{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
012011-01-01101006010.240.28790.81031316
122011-01-01101106010.220.27270.80083240
232011-01-01101206010.220.27270.80052732
342011-01-01101306010.240.28790.75031013
452011-01-01101406010.240.28790.750011
\n", "
" ], "text/plain": [ " instant dteday season yr mnth hr holiday weekday workingday \\\n", "0 1 2011-01-01 1 0 1 0 0 6 0 \n", "1 2 2011-01-01 1 0 1 1 0 6 0 \n", "2 3 2011-01-01 1 0 1 2 0 6 0 \n", "3 4 2011-01-01 1 0 1 3 0 6 0 \n", "4 5 2011-01-01 1 0 1 4 0 6 0 \n", "\n", " weathersit temp atemp hum windspeed casual registered cnt \n", "0 1 0.24 0.2879 0.81 0 3 13 16 \n", "1 1 0.22 0.2727 0.80 0 8 32 40 \n", "2 1 0.22 0.2727 0.80 0 5 27 32 \n", "3 1 0.24 0.2879 0.75 0 3 10 13 \n", "4 1 0.24 0.2879 0.75 0 0 1 1 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas\n", "\n", "bike_rentals = pandas.read_csv(\"bike_rental_hour.csv\")\n", "bike_rentals.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 6972., 3705., 2659., 1660., 987., 663., 369., 188.,\n", " 139., 37.]),\n", " array([ 1. , 98.6, 196.2, 293.8, 391.4, 489. , 586.6, 684.2,\n", " 781.8, 879.4, 977. ]),\n", " )" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.hist(bike_rentals[\"cnt\"])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "instant 0.278379\n", "season 0.178056\n", "yr 0.250495\n", "mnth 0.120638\n", "hr 0.394071\n", "holiday -0.030927\n", "weekday 0.026900\n", "workingday 0.030284\n", "weathersit -0.142426\n", "temp 0.404772\n", "atemp 0.400929\n", "hum -0.322911\n", "windspeed 0.093234\n", "casual 0.694564\n", "registered 0.972151\n", "cnt 1.000000\n", "Name: cnt, dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bike_rentals.corr()[\"cnt\"]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def assign_label(hour):\n", " if hour >=0 and hour < 6:\n", " return 4\n", " elif hour >=6 and hour < 12:\n", " return 1\n", " elif hour >= 12 and hour < 18:\n", " return 2\n", " elif hour >= 18 and hour <=24:\n", " return 3\n", "\n", "bike_rentals[\"time_label\"] = bike_rentals[\"hr\"].apply(assign_label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Error metric\n", "\n", "The mean squared error metric makes the most sense to evaluate our error. MSE works on continuous numeric data, which fits our data quite well." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "train = bike_rentals.sample(frac=.8)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test = bike_rentals.loc[~bike_rentals.index.isin(train.index)]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "predictors = list(train.columns)\n", "predictors.remove(\"cnt\")\n", "predictors.remove(\"casual\")\n", "predictors.remove(\"registered\")\n", "predictors.remove(\"dteday\")\n", "\n", "reg = LinearRegression()\n", "\n", "reg.fit(train[predictors], train[\"cnt\"])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16586.154698429491" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy\n", "predictions = reg.predict(test[predictors])\n", "\n", "numpy.mean((predictions - test[\"cnt\"]) ** 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Error\n", "\n", "The error is very high, which may be due to the fact that the data has a few extremely high rental counts, but otherwise mostly low counts. Larger errors are penalized more with MSE, which leads to a higher total error." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DecisionTreeRegressor(compute_importances=None, criterion='mse',\n", " max_depth=None, max_features=None, max_leaf_nodes=None,\n", " min_density=None, min_samples_leaf=5, min_samples_split=2,\n", " random_state=None, splitter='best')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", "reg = DecisionTreeRegressor(min_samples_leaf=5)\n", "\n", "reg.fit(train[predictors], train[\"cnt\"])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2644.2820429330714" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictions = reg.predict(test[predictors])\n", "\n", "numpy.mean((predictions - test[\"cnt\"]) ** 2)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2964.7288070579207" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg = DecisionTreeRegressor(min_samples_leaf=2)\n", "\n", "reg.fit(train[predictors], train[\"cnt\"])\n", "\n", "predictions = reg.predict(test[predictors])\n", "\n", "numpy.mean((predictions - test[\"cnt\"]) ** 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision tree error\n", "\n", "By taking the nonlinear predictors into account, the decision tree regressor appears to have much higher accuracy than linear regression." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RandomForestRegressor(bootstrap=True, compute_importances=None,\n", " criterion='mse', max_depth=None, max_features='auto',\n", " max_leaf_nodes=None, min_density=None, min_samples_leaf=5,\n", " min_samples_split=2, n_estimators=10, n_jobs=1, oob_score=False,\n", " random_state=None, verbose=0)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "\n", "reg = RandomForestRegressor(min_samples_leaf=5)\n", "reg.fit(train[predictors], train[\"cnt\"])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1911.9827104170736" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictions = reg.predict(test[predictors])\n", "\n", "numpy.mean((predictions - test[\"cnt\"]) ** 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random forest error\n", "\n", "By removing some of the sources of overfitting, the random forest accuracy is improved over the decision tree accuracy." ] } ], "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 }