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+{
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 9,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/html": [
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+ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>instant</th>\n",
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+ " <th>dteday</th>\n",
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+ " <th>season</th>\n",
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+ " <th>yr</th>\n",
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+ " <th>mnth</th>\n",
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+ " <th>hr</th>\n",
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+ " <th>holiday</th>\n",
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+ " <th>weekday</th>\n",
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+ " <th>workingday</th>\n",
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+ " <th>weathersit</th>\n",
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+ " <th>temp</th>\n",
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+ " <th>atemp</th>\n",
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+ " <th>hum</th>\n",
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+ " <th>windspeed</th>\n",
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+ " <th>casual</th>\n",
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+ " <th>registered</th>\n",
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+ " <th>cnt</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>1</td>\n",
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+ " <td>2011-01-01</td>\n",
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+ " <td>1</td>\n",
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+ " <td>0.24</td>\n",
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+ " <td>0.2879</td>\n",
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+ " <td>0.81</td>\n",
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+ " <td>16</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>2</td>\n",
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+ " <td>2011-01-01</td>\n",
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+ " <td>0.22</td>\n",
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+ " <td>0.2727</td>\n",
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+ " <td>0.80</td>\n",
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+ " <td>0</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>3</td>\n",
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+ " <td>2011-01-01</td>\n",
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+ " <td>0.22</td>\n",
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+ " <td>0.2727</td>\n",
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+ " <td>0.80</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>2011-01-01</td>\n",
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+ " <td>0.24</td>\n",
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+ " <td>0.2879</td>\n",
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+ " <td>0.75</td>\n",
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+ " <td>0</td>\n",
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+ " <td>10</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
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+ " <td>5</td>\n",
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+ " <td>2011-01-01</td>\n",
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+ " <td>1</td>\n",
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+ " <td>0</td>\n",
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+ " <td>1</td>\n",
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+ " <td>0.24</td>\n",
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+ " <td>0.2879</td>\n",
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+ " <td>0.75</td>\n",
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+ " <td>0</td>\n",
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+ " <td>0</td>\n",
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+ " <td>1</td>\n",
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+ " <td>1</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "</div>"
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+ ],
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+ "text/plain": [
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+ " instant dteday season yr mnth hr holiday weekday workingday \\\n",
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+ "0 1 2011-01-01 1 0 1 0 0 6 0 \n",
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+ "1 2 2011-01-01 1 0 1 1 0 6 0 \n",
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+ "2 3 2011-01-01 1 0 1 2 0 6 0 \n",
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+ "3 4 2011-01-01 1 0 1 3 0 6 0 \n",
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+ "4 5 2011-01-01 1 0 1 4 0 6 0 \n",
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+ "\n",
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+ " weathersit temp atemp hum windspeed casual registered cnt \n",
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+ "0 1 0.24 0.2879 0.81 0 3 13 16 \n",
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+ "1 1 0.22 0.2727 0.80 0 8 32 40 \n",
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+ "2 1 0.22 0.2727 0.80 0 5 27 32 \n",
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+ "3 1 0.24 0.2879 0.75 0 3 10 13 \n",
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+ "4 1 0.24 0.2879 0.75 0 0 1 1 "
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+ ]
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+ },
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+ "execution_count": 9,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "import pandas\n",
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+ "\n",
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+ "bike_rentals = pandas.read_csv(\"bike_rental_hour.csv\")\n",
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+ "bike_rentals.head()"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 10,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "(array([ 6972., 3705., 2659., 1660., 987., 663., 369., 188.,\n",
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+ " 139., 37.]),\n",
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+ " array([ 1. , 98.6, 196.2, 293.8, 391.4, 489. , 586.6, 684.2,\n",
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+ " 781.8, 879.4, 977. ]),\n",
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+ " <a list of 10 Patch objects>)"
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+ ]
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+ },
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+ "execution_count": 10,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ },
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+ {
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+ "data": {
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|
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+ "wD9lsBRyJ/C+JP94uM00zMPVLOP4r8mkhMI8sHloezP/f7q9KyW5hUEgPF1VR1r1QpI72v6NwLlW\n",
|
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+ "f/kcbWp173Q/CexK8t+BQ8BPJ3ma6ZuHRWeAM1X1/bb9TQYhcXbK5uNvA/+pqv5XVV0A/j3wd5m+\n",
|
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+ "ebjcSn4uzrT6TZfVX3VeJiUUfgDMJPlAkrXAQ8DRMY/phkoS4CvAiar64tCuowwuqNH+PDJU/3CS\n",
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+ "tUm2ADMMLiC9o1XV56tqc1VtAR4GfqeqdjNl87Coqs4CryW5u1V9AngJ+BbTNR8ngY8kWdd+Vj4B\n",
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+ "nGD65uFyK/q5aP+e3mh3sAXYPdTnysZ9hX3oqvrPMLgD5xSwf9zjWYXj/SiDNfQXgRfaYydwO/Ad\n",
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+ "4BXgOWD9UJ/Pt/k5CfzDcR/DDZiTn+LS3UfTPA9/C/g+8J8Z/A/5tmmcD+CXGQTicQYXVW+Zpnlg\n",
|
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+ "cOb8R8CbDK65/ty1HD+wrc3hKeBLS72uH16TJHWTsnwkSZoAhoIkqTMUJEmdoSBJ6gwFSVJnKEiS\n",
|
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+ "OkNBktQZCpKk7v8BIgy2anPl5soAAAAASUVORK5CYII=\n"
|
|
|
+ ],
|
|
|
+ "text/plain": [
|
|
|
+ "<matplotlib.figure.Figure at 0x10790ef28>"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ "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": "code",
|
|
|
+ "execution_count": 20,
|
|
|
+ "metadata": {
|
|
|
+ "collapsed": false
|
|
|
+ },
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "data": {
|
|
|
+ "text/plain": [
|
|
|
+ "array([ -75.31906346, 144.15652539, 125.29713548, ..., 167.94469909,\n",
|
|
|
+ " 181.44415684, 165.3047817 ])"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ "execution_count": 20,
|
|
|
+ "metadata": {},
|
|
|
+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "actual"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 21,
|
|
|
+ "metadata": {
|
|
|
+ "collapsed": false
|
|
|
+ },
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "data": {
|
|
|
+ "text/plain": [
|
|
|
+ "4 1\n",
|
|
|
+ "10 36\n",
|
|
|
+ "16 93\n",
|
|
|
+ "24 17\n",
|
|
|
+ "36 75\n",
|
|
|
+ "39 76\n",
|
|
|
+ "40 65\n",
|
|
|
+ "45 9\n",
|
|
|
+ "48 2\n",
|
|
|
+ "52 64\n",
|
|
|
+ "68 12\n",
|
|
|
+ "72 2\n",
|
|
|
+ "76 179\n",
|
|
|
+ "80 78\n",
|
|
|
+ "81 97\n",
|
|
|
+ "87 112\n",
|
|
|
+ "88 54\n",
|
|
|
+ "90 35\n",
|
|
|
+ "92 6\n",
|
|
|
+ "109 169\n",
|
|
|
+ "111 89\n",
|
|
|
+ "112 43\n",
|
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|
+ "113 42\n",
|
|
|
+ "115 11\n",
|
|
|
+ "122 219\n",
|
|
|
+ "133 112\n",
|
|
|
+ "138 17\n",
|
|
|
+ "144 84\n",
|
|
|
+ "146 134\n",
|
|
|
+ "147 63\n",
|
|
|
+ " ... \n",
|
|
|
+ "17232 34\n",
|
|
|
+ "17243 31\n",
|
|
|
+ "17245 8\n",
|
|
|
+ "17255 32\n",
|
|
|
+ "17265 45\n",
|
|
|
+ "17269 75\n",
|
|
|
+ "17280 63\n",
|
|
|
+ "17289 51\n",
|
|
|
+ "17291 239\n",
|
|
|
+ "17292 191\n",
|
|
|
+ "17298 225\n",
|
|
|
+ "17301 213\n",
|
|
|
+ "17302 128\n",
|
|
|
+ "17304 92\n",
|
|
|
+ "17309 19\n",
|
|
|
+ "17311 3\n",
|
|
|
+ "17312 3\n",
|
|
|
+ "17315 44\n",
|
|
|
+ "17316 49\n",
|
|
|
+ "17327 66\n",
|
|
|
+ "17339 33\n",
|
|
|
+ "17340 74\n",
|
|
|
+ "17343 144\n",
|
|
|
+ "17346 138\n",
|
|
|
+ "17348 123\n",
|
|
|
+ "17349 125\n",
|
|
|
+ "17351 72\n",
|
|
|
+ "17353 36\n",
|
|
|
+ "17354 49\n",
|
|
|
+ "17373 122\n",
|
|
|
+ "Name: cnt, dtype: int64"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ "execution_count": 21,
|
|
|
+ "metadata": {},
|
|
|
+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "test[\"cnt\"]"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "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"
|
|
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+ }
|
|
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+ ],
|
|
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+ "source": [
|
|
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+ "from sklearn.tree import DecisionTreeRegressor\n",
|
|
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+ "\n",
|
|
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+ "reg = DecisionTreeRegressor(min_samples_leaf=5)\n",
|
|
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+ "\n",
|
|
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+ "reg.fit(train[predictors], train[\"cnt\"])"
|
|
|
+ ]
|
|
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+ },
|
|
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+ {
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|
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+ "cell_type": "code",
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|
+ "execution_count": 26,
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+ "metadata": {
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+ "collapsed": false
|
|
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+ },
|
|
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+ "outputs": [
|
|
|
+ {
|
|
|
+ "data": {
|
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+ "text/plain": [
|
|
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+ "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": "3.4.2"
|
|
|
+ }
|
|
|
+ },
|
|
|
+ "nbformat": 4,
|
|
|
+ "nbformat_minor": 0
|
|
|
+}
|
Vik Paruchuri