diff --git a/Assignment 1/Assignment1_ViraatGoyal/_Assignment1_Matplotlib_ViraatGoyal.ipynb b/Assignment 1/Assignment1_ViraatGoyal/_Assignment1_Matplotlib_ViraatGoyal.ipynb
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+{"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":"92ddd03b-1d1a-4585-d5d0-1137281c9704","executionInfo":{"status":"ok","timestamp":1764865053112,"user_tz":-330,"elapsed":126,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"colab":{"base_uri":"https://localhost:8080/","height":582}},"outputs":[{"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","plt.title('title')\n","plt.xticks([0,20,40,60,80,100])\n","plt.yticks([0,50,100,150,200])\n","ax.set_xlim(-10, 110)\n","ax.set_ylim(-10, 210)\n","ax.set_xlabel('x')\n","ax.set_ylabel('y')\n","x =np.array([0,100])\n","y = 2*x\n","ax.plot(x,y,'b')\n","plt.show()"]},{"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":37,"metadata":{"id":"D4nUfU26ST2k","executionInfo":{"status":"ok","timestamp":1764865565478,"user_tz":-330,"elapsed":176,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"colab":{"base_uri":"https://localhost:8080/","height":546},"outputId":"ded8508f-c13d-457c-8367-97b8dd218db9"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"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, 0.2, 0.2])\n","plt.xticks([0,0.2,0.4,0.6,0.8,1])\n","plt.yticks([0,0.2,0.4,0.6,0.8,1])\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":47,"metadata":{"id":"m_2gVMryST2m","outputId":"354f78d8-3200-4536-b773-ee15a1047b22","colab":{"base_uri":"https://localhost:8080/","height":540},"executionInfo":{"status":"ok","timestamp":1764866576512,"user_tz":-330,"elapsed":211,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"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, 0.2, 0.2])\n","\n","ax1.set_xticks([0,20,40,60,80,100])\n","ax1.set_yticks([0,50,100,150,200])\n","\n","ax2.set_xticks([0,20,40,60,80,100])\n","ax2.set_yticks([0,50,100,150,200])\n","\n","ax1.set_xlim(-10, 110)\n","ax1.set_ylim(-10, 210)\n","\n","x =np.array([0,100])\n","y = 2*x\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":48,"metadata":{"id":"BuiYx0xbST2o","outputId":"fd4303ed-bb75-4632-a050-8a3d72bb2a43","colab":{"base_uri":"https://localhost:8080/","height":546},"executionInfo":{"status":"ok","timestamp":1764866644875,"user_tz":-330,"elapsed":172,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"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.xticks([0,0.2,0.4,0.6,0.8,1])\n","plt.yticks([0,0.2,0.4,0.6,0.8,1])\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":73,"metadata":{"id":"jhy7t5AKST2p","outputId":"80478824-d26a-408a-943f-282454bb8629","colab":{"base_uri":"https://localhost:8080/","height":565},"executionInfo":{"status":"ok","timestamp":1764869973386,"user_tz":-330,"elapsed":286,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"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, 0.4, 0.4])\n","\n","ax1.set_xticks([0,20,40,60,80,100])\n","ax1.set_yticks(np.array([0,2,4,6,8,10])*1000)\n","\n","ax2.set_xticks([20,20.5,21,21.5,22])\n","ax2.set_yticks([30,35,40,45,50])\n","\n","ax2.set_xlim(20,22)\n","ax2.set_ylim(30,50)\n","\n","ax1.set_xlim(0,100)\n","ax1.set_ylim(0,10000)\n","\n","ax2.set_title('zoom')\n","\n","ax1.set_xlabel('X')\n","ax1.set_ylabel('Z')\n","\n","ax2.set_xlabel('X')\n","ax2.set_ylabel('Y')\n","\n","x =np.linspace(0,100,500)\n","z = x**2\n","y = (x**2)/10\n","ax1.plot(x,z,'b')\n","ax2.plot(x,y,'b')\n","plt.show()\n"]},{"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":75,"metadata":{"id":"osk0Jco2ST2r","outputId":"f5aa372e-40cb-487f-d8a4-6d2869939651","colab":{"base_uri":"https://localhost:8080/","height":452},"executionInfo":{"status":"ok","timestamp":1764870753683,"user_tz":-330,"elapsed":263,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["fig = plt.figure()\n","fig,axes = plt.subplots(nrows=1, ncols=2)\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":80,"metadata":{"id":"ZoEMYyLOST2r","outputId":"2461c567-85c1-43ec-be94-cecaa68ca668","colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"status":"ok","timestamp":1764871701906,"user_tz":-330,"elapsed":324,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["\n","fig,axes = plt.subplots(nrows=1, ncols=2)\n","ax1 = axes[0]\n","ax2 = axes[1]\n","x = np.linspace(0,100,500)\n","z = x**2\n","y = x*2\n","ax1.set_xticks(np.linspace(0,100,5))\n","ax1.set_yticks(np.linspace(0,200,9))\n","\n","ax2.set_xticks(np.linspace(0,100,5))\n","ax2.set_yticks(np.linspace(0,10000,6))\n","\n","ax1.set_xlim(-10,110)\n","ax1.set_ylim(-10,210)\n","\n","ax2.set_xlim(-10,110)\n","ax2.set_ylim(-1000,11000)\n","\n","ax1.plot(x,y,'b--')\n","ax2.plot(x,z,'r')\n","plt.show()"]},{"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":92,"metadata":{"id":"0pq5vAFZST2s","outputId":"39ecc457-e086-4e41-b343-3ce515e1e749","colab":{"base_uri":"https://localhost:8080/","height":291},"executionInfo":{"status":"ok","timestamp":1764873317078,"user_tz":-330,"elapsed":218,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x300 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["fig,axes = plt.subplots(nrows=1, ncols=2,figsize=(15,3),dpi = 100)\n","ax1 = axes[0]\n","ax2 = axes[1]\n","x = np.linspace(0,100,500)\n","z = x**2\n","y = x*2\n","ax1.set_xticks(np.linspace(0,100,5))\n","ax1.set_yticks(np.linspace(0,200,9))\n","\n","ax2.set_xticks(np.linspace(0,100,5))\n","ax2.set_yticks(np.linspace(0,10000,6))\n","\n","ax1.set_xlim(-10,110)\n","ax1.set_ylim(-10,210)\n","\n","ax2.set_xlim(-10,110)\n","ax2.set_ylim(-1000,11000)\n","\n","ax1.plot(x,y,'b--',linewidth = 3)\n","ax2.plot(x,z,'r',linewidth = 1)\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":[{"file_id":"1hYhHtDxal3ubd7qG8XFZDk8-XHoB_b1P","timestamp":1764858916161},{"file_id":"1TktwCMc95E3iZHIyOHJO0-h_UzQpVe4U","timestamp":1764745279737},{"file_id":"1tZurHlHqMzny5-rSxWhXrkRUpMckdLl5","timestamp":1731497437912}]}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/Assignment 1/Assignment1_ViraatGoyal/_Assignment1_Numpy _ViraatGoyal.ipynb b/Assignment 1/Assignment1_ViraatGoyal/_Assignment1_Numpy _ViraatGoyal.ipynb
new file mode 100644
index 00000000..de941657
--- /dev/null
+++ b/Assignment 1/Assignment1_ViraatGoyal/_Assignment1_Numpy _ViraatGoyal.ipynb	
@@ -0,0 +1 @@
+{"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","executionInfo":{"status":"ok","timestamp":1764924353277,"user_tz":-330,"elapsed":24,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[],"source":["import numpy as np"]},{"cell_type":"markdown","metadata":{"id":"ehlpKUPc9xlM"},"source":["#### Create an array of 10 zeros"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"aEQkK-Dw9xlN","outputId":"2e76c2ba-50c4-44b5-eb24-b39626a6b0dc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764924365645,"user_tz":-330,"elapsed":31,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 0, 0, 0, 0, 0])"]},"metadata":{},"execution_count":4}],"source":["arr = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])\n","arr"]},{"cell_type":"markdown","metadata":{"id":"6QHp05Ei9xlN"},"source":["#### Create an array of 10 ones"]},{"cell_type":"code","execution_count":3,"metadata":{"scrolled":true,"id":"ebe9xrC29xlN","outputId":"481bb44c-8348-46f0-f973-332f8db47815","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764873591161,"user_tz":-330,"elapsed":61,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"]},"metadata":{},"execution_count":3}],"source":["arr = np.ones(10)\n","arr"]},{"cell_type":"markdown","metadata":{"id":"ziS-l3xp9xlO"},"source":["#### Create an array of 10 fives"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"fFC7bqLM9xlO","outputId":"625a48f9-bd87-475c-93b3-627a875ce6d8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764873738145,"user_tz":-330,"elapsed":18,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"]},"metadata":{},"execution_count":6}],"source":["arr = np.full(10,5.0)\n","arr"]},{"cell_type":"markdown","metadata":{"id":"kBugMXlC9xlO"},"source":["#### Create an array of the integers from 10 to 50"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"JsO6qS9R9xlO","outputId":"d3cdaef9-d695-4460-9117-85ce0e87746d","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764873854299,"user_tz":-330,"elapsed":31,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"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":11}],"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":12,"metadata":{"id":"4lK-5SQV9xlO","outputId":"31c451a7-1ce7-4bdd-a5ea-435e89038e2c","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764873892954,"user_tz":-330,"elapsed":29,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"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":12}],"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":6,"metadata":{"id":"MmVXCn0K9xlP","outputId":"c8b0f517-7fe0-425f-dcf2-cceb79e88cb9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764924541101,"user_tz":-330,"elapsed":520,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0, 1, 2],\n","       [3, 4, 5],\n","       [6, 7, 8]])"]},"metadata":{},"execution_count":6}],"source":["np.array([[0,1,2],\n","          [3,4,5],\n","          [6,7,8]\n","          ])"]},{"cell_type":"markdown","metadata":{"id":"4YfT0aLo9xlP"},"source":["#### Create a 3x3 identity matrix"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"UHa42UpQ9xlP","outputId":"dc4cc0bc-4ce7-4cd9-ad77-461352d36d0f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764924486400,"user_tz":-330,"elapsed":29,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[1., 0., 0.],\n","       [0., 1., 0.],\n","       [0., 0., 1.]])"]},"metadata":{},"execution_count":5}],"source":["np.eye(3)"]},{"cell_type":"markdown","metadata":{"id":"bfwDbjhI9xlP"},"source":["#### Use NumPy to generate a random number between 0 and 1"]},{"cell_type":"code","execution_count":17,"metadata":{"id":"Z0OroZxW9xlP","outputId":"13afdd00-ef6c-4e14-a02e-8fdc438d64b0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764924655219,"user_tz":-330,"elapsed":16,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["np.float64(0.16209307464769218)"]},"metadata":{},"execution_count":17}],"source":["np.random.rand(1)[0]"]},{"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":18,"metadata":{"id":"szluy14n9xlP","outputId":"d82bf31f-8459-496c-8440-13f3030fccf4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764924663384,"user_tz":-330,"elapsed":30,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([-0.48267707, -0.01849618, -0.97346214, -1.81548646, -0.47550739,\n","        1.79793312,  0.07489869,  1.39878539, -0.79663038,  0.18243196,\n","       -0.58899305, -0.08828152,  0.63501786,  0.77096851,  0.47174184,\n","       -0.28930723, -0.13836799,  0.71492099, -1.80204815, -0.66125728,\n","        0.63915339, -0.83954667,  1.02126025,  0.3431397 ,  1.25397928])"]},"metadata":{},"execution_count":18}],"source":["np.random.randn(25)"]},{"cell_type":"markdown","metadata":{"id":"_GhI8LYn9xlP"},"source":["#### Create the following matrix:"]},{"cell_type":"code","execution_count":25,"metadata":{"id":"wS1ZBddV9xlP","outputId":"7989eee6-0100-4f6c-9049-7f339e1cf81a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764925084664,"user_tz":-330,"elapsed":46,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"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":25}],"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":27,"metadata":{"id":"FNXTugQ29xlQ","outputId":"8e9a4cd6-cc09-4108-fb96-d41c3eb49abd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764925211854,"user_tz":-330,"elapsed":57,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"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":27}],"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":29,"metadata":{"id":"Ft8P8e249xlQ","outputId":"3bd1b51d-0f89-48af-b308-7c53cb12fff6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764925287145,"user_tz":-330,"elapsed":31,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"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":29}],"source":["np.arange(1,26,1).reshape(5,5)"]},{"cell_type":"code","execution_count":44,"metadata":{"id":"WrcFkpGL9xlQ","outputId":"1de36aae-91e9-4da6-be06-4c409c301997","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764927236389,"user_tz":-330,"elapsed":29,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"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":44}],"source":["a=np.arange(12,26,1)\n","A = np.delete(a,[4,9])\n","A.reshape(3,4)"]},{"cell_type":"code","execution_count":45,"metadata":{"id":"rnUSntEa9xlQ","outputId":"4e0a4f72-ef77-403f-ad21-54b3c138fcee","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764927276100,"user_tz":-330,"elapsed":28,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[20]])"]},"metadata":{},"execution_count":45}],"source":["np.array([20]).reshape(1,1)"]},{"cell_type":"code","execution_count":46,"metadata":{"id":"3DMBC_wp9xlQ","outputId":"4259f126-ed97-4687-b8ce-9af65fd58e32","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764927309551,"user_tz":-330,"elapsed":27,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 2],\n","       [ 7],\n","       [12]])"]},"metadata":{},"execution_count":46}],"source":["np.array([2,7,12]).reshape(3,1)"]},{"cell_type":"code","execution_count":47,"metadata":{"id":"pGjD4HUK9xlQ","outputId":"7abaf084-e9c8-48fe-ab3f-084c0c4531bd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764927333698,"user_tz":-330,"elapsed":50,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([21, 22, 23, 24, 25])"]},"metadata":{},"execution_count":47}],"source":["np.arange(21,26,1)"]},{"cell_type":"code","execution_count":48,"metadata":{"id":"dqsdpxUo9xlR","outputId":"ffb1af5f-b50a-411b-9725-a8bc1f3678e2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764927358258,"user_tz":-330,"elapsed":26,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[16, 17, 18, 19, 20],\n","       [21, 22, 23, 24, 25]])"]},"metadata":{},"execution_count":48}],"source":["np.arange(16,26,1).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":[{"file_id":"1uvwMRRqMFNlaYoWGT6JhFghskgPy9dzJ","timestamp":1764873560327},{"file_id":"1PacBskAxI7FPP4LIyB_3MysbL8ArUHsV","timestamp":1764745354238},{"file_id":"19ZajGBzqADvFnQ0kzbkad_udXeAh26pj","timestamp":1731497620277}]}},"nbformat":4,"nbformat_minor":0}
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+{"cells":[{"cell_type":"markdown","source":["## General Instructions for all Assignments"],"metadata":{"id":"AWDeauE3Bdzs"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"CwCNNc99Ra5W","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764786840636,"user_tz":-330,"elapsed":28600,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"76b53240-868d-4cd8-cc92-2642a6b28fc9"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"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","executionInfo":{"status":"ok","timestamp":1764786853926,"user_tz":-330,"elapsed":344,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[],"source":["#import pandas library\n","import pandas as pd"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"JUtm9KricAYu","executionInfo":{"status":"ok","timestamp":1764786954336,"user_tz":-330,"elapsed":142,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[],"source":["df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/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":null,"metadata":{"id":"qzUnSkfRcAYu"},"outputs":[],"source":["dictionary = {'col':[4,2,3], 'col2':[4,7,6], 'col3':[7,10,9]}"]},{"cell_type":"code","execution_count":null,"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","executionInfo":{"status":"ok","timestamp":1764745217710,"user_tz":-330,"elapsed":46,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"outputId":"366bc39c-7df6-4cc7-ae60-03aedd2571f4"},"execution_count":null,"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-5fb77386-d5e3-476d-82ce-c6c6f738c24c\" 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","   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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-20977554-2a4f-4809-8a98-d04c0940167d\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20977554-2a4f-4809-8a98-d04c0940167d')\"\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-20977554-2a4f-4809-8a98-d04c0940167d button');\n","          quickchartButtonEl.style.display =\n","            google.colab.kernel.accessAllowed ? 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\"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":32}]},{"cell_type":"code","execution_count":11,"metadata":{"id":"iAx3B-TacAYu","colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"status":"ok","timestamp":1764788613858,"user_tz":-330,"elapsed":58,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"9ad55e2f-99a2-4559-92cf-73c98541a2ce"},"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-e6bd641d-09ec-4ddb-9b5d-53ffcc6562ac\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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    }\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":11}],"source":["#Similiar to above, show 10 rows from the top`\n","df.head(10)"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"P9RtjFLQcAYv","colab":{"base_uri":"https://localhost:8080/","height":519},"executionInfo":{"status":"ok","timestamp":1764788607495,"user_tz":-330,"elapsed":126,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"97215584-35d9-47ad-e5a0-b89a62c3639f"},"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-20997e8c-8c54-419c-b964-cfba525d7611\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","   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<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-20997e8c-8c54-419c-b964-cfba525d7611')\"\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 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{\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        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'PetalLengthCm', 'PetalWidthCm',\n","       'Species'],\n","      dtype='object')"]},"metadata":{},"execution_count":6}],"source":["#list all the columns of the dataset\n","df.columns"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5yPReLqxcAYv","executionInfo":{"status":"ok","timestamp":1764786448409,"user_tz":-330,"elapsed":84,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"569aebb4-3156-4e46-8679-11c6a35b433a","colab":{"base_uri":"https://localhost:8080/","height":272}},"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","    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name\n","df.dtypes"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lT3OEUNDcAYv","executionInfo":{"status":"ok","timestamp":1764786483308,"user_tz":-330,"elapsed":205,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"b04fecc7-9533-49a3-a583-a6db2c1eb5f5","colab":{"base_uri":"https://localhost:8080/","height":300}},"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-d4d3f04e-69af-4b11-959d-640a9779e035\" 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","    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<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-d4d3f04e-69af-4b11-959d-640a9779e035')\"\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 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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":8}],"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","executionInfo":{"status":"ok","timestamp":1764745217940,"user_tz":-330,"elapsed":87,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"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: 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\"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JWYXoLl0cAYw","executionInfo":{"status":"ok","timestamp":1764745217997,"user_tz":-330,"elapsed":19,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"outputId":"644abfd4-2a4b-45d1-f0d7-828df37a37a1"},"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":39}],"source":["df.SepalLengthCm.unique()\n","#gives the unique values of Sepal Length present in that particular column"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MiOVp1WacAYw","executionInfo":{"status":"ok","timestamp":1764786539411,"user_tz":-330,"elapsed":45,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"3c9c6a94-8032-429e-d335-669f156c4344","colab":{"base_uri":"https://localhost:8080/","height":272}},"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":9}],"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":36},"id":"TF03jh_MovET","executionInfo":{"status":"ok","timestamp":1764788596882,"user_tz":-330,"elapsed":43,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"e331679c-4f99-4c0b-cc23-11e5f19a016e"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Iris-setosa'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["#Display the second last entry of the 10th row\n","print(df.iloc[10,-2])"],"metadata":{"id":"gV-h3rK-g5lZ","executionInfo":{"status":"ok","timestamp":1764786612442,"user_tz":-330,"elapsed":133,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"2a7760a6-65e3-47f9-e04f-086d0b91bcea","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.2\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"KXWCH34ecAYx","executionInfo":{"status":"ok","timestamp":1764745218194,"user_tz":-330,"elapsed":141,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"outputId":"15f4faed-a888-4e95-f20d-02ba16939636"},"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-aa83c2a6-f2e6-4192-af88-2c4a4734f4f5\" 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: 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<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-aa83c2a6-f2e6-4192-af88-2c4a4734f4f5')\"\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 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Jaitley","userId":"11414106947643866646"}},"outputId":"46c03362-af5f-47a5-968e-7f38f5bcb53e"},"execution_count":null,"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-8885b03f-87d3-4cf2-a529-b3ae2ab2b0c1\" 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-8885b03f-87d3-4cf2-a529-b3ae2ab2b0c1')\"\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 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\"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":44}]},{"cell_type":"code","execution_count":8,"metadata":{"id":"kZg8z57fcAY2","executionInfo":{"status":"ok","timestamp":1764788544058,"user_tz":-330,"elapsed":34,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[],"source":["df.dropna(axis=0, inplace=True)\n","#drops all the rows with missing values"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":272},"id":"FRWcDJA2cAY2","executionInfo":{"status":"ok","timestamp":1764745218284,"user_tz":-330,"elapsed":21,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"outputId":"4fbbfdfa-8eff-4e2d-e3a8-5bfc947f3abc"},"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","   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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":[{"file_id":"1tVNRnxnutLstrGKY3P0fbmpEVSwXiDnJ","timestamp":1764786683499},{"file_id":"1PJDPRd4uOSxvoSK0o_O22PiQ2-AkDis4","timestamp":1764744257366},{"file_id":"1AjoKNd7ieBCZ4NDPgzWDLDMsHKuYsb5A","timestamp":1731497483327}]}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/Assignment 2/Assignment2_ViraatGoyal/_Assignment2_ViraatGoyal.ipynb b/Assignment 2/Assignment2_ViraatGoyal/_Assignment2_ViraatGoyal.ipynb
new file mode 100644
index 00000000..baed8e5b
--- /dev/null
+++ b/Assignment 2/Assignment2_ViraatGoyal/_Assignment2_ViraatGoyal.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyN/il+yL1rdpq4Icoq0Kni/"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":2,"metadata":{"id":"to6u5h6EUFVk","executionInfo":{"status":"ok","timestamp":1765703123773,"user_tz":-330,"elapsed":5386,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"outputs":[],"source":["import yfinance as yf\n","import plotly.graph_objects as go"]},{"cell_type":"code","source":["ticker  = yf.Ticker(\"AAPL\")\n","df = ticker.history(period=\"6mo\",interval = \"1d\")\n","fig = go.Figure(data = [go.Candlestick(x = df.index,open=df['Open'],high=df['High'],low=df['Low'],close=df['Close'])])\n","fig.update_layout(title_text = \"Apple stock prices of past 6 months\",\n","                  title_xanchor = \"center\",\n","                  title_x =0.5,\n","                  xaxis_rangeslider_visible=True,\n","                  xaxis_title = \"DAYS\",\n","                  yaxis_title = \"STOCKPRICE(USD)\")\n","fig.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"Cu0yLrRZJP2U","executionInfo":{"status":"ok","timestamp":1765703169738,"user_tz":-330,"elapsed":31,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"aa88e84c-81cc-4cfc-af46-8a660c2810d6"},"execution_count":8,"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: 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NOV : BULLISH ENGULFING                         \n"," prices went up"],"metadata":{"id":"iGONCdcBzgUK"}},{"cell_type":"markdown","source":["5 AUG: MORNING STAR               \n","prices went up"],"metadata":{"id":"0RLd5UxzHWaH"}},{"cell_type":"markdown","source":["6,7,8 AUG: WEAK 3 WHITE SOLDIER                        \n","prices went up"],"metadata":{"id":"zjzXs9E0HXDA"}},{"cell_type":"markdown","source":["16 OCT: THE HAMMER  \n","prices went up"],"metadata":{"id":"2Qolh4V8HXKH"}},{"cell_type":"markdown","source":["3 NOV: PIERCING LINE                       \n","prices went up"],"metadata":{"id":"zzKt8p52HXM0"}}]}
\ No newline at end of file
diff --git a/Assignment 3/Assignment3_ViraatGoyal/Assignment3_Viraat b/Assignment 3/Assignment3_ViraatGoyal/Assignment3_Viraat
new file mode 100644
index 00000000..f201ccb6
--- /dev/null
+++ b/Assignment 3/Assignment3_ViraatGoyal/Assignment3_Viraat	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyN8fD1EvPHG5cfVrhCdyAxv"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sG1PmsjOugNX","executionInfo":{"status":"ok","timestamp":1767462852012,"user_tz":-330,"elapsed":24983,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"a0c186b8-9ee1-43ab-cbfe-95382149f060"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import tensorflow as tf\n","import numpy as np\n","from sklearn.metrics import classification_report, confusion_matrix\n","from sklearn.utils import class_weight\n","\n","BATCH_SIZE = 32\n","train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n","    '/content/drive/MyDrive/Colab Notebooks/Train', validation_split=0.2, subset=\"training\", seed=123,\n","    image_size=(128,128), batch_size=BATCH_SIZE\n",")\n","\n","val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n","    '/content/drive/MyDrive/Colab Notebooks/Train', seed=123, validation_split=0.2, subset=\"validation\",\n","    image_size=(128,128), batch_size=BATCH_SIZE, shuffle=False\n",")\n","\n","test_ds = tf.keras.preprocessing.image_dataset_from_directory(\n","    '/content/drive/MyDrive/Colab Notebooks/Test', seed=123,\n","    image_size=(128,128), batch_size=BATCH_SIZE, shuffle=False\n",")\n","\n","data_augmentation = tf.keras.Sequential(\n","    [\n","    tf.keras.layers.RandomZoom(0.1),\n","    tf.keras.layers.RandomContrast(0.15),\n","    tf.keras.layers.RandomBrightness(0.1),\n","    ],\n","    name=\"data_augmentation\"\n",")\n","\n","\n","model = tf.keras.Sequential([\n","    tf.keras.layers.Input(shape=(128,128,3)),\n","    data_augmentation,\n","    tf.keras.layers.Rescaling(1./255),\n","\n","    tf.keras.layers.Conv2D(32, 3, padding=\"same\"),\n","    tf.keras.layers.BatchNormalization(),\n","    tf.keras.layers.ReLU(),\n","    tf.keras.layers.MaxPooling2D(),\n","\n","    tf.keras.layers.Conv2D(64, 3, padding=\"same\"),\n","    tf.keras.layers.BatchNormalization(),\n","    tf.keras.layers.ReLU(),\n","    tf.keras.layers.MaxPooling2D(),\n","\n","    tf.keras.layers.Conv2D(128, 3, padding=\"same\"),\n","    tf.keras.layers.BatchNormalization(),\n","    tf.keras.layers.ReLU(),\n","    tf.keras.layers.MaxPooling2D(),\n","\n","    tf.keras.layers.GlobalAveragePooling2D(),\n","    tf.keras.layers.Dense(128, activation=\"relu\"),\n","    tf.keras.layers.Dropout(0.4),\n","    tf.keras.layers.Dense(1, activation=\"sigmoid\")\n","])\n","\n","loss = tf.keras.losses.BinaryCrossentropy(label_smoothing=0.1)\n","model.compile(loss=loss,\n","              optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4),\n","              metrics=[\"accuracy\"])\n","callbacks = [\n","   tf.keras.callbacks.EarlyStopping(\n","        monitor=\"val_loss\",\n","        patience=5,\n","        restore_best_weights=True\n","    ),\n","    tf.keras.callbacks.ModelCheckpoint(\n","        \"model.keras\",\n","        monitor=\"val_loss\",\n","        save_best_only=True\n","    )\n","]\n","\n","history = model.fit(\n","    train_ds,\n","    epochs=50,\n","    validation_data=val_ds,\n","    callbacks=callbacks,\n",")\n","\n","model = tf.keras.models.load_model(\"model.keras\")\n","\n","y_prob = model.predict(test_ds)\n","y_prob = y_prob.ravel()\n","y_pred = (y_prob > 0.4).astype(int)\n","\n","y_true = np.concatenate([y for x, y in test_ds], axis=0)\n","\n","print(classification_report(y_true, y_pred))\n","print(confusion_matrix(y_true,y_pred))\n","\n","\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bJDn30Kash3C","executionInfo":{"status":"ok","timestamp":1767464240472,"user_tz":-330,"elapsed":169557,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"1b60c6c1-4de6-4c3d-9158-c4d9c04cbf4f"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 1434 files belonging to 2 classes.\n","Using 1148 files for training.\n","Found 1434 files belonging to 2 classes.\n","Using 286 files for validation.\n","Found 351 files belonging to 2 classes.\n","Using Class Weights: {0: np.float64(1.1643002028397567), 1: np.float64(0.8763358778625954)}\n","Epoch 1/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 190ms/step - accuracy: 0.5492 - loss: 0.7071 - val_accuracy: 1.0000 - val_loss: 0.6772\n","Epoch 2/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 215ms/step - accuracy: 0.5216 - loss: 0.7140 - val_accuracy: 1.0000 - val_loss: 0.6878\n","Epoch 3/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 184ms/step - accuracy: 0.5245 - loss: 0.7104 - val_accuracy: 0.8811 - val_loss: 0.6916\n","Epoch 4/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 210ms/step - accuracy: 0.5448 - loss: 0.6929 - val_accuracy: 1.0000 - val_loss: 0.6725\n","Epoch 5/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 202ms/step - accuracy: 0.5277 - loss: 0.7059 - val_accuracy: 1.0000 - val_loss: 0.6734\n","Epoch 6/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 200ms/step - accuracy: 0.5714 - loss: 0.6919 - val_accuracy: 1.0000 - val_loss: 0.6613\n","Epoch 7/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 217ms/step - accuracy: 0.5569 - loss: 0.6973 - val_accuracy: 1.0000 - val_loss: 0.6458\n","Epoch 8/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 185ms/step - accuracy: 0.5425 - loss: 0.6944 - val_accuracy: 1.0000 - val_loss: 0.6315\n","Epoch 9/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 217ms/step - accuracy: 0.5404 - loss: 0.6888 - val_accuracy: 1.0000 - val_loss: 0.6036\n","Epoch 10/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 183ms/step - accuracy: 0.5543 - loss: 0.6907 - val_accuracy: 1.0000 - val_loss: 0.6365\n","Epoch 11/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 214ms/step - accuracy: 0.5510 - loss: 0.6862 - val_accuracy: 1.0000 - val_loss: 0.6393\n","Epoch 12/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 184ms/step - accuracy: 0.5552 - loss: 0.6912 - val_accuracy: 1.0000 - val_loss: 0.5854\n","Epoch 13/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 232ms/step - accuracy: 0.5456 - loss: 0.6935 - val_accuracy: 1.0000 - val_loss: 0.5834\n","Epoch 14/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 206ms/step - accuracy: 0.5722 - loss: 0.6853 - val_accuracy: 1.0000 - val_loss: 0.5895\n","Epoch 15/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 186ms/step - accuracy: 0.5730 - loss: 0.6811 - val_accuracy: 1.0000 - val_loss: 0.5804\n","Epoch 16/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 212ms/step - accuracy: 0.5496 - loss: 0.6891 - val_accuracy: 1.0000 - val_loss: 0.6026\n","Epoch 17/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 183ms/step - accuracy: 0.5659 - loss: 0.6883 - val_accuracy: 0.9965 - val_loss: 0.6288\n","Epoch 18/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 216ms/step - accuracy: 0.5551 - loss: 0.6889 - val_accuracy: 1.0000 - val_loss: 0.6177\n","Epoch 19/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 191ms/step - accuracy: 0.5600 - loss: 0.6856 - val_accuracy: 1.0000 - val_loss: 0.6162\n","Epoch 20/50\n","\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 185ms/step - accuracy: 0.5725 - loss: 0.6846 - val_accuracy: 1.0000 - val_loss: 0.5808\n","\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 158ms/step\n","0.3\n","              precision    recall  f1-score   support\n","\n","           0       0.00      0.00      0.00       157\n","           1       0.55      1.00      0.71       194\n","\n","    accuracy                           0.55       351\n","   macro avg       0.28      0.50      0.36       351\n","weighted avg       0.31      0.55      0.39       351\n","\n","[[  0 157]\n"," [  0 194]]\n","0.35\n","              precision    recall  f1-score   support\n","\n","           0       0.00      0.00      0.00       157\n","           1       0.55      1.00      0.71       194\n","\n","    accuracy                           0.55       351\n","   macro avg       0.28      0.50      0.36       351\n","weighted avg       0.31      0.55      0.39       351\n","\n","[[  0 157]\n"," [  0 194]]\n","0.4\n","              precision    recall  f1-score   support\n","\n","           0       0.00      0.00      0.00       157\n","           1       0.55      1.00      0.71       194\n","\n","    accuracy                           0.55       351\n","   macro avg       0.28      0.50      0.36       351\n","weighted avg       0.31      0.55      0.39       351\n","\n","[[  0 157]\n"," [  0 194]]\n","0.45\n","              precision    recall  f1-score   support\n","\n","           0       0.00      0.00      0.00       157\n","           1       0.55      1.00      0.71       194\n","\n","    accuracy                           0.55       351\n","   macro avg       0.28      0.50      0.36       351\n","weighted avg       0.31      0.55      0.39       351\n","\n","[[  0 157]\n"," [  0 194]]\n","0.5\n","              precision    recall  f1-score   support\n","\n","           0       0.00      0.00      0.00       157\n","           1       0.55      1.00      0.71       194\n","\n","    accuracy                           0.55       351\n","   macro avg       0.28      0.50      0.36       351\n","weighted avg       0.31      0.55      0.39       351\n","\n","[[  0 157]\n"," [  0 194]]\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","/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","/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","/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","/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"]}]}]}
\ No newline at end of file
diff --git a/MidEval Code/ViraatGoyal_mideval.ipynb b/MidEval Code/ViraatGoyal_mideval.ipynb
new file mode 100644
index 00000000..d3cc5afb
--- /dev/null
+++ b/MidEval Code/ViraatGoyal_mideval.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyN6mivfu7v53PUFqZ0Fp1RR"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"rHtdTPt1Glrp","executionInfo":{"status":"ok","timestamp":1766424491705,"user_tz":-330,"elapsed":2012,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"5953b39d-d0b2-491d-b307-dfbad36ad26b"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","execution_count":57,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wu8tOc9aYlL7","executionInfo":{"status":"ok","timestamp":1766430264625,"user_tz":-330,"elapsed":752,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}},"outputId":"86da635a-5645-485a-f776-5eddd7ecce09"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","======= Classification Report for logmodel =======\n","               precision    recall  f1-score   support\n","\n","           0       0.67      0.50      0.57         4\n","           1       0.96      0.98      0.97        56\n","\n","    accuracy                           0.95        60\n","   macro avg       0.82      0.74      0.77        60\n","weighted avg       0.95      0.95      0.95        60\n","\n","\n","Confusion mstrix :\n"," [[ 2  2]\n"," [ 1 55]]\n","\n","======= Classification Report for nn_model =======\n","               precision    recall  f1-score   support\n","\n","           0       0.75      0.75      0.75         4\n","           1       0.98      0.98      0.98        56\n","\n","    accuracy                           0.97        60\n","   macro avg       0.87      0.87      0.87        60\n","weighted avg       0.97      0.97      0.97        60\n","\n","\n","Confusion mstrix :\n"," [[ 3  1]\n"," [ 1 55]]\n"]}],"source":["import pandas as pd\n","\n","from sklearn.preprocessing import StandardScaler, OneHotEncoder\n","from sklearn.compose import ColumnTransformer\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.neural_network import MLPClassifier\n","from sklearn.metrics import classification_report, confusion_matrix\n","from sklearn.pipeline import Pipeline\n","\n","df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/quantvision_financial_dataset_200.csv')\n","num_features = ['lookback_days','high_volatility','trend_continuation', 'technical_score', 'edge_density',\n","                'slope_strength', 'candlestick_variance', 'pattern_symmetry']\n","cat_features = ['asset_type', 'market_regime']\n","x = df.drop('future_trend', axis=1)\n","y = df['future_trend']\n","\n","preprocessing = ColumnTransformer(\n","    transformers=[\n","        ('num', StandardScaler(), num_features),\n","        ('cat', OneHotEncoder(drop = \"first\"), cat_features)\n","    ]\n",")\n","\n","x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42,stratify = y)\n","\n","logmodel = log_model=Pipeline(steps=[\n","    (\"preprocessor\", preprocessing),\n","     (\"classifier\", LogisticRegression(\n","         random_state=7,\n","         max_iter=1000\n","         ))\n","     ])\n","\n","logmodel.fit(x_train, y_train)\n","y_pred_logmodel = logmodel.predict(x_test)\n","print(\"\\n======= Classification Report for logmodel =======\\n\", classification_report(y_test, y_pred_logmodel))\n","print( \"\\nConfusion mstrix :\\n\",confusion_matrix(y_test, y_pred_logmodel))\n","\n","nn_model=Pipeline(steps=[\n","    (\"preprocessor\", preprocessing),\n","    (\"classifier\", MLPClassifier(\n","        hidden_layer_sizes=(64, 32),\n","        activation=\"relu\",\n","        solver=\"adam\",\n","        max_iter=1000,\n","        random_state=7\n","    ))\n","])\n","nn_model.fit(x_train, y_train)\n","y_pred_nn_model = nn_model.predict(x_test)\n","print(\"\\n======= Classification Report for nn_model =======\\n\", classification_report(y_test, y_pred_nn_model))\n","print( \"\\nConfusion mstrix :\\n\",confusion_matrix(y_test, y_pred_nn_model))"]},{"cell_type":"code","source":["# Why Logistic Regression performs reasonably good or bad\n","#due to it's linear nature the model might not be able to fit certain data points on a straight line.\n","\n","# Why Neural Network performs better or worse\n","#neural networks work in a non linear manner.It identifies trends on various factor and works like a decision tree.\n","\n","#The effect of volatility on predictions\n","#high volatility means high engy in the market which can either cause prices to soar high or a caos in the price trends,\n","# logistic regression struggles with this as there will many outliers in its patter which will result in bad prediction.\n","\n","# The role of trend continuation\n","#helps in increasing accuracy for both the models as the classification of datapoints are predictible\n","\n","# Situations where the model fails and why\n","#models fails in situations of high volatility with causes and unstable market price,this makes it difficult for the models to make clear trends.\n","# the logistic regrssion model might not be able to fit a straight line through the messy datapoints\n","# whereas NN might overfit(memorize) certain patters in the data (like high volatility +high technical score)\n","# which will not happen in the future leading to poor predictions."],"metadata":{"id":"BNTzZvYNdncB","executionInfo":{"status":"ok","timestamp":1766430522147,"user_tz":-330,"elapsed":17,"user":{"displayName":"VIRAAT GOYAL","userId":"17639306585596031002"}}},"execution_count":58,"outputs":[]}]}
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