{ "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3-final" }, "orig_nbformat": 2, "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "source": [ "## Linear Regression Solution" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "Import needed libraries" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn import datasets, linear_model, model_selection\n" ] }, { "source": [ "Load the diabetes dataset, divided into `X` data and `y` features" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "(442, 10)\n[ 0.03807591 0.05068012 0.06169621 0.02187235 -0.0442235 -0.03482076\n -0.04340085 -0.00259226 0.01990842 -0.01764613]\n" ] } ], "source": [ "X, y = datasets.load_diabetes(return_X_y=True)\n", "print(X.shape)\n", "print(X[0])" ] }, { "source": [ "Select just one feature to target for this exercise" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "X = X[:, np.newaxis, 2]\n" ] }, { "source": [ "Split the training and test data for both `X` and `y`" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n" ] }, { "source": [ "Select the model and fit it with the training data" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "LinearRegression()" ] }, "metadata": {}, "execution_count": 6 } ], "source": [ "model = linear_model.LinearRegression()\n", "model.fit(X_train, y_train)" ] }, { "source": [ "Use test data to predict a line" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "y_pred = model.predict(X_test)\n" ] }, { "source": [ "Display the results in a plot" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "

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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "plt.scatter(X_test, y_test, color='black')\n", "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ] }