{ "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": [ "## Logistic Regression - Lesson 4\n", "\n", "Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " City Name Type Package Variety Sub Variety Grade Date \\\n", "0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \n", "1 BALTIMORE NaN 24 inch bins NaN NaN NaN 5/6/17 \n", "2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", "3 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", "4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n", "\n", " Low Price High Price Mostly Low ... Unit of Sale Quality Condition \\\n", "0 270.0 280.0 270.0 ... NaN NaN NaN \n", "1 270.0 280.0 270.0 ... NaN NaN NaN \n", "2 160.0 160.0 160.0 ... NaN NaN NaN \n", "3 160.0 160.0 160.0 ... NaN NaN NaN \n", "4 90.0 100.0 90.0 ... NaN NaN NaN \n", "\n", " Appearance Storage Crop Repack Trans Mode Unnamed: 24 Unnamed: 25 \n", "0 NaN NaN NaN E NaN NaN NaN \n", "1 NaN NaN NaN E NaN NaN NaN \n", "2 NaN NaN NaN N NaN NaN NaN \n", "3 NaN NaN NaN N NaN NaN NaN \n", "4 NaN NaN NaN N NaN NaN NaN \n", "\n", "[5 rows x 26 columns]" ], "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
City NameTypePackageVarietySub VarietyGradeDateLow PriceHigh PriceMostly Low...Unit of SaleQualityConditionAppearanceStorageCropRepackTrans ModeUnnamed: 24Unnamed: 25
0BALTIMORENaN24 inch binsNaNNaNNaN4/29/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
1BALTIMORENaN24 inch binsNaNNaNNaN5/6/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
2BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN9/24/16160.0160.0160.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
3BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN9/24/16160.0160.0160.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
4BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN11/5/1690.0100.090.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
\n

5 rows × 26 columns

\n
" }, "metadata": {}, "execution_count": 7 } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n", "\n", "pumpkins.head()\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "\n", "new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']\n", "\n", "new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n", "\n", "new_pumpkins.dropna(inplace=True)\n", "\n", "new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)" ] }, { "source": [ "Check the data shape, size, and quality" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 11 } ], "source": [ "new_pumpkins.info" ] }, { "source": [ "Working with Item Size to Color, create a scatterplot using Seaborn" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 13 }, { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "import seaborn as sns\n", "\n", "g = sns.PairGrid(new_pumpkins)\n", "g.map(sns.scatterplot)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.swarmplot(x=\"Color\", y=\"Item Size\", data=new_pumpkins)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 10 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "sns.catplot(x=\"Color\", y=\"Item Size\",\n", " kind=\"violin\", data=new_pumpkins)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "Selected_features = ['Origin','Item Size','Variety','City Name','Package']\n", "\n", "X = new_pumpkins[Selected_features]\n", "y = new_pumpkins['Color']\n", "\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'fit' is not defined", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'fit' is not defined" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score, classification_report \n", "from sklearn.linear_model import LogisticRegression\n", "model = LogisticRegression()\n", "model.fit(X_train, y_train)\n", "predictions = fit.predict(X_test)\n", "\n", "print(classification_report(y_test, predictions))\n", "print('Predicted labels: ', predictions)\n", "print('Accuracy: ', accuracy_score(y_test, predictions))\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 16 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "from sklearn.metrics import roc_curve, roc_auc_score\n", "\n", "y_scores = model.predict_proba(X_test)\n", "# calculate ROC curve\n", "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n", "sns.lineplot([0, 1], [0, 1])\n", "sns.lineplot(fpr, tpr)" ] }, { "source": [ "auc = roc_auc_score(y_test,y_scores[:,1])\n", "print(auc)" ], "cell_type": "code", "metadata": {}, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "0.6976998904709748\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ] }