From ac792f273888b1e8e946ebc26247672458361eb2 Mon Sep 17 00:00:00 2001 From: hemant-hk25 Date: Wed, 17 Dec 2025 21:00:31 +0530 Subject: [PATCH 1/6] Added Assignment1_Hemant --- .../Assignment1_Hemant_Matplotlib.ipynb | 446 +++ .../Assignment1_Hemant_Numpy.ipynb | 512 +++ .../Assignment1_Hemant_Pandas.ipynb | 3085 +++++++++++++++++ 3 files changed, 4043 insertions(+) create mode 100644 Assignment 1/Assignment1_Hemant/Assignment1_Hemant_Matplotlib.ipynb create mode 100644 Assignment 1/Assignment1_Hemant/Assignment1_Hemant_Numpy.ipynb create mode 100644 Assignment 1/Assignment1_Hemant/Assignment1_Hemant_Pandas.ipynb diff --git a/Assignment 1/Assignment1_Hemant/Assignment1_Hemant_Matplotlib.ipynb b/Assignment 1/Assignment1_Hemant/Assignment1_Hemant_Matplotlib.ipynb new file mode 100644 index 00000000..ffb1d43d --- /dev/null +++ b/Assignment 1/Assignment1_Hemant/Assignment1_Hemant_Matplotlib.ipynb @@ -0,0 +1,446 @@ +{ + "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": 2, + "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": 3, + "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": 4, + "metadata": { + "id": "03Ts4r5SST2g", + "outputId": "31098077-7b08-470c-94e2-6c7c0d9decb9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 600 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'title')" + ] + }, + "metadata": {}, + "execution_count": 4 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "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)\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "ax.set_title('title')\n" + ] + }, + { + "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": 5, + "metadata": { + "id": "D4nUfU26ST2k", + "outputId": "da62478f-79d0-4a4c-97e1-3a55cc954704", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig=plt.figure()\n", + "ax1=fig.add_axes([0,0,1,1])\n", + "ax2=fig.add_axes([0.2,0.5,.2,.2])\n", + "plt.show()" + ] + }, + { + "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", + "execution_count": 6, + "metadata": { + "id": "m_2gVMryST2m", + "outputId": "4110cff0-83a9-4da7-bced-faa80c2fab7b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 540 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig=plt.figure()\n", + "ax1=fig.add_axes([0,0,1,1])\n", + "ax2=fig.add_axes([0.2,0.5,.2,.2])\n", + "ax1.plot(x,y,color='brown')\n", + "ax2.plot(x,y,color='brown')\n", + "plt.show()" + ] + }, + { + "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": 7, + "metadata": { + "id": "BuiYx0xbST2o", + "outputId": "9d16591c-79f2-4fa6-8a16-50490d69eb20", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig=plt.figure()\n", + "ax1=fig.add_axes([0,0,1,1])\n", + "ax2=fig.add_axes([0.2,0.5,.4,.4])\n", + "plt.show()" + ] + }, + { + "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": 8, + "metadata": { + "id": "jhy7t5AKST2p", + "outputId": "19fcdb12-eb24-4129-e27f-dc337c5c277d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 565 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig=plt.figure()\n", + "\n", + "ax1=fig.add_axes([0,0,1,1])\n", + "ax2=fig.add_axes([0.2,0.5,.4,.4])\n", + "\n", + "ax1.set_xlabel('X')\n", + "ax1.set_ylabel('Z')\n", + "ax2.set_title(\"zoom\")\n", + "ax2.set_xlabel('X')\n", + "ax2.set_ylabel('Y')\n", + "\n", + "ax2.set_xlim(20, 22)\n", + "ax2.set_ylim(30, 50)\n", + "ax1.set_xlim(0, 100)\n", + "ax1.set_ylim(0, 10000)\n", + "\n", + "ax1.plot(x,z)\n", + "ax2.plot(x,y)\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": 20, + "metadata": { + "id": "osk0Jco2ST2r", + "outputId": "4a94d076-ede5-48f3-9f6f-2305bdf038ab", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 435 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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9GsuWLYuIiCVLlsT06dOjpaUlqqqq4uKLLx5w/XnnnRcRccJx4PRmdwClUHS4LFq0KF599dVYs2ZNtLe3x+zZs2P79u39P3R34MCBKC/3B3mBgewOoBTKUkpprId4O11dXVFTUxOdnZ1RXV091uPAGSfHx2COM8PpZiQeh769AQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8MKlw0bNsSMGTOiqqoq6uvrY9euXUOeu2nTprjyyitj4sSJMXHixGhsbHzL84HTl90BnKqiw2XLli3R1NQUzc3NsWfPnpg1a1bMnz8/XnnllUHP37FjR1x33XXxxBNPxM6dO6Ouri4+/vGPx5///OdTHh7Ih90BlEJZSikVc0F9fX1cdtllcf/990dERF9fX9TV1cWtt94aK1eufNvre3t7Y+LEiXH//ffHkiVLBj2nu7s7uru7+z/u6uqKurq66OzsjOrq6mLGBUqgq6srampqTukxONK7w96A8acUu+N4RT3j0tPTE7t3747GxsZ/fYLy8mhsbIydO3ee1Od47bXX4o033oh3vvOdQ57T0tISNTU1/be6urpixgTGmdHYHfYGnBmKCpdDhw5Fb29v1NbWDjheW1sb7e3tJ/U5brvttpg2bdqABXa8VatWRWdnZ//t4MGDxYwJjDOjsTvsDTgzTBjNO1u3bl1s3rw5duzYEVVVVUOeVygUolAojOJkwHh2MrvD3oAzQ1HhMmnSpKioqIiOjo4Bxzs6OmLKlClvee0999wT69ati1//+tdx6aWXFj8pkC27AyiVol4qqqysjDlz5kRra2v/sb6+vmhtbY2GhoYhr7v77rvjrrvuiu3bt8fcuXOHPy2QJbsDKJWiXypqamqKpUuXxty5c2PevHmxfv36OHr0aCxbtiwiIpYsWRLTp0+PlpaWiIj45je/GWvWrIkf//jHMWPGjP7Xs9/xjnfEO97xjhJ+KcB4ZncApVB0uCxatCheffXVWLNmTbS3t8fs2bNj+/bt/T90d+DAgSgv/9cTOd/97nejp6cnPvnJTw74PM3NzfHVr3711KYHsmF3AKVQ9N9xGQsj8XvgwMnL8TGY48xwuhnzv+MCADCWhAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkY1jhsmHDhpgxY0ZUVVVFfX197Nq16y3P/+lPfxoXXXRRVFVVxSWXXBLbtm0b1rBA3uwO4FQVHS5btmyJpqamaG5ujj179sSsWbNi/vz58corrwx6/lNPPRXXXXdd3HDDDbF3795YuHBhLFy4MP7whz+c8vBAPuwOoBTKUkqpmAvq6+vjsssui/vvvz8iIvr6+qKuri5uvfXWWLly5QnnL1q0KI4ePRq/+MUv+o99+MMfjtmzZ8fGjRsHvY/u7u7o7u7u/7izszPOP//8OHjwYFRXVxczLlACXV1dUVdXF4cPH46ampphfY6R3h32Bow/pdgdJ0hF6O7uThUVFemxxx4bcHzJkiXp2muvHfSaurq69J3vfGfAsTVr1qRLL710yPtpbm5OEeHm5jbObi+++GIxK2NUd4e94eY2fm/D3R2DmRBFOHToUPT29kZtbe2A47W1tfHss88Oek17e/ug57e3tw95P6tWrYqmpqb+jw8fPhzvfve748CBA6UrthH2ZmXm9N2emUdHjjO/+ezFO9/5zmFdPxq7w94YGznOHJHn3DnOfKq7YzBFhctoKRQKUSgUTjheU1OTzX+sN1VXV5t5FJh5dJSXj99fRLQ3xlaOM0fkOXeOM5dydxT1mSZNmhQVFRXR0dEx4HhHR0dMmTJl0GumTJlS1PnA6cfuAEqlqHCprKyMOXPmRGtra/+xvr6+aG1tjYaGhkGvaWhoGHB+RMSvfvWrIc8HTj92B1Ayxf5QzObNm1OhUEgPP/xweuaZZ9LNN9+czjvvvNTe3p5SSmnx4sVp5cqV/ef/7ne/SxMmTEj33HNP2rdvX2pubk5nnXVWevrpp0/6Pl9//fXU3NycXn/99WLHHTNmHh1mHh2lmHm0d8eZ+u95tOU4c0p5zm3mfyo6XFJK6b777kvnn39+qqysTPPmzUu///3v+//ZVVddlZYuXTrg/J/85CfpwgsvTJWVlekDH/hA2rp16ykNDeTJ7gBOVdF/xwUAYKyM318RAAA4jnABALIhXACAbAgXACAb4yZccny7+2Jm3rRpU1x55ZUxceLEmDhxYjQ2Nr7t1zgSiv33/KbNmzdHWVlZLFy4cGQHHESxMx8+fDiWL18eU6dOjUKhEBdeeOGo//9R7Mzr16+P9773vXH22WdHXV1drFixIl5//fVRmjbit7/9bSxYsCCmTZsWZWVl8bOf/extr9mxY0d86EMfikKhEO95z3vi4YcfHvE5j2dvjA57Y/TktDvGbG+M9a81pfTPv+9QWVmZHnroofS///u/6aabbkrnnXde6ujoGPT83/3ud6mioiLdfffd6Zlnnkm333570X8bZrRnvv7669OGDRvS3r170759+9JnPvOZVFNTk/70pz+N25nf9NJLL6Xp06enK6+8Mv3Hf/zH6Az7/xQ7c3d3d5o7d2665ppr0pNPPpleeumltGPHjtTW1jZuZ/7Rj36UCoVC+tGPfpReeuml9Pjjj6epU6emFStWjNrM27ZtS6tXr06PPvpoiogT3gzxePv370/nnHNOampqSs8880y67777UkVFRdq+ffvoDJzsjfE685vsjZGfe6x3x1jtjXERLvPmzUvLly/v/7i3tzdNmzYttbS0DHr+pz71qfSJT3xiwLH6+vr0X//1XyM65/+v2JmPd+zYsXTuueemH/7whyM14gmGM/OxY8fS5Zdfnr7//e+npUuXjvoCKnbm7373u2nmzJmpp6dntEY8QbEzL1++PH30ox8dcKypqSldccUVIzrnUE5mAX35y19OH/jABwYcW7RoUZo/f/4ITjaQvTE67I3Rk/PuGM29MeYvFfX09MTu3bujsbGx/1h5eXk0NjbGzp07B71m586dA86PiJg/f/6Q55facGY+3muvvRZvvPFGSd8x860Md+avfe1rMXny5LjhhhtGY8wBhjPzz3/+82hoaIjly5dHbW1tXHzxxbF27dro7e0dtzNffvnlsXv37v6nhPfv3x/btm2La665ZlRmHo4cH4M5znw8e+Pt5bg3Is6M3VGqx+CYvzv0aLzdfakNZ+bj3XbbbTFt2rQT/iOOlOHM/OSTT8aDDz4YbW1tozDhiYYz8/79++M3v/lNfPrTn45t27bFCy+8EJ/73OfijTfeiObm5nE58/XXXx+HDh2Kj3zkI5FSimPHjsUtt9wSX/nKV0Z83uEa6jHY1dUV//jHP+Lss88e0fu3N+yNoeS4NyLOjN1Rqr0x5s+4nInWrVsXmzdvjsceeyyqqqrGepxBHTlyJBYvXhybNm2KSZMmjfU4J62vry8mT54c3/ve92LOnDmxaNGiWL16dWzcuHGsRxvSjh07Yu3atfHAAw/Enj174tFHH42tW7fGXXfdNdajMY7YGyMnx70RcebujjF/xiXHt7sfzsxvuueee2LdunXx61//Oi699NKRHHOAYmd+8cUX4+WXX44FCxb0H+vr64uIiAkTJsRzzz0XF1xwwbiaOSJi6tSpcdZZZ0VFRUX/sfe9733R3t4ePT09UVlZOe5mvuOOO2Lx4sVx4403RkTEJZdcEkePHo2bb745Vq9eHeXl4+/7i6Eeg9XV1SP+bEuEvTFa7I3R2RsRZ8buKNXeGPOvKse3ux/OzBERd999d9x1112xffv2mDt37miM2q/YmS+66KJ4+umno62trf927bXXxtVXXx1tbW1RV1c37maOiLjiiivihRde6F+WERHPP/98TJ06dVSWz3Bmfu21105YMG8u0DRO30osx8dgjjNH2BsjPXPE2O+NiDNjd5TsMVjUj/KOkNF+u/uxmHndunWpsrIyPfLII+kvf/lL/+3IkSPjdubjjcVvBxQ784EDB9K5556b/vu//zs999xz6Re/+EWaPHly+vrXvz5uZ25ubk7nnntu+p//+Z+0f//+9Mtf/jJdcMEF6VOf+tSozXzkyJG0d+/etHfv3hQR6d5770179+5Nf/zjH1NKKa1cuTItXry4//w3f63xS1/6Utq3b1/asGHDmPw6tL0x/mY+nr0xcnOP9e4Yq70xLsIlpTzf7r6Ymd/97neniDjh1tzcPG5nPt5YLKCUip/5qaeeSvX19alQKKSZM2emb3zjG+nYsWPjduY33ngjffWrX00XXHBBqqqqSnV1delzn/tc+tvf/jZq8z7xxBOD/v/55pxLly5NV1111QnXzJ49O1VWVqaZM2emH/zgB6M275vsjfE38/HsjeLktDvGam+UpTQOn08CABjEmP+MCwDAyRIuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQjf8DOu0CiLveMb4AAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=2)\n", + "\n", + "plt.show()" + ] + }, + { + "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": 21, + "metadata": { + "id": "ZoEMYyLOST2r", + "outputId": "23c8c742-bd78-49cd-8904-7d8837a7dffc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": {}, + "execution_count": 21 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=2)\n", + "\n", + "axes[0].plot(x, y, color='blue', linestyle='--', linewidth=2)\n", + "axes[1].plot(x, z, color='red', linewidth=3)" + ] + }, + { + "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": 26, + "metadata": { + "id": "0pq5vAFZST2s", + "outputId": "31656c7a-62d8-4552-ca8a-52f2a1c2c143", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "axes[0].plot(x, y, color='blue', linestyle='--', linewidth=2)\n", + "axes[1].plot(x, z, color='red', linewidth=3)\n", + "plt.show()" + ] + }, + { + "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_Hemant/Assignment1_Hemant_Numpy.ipynb b/Assignment 1/Assignment1_Hemant/Assignment1_Hemant_Numpy.ipynb new file mode 100644 index 00000000..928569af --- /dev/null +++ b/Assignment 1/Assignment1_Hemant/Assignment1_Hemant_Numpy.ipynb @@ -0,0 +1,512 @@ +{ + "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": 1, + "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", + "execution_count": 11, + "metadata": { + "id": "aEQkK-Dw9xlN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8d25f7e9-ae61-426c-ffc8-a23e21f79e5f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "arr = np.array([0]*10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6QHp05Ei9xlN" + }, + "source": [ + "#### Create an array of 10 ones" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true, + "id": "ebe9xrC29xlN" + }, + "outputs": [], + "source": [ + "arr=np.array([1]*10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ziS-l3xp9xlO" + }, + "source": [ + "#### Create an array of 10 fives" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "fFC7bqLM9xlO" + }, + "outputs": [], + "source": [ + "arr=np.array([5]*10)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kBugMXlC9xlO" + }, + "source": [ + "#### Create an array of the integers from 10 to 50" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "JsO6qS9R9xlO" + }, + "outputs": [], + "source": [ + "arr=np.array([list(range(10,51))])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dMw4V2L79xlO" + }, + "source": [ + "#### Create an array of all the even integers from 10 to 50" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "4lK-5SQV9xlO" + }, + "outputs": [], + "source": [ + "arr=np.array([list(range(10,51,2))])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g__F0rB39xlP" + }, + "source": [ + "#### Create a 3x3 matrix with values ranging from 0 to 8" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "MmVXCn0K9xlP", + "outputId": "03b15ed7-689f-47af-a69e-ff332cd3e9df", + "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": 18 + } + ], + "source": [ + "arr=(np.arange(9)).reshape(3,3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4YfT0aLo9xlP" + }, + "source": [ + "#### Create a 3x3 identity matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "UHa42UpQ9xlP" + }, + "outputs": [], + "source": [ + "arr=np.identity(3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bfwDbjhI9xlP" + }, + "source": [ + "#### Use NumPy to generate a random number between 0 and 1" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "Z0OroZxW9xlP" + }, + "outputs": [], + "source": [ + "from numpy import random\n", + "arr=random.rand()\n" + ] + }, + { + "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": 16, + "metadata": { + "id": "szluy14n9xlP", + "outputId": "39c5d32f-5b64-4c29-c2ca-883c276e9bcc", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([-1.20328511, 0.24615778, -0.54428102, -0.60534616, -1.46874635,\n", + " 0.60731453, 1.08433543, -0.842758 , 0.38238288, 0.16531261,\n", + " 0.07403422, -1.02209496, -0.21699431, 0.19910139, 0.19277281,\n", + " 1.34080541, -0.10600271, 1.25169679, 0.46326342, 2.31687013,\n", + " 1.14144141, 0.10201743, -0.52266774, -0.37216551, -0.85106569])" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "from numpy import random\n", + "arr=random.normal(0,1,25)\n", + "arr" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_GhI8LYn9xlP" + }, + "source": [ + "#### Create the following matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "wS1ZBddV9xlP" + }, + "outputs": [], + "source": [ + "arr=np.arange(0.01,1.01,0.01).reshape(10,10)\n", + "\n" + ] + }, + { + "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": 26, + "metadata": { + "id": "FNXTugQ29xlQ" + }, + "outputs": [], + "source": [ + "arr=np.linspace(0,1,20)\n" + ] + }, + { + "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": 40, + "metadata": { + "id": "Ft8P8e249xlQ", + "outputId": "b02631b4-dc02-45ac-ded1-2332ff0cb08e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[ 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]]\n" + ] + } + ], + "source": [ + "arr1=np.array([list(range(1,26))]).reshape(5,5)\n", + "print(arr1)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "WrcFkpGL9xlQ", + "outputId": "5445b615-08a6-491c-cbad-e67d303ffcf2", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[12 13 14 15]\n", + " [17 18 19 20]\n", + " [22 23 24 25]]\n" + ] + } + ], + "source": [ + "arr2=np.array([list(range(12,27))]).reshape(3,5)[:, :4]\n", + "print(arr2)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "id": "rnUSntEa9xlQ", + "outputId": "5671a2da-f82e-4ed0-b863-70dacce1c0d1", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(arr1[3,4])" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "id": "3DMBC_wp9xlQ", + "outputId": "35e99a9b-28c0-4a01-fffe-6c257bdf5e52", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[ 2]\n", + " [ 7]\n", + " [12]]\n" + ] + } + ], + "source": [ + "print(arr1[:3,1:2])" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "id": "pGjD4HUK9xlQ", + "outputId": "f6ad1090-17ab-4fdf-84c0-dee3605ee03a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[22 23 24 25]\n" + ] + } + ], + "source": [ + "print(arr2[2,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "id": "dqsdpxUo9xlR", + "outputId": "3ff5e9ac-389f-4a20-ac8a-b31806f55232", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[16 17 18 19 20]\n", + " [21 22 23 24 25]]\n" + ] + } + ], + "source": [ + "print(arr1[3:5 , :])" + ] + }, + { + "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_Hemant/Assignment1_Hemant_Pandas.ipynb b/Assignment 1/Assignment1_Hemant/Assignment1_Hemant_Pandas.ipynb new file mode 100644 index 00000000..dfbb85af --- /dev/null +++ b/Assignment 1/Assignment1_Hemant/Assignment1_Hemant_Pandas.ipynb @@ -0,0 +1,3085 @@ +{ + "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": 2, + "metadata": { + "id": "BiD4gl6_cAYs" + }, + "outputs": [], + "source": [ + "#import pandas library\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "JUtm9KricAYu" + }, + "outputs": [], + "source": [ + "df = pd.read_csv('/content/sample_data/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": 6, + "metadata": { + "id": "qzUnSkfRcAYu" + }, + "outputs": [], + "source": [ + "dictionary = {'col':[4,2,3], 'col2':[4,7,6], 'col3':[7,10,9]}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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": "ffb215d3-a69b-4107-d71e-91b37c923e41" + }, + "execution_count": 8, + "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", + "
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30314.83.11.60.2Iris-setosa
31325.43.41.50.4Iris-setosa
32335.24.11.50.1Iris-setosa
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455.03.61.40.2Iris-setosa
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1451466.73.05.22.3Iris-virginica
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+ ], + "metadata": { + "id": "pm6VvR-rjLoL" + } + }, + { + "cell_type": "markdown", + "source": [ + 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+ ], + "metadata": { + "id": "_J1j50_ajnFe" + } + }, + { + "cell_type": "markdown", + "source": [ + "* Date of pattern: **August 6, 2025**\n", + "\n", + "* Pattern name: **Bullish Engulfing**\n", + "\n", + "* **Price went up** immediately after that pattern" + ], + "metadata": { + "id": "_arL7yabB--M" + } + } + ] +} \ No newline at end of file From bec8115f8edfce6b948bd3207c86b7915ae986df Mon Sep 17 00:00:00 2001 From: hemant-hk25 Date: Mon, 22 Dec 2025 23:24:19 +0530 Subject: [PATCH 3/6] Created MidEval_Hemant --- MidEval Code/MidEval_Hemant | 1 + 1 file changed, 1 insertion(+) create mode 100644 MidEval Code/MidEval_Hemant diff --git a/MidEval Code/MidEval_Hemant b/MidEval Code/MidEval_Hemant new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/MidEval Code/MidEval_Hemant @@ -0,0 +1 @@ + From 3d995bb7901b58c8f8363bcabfb8f44d14494d27 Mon Sep 17 00:00:00 2001 From: hemant-hk25 Date: Mon, 22 Dec 2025 23:38:38 +0530 Subject: [PATCH 4/6] Added Mideval code --- .../MidEval_Hemant_/Hemant_Mideval.ipynb | 605 ++++++++++++++++++ 1 file changed, 605 insertions(+) create mode 100644 MidEval Code/MidEval_Hemant_/Hemant_Mideval.ipynb diff --git a/MidEval Code/MidEval_Hemant_/Hemant_Mideval.ipynb b/MidEval Code/MidEval_Hemant_/Hemant_Mideval.ipynb new file mode 100644 index 00000000..2bd2e1a4 --- /dev/null +++ b/MidEval Code/MidEval_Hemant_/Hemant_Mideval.ipynb @@ -0,0 +1,605 @@ +{ + "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": 5, + "metadata": { + "id": "TY7ftWhjWzdv" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "source": [ + "# Loading the dataset\n", + "df = pd.read_csv(\"/content/quantvision_financial_dataset_200.csv\")\n", + "print(df.shape)\n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 243 + }, + "id": "MxBcm6yVXFNa", + "outputId": "6cd3f008-762f-4f51-ec85-4a5f7b620971" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(200, 11)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " lookback_days asset_type market_regime high_volatility \\\n", + "0 48 equity bullish 0 \n", + "1 38 index bullish 1 \n", + "2 24 equity bullish 1 \n", + "3 52 equity bullish 0 \n", + "4 17 equity bullish 1 \n", + "\n", + " trend_continuation technical_score edge_density slope_strength \\\n", + "0 1 59.99 0.504 0.298 \n", + "1 1 78.54 0.559 0.037 \n", + "2 0 56.03 0.617 0.212 \n", + "3 0 66.51 0.360 0.347 \n", + "4 1 61.21 0.492 0.144 \n", + "\n", + " candlestick_variance pattern_symmetry future_trend \n", + "0 1.572 0.768 1 \n", + "1 0.692 0.538 1 \n", + "2 1.419 0.301 1 \n", + "3 0.699 0.498 1 \n", + "4 2.520 0.828 1 " + ], + "text/html": [ + "\n", + "
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048equitybullish0159.990.5040.2981.5720.7681
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\"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"trend_continuation\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"technical_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 15.028468080337015,\n \"min\": 24.2,\n \"max\": 100.0,\n \"num_unique_values\": 195,\n \"samples\": [\n 71.25,\n 69.01\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"edge_density\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.11605043079384576,\n \"min\": 0.191,\n \"max\": 0.798,\n \"num_unique_values\": 159,\n \"samples\": [\n 0.384,\n 0.695\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"slope_strength\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.6199660874985351,\n \"min\": -1.217,\n \"max\": 1.833,\n \"num_unique_values\": 189,\n \"samples\": [\n -0.285,\n 0.217\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"candlestick_variance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.5755348790123836,\n \"min\": 0.028,\n \"max\": 2.52,\n \"num_unique_values\": 195,\n \"samples\": [\n 2.085,\n 0.634\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pattern_symmetry\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.17357671352283097,\n \"min\": 0.21,\n \"max\": 1.0,\n \"num_unique_values\": 172,\n \"samples\": [\n 0.919,\n 0.318\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"future_trend\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# Separate input features (X) and target variable (y)\n", + "# X contains all independent variables\n", + "# y contains the future price movement (0 = Down, 1 = Up)\n", + "\n", + "X = df.drop(\"future_trend\", axis=1)\n", + "y = df[\"future_trend\"]\n", + "\n", + "# Convert categorical variables into numerical form\n", + "X = pd.get_dummies(X, drop_first=True)\n", + "\n", + "# Standardize numerical features so that all features are on a similar scale\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X_scaled, y, test_size=0.2, random_state=42\n", + ")\n" + ], + "metadata": { + "id": "taw-o-k7ZAcO" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n", + "\n", + "# Train Logistic Regression model\n", + "log_model = LogisticRegression(max_iter=500)\n", + "log_model.fit(X_train, y_train)\n", + "# Predictions\n", + "y_pred_log = log_model.predict(X_test)\n", + "\n", + "# Evaluation\n", + "print(\"Logistic Regression Results\")\n", + "print(\"Accuracy:\", accuracy_score(y_test, y_pred_log))\n", + "print(\"Precision:\", precision_score(y_test, y_pred_log))\n", + "print(\"Recall:\", recall_score(y_test, y_pred_log))\n", + "print(\"F1-score:\", f1_score(y_test, y_pred_log))\n", + "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred_log))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LHkxM37fg3m6", + "outputId": "79a92e30-07a7-4c69-d12e-a02b97ae38a1" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Logistic Regression Results\n", + "Accuracy: 0.925\n", + "Precision: 0.9487179487179487\n", + "Recall: 0.9736842105263158\n", + "F1-score: 0.961038961038961\n", + "Confusion Matrix:\n", + " [[ 0 2]\n", + " [ 1 37]]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.neural_network import MLPClassifier\n", + "# Train Neural Network (MLP)\n", + "mlp_model = MLPClassifier(\n", + " hidden_layer_sizes=(64, 32), # 2 dense layers\n", + " activation='relu',\n", + " max_iter=500,\n", + " random_state=42\n", + ")\n", + "\n", + "mlp_model.fit(X_train, y_train)\n", + "\n", + "# Predictions\n", + "y_pred_mlp = mlp_model.predict(X_test)\n", + "\n", + "# Evaluation\n", + "print(\"Neural Network (MLP) Results\")\n", + "print(\"Accuracy:\", accuracy_score(y_test, y_pred_mlp))\n", + "print(\"Precision:\", precision_score(y_test, y_pred_mlp))\n", + "print(\"Recall:\", recall_score(y_test, y_pred_mlp))\n", + "print(\"F1-score:\", f1_score(y_test, y_pred_mlp))\n", + "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred_mlp))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iXEu6c1ujtRq", + "outputId": "c3460a15-60d4-4cd9-a530-fc961a324e91" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Neural Network (MLP) Results\n", + "Accuracy: 0.925\n", + "Precision: 0.9487179487179487\n", + "Recall: 0.9736842105263158\n", + "F1-score: 0.961038961038961\n", + "Confusion Matrix:\n", + " [[ 0 2]\n", + " [ 1 37]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## **1. Performance of Logistic Regression**\n", + "\n", + "Logistic Regression performs reasonably well on this dataset due to the nature of the input features.\n", + "\n", + "* Features such as **trend_continuation**, **technical_score**, and **slope_strength** have a **direct relationship** with future price movement.\n", + "* These indicators clearly capture market direction and momentum.\n", + "* Logistic Regression is effective when the decision boundary between classes can be approximated using a **linear relationship**.\n", + "\n", + "As a result, the model achieves **high accuracy and recall**, making it a strong baseline model for this problem.\n", + "\n", + "\n", + "\n", + "## **2. Performance of the Neural Network**\n", + "\n", + "Neural Networks are capable of learning complex and non-linear relationships. However, in this case:\n", + "\n", + "* The dataset is relatively **small**.\n", + "* The features are already **well-engineered technical indicators**.\n", + "* The relationship between features and the target variable is mostly **linear**.\n", + "\n", + "Because of these reasons, the Neural Network learns the **same decision boundary** as Logistic Regression. Consequently, both models produce **identical predictions and evaluation scores**, indicating that additional model complexity does not improve performance for this dataset.\n", + "\n", + "\n", + "\n", + "## **3. Effect of Volatility on Predictions**\n", + "\n", + "The **high_volatility** feature represents periods of increased market uncertainty.\n", + "\n", + "* During high volatility, price movements become noisy and less predictable.\n", + "* Technical indicators may generate false or misleading signals.\n", + "* Both models show increased misclassification during such periods.\n", + "\n", + "This demonstrates that volatility negatively impacts prediction reliability for both linear and non-linear models.\n", + "\n", + "\n", + "\n", + "## **4. Role of Trend Continuation**\n", + "\n", + "**Trend_continuation** plays a significant role in predicting future price movement.\n", + "\n", + "* Financial markets often exhibit momentum, where existing trends persist.\n", + "* When a trend is continuing, both models correctly predict upward price movement in most cases.\n", + "* This feature strongly contributes to the **high recall** observed in the results.\n", + "\n", + "Trend-following behavior is therefore a key driver of model performance.\n", + "\n", + "\n", + "## **5. Situations Where the Models Fail**\n", + "\n", + "The models tend to fail in the following scenarios:\n", + "\n", + "* Sudden market regime changes (e.g., bullish to bearish)\n", + "* Highly volatile or sideways markets\n", + "* Unexpected news or external economic events\n", + "\n", + "Additionally, the dataset is **imbalanced**, with more instances of upward price movement than downward movement. This causes both models to favor predicting the majority class (“Price Up”), leading to high accuracy but weaker performance in predicting rare downward movements.\n", + "\n", + "\n", + "\n", + "## **6. Overall Interpretation**\n", + "\n", + "The identical performance of Logistic Regression and the Neural Network indicates that the dataset is **largely linearly separable**. The engineered technical features already capture most of the predictive information, making a simple linear model as effective as a more complex neural network for this task.\n", + "\n" + ], + "metadata": { + "id": "9wCUrssFKAEk" + } + } + ] +} \ No newline at end of file From 57eeba5349eecdd4866575bb4d435bb662e1f37e Mon Sep 17 00:00:00 2001 From: hemant-hk25 Date: Mon, 22 Dec 2025 23:40:06 +0530 Subject: [PATCH 5/6] Deleted blank file --- MidEval Code/MidEval_Hemant | 1 - 1 file changed, 1 deletion(-) delete mode 100644 MidEval Code/MidEval_Hemant diff --git a/MidEval Code/MidEval_Hemant b/MidEval Code/MidEval_Hemant deleted file mode 100644 index 8b137891..00000000 --- a/MidEval Code/MidEval_Hemant +++ /dev/null @@ -1 +0,0 @@ - From cf3d1c85019fbc35a06a1f7ff045eef9531dded1 Mon Sep 17 00:00:00 2001 From: hemant-hk25 Date: Tue, 6 Jan 2026 00:08:30 +0530 Subject: [PATCH 6/6] Hemant_Assignment3 --- .../Assignment3_Hemant/Hemant_Assignment3.py | 92 +++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 Assignment 3/Assignment3_Hemant/Hemant_Assignment3.py diff --git a/Assignment 3/Assignment3_Hemant/Hemant_Assignment3.py b/Assignment 3/Assignment3_Hemant/Hemant_Assignment3.py new file mode 100644 index 00000000..9901a219 --- /dev/null +++ b/Assignment 3/Assignment3_Hemant/Hemant_Assignment3.py @@ -0,0 +1,92 @@ +import tensorflow as tf +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout +from tensorflow.keras.preprocessing.image import ImageDataGenerator +import matplotlib.pyplot as plt + +IMG_SIZE = (128, 128) +BATCH_SIZE = 32 +EPOCHS = 20 + +DATASET_DIR = "/kaggle/input/candlestick-dataset" # update path if needed + +# Data generators +train_datagen = ImageDataGenerator( + rescale=1./255, + rotation_range=10, + width_shift_range=0.05, + height_shift_range=0.05, + zoom_range=0.1, + validation_split=0.2 +) + +val_datagen = ImageDataGenerator( + rescale=1./255, + validation_split=0.2 +) + +train_generator = train_datagen.flow_from_directory( + DATASET_DIR, + target_size=IMG_SIZE, + batch_size=BATCH_SIZE, + class_mode="binary", + subset="training" +) + +val_generator = val_datagen.flow_from_directory( + DATASET_DIR, + target_size=IMG_SIZE, + batch_size=BATCH_SIZE, + class_mode="binary", + subset="validation" +) + +# CNN model +model = Sequential([ + Conv2D(32, (3,3), activation="relu", input_shape=IMG_SIZE + (3,)), + MaxPooling2D((2,2)), + + Conv2D(64, (3,3), activation="relu"), + MaxPooling2D((2,2)), + + Conv2D(128, (3,3), activation="relu"), + MaxPooling2D((2,2)), + + Flatten(), + Dense(128, activation="relu"), + Dropout(0.5), + Dense(1, activation="sigmoid") +]) + +model.compile( + optimizer="adam", + loss="binary_crossentropy", + metrics=["accuracy"] +) + +model.summary() + +history = model.fit( + train_generator, + epochs=EPOCHS, + validation_data=val_generator +) + +# Plots +plt.figure(figsize=(12,5)) + +plt.subplot(1,2,1) +plt.plot(history.history["accuracy"], label="Train") +plt.plot(history.history["val_accuracy"], label="Validation") +plt.title("Accuracy") +plt.legend() + +plt.subplot(1,2,2) +plt.plot(history.history["loss"], label="Train") +plt.plot(history.history["val_loss"], label="Validation") +plt.title("Loss") +plt.legend() + +plt.show() + +model.save("candlestick_cnn_up_down.h5")