sales_by_product_and_date.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sales Predictions\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.tsa.stattools import adfuller, acf, pacf,arma_order_select_ic\n",
    "from pmdarima.arima import auto_arima\n",
    "import xgboost as xgb\n",
    "from tensorflow import keras\n",
    "import lightgbm as lgb\n",
    "import itertools \n",
    "import calendar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the data\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing The Data\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Format Converting\n",
    "****\n",
    "\n",
    "Changing the file type from csv to tsv (tab delimiter) and then back to csv because the decimal point separator isn't a dot but a comma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.read_csv('X_train.tsv', delimiter='\\t', decimal=\",\")\n",
    "X_train.to_csv(\"X_train_csv.csv\", sep=',', encoding='utf-8', quotechar='\"', decimal='.')\n",
    "\n",
    "X_test = pd.read_csv('X_test.tsv', delimiter='\\t', decimal=\",\")\n",
    "X_test.to_csv(\"X_test_csv.csv\", sep=',', encoding='utf-8', quotechar='\"', decimal='.')\n",
    "\n",
    "y_train = pd.read_csv('y_train.tsv', delimiter='\\t', decimal=\",\")\n",
    "y_train.to_csv(\"y_train_csv.csv\", sep=',', encoding='utf-8', quotechar='\"', decimal='.')\n",
    "\n",
    "y_test = pd.read_csv('y_test.tsv', delimiter='\\t', decimal=\",\")\n",
    "y_test.to_csv(\"y_test_csv.csv\", sep=',', encoding='utf-8', quotechar='\"', decimal='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking Into The Data\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>date</th>\n",
       "      <th>x1</th>\n",
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       "      <th>0</th>\n",
       "      <td>683</td>\n",
       "      <td>01Jan2012</td>\n",
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       "      <td>0.690346</td>\n",
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       "      <td>502.893893</td>\n",
       "      <td>10235.637780</td>\n",
       "      <td>...</td>\n",
       "      <td>0.053509</td>\n",
       "      <td>120.669814</td>\n",
       "      <td>0.955509</td>\n",
       "      <td>616.883023</td>\n",
       "      <td>200.144478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.391554</td>\n",
       "      <td>0.509713</td>\n",
       "      <td>216.851245</td>\n",
       "      <td>56.139906</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>683</td>\n",
       "      <td>01Feb2012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000686</td>\n",
       "      <td>0.590133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>362.021309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.833160</td>\n",
       "      <td>88.793012</td>\n",
       "      <td>...</td>\n",
       "      <td>112.074385</td>\n",
       "      <td>69.943189</td>\n",
       "      <td>2.704237</td>\n",
       "      <td>292.934714</td>\n",
       "      <td>138.241134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.316564</td>\n",
       "      <td>2.526962</td>\n",
       "      <td>39.451960</td>\n",
       "      <td>7.172635</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>683</td>\n",
       "      <td>01Mar2012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.672651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>880.917406</td>\n",
       "      <td>553.998042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998247</td>\n",
       "      <td>273.213135</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.264811</td>\n",
       "      <td>100.972356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.774053</td>\n",
       "      <td>0.242973</td>\n",
       "      <td>35.226264</td>\n",
       "      <td>50.614383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>683</td>\n",
       "      <td>01Apr2012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.711943</td>\n",
       "      <td>150.778812</td>\n",
       "      <td>11.951856</td>\n",
       "      <td>...</td>\n",
       "      <td>182.047997</td>\n",
       "      <td>18.272077</td>\n",
       "      <td>-0.357520</td>\n",
       "      <td>5.272196</td>\n",
       "      <td>49.725924</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.904427</td>\n",
       "      <td>29.437773</td>\n",
       "      <td>5.591309</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>683</td>\n",
       "      <td>01May2012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5116.833341</td>\n",
       "      <td>114.048014</td>\n",
       "      <td>...</td>\n",
       "      <td>15.836277</td>\n",
       "      <td>1629.479264</td>\n",
       "      <td>59.613338</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>4.878556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.081303</td>\n",
       "      <td>0.125393</td>\n",
       "      <td>5.872891</td>\n",
       "      <td>0.000000</td>\n",
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       "<p>5 rows × 114 columns</p>\n",
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       "   key       date        x1        x2        x3        x4          x5  \\\n",
       "0  683  01Jan2012  0.793085  0.000000  0.690346  0.000000         NaN   \n",
       "1  683  01Feb2012  0.000000  0.000686  0.590133  0.000000  362.021309   \n",
       "2  683  01Mar2012  0.000000  0.000000  0.000000  0.672651         NaN   \n",
       "3  683  01Apr2012  0.000000  0.000000  0.000000  0.022906         NaN   \n",
       "4  683  01May2012  0.000000  0.000000  0.000000  0.000000         NaN   \n",
       "\n",
       "         x6           x7            x8  ...        x103         x104  \\\n",
       "0       NaN   502.893893  10235.637780  ...    0.053509   120.669814   \n",
       "1       NaN    72.833160     88.793012  ...  112.074385    69.943189   \n",
       "2       NaN   880.917406    553.998042  ...    0.998247   273.213135   \n",
       "3  6.711943   150.778812     11.951856  ...  182.047997    18.272077   \n",
       "4       NaN  5116.833341    114.048014  ...   15.836277  1629.479264   \n",
       "\n",
       "        x105        x106        x107  x108       x109       x110        x111  \\\n",
       "0   0.955509  616.883023  200.144478   0.0  20.391554   0.509713  216.851245   \n",
       "1   2.704237  292.934714  138.241134   0.0  15.316564   2.526962   39.451960   \n",
       "2   0.000000   92.264811  100.972356   0.0  12.774053   0.242973   35.226264   \n",
       "3  -0.357520    5.272196   49.725924   0.0  17.904427  29.437773    5.591309   \n",
       "4  59.613338    0.060753    4.878556   0.0  14.081303   0.125393    5.872891   \n",
       "\n",
       "        x112  \n",
       "0  56.139906  \n",
       "1   7.172635  \n",
       "2  50.614383  \n",
       "3   0.000000  \n",
       "4   0.000000  \n",
       "\n",
       "[5 rows x 114 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8437.00000</td>\n",
       "      <td>7800.000000</td>\n",
       "      <td>7793.000000</td>\n",
       "      <td>7797.000000</td>\n",
       "      <td>7795.000000</td>\n",
       "      <td>2876.000000</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>7780.000000</td>\n",
       "      <td>7780.000000</td>\n",
       "      <td>7800.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7797.000000</td>\n",
       "      <td>7793.000000</td>\n",
       "      <td>7796.000000</td>\n",
       "      <td>7796.000000</td>\n",
       "      <td>7817.000000</td>\n",
       "      <td>7817.000000</td>\n",
       "      <td>7805.000000</td>\n",
       "      <td>7820.000000</td>\n",
       "      <td>7817.000000</td>\n",
       "      <td>7818.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51326.51689</td>\n",
       "      <td>0.347830</td>\n",
       "      <td>0.354310</td>\n",
       "      <td>0.319932</td>\n",
       "      <td>0.336695</td>\n",
       "      <td>372.786005</td>\n",
       "      <td>382.011899</td>\n",
       "      <td>951.109916</td>\n",
       "      <td>924.281006</td>\n",
       "      <td>676.835635</td>\n",
       "      <td>...</td>\n",
       "      <td>44.668328</td>\n",
       "      <td>465.193962</td>\n",
       "      <td>12.705929</td>\n",
       "      <td>945.771714</td>\n",
       "      <td>679.741590</td>\n",
       "      <td>0.315714</td>\n",
       "      <td>82.991057</td>\n",
       "      <td>45.698039</td>\n",
       "      <td>454.877942</td>\n",
       "      <td>13.009074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29839.15113</td>\n",
       "      <td>1.936792</td>\n",
       "      <td>1.965066</td>\n",
       "      <td>1.829656</td>\n",
       "      <td>2.027163</td>\n",
       "      <td>1241.064389</td>\n",
       "      <td>1326.111969</td>\n",
       "      <td>2825.566845</td>\n",
       "      <td>2643.918400</td>\n",
       "      <td>1984.928078</td>\n",
       "      <td>...</td>\n",
       "      <td>244.642605</td>\n",
       "      <td>1296.109097</td>\n",
       "      <td>46.145829</td>\n",
       "      <td>2657.296779</td>\n",
       "      <td>1955.014962</td>\n",
       "      <td>1.536839</td>\n",
       "      <td>356.505770</td>\n",
       "      <td>226.049705</td>\n",
       "      <td>1231.749937</td>\n",
       "      <td>49.115564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>683.00000</td>\n",
       "      <td>-33.228353</td>\n",
       "      <td>-52.023791</td>\n",
       "      <td>-61.645734</td>\n",
       "      <td>-66.456706</td>\n",
       "      <td>-0.218961</td>\n",
       "      <td>-0.175169</td>\n",
       "      <td>-80.348046</td>\n",
       "      <td>-26.782682</td>\n",
       "      <td>-174.883211</td>\n",
       "      <td>...</td>\n",
       "      <td>-329.086066</td>\n",
       "      <td>-247.860736</td>\n",
       "      <td>-251.749756</td>\n",
       "      <td>-128.023454</td>\n",
       "      <td>-207.568811</td>\n",
       "      <td>-16.389952</td>\n",
       "      <td>-927.615894</td>\n",
       "      <td>-485.108770</td>\n",
       "      <td>-250.392248</td>\n",
       "      <td>-317.319763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20423.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.809152</td>\n",
       "      <td>1.753411</td>\n",
       "      <td>11.499549</td>\n",
       "      <td>12.351966</td>\n",
       "      <td>9.102475</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.133734</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.664360</td>\n",
       "      <td>9.217013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.114887</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>57423.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.944190</td>\n",
       "      <td>20.159825</td>\n",
       "      <td>83.294584</td>\n",
       "      <td>84.247536</td>\n",
       "      <td>63.515596</td>\n",
       "      <td>...</td>\n",
       "      <td>1.711016</td>\n",
       "      <td>37.459257</td>\n",
       "      <td>0.323453</td>\n",
       "      <td>84.145884</td>\n",
       "      <td>61.588430</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.741318</td>\n",
       "      <td>1.670990</td>\n",
       "      <td>36.874337</td>\n",
       "      <td>0.303223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77827.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>136.762118</td>\n",
       "      <td>135.828419</td>\n",
       "      <td>455.481914</td>\n",
       "      <td>471.741556</td>\n",
       "      <td>369.282624</td>\n",
       "      <td>...</td>\n",
       "      <td>17.664795</td>\n",
       "      <td>232.762592</td>\n",
       "      <td>5.281627</td>\n",
       "      <td>446.065219</td>\n",
       "      <td>365.907941</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.395514</td>\n",
       "      <td>18.314854</td>\n",
       "      <td>229.778188</td>\n",
       "      <td>5.334830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99444.00000</td>\n",
       "      <td>65.134739</td>\n",
       "      <td>51.636600</td>\n",
       "      <td>53.112209</td>\n",
       "      <td>58.471756</td>\n",
       "      <td>17036.688540</td>\n",
       "      <td>19046.124220</td>\n",
       "      <td>38518.234870</td>\n",
       "      <td>39314.030240</td>\n",
       "      <td>40010.669150</td>\n",
       "      <td>...</td>\n",
       "      <td>7909.322436</td>\n",
       "      <td>19508.953940</td>\n",
       "      <td>1241.243056</td>\n",
       "      <td>35366.547670</td>\n",
       "      <td>38298.484340</td>\n",
       "      <td>41.261416</td>\n",
       "      <td>6733.987892</td>\n",
       "      <td>5499.072415</td>\n",
       "      <td>18661.314080</td>\n",
       "      <td>2222.065806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 113 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               key           x1           x2           x3           x4  \\\n",
       "count   8437.00000  7800.000000  7793.000000  7797.000000  7795.000000   \n",
       "mean   51326.51689     0.347830     0.354310     0.319932     0.336695   \n",
       "std    29839.15113     1.936792     1.965066     1.829656     2.027163   \n",
       "min      683.00000   -33.228353   -52.023791   -61.645734   -66.456706   \n",
       "25%    20423.00000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%    57423.00000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%    77827.00000     0.000000     0.000000     0.000000     0.000000   \n",
       "max    99444.00000    65.134739    51.636600    53.112209    58.471756   \n",
       "\n",
       "                 x5            x6            x7            x8            x9  \\\n",
       "count   2876.000000   2906.000000   7780.000000   7780.000000   7800.000000   \n",
       "mean     372.786005    382.011899    951.109916    924.281006    676.835635   \n",
       "std     1241.064389   1326.111969   2825.566845   2643.918400   1984.928078   \n",
       "min       -0.218961     -0.175169    -80.348046    -26.782682   -174.883211   \n",
       "25%        1.809152      1.753411     11.499549     12.351966      9.102475   \n",
       "50%       19.944190     20.159825     83.294584     84.247536     63.515596   \n",
       "75%      136.762118    135.828419    455.481914    471.741556    369.282624   \n",
       "max    17036.688540  19046.124220  38518.234870  39314.030240  40010.669150   \n",
       "\n",
       "       ...         x103          x104         x105          x106  \\\n",
       "count  ...  7797.000000   7793.000000  7796.000000   7796.000000   \n",
       "mean   ...    44.668328    465.193962    12.705929    945.771714   \n",
       "std    ...   244.642605   1296.109097    46.145829   2657.296779   \n",
       "min    ...  -329.086066   -247.860736  -251.749756   -128.023454   \n",
       "25%    ...     0.000000      5.133734     0.000000     11.664360   \n",
       "50%    ...     1.711016     37.459257     0.323453     84.145884   \n",
       "75%    ...    17.664795    232.762592     5.281627    446.065219   \n",
       "max    ...  7909.322436  19508.953940  1241.243056  35366.547670   \n",
       "\n",
       "               x107         x108         x109         x110          x111  \\\n",
       "count   7817.000000  7817.000000  7805.000000  7820.000000   7817.000000   \n",
       "mean     679.741590     0.315714    82.991057    45.698039    454.877942   \n",
       "std     1955.014962     1.536839   356.505770   226.049705   1231.749937   \n",
       "min     -207.568811   -16.389952  -927.615894  -485.108770   -250.392248   \n",
       "25%        9.217013     0.000000     0.000000     0.000000      5.114887   \n",
       "50%       61.588430     0.000000     4.741318     1.670990     36.874337   \n",
       "75%      365.907941     0.000000    45.395514    18.314854    229.778188   \n",
       "max    38298.484340    41.261416  6733.987892  5499.072415  18661.314080   \n",
       "\n",
       "              x112  \n",
       "count  7818.000000  \n",
       "mean     13.009074  \n",
       "std      49.115564  \n",
       "min    -317.319763  \n",
       "25%       0.000000  \n",
       "50%       0.303223  \n",
       "75%       5.334830  \n",
       "max    2222.065806  \n",
       "\n",
       "[8 rows x 113 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key       int64\n",
       "date     object\n",
       "x1      float64\n",
       "x2      float64\n",
       "x3      float64\n",
       "         ...   \n",
       "x108    float64\n",
       "x109    float64\n",
       "x110    float64\n",
       "x111    float64\n",
       "x112    float64\n",
       "Length: 114, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The key attribute presents the product, and the data type is categorical. Distinct products and their count:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8200     78\n",
       "99444    78\n",
       "61127    78\n",
       "57423    78\n",
       "37081    78\n",
       "         ..\n",
       "93876    42\n",
       "66606    42\n",
       "24248    34\n",
       "59945    30\n",
       "74523    29\n",
       "Name: key, Length: 119, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[\"key\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to get distinct values but easier to see all the values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.unique(X_train[\"key\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with Missing Values\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_data_percentage(column):\n",
    "    percentage = (X_train[column].isna().sum()/len(X_train[column])) * 100\n",
    "    print('{} column - missing data percentage: {:.2f} %' .format(column, percentage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key column - missing data percentage: 0.00 %\n",
      "date column - missing data percentage: 0.00 %\n",
      "x1 column - missing data percentage: 0.00 %\n",
      "x2 column - missing data percentage: 7.63 %\n",
      "x3 column - missing data percentage: 7.59 %\n",
      "x4 column - missing data percentage: 7.61 %\n",
      "x5 column - missing data percentage: 65.91 %\n",
      "x6 column - missing data percentage: 65.56 %\n",
      "x7 column - missing data percentage: 7.79 %\n",
      "x8 column - missing data percentage: 7.79 %\n",
      "x9 column - missing data percentage: 7.55 %\n",
      "x10 column - missing data percentage: 7.70 %\n",
      "x11 column - missing data percentage: 66.01 %\n",
      "x12 column - missing data percentage: 7.53 %\n",
      "x13 column - missing data percentage: 7.55 %\n",
      "x14 column - missing data percentage: 7.53 %\n",
      "x15 column - missing data percentage: 7.82 %\n",
      "x16 column - missing data percentage: 7.61 %\n",
      "x17 column - missing data percentage: 75.76 %\n",
      "x18 column - missing data percentage: 65.83 %\n",
      "x19 column - missing data percentage: 7.59 %\n",
      "x20 column - missing data percentage: 7.61 %\n",
      "x21 column - missing data percentage: 7.60 %\n",
      "x22 column - missing data percentage: 7.50 %\n",
      "x23 column - missing data percentage: 7.55 %\n",
      "x24 column - missing data percentage: 7.55 %\n",
      "x25 column - missing data percentage: 65.41 %\n",
      "x26 column - missing data percentage: 7.53 %\n",
      "x27 column - missing data percentage: 7.55 %\n",
      "x28 column - missing data percentage: 7.53 %\n",
      "x29 column - missing data percentage: 7.73 %\n",
      "x30 column - missing data percentage: 7.50 %\n",
      "x31 column - missing data percentage: 7.50 %\n",
      "x32 column - missing data percentage: 65.82 %\n",
      "x33 column - missing data percentage: 7.49 %\n",
      "x34 column - missing data percentage: 7.50 %\n",
      "x35 column - missing data percentage: 7.50 %\n",
      "x36 column - missing data percentage: 7.81 %\n",
      "x37 column - missing data percentage: 7.59 %\n",
      "x38 column - missing data percentage: 7.59 %\n",
      "x39 column - missing data percentage: 65.68 %\n",
      "x40 column - missing data percentage: 7.56 %\n",
      "x41 column - missing data percentage: 7.59 %\n",
      "x42 column - missing data percentage: 7.56 %\n",
      "x43 column - missing data percentage: 7.79 %\n",
      "x44 column - missing data percentage: 7.59 %\n",
      "x45 column - missing data percentage: 7.51 %\n",
      "x46 column - missing data percentage: 65.82 %\n",
      "x47 column - missing data percentage: 7.54 %\n",
      "x48 column - missing data percentage: 7.59 %\n",
      "x49 column - missing data percentage: 7.56 %\n",
      "x50 column - missing data percentage: 7.68 %\n",
      "x51 column - missing data percentage: 7.48 %\n",
      "x52 column - missing data percentage: 7.48 %\n",
      "x53 column - missing data percentage: 66.00 %\n",
      "x54 column - missing data percentage: 7.50 %\n",
      "x55 column - missing data percentage: 7.48 %\n",
      "x56 column - missing data percentage: 7.50 %\n",
      "x57 column - missing data percentage: 7.74 %\n",
      "x58 column - missing data percentage: 7.50 %\n",
      "x59 column - missing data percentage: 7.50 %\n",
      "x60 column - missing data percentage: 65.78 %\n",
      "x61 column - missing data percentage: 7.46 %\n",
      "x62 column - missing data percentage: 7.50 %\n",
      "x63 column - missing data percentage: 7.49 %\n",
      "x64 column - missing data percentage: 7.64 %\n",
      "x65 column - missing data percentage: 7.43 %\n",
      "x66 column - missing data percentage: 7.43 %\n",
      "x67 column - missing data percentage: 66.01 %\n",
      "x68 column - missing data percentage: 7.44 %\n",
      "x69 column - missing data percentage: 7.43 %\n",
      "x70 column - missing data percentage: 7.43 %\n",
      "x71 column - missing data percentage: 7.64 %\n",
      "x72 column - missing data percentage: 7.42 %\n",
      "x73 column - missing data percentage: 7.42 %\n",
      "x74 column - missing data percentage: 66.00 %\n",
      "x75 column - missing data percentage: 7.40 %\n",
      "x76 column - missing data percentage: 7.42 %\n",
      "x77 column - missing data percentage: 7.41 %\n",
      "x78 column - missing data percentage: 7.56 %\n",
      "x79 column - missing data percentage: 7.38 %\n",
      "x80 column - missing data percentage: 7.38 %\n",
      "x81 column - missing data percentage: 66.08 %\n",
      "x82 column - missing data percentage: 7.37 %\n",
      "x83 column - missing data percentage: 7.38 %\n",
      "x84 column - missing data percentage: 7.38 %\n",
      "x85 column - missing data percentage: 7.83 %\n",
      "x86 column - missing data percentage: 7.61 %\n",
      "x87 column - missing data percentage: 7.61 %\n",
      "x88 column - missing data percentage: 7.51 %\n",
      "x89 column - missing data percentage: 7.59 %\n",
      "x90 column - missing data percentage: 7.61 %\n",
      "x91 column - missing data percentage: 7.60 %\n",
      "x92 column - missing data percentage: 7.75 %\n",
      "x93 column - missing data percentage: 7.55 %\n",
      "x94 column - missing data percentage: 7.55 %\n",
      "x95 column - missing data percentage: 65.97 %\n",
      "x96 column - missing data percentage: 7.53 %\n",
      "x97 column - missing data percentage: 7.55 %\n",
      "x98 column - missing data percentage: 7.54 %\n",
      "x99 column - missing data percentage: 7.87 %\n",
      "x100 column - missing data percentage: 7.63 %\n",
      "x101 column - missing data percentage: 7.51 %\n",
      "x102 column - missing data percentage: 66.36 %\n",
      "x103 column - missing data percentage: 7.59 %\n",
      "x104 column - missing data percentage: 7.63 %\n",
      "x105 column - missing data percentage: 7.60 %\n",
      "x106 column - missing data percentage: 7.60 %\n",
      "x107 column - missing data percentage: 7.35 %\n",
      "x108 column - missing data percentage: 7.35 %\n",
      "x109 column - missing data percentage: 7.49 %\n",
      "x110 column - missing data percentage: 7.31 %\n",
      "x111 column - missing data percentage: 7.35 %\n",
      "x112 column - missing data percentage: 7.34 %\n"
     ]
    }
   ],
   "source": [
    "for column in X_train.columns:\n",
    "    missing_data_percentage(column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns with more than 50% of missing values will be removed from the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.drop(['x5', 'x6', 'x11', 'x17', 'x18', 'x25', 'x32', 'x39', 'x46', 'x53', 'x60', 'x67', 'x74', 'x81', 'x95', 'x102' ], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dealing with missing data - using median value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FillNa(BaseEstimator, TransformerMixin):\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(X, column):\n",
    "        return X[column].fillna(X[column].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x1 column - missing data percentage: 0.00 %\n",
      "x2 column - missing data percentage: 0.00 %\n",
      "x3 column - missing data percentage: 0.00 %\n",
      "x4 column - missing data percentage: 0.00 %\n",
      "x5 column - missing data percentage: 0.00 %\n",
      "x6 column - missing data percentage: 0.00 %\n",
      "x7 column - missing data percentage: 0.00 %\n",
      "x8 column - missing data percentage: 0.00 %\n",
      "x9 column - missing data percentage: 0.00 %\n",
      "x10 column - missing data percentage: 0.00 %\n",
      "x11 column - missing data percentage: 0.00 %\n",
      "x12 column - missing data percentage: 0.00 %\n",
      "x13 column - missing data percentage: 0.00 %\n",
      "x14 column - missing data percentage: 0.00 %\n",
      "x15 column - missing data percentage: 0.00 %\n",
      "x16 column - missing data percentage: 0.00 %\n",
      "x17 column - missing data percentage: 0.00 %\n",
      "x18 column - missing data percentage: 0.00 %\n",
      "x19 column - missing data percentage: 0.00 %\n",
      "x20 column - missing data percentage: 0.00 %\n",
      "x21 column - missing data percentage: 0.00 %\n",
      "x22 column - missing data percentage: 0.00 %\n",
      "x23 column - missing data percentage: 0.00 %\n",
      "x24 column - missing data percentage: 0.00 %\n",
      "x25 column - missing data percentage: 0.00 %\n",
      "x26 column - missing data percentage: 0.00 %\n",
      "x27 column - missing data percentage: 0.00 %\n",
      "x28 column - missing data percentage: 0.00 %\n",
      "x29 column - missing data percentage: 0.00 %\n",
      "x30 column - missing data percentage: 0.00 %\n",
      "x31 column - missing data percentage: 0.00 %\n",
      "x32 column - missing data percentage: 0.00 %\n",
      "x33 column - missing data percentage: 0.00 %\n",
      "x34 column - missing data percentage: 0.00 %\n",
      "x35 column - missing data percentage: 0.00 %\n",
      "x36 column - missing data percentage: 0.00 %\n",
      "x37 column - missing data percentage: 0.00 %\n",
      "x38 column - missing data percentage: 0.00 %\n",
      "x39 column - missing data percentage: 0.00 %\n",
      "x40 column - missing data percentage: 0.00 %\n",
      "x41 column - missing data percentage: 0.00 %\n",
      "x42 column - missing data percentage: 0.00 %\n",
      "x43 column - missing data percentage: 0.00 %\n",
      "x44 column - missing data percentage: 0.00 %\n",
      "x45 column - missing data percentage: 0.00 %\n",
      "x46 column - missing data percentage: 0.00 %\n",
      "x47 column - missing data percentage: 0.00 %\n",
      "x48 column - missing data percentage: 0.00 %\n",
      "x49 column - missing data percentage: 0.00 %\n",
      "x50 column - missing data percentage: 0.00 %\n",
      "x51 column - missing data percentage: 0.00 %\n",
      "x52 column - missing data percentage: 0.00 %\n",
      "x53 column - missing data percentage: 0.00 %\n",
      "x54 column - missing data percentage: 0.00 %\n",
      "x55 column - missing data percentage: 0.00 %\n",
      "x56 column - missing data percentage: 0.00 %\n",
      "x57 column - missing data percentage: 0.00 %\n",
      "x58 column - missing data percentage: 0.00 %\n",
      "x59 column - missing data percentage: 0.00 %\n",
      "x60 column - missing data percentage: 0.00 %\n",
      "x61 column - missing data percentage: 0.00 %\n",
      "x62 column - missing data percentage: 0.00 %\n",
      "x63 column - missing data percentage: 0.00 %\n",
      "x64 column - missing data percentage: 0.00 %\n",
      "x65 column - missing data percentage: 0.00 %\n",
      "x66 column - missing data percentage: 0.00 %\n",
      "x67 column - missing data percentage: 0.00 %\n",
      "x68 column - missing data percentage: 0.00 %\n",
      "x69 column - missing data percentage: 0.00 %\n",
      "x70 column - missing data percentage: 0.00 %\n",
      "x71 column - missing data percentage: 0.00 %\n",
      "x72 column - missing data percentage: 0.00 %\n",
      "x73 column - missing data percentage: 0.00 %\n",
      "x74 column - missing data percentage: 0.00 %\n",
      "x75 column - missing data percentage: 0.00 %\n",
      "x76 column - missing data percentage: 0.00 %\n",
      "x77 column - missing data percentage: 0.00 %\n",
      "x78 column - missing data percentage: 0.00 %\n",
      "x79 column - missing data percentage: 0.00 %\n",
      "x80 column - missing data percentage: 0.00 %\n",
      "x81 column - missing data percentage: 0.00 %\n",
      "x82 column - missing data percentage: 0.00 %\n",
      "x83 column - missing data percentage: 0.00 %\n",
      "x84 column - missing data percentage: 0.00 %\n",
      "x85 column - missing data percentage: 0.00 %\n",
      "x86 column - missing data percentage: 0.00 %\n",
      "x87 column - missing data percentage: 0.00 %\n",
      "x88 column - missing data percentage: 0.00 %\n",
      "x89 column - missing data percentage: 0.00 %\n",
      "x90 column - missing data percentage: 0.00 %\n",
      "x91 column - missing data percentage: 0.00 %\n",
      "x92 column - missing data percentage: 0.00 %\n",
      "x93 column - missing data percentage: 0.00 %\n",
      "x94 column - missing data percentage: 0.00 %\n",
      "x95 column - missing data percentage: 0.00 %\n",
      "x96 column - missing data percentage: 0.00 %\n",
      "x97 column - missing data percentage: 0.00 %\n",
      "x98 column - missing data percentage: 0.00 %\n",
      "x99 column - missing data percentage: 0.00 %\n",
      "x100 column - missing data percentage: 0.00 %\n",
      "x101 column - missing data percentage: 0.00 %\n",
      "x102 column - missing data percentage: 0.00 %\n",
      "x103 column - missing data percentage: 0.00 %\n",
      "x104 column - missing data percentage: 0.00 %\n",
      "x105 column - missing data percentage: 0.00 %\n",
      "x106 column - missing data percentage: 0.00 %\n",
      "x107 column - missing data percentage: 0.00 %\n",
      "x108 column - missing data percentage: 0.00 %\n",
      "x109 column - missing data percentage: 0.00 %\n",
      "x110 column - missing data percentage: 0.00 %\n",
      "x111 column - missing data percentage: 0.00 %\n",
      "x112 column - missing data percentage: 0.00 %\n",
      "month column - missing data percentage: 0.00 %\n",
      "year column - missing data percentage: 0.00 %\n"
     ]
    }
   ],
   "source": [
    "for column in X_train.iloc[:,2:].columns:\n",
    "    FillNa.transform(X_train, column)\n",
    "    missing_data_percentage(column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking again if the attributes hold any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Outliers\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interquartile range (IQR) is a measure of statistical dispersion and is calculated as the difference between the 75th and 25th percentiles.\n",
    "It is represented by the formula IQR = Q3 − Q1.\n",
    "The lines of code below calculate and print the interquartile range for each of the variables in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key     57404.000000\n",
      "x1          0.000000\n",
      "x2          0.000000\n",
      "x3          0.000000\n",
      "x4          0.000000\n",
      "            ...     \n",
      "x108        0.000000\n",
      "x109       38.167024\n",
      "x110       15.743874\n",
      "x111      190.101397\n",
      "x112        4.255356\n",
      "Length: 113, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "columns = X_train.columns\n",
    "\n",
    "Q1 = X_train[columns].quantile(0.25)\n",
    "Q3 = X_train[columns].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the outliers and calculating the skewness value again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Flooring_Capping(BaseEstimator, TransformerMixin):\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(X, column):\n",
    "        X[column] = np.where(X[column] < X[column].quantile(0.10), X[column].quantile(0.10), X[column])\n",
    "        X[column] = np.where(X[column] > X[column].quantile(0.90), X[column].quantile(0.90), X[column])\n",
    "        return X[column]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness of column x1 BEFORE the flooring and capping: 2.07\n",
      "Skewness of column x1 AFTER the flooring and capping: 2.07\n",
      "\n",
      "Skewness of column x2 BEFORE the flooring and capping: 2.04\n",
      "Skewness of column x2 AFTER the flooring and capping: 2.04\n",
      "\n",
      "Skewness of column x3 BEFORE the flooring and capping: 2.07\n",
      "Skewness of column x3 AFTER the flooring and capping: 2.07\n",
      "\n",
      "Skewness of column x4 BEFORE the flooring and capping: 2.07\n",
      "Skewness of column x4 AFTER the flooring and capping: 2.07\n",
      "\n",
      "Skewness of column x5 BEFORE the flooring and capping: 1.98\n",
      "Skewness of column x5 AFTER the flooring and capping: 1.98\n",
      "\n",
      "Skewness of column x6 BEFORE the flooring and capping: 2.01\n",
      "Skewness of column x6 AFTER the flooring and capping: 2.01\n",
      "\n",
      "Skewness of column x7 BEFORE the flooring and capping: 1.81\n",
      "Skewness of column x7 AFTER the flooring and capping: 1.81\n",
      "\n",
      "Skewness of column x8 BEFORE the flooring and capping: 1.77\n",
      "Skewness of column x8 AFTER the flooring and capping: 1.77\n",
      "\n",
      "Skewness of column x9 BEFORE the flooring and capping: 1.74\n",
      "Skewness of column x9 AFTER the flooring and capping: 1.74\n",
      "\n",
      "Skewness of column x10 BEFORE the flooring and capping: 1.75\n",
      "Skewness of column x10 AFTER the flooring and capping: 1.75\n",
      "\n",
      "Skewness of column x11 BEFORE the flooring and capping: 1.96\n",
      "Skewness of column x11 AFTER the flooring and capping: 1.96\n",
      "\n",
      "Skewness of column x12 BEFORE the flooring and capping: 1.70\n",
      "Skewness of column x12 AFTER the flooring and capping: 1.70\n",
      "\n",
      "Skewness of column x13 BEFORE the flooring and capping: 1.80\n",
      "Skewness of column x13 AFTER the flooring and capping: 1.80\n",
      "\n",
      "Skewness of column x14 BEFORE the flooring and capping: 1.84\n",
      "Skewness of column x14 AFTER the flooring and capping: 1.84\n",
      "\n",
      "Skewness of column x15 BEFORE the flooring and capping: 1.82\n",
      "Skewness of column x15 AFTER the flooring and capping: 1.82\n",
      "\n",
      "Skewness of column x16 BEFORE the flooring and capping: 1.74\n",
      "Skewness of column x16 AFTER the flooring and capping: 1.74\n",
      "\n",
      "Skewness of column x17 BEFORE the flooring and capping: 1.09\n",
      "Skewness of column x17 AFTER the flooring and capping: 1.09\n",
      "\n",
      "Skewness of column x18 BEFORE the flooring and capping: 2.02\n",
      "Skewness of column x18 AFTER the flooring and capping: 2.02\n",
      "\n",
      "Skewness of column x19 BEFORE the flooring and capping: 1.69\n",
      "Skewness of column x19 AFTER the flooring and capping: 1.69\n",
      "\n",
      "Skewness of column x20 BEFORE the flooring and capping: 1.78\n",
      "Skewness of column x20 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x21 BEFORE the flooring and capping: 1.82\n",
      "Skewness of column x21 AFTER the flooring and capping: 1.82\n",
      "\n",
      "Skewness of column x22 BEFORE the flooring and capping: 1.81\n",
      "Skewness of column x22 AFTER the flooring and capping: 1.81\n",
      "\n",
      "Skewness of column x23 BEFORE the flooring and capping: 1.76\n",
      "Skewness of column x23 AFTER the flooring and capping: 1.76\n",
      "\n",
      "Skewness of column x24 BEFORE the flooring and capping: 2.04\n",
      "Skewness of column x24 AFTER the flooring and capping: 2.04\n",
      "\n",
      "Skewness of column x25 BEFORE the flooring and capping: 2.07\n",
      "Skewness of column x25 AFTER the flooring and capping: 2.07\n",
      "\n",
      "Skewness of column x26 BEFORE the flooring and capping: 1.69\n",
      "Skewness of column x26 AFTER the flooring and capping: 1.69\n",
      "\n",
      "Skewness of column x27 BEFORE the flooring and capping: 1.83\n",
      "Skewness of column x27 AFTER the flooring and capping: 1.83\n",
      "\n",
      "Skewness of column x28 BEFORE the flooring and capping: 1.82\n",
      "Skewness of column x28 AFTER the flooring and capping: 1.82\n",
      "\n",
      "Skewness of column x29 BEFORE the flooring and capping: 1.80\n",
      "Skewness of column x29 AFTER the flooring and capping: 1.80\n",
      "\n",
      "Skewness of column x30 BEFORE the flooring and capping: 1.74\n",
      "Skewness of column x30 AFTER the flooring and capping: 1.74\n",
      "\n",
      "Skewness of column x31 BEFORE the flooring and capping: 2.08\n",
      "Skewness of column x31 AFTER the flooring and capping: 2.08\n",
      "\n",
      "Skewness of column x32 BEFORE the flooring and capping: 2.00\n",
      "Skewness of column x32 AFTER the flooring and capping: 2.00\n",
      "\n",
      "Skewness of column x33 BEFORE the flooring and capping: 1.71\n",
      "Skewness of column x33 AFTER the flooring and capping: 1.71\n",
      "\n",
      "Skewness of column x34 BEFORE the flooring and capping: 1.77\n",
      "Skewness of column x34 AFTER the flooring and capping: 1.77\n",
      "\n",
      "Skewness of column x35 BEFORE the flooring and capping: 1.84\n",
      "Skewness of column x35 AFTER the flooring and capping: 1.84\n",
      "\n",
      "Skewness of column x36 BEFORE the flooring and capping: 1.80\n",
      "Skewness of column x36 AFTER the flooring and capping: 1.80\n",
      "\n",
      "Skewness of column x37 BEFORE the flooring and capping: 1.75\n",
      "Skewness of column x37 AFTER the flooring and capping: 1.75\n",
      "\n",
      "Skewness of column x38 BEFORE the flooring and capping: 2.07\n",
      "Skewness of column x38 AFTER the flooring and capping: 2.07\n",
      "\n",
      "Skewness of column x39 BEFORE the flooring and capping: 2.04\n",
      "Skewness of column x39 AFTER the flooring and capping: 2.04\n",
      "\n",
      "Skewness of column x40 BEFORE the flooring and capping: 1.69\n",
      "Skewness of column x40 AFTER the flooring and capping: 1.69\n",
      "\n",
      "Skewness of column x41 BEFORE the flooring and capping: 1.82\n",
      "Skewness of column x41 AFTER the flooring and capping: 1.82\n",
      "\n",
      "Skewness of column x42 BEFORE the flooring and capping: 1.82\n",
      "Skewness of column x42 AFTER the flooring and capping: 1.82\n",
      "\n",
      "Skewness of column x43 BEFORE the flooring and capping: 1.79\n",
      "Skewness of column x43 AFTER the flooring and capping: 1.79\n",
      "\n",
      "Skewness of column x44 BEFORE the flooring and capping: 1.79\n",
      "Skewness of column x44 AFTER the flooring and capping: 1.79\n",
      "\n",
      "Skewness of column x45 BEFORE the flooring and capping: 1.97\n",
      "Skewness of column x45 AFTER the flooring and capping: 1.97\n",
      "\n",
      "Skewness of column x46 BEFORE the flooring and capping: 1.94\n",
      "Skewness of column x46 AFTER the flooring and capping: 1.94\n",
      "\n",
      "Skewness of column x47 BEFORE the flooring and capping: 1.68\n",
      "Skewness of column x47 AFTER the flooring and capping: 1.68\n",
      "\n",
      "Skewness of column x48 BEFORE the flooring and capping: 1.80\n",
      "Skewness of column x48 AFTER the flooring and capping: 1.80\n",
      "\n",
      "Skewness of column x49 BEFORE the flooring and capping: 1.84\n",
      "Skewness of column x49 AFTER the flooring and capping: 1.84\n",
      "\n",
      "Skewness of column x50 BEFORE the flooring and capping: 1.79\n",
      "Skewness of column x50 AFTER the flooring and capping: 1.79\n",
      "\n",
      "Skewness of column x51 BEFORE the flooring and capping: 1.73\n",
      "Skewness of column x51 AFTER the flooring and capping: 1.73\n",
      "\n",
      "Skewness of column x52 BEFORE the flooring and capping: 2.08\n",
      "Skewness of column x52 AFTER the flooring and capping: 2.08\n",
      "\n",
      "Skewness of column x53 BEFORE the flooring and capping: 1.97\n",
      "Skewness of column x53 AFTER the flooring and capping: 1.97\n",
      "\n",
      "Skewness of column x54 BEFORE the flooring and capping: 1.69\n",
      "Skewness of column x54 AFTER the flooring and capping: 1.69\n",
      "\n",
      "Skewness of column x55 BEFORE the flooring and capping: 1.81\n",
      "Skewness of column x55 AFTER the flooring and capping: 1.81\n",
      "\n",
      "Skewness of column x56 BEFORE the flooring and capping: 1.84\n",
      "Skewness of column x56 AFTER the flooring and capping: 1.84\n",
      "\n",
      "Skewness of column x57 BEFORE the flooring and capping: 1.78\n",
      "Skewness of column x57 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x58 BEFORE the flooring and capping: 1.75\n",
      "Skewness of column x58 AFTER the flooring and capping: 1.75\n",
      "\n",
      "Skewness of column x59 BEFORE the flooring and capping: 2.08\n",
      "Skewness of column x59 AFTER the flooring and capping: 2.08\n",
      "\n",
      "Skewness of column x60 BEFORE the flooring and capping: 2.01\n",
      "Skewness of column x60 AFTER the flooring and capping: 2.01\n",
      "\n",
      "Skewness of column x61 BEFORE the flooring and capping: 1.70\n",
      "Skewness of column x61 AFTER the flooring and capping: 1.70\n",
      "\n",
      "Skewness of column x62 BEFORE the flooring and capping: 1.78\n",
      "Skewness of column x62 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x63 BEFORE the flooring and capping: 1.85\n",
      "Skewness of column x63 AFTER the flooring and capping: 1.85\n",
      "\n",
      "Skewness of column x64 BEFORE the flooring and capping: 1.78\n",
      "Skewness of column x64 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x65 BEFORE the flooring and capping: 1.74\n",
      "Skewness of column x65 AFTER the flooring and capping: 1.74\n",
      "\n",
      "Skewness of column x66 BEFORE the flooring and capping: 2.06\n",
      "Skewness of column x66 AFTER the flooring and capping: 2.06\n",
      "\n",
      "Skewness of column x67 BEFORE the flooring and capping: 1.99\n",
      "Skewness of column x67 AFTER the flooring and capping: 1.99\n",
      "\n",
      "Skewness of column x68 BEFORE the flooring and capping: 1.68\n",
      "Skewness of column x68 AFTER the flooring and capping: 1.68\n",
      "\n",
      "Skewness of column x69 BEFORE the flooring and capping: 1.79\n",
      "Skewness of column x69 AFTER the flooring and capping: 1.79\n",
      "\n",
      "Skewness of column x70 BEFORE the flooring and capping: 1.83\n",
      "Skewness of column x70 AFTER the flooring and capping: 1.83\n",
      "\n",
      "Skewness of column x71 BEFORE the flooring and capping: 1.78\n",
      "Skewness of column x71 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x72 BEFORE the flooring and capping: 1.75\n",
      "Skewness of column x72 AFTER the flooring and capping: 1.75\n",
      "\n",
      "Skewness of column x73 BEFORE the flooring and capping: 2.07\n",
      "Skewness of column x73 AFTER the flooring and capping: 2.07\n",
      "\n",
      "Skewness of column x74 BEFORE the flooring and capping: 2.04\n",
      "Skewness of column x74 AFTER the flooring and capping: 2.04\n",
      "\n",
      "Skewness of column x75 BEFORE the flooring and capping: 1.67\n",
      "Skewness of column x75 AFTER the flooring and capping: 1.67\n",
      "\n",
      "Skewness of column x76 BEFORE the flooring and capping: 1.78\n",
      "Skewness of column x76 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x77 BEFORE the flooring and capping: 1.83\n",
      "Skewness of column x77 AFTER the flooring and capping: 1.83\n",
      "\n",
      "Skewness of column x78 BEFORE the flooring and capping: 1.80\n",
      "Skewness of column x78 AFTER the flooring and capping: 1.80\n",
      "\n",
      "Skewness of column x79 BEFORE the flooring and capping: 1.73\n",
      "Skewness of column x79 AFTER the flooring and capping: 1.73\n",
      "\n",
      "Skewness of column x80 BEFORE the flooring and capping: 2.07\n",
      "Skewness of column x80 AFTER the flooring and capping: 2.07\n",
      "\n",
      "Skewness of column x81 BEFORE the flooring and capping: 2.02\n",
      "Skewness of column x81 AFTER the flooring and capping: 2.02\n",
      "\n",
      "Skewness of column x82 BEFORE the flooring and capping: 1.67\n",
      "Skewness of column x82 AFTER the flooring and capping: 1.67\n",
      "\n",
      "Skewness of column x83 BEFORE the flooring and capping: 1.78\n",
      "Skewness of column x83 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x84 BEFORE the flooring and capping: 1.85\n",
      "Skewness of column x84 AFTER the flooring and capping: 1.85\n",
      "\n",
      "Skewness of column x85 BEFORE the flooring and capping: 1.79\n",
      "Skewness of column x85 AFTER the flooring and capping: 1.79\n",
      "\n",
      "Skewness of column x86 BEFORE the flooring and capping: 1.76\n",
      "Skewness of column x86 AFTER the flooring and capping: 1.76\n",
      "\n",
      "Skewness of column x87 BEFORE the flooring and capping: 2.09\n",
      "Skewness of column x87 AFTER the flooring and capping: 2.09\n",
      "\n",
      "Skewness of column x88 BEFORE the flooring and capping: 1.91\n",
      "Skewness of column x88 AFTER the flooring and capping: 1.91\n",
      "\n",
      "Skewness of column x89 BEFORE the flooring and capping: 1.70\n",
      "Skewness of column x89 AFTER the flooring and capping: 1.70\n",
      "\n",
      "Skewness of column x90 BEFORE the flooring and capping: 1.79\n",
      "Skewness of column x90 AFTER the flooring and capping: 1.79\n",
      "\n",
      "Skewness of column x91 BEFORE the flooring and capping: 1.81\n",
      "Skewness of column x91 AFTER the flooring and capping: 1.81\n",
      "\n",
      "Skewness of column x92 BEFORE the flooring and capping: 1.76\n",
      "Skewness of column x92 AFTER the flooring and capping: 1.76\n",
      "\n",
      "Skewness of column x93 BEFORE the flooring and capping: 1.76\n",
      "Skewness of column x93 AFTER the flooring and capping: 1.76\n",
      "\n",
      "Skewness of column x94 BEFORE the flooring and capping: 2.07\n",
      "Skewness of column x94 AFTER the flooring and capping: 2.07\n",
      "\n",
      "Skewness of column x95 BEFORE the flooring and capping: 2.00\n",
      "Skewness of column x95 AFTER the flooring and capping: 2.00\n",
      "\n",
      "Skewness of column x96 BEFORE the flooring and capping: 1.69\n",
      "Skewness of column x96 AFTER the flooring and capping: 1.69\n",
      "\n",
      "Skewness of column x97 BEFORE the flooring and capping: 1.79\n",
      "Skewness of column x97 AFTER the flooring and capping: 1.79\n",
      "\n",
      "Skewness of column x98 BEFORE the flooring and capping: 1.80\n",
      "Skewness of column x98 AFTER the flooring and capping: 1.80\n",
      "\n",
      "Skewness of column x99 BEFORE the flooring and capping: 1.78\n",
      "Skewness of column x99 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x100 BEFORE the flooring and capping: 1.78\n",
      "Skewness of column x100 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x101 BEFORE the flooring and capping: 1.78\n",
      "Skewness of column x101 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x102 BEFORE the flooring and capping: 1.96\n",
      "Skewness of column x102 AFTER the flooring and capping: 1.96\n",
      "\n",
      "Skewness of column x103 BEFORE the flooring and capping: 1.67\n",
      "Skewness of column x103 AFTER the flooring and capping: 1.67\n",
      "\n",
      "Skewness of column x104 BEFORE the flooring and capping: 1.79\n",
      "Skewness of column x104 AFTER the flooring and capping: 1.79\n",
      "\n",
      "Skewness of column x105 BEFORE the flooring and capping: 1.81\n",
      "Skewness of column x105 AFTER the flooring and capping: 1.81\n",
      "\n",
      "Skewness of column x106 BEFORE the flooring and capping: 1.82\n",
      "Skewness of column x106 AFTER the flooring and capping: 1.82\n",
      "\n",
      "Skewness of column x107 BEFORE the flooring and capping: 1.74\n",
      "Skewness of column x107 AFTER the flooring and capping: 1.74\n",
      "\n",
      "Skewness of column x108 BEFORE the flooring and capping: 2.09\n",
      "Skewness of column x108 AFTER the flooring and capping: 2.09\n",
      "\n",
      "Skewness of column x109 BEFORE the flooring and capping: 1.60\n",
      "Skewness of column x109 AFTER the flooring and capping: 1.60\n",
      "\n",
      "Skewness of column x110 BEFORE the flooring and capping: 1.70\n",
      "Skewness of column x110 AFTER the flooring and capping: 1.70\n",
      "\n",
      "Skewness of column x111 BEFORE the flooring and capping: 1.79\n",
      "Skewness of column x111 AFTER the flooring and capping: 1.79\n",
      "\n",
      "Skewness of column x112 BEFORE the flooring and capping: 1.82\n",
      "Skewness of column x112 AFTER the flooring and capping: 1.82\n",
      "\n",
      "Skewness of column month BEFORE the flooring and capping: 0.12\n",
      "Skewness of column month AFTER the flooring and capping: 0.12\n",
      "\n",
      "Skewness of column year BEFORE the flooring and capping: -0.24\n",
      "Skewness of column year AFTER the flooring and capping: -0.24\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in X_train.iloc[:,2:].columns:\n",
    "    print(\"Skewness of column {} BEFORE the flooring and capping: {:.2f}\".format(column, X_train[column].skew()))\n",
    "    Flooring_Capping.transform(X_train, column)\n",
    "    print(\"Skewness of column {} AFTER the flooring and capping: {:.2f}\\n\".format(column, X_train[column].skew()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is visible that removing the outliers below the 10th quantile and above 90th quantile results in better skewness of every column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformations\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changing numerical columns from object into datetime type, as well as other columns' types from object to float/int:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key               int64\n",
       "date     datetime64[ns]\n",
       "x1              float64\n",
       "x2              float64\n",
       "x3              float64\n",
       "              ...      \n",
       "x110            float64\n",
       "x111            float64\n",
       "x112            float64\n",
       "month             int64\n",
       "year              int64\n",
       "Length: 100, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.infer_objects()\n",
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key        int64\n",
       "date      object\n",
       "y        float64\n",
       "month      int64\n",
       "year       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.infer_objects()\n",
    "y_train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Transformers\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding new month and year columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing a date column from object into datetime type:\n",
    "X_train['date'] = pd.to_datetime(X_train['date'])\n",
    "\n",
    "class MonthYearAdder(BaseEstimator, TransformerMixin):\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(X):\n",
    "        X[\"month\"] = pd.DatetimeIndex(X[\"date\"]).month\n",
    "        X[\"year\"] = pd.DatetimeIndex(X[\"date\"]).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "MonthYearAdder.transform(X_train)\n",
    "MonthYearAdder.transform(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformation Pipeline\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "transform_pipeline = Pipeline(\n",
    "[\n",
    "    ('fill_na', FillNa()),\n",
    "    ('flooring_capping_outliers', Flooring_Capping()),\n",
    "    ('add_month_year', MonthYearAdder())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'FillNa' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-120-6fdc125022f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtransform_pipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    365\u001b[0m         \"\"\"\n\u001b[1;32m    366\u001b[0m         \u001b[0mfit_params_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_fit_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0mXt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m         \u001b[0mlast_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, **fit_params_steps)\u001b[0m\n\u001b[1;32m    290\u001b[0m                 \u001b[0mcloned_transformer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m             \u001b[0;31m# Fit or load from cache the current transformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 292\u001b[0;31m             X, fitted_transformer = fit_transform_one_cached(\n\u001b[0m\u001b[1;32m    293\u001b[0m                 \u001b[0mcloned_transformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m                 \u001b[0mmessage_clsname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Pipeline'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/joblib/memory.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_and_shelve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    738\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_print_elapsed_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_clsname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 740\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    741\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    742\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    688\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    689\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 690\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    691\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-110-86a8fa5f1891>\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(X, column)\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'FillNa' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "transform_pipeline.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attributes Correlation\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pearson’s correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables.\n",
    "\n",
    "When there are two independent variables that are very highly correlated, you definitely should remove one of them because you run into the multicollinearity conundrum and your regression model's coefficients related to the two highly correlated variables will be unreliable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.005726</td>\n",
       "      <td>-0.003948</td>\n",
       "      <td>0.003573</td>\n",
       "      <td>-0.000734</td>\n",
       "      <td>0.018214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x3</th>\n",
       "      <td>-0.000696</td>\n",
       "      <td>0.002758</td>\n",
       "      <td>0.008258</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012233</td>\n",
       "      <td>0.010476</td>\n",
       "      <td>-0.009032</td>\n",
       "      <td>-0.010588</td>\n",
       "      <td>0.008027</td>\n",
       "      <td>-0.005360</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016126</td>\n",
       "      <td>-0.008118</td>\n",
       "      <td>0.009803</td>\n",
       "      <td>0.010984</td>\n",
       "      <td>0.012663</td>\n",
       "      <td>0.010429</td>\n",
       "      <td>0.001106</td>\n",
       "      <td>-0.004909</td>\n",
       "      <td>0.004193</td>\n",
       "      <td>-0.000368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x4</th>\n",
       "      <td>-0.003560</td>\n",
       "      <td>-0.004958</td>\n",
       "      <td>0.019026</td>\n",
       "      <td>0.012233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010267</td>\n",
       "      <td>-0.023509</td>\n",
       "      <td>-0.020737</td>\n",
       "      <td>-0.003113</td>\n",
       "      <td>-0.006516</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001690</td>\n",
       "      <td>-0.006962</td>\n",
       "      <td>-0.005505</td>\n",
       "      <td>-0.000878</td>\n",
       "      <td>-0.010438</td>\n",
       "      <td>-0.008539</td>\n",
       "      <td>0.001754</td>\n",
       "      <td>-0.008606</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>0.011572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x110</th>\n",
       "      <td>0.004044</td>\n",
       "      <td>0.004062</td>\n",
       "      <td>0.005726</td>\n",
       "      <td>0.010429</td>\n",
       "      <td>-0.008539</td>\n",
       "      <td>0.021358</td>\n",
       "      <td>0.006825</td>\n",
       "      <td>0.009173</td>\n",
       "      <td>0.010593</td>\n",
       "      <td>-0.002485</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015692</td>\n",
       "      <td>0.386250</td>\n",
       "      <td>0.424457</td>\n",
       "      <td>0.421616</td>\n",
       "      <td>0.007151</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.363990</td>\n",
       "      <td>0.293007</td>\n",
       "      <td>-0.002754</td>\n",
       "      <td>-0.007135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x111</th>\n",
       "      <td>-0.005983</td>\n",
       "      <td>0.008134</td>\n",
       "      <td>-0.003948</td>\n",
       "      <td>0.001106</td>\n",
       "      <td>0.001754</td>\n",
       "      <td>0.009555</td>\n",
       "      <td>-0.003607</td>\n",
       "      <td>0.007194</td>\n",
       "      <td>0.009450</td>\n",
       "      <td>-0.010532</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001358</td>\n",
       "      <td>0.806815</td>\n",
       "      <td>0.800378</td>\n",
       "      <td>0.149363</td>\n",
       "      <td>-0.002074</td>\n",
       "      <td>0.363990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651730</td>\n",
       "      <td>-0.001209</td>\n",
       "      <td>-0.000560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x112</th>\n",
       "      <td>-0.008894</td>\n",
       "      <td>-0.006453</td>\n",
       "      <td>0.003573</td>\n",
       "      <td>-0.004909</td>\n",
       "      <td>-0.008606</td>\n",
       "      <td>0.008580</td>\n",
       "      <td>0.009550</td>\n",
       "      <td>0.017658</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000453</td>\n",
       "      <td>0.658148</td>\n",
       "      <td>0.643110</td>\n",
       "      <td>0.105122</td>\n",
       "      <td>-0.001024</td>\n",
       "      <td>0.293007</td>\n",
       "      <td>0.651730</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000677</td>\n",
       "      <td>-0.002726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>-0.000657</td>\n",
       "      <td>0.022850</td>\n",
       "      <td>-0.000734</td>\n",
       "      <td>0.004193</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>0.005103</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>-0.005554</td>\n",
       "      <td>0.024554</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006657</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>-0.004181</td>\n",
       "      <td>-0.017083</td>\n",
       "      <td>-0.010068</td>\n",
       "      <td>-0.002754</td>\n",
       "      <td>-0.001209</td>\n",
       "      <td>-0.000677</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.122319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>-0.001066</td>\n",
       "      <td>-0.002896</td>\n",
       "      <td>0.018214</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>0.011572</td>\n",
       "      <td>0.005921</td>\n",
       "      <td>-0.007053</td>\n",
       "      <td>-0.007616</td>\n",
       "      <td>0.009005</td>\n",
       "      <td>-0.004031</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029329</td>\n",
       "      <td>0.001751</td>\n",
       "      <td>0.001595</td>\n",
       "      <td>0.013115</td>\n",
       "      <td>0.067350</td>\n",
       "      <td>-0.007135</td>\n",
       "      <td>-0.000560</td>\n",
       "      <td>-0.002726</td>\n",
       "      <td>-0.122319</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            key        x1        x2        x3        x4        x7        x8  \\\n",
       "key    1.000000  0.009020  0.010855 -0.000696 -0.003560  0.012958 -0.007995   \n",
       "x1     0.009020  1.000000 -0.001504  0.002758 -0.004958 -0.002514  0.168715   \n",
       "x2     0.010855 -0.001504  1.000000  0.008258  0.019026  0.006475 -0.008417   \n",
       "x3    -0.000696  0.002758  0.008258  1.000000  0.012233  0.010476 -0.009032   \n",
       "x4    -0.003560 -0.004958  0.019026  0.012233  1.000000  0.010267 -0.023509   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "x110   0.004044  0.004062  0.005726  0.010429 -0.008539  0.021358  0.006825   \n",
       "x111  -0.005983  0.008134 -0.003948  0.001106  0.001754  0.009555 -0.003607   \n",
       "x112  -0.008894 -0.006453  0.003573 -0.004909 -0.008606  0.008580  0.009550   \n",
       "month -0.000657  0.022850 -0.000734  0.004193  0.001023  0.005103  0.001335   \n",
       "year  -0.001066 -0.002896  0.018214 -0.000368  0.011572  0.005921 -0.007053   \n",
       "\n",
       "             x9       x10       x12  ...      x105      x106      x107  \\\n",
       "key   -0.002685 -0.033451 -0.006153  ... -0.013070 -0.006636 -0.009420   \n",
       "x1     0.184093  0.002808  0.441816  ...  0.017767  0.008475 -0.002285   \n",
       "x2    -0.002371  0.001949 -0.010871  ...  0.101134  0.002421 -0.008118   \n",
       "x3    -0.010588  0.008027 -0.005360  ... -0.016126 -0.008118  0.009803   \n",
       "x4    -0.020737 -0.003113 -0.006516  ...  0.001690 -0.006962 -0.005505   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "x110   0.009173  0.010593 -0.002485  ... -0.015692  0.386250  0.424457   \n",
       "x111   0.007194  0.009450 -0.010532  ... -0.001358  0.806815  0.800378   \n",
       "x112   0.017658  0.012812  0.000110  ... -0.000453  0.658148  0.643110   \n",
       "month  0.000242 -0.005554  0.024554  ...  0.006657  0.000798 -0.004181   \n",
       "year  -0.007616  0.009005 -0.004031  ... -0.029329  0.001751  0.001595   \n",
       "\n",
       "           x108      x109      x110      x111      x112     month      year  \n",
       "key    0.019460 -0.000734  0.004044 -0.005983 -0.008894 -0.000657 -0.001066  \n",
       "x1     0.000192  0.010430  0.004062  0.008134 -0.006453  0.022850 -0.002896  \n",
       "x2    -0.002993  0.009667  0.005726 -0.003948  0.003573 -0.000734  0.018214  \n",
       "x3     0.010984  0.012663  0.010429  0.001106 -0.004909  0.004193 -0.000368  \n",
       "x4    -0.000878 -0.010438 -0.008539  0.001754 -0.008606  0.001023  0.011572  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "x110   0.421616  0.007151  1.000000  0.363990  0.293007 -0.002754 -0.007135  \n",
       "x111   0.149363 -0.002074  0.363990  1.000000  0.651730 -0.001209 -0.000560  \n",
       "x112   0.105122 -0.001024  0.293007  0.651730  1.000000 -0.000677 -0.002726  \n",
       "month -0.017083 -0.010068 -0.002754 -0.001209 -0.000677  1.000000 -0.122319  \n",
       "year   0.013115  0.067350 -0.007135 -0.000560 -0.002726 -0.122319  1.000000  \n",
       "\n",
       "[99 rows x 99 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = X_train.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are taking only the upper triangle of the correlation matrix to remove only one highly correlated attribute (and to look only positive correlation (Pearson's coefficient ranges from -1 to 1))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally, if the correlation between a pair of variables is greater than 0.5-0.6, we should seriously consider dropping one of those variables.\n",
    "We are selecting the columns which are having absolute correlation greater than 0.8 and making a list of those columns named 'to_drop'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pearson's r = correlation coefficient = r:\n",
    "\n",
    "0.00-0.19: very weak\n",
    "0.20-0.39: weak\n",
    "0.40-0.59: moderate \n",
    "0.60-0.79: strong\n",
    "0.80-1.00: very strong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-5563e36824c2>:1: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  upper_tri = corr.where(np.triu(np.ones(corr.shape),k=1).astype(np.bool))\n"
     ]
    }
   ],
   "source": [
    "upper_tri = corr.where(np.triu(np.ones(corr.shape),k=1).astype(np.bool))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x9', 'x13', 'x16', 'x20', 'x23', 'x27', 'x30', 'x34', 'x37', 'x41', 'x44', 'x48', 'x55', 'x62', 'x65', 'x69', 'x72', 'x79', 'x83', 'x86', 'x88', 'x90', 'x93', 'x97', 'x100', 'x101', 'x104', 'x107', 'x111']\n"
     ]
    }
   ],
   "source": [
    "to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > 0.8)]\n",
    "print(to_drop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Pearson's Correlation Coefficient, there is a couple of  variables that are highly correlated (𝜌 > 0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9653448406738077"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr.loc[\"x88\", \"x101\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.drop(to_drop, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explorative Data Analysis\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Joining the datasets to visualize the sales for products and for other information in the X_train dataset):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = X_train.append(y_train)\n",
    "dataset = pd.merge(X_train, y_train, how = 'left', left_on = [\"key\", \"month\", \"year\"], right_on = [\"key\", \"month\", \"year\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look into the scaled data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping data for EDA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_sales_monthly_mean = dataset.groupby([\"month\"], as_index = False)[\"y\"].mean()\n",
    "total_sales_monthly_sum = dataset.groupby([\"month\"], as_index = False)[\"y\"].sum()\n",
    "\n",
    "total_sales_yearly_mean = dataset.groupby([\"year\"], as_index = False)[\"y\"].mean()\n",
    "total_sales_yearly_sum = dataset.groupby([\"year\"], as_index = False)[\"y\"].sum()\n",
    "\n",
    "total_sales_daily_mean = dataset.groupby([\"date_x\"], as_index = False)[\"y\"].mean()\n",
    "total_sales_daily_sum = dataset.groupby([\"date_x\"], as_index = False)[\"y\"].sum()\n",
    "\n",
    "group_product_sales_mean = dataset.groupby([\"key\"], as_index=False)[\"y\"].mean()\n",
    "group_product_sales_sum = dataset.groupby([\"key\"], as_index=False)[\"y\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monthly Total Sales - Showing Seasonality\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'month'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_sales_monthly_mean.columns[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_total_sales(measure):\n",
    "    measure.plot(x = measure.columns[0], \n",
    "                 y = \"y\", \n",
    "                 figsize = (6, 4), \n",
    "                 title = \"Sales - {}ly\".format(measure.columns[0]), \n",
    "                 xlabel = measure.columns[0], \n",
    "                 ylabel = \"Sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_total_sales(total_sales_monthly_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_total_sales(total_sales_monthly_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an obvious \"seasonality\" (Eg: peak sales around a time of year) - it is visible that the cummulative total sales of the company peak in May and June, then drop in winter months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yearly Total Sales - Showing Trends\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_total_sales(total_sales_yearly_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*As we can see we have a trending increase of item sales count (mean) since 2012 to 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_total_sales(total_sales_yearly_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a visible drop in sales since 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sales by Date\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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_x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1245.364114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>1559.680929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>3085.166393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>2703.845190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>3489.027823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>2544.695370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>4108.612587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>4605.923126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>5637.412732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>6262.250929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       date_x            y\n",
       "0  2012-01-01  1245.364114\n",
       "1  2012-02-01  1559.680929\n",
       "2  2012-03-01  3085.166393\n",
       "3  2012-04-01  2703.845190\n",
       "4  2012-05-01  3489.027823\n",
       "..        ...          ...\n",
       "73 2018-02-01  2544.695370\n",
       "74 2018-03-01  4108.612587\n",
       "75 2018-04-01  4605.923126\n",
       "76 2018-05-01  5637.412732\n",
       "77 2018-06-01  6262.250929\n",
       "\n",
       "[78 rows x 2 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_sales_daily_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_total_sales(total_sales_daily_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_total_sales(total_sales_daily_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Growth in sales (mean sales and sum of sales) is clearly visible. Also, sales tend to peak in the summer months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sales by Product\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sales_by_product(dataset):\n",
    "    axes = plt.subplots(1, 1, figsize=(45, 15), sharex=True)\n",
    "    ax = sn.barplot(x=\"key\", y=\"y\", data=dataset, palette=\"rocket\").set_title(\"Yearly mean\")\n",
    "    plt.xticks(rotation = 90)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 3240x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sales_by_product(group_product_sales_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3240x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sales_by_product(group_product_sales_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's visible that some of the products have much higher sales than the other. Let's see what are the top 10 products by mean and summed yearly sales:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>key</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>52602</td>\n",
       "      <td>6788.668269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>36516</td>\n",
       "      <td>5512.768687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>76872</td>\n",
       "      <td>5469.976213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>55896</td>\n",
       "      <td>5157.350431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>24488</td>\n",
       "      <td>5130.673219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>43045</td>\n",
       "      <td>5118.050320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>98721</td>\n",
       "      <td>5038.642555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20118</td>\n",
       "      <td>4955.508885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20423</td>\n",
       "      <td>4950.929064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>48768</td>\n",
       "      <td>4927.716751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       key            y\n",
       "56   52602  6788.668269\n",
       "42   36516  5512.768687\n",
       "88   76872  5469.976213\n",
       "57   55896  5157.350431\n",
       "34   24488  5130.673219\n",
       "48   43045  5118.050320\n",
       "117  98721  5038.642555\n",
       "27   20118  4955.508885\n",
       "28   20423  4950.929064\n",
       "54   48768  4927.716751"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_product_sales_mean.nlargest(10, \"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>key</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>52602</td>\n",
       "      <td>529516.124969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>36516</td>\n",
       "      <td>429995.957601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>55896</td>\n",
       "      <td>402273.333639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>43045</td>\n",
       "      <td>399207.924981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>98721</td>\n",
       "      <td>393014.119313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20423</td>\n",
       "      <td>386172.466955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>48768</td>\n",
       "      <td>384361.906579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>32473</td>\n",
       "      <td>378162.113425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4361</td>\n",
       "      <td>376849.859478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16071</td>\n",
       "      <td>372146.150452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       key              y\n",
       "56   52602  529516.124969\n",
       "42   36516  429995.957601\n",
       "57   55896  402273.333639\n",
       "48   43045  399207.924981\n",
       "117  98721  393014.119313\n",
       "28   20423  386172.466955\n",
       "54   48768  384361.906579\n",
       "41   32473  378162.113425\n",
       "4     4361  376849.859478\n",
       "20   16071  372146.150452"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_product_sales_sum.nlargest(10, \"y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This way it can be seen that the sum of products sold and the median value show different products."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Space for improvement: Let's see which products had sales growth in the last 6 and 12 months, and which had a decrease in sales, and find the product with biggest growth and with biggest fall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seasonal Decomposition Using Moving Averages\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"y\"].fillna(dataset[\"y\"].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additive Model\n",
    "\n",
    "An additive model suggests that the components are added together as follows:\n",
    "\n",
    "y(t) = Level + Trend + Seasonality + Noise\n",
    "An additive model is linear where changes over time are consistently made by the same amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01', '2012-02-01', '2012-03-01', '2012-04-01',\n",
       "               '2012-05-01', '2012-06-01', '2012-07-01', '2012-08-01',\n",
       "               '2012-09-01', '2012-10-01',\n",
       "               ...\n",
       "               '2017-09-01', '2017-10-01', '2017-11-01', '2017-12-01',\n",
       "               '2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',\n",
       "               '2018-05-01', '2018-06-01'],\n",
       "              dtype='datetime64[ns]', name='date_x', length=8437, freq=None)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.set_index('date_x')\n",
    "dataset.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the average daily sales value for that month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset[\"y\"].resample('MS').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import rcParams\n",
    "import statsmodels.api as sm\n",
    "\n",
    "rcParams['figure.figsize'] = 18, 8\n",
    "decomposition = sm.tsa.seasonal_decompose(y, model='additive')\n",
    "fig = decomposition.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above we can clearly see the seasonal component of the data, and we can also see the separated upward trend of sales in time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stationarity Test\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stationarity refers to time-invariance of a series. (ie) Two points in a time series are related to each other by only how far apart they are, and not by the direction(forward/backward)\n",
    "\n",
    "When a time series is stationary, it can be easier to model. Statistical modeling methods assume or require the time series to be stationary.\n",
    "\n",
    "The Augmented Dickey-Fuller test is a type of statistical test called a unit root test.\n",
    "\n",
    "The intuition behind a unit root test is that it determines how strongly a time series is defined by a trend.\n",
    "\n",
    "* Null Hypothesis (H0): If failed to be rejected, it suggests the time series has a unit root, meaning it is non-stationary. It has some time dependent structure.\n",
    "\n",
    "* Alternate Hypothesis (H1): The null hypothesis is rejected; it suggests the time series does not have a unit root, meaning it is stationary. It does not have time-dependent structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_stationarity(timeseries):\n",
    "    \n",
    "    #Perform Dickey-Fuller test:\n",
    "    print('Results of Dickey-Fuller Test:')\n",
    "    dftest = adfuller(timeseries, autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    \n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print (dfoutput)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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_x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>95893.036745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>118535.750612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>234472.645893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>205492.234465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>268655.142392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>297729.358282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>488924.897841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>548104.851969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>670852.115058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>745207.860510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       date_x              y\n",
       "0  2012-01-01   95893.036745\n",
       "1  2012-02-01  118535.750612\n",
       "2  2012-03-01  234472.645893\n",
       "3  2012-04-01  205492.234465\n",
       "4  2012-05-01  268655.142392\n",
       "..        ...            ...\n",
       "73 2018-02-01  297729.358282\n",
       "74 2018-03-01  488924.897841\n",
       "75 2018-04-01  548104.851969\n",
       "76 2018-05-01  670852.115058\n",
       "77 2018-06-01  745207.860510\n",
       "\n",
       "[78 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_sales_daily_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                 -4.304432\n",
      "p-value                         0.000436\n",
      "#Lags Used                     11.000000\n",
      "Number of Observations Used    66.000000\n",
      "Critical Value (1%)            -3.533560\n",
      "Critical Value (5%)            -2.906444\n",
      "Critical Value (10%)           -2.590724\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "test_stationarity(total_sales_daily_sum[\"y\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p-value lower than 1% shows that the data probably isn't stationary.\n",
    "\n",
    "As part of the output, we get a look-up table to help determine the ADF statistic. We can see that our statistic value of -4 is less than the value of -3.533 at 1%.\n",
    "\n",
    "This suggests that we can reject the null hypothesis with a significance level of less than 1% (i.e. a low probability that the result is a statistical fluke).\n",
    "\n",
    "Rejecting the null hypothesis means that the process has no unit root, and in turn that the time series is stationary or does not have time-dependent structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardization of datasets is a common requirement for many machine learning estimators implemented in scikit-learn; they might behave badly if the individual features do not more or less look like standard normally distributed data: Gaussian with zero mean and unit variance.\n",
    "\n",
    "Since we trained tree-based models on the data, feature scaling isn't needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformations on the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making the same transformations on the test set as on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = X_test.drop(to_drop, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness of column x1 BEFORE the flooring and capping: 10.15\n",
      "Skewness of column x1 AFTER the flooring and capping: 1.97\n",
      "\n",
      "Skewness of column x2 BEFORE the flooring and capping: 16.30\n",
      "Skewness of column x2 AFTER the flooring and capping: 2.11\n",
      "\n",
      "Skewness of column x3 BEFORE the flooring and capping: 11.93\n",
      "Skewness of column x3 AFTER the flooring and capping: 2.04\n",
      "\n",
      "Skewness of column x4 BEFORE the flooring and capping: 13.97\n",
      "Skewness of column x4 AFTER the flooring and capping: 1.98\n",
      "\n",
      "Skewness of column x7 BEFORE the flooring and capping: 5.00\n",
      "Skewness of column x7 AFTER the flooring and capping: 1.74\n",
      "\n",
      "Skewness of column x8 BEFORE the flooring and capping: 5.33\n",
      "Skewness of column x8 AFTER the flooring and capping: 1.86\n",
      "\n",
      "Skewness of column x10 BEFORE the flooring and capping: 3.74\n",
      "Skewness of column x10 AFTER the flooring and capping: 1.72\n",
      "\n",
      "Skewness of column x12 BEFORE the flooring and capping: 10.55\n",
      "Skewness of column x12 AFTER the flooring and capping: 1.64\n",
      "\n",
      "Skewness of column x14 BEFORE the flooring and capping: 6.17\n",
      "Skewness of column x14 AFTER the flooring and capping: 1.80\n",
      "\n",
      "Skewness of column x15 BEFORE the flooring and capping: 4.66\n",
      "Skewness of column x15 AFTER the flooring and capping: 1.74\n",
      "\n",
      "Skewness of column x19 BEFORE the flooring and capping: 12.15\n",
      "Skewness of column x19 AFTER the flooring and capping: 1.64\n",
      "\n",
      "Skewness of column x21 BEFORE the flooring and capping: 6.23\n",
      "Skewness of column x21 AFTER the flooring and capping: 1.75\n",
      "\n",
      "Skewness of column x22 BEFORE the flooring and capping: 10.10\n",
      "Skewness of column x22 AFTER the flooring and capping: 1.70\n",
      "\n",
      "Skewness of column x24 BEFORE the flooring and capping: 12.38\n",
      "Skewness of column x24 AFTER the flooring and capping: 1.96\n",
      "\n",
      "Skewness of column x26 BEFORE the flooring and capping: 14.39\n",
      "Skewness of column x26 AFTER the flooring and capping: 1.65\n",
      "\n",
      "Skewness of column x28 BEFORE the flooring and capping: 8.78\n",
      "Skewness of column x28 AFTER the flooring and capping: 1.81\n",
      "\n",
      "Skewness of column x29 BEFORE the flooring and capping: 4.74\n",
      "Skewness of column x29 AFTER the flooring and capping: 1.81\n",
      "\n",
      "Skewness of column x31 BEFORE the flooring and capping: 12.69\n",
      "Skewness of column x31 AFTER the flooring and capping: 1.96\n",
      "\n",
      "Skewness of column x33 BEFORE the flooring and capping: 14.08\n",
      "Skewness of column x33 AFTER the flooring and capping: 1.64\n",
      "\n",
      "Skewness of column x35 BEFORE the flooring and capping: 5.96\n",
      "Skewness of column x35 AFTER the flooring and capping: 1.75\n",
      "\n",
      "Skewness of column x36 BEFORE the flooring and capping: 6.40\n",
      "Skewness of column x36 AFTER the flooring and capping: 1.72\n",
      "\n",
      "Skewness of column x38 BEFORE the flooring and capping: 13.65\n",
      "Skewness of column x38 AFTER the flooring and capping: 2.04\n",
      "\n",
      "Skewness of column x40 BEFORE the flooring and capping: 21.77\n",
      "Skewness of column x40 AFTER the flooring and capping: 1.65\n",
      "\n",
      "Skewness of column x42 BEFORE the flooring and capping: 5.82\n",
      "Skewness of column x42 AFTER the flooring and capping: 1.73\n",
      "\n",
      "Skewness of column x43 BEFORE the flooring and capping: 5.12\n",
      "Skewness of column x43 AFTER the flooring and capping: 1.76\n",
      "\n",
      "Skewness of column x45 BEFORE the flooring and capping: 7.65\n",
      "Skewness of column x45 AFTER the flooring and capping: 1.84\n",
      "\n",
      "Skewness of column x47 BEFORE the flooring and capping: 13.72\n",
      "Skewness of column x47 AFTER the flooring and capping: 1.61\n",
      "\n",
      "Skewness of column x49 BEFORE the flooring and capping: 15.68\n",
      "Skewness of column x49 AFTER the flooring and capping: 1.81\n",
      "\n",
      "Skewness of column x50 BEFORE the flooring and capping: 6.06\n",
      "Skewness of column x50 AFTER the flooring and capping: 1.79\n",
      "\n",
      "Skewness of column x51 BEFORE the flooring and capping: 6.81\n",
      "Skewness of column x51 AFTER the flooring and capping: 1.67\n",
      "\n",
      "Skewness of column x52 BEFORE the flooring and capping: 12.66\n",
      "Skewness of column x52 AFTER the flooring and capping: 1.97\n",
      "\n",
      "Skewness of column x54 BEFORE the flooring and capping: 14.77\n",
      "Skewness of column x54 AFTER the flooring and capping: 1.69\n",
      "\n",
      "Skewness of column x56 BEFORE the flooring and capping: 14.78\n",
      "Skewness of column x56 AFTER the flooring and capping: 1.87\n",
      "\n",
      "Skewness of column x57 BEFORE the flooring and capping: 5.11\n",
      "Skewness of column x57 AFTER the flooring and capping: 1.75\n",
      "\n",
      "Skewness of column x58 BEFORE the flooring and capping: 6.89\n",
      "Skewness of column x58 AFTER the flooring and capping: 1.82\n",
      "\n",
      "Skewness of column x59 BEFORE the flooring and capping: 10.57\n",
      "Skewness of column x59 AFTER the flooring and capping: 1.93\n",
      "\n",
      "Skewness of column x61 BEFORE the flooring and capping: 11.76\n",
      "Skewness of column x61 AFTER the flooring and capping: 1.69\n",
      "\n",
      "Skewness of column x63 BEFORE the flooring and capping: 5.73\n",
      "Skewness of column x63 AFTER the flooring and capping: 1.72\n",
      "\n",
      "Skewness of column x64 BEFORE the flooring and capping: 4.78\n",
      "Skewness of column x64 AFTER the flooring and capping: 1.74\n",
      "\n",
      "Skewness of column x66 BEFORE the flooring and capping: 11.79\n",
      "Skewness of column x66 AFTER the flooring and capping: 2.10\n",
      "\n",
      "Skewness of column x68 BEFORE the flooring and capping: 13.12\n",
      "Skewness of column x68 AFTER the flooring and capping: 1.74\n",
      "\n",
      "Skewness of column x70 BEFORE the flooring and capping: 6.25\n",
      "Skewness of column x70 AFTER the flooring and capping: 1.76\n",
      "\n",
      "Skewness of column x71 BEFORE the flooring and capping: 5.24\n",
      "Skewness of column x71 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x73 BEFORE the flooring and capping: 10.19\n",
      "Skewness of column x73 AFTER the flooring and capping: 2.02\n",
      "\n",
      "Skewness of column x75 BEFORE the flooring and capping: 16.38\n",
      "Skewness of column x75 AFTER the flooring and capping: 1.58\n",
      "\n",
      "Skewness of column x76 BEFORE the flooring and capping: 4.61\n",
      "Skewness of column x76 AFTER the flooring and capping: 1.75\n",
      "\n",
      "Skewness of column x77 BEFORE the flooring and capping: 23.18\n",
      "Skewness of column x77 AFTER the flooring and capping: 1.82\n",
      "\n",
      "Skewness of column x78 BEFORE the flooring and capping: 5.54\n",
      "Skewness of column x78 AFTER the flooring and capping: 1.75\n",
      "\n",
      "Skewness of column x80 BEFORE the flooring and capping: 9.09\n",
      "Skewness of column x80 AFTER the flooring and capping: 1.93\n",
      "\n",
      "Skewness of column x82 BEFORE the flooring and capping: 10.76\n",
      "Skewness of column x82 AFTER the flooring and capping: 1.62\n",
      "\n",
      "Skewness of column x84 BEFORE the flooring and capping: 5.85\n",
      "Skewness of column x84 AFTER the flooring and capping: 1.69\n",
      "\n",
      "Skewness of column x85 BEFORE the flooring and capping: 5.03\n",
      "Skewness of column x85 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x87 BEFORE the flooring and capping: -12.78\n",
      "Skewness of column x87 AFTER the flooring and capping: 2.02\n",
      "\n",
      "Skewness of column x89 BEFORE the flooring and capping: 16.58\n",
      "Skewness of column x89 AFTER the flooring and capping: 1.77\n",
      "\n",
      "Skewness of column x91 BEFORE the flooring and capping: 12.62\n",
      "Skewness of column x91 AFTER the flooring and capping: 1.81\n",
      "\n",
      "Skewness of column x92 BEFORE the flooring and capping: 6.01\n",
      "Skewness of column x92 AFTER the flooring and capping: 1.69\n",
      "\n",
      "Skewness of column x94 BEFORE the flooring and capping: 18.33\n",
      "Skewness of column x94 AFTER the flooring and capping: 2.00\n",
      "\n",
      "Skewness of column x96 BEFORE the flooring and capping: 13.29\n",
      "Skewness of column x96 AFTER the flooring and capping: 1.55\n",
      "\n",
      "Skewness of column x98 BEFORE the flooring and capping: 6.29\n",
      "Skewness of column x98 AFTER the flooring and capping: 1.88\n",
      "\n",
      "Skewness of column x99 BEFORE the flooring and capping: 5.51\n",
      "Skewness of column x99 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x103 BEFORE the flooring and capping: 11.84\n",
      "Skewness of column x103 AFTER the flooring and capping: 1.70\n",
      "\n",
      "Skewness of column x105 BEFORE the flooring and capping: 4.98\n",
      "Skewness of column x105 AFTER the flooring and capping: 1.78\n",
      "\n",
      "Skewness of column x106 BEFORE the flooring and capping: 5.73\n",
      "Skewness of column x106 AFTER the flooring and capping: 1.73\n",
      "\n",
      "Skewness of column x108 BEFORE the flooring and capping: 14.08\n",
      "Skewness of column x108 AFTER the flooring and capping: 1.95\n",
      "\n",
      "Skewness of column x109 BEFORE the flooring and capping: 8.95\n",
      "Skewness of column x109 AFTER the flooring and capping: 1.56\n",
      "\n",
      "Skewness of column x110 BEFORE the flooring and capping: 12.13\n",
      "Skewness of column x110 AFTER the flooring and capping: 1.70\n",
      "\n",
      "Skewness of column x112 BEFORE the flooring and capping: 14.85\n",
      "Skewness of column x112 AFTER the flooring and capping: 1.69\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_test.drop(['x5', 'x6', 'x11', 'x17', 'x18', 'x25', 'x32', 'x39', 'x46', 'x53', 'x60', 'x67', 'x74', 'x81', 'x95', 'x102' ], axis = 1, inplace=True)\n",
    "\n",
    "for column in X_test.iloc[:,2:].columns:\n",
    "    FillNa.transform(X_test)\n",
    "    \n",
    "for column in X_test.iloc[:,2:].columns:\n",
    "    Flooring_Capping.transform(X_test, column)\n",
    "    \n",
    "X_test['date'] = pd.to_datetime(X_test['date'])\n",
    "\n",
    "MonthYearAdder(X_test)\n",
    "\n",
    "X_test.infer_objects()\n",
    "\n",
    "len(X_test.columns)\n",
    "\n",
    "dataset = dataset.drop([\"date_y\"] ,axis = 1)\n",
    "X_test = X_test.drop(\"date\", axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tree-based estimators can be used to compute impurity-based feature importances, which in turn can be used to discard irrelevant features (when coupled with the SelectFromModel meta-transformer):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=100)\n",
    "rf.fit(dataset.iloc[:,:-1], dataset.iloc[:,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_idx = rf.feature_importances_.argsort()\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.barh(X_train.columns[sorted_idx], rf.feature_importances_[sorted_idx])\n",
    "plt.xlabel(\"Random Forest Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "model = SelectFromModel(rf, prefit=True)\n",
    "#X_new = model.transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is visible that the most important features are key, year, month, x10, x109 and x22. We will make predictions on these variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Models\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This problem is Multi Variate Time Series Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we don't have the actual y_test values to be predicted, but an empty column, we will take a part of the dataset for validation and check the model on the validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "dataset, validation_set = train_test_split(dataset, train_size = 0.8, shuffle = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sets for predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_set_features = validation_set[[\"key\", \"year\", \"month\", \"x10\", \"x22\", \"x85\", \"x92\", \"x109\"]]\n",
    "validation_set_y = validation_set.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_features = dataset.iloc[:,:-1]\n",
    "dataset_features = dataset_features[[\"key\", \"year\", \"month\", \"x10\", \"x22\", \"x85\", \"x92\", \"x109\"]]\n",
    "y_test = dataset.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "wmape = sum(np.abs(actual - forecast))/sum(actual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def WMAPE (forecast, actual):\n",
    "    wmape = 0\n",
    "    sum_j = 0\n",
    "    sum_abs = 0\n",
    "    for i,j in zip(forecast, actual):\n",
    "        sum_abs += np.abs(i - j)\n",
    "        sum_j += j \n",
    "    wmape = sum_abs/sum_j\n",
    "    print(round(wmape,2))\n",
    "    print('WMAPE Score: ', str(round(wmape * 100, 2)) , ' %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from xgboost import plot_importance\n",
    "\n",
    "def xboost(x_train, y_train, x_test):\n",
    "    dtrain = xgb.DMatrix(x_train, label = y_train, feature_names = list(x_train.columns))\n",
    "    dtest = xgb.DMatrix(x_test, feature_names = list(x_test.columns))\n",
    "\n",
    "    params = {'max_depth':10,\n",
    "              'eta':0.2,\n",
    "              'silent':1,\n",
    "              'subsample':1}\n",
    "    \n",
    "    num_rounds = 4000\n",
    "\n",
    "    bst = xgb.train(params, dtrain, num_rounds)\n",
    "    plot_importance(bst)\n",
    "    \n",
    "    return pd.Series(bst.predict(dtest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:07:59] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:573: \n",
      "Parameters: { \"silent\" } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions_validation = xboost(dataset_features, y_test, validation_set_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47\n",
      "WMAPE Score:  46.94  %\n"
     ]
    }
   ],
   "source": [
    "WMAPE(predictions_validation, validation_set_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM Model\n",
    "****\n",
    "\n",
    "XGBoost applies level-wise tree growth where LightGBM applies leaf-wise tree growth. Level-wise approach grows horizontal whereas leaf-wise grows vertical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_params =  { 'metric': 'mae',\n",
    "                'num_leaves' : 12,\n",
    "                'max_depth': 10,\n",
    "                'min_child_samples': 5,\n",
    "                'learning_rate': 0.03,\n",
    "                'colsample_bytree': 0.5,\n",
    "                'verbose': 0,\n",
    "                'num_boost_round': 2000,\n",
    "                'early_stopping_rounds': 200,\n",
    "                'min_child_weight' : 0.1,\n",
    "                'nthread': -1}\n",
    "\n",
    "cols = [col for col in dataset_features.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002307 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "Training until validation scores don't improve for 200 rounds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/lightgbm/engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/anaconda3/lib/python3.8/site-packages/lightgbm/engine.py:181: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/lightgbm/engine.py:240: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100]\ttraining's l1: 1408.79\tvalid_1's l1: 1518.93\n",
      "[200]\ttraining's l1: 1276.39\tvalid_1's l1: 1482.34\n",
      "[300]\ttraining's l1: 1199.2\tvalid_1's l1: 1473.56\n",
      "[400]\ttraining's l1: 1146\tvalid_1's l1: 1477.88\n",
      "[500]\ttraining's l1: 1102.98\tvalid_1's l1: 1491.21\n",
      "Early stopping, best iteration is:\n",
      "[303]\ttraining's l1: 1196.71\tvalid_1's l1: 1473.26\n"
     ]
    }
   ],
   "source": [
    "lgbtrain = lgb.Dataset(data = dataset_features, label = y_test, feature_name = cols)\n",
    "\n",
    "lgbval = lgb.Dataset(data = validation_set_features, \n",
    "                     label = validation_set_y, \n",
    "                     reference = lgbtrain, \n",
    "                     feature_name = cols)\n",
    "\n",
    "model_lgb = lgb.train(lgb_params, \n",
    "                  lgbtrain,\n",
    "                  valid_sets = [lgbtrain, lgbval],\n",
    "                  num_boost_round = lgb_params['num_boost_round'],\n",
    "                  early_stopping_rounds = lgb_params['early_stopping_rounds'],\n",
    "                  verbose_eval=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_val = model_lgb.predict(validation_set_features, num_iteration = model_lgb.best_iteration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4\n",
      "WMAPE Score:  39.7  %\n"
     ]
    }
   ],
   "source": [
    "WMAPE(y_pred_val, validation_set_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CatBoost\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_cb = CatBoostRegressor(iterations=2,\n",
    "                          learning_rate=1,\n",
    "                          depth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 2067.5306282\ttotal: 53ms\tremaining: 53ms\n",
      "1:\tlearn: 1964.0145043\ttotal: 54.8ms\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "model_cb.fit(dataset_features, y_test)\n",
    "\n",
    "predictions_cb = model_cb.predict(validation_set_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42\n",
      "WMAPE Score:  41.97  %\n"
     ]
    }
   ],
   "source": [
    "WMAPE(predictions_cb, validation_set_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "****\n",
    "\n",
    "LightGBM model showed the best results when testing on the validation_set, with 39.7%. Next, we will change the hyperparameters to get the best values and to lower the WMAPE measure of error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {\n",
    "    'num_leaves' : [10, 12, 14],\n",
    "    'max_depth': [8, 10, 12],\n",
    "    'min_child_samples': [4, 5],\n",
    "    'learning_rate': [0.03],\n",
    "    'colsample_bytree': [0.4, 0.5],\n",
    "    'verbose': [0, 1],\n",
    "    'num_boost_round': [2500],\n",
    "    'early_stopping_rounds': [300],\n",
    "    'min_child_weight' : [0.1, 0.2],\n",
    "    'nthread': [-1]\n",
    "    }\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lgb_1 = lgb.LGBMRegressor()\n",
    "\n",
    "gs = GridSearchCV(\n",
    "    estimator=model_lgb_1,\n",
    "    param_grid=param_grid, \n",
    "    cv=5, \n",
    "    n_jobs=-1, \n",
    "    scoring='neg_mean_squared_error',\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002220 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\ttraining's l1: 1409.16\tvalid_1's l1: 1519.27\n",
      "[200]\ttraining's l1: 1279.67\tvalid_1's l1: 1479.26\n",
      "[300]\ttraining's l1: 1205.08\tvalid_1's l1: 1472.18\n",
      "[400]\ttraining's l1: 1149.15\tvalid_1's l1: 1475.38\n",
      "Early stopping, best iteration is:\n",
      "[291]\ttraining's l1: 1210.68\tvalid_1's l1: 1471.19\n"
     ]
    }
   ],
   "source": [
    "lgb_params =  { 'metric': 'mae',\n",
    "                'num_leaves' : 12,\n",
    "                'max_depth': 10,\n",
    "                'min_child_samples': 14,\n",
    "                'learning_rate': 0.03,\n",
    "                'colsample_bytree': 0.5,\n",
    "                'verbose': 0,\n",
    "                'num_boost_round': 2000,\n",
    "                'early_stopping_rounds': 200,\n",
    "                'min_child_weight' : 0.1,\n",
    "                'nthread': -1}\n",
    "\n",
    "lgbtrain = lgb.Dataset(data = dataset_features, label = y_test, feature_name = cols)\n",
    "\n",
    "lgbval = lgb.Dataset(data = validation_set_features, \n",
    "                     label = validation_set_y, \n",
    "                     reference = lgbtrain, \n",
    "                     feature_name = cols)\n",
    "\n",
    "model_lgb = lgb.train(lgb_params, \n",
    "                  lgbtrain,\n",
    "                  valid_sets = [lgbtrain, lgbval],\n",
    "                  num_boost_round = lgb_params['num_boost_round'],\n",
    "                  early_stopping_rounds = lgb_params['early_stopping_rounds'],\n",
    "                  verbose_eval=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_val = model_lgb.predict(validation_set_features, num_iteration = model_lgb.best_iteration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4\n",
      "WMAPE Score:  39.65  %\n"
     ]
    }
   ],
   "source": [
    "WMAPE(y_pred_val, validation_set_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions on the Test Set\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After taking only the most important features, using the best performing model on the validation set to predict test set results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_features = X_test[[\"key\", \"year\", \"month\", \"x10\", \"x22\", \"x85\", \"x92\", \"x109\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = model_lgb.predict(X_test_features, num_iteration = model_lgb.best_iteration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = pd.DataFrame(y_test, columns = [\"y\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "             x106      x108        x109       x110       x112  month  year  \\\n",
       "0     2206.040376  0.000000   39.599762   7.289597  27.328970      7  2018   \n",
       "1      130.027455  0.357027   33.098055  17.244210   3.189301      8  2018   \n",
       "2             NaN       NaN    8.638553        NaN        NaN      9  2018   \n",
       "3      102.735687  0.000000   14.646718   2.719439   0.267833     10  2018   \n",
       "4       71.384402  0.000000    0.000000   2.190175   0.833723     11  2018   \n",
       "...           ...       ...         ...        ...        ...    ...   ...   \n",
       "1423    53.162367  0.000000   52.082725   0.764603   0.262702      2  2019   \n",
       "1424    11.386100  0.000000  142.196722   0.356890   0.000000      3  2019   \n",
       "1425     2.814448  0.000000  165.414227   0.038380   0.073461      4  2019   \n",
       "1426    28.990518  0.026931   58.296873   3.361134   0.000000      5  2019   \n",
       "1427    30.554309  0.000000  273.088747   0.000000   0.000000      6  2019   \n",
       "\n",
       "        id  \n",
       "0        0  \n",
       "1        1  \n",
       "2        2  \n",
       "3        3  \n",
       "4        4  \n",
       "...    ...  \n",
       "1423  1423  \n",
       "1424  1424  \n",
       "1425  1425  \n",
       "1426  1426  \n",
       "1427  1427  \n",
       "\n",
       "[1428 rows x 71 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test['id'] = np.arange(len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test['id'] = np.arange(len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "combine x_test and y_test into y_test, and concat only 01 + month + year, and key and y prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = pd.merge(y_test, X_test, left_on = \"id\", right_on = \"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "months = y_test['month'].astype(int)\n",
    "month_abbr = []\n",
    "date = []\n",
    "\n",
    "for month in months:\n",
    "    abbr = calendar.month_abbr[month]\n",
    "    month_abbr.append(abbr)\n",
    "\n",
    "for abbr in month_abbr:\n",
    "    date.append(\"01\" + abbr)\n",
    "    \n",
    "y_test['date'] = date + y_test['year'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test[[\"key\", \"date\", \"y\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>date</th>\n",
       "      <th>y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>683</td>\n",
       "      <td>01Jul2018</td>\n",
       "      <td>5080.077683</td>\n",
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       "    <tr>\n",
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       "      <td>683</td>\n",
       "      <td>01Aug2018</td>\n",
       "      <td>4286.903518</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>683</td>\n",
       "      <td>01Sep2018</td>\n",
       "      <td>3613.895160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>683</td>\n",
       "      <td>01Oct2018</td>\n",
       "      <td>2900.301780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>683</td>\n",
       "      <td>01Nov2018</td>\n",
       "      <td>2437.221981</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>99444</td>\n",
       "      <td>01Feb2019</td>\n",
       "      <td>3026.742473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1424</th>\n",
       "      <td>99444</td>\n",
       "      <td>01Mar2019</td>\n",
       "      <td>4804.224906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>99444</td>\n",
       "      <td>01Apr2019</td>\n",
       "      <td>5141.778987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426</th>\n",
       "      <td>99444</td>\n",
       "      <td>01May2019</td>\n",
       "      <td>5849.527317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>99444</td>\n",
       "      <td>01Jun2019</td>\n",
       "      <td>6671.697500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1428 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        key       date            y\n",
       "0       683  01Jul2018  5080.077683\n",
       "1       683  01Aug2018  4286.903518\n",
       "2       683  01Sep2018  3613.895160\n",
       "3       683  01Oct2018  2900.301780\n",
       "4       683  01Nov2018  2437.221981\n",
       "...     ...        ...          ...\n",
       "1423  99444  01Feb2019  3026.742473\n",
       "1424  99444  01Mar2019  4804.224906\n",
       "1425  99444  01Apr2019  5141.778987\n",
       "1426  99444  01May2019  5849.527317\n",
       "1427  99444  01Jun2019  6671.697500\n",
       "\n",
       "[1428 rows x 3 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test.to_csv(\"y_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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