From bb40b8823f411d2b05e8a5a353f3c9e7f79c97d7 Mon Sep 17 00:00:00 2001 From: Alvin Date: Sat, 17 Aug 2024 18:09:01 +0100 Subject: [PATCH] linnerud dataset modelled by linear regression --- 2-Regression/1-Tools/assignment.ipynb | 625 ++++++++++++++++++++++++++ 2-Regression/1-Tools/notebook.ipynb | 16 +- 2 files changed, 633 insertions(+), 8 deletions(-) create mode 100644 2-Regression/1-Tools/assignment.ipynb diff --git a/2-Regression/1-Tools/assignment.ipynb b/2-Regression/1-Tools/assignment.ipynb new file mode 100644 index 000000000..ab05d95ca --- /dev/null +++ b/2-Regression/1-Tools/assignment.ipynb @@ -0,0 +1,625 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Importing necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import datasets, linear_model, model_selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data processing" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(20, 3)\n", + "(20, 3)\n", + "[[191. 36. 50.]\n", + " [189. 37. 52.]\n", + " [193. 38. 58.]\n", + " [162. 35. 62.]\n", + " [189. 35. 46.]\n", + " [182. 36. 56.]\n", + " [211. 38. 56.]\n", + " [167. 34. 60.]\n", + " [176. 31. 74.]\n", + " [154. 33. 56.]\n", + " [169. 34. 50.]\n", + " [166. 33. 52.]\n", + " [154. 34. 64.]\n", + " [247. 46. 50.]\n", + " [193. 36. 46.]\n", + " [202. 37. 62.]\n", + " [176. 37. 54.]\n", + " [157. 32. 52.]\n", + " [156. 33. 54.]\n", + " [138. 33. 68.]]\n", + "[[ 5. 162. 60.]\n", + " [ 2. 110. 60.]\n", + " [ 12. 101. 101.]\n", + " [ 12. 105. 37.]\n", + " [ 13. 155. 58.]\n", + " [ 4. 101. 42.]\n", + " [ 8. 101. 38.]\n", + " [ 6. 125. 40.]\n", + " [ 15. 200. 40.]\n", + " [ 17. 251. 250.]\n", + " [ 17. 120. 38.]\n", + " [ 13. 210. 115.]\n", + " [ 14. 215. 105.]\n", + " [ 1. 50. 50.]\n", + " [ 6. 70. 31.]\n", + " [ 12. 210. 120.]\n", + " [ 4. 60. 25.]\n", + " [ 11. 230. 80.]\n", + " [ 15. 225. 73.]\n", + " [ 2. 110. 43.]]\n" + ] + } + ], + "source": [ + "XColumns= [\"weight\", \"waist\", \"pulse\"]\n", + "yColumns = [\"chins\", \"situps\", \"jumps\"]\n", + "columnNumber = 0\n", + "targetColumnNumber = 1\n", + "y,X = datasets.load_linnerud(return_X_y= True)\n", + "print(X.shape)\n", + "print(y.shape)\n", + "print(X)\n", + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(20,)\n", + "(20, 1)\n", + "(20,)\n", + "(20, 1)\n" + ] + } + ], + "source": [ + "\n", + "X= X[:,columnNumber]\n", + "print(X.shape)\n", + "X = X.reshape((-1,1))\n", + "print(X.shape)\n", + "\n", + "y = y[:,targetColumnNumber]\n", + "print(y.shape)\n", + "y= y.reshape((-1,1))\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Splitting training and test data and training the model" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, train_size=0.75)\n", + "model = linear_model.LinearRegression()\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Predicting, Plotting the data and determining accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_pred = model.predict(X_test)\n", + "plt.scatter(X_test, y_test, color=\"red\")\n", + "plt.plot(X_test, y_pred, color=\"green\", linewidth=3)\n", + "plt.xlabel(f'{XColumns[columnNumber]}')\n", + "plt.ylabel(f'{yColumns[targetColumnNumber]}')\n", + "plt.title(f'A Graph Plot Showing Prediction of {yColumns[targetColumnNumber]} by {XColumns[columnNumber]}')\n", + "plt.plot()\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2-Regression/1-Tools/notebook.ipynb b/2-Regression/1-Tools/notebook.ipynb index 184e20bfd..d178554b8 100644 --- a/2-Regression/1-Tools/notebook.ipynb +++ b/2-Regression/1-Tools/notebook.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -545,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -561,7 +561,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": null, "metadata": {}, "outputs": [ {