Prediction  of beer consumption.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n",
    "import os\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import *\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from yellowbrick.model_selection import FeatureImportances\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from yellowbrick.regressor import AlphaSelection\n",
    "from sklearn.linear_model import RidgeCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\leand'"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Temperatura Media (C)</th>\n",
       "      <th>Temperatura Minima (C)</th>\n",
       "      <th>Temperatura Maxima (C)</th>\n",
       "      <th>Precipitacao (mm)</th>\n",
       "      <th>Final de Semana</th>\n",
       "      <th>Consumo de cerveja (litros)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>27,3</td>\n",
       "      <td>23,9</td>\n",
       "      <td>32,5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>27,02</td>\n",
       "      <td>24,5</td>\n",
       "      <td>33,5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>24,82</td>\n",
       "      <td>22,4</td>\n",
       "      <td>29,9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>23,98</td>\n",
       "      <td>21,5</td>\n",
       "      <td>28,6</td>\n",
       "      <td>1,2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>23,82</td>\n",
       "      <td>21</td>\n",
       "      <td>28,3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Data Temperatura Media (C) Temperatura Minima (C)  \\\n",
       "0  2015-01-01                  27,3                   23,9   \n",
       "1  2015-01-02                 27,02                   24,5   \n",
       "2  2015-01-03                 24,82                   22,4   \n",
       "3  2015-01-04                 23,98                   21,5   \n",
       "4  2015-01-05                 23,82                     21   \n",
       "\n",
       "  Temperatura Maxima (C) Precipitacao (mm)  Final de Semana  \\\n",
       "0                   32,5                 0              0.0   \n",
       "1                   33,5                 0              0.0   \n",
       "2                   29,9                 0              1.0   \n",
       "3                   28,6               1,2              1.0   \n",
       "4                   28,3                 0              0.0   \n",
       "\n",
       "   Consumo de cerveja (litros)  \n",
       "0                       25.461  \n",
       "1                       28.972  \n",
       "2                       30.814  \n",
       "3                       29.799  \n",
       "4                       28.900  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_data=pd.read_csv('Consumo_cerveja.csv')\n",
    "beer_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#translating the column names fom spanish to english\n",
    "beer_data.columns=['date','mean_temp','max_temp','min_temp','precipitation','endofweek','beer_consumption_1000']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(941, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#missing values\n",
    "beer_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this data is only fo the year 2015 comprising of 366 days so we can ignore rows 366 to 941 which contain missing data\n",
    "beer_data=beer_data.iloc[0:365,:]\n",
    "beer_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                     0\n",
       "mean_temp                0\n",
       "max_temp                 0\n",
       "min_temp                 0\n",
       "precipitation            0\n",
       "endofweek                0\n",
       "beer_consumption_1000    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values\n",
    "beer_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing the index to start from 1 so it matches with the day of the year\n",
    "beer_data.index= beer_data.index+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>mean_temp</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>endofweek</th>\n",
       "      <th>beer_consumption_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>27,3</td>\n",
       "      <td>23,9</td>\n",
       "      <td>32,5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>27,02</td>\n",
       "      <td>24,5</td>\n",
       "      <td>33,5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>24,82</td>\n",
       "      <td>22,4</td>\n",
       "      <td>29,9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>23,98</td>\n",
       "      <td>21,5</td>\n",
       "      <td>28,6</td>\n",
       "      <td>1,2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>23,82</td>\n",
       "      <td>21</td>\n",
       "      <td>28,3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date mean_temp max_temp min_temp precipitation  endofweek  \\\n",
       "1  2015-01-01      27,3     23,9     32,5             0        0.0   \n",
       "2  2015-01-02     27,02     24,5     33,5             0        0.0   \n",
       "3  2015-01-03     24,82     22,4     29,9             0        1.0   \n",
       "4  2015-01-04     23,98     21,5     28,6           1,2        1.0   \n",
       "5  2015-01-05     23,82       21     28,3             0        0.0   \n",
       "\n",
       "   beer_consumption_1000  \n",
       "1                 25.461  \n",
       "2                 28.972  \n",
       "3                 30.814  \n",
       "4                 29.799  \n",
       "5                 28.900  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we need to convert , into . and vice vesra as temp and rainfaill are usually epesented by decimal point notation and\n",
    "#beer_consumption in thousands of litres\n",
    "\n",
    "comma_to_dot = lambda x: x.replace(',','.')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "beer_data.loc[:,['mean_temp','max_temp','min_temp','precipitation']]=beer_data.select_dtypes(['object']).iloc[:,1:5].applymap(comma_to_dot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#assigning proper datatypes to the columns\n",
    "beer_data['date']=pd.to_datetime(beer_data['date'])\n",
    "str_cols=beer_data.select_dtypes(['object']).columns\n",
    "\n",
    "for val in str_cols:\n",
    "    beer_data[val]=pd.to_numeric(beer_data[val])\n",
    "    \n",
    "\n",
    "endofweekconv = lambda x : str(x)[0]\n",
    "beer_data['endofweek']=beer_data.endofweek.apply(endofweekconv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                     datetime64[ns]\n",
       "mean_temp                       float64\n",
       "max_temp                        float64\n",
       "min_temp                        float64\n",
       "precipitation                   float64\n",
       "endofweek                        object\n",
       "beer_consumption_1000           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding day of week as a new column\n",
    "beer_data['day']=beer_data['date'].dt.strftime(\"%A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>mean_temp</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>endofweek</th>\n",
       "      <th>beer_consumption_1000</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>27.30</td>\n",
       "      <td>23.9</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.461</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>27.02</td>\n",
       "      <td>24.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.972</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>24.82</td>\n",
       "      <td>22.4</td>\n",
       "      <td>29.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.814</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>23.98</td>\n",
       "      <td>21.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>29.799</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>23.82</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.900</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>23.78</td>\n",
       "      <td>20.1</td>\n",
       "      <td>30.5</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0</td>\n",
       "      <td>28.218</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>24.00</td>\n",
       "      <td>19.5</td>\n",
       "      <td>33.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.732</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>24.90</td>\n",
       "      <td>19.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>48.6</td>\n",
       "      <td>0</td>\n",
       "      <td>28.397</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-01-09</td>\n",
       "      <td>28.20</td>\n",
       "      <td>21.9</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0</td>\n",
       "      <td>24.886</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  mean_temp  max_temp  min_temp  precipitation endofweek  \\\n",
       "1 2015-01-01      27.30      23.9      32.5            0.0         0   \n",
       "2 2015-01-02      27.02      24.5      33.5            0.0         0   \n",
       "3 2015-01-03      24.82      22.4      29.9            0.0         1   \n",
       "4 2015-01-04      23.98      21.5      28.6            1.2         1   \n",
       "5 2015-01-05      23.82      21.0      28.3            0.0         0   \n",
       "6 2015-01-06      23.78      20.1      30.5           12.2         0   \n",
       "7 2015-01-07      24.00      19.5      33.7            0.0         0   \n",
       "8 2015-01-08      24.90      19.5      32.8           48.6         0   \n",
       "9 2015-01-09      28.20      21.9      34.0            4.4         0   \n",
       "\n",
       "   beer_consumption_1000        day  \n",
       "1                 25.461   Thursday  \n",
       "2                 28.972     Friday  \n",
       "3                 30.814   Saturday  \n",
       "4                 29.799     Sunday  \n",
       "5                 28.900     Monday  \n",
       "6                 28.218    Tuesday  \n",
       "7                 29.732  Wednesday  \n",
       "8                 28.397   Thursday  \n",
       "9                 24.886     Friday  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_data.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 276,
     "metadata": {},
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#It seems there is higher beer consumtion on weekends\n",
    "sns.catplot(x=\"endofweek\", y=\"beer_consumption_1000\", kind=\"box\", data=beer_data,palette='OrRd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2a16e6fe2c8>"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#There is a marked increase in beer consumtion on SATUDRAY and SUNDAY\n",
    "sns.catplot(x=\"day\", y=\"beer_consumption_1000\", kind=\"box\", data=beer_data,palette='Spectral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2520x360 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#There seems to be a positive relationship between temperature and beer consumption fo evey day of the week.\n",
    "sns.lmplot(x=\"mean_temp\", y=\"beer_consumption_1000\", data=beer_data,hue='day',col='day');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'consumption vs rainfall')"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 449x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Since most days of the year in So Paulo have little o no rainfall,it is hard to infer any relationship between rainfall and beer consumption \n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.relplot(x=\"precipitation\", y=\"beer_consumption_1000\", data=beer_data,hue='mean_temp',palette=\"YlOrBr\")\n",
    "plt.title('consumption vs rainfall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding a new binary ain? column\n",
    "rainy=[]\n",
    "def did_it_rain(rain):\n",
    "    for val in rain:\n",
    "        if val>0:\n",
    "            rainy.append(1)\n",
    "           \n",
    "        elif val==0:\n",
    "            rainy.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>date</th>\n",
       "      <th>mean_temp</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>endofweek</th>\n",
       "      <th>beer_consumption_1000</th>\n",
       "      <th>day</th>\n",
       "      <th>rain?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>27.30</td>\n",
       "      <td>23.9</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.461</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>27.02</td>\n",
       "      <td>24.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.972</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>24.82</td>\n",
       "      <td>22.4</td>\n",
       "      <td>29.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.814</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>23.98</td>\n",
       "      <td>21.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>29.799</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>23.82</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.900</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  mean_temp  max_temp  min_temp  precipitation endofweek  \\\n",
       "1 2015-01-01      27.30      23.9      32.5            0.0         0   \n",
       "2 2015-01-02      27.02      24.5      33.5            0.0         0   \n",
       "3 2015-01-03      24.82      22.4      29.9            0.0         1   \n",
       "4 2015-01-04      23.98      21.5      28.6            1.2         1   \n",
       "5 2015-01-05      23.82      21.0      28.3            0.0         0   \n",
       "\n",
       "   beer_consumption_1000       day  rain?  \n",
       "1                 25.461  Thursday      0  \n",
       "2                 28.972    Friday      0  \n",
       "3                 30.814  Saturday      0  \n",
       "4                 29.799    Sunday      1  \n",
       "5                 28.900    Monday      0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "did_it_rain(beer_data['precipitation'])\n",
    "\n",
    "k=pd.Series(rainy,name=\"rain?\")\n",
    "k.index=k.index+1\n",
    "beer_data=pd.concat([beer_data,k],axis=1)\n",
    "\n",
    "beer_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2a170b2fb08>"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we notice a slight increase in consumption on rainy days\n",
    "sns.catplot(x=\"rain?\", y=\"beer_consumption_1000\",kind=\"violin\", data=beer_data,palette='GnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\leand\\Anaconda3\\lib\\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"
     ]
    }
   ],
   "source": [
    "#grouping consumption by month of year into a new dataset called monthly_data\n",
    "monthly_data=beer_data.iloc[:,[0,6]]\n",
    "monthly_data['month']=monthly_data.date.dt.month\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "monthly_data.index=np.arange(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "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>beer_consumption_1000</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.677645</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.673143</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.036387</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.624100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.798387</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.627067</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22.572871</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25.908968</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25.198133</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27.653194</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>25.828200</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27.169516</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    beer_consumption_1000  month\n",
       "0               28.677645      1\n",
       "1               25.673143      2\n",
       "2               25.036387      3\n",
       "3               24.624100      4\n",
       "4               22.798387      5\n",
       "5               23.627067      6\n",
       "6               22.572871      7\n",
       "7               25.908968      8\n",
       "8               25.198133      9\n",
       "9               27.653194     10\n",
       "10              25.828200     11\n",
       "11              27.169516     12"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Adding the month to the data we notice that the consumption of beer dips towards the middle of the year but not by much\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.relplot(x=\"month\", y=\"beer_consumption_1000\",kind=\"line\", data=monthly_data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_columns=beer_data.iloc[:,[1,2,3,4,6]].corr()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cab147e8c8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#None of the features are too correlated with beer consumtio\n",
    "sns.heatmap(numeric_columns, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min_temp                 0.642672\n",
       "beer_consumption_1000    1.000000\n",
       "Name: beer_consumption_1000, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_target = abs(numeric_columns[\"beer_consumption_1000\"])\n",
    "\n",
    "cor_target[cor_target>0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>mean_temp</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>endofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.30</td>\n",
       "      <td>23.9</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.02</td>\n",
       "      <td>24.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.82</td>\n",
       "      <td>22.4</td>\n",
       "      <td>29.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.98</td>\n",
       "      <td>21.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.82</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>24.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>28.2</td>\n",
       "      <td>13.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>22.64</td>\n",
       "      <td>21.1</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>21.68</td>\n",
       "      <td>20.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>21.38</td>\n",
       "      <td>19.3</td>\n",
       "      <td>22.4</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>24.76</td>\n",
       "      <td>20.2</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_temp  max_temp  min_temp  precipitation endofweek\n",
       "1        27.30      23.9      32.5            0.0         0\n",
       "2        27.02      24.5      33.5            0.0         0\n",
       "3        24.82      22.4      29.9            0.0         1\n",
       "4        23.98      21.5      28.6            1.2         1\n",
       "5        23.82      21.0      28.3            0.0         0\n",
       "..         ...       ...       ...            ...       ...\n",
       "361      24.00      21.1      28.2           13.6         1\n",
       "362      22.64      21.1      26.7            0.0         0\n",
       "363      21.68      20.3      24.1           10.3         0\n",
       "364      21.38      19.3      22.4            6.3         0\n",
       "365      24.76      20.2      29.0            0.0         0\n",
       "\n",
       "[365 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_columns_data=beer_data.iloc[:,[1,2,3,4,5]]\n",
    "reg_columns_target=beer_data.iloc[:,6]\n",
    "reg_columns_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\leand\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cab1d3c6c8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding important features using a random forest model\n",
    "rf = RandomForestRegressor(max_depth=2, random_state=0)\n",
    "main_features = FeatureImportances(rf)\n",
    "main_features.fit(reg_columns_data, reg_columns_target)\n",
    "main_features.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Choosing the temperature variables and end-of-week as the final features and split the data into test and train datasets\n",
    "reg_data=reg_columns_data.iloc[:,[2,4]]\n",
    "\n",
    "reg_target=beer_data.iloc[:,6]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(reg_data,reg_target,random_state=1,shuffle=True,test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Running 5 fold cross validation on five different models\n",
    "#(linear regression,LASSO,RIDGE,Random Forest,K neares neighbours)\n",
    "models = []\n",
    "models.append(('LR', LinearRegression()))\n",
    "models.append(('LASSO', linear_model.Lasso()))\n",
    "models.append(('KNN', KNeighborsRegressor()))\n",
    "models.append(('DTREE', DecisionTreeRegressor()))\n",
    "models.append(('RIDGE',Ridge(alpha=0.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: -2.024904 (0.116993)\n",
      "LASSO: -2.731539 (0.129341)\n",
      "KNN: -2.248236 (0.086699)\n",
      "DTREE: -2.702475 (0.140801)\n",
      "RIDGE: -2.024084 (0.117107)\n"
     ]
    }
   ],
   "source": [
    "#lnear regression and Ridge have the lowest absolute error\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "    \n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv=3, scoring='neg_mean_absolute_error')\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCALEDLR: -2.034520 (0.137741)\n",
      "ScaledLASSO: -2.339683 (0.256080)\n",
      "ScaledIDGE: -2.033867 (0.137458)\n",
      "ScaledKNN: -2.149664 (0.114568)\n",
      "ScaledTREE: -2.590317 (0.241393)\n"
     ]
    }
   ],
   "source": [
    "#Even after acaling the data though standard scaler transformation ,lnear regression and Ridge have the lowest absolute error\n",
    "pre_process = ColumnTransformer([\n",
    "                      ('scale',StandardScaler(),['min_temp','endofweek']) ])\n",
    "pipelines = []\n",
    "pipelines.append(('SCALEDLR', Pipeline([('PR',pre_process),('LR', LinearRegression())])))\n",
    "pipelines.append(('ScaledLASSO', Pipeline([('PR',pre_process),('LASSO', linear_model.Lasso())])))\n",
    "pipelines.append(('ScaledIDGE', Pipeline([('PR',pre_process),('RIDGE', Ridge())])))\n",
    "pipelines.append(('ScaledKNN', Pipeline([('PR',pre_process),('KNN', KNeighborsRegressor())])))\n",
    "pipelines.append(('ScaledTREE', Pipeline([('PR',pre_process),('CART', DecisionTreeRegressor())])))\n",
    "\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name, model in pipelines:\n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_absolute_error')\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2653061224489796"
      ]
     },
     "execution_count": 653,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tuning the alpha parameter\n",
    "alphas = np.linspace(0.1, 1, 50)\n",
    "clf = RidgeCV(alphas=alphas).fit(X_train, y_train)\n",
    "clf.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a17aaa0108>"
      ]
     },
     "execution_count": 671,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plotting the residuals for the train and test data fo the Ridge model.\n",
    "from yellowbrick.regressor import ResidualsPlot\n",
    "lr = Ridge(alpha=.265)\n",
    "\n",
    "lr.fit(X_train,y_train)\n",
    "ypred=lr.predict(X_test)\n",
    "visualizer = ResidualsPlot(lr,hist=False)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "visualizer.show()                 # Finalize and render the figure"
   ]
  }
 ],
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