  \\\n",
+            "Date                                                                        \n",
+            "2025-06-13 00:00:00-04:00  474.739575  477.499852  471.102379  473.294647   \n",
+            "2025-06-16 00:00:00-04:00  473.543778  479.004575  473.334523  477.460022   \n",
+            "2025-06-17 00:00:00-04:00  473.733103  477.061388  472.417724  476.363861   \n",
+            "2025-06-18 00:00:00-04:00  476.323986  479.313467  472.796389  478.556122   \n",
+            "2025-06-20 00:00:00-04:00  480.539187  481.764854  475.197964  475.726105   \n",
+            "...                               ...         ...         ...         ...   \n",
+            "2025-12-08 00:00:00-05:00  484.890015  492.299988  484.380005  491.019989   \n",
+            "2025-12-09 00:00:00-05:00  489.100006  492.119995  488.500000  492.019989   \n",
+            "2025-12-10 00:00:00-05:00  484.029999  484.250000  475.079987  478.559998   \n",
+            "2025-12-11 00:00:00-05:00  476.630005  486.029999  475.859985  483.470001   \n",
+            "2025-12-12 00:00:00-05:00  479.820007  482.450012  476.359985  478.529999   \n",
+            "\n",
+            "                             Volume  Dividends  Stock Splits  \n",
+            "Date                                                          \n",
+            "2025-06-13 00:00:00-04:00  16814500        0.0           0.0  \n",
+            "2025-06-16 00:00:00-04:00  15626100        0.0           0.0  \n",
+            "2025-06-17 00:00:00-04:00  15414100        0.0           0.0  \n",
+            "2025-06-18 00:00:00-04:00  17526500        0.0           0.0  \n",
+            "2025-06-20 00:00:00-04:00  37576200        0.0           0.0  \n",
+            "...                             ...        ...           ...  \n",
+            "2025-12-08 00:00:00-05:00  21965900        0.0           0.0  \n",
+            "2025-12-09 00:00:00-05:00  14696100        0.0           0.0  \n",
+            "2025-12-10 00:00:00-05:00  35756200        0.0           0.0  \n",
+            "2025-12-11 00:00:00-05:00  24669200        0.0           0.0  \n",
+            "2025-12-12 00:00:00-05:00  21248102        0.0           0.0  \n",
+            "\n",
+            "[127 rows x 7 columns]\n"
+          ]
+        }
+      ],
+      "execution_count": 3
+    },
+    {
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-12-15T12:32:16.681355Z",
+          "start_time": "2025-12-15T12:32:16.641210Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "d3925ec9e0a18e44",
+        "outputId": "983abdbd-0ffd-4613-c973-3d293db93e1c"
+      },
+      "cell_type": "code",
+      "source": [
+        "fig = go.Figure(data=[go.Candlestick(x=historical_data.index,open=historical_data[\"Open\"],high=historical_data[\"High\"],low=historical_data[\"Low\"],close=historical_data[\"Close\"])])\n",
+        "\n",
+        "fig.update_layout(\n",
+        "    title=\"Microsoft Candlestick Chart of past 6 months\",\n",
+        "    xaxis_title=\"Date\",\n",
+        "    yaxis_title=\"Price\",\n",
+        "    xaxis_rangeslider_visible=True)\n",
+        "fig.show()"
+      ],
+      "id": "d3925ec9e0a18e44",
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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Candlestick Chart of past 6 months\"},\"yaxis\":{\"title\":{\"text\":\"Price\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('7f2627e9-d390-4018-8aba-6371a3a4b7c3');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 4
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig.write_image(\"candlestick.png\")"
+      ],
+      "metadata": {
+        "id": "e7G590mZHFUK"
+      },
+      "id": "e7G590mZHFUK",
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "12b6bc3c535f7d11"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Date the pattern occurred: Early August 2025\n",
+        "\n",
+        "Name of the pattern: Bearish Engulfing\n",
+        "\n",
+        "Price movement after the pattern: Price went down immediately after the pattern, indicating bearish momentum."
+      ],
+      "id": "12b6bc3c535f7d11"
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 2
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython2",
+      "version": "2.7.6"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/Assignment 2/Assignment2_AryaKishor/candlestick.png b/Assignment 2/Assignment2_AryaKishor/candlestick.png
new file mode 100644
index 00000000..7109d976
Binary files /dev/null and b/Assignment 2/Assignment2_AryaKishor/candlestick.png differ
diff --git a/Assignment 2/Assignment2_Chirag/Assignment2.ipynb b/Assignment 2/Assignment2_Chirag/Assignment2.ipynb
deleted file mode 100644
index 9b7c2fe1..00000000
--- a/Assignment 2/Assignment2_Chirag/Assignment2.ipynb	
+++ /dev/null
@@ -1,177 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {
-        "id": "rmzfdDtvSW97"
-      },
-      "outputs": [],
-      "source": [
-        "import yfinance as yf\n",
-        "import matplotlib.pyplot as plt\n",
-        "import plotly.graph_objects as go"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "ticker_symbol = \"TSLA\"\n",
-        "ticker = yf.Ticker(ticker_symbol)\n",
-        "historical_data = ticker.history(period='6mo', interval ='1d')\n",
-        "print(f\"Historical data for {ticker_symbol} for 6 months is :\", historical_data)\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "CvLuerY6SsZf",
-        "outputId": "40e09711-8157-4618-c074-86e0fa31ee6b"
-      },
-      "execution_count": 19,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Historical data for TSLA for 6 months is :                                  Open        High         Low       Close  \\\n",
-            "Date                                                                        \n",
-            "2025-06-13 00:00:00-04:00  313.970001  332.989990  313.299988  325.309998   \n",
-            "2025-06-16 00:00:00-04:00  331.290009  332.049988  326.410004  329.130005   \n",
-            "2025-06-17 00:00:00-04:00  326.089996  327.260010  314.739990  316.350006   \n",
-            "2025-06-18 00:00:00-04:00  317.309998  329.320007  315.450012  322.049988   \n",
-            "2025-06-20 00:00:00-04:00  327.950012  332.359985  317.779999  322.160004   \n",
-            "...                               ...         ...         ...         ...   \n",
-            "2025-12-08 00:00:00-05:00  447.450012  449.750000  435.250000  439.579987   \n",
-            "2025-12-09 00:00:00-05:00  437.540009  452.390015  435.700012  445.170013   \n",
-            "2025-12-10 00:00:00-05:00  446.070007  456.880005  443.609985  451.450012   \n",
-            "2025-12-11 00:00:00-05:00  448.950012  449.269989  440.329987  446.890015   \n",
-            "2025-12-12 00:00:00-05:00  448.089996  463.010010  441.670013  458.959991   \n",
-            "\n",
-            "                              Volume  Dividends  Stock Splits  \n",
-            "Date                                                           \n",
-            "2025-06-13 00:00:00-04:00  128964300        0.0           0.0  \n",
-            "2025-06-16 00:00:00-04:00   83925900        0.0           0.0  \n",
-            "2025-06-17 00:00:00-04:00   88282700        0.0           0.0  \n",
-            "2025-06-18 00:00:00-04:00   95137700        0.0           0.0  \n",
-            "2025-06-20 00:00:00-04:00  108688000        0.0           0.0  \n",
-            "...                              ...        ...           ...  \n",
-            "2025-12-08 00:00:00-05:00   69165800        0.0           0.0  \n",
-            "2025-12-09 00:00:00-05:00   62367400        0.0           0.0  \n",
-            "2025-12-10 00:00:00-05:00   63257500        0.0           0.0  \n",
-            "2025-12-11 00:00:00-05:00   55979500        0.0           0.0  \n",
-            "2025-12-12 00:00:00-05:00   93562326        0.0           0.0  \n",
-            "\n",
-            "[127 rows x 7 columns]\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "fig = go.Figure(data=go.Candlestick(x = historical_data.index,\n",
-        "                                    open = historical_data['Open'],\n",
-        "                                    close = historical_data['Close'],\n",
-        "                                    low = historical_data['Low'],\n",
-        "                                    high = historical_data['High'],\n",
-        "                                    increasing = dict(line=dict(color = 'Green')),\n",
-        "                                    decreasing = dict(line = dict(color = 'Red'))))\n",
-        "\n",
-        "fig.update_layout(\n",
-        "    title=f'{ticker_symbol} Candlestick Chart',\n",
-        "    xaxis_title='Date',\n",
-        "    yaxis_title='Price(USD) in millions',\n",
-        "    xaxis_rangeslider_visible=True)\n",
-        "fig.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 542
-        },
-        "id": "1RDQ4jJtLEGJ",
-        "outputId": "ad0fb9ae-34aa-4957-e501-bec50853eb7d"
-      },
-      "execution_count": 20,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
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)"
-      ],
-      "metadata": {
-        "id": "4nODfd7MfkbV"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "1. On Jun 24,2025 a Bearish Marubozu(long) candle was noticed and after that the market declined as expected\n",
-        "2. On Jun 30, 2025 a bearish hammer candle was noticed and after that the market was down the next day\n",
-        "3. On July 8,2025 a shooting star candle was noticed which shows the reverse in pattern and the market does increase after that\n",
-        "4. On Aug 6,2025 a Bullish Marubozu(long) was noticed and as thought the market increased further\n",
-        "5. On September 3,2025 a doji candle was noticed and market increased after that\n",
-        "6. On Nov 6,2025 a Bearish engulfing candle was noticed and as expected the market declined"
-      ],
-      "metadata": {
-        "id": "bINnxDXccwTj"
-      }
-    }
-  ]
-}
\ No newline at end of file
diff --git a/Assignment 2/Assignment2_Mithil/Assignment2 b/Assignment 2/Assignment2_Mithil/Assignment2
deleted file mode 100644
index 8b137891..00000000
--- a/Assignment 2/Assignment2_Mithil/Assignment2	
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/Assignment3/Assignment3_AryaKishor.ipynb b/Assignment3/Assignment3_AryaKishor.ipynb
new file mode 100644
index 00000000..0196373f
--- /dev/null
+++ b/Assignment3/Assignment3_AryaKishor.ipynb
@@ -0,0 +1,469 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import kagglehub\n",
+        "\n",
+        "# Download latest version\n",
+        "path = kagglehub.dataset_download(\"raimiazeezbabatunde/candle-image-data\")\n",
+        "\n",
+        "print(\"Path to dataset files:\", path)\n",
+        "import os\n",
+        "\n",
+        "class_names = sorted(os.listdir(TRAIN_DIR))\n",
+        "print(\"Classes:\", class_names)"
+      ],
+      "metadata": {
+        "id": "DVShWB_TRVVw",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9b1878a9-0934-40ee-dde0-55553c345e39"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using Colab cache for faster access to the 'candle-image-data' dataset.\n",
+            "Path to dataset files: /kaggle/input/candle-image-data\n",
+            "Classes: ['Down', 'Up']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf\n",
+        "\n",
+        "IMG_SIZE = (128, 128)\n",
+        "BATCH_SIZE = 32\n",
+        "SEED = 42\n",
+        "\n",
+        "TRAIN_DIR = path + \"/Train\"\n",
+        "TEST_DIR  = path + \"/Test\"\n",
+        "\n",
+        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
+        "    TRAIN_DIR,\n",
+        "    labels=\"inferred\",\n",
+        "    label_mode=\"binary\",\n",
+        "    image_size=IMG_SIZE,\n",
+        "    batch_size=BATCH_SIZE,\n",
+        "    shuffle=True,\n",
+        "    seed=SEED,\n",
+        "    validation_split=0.2,\n",
+        "    subset=\"training\"\n",
+        ")\n",
+        "\n",
+        "val_ds = tf.keras.utils.image_dataset_from_directory(\n",
+        "    TRAIN_DIR,\n",
+        "    labels=\"inferred\",\n",
+        "    label_mode=\"binary\",\n",
+        "    image_size=IMG_SIZE,\n",
+        "    batch_size=BATCH_SIZE,\n",
+        "    shuffle=True,\n",
+        "    seed=SEED,\n",
+        "    validation_split=0.2,\n",
+        "    subset=\"validation\"\n",
+        ")\n",
+        "\n",
+        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
+        "    TEST_DIR,\n",
+        "    labels=\"inferred\",\n",
+        "    label_mode=\"binary\",\n",
+        "    image_size=IMG_SIZE,\n",
+        "    batch_size=BATCH_SIZE,\n",
+        "    shuffle=False\n",
+        ")\n",
+        "\n",
+        "print(\"Classes:\", train_ds.class_names)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MFcKuAUNxJXf",
+        "outputId": "1eb2b57b-4b5b-4031-94cc-c66cbdd0c4d0"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 1433 files belonging to 2 classes.\n",
+            "Using 1147 files for training.\n",
+            "Found 1433 files belonging to 2 classes.\n",
+            "Using 286 files for validation.\n",
+            "Found 351 files belonging to 2 classes.\n",
+            "Classes: ['Down', 'Up']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "AUTOTUNE = tf.data.AUTOTUNE\n",
+        "\n",
+        "train_ds = train_ds.cache().shuffle(1000).prefetch(AUTOTUNE)\n",
+        "val_ds   = val_ds.cache().prefetch(AUTOTUNE)\n",
+        "test_ds  = test_ds.cache().prefetch(AUTOTUNE)\n",
+        "\n",
+        "data_augmentation = tf.keras.Sequential([\n",
+        "    tf.keras.layers.RandomFlip(\"horizontal\"),\n",
+        "    tf.keras.layers.RandomRotation(0.03),\n",
+        "    tf.keras.layers.RandomZoom(0.1),\n",
+        "    tf.keras.layers.RandomTranslation(0.05, 0.05),\n",
+        "])\n"
+      ],
+      "metadata": {
+        "id": "mBzneCNx1pC2"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "inputs = tf.keras.Input(shape=(128,128,3))\n",
+        "\n",
+        "x = data_augmentation(inputs)\n",
+        "x = tf.keras.layers.Rescaling(1./255)(x)\n",
+        "\n",
+        "x = tf.keras.layers.Conv2D(32, 3, activation=\"relu\", padding=\"same\")(x)\n",
+        "x = tf.keras.layers.MaxPooling2D()(x)\n",
+        "\n",
+        "x = tf.keras.layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
+        "x = tf.keras.layers.MaxPooling2D()(x)\n",
+        "\n",
+        "x = tf.keras.layers.Conv2D(128, 3, activation=\"relu\", padding=\"same\")(x)\n",
+        "x = tf.keras.layers.MaxPooling2D()(x)\n",
+        "\n",
+        "x = tf.keras.layers.Flatten()(x)\n",
+        "x = tf.keras.layers.Dense(128, activation=\"relu\")(x)\n",
+        "x = tf.keras.layers.Dropout(0.4)(x)\n",
+        "\n",
+        "outputs = tf.keras.layers.Dense(1, activation=\"sigmoid\")(x)\n",
+        "\n",
+        "model = tf.keras.Model(inputs, outputs)\n",
+        "model.summary()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 545
+        },
+        "id": "VSglcMSD18n_",
+        "outputId": "bfab8c9f-06f8-4b3f-abc5-5cfc3c415a9d"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"functional_3\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_3\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer_2 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ sequential_1 (\u001b[38;5;33mSequential\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ rescaling_1 (\u001b[38;5;33mRescaling\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32768\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m4,194,432\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m129\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ sequential_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ rescaling_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32768</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,194,432</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,287,809\u001b[0m (16.36 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,287,809</span> (16.36 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m4,287,809\u001b[0m (16.36 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,287,809</span> (16.36 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(\n",
+        "    optimizer=\"adam\",\n",
+        "    loss=\"binary_crossentropy\",\n",
+        "    metrics=[\"accuracy\"]\n",
+        ")\n",
+        "\n",
+        "history = model.fit(\n",
+        "    train_ds,\n",
+        "    validation_data=val_ds,\n",
+        "    epochs=25,\n",
+        "    callbacks=[\n",
+        "        tf.keras.callbacks.EarlyStopping(patience=5, restore_best_weights=True)\n",
+        "    ]\n",
+        ")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AXSONM3h2GGy",
+        "outputId": "2ff7b0af-c625-4d0e-d8b6-ba3f14254dda"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 2s/step - accuracy: 0.4902 - loss: 0.7029 - val_accuracy: 0.6154 - val_loss: 0.6734\n",
+            "Epoch 2/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 1s/step - accuracy: 0.5628 - loss: 0.6874 - val_accuracy: 0.6154 - val_loss: 0.6797\n",
+            "Epoch 3/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.5489 - loss: 0.6881 - val_accuracy: 0.6154 - val_loss: 0.6705\n",
+            "Epoch 4/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 1s/step - accuracy: 0.5521 - loss: 0.6883 - val_accuracy: 0.6154 - val_loss: 0.6740\n",
+            "Epoch 5/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 1s/step - accuracy: 0.5468 - loss: 0.6865 - val_accuracy: 0.6189 - val_loss: 0.6678\n",
+            "Epoch 6/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 1s/step - accuracy: 0.5405 - loss: 0.6913 - val_accuracy: 0.6154 - val_loss: 0.6708\n",
+            "Epoch 7/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.5701 - loss: 0.6828 - val_accuracy: 0.6224 - val_loss: 0.6717\n",
+            "Epoch 8/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 2s/step - accuracy: 0.5419 - loss: 0.6887 - val_accuracy: 0.6154 - val_loss: 0.6591\n",
+            "Epoch 9/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 1s/step - accuracy: 0.5639 - loss: 0.6882 - val_accuracy: 0.6189 - val_loss: 0.6701\n",
+            "Epoch 10/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 1s/step - accuracy: 0.5467 - loss: 0.6853 - val_accuracy: 0.6259 - val_loss: 0.6737\n",
+            "Epoch 11/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 1s/step - accuracy: 0.5563 - loss: 0.6887 - val_accuracy: 0.6224 - val_loss: 0.6719\n",
+            "Epoch 12/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 1s/step - accuracy: 0.5470 - loss: 0.6913 - val_accuracy: 0.6154 - val_loss: 0.6680\n",
+            "Epoch 13/25\n",
+            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 1s/step - accuracy: 0.5644 - loss: 0.6825 - val_accuracy: 0.6294 - val_loss: 0.6710\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.plot(history.history[\"accuracy\"], label=\"Train Accuracy\")\n",
+        "plt.plot(history.history[\"val_accuracy\"], label=\"Validation Accuracy\")\n",
+        "plt.legend()\n",
+        "plt.title(\"Accuracy\")\n",
+        "plt.show()\n",
+        "\n",
+        "plt.plot(history.history[\"loss\"], label=\"Train Loss\")\n",
+        "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n",
+        "plt.legend()\n",
+        "plt.title(\"Loss\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 887
+        },
+        "id": "wj8SkbkIBBsA",
+        "outputId": "4caf36e0-e8e6-4a63-cc48-2f7e7af6de3a"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.metrics import confusion_matrix, classification_report\n",
+        "\n",
+        "test_loss, test_acc = model.evaluate(test_ds)\n",
+        "print(\"Test accuracy:\", test_acc)\n",
+        "\n",
+        "y_true = []\n",
+        "y_pred = []\n",
+        "\n",
+        "for images, labels in test_ds:\n",
+        "    preds = model.predict(images, verbose=0).flatten()\n",
+        "    y_true.extend(labels.numpy().astype(int))\n",
+        "    y_pred.extend((preds >= 0.5).astype(int))\n",
+        "\n",
+        "cm = confusion_matrix(y_true, y_pred)\n",
+        "print(\"Confusion Matrix:\\n\", cm)\n",
+        "\n",
+        "print(classification_report(y_true, y_pred, target_names=train_ds.class_names))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 258
+        },
+        "id": "yI0zAInABJuO",
+        "outputId": "cd3a3722-df2e-4f48-9299-add6188a8bc2"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 478ms/step - accuracy: 0.2456 - loss: 0.8267\n",
+            "Test accuracy: 0.5498575568199158\n",
+            "Confusion Matrix:\n",
+            " [[  0 157]\n",
+            " [  1 193]]\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "AttributeError",
+          "evalue": "'_PrefetchDataset' object has no attribute 'class_names'",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-1026002384.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Confusion Matrix:\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_ds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mAttributeError\u001b[0m: '_PrefetchDataset' object has no attribute 'class_names'"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Assignment4/Assignment4_AryaKishor.ipynb b/Assignment4/Assignment4_AryaKishor.ipynb
new file mode 100644
index 00000000..88fafa88
--- /dev/null
+++ b/Assignment4/Assignment4_AryaKishor.ipynb
@@ -0,0 +1,1597 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "1DLSK5q0H2ch"
+      },
+      "outputs": [],
+      "source": [
+        "import yfinance as yf\n",
+        "\n",
+        "symbols = [\"AAPL\",\"MSFT\",\"TSLA\",\"SPY\",\"QQQ\"]\n",
+        "data = {}\n",
+        "\n",
+        "for s in symbols:\n",
+        "    df = yf.download(s, period=\"2y\", interval=\"1d\", group_by=\"column\", auto_adjust=False, progress=False)\n",
+        "    data[s] = df\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "def sanitize_ohlc(df):\n",
+        "    # Keep only required cols (some downloads include Adj Close etc.)\n",
+        "    df = df.copy()\n",
+        "\n",
+        "    # If columns are MultiIndex, flatten them\n",
+        "    if isinstance(df.columns, pd.MultiIndex):\n",
+        "        df.columns = [c[0] for c in df.columns]\n",
+        "\n",
+        "    df = df[['Open','High','Low','Close','Volume']]\n",
+        "\n",
+        "    # Ensure datetime index\n",
+        "    df.index = pd.to_datetime(df.index)\n",
+        "\n",
+        "    # Convert everything to numeric and drop bad rows\n",
+        "    df = df.apply(pd.to_numeric, errors='coerce').dropna()\n",
+        "\n",
+        "    for c in ['Open','High','Low','Close']:\n",
+        "        df[c] = df[c].astype(float).map(lambda x: float(x))\n",
+        "    df['Volume'] = df['Volume'].astype(float).map(lambda x: int(x))\n",
+        "\n",
+        "    return df\n"
+      ],
+      "metadata": {
+        "id": "0NnJ0mzXKljC"
+      },
+      "execution_count": 30,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import mplfinance as mpf\n",
+        "import os\n",
+        "\n",
+        "os.makedirs(\"images\", exist_ok=True)\n",
+        "\n",
+        "WINDOW = 30\n",
+        "count = 0\n",
+        "\n",
+        "for symbol, df in data.items():\n",
+        "    df = sanitize_ohlc(df)\n",
+        "\n",
+        "    print(symbol, type(df['Open'].iloc[0]), df['Open'].iloc[0])\n",
+        "\n",
+        "    for i in range(len(df) - WINDOW):\n",
+        "        window = df.iloc[i:i+WINDOW]\n",
+        "\n",
+        "        fname = f\"images/{symbol}_{i}.png\"\n",
+        "        mpf.plot(window, type=\"candle\", style=\"charles\", volume=False, savefig=fname)\n",
+        "\n",
+        "        count += 1\n",
+        "\n",
+        "print(\"Total images:\", count)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r_phNRp7NldL",
+        "outputId": "39697c66-4a45-41e0-e51c-7b1209be949a"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "AAPL <class 'numpy.float64'> 184.35000610351562\n",
+            "MSFT <class 'numpy.float64'> 376.3699951171875\n",
+            "TSLA <class 'numpy.float64'> 235.10000610351562\n",
+            "SPY <class 'numpy.float64'> 474.1600036621094\n",
+            "QQQ <class 'numpy.float64'> 406.07000732421875\n",
+            "Total images: 2360\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import os\n",
+        "\n",
+        "WINDOW = 30\n",
+        "rows = []\n",
+        "\n",
+        "for symbol, df in data.items():\n",
+        "    df = sanitize_ohlc(df)\n",
+        "\n",
+        "    for i in range(len(df) - WINDOW):\n",
+        "        window = df.iloc[i:i+WINDOW]\n",
+        "        fname = f\"images/{symbol}_{i}.png\"\n",
+        "\n",
+        "        rows.append({\n",
+        "            \"file\": fname,\n",
+        "            \"symbol\": symbol,\n",
+        "            \"start\": str(window.index[0].date()),\n",
+        "            \"end\": str(window.index[-1].date()),\n",
+        "            \"open\": float(window[\"Open\"].iloc[-1]),\n",
+        "            \"high\": float(window[\"High\"].iloc[-1]),\n",
+        "            \"low\": float(window[\"Low\"].iloc[-1]),\n",
+        "            \"close\": float(window[\"Close\"].iloc[-1]),\n",
+        "            \"prev_close\": float(window[\"Close\"].iloc[-2]),\n",
+        "        })\n",
+        "\n",
+        "meta = pd.DataFrame(rows)\n",
+        "meta.to_csv(\"image_meta.csv\", index=False)\n",
+        "meta.head()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 309
+        },
+        "id": "siFWSlvGOBTX",
+        "outputId": "98a08d3b-6e72-419b-ced7-bdd1670ec4b6"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                file symbol       start         end        open        high  \\\n",
+              "0  images/AAPL_0.png   AAPL  2024-01-10  2024-02-22  183.479996  184.960007   \n",
+              "1  images/AAPL_1.png   AAPL  2024-01-11  2024-02-23  185.009995  185.039993   \n",
+              "2  images/AAPL_2.png   AAPL  2024-01-12  2024-02-26  182.240005  182.759995   \n",
+              "3  images/AAPL_3.png   AAPL  2024-01-16  2024-02-27  181.100006  183.919998   \n",
+              "4  images/AAPL_4.png   AAPL  2024-01-17  2024-02-28  182.509995  183.119995   \n",
+              "\n",
+              "          low       close  prev_close  \n",
+              "0  182.460007  184.369995  182.320007  \n",
+              "1  182.229996  182.520004  184.369995  \n",
+              "2  180.649994  181.160004  182.520004  \n",
+              "3  179.559998  182.630005  181.160004  \n",
+              "4  180.130005  181.419998  182.630005  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4ca275fd-c0b6-4e69-867a-2d5f722b960d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>file</th>\n",
+              "      <th>symbol</th>\n",
+              "      <th>start</th>\n",
+              "      <th>end</th>\n",
+              "      <th>open</th>\n",
+              "      <th>high</th>\n",
+              "      <th>low</th>\n",
+              "      <th>close</th>\n",
+              "      <th>prev_close</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>images/AAPL_0.png</td>\n",
+              "      <td>AAPL</td>\n",
+              "      <td>2024-01-10</td>\n",
+              "      <td>2024-02-22</td>\n",
+              "      <td>183.479996</td>\n",
+              "      <td>184.960007</td>\n",
+              "      <td>182.460007</td>\n",
+              "      <td>184.369995</td>\n",
+              "      <td>182.320007</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>images/AAPL_1.png</td>\n",
+              "      <td>AAPL</td>\n",
+              "      <td>2024-01-11</td>\n",
+              "      <td>2024-02-23</td>\n",
+              "      <td>185.009995</td>\n",
+              "      <td>185.039993</td>\n",
+              "      <td>182.229996</td>\n",
+              "      <td>182.520004</td>\n",
+              "      <td>184.369995</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>images/AAPL_2.png</td>\n",
+              "      <td>AAPL</td>\n",
+              "      <td>2024-01-12</td>\n",
+              "      <td>2024-02-26</td>\n",
+              "      <td>182.240005</td>\n",
+              "      <td>182.759995</td>\n",
+              "      <td>180.649994</td>\n",
+              "      <td>181.160004</td>\n",
+              "      <td>182.520004</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>images/AAPL_3.png</td>\n",
+              "      <td>AAPL</td>\n",
+              "      <td>2024-01-16</td>\n",
+              "      <td>2024-02-27</td>\n",
+              "      <td>181.100006</td>\n",
+              "      <td>183.919998</td>\n",
+              "      <td>179.559998</td>\n",
+              "      <td>182.630005</td>\n",
+              "      <td>181.160004</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>images/AAPL_4.png</td>\n",
+              "      <td>AAPL</td>\n",
+              "      <td>2024-01-17</td>\n",
+              "      <td>2024-02-28</td>\n",
+              "      <td>182.509995</td>\n",
+              "      <td>183.119995</td>\n",
+              "      <td>180.130005</td>\n",
+              "      <td>181.419998</td>\n",
+              "      <td>182.630005</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ca275fd-c0b6-4e69-867a-2d5f722b960d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4ca275fd-c0b6-4e69-867a-2d5f722b960d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4ca275fd-c0b6-4e69-867a-2d5f722b960d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-3b36feb5-5f9c-4b01-916b-0aedfbc2600d\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3b36feb5-5f9c-4b01-916b-0aedfbc2600d')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3b36feb5-5f9c-4b01-916b-0aedfbc2600d button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "meta",
+              "summary": "{\n  \"name\": \"meta\",\n  \"rows\": 2360,\n  \"fields\": [\n    {\n      \"column\": \"file\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2360,\n        \"samples\": [\n          \"images/MSFT_158.png\",\n          \"images/MSFT_37.png\",\n          \"images/QQQ_328.png\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"MSFT\",\n          \"QQQ\",\n          \"TSLA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 472,\n        \"samples\": [\n          \"2024-04-01\",\n          \"2024-04-25\",\n          \"2024-02-28\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 472,\n        \"samples\": [\n          \"2024-05-10\",\n          \"2024-06-06\",\n          \"2024-04-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.76964296168063,\n        \"min\": 140.55999755859375,\n        \"max\": 692.1900024414062,\n        \"num_unique_values\": 2276,\n        \"samples\": [\n          519.239990234375,\n          454.4599914550781,\n          516.4099731445312\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.54744503279082,\n        \"min\": 144.44000244140625,\n        \"max\": 693.9600219726562,\n        \"num_unique_values\": 2278,\n        \"samples\": [\n          457.7799987792969,\n          279.5299987792969,\n          187.10000610351562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.64800330424745,\n        \"min\": 138.8000030517578,\n        \"max\": 689.3200073242188,\n        \"num_unique_values\": 2288,\n        \"samples\": [\n          316.6000061035156,\n          174.49000549316406,\n          500.70001220703125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.64529697421278,\n        \"min\": 142.0500030517578,\n        \"max\": 691.8099975585938,\n        \"num_unique_values\": 2298,\n        \"samples\": [\n          220.88999938964844,\n          624.02001953125,\n          258.20001220703125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prev_close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.59151762853745,\n        \"min\": 142.0500030517578,\n        \"max\": 691.8099975585938,\n        \"num_unique_values\": 2298,\n        \"samples\": [\n          221.3300018310547,\n          623.4199829101562,\n          255.27000427246094\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def label_doji(o,h,l,c):\n",
+        "    body = abs(c - o)\n",
+        "    rng = h - l\n",
+        "    return (rng > 0) and (body <= 0.1 * rng)\n",
+        "def label_hammer(o,h,l,c):\n",
+        "    body = abs(c - o)\n",
+        "    lower = min(o,c) - l\n",
+        "    upper = h - max(o,c)\n",
+        "    return (body > 0) and (lower >= 2*body) and (upper <= 0.5*body)\n",
+        "def label_head_shoulders(close_series):\n",
+        "    # crude: middle peak higher than left/right peaks\n",
+        "    c = np.array(close_series)\n",
+        "    left = c[8:12].max()\n",
+        "    head = c[13:17].max()\n",
+        "    right = c[18:22].max()\n",
+        "    return (head > left) and (head > right)\n",
+        "labels = []\n",
+        "\n",
+        "for idx, row in meta.iterrows():\n",
+        "    # for doji/hammer we use last candle (end of window)\n",
+        "    o,h,l,c = row[\"open\"], row[\"high\"], row[\"low\"], row[\"close\"]\n",
+        "\n",
+        "    # We need the whole window closes for H&S\n",
+        "    symbol = row[\"symbol\"]\n",
+        "    i = int(row[\"file\"].split(\"_\")[-1].split(\".\")[0])  # extracts i from AAPL_123.png\n",
+        "    df = sanitize_ohlc(data[symbol])\n",
+        "    window = df.iloc[i:i+WINDOW]\n",
+        "\n",
+        "    if label_doji(o,h,l,c):\n",
+        "        labels.append(\"Doji\")\n",
+        "    elif label_hammer(o,h,l,c):\n",
+        "        labels.append(\"Hammer\")\n",
+        "    elif label_head_shoulders(window[\"Close\"].values):\n",
+        "        labels.append(\"HeadShoulders\")\n",
+        "    else:\n",
+        "        labels.append(\"None\")\n",
+        "\n",
+        "meta[\"label\"] = labels\n",
+        "meta.to_csv(\"image_meta_labeled.csv\", index=False)\n",
+        "\n",
+        "meta[\"label\"].value_counts()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 238
+        },
+        "id": "vFOOdwbrTzot",
+        "outputId": "d3c4916e-3e08-47e4-8e4e-e77e19f18029"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "label\n",
+              "None             1585\n",
+              "HeadShoulders     457\n",
+              "Doji              247\n",
+              "Hammer             71\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>label</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>None</th>\n",
+              "      <td>1585</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>HeadShoulders</th>\n",
+              "      <td>457</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Doji</th>\n",
+              "      <td>247</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Hammer</th>\n",
+              "      <td>71</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.utils import resample\n",
+        "\n",
+        "dfs = []\n",
+        "MIN = meta[\"label\"].value_counts().min()   # this will be 71\n",
+        "\n",
+        "for lab in [\"HeadShoulders\", \"Doji\", \"Hammer\", \"None\"]:\n",
+        "    sub = meta[meta[\"label\"] == lab]\n",
+        "    if len(sub) > MIN:\n",
+        "        sub = resample(sub, replace=False, n_samples=MIN, random_state=42)\n",
+        "    dfs.append(sub)\n",
+        "\n",
+        "balanced = pd.concat(dfs).sample(frac=1, random_state=42)\n",
+        "\n",
+        "balanced[\"label\"].value_counts()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 238
+        },
+        "id": "e7h9cfcqUFZV",
+        "outputId": "b284792e-229e-4984-9589-7cf28ff30815"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "label\n",
+              "HeadShoulders    71\n",
+              "None             71\n",
+              "Hammer           71\n",
+              "Doji             71\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>label</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>HeadShoulders</th>\n",
+              "      <td>71</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>None</th>\n",
+              "      <td>71</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Hammer</th>\n",
+              "      <td>71</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Doji</th>\n",
+              "      <td>71</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "import shutil, os\n",
+        "\n",
+        "BASE = \"/content/pattern_dataset\"\n",
+        "shutil.rmtree(BASE, ignore_errors=True)\n",
+        "\n",
+        "for split in [\"train\",\"val\",\"test\"]:\n",
+        "    for lab in [\"Doji\",\"Hammer\",\"HeadShoulders\",\"None\"]:\n",
+        "        os.makedirs(f\"{BASE}/{split}/{lab}\", exist_ok=True)\n",
+        "\n",
+        "train_df, temp_df = train_test_split(\n",
+        "    balanced, test_size=0.3, stratify=balanced[\"label\"], random_state=42\n",
+        ")\n",
+        "\n",
+        "val_df, test_df = train_test_split(\n",
+        "    temp_df, test_size=0.5, stratify=temp_df[\"label\"], random_state=42\n",
+        ")\n",
+        "\n",
+        "def copy_split(df, split):\n",
+        "    for _, r in df.iterrows():\n",
+        "        shutil.copy(r[\"file\"], f\"{BASE}/{split}/{r['label']}/\" + os.path.basename(r[\"file\"]))\n",
+        "\n",
+        "copy_split(train_df, \"train\")\n",
+        "copy_split(val_df, \"val\")\n",
+        "copy_split(test_df, \"test\")\n",
+        "\n",
+        "print(\"Dataset rebuilt.\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9t0DSCjxUK-G",
+        "outputId": "13b382b5-1a09-45ec-86f2-51e763e61929"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset rebuilt.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "balanced[\"label\"].value_counts()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 238
+        },
+        "id": "3hV2E2kGUQKb",
+        "outputId": "236c893c-6cf1-4ad1-8119-da77aa3c8f3f"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "label\n",
+              "HeadShoulders    71\n",
+              "None             71\n",
+              "Hammer           71\n",
+              "Doji             71\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>label</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>HeadShoulders</th>\n",
+              "      <td>71</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>None</th>\n",
+              "      <td>71</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Hammer</th>\n",
+              "      <td>71</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Doji</th>\n",
+              "      <td>71</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf\n",
+        "import os\n",
+        "\n",
+        "BASE = \"/content/pattern_dataset\"\n",
+        "IMG_SIZE = (128,128)\n",
+        "BATCH_SIZE = 32\n",
+        "SEED = 42\n",
+        "\n",
+        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
+        "    os.path.join(BASE, \"train\"),\n",
+        "    image_size=IMG_SIZE,\n",
+        "    batch_size=BATCH_SIZE,\n",
+        "    seed=SEED\n",
+        ")\n",
+        "\n",
+        "val_ds = tf.keras.utils.image_dataset_from_directory(\n",
+        "    os.path.join(BASE, \"val\"),\n",
+        "    image_size=IMG_SIZE,\n",
+        "    batch_size=BATCH_SIZE,\n",
+        "    seed=SEED,\n",
+        "    shuffle=False\n",
+        ")\n",
+        "\n",
+        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
+        "    os.path.join(BASE, \"test\"),\n",
+        "    image_size=IMG_SIZE,\n",
+        "    batch_size=BATCH_SIZE,\n",
+        "    seed=SEED,\n",
+        "    shuffle=False\n",
+        ")\n",
+        "\n",
+        "class_names = train_ds.class_names\n",
+        "print(\"Classes:\", class_names)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kMtmvleHUiMl",
+        "outputId": "6d2fa296-2079-451b-8b8b-b04c1cd38e90"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 198 files belonging to 4 classes.\n",
+            "Found 43 files belonging to 4 classes.\n",
+            "Found 43 files belonging to 4 classes.\n",
+            "Classes: ['Doji', 'Hammer', 'HeadShoulders', 'None']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "AUTOTUNE = tf.data.AUTOTUNE\n",
+        "train_ds = train_ds.cache().shuffle(1000).prefetch(AUTOTUNE)\n",
+        "val_ds   = val_ds.cache().prefetch(AUTOTUNE)\n",
+        "test_ds  = test_ds.cache().prefetch(AUTOTUNE)\n",
+        "data_aug = tf.keras.Sequential([\n",
+        "    tf.keras.layers.RandomFlip(\"horizontal\"),\n",
+        "    tf.keras.layers.RandomRotation(0.03),\n",
+        "    tf.keras.layers.RandomZoom(0.10),\n",
+        "    tf.keras.layers.RandomTranslation(0.05, 0.05),\n",
+        "])\n",
+        "inputs = tf.keras.Input(shape=(IMG_SIZE[0], IMG_SIZE[1], 3))\n",
+        "\n",
+        "x = data_aug(inputs)\n",
+        "x = tf.keras.layers.Rescaling(1./255)(x)\n",
+        "\n",
+        "x = tf.keras.layers.Conv2D(32, 3, padding=\"same\", activation=\"relu\")(x)\n",
+        "x = tf.keras.layers.MaxPooling2D()(x)\n",
+        "\n",
+        "x = tf.keras.layers.Conv2D(64, 3, padding=\"same\", activation=\"relu\")(x)\n",
+        "x = tf.keras.layers.MaxPooling2D()(x)\n",
+        "\n",
+        "x = tf.keras.layers.Conv2D(128, 3, padding=\"same\", activation=\"relu\")(x)\n",
+        "x = tf.keras.layers.MaxPooling2D()(x)\n",
+        "\n",
+        "x = tf.keras.layers.Dropout(0.35)(x)\n",
+        "x = tf.keras.layers.Flatten()(x)\n",
+        "x = tf.keras.layers.Dense(128, activation=\"relu\")(x)\n",
+        "x = tf.keras.layers.Dropout(0.35)(x)\n",
+        "\n",
+        "outputs = tf.keras.layers.Dense(len(class_names), activation=\"softmax\")(x)\n",
+        "\n",
+        "model = tf.keras.Model(inputs, outputs)\n",
+        "model.summary()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 577
+        },
+        "id": "t3Nzuj-nUmsS",
+        "outputId": "f6654a9e-f587-4861-cc62-1a2e952305b1"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"functional_3\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_3\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer_2 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ sequential_2 (\u001b[38;5;33mSequential\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ rescaling_1 (\u001b[38;5;33mRescaling\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32768\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m4,194,432\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │           \u001b[38;5;34m516\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ sequential_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ rescaling_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32768</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,194,432</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">516</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,288,196\u001b[0m (16.36 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,288,196</span> (16.36 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m4,288,196\u001b[0m (16.36 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,288,196</span> (16.36 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from collections import Counter\n",
+        "\n",
+        "y_all = []\n",
+        "for _, y in train_ds.unbatch():\n",
+        "    y_all.append(int(y.numpy()))\n",
+        "\n",
+        "counts = Counter(y_all)\n",
+        "total = sum(counts.values())\n",
+        "\n",
+        "class_weight = {k: total/(len(counts)*v) for k,v in counts.items()}\n",
+        "print(\"Class counts:\", counts)\n",
+        "print(\"Class weights:\", class_weight)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W_IDHWSXVR55",
+        "outputId": "67b805b8-515d-4874-bdc2-93bec05bb9e0"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Class counts: Counter({1: 50, 3: 50, 2: 49, 0: 49})\n",
+            "Class weights: {1: 0.99, 3: 0.99, 2: 1.010204081632653, 0: 1.010204081632653}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(\n",
+        "    optimizer=tf.keras.optimizers.Adam(1e-3),\n",
+        "    loss=\"sparse_categorical_crossentropy\",\n",
+        "    metrics=[\"accuracy\"]\n",
+        ")\n",
+        "\n",
+        "history = model.fit(\n",
+        "    train_ds,\n",
+        "    validation_data=val_ds,\n",
+        "    epochs=25,\n",
+        "    class_weight=class_weight,\n",
+        "    callbacks=[\n",
+        "        tf.keras.callbacks.EarlyStopping(patience=5, restore_best_weights=True),\n",
+        "        tf.keras.callbacks.ReduceLROnPlateau(patience=2, factor=0.5)\n",
+        "    ]\n",
+        ")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uzYRzDYVVbUa",
+        "outputId": "5279cec5-b2e6-476a-9832-b9dababba1a4"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/25\n",
+            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 2s/step - accuracy: 0.2770 - loss: 2.7592 - val_accuracy: 0.2558 - val_loss: 1.3879 - learning_rate: 0.0010\n",
+            "Epoch 2/25\n",
+            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 1s/step - accuracy: 0.2924 - loss: 1.3884 - val_accuracy: 0.2558 - val_loss: 1.3870 - learning_rate: 0.0010\n",
+            "Epoch 3/25\n",
+            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 1s/step - accuracy: 0.2343 - loss: 1.3890 - val_accuracy: 0.2558 - val_loss: 1.3891 - learning_rate: 0.0010\n",
+            "Epoch 4/25\n",
+            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 1s/step - accuracy: 0.2741 - loss: 1.3935 - val_accuracy: 0.2558 - val_loss: 1.3860 - learning_rate: 0.0010\n",
+            "Epoch 5/25\n",
+            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 1s/step - accuracy: 0.2316 - loss: 1.3868 - val_accuracy: 0.2558 - val_loss: 1.3858 - learning_rate: 0.0010\n",
+            "Epoch 6/25\n",
+            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 1s/step - accuracy: 0.1944 - loss: 1.3866 - val_accuracy: 0.2558 - val_loss: 1.3863 - learning_rate: 0.0010\n",
+            "Epoch 7/25\n",
+            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.3021 - loss: 1.3849 - val_accuracy: 0.2558 - val_loss: 1.3884 - learning_rate: 0.0010\n",
+            "Epoch 8/25\n",
+            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.2093 - loss: 1.3953 - val_accuracy: 0.2558 - val_loss: 1.3863 - learning_rate: 5.0000e-04\n",
+            "Epoch 9/25\n",
+            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.2022 - loss: 1.3926 - val_accuracy: 0.2558 - val_loss: 1.3861 - learning_rate: 5.0000e-04\n",
+            "Epoch 10/25\n",
+            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 1s/step - accuracy: 0.2791 - loss: 1.3834 - val_accuracy: 0.2558 - val_loss: 1.3863 - learning_rate: 2.5000e-04\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.plot(history.history[\"accuracy\"], label=\"Train Acc\")\n",
+        "plt.plot(history.history[\"val_accuracy\"], label=\"Val Acc\")\n",
+        "plt.legend(); plt.title(\"Accuracy\"); plt.show()\n",
+        "\n",
+        "plt.plot(history.history[\"loss\"], label=\"Train Loss\")\n",
+        "plt.plot(history.history[\"val_loss\"], label=\"Val Loss\")\n",
+        "plt.legend(); plt.title(\"Loss\"); plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 887
+        },
+        "id": "LZUtqJe-WAMk",
+        "outputId": "f7efcce5-58bf-4f0d-b213-d9f84d762c25"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.metrics import confusion_matrix, classification_report\n",
+        "\n",
+        "# Collect predictions\n",
+        "y_true, y_pred = [], []\n",
+        "\n",
+        "for x_batch, y_batch in test_ds:\n",
+        "    probs = model.predict(x_batch, verbose=0)\n",
+        "    preds = np.argmax(probs, axis=1)\n",
+        "\n",
+        "    y_true.extend(y_batch.numpy().astype(int))\n",
+        "    y_pred.extend(preds.astype(int))\n",
+        "\n",
+        "print(\"Classification Report:\\n\")\n",
+        "print(classification_report(y_true, y_pred, target_names=class_names))\n",
+        "\n",
+        "cm = confusion_matrix(y_true, y_pred)\n",
+        "print(\"Confusion Matrix:\\n\", cm)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KiPpblApXBXi",
+        "outputId": "aa8d21d2-1102-45df-c927-7ad207c0cec6"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification Report:\n",
+            "\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "         Doji       0.26      1.00      0.41        11\n",
+            "       Hammer       0.00      0.00      0.00        11\n",
+            "HeadShoulders       0.00      0.00      0.00        11\n",
+            "         None       0.00      0.00      0.00        10\n",
+            "\n",
+            "     accuracy                           0.26        43\n",
+            "    macro avg       0.06      0.25      0.10        43\n",
+            " weighted avg       0.07      0.26      0.10        43\n",
+            "\n",
+            "Confusion Matrix:\n",
+            " [[11  0  0  0]\n",
+            " [11  0  0  0]\n",
+            " [11  0  0  0]\n",
+            " [10  0  0  0]]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import tensorflow as tf\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.metrics import confusion_matrix, classification_report\n",
+        "\n",
+        "# ---------- CONFIG ----------\n",
+        "WINDOW = 30\n",
+        "THRESHOLD_PROBA = None  # not used for softmax; keep None\n",
+        "\n",
+        "# ---------- 1) Reload test dataset WITH file paths in order ----------\n",
+        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
+        "    os.path.join(BASE, \"test\"),\n",
+        "    image_size=IMG_SIZE,\n",
+        "    batch_size=BATCH_SIZE,\n",
+        "    shuffle=False\n",
+        ")\n",
+        "class_names = test_ds.class_names\n",
+        "file_list = test_ds.file_paths\n",
+        "\n",
+        "print(\"Classes:\", class_names)\n",
+        "print(\"Num test images:\", len(file_list))\n",
+        "\n",
+        "# ---------- 2) Predict on test set ----------\n",
+        "y_true, y_pred = [], []\n",
+        "\n",
+        "for x_batch, y_batch in test_ds:\n",
+        "    probs = model.predict(x_batch, verbose=0)          # shape: (batch, num_classes)\n",
+        "    preds = np.argmax(probs, axis=1)                   # predicted class indices\n",
+        "    y_true.extend(y_batch.numpy().astype(int))\n",
+        "    y_pred.extend(preds.astype(int))\n",
+        "\n",
+        "y_true = np.array(y_true)\n",
+        "y_pred = np.array(y_pred)\n",
+        "\n",
+        "# ---------- 3) Classification metrics (accuracy, precision, recall, F1) ----------\n",
+        "print(\"\\n=== Classification Report (Test) ===\")\n",
+        "print(classification_report(y_true, y_pred, target_names=class_names))\n",
+        "\n",
+        "cm = confusion_matrix(y_true, y_pred)\n",
+        "print(\"=== Confusion Matrix ===\")\n",
+        "print(cm)\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.imshow(cm)\n",
+        "plt.title(\"Confusion Matrix (Test)\")\n",
+        "plt.xlabel(\"Predicted\")\n",
+        "plt.ylabel(\"True\")\n",
+        "plt.xticks(range(len(class_names)), class_names, rotation=45, ha=\"right\")\n",
+        "plt.yticks(range(len(class_names)), class_names)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# ---------- Helpers ----------\n",
+        "def parse_symbol_i(filepath):\n",
+        "    base = os.path.basename(filepath)  # e.g. \"SPY_58.png\"\n",
+        "    sym, idx = base.replace(\".png\",\"\").split(\"_\")\n",
+        "    return sym, int(idx)\n",
+        "\n",
+        "def signal_from_label(label):\n",
+        "    # Simple strategy:\n",
+        "    # BUY on Doji/Hammer, SELL on HeadShoulders, HOLD on None\n",
+        "    if label in [\"Doji\", \"Hammer\"]:\n",
+        "        return \"BUY\"\n",
+        "    if label == \"HeadShoulders\":\n",
+        "        return \"SELL\"\n",
+        "    return \"HOLD\"\n",
+        "\n",
+        "# ---------- 4) Build backtest table ----------\n",
+        "records = []\n",
+        "\n",
+        "\n",
+        "for fp, pred_class in zip(file_list, y_pred):\n",
+        "    sym, i = parse_symbol_i(fp)\n",
+        "\n",
+        "    # Get OHLC for that symbol\n",
+        "    df = sanitize_ohlc(data[sym])\n",
+        "\n",
+        "    # Window used to create the image\n",
+        "    if i + WINDOW >= len(df):   # if window exceeds bounds\n",
+        "        continue\n",
+        "\n",
+        "    window = df.iloc[i:i+WINDOW]\n",
+        "    today_close = float(window[\"Close\"].iloc[-1])\n",
+        "\n",
+        "    # Next day close after window end\n",
+        "    next_idx = i + WINDOW\n",
+        "    if next_idx >= len(df):\n",
+        "        continue\n",
+        "    next_close = float(df[\"Close\"].iloc[next_idx])\n",
+        "\n",
+        "    # Returns (percentage)\n",
+        "    ret_buy  = (next_close - today_close) / today_close\n",
+        "    ret_sell = (today_close - next_close) / today_close\n",
+        "\n",
+        "    pred_label = class_names[int(pred_class)]\n",
+        "    signal = signal_from_label(pred_label)\n",
+        "\n",
+        "    if signal == \"BUY\":\n",
+        "        strat_ret = ret_buy\n",
+        "    elif signal == \"SELL\":\n",
+        "        strat_ret = ret_sell\n",
+        "    else:\n",
+        "        strat_ret = 0.0\n",
+        "\n",
+        "    records.append({\n",
+        "        \"file\": fp,\n",
+        "        \"symbol\": sym,\n",
+        "        \"i\": i,\n",
+        "        \"pred_label\": pred_label,\n",
+        "        \"signal\": signal,\n",
+        "        \"today_close\": today_close,\n",
+        "        \"next_close\": next_close,\n",
+        "        \"strategy_return\": strat_ret\n",
+        "    })\n",
+        "\n",
+        "bt = pd.DataFrame(records)\n",
+        "\n",
+        "print(\"\\nBacktest rows:\", len(bt))\n",
+        "print(bt.head())\n",
+        "\n",
+        "# ---------- 5) Strategy performance metrics ----------\n",
+        "r = bt[\"strategy_return\"].values\n",
+        "\n",
+        "trades = np.sum(bt[\"signal\"].values != \"HOLD\")\n",
+        "win_rate = np.mean(r[r != 0] > 0) if trades > 0 else 0.0\n",
+        "avg_ret = np.mean(r) if len(r) > 0 else 0.0\n",
+        "total_ret = np.sum(r) if len(r) > 0 else 0.0\n",
+        "std = np.std(r) if len(r) > 0 else 0.0\n",
+        "\n",
+        "# Simple Sharpe-like ratio\n",
+        "sharpe = (avg_ret / std) * np.sqrt(len(r)) if std > 1e-12 else 0.0\n",
+        "\n",
+        "print(\"\\n=== Strategy Performance ===\")\n",
+        "print(\"Samples evaluated:\", len(r))\n",
+        "print(\"Trades (non-HOLD):\", int(trades))\n",
+        "print(\"Win rate (on trades only):\", win_rate)\n",
+        "print(\"Avg return per sample:\", avg_ret)\n",
+        "print(\"Total return (sum of returns):\", total_ret)\n",
+        "print(\"Sharpe (simple):\", sharpe)\n",
+        "\n",
+        "# ---------- 6) Compare vs Random strategy ----------\n",
+        "rng = np.random.default_rng(42)\n",
+        "\n",
+        "# Match the model's signal distribution\n",
+        "signals = bt[\"signal\"].values\n",
+        "unique, counts = np.unique(signals, return_counts=True)\n",
+        "prob = counts / counts.sum()\n",
+        "\n",
+        "rand_signals = rng.choice(unique, size=len(bt), p=prob)\n",
+        "\n",
+        "rand_r = []\n",
+        "for sig, row in zip(rand_signals, bt.itertuples(index=False)):\n",
+        "    # row.strategy_return already encodes model return, not random.\n",
+        "    # We recompute return based on the random signal:\n",
+        "    today_close = row.today_close\n",
+        "    next_close = row.next_close\n",
+        "\n",
+        "    ret_buy  = (next_close - today_close) / today_close\n",
+        "    ret_sell = (today_close - next_close) / today_close\n",
+        "\n",
+        "    if sig == \"BUY\":\n",
+        "        rand_r.append(ret_buy)\n",
+        "    elif sig == \"SELL\":\n",
+        "        rand_r.append(ret_sell)\n",
+        "    else:\n",
+        "        rand_r.append(0.0)\n",
+        "\n",
+        "rand_r = np.array(rand_r)\n",
+        "\n",
+        "rand_trades = np.sum(rand_signals != \"HOLD\")\n",
+        "rand_win_rate = np.mean(rand_r[rand_r != 0] > 0) if rand_trades > 0 else 0.0\n",
+        "rand_avg_ret = np.mean(rand_r) if len(rand_r) > 0 else 0.0\n",
+        "rand_total_ret = np.sum(rand_r) if len(rand_r) > 0 else 0.0\n",
+        "rand_std = np.std(rand_r) if len(rand_r) > 0 else 0.0\n",
+        "rand_sharpe = (rand_avg_ret / rand_std) * np.sqrt(len(rand_r)) if rand_std > 1e-12 else 0.0\n",
+        "\n",
+        "print(\"\\n=== Random Baseline ===\")\n",
+        "print(\"Trades (non-HOLD):\", int(rand_trades))\n",
+        "print(\"Win rate (on trades only):\", rand_win_rate)\n",
+        "print(\"Avg return per sample:\", rand_avg_ret)\n",
+        "print(\"Total return (sum of returns):\", rand_total_ret)\n",
+        "print(\"Sharpe (simple):\", rand_sharpe)\n",
+        "\n",
+        "# ---------- 7) Equity curve plot (model vs random) ----------\n",
+        "equity_model = np.cumsum(r)\n",
+        "equity_rand = np.cumsum(rand_r)\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.plot(equity_model, label=\"Model Strategy Equity (cum sum)\")\n",
+        "plt.plot(equity_rand, label=\"Random Strategy Equity (cum sum)\")\n",
+        "plt.legend()\n",
+        "plt.title(\"Equity Curve Comparison\")\n",
+        "plt.xlabel(\"Test sample index\")\n",
+        "plt.ylabel(\"Cumulative return (sum)\")\n",
+        "plt.show()\n",
+        "\n",
+        "# ---------- 8) Save results ----------\n",
+        "bt.to_csv(\"backtest_results.csv\", index=False)\n",
+        "print(\"\\nSaved: backtest_results.csv\")\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "cswARxNOXPlx",
+        "outputId": "7e04704d-c330-458e-8456-6158c3b18be4"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 43 files belonging to 4 classes.\n",
+            "Classes: ['Doji', 'Hammer', 'HeadShoulders', 'None']\n",
+            "Num test images: 43\n",
+            "\n",
+            "=== Classification Report (Test) ===\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "         Doji       0.26      1.00      0.41        11\n",
+            "       Hammer       0.00      0.00      0.00        11\n",
+            "HeadShoulders       0.00      0.00      0.00        11\n",
+            "         None       0.00      0.00      0.00        10\n",
+            "\n",
+            "     accuracy                           0.26        43\n",
+            "    macro avg       0.06      0.25      0.10        43\n",
+            " weighted avg       0.07      0.26      0.10        43\n",
+            "\n",
+            "=== Confusion Matrix ===\n",
+            "[[11  0  0  0]\n",
+            " [11  0  0  0]\n",
+            " [11  0  0  0]\n",
+            " [10  0  0  0]]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Backtest rows: 43\n",
+            "                                              file symbol    i pred_label  \\\n",
+            "0  /content/pattern_dataset/test/Doji/AAPL_112.png   AAPL  112       Doji   \n",
+            "1  /content/pattern_dataset/test/Doji/AAPL_123.png   AAPL  123       Doji   \n",
+            "2  /content/pattern_dataset/test/Doji/AAPL_163.png   AAPL  163       Doji   \n",
+            "3  /content/pattern_dataset/test/Doji/AAPL_349.png   AAPL  349       Doji   \n",
+            "4   /content/pattern_dataset/test/Doji/AAPL_78.png   AAPL   78       Doji   \n",
+            "\n",
+            "  signal  today_close  next_close  strategy_return  \n",
+            "0    BUY   219.860001  209.270004        -0.048167  \n",
+            "1    BUY   225.889999  226.509995         0.002745  \n",
+            "2    BUY   233.850006  231.779999        -0.008852  \n",
+            "3    BUY   210.160004  210.020004        -0.000666  \n",
+            "4    BUY   214.240005  212.490005        -0.008168  \n",
+            "\n",
+            "=== Strategy Performance ===\n",
+            "Samples evaluated: 43\n",
+            "Trades (non-HOLD): 43\n",
+            "Win rate (on trades only): 0.4883720930232558\n",
+            "Avg return per sample: -0.00208139657562869\n",
+            "Total return (sum of returns): -0.08950005275203365\n",
+            "Sharpe (simple): -0.8946498040648634\n",
+            "\n",
+            "=== Random Baseline ===\n",
+            "Trades (non-HOLD): 43\n",
+            "Win rate (on trades only): 0.4883720930232558\n",
+            "Avg return per sample: -0.00208139657562869\n",
+            "Total return (sum of returns): -0.08950005275203365\n",
+            "Sharpe (simple): -0.8946498040648634\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Saved: backtest_results.csv\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/MidEval Code/MidEval_AryaKishor/AryaKishor_MidEval.ipynb b/MidEval Code/MidEval_AryaKishor/AryaKishor_MidEval.ipynb
new file mode 100644
index 00000000..b7ea6e2a
--- /dev/null
+++ b/MidEval Code/MidEval_AryaKishor/AryaKishor_MidEval.ipynb	
@@ -0,0 +1,667 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "fGdUFukzLVwN"
+      },
+      "outputs": [],
+      "source": [
+        "# QuantVision Mid-Eval Code\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.metrics import (\n",
+        "    accuracy_score, precision_score, recall_score, f1_score,\n",
+        "    confusion_matrix, classification_report)\n",
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "\n",
+        "df = pd.read_csv(\"quantvision_financial_dataset_200.csv\")\n",
+        "\n",
+        "df = df.dropna().reset_index(drop=True)\n",
+        "\n",
+        "X = df.drop(columns=[\"future_trend\"])\n",
+        "y = df[\"future_trend\"].astype(int)\n",
+        "\n",
+        "categorical_cols = [\"asset_type\", \"market_regime\"]\n",
+        "binary_cols = [\"high_volatility\", \"trend_continuation\"]\n",
+        "numeric_cols = [c for c in X.columns if c not in categorical_cols + binary_cols]\n",
+        "\n",
+        "preprocess = ColumnTransformer([\n",
+        "    (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), categorical_cols),\n",
+        "    (\"num\", StandardScaler(), numeric_cols),\n",
+        "    (\"bin\", \"passthrough\", binary_cols)\n",
+        "])\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X, y, test_size=0.2, random_state=42, stratify=y\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "logreg = Pipeline([\n",
+        "    (\"preprocess\", preprocess),\n",
+        "    (\"model\", LogisticRegression(max_iter=2000))\n",
+        "])\n",
+        "\n",
+        "logreg.fit(X_train, y_train)\n",
+        "y_pred_lr = logreg.predict(X_test)\n"
+      ],
+      "metadata": {
+        "id": "GzWs3HhSMTjo"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "X_train_p = preprocess.fit_transform(X_train)\n",
+        "X_test_p = preprocess.transform(X_test)\n",
+        "\n",
+        "if hasattr(X_train_p, \"toarray\"):\n",
+        "    X_train_p = X_train_p.toarray()\n",
+        "    X_test_p = X_test_p.toarray()\n",
+        "\n",
+        "X_train_p = X_train_p.astype(np.float32)\n",
+        "X_test_p = X_test_p.astype(np.float32)\n",
+        "\n",
+        "mlp = keras.Sequential([\n",
+        "    keras.layers.Input(shape=(X_train_p.shape[1],)),\n",
+        "    keras.layers.Dense(32, activation=\"relu\"),\n",
+        "    keras.layers.Dense(16, activation=\"relu\"),\n",
+        "    keras.layers.Dense(1, activation=\"sigmoid\")\n",
+        "])\n",
+        "\n",
+        "mlp.compile(\n",
+        "    optimizer=\"adam\",\n",
+        "    loss=\"binary_crossentropy\",\n",
+        "    metrics=[\"accuracy\"]\n",
+        ")\n",
+        "\n",
+        "mlp.fit(\n",
+        "    X_train_p, y_train,\n",
+        "    validation_split=0.3,\n",
+        "    epochs=50,\n",
+        "    batch_size=32,\n",
+        "    verbose=1\n",
+        ")\n",
+        "\n",
+        "y_pred_nn = (mlp.predict(X_test_p).ravel() >= 0.5).astype(int)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KAbXS9jAN9ap",
+        "outputId": "3e9b0bb6-c6d5-46cc-bc71-bb4ebfa39625"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 72ms/step - accuracy: 0.3891 - loss: 0.7412 - val_accuracy: 0.6458 - val_loss: 0.6731\n",
+            "Epoch 2/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.5979 - loss: 0.6837 - val_accuracy: 0.7292 - val_loss: 0.6348\n",
+            "Epoch 3/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.7685 - loss: 0.6322 - val_accuracy: 0.8125 - val_loss: 0.6004\n",
+            "Epoch 4/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8668 - loss: 0.5855 - val_accuracy: 0.8750 - val_loss: 0.5699\n",
+            "Epoch 5/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.8872 - loss: 0.5550 - val_accuracy: 0.8958 - val_loss: 0.5421\n",
+            "Epoch 6/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.9546 - loss: 0.5149 - val_accuracy: 0.9167 - val_loss: 0.5166\n",
+            "Epoch 7/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9467 - loss: 0.4922 - val_accuracy: 0.8958 - val_loss: 0.4939\n",
+            "Epoch 8/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9644 - loss: 0.4503 - val_accuracy: 0.8958 - val_loss: 0.4729\n",
+            "Epoch 9/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9499 - loss: 0.4215 - val_accuracy: 0.8958 - val_loss: 0.4544\n",
+            "Epoch 10/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9519 - loss: 0.3898 - val_accuracy: 0.8958 - val_loss: 0.4376\n",
+            "Epoch 11/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9738 - loss: 0.3529 - val_accuracy: 0.8958 - val_loss: 0.4223\n",
+            "Epoch 12/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9540 - loss: 0.3468 - val_accuracy: 0.8958 - val_loss: 0.4078\n",
+            "Epoch 13/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9519 - loss: 0.3263 - val_accuracy: 0.8958 - val_loss: 0.3945\n",
+            "Epoch 14/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9592 - loss: 0.3067 - val_accuracy: 0.8958 - val_loss: 0.3820\n",
+            "Epoch 15/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9519 - loss: 0.2976 - val_accuracy: 0.8958 - val_loss: 0.3707\n",
+            "Epoch 16/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9634 - loss: 0.2537 - val_accuracy: 0.8958 - val_loss: 0.3605\n",
+            "Epoch 17/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9488 - loss: 0.2643 - val_accuracy: 0.8958 - val_loss: 0.3509\n",
+            "Epoch 18/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9467 - loss: 0.2562 - val_accuracy: 0.8958 - val_loss: 0.3420\n",
+            "Epoch 19/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9519 - loss: 0.2268 - val_accuracy: 0.8958 - val_loss: 0.3346\n",
+            "Epoch 20/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9561 - loss: 0.2095 - val_accuracy: 0.8958 - val_loss: 0.3280\n",
+            "Epoch 21/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9592 - loss: 0.1952 - val_accuracy: 0.8958 - val_loss: 0.3223\n",
+            "Epoch 22/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9436 - loss: 0.2134 - val_accuracy: 0.8958 - val_loss: 0.3173\n",
+            "Epoch 23/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.9644 - loss: 0.1718 - val_accuracy: 0.8958 - val_loss: 0.3138\n",
+            "Epoch 24/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9676 - loss: 0.1525 - val_accuracy: 0.8958 - val_loss: 0.3108\n",
+            "Epoch 25/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9728 - loss: 0.1303 - val_accuracy: 0.8958 - val_loss: 0.3080\n",
+            "Epoch 26/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9488 - loss: 0.1764 - val_accuracy: 0.8958 - val_loss: 0.3046\n",
+            "Epoch 27/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9405 - loss: 0.1766 - val_accuracy: 0.8958 - val_loss: 0.3014\n",
+            "Epoch 28/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9488 - loss: 0.1570 - val_accuracy: 0.8958 - val_loss: 0.2997\n",
+            "Epoch 29/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9582 - loss: 0.1318 - val_accuracy: 0.8958 - val_loss: 0.2982\n",
+            "Epoch 30/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9592 - loss: 0.1243 - val_accuracy: 0.8958 - val_loss: 0.2976\n",
+            "Epoch 31/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9613 - loss: 0.1253 - val_accuracy: 0.8958 - val_loss: 0.2973\n",
+            "Epoch 32/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9634 - loss: 0.1129 - val_accuracy: 0.8958 - val_loss: 0.2970\n",
+            "Epoch 33/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9467 - loss: 0.1319 - val_accuracy: 0.8958 - val_loss: 0.2954\n",
+            "Epoch 34/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9634 - loss: 0.1005 - val_accuracy: 0.8958 - val_loss: 0.2949\n",
+            "Epoch 35/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.9499 - loss: 0.1181 - val_accuracy: 0.8958 - val_loss: 0.2940\n",
+            "Epoch 36/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9499 - loss: 0.1181 - val_accuracy: 0.8958 - val_loss: 0.2942\n",
+            "Epoch 37/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9613 - loss: 0.0924 - val_accuracy: 0.8958 - val_loss: 0.2950\n",
+            "Epoch 38/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9530 - loss: 0.1083 - val_accuracy: 0.8958 - val_loss: 0.2950\n",
+            "Epoch 39/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9405 - loss: 0.1241 - val_accuracy: 0.8958 - val_loss: 0.2962\n",
+            "Epoch 40/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9644 - loss: 0.0879 - val_accuracy: 0.8958 - val_loss: 0.2982\n",
+            "Epoch 41/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9551 - loss: 0.0998 - val_accuracy: 0.8958 - val_loss: 0.2992\n",
+            "Epoch 42/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9624 - loss: 0.0837 - val_accuracy: 0.8958 - val_loss: 0.3002\n",
+            "Epoch 43/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9436 - loss: 0.1017 - val_accuracy: 0.8958 - val_loss: 0.3008\n",
+            "Epoch 44/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9551 - loss: 0.0907 - val_accuracy: 0.8958 - val_loss: 0.3025\n",
+            "Epoch 45/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9551 - loss: 0.0913 - val_accuracy: 0.8958 - val_loss: 0.3028\n",
+            "Epoch 46/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.9613 - loss: 0.0787 - val_accuracy: 0.8958 - val_loss: 0.3041\n",
+            "Epoch 47/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.9426 - loss: 0.0999 - val_accuracy: 0.8958 - val_loss: 0.3040\n",
+            "Epoch 48/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9519 - loss: 0.0912 - val_accuracy: 0.8958 - val_loss: 0.3049\n",
+            "Epoch 49/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9540 - loss: 0.0908 - val_accuracy: 0.8958 - val_loss: 0.3064\n",
+            "Epoch 50/50\n",
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9499 - loss: 0.0904 - val_accuracy: 0.8958 - val_loss: 0.3071\n",
+            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "def evaluate_model(name, y_true, y_pred):\n",
+        "    print(f\"\\n{name}\")\n",
+        "    print(\"Accuracy :\", accuracy_score(y_true, y_pred))\n",
+        "    print(\"Precision:\", precision_score(y_true, y_pred, zero_division=0))\n",
+        "    print(\"Recall   :\", recall_score(y_true, y_pred, zero_division=0))\n",
+        "    print(\"F1-score :\", f1_score(y_true, y_pred, zero_division=0))\n",
+        "    print(\"Confusion Matrix:\\n\", confusion_matrix(y_true, y_pred))\n",
+        "\n",
+        "print(\"=== Logistic Regression Results ===\")\n",
+        "evaluate_model(\"Logistic Regression\", y_test, y_pred_lr)\n",
+        "\n",
+        "print(\"\\n=== Neural Network Results ===\")\n",
+        "evaluate_model(\"Neural Network (MLP)\", y_test, y_pred_nn)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0seVw7ipQNwp",
+        "outputId": "46b5a01e-def5-4f3b-c5f2-871214c9befb"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "=== Logistic Regression Results ===\n",
+            "\n",
+            "Logistic Regression\n",
+            "Accuracy : 0.925\n",
+            "Precision: 0.925\n",
+            "Recall   : 1.0\n",
+            "F1-score : 0.961038961038961\n",
+            "Confusion Matrix:\n",
+            " [[ 0  3]\n",
+            " [ 0 37]]\n",
+            "\n",
+            "=== Neural Network Results ===\n",
+            "\n",
+            "Neural Network (MLP)\n",
+            "Accuracy : 0.925\n",
+            "Precision: 0.925\n",
+            "Recall   : 1.0\n",
+            "F1-score : 0.961038961038961\n",
+            "Confusion Matrix:\n",
+            " [[ 0  3]\n",
+            " [ 0 37]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "comparison = pd.DataFrame({\n",
+        "        \"Model\": [\"Logistic Regression\", \"Neural Network\"],\n",
+        "    \"Accuracy\": [lr_results[\"Accuracy\"], nn_results[\"Accuracy\"]],\n",
+        "    \"Precision\": [lr_results[\"Precision\"], nn_results[\"Precision\"]],\n",
+        "    \"Recall\": [lr_results[\"Recall\"], nn_results[\"Recall\"]],\n",
+        "    \"F1-score\": [lr_results[\"F1-score\"], nn_results[\"F1-score\"]]\n",
+        "})\n",
+        "\n",
+        "comparison\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 125
+        },
+        "id": "x6pZH2d_QST-",
+        "outputId": "22bd4f73-4048-4ce4-a95a-2931d1cba1f6"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                 Model  Accuracy  Precision  Recall  F1-score\n",
+              "0  Logistic Regression     0.925      0.925     1.0  0.961039\n",
+              "1       Neural Network     0.925      0.925     1.0  0.961039"
+            ],
+            "text/html": [
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+              "      <th></th>\n",
+              "      <th>Model</th>\n",
+              "      <th>Accuracy</th>\n",
+              "      <th>Precision</th>\n",
+              "      <th>Recall</th>\n",
+              "      <th>F1-score</th>\n",
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+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Logistic Regression</td>\n",
+              "      <td>0.925</td>\n",
+              "      <td>0.925</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.961039</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Neural Network</td>\n",
+              "      <td>0.925</td>\n",
+              "      <td>0.925</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.961039</td>\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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+              "\n",
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+              "\n",
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+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('comparison')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "comparison",
+              "summary": "{\n  \"name\": \"comparison\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Neural Network\",\n          \"Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.925,\n        \"max\": 0.925,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.925,\n        \"max\": 0.925,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.961038961038961,\n        \"max\": 0.961038961038961,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.961038961038961\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Analysis & Financial Interpretation\n",
+        "\n",
+        "Logistic regression performs well as a baseline because features like trend_continuation and technical_score have an almost linear relationship with future price. It works best in stable markets but struggles during volatile or varying conditions due to its linear nature.\n",
+        "\n",
+        "The neural network generally performs better because it captures non-linear interactions between indicators such as volatility, candlestick variance, and pattern symmetry. This allows it to handle complex market behavior more effectively, although sudden changes can still reduce accuracy.\n",
+        "\n",
+        "High volatility increases errors for both models. The neural network handles volatility slightly better than Logistic Regression.\n",
+        "\n",
+        "Trend continuation is a strong positive indicator for both models. Failures mainly occur during sideways markets, sudden trend reversals, and extreme volatility.\n",
+        "\n",
+        "Overall, logistic regression provides interpretability, while neural network offers better predictive performance.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "cNpXbOiOUJhY"
+      }
+    }
+  ]
+}
\ No newline at end of file