forecast_bike_rentals_187.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor \n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of        instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "0            1  2011-01-01       1   0     1   0        0        6   \n",
       "1            2  2011-01-01       1   0     1   1        0        6   \n",
       "2            3  2011-01-01       1   0     1   2        0        6   \n",
       "3            4  2011-01-01       1   0     1   3        0        6   \n",
       "4            5  2011-01-01       1   0     1   4        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "0               0           1  0.24  0.2879  0.81     0.0000       3   \n",
       "1               0           1  0.22  0.2727  0.80     0.0000       8   \n",
       "2               0           1  0.22  0.2727  0.80     0.0000       5   \n",
       "3               0           1  0.24  0.2879  0.75     0.0000       3   \n",
       "4               0           1  0.24  0.2879  0.75     0.0000       0   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt  \n",
       "0              13   16  \n",
       "1              32   40  \n",
       "2              27   32  \n",
       "3              10   13  \n",
       "4               1    1  \n",
       "...           ...  ...  \n",
       "17374         108  119  \n",
       "17375          81   89  \n",
       "17376          83   90  \n",
       "17377          48   61  \n",
       "17378          37   49  \n",
       "\n",
       "[17379 rows x 17 columns]>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filePath = '/cxldata/datasets/project/bikes.csv'\n",
    "bikesData = pd.read_csv(filePath)\n",
    "bikesData.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "bikesData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData['yr'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>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>count</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>2.501640</td>\n",
       "      <td>0.502561</td>\n",
       "      <td>6.537775</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.475775</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>189.463088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5017.0295</td>\n",
       "      <td>1.106918</td>\n",
       "      <td>0.500008</td>\n",
       "      <td>3.438776</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>181.387599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4345.5000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484800</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13034.5000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.621200</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant        season            yr          mnth            hr  \\\n",
       "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
       "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
       "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
       "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
       "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
       "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
       "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
       "\n",
       "            holiday       weekday    workingday    weathersit          temp  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
       "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
       "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
       "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
       "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
       "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
       "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
       "\n",
       "              atemp           hum     windspeed        casual    registered  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
       "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
       "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
       "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
       "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
       "\n",
       "                cnt  \n",
       "count  17379.000000  \n",
       "mean     189.463088  \n",
       "std      181.387599  \n",
       "min        1.000000  \n",
       "25%       40.000000  \n",
       "50%      142.000000  \n",
       "75%      281.000000  \n",
       "max      977.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikesData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnsToDrop = [\"instant\", \"casual\", \"registered\", \"atemp\", \"dteday\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikesData = bikesData.drop(columnsToDrop, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikesData['dayCount'] = pd.Series(range(bikesData.shape[0]))/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = train_test_split(bikesData, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12165 train + 5214 test\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "train_set.sort_values('dayCount', axis= 0, inplace=True)\n",
    "test_set.sort_values('dayCount', axis= 0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnsToScale = ['temp', 'hum', 'windspeed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/usr/local/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:1734: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(loc, value[:, i].tolist())\n",
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/usr/local/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:1734: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(loc, value[:, i].tolist())\n"
     ]
    }
   ],
   "source": [
    "train_set[columnsToScale] = scaler.fit_transform(train_set[columnsToScale])\n",
    "test_set[columnsToScale] = scaler.transform(test_set[columnsToScale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp         1.658955e-15\n",
       "hum          4.775031e-17\n",
       "windspeed   -1.367550e-15\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[columnsToScale].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp         1.000041\n",
       "hum          1.000041\n",
       "windspeed    1.000041\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[columnsToScale].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainingCols = train_set.drop(['cnt'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainingLabels = train_set['cnt'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [42.94494659 50.37222679 36.95891537 44.26211997 46.99589154 71.98026316\n",
      " 58.19901316 48.87417763 50.84868421 96.46217105]\n",
      "Mean: 54.7898409457034\n",
      "Standard deviation: 16.563759407187572\n"
     ]
    }
   ],
   "source": [
    "dec_reg = DecisionTreeRegressor(random_state = 42)\n",
    "dt_mae_scores = -cross_val_score(dec_reg, trainingCols, \n",
    "                                 trainingLabels, cv=10, \n",
    "                                 scoring=\"neg_mean_absolute_error\")\n",
    "display_scores(dt_mae_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 65.39786583  77.67402864  60.57274567  73.73250527  75.48574011\n",
      " 113.22922285  96.5884429   82.11639785  86.86752618 149.13680359]\n",
      "Mean: 88.0801278896052\n",
      "Standard deviation: 24.927341207369675\n"
     ]
    }
   ],
   "source": [
    "dt_mse_scores = np.sqrt(-cross_val_score(dec_reg, trainingCols, \n",
    "                                         trainingLabels, cv=10, \n",
    "                                         scoring=\"neg_mean_squared_error\"))\n",
    "display_scores(dt_mse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 66.96340699  80.48809095 113.84704981  93.17230086  76.11197672\n",
      "  96.5220689  133.13798218 158.02254734 158.90195479 127.15674717]\n",
      "Mean: 110.43241256942319\n",
      "Standard deviation: 31.42696570529541\n"
     ]
    }
   ],
   "source": [
    "lr_mae_scores = -cross_val_score(lin_reg, trainingCols, \n",
    "                                 trainingLabels, cv =10, \n",
    "                                 scoring =\"neg_mean_absolute_error\")\n",
    "display_scores(lr_mae_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 84.63836676 111.12038541 131.88324414 119.16350622 105.17621319\n",
      " 127.72562924 174.97188817 187.31691741 205.60028279 164.30585678]\n",
      "Mean: 141.19022901181862\n",
      "Standard deviation: 37.55565075919544\n"
     ]
    }
   ],
   "source": [
    "lr_mse_scores = np.sqrt(-cross_val_score(lin_reg, trainingCols, \n",
    "                                 trainingLabels, cv =10, \n",
    "                                 scoring =\"neg_mean_squared_error\"))\n",
    "display_scores(lr_mse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_reg = RandomForestRegressor(random_state = 42, n_estimators = 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [33.39666393 33.54451931 28.50225692 31.78826623 36.55658724 57.81963268\n",
      " 40.96405702 40.84652961 37.57766447 84.69771382]\n",
      "Mean: 42.56938912059061\n",
      "Standard deviation: 15.980256848600963\n"
     ]
    }
   ],
   "source": [
    "rf_mae_scores = -cross_val_score(forest_reg, trainingCols, \n",
    "                                 trainingLabels, cv =10, \n",
    "                                 scoring =\"neg_mean_absolute_error\")\n",
    "display_scores(rf_mae_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 45.64176074  50.97205843  43.37588352  52.2640926   60.46557726\n",
      "  94.24478873  66.26045287  65.45672124  61.69916554 131.9727285 ]\n",
      "Mean: 67.23532294382946\n",
      "Standard deviation: 25.544513111074128\n"
     ]
    }
   ],
   "source": [
    "rf_mse_scores = np.sqrt(-cross_val_score(forest_reg, trainingCols, \n",
    "                                 trainingLabels, cv =10, \n",
    "                                 scoring =\"neg_mean_squared_error\"))\n",
    "display_scores(rf_mse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [{'n_estimators': [120, 150], \n",
    "               'max_features' : [10, 12], \n",
    "               'max_depth' : [15, 28]},]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=150, n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'max_depth': [15, 28], 'max_features': [10, 12],\n",
       "                          'n_estimators': [120, 150]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(trainingCols, trainingLabels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00424888 0.00145493 0.00570279 0.58348648 0.00215107 0.01790669\n",
      " 0.06993018 0.01688336 0.09373438 0.03176755 0.00907719 0.16365649]\n"
     ]
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "print(feature_importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 28, 'max_features': 10, 'n_estimators': 150}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.ensemble._forest.RandomForestRegressor'>\n"
     ]
    }
   ],
   "source": [
    "print(type(final_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test_set.sort_values('dayCount', axis =0, inplace=True)\n",
    "test_x_cols = (test_set.drop(['cnt'], axis=1)).columns.values\n",
    "X_test = test_set.loc[:,test_x_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y_cols = 'cnt'\n",
    "y_test = test_set.loc[:, test_y_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:1596: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/usr/local/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:1743: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(ilocs[0], value)\n"
     ]
    }
   ],
   "source": [
    "test_set.loc[:, 'predictedCounts_test'] = final_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.47930005837265\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, test_set.loc[:,'predictedCounts_test'])\n",
    "final_mse=np.sqrt(mse)\n",
    "print(final_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = [9,18]\n",
    "for time in times:\n",
    "    fig = plt.figure(figsize=(8, 6))\n",
    "    fig.clf()\n",
    "    ax = fig.gca()\n",
    "    test_set_freg_time = test_set[test_set.hr == time]\n",
    "    test_set_freg_time.plot(kind = 'line', x = 'dayCount', y = 'cnt', ax = ax)\n",
    "    test_set_freg_time.plot(kind = 'line', x = 'dayCount', y = 'predictedCounts_test', ax =ax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}