Predicting Bike Rentals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Predicting Bike Rentals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many American cities have communal bike sharing stations where you can rent bicycles by the hour or day. Washington, D.C. is one of these cities. The District collects detailed data on the number of bicycles people rent by the hour and day.\n",
    "\n",
    "[Hadi Fanaee-T](http://www.liaad.up.pt/area/fanaee) at the [University of Porto](http://www.up.pt/) compiled this data into a CSV file, which you'll be working with in this project. The file contains 17380 rows, with each row representing the number of bike rentals for a single hour of a single day. You can download the data from the [University of California, Irvine's website](http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "bike_rentals = pd.read_csv('bike_rental_hour.csv')\n",
    "bike_rentals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([6972., 3705., 2659., 1660.,  987.,  663.,  369.,  188.,  139.,\n",
       "          37.]),\n",
       " array([  1. ,  98.6, 196.2, 293.8, 391.4, 489. , 586.6, 684.2, 781.8,\n",
       "        879.4, 977. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u1wEA5JZZZw7vvvuuYrGYjDGanp7Wpk2bdPfdd6u8vFz19fVqaWlRQUGBOjs7JUmhUEj1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\nBwCAw/8HkCb6D0gnLtwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47b3bfea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.hist(bike_rentals['cnt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column **cnt** which is the total number of bike rentals has a skewed distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation between 'cnt' and other columns\n",
    "bike_rentals.corr()['cnt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that 'cnt' is highly correlated to 'casual' and 'registered', that's because cnt = casual + registred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **hr** column in **bike_rentals** contains the hours during which bikes are rented, from 1 to 24. A machine will treat each hour differently, without understanding that certain hours are related. We can introduce some order into the process by creating a new column with labels for **morning**, **afternoon**, **evening**, and **night**. This will bundle similar times together, enabling the model to make better decisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def assign_label(hour):\n",
    "    if hour>=6 and hour<12:\n",
    "        return 1\n",
    "    if hour>=12 and hour<18:\n",
    "        return 2\n",
    "    if hour>=18 and hour<24:\n",
    "        return 3\n",
    "    if hour>=0 and hour<6:\n",
    "        return 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bike_rentals['time_label'] = bike_rentals['hr'].apply(assign_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error Metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test our model, I'm going to use the mean squared error since it works on contunuous numeric data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = bike_rentals.sample(frac=0.8)\n",
    "test = bike_rentals.loc[~bike_rentals.index.isin(train.index)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['instant',\n",
       " 'season',\n",
       " 'yr',\n",
       " 'mnth',\n",
       " 'hr',\n",
       " 'holiday',\n",
       " 'weekday',\n",
       " 'workingday',\n",
       " 'weathersit',\n",
       " 'temp',\n",
       " 'atemp',\n",
       " 'hum',\n",
       " 'windspeed',\n",
       " 'time_label']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choose features that don't give away information about the target\n",
    "features = [c for c in train.columns if not c in ['cnt', 'casual', 'dteday', 'registered']]\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(train[features], train['cnt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = lr.predict(test[features])\n",
    "mse = mean_squared_error(predictions, test['cnt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17355.03601884258"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3400.554085155351"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree = DecisionTreeRegressor()\n",
    "tree.fit(train[features], train['cnt'])\n",
    "predictions = tree.predict(test[features])\n",
    "mse = mean_squared_error(predictions, test['cnt'])\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's experiment with other parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2658.7828943201407"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeRegressor(min_samples_leaf=5)\n",
    "tree.fit(train[features], train['cnt'])\n",
    "predictions = tree.predict(test[features])\n",
    "mse = mean_squared_error(predictions, test['cnt'])\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11556.966982655524"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeRegressor(max_depth=5)\n",
    "tree.fit(train[features], train['cnt'])\n",
    "predictions = tree.predict(test[features])\n",
    "mse = mean_squared_error(predictions, test['cnt'])\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15990.309819082107"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeRegressor(min_samples_leaf=5, \n",
    "                             max_depth=3)\n",
    "tree.fit(train[features], train['cnt'])\n",
    "predictions = tree.predict(test[features])\n",
    "mse = mean_squared_error(predictions, test['cnt'])\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default decisio tree model already performed better than linear regression. Tweaking the **min_samples_leaf** parameter improved it even more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1813.5567779056387"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest = RandomForestRegressor()\n",
    "forest.fit(train[features], train['cnt'])\n",
    "predictions = forest.predict(test[features])\n",
    "mse = mean_squared_error(predictions, test['cnt'])\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It outperformed both models. Let's experiment with other parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1997.479533318578"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestRegressor(min_samples_leaf=5)\n",
    "forest.fit(train[features], train['cnt'])\n",
    "predictions = forest.predict(test[features])\n",
    "mse = mean_squared_error(predictions, test['cnt'])\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1928.6938286195884"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestRegressor(min_samples_leaf=3)\n",
    "forest.fit(train[features], train['cnt'])\n",
    "predictions = forest.predict(test[features])\n",
    "mse = mean_squared_error(predictions, test['cnt'])\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default model performs the best."
   ]
  }
 ],
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