{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizing Linear Regression Model in Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize a Simple Linear Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Goal: visualzie the relationship between the topsoil lead concentration (`lead` column, as y-axis) and the topsoil cadmium concentration (`cadmium` column, as x-axis). " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/lizhoufan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " # Remove the CWD from sys.path while we load stuff.\n" ] } ], "source": [ "### Previous steps necessary\n", "# import packages\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.linear_model import LinearRegression\n", "# import dataset\n", "data = pd.read_csv(\"meuse.csv\")\n", "# build the model\n", "regression_model = LinearRegression()\n", "lr = LinearRegression().fit(data.cadmium.reshape((-1, 1)), data.lead)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the matplotlib package to visualize:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/lizhoufan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " \n", "/Users/lizhoufan/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# reference: https://becominghuman.ai/implementing-and-visualizing-linear-regression-in-python-with-scikit-learn-a073768dc688\n", "import matplotlib.pyplot as plt\n", "plt.scatter(data.cadmium.reshape((-1, 1)),data.lead, color = \"red\")\n", "plt.plot(data.cadmium.reshape((-1, 1)), lr.predict(data.cadmium.reshape((-1, 1))), color = \"green\")\n", "plt.title(\"Lead vs Cadmium\")\n", "plt.xlabel(\"Cadmium\")\n", "plt.ylabel(\"Lead\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please follow the next post on how do we analyze the model." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }