BCG_VI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plan outline\n",
    "1. Import libraries and data\n",
    "2. Perform exploratory data analysis \n",
    "3. Find missing data/corrupt data \n",
    "4. Visualize necessary data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Serg\\\\Untitled Folder'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"D:\\Documents\\BCG Data Science\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BCG_VI.html',\n",
       " 'BCG_VI.pdf',\n",
       " 'Executive summary.pptx',\n",
       " 'ml_case_training_data.csv',\n",
       " 'ml_case_training_hist_data.csv',\n",
       " 'ml_case_training_output.csv',\n",
       " 'New Microsoft Word Document.docx']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = pd.read_csv(\"ml_case_training_data.csv\")\n",
    "churn_data = pd.read_csv(\"ml_case_training_output.csv\")\n",
    "historical_data = pd.read_csv(\"ml_case_training_hist_data.csv\")"
   ]
  },
  {
   "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>id</th>\n",
       "      <th>activity_new</th>\n",
       "      <th>campaign_disc_ele</th>\n",
       "      <th>channel_sales</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <th>cons_last_month</th>\n",
       "      <th>date_activ</th>\n",
       "      <th>date_end</th>\n",
       "      <th>date_first_activ</th>\n",
       "      <th>...</th>\n",
       "      <th>forecast_price_pow_p1</th>\n",
       "      <th>has_gas</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>margin_net_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>net_margin</th>\n",
       "      <th>num_years_antig</th>\n",
       "      <th>origin_up</th>\n",
       "      <th>pow_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>2016-11-06</td>\n",
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       "      <td>24011ae4ebbe3035111d65fa7c15bc57</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2013-06-15</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>40.606701</td>\n",
       "      <td>t</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.44</td>\n",
       "      <td>25.44</td>\n",
       "      <td>2</td>\n",
       "      <td>678.99</td>\n",
       "      <td>3</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>43.648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4660</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-08-21</td>\n",
       "      <td>2016-08-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.311378</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.38</td>\n",
       "      <td>16.38</td>\n",
       "      <td>1</td>\n",
       "      <td>18.89</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.800</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>764c75f661154dac3a6c254cd082ea7d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foosdfpfkusacimwkcsosbicdxkicaua</td>\n",
       "      <td>544</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-04-16</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.311378</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.60</td>\n",
       "      <td>28.60</td>\n",
       "      <td>1</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bba03439a292a1e166f80264c16191cb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lmkebamcaaclubfxadlmueccxoimlema</td>\n",
       "      <td>1584</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-03-30</td>\n",
       "      <td>2016-03-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.311378</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.22</td>\n",
       "      <td>30.22</td>\n",
       "      <td>1</td>\n",
       "      <td>25.46</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.200</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 32 columns</p>\n",
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      ],
      "text/plain": [
       "                                 id                      activity_new  \\\n",
       "0  48ada52261e7cf58715202705a0451c9  esoiiifxdlbkcsluxmfuacbdckommixw   \n",
       "1  24011ae4ebbe3035111d65fa7c15bc57                               NaN   \n",
       "2  d29c2c54acc38ff3c0614d0a653813dd                               NaN   \n",
       "3  764c75f661154dac3a6c254cd082ea7d                               NaN   \n",
       "4  bba03439a292a1e166f80264c16191cb                               NaN   \n",
       "\n",
       "   campaign_disc_ele                     channel_sales  cons_12m  \\\n",
       "0                NaN  lmkebamcaaclubfxadlmueccxoimlema    309275   \n",
       "1                NaN  foosdfpfkusacimwkcsosbicdxkicaua         0   \n",
       "2                NaN                               NaN      4660   \n",
       "3                NaN  foosdfpfkusacimwkcsosbicdxkicaua       544   \n",
       "4                NaN  lmkebamcaaclubfxadlmueccxoimlema      1584   \n",
       "\n",
       "   cons_gas_12m  cons_last_month  date_activ    date_end date_first_activ  \\\n",
       "0             0            10025  2012-11-07  2016-11-06              NaN   \n",
       "1         54946                0  2013-06-15  2016-06-15              NaN   \n",
       "2             0                0  2009-08-21  2016-08-30              NaN   \n",
       "3             0                0  2010-04-16  2016-04-16              NaN   \n",
       "4             0                0  2010-03-30  2016-03-30              NaN   \n",
       "\n",
       "   ... forecast_price_pow_p1 has_gas  imp_cons  margin_gross_pow_ele  \\\n",
       "0  ...             58.995952       f     831.8                -41.76   \n",
       "1  ...             40.606701       t       0.0                 25.44   \n",
       "2  ...             44.311378       f       0.0                 16.38   \n",
       "3  ...             44.311378       f       0.0                 28.60   \n",
       "4  ...             44.311378       f       0.0                 30.22   \n",
       "\n",
       "   margin_net_pow_ele  nb_prod_act  net_margin  num_years_antig  \\\n",
       "0              -41.76            1     1732.36                3   \n",
       "1               25.44            2      678.99                3   \n",
       "2               16.38            1       18.89                6   \n",
       "3               28.60            1        6.60                6   \n",
       "4               30.22            1       25.46                6   \n",
       "\n",
       "                          origin_up  pow_max  \n",
       "0  ldkssxwpmemidmecebumciepifcamkci  180.000  \n",
       "1  lxidpiddsbxsbosboudacockeimpuepw   43.648  \n",
       "2  kamkkxfxxuwbdslkwifmmcsiusiuosws   13.800  \n",
       "3  kamkkxfxxuwbdslkwifmmcsiusiuosws   13.856  \n",
       "4  kamkkxfxxuwbdslkwifmmcsiusiuosws   13.200  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that this dataframe has a lot of null values, which we will have to deal with later "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "                                 id  churn\n",
       "0  48ada52261e7cf58715202705a0451c9      0\n",
       "1  24011ae4ebbe3035111d65fa7c15bc57      1\n",
       "2  d29c2c54acc38ff3c0614d0a653813dd      0\n",
       "3  764c75f661154dac3a6c254cd082ea7d      0\n",
       "4  bba03439a292a1e166f80264c16191cb      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Churn data seems to be formated correctly as either 0 or 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price_date</th>\n",
       "      <th>price_p1_var</th>\n",
       "      <th>price_p2_var</th>\n",
       "      <th>price_p3_var</th>\n",
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       "      <td>0.151367</td>\n",
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       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>2015-03-01</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>0.149626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>0.149626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "                                 id  price_date  price_p1_var  price_p2_var  \\\n",
       "0  038af19179925da21a25619c5a24b745  2015-01-01      0.151367           0.0   \n",
       "1  038af19179925da21a25619c5a24b745  2015-02-01      0.151367           0.0   \n",
       "2  038af19179925da21a25619c5a24b745  2015-03-01      0.151367           0.0   \n",
       "3  038af19179925da21a25619c5a24b745  2015-04-01      0.149626           0.0   \n",
       "4  038af19179925da21a25619c5a24b745  2015-05-01      0.149626           0.0   \n",
       "\n",
       "   price_p3_var  price_p1_fix  price_p2_fix  price_p3_fix  \n",
       "0           0.0     44.266931           0.0           0.0  \n",
       "1           0.0     44.266931           0.0           0.0  \n",
       "2           0.0     44.266931           0.0           0.0  \n",
       "3           0.0     44.266931           0.0           0.0  \n",
       "4           0.0     44.266931           0.0           0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like this dataframe has too many 0's for periods 2 and 3 for both energy and power prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can combine churn data with train data now to access them more easily,they would have to be combined later anyway. The common key is ID.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.merge(training_data,churn_data, on = \"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>activity_new</th>\n",
       "      <th>campaign_disc_ele</th>\n",
       "      <th>channel_sales</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <th>cons_last_month</th>\n",
       "      <th>date_activ</th>\n",
       "      <th>date_end</th>\n",
       "      <th>date_first_activ</th>\n",
       "      <th>...</th>\n",
       "      <th>has_gas</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>margin_net_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>net_margin</th>\n",
       "      <th>num_years_antig</th>\n",
       "      <th>origin_up</th>\n",
       "      <th>pow_max</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48ada52261e7cf58715202705a0451c9</td>\n",
       "      <td>esoiiifxdlbkcsluxmfuacbdckommixw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lmkebamcaaclubfxadlmueccxoimlema</td>\n",
       "      <td>309275</td>\n",
       "      <td>0</td>\n",
       "      <td>10025</td>\n",
       "      <td>2012-11-07</td>\n",
       "      <td>2016-11-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>831.8</td>\n",
       "      <td>-41.76</td>\n",
       "      <td>-41.76</td>\n",
       "      <td>1</td>\n",
       "      <td>1732.36</td>\n",
       "      <td>3</td>\n",
       "      <td>ldkssxwpmemidmecebumciepifcamkci</td>\n",
       "      <td>180.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24011ae4ebbe3035111d65fa7c15bc57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foosdfpfkusacimwkcsosbicdxkicaua</td>\n",
       "      <td>0</td>\n",
       "      <td>54946</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-06-15</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>t</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.44</td>\n",
       "      <td>25.44</td>\n",
       "      <td>2</td>\n",
       "      <td>678.99</td>\n",
       "      <td>3</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>43.648</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d29c2c54acc38ff3c0614d0a653813dd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4660</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-08-21</td>\n",
       "      <td>2016-08-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.38</td>\n",
       "      <td>16.38</td>\n",
       "      <td>1</td>\n",
       "      <td>18.89</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>764c75f661154dac3a6c254cd082ea7d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foosdfpfkusacimwkcsosbicdxkicaua</td>\n",
       "      <td>544</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-04-16</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.60</td>\n",
       "      <td>28.60</td>\n",
       "      <td>1</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.856</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bba03439a292a1e166f80264c16191cb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lmkebamcaaclubfxadlmueccxoimlema</td>\n",
       "      <td>1584</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-03-30</td>\n",
       "      <td>2016-03-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.22</td>\n",
       "      <td>30.22</td>\n",
       "      <td>1</td>\n",
       "      <td>25.46</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id                      activity_new  \\\n",
       "0  48ada52261e7cf58715202705a0451c9  esoiiifxdlbkcsluxmfuacbdckommixw   \n",
       "1  24011ae4ebbe3035111d65fa7c15bc57                               NaN   \n",
       "2  d29c2c54acc38ff3c0614d0a653813dd                               NaN   \n",
       "3  764c75f661154dac3a6c254cd082ea7d                               NaN   \n",
       "4  bba03439a292a1e166f80264c16191cb                               NaN   \n",
       "\n",
       "   campaign_disc_ele                     channel_sales  cons_12m  \\\n",
       "0                NaN  lmkebamcaaclubfxadlmueccxoimlema    309275   \n",
       "1                NaN  foosdfpfkusacimwkcsosbicdxkicaua         0   \n",
       "2                NaN                               NaN      4660   \n",
       "3                NaN  foosdfpfkusacimwkcsosbicdxkicaua       544   \n",
       "4                NaN  lmkebamcaaclubfxadlmueccxoimlema      1584   \n",
       "\n",
       "   cons_gas_12m  cons_last_month  date_activ    date_end date_first_activ  \\\n",
       "0             0            10025  2012-11-07  2016-11-06              NaN   \n",
       "1         54946                0  2013-06-15  2016-06-15              NaN   \n",
       "2             0                0  2009-08-21  2016-08-30              NaN   \n",
       "3             0                0  2010-04-16  2016-04-16              NaN   \n",
       "4             0                0  2010-03-30  2016-03-30              NaN   \n",
       "\n",
       "   ... has_gas imp_cons  margin_gross_pow_ele  margin_net_pow_ele  \\\n",
       "0  ...       f    831.8                -41.76              -41.76   \n",
       "1  ...       t      0.0                 25.44               25.44   \n",
       "2  ...       f      0.0                 16.38               16.38   \n",
       "3  ...       f      0.0                 28.60               28.60   \n",
       "4  ...       f      0.0                 30.22               30.22   \n",
       "\n",
       "   nb_prod_act  net_margin  num_years_antig                         origin_up  \\\n",
       "0            1     1732.36                3  ldkssxwpmemidmecebumciepifcamkci   \n",
       "1            2      678.99                3  lxidpiddsbxsbosboudacockeimpuepw   \n",
       "2            1       18.89                6  kamkkxfxxuwbdslkwifmmcsiusiuosws   \n",
       "3            1        6.60                6  kamkkxfxxuwbdslkwifmmcsiusiuosws   \n",
       "4            1       25.46                6  kamkkxfxxuwbdslkwifmmcsiusiuosws   \n",
       "\n",
       "   pow_max  churn  \n",
       "0  180.000      0  \n",
       "1   43.648      1  \n",
       "2   13.800      0  \n",
       "3   13.856      0  \n",
       "4   13.200      0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>campaign_disc_ele</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <th>cons_last_month</th>\n",
       "      <th>forecast_base_bill_ele</th>\n",
       "      <th>forecast_base_bill_year</th>\n",
       "      <th>forecast_bill_12m</th>\n",
       "      <th>forecast_cons</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>forecast_cons_year</th>\n",
       "      <th>...</th>\n",
       "      <th>forecast_price_energy_p2</th>\n",
       "      <th>forecast_price_pow_p1</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>margin_net_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>net_margin</th>\n",
       "      <th>num_years_antig</th>\n",
       "      <th>pow_max</th>\n",
       "      <th>churn</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.609600e+04</td>\n",
       "      <td>1.609600e+04</td>\n",
       "      <td>1.609600e+04</td>\n",
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       "      <td>16096.000000</td>\n",
       "      <td>16096.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15970.000000</td>\n",
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       "      <td>16083.000000</td>\n",
       "      <td>16096.000000</td>\n",
       "      <td>16081.000000</td>\n",
       "      <td>16096.000000</td>\n",
       "      <td>16093.000000</td>\n",
       "      <td>16096.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.948044e+05</td>\n",
       "      <td>3.191164e+04</td>\n",
       "      <td>1.946154e+04</td>\n",
       "      <td>335.843857</td>\n",
       "      <td>335.843857</td>\n",
       "      <td>3837.441866</td>\n",
       "      <td>206.845165</td>\n",
       "      <td>2370.555949</td>\n",
       "      <td>1907.347229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052951</td>\n",
       "      <td>43.533496</td>\n",
       "      <td>196.123447</td>\n",
       "      <td>22.462276</td>\n",
       "      <td>21.460318</td>\n",
       "      <td>1.347788</td>\n",
       "      <td>217.987028</td>\n",
       "      <td>5.030629</td>\n",
       "      <td>20.604131</td>\n",
       "      <td>0.099093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.795151e+05</td>\n",
       "      <td>1.775885e+05</td>\n",
       "      <td>8.235676e+04</td>\n",
       "      <td>649.406000</td>\n",
       "      <td>649.406000</td>\n",
       "      <td>5425.744327</td>\n",
       "      <td>455.634288</td>\n",
       "      <td>4035.085664</td>\n",
       "      <td>5257.364759</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048617</td>\n",
       "      <td>5.212252</td>\n",
       "      <td>494.366979</td>\n",
       "      <td>23.700883</td>\n",
       "      <td>27.917349</td>\n",
       "      <td>1.459808</td>\n",
       "      <td>366.742030</td>\n",
       "      <td>1.676101</td>\n",
       "      <td>21.772421</td>\n",
       "      <td>0.298796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.252760e+05</td>\n",
       "      <td>-3.037000e+03</td>\n",
       "      <td>-9.138600e+04</td>\n",
       "      <td>-364.940000</td>\n",
       "      <td>-364.940000</td>\n",
       "      <td>-2503.480000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-16689.260000</td>\n",
       "      <td>-85627.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.122184</td>\n",
       "      <td>-9038.210000</td>\n",
       "      <td>-525.540000</td>\n",
       "      <td>-615.660000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-4148.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.906250e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1158.175000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>513.230000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.606701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.960000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.970000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.533250e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.010000e+02</td>\n",
       "      <td>162.955000</td>\n",
       "      <td>162.955000</td>\n",
       "      <td>2187.230000</td>\n",
       "      <td>42.215000</td>\n",
       "      <td>1179.160000</td>\n",
       "      <td>378.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086163</td>\n",
       "      <td>44.311378</td>\n",
       "      <td>44.465000</td>\n",
       "      <td>21.090000</td>\n",
       "      <td>20.970000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>119.680000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.856000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.022150e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.127000e+03</td>\n",
       "      <td>396.185000</td>\n",
       "      <td>396.185000</td>\n",
       "      <td>4246.555000</td>\n",
       "      <td>228.117500</td>\n",
       "      <td>2692.077500</td>\n",
       "      <td>1994.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098837</td>\n",
       "      <td>44.311378</td>\n",
       "      <td>218.090000</td>\n",
       "      <td>29.640000</td>\n",
       "      <td>29.640000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>275.810000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
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       "      <td>1.609711e+07</td>\n",
       "      <td>4.188440e+06</td>\n",
       "      <td>4.538720e+06</td>\n",
       "      <td>12566.080000</td>\n",
       "      <td>12566.080000</td>\n",
       "      <td>81122.630000</td>\n",
       "      <td>9682.890000</td>\n",
       "      <td>103801.930000</td>\n",
       "      <td>175375.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.195975</td>\n",
       "      <td>59.444710</td>\n",
       "      <td>15042.790000</td>\n",
       "      <td>374.640000</td>\n",
       "      <td>374.640000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>24570.650000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       campaign_disc_ele      cons_12m  cons_gas_12m  cons_last_month  \\\n",
       "count                0.0  1.609600e+04  1.609600e+04     1.609600e+04   \n",
       "mean                 NaN  1.948044e+05  3.191164e+04     1.946154e+04   \n",
       "std                  NaN  6.795151e+05  1.775885e+05     8.235676e+04   \n",
       "min                  NaN -1.252760e+05 -3.037000e+03    -9.138600e+04   \n",
       "25%                  NaN  5.906250e+03  0.000000e+00     0.000000e+00   \n",
       "50%                  NaN  1.533250e+04  0.000000e+00     9.010000e+02   \n",
       "75%                  NaN  5.022150e+04  0.000000e+00     4.127000e+03   \n",
       "max                  NaN  1.609711e+07  4.188440e+06     4.538720e+06   \n",
       "\n",
       "       forecast_base_bill_ele  forecast_base_bill_year  forecast_bill_12m  \\\n",
       "count             3508.000000              3508.000000        3508.000000   \n",
       "mean               335.843857               335.843857        3837.441866   \n",
       "std                649.406000               649.406000        5425.744327   \n",
       "min               -364.940000              -364.940000       -2503.480000   \n",
       "25%                  0.000000                 0.000000        1158.175000   \n",
       "50%                162.955000               162.955000        2187.230000   \n",
       "75%                396.185000               396.185000        4246.555000   \n",
       "max              12566.080000             12566.080000       81122.630000   \n",
       "\n",
       "       forecast_cons  forecast_cons_12m  forecast_cons_year  ...  \\\n",
       "count    3508.000000       16096.000000        16096.000000  ...   \n",
       "mean      206.845165        2370.555949         1907.347229  ...   \n",
       "std       455.634288        4035.085664         5257.364759  ...   \n",
       "min         0.000000      -16689.260000       -85627.000000  ...   \n",
       "25%         0.000000         513.230000            0.000000  ...   \n",
       "50%        42.215000        1179.160000          378.000000  ...   \n",
       "75%       228.117500        2692.077500         1994.250000  ...   \n",
       "max      9682.890000      103801.930000       175375.000000  ...   \n",
       "\n",
       "       forecast_price_energy_p2  forecast_price_pow_p1      imp_cons  \\\n",
       "count              15970.000000           15970.000000  16096.000000   \n",
       "mean                   0.052951              43.533496    196.123447   \n",
       "std                    0.048617               5.212252    494.366979   \n",
       "min                    0.000000              -0.122184  -9038.210000   \n",
       "25%                    0.000000              40.606701      0.000000   \n",
       "50%                    0.086163              44.311378     44.465000   \n",
       "75%                    0.098837              44.311378    218.090000   \n",
       "max                    0.195975              59.444710  15042.790000   \n",
       "\n",
       "       margin_gross_pow_ele  margin_net_pow_ele   nb_prod_act    net_margin  \\\n",
       "count          16083.000000        16083.000000  16096.000000  16081.000000   \n",
       "mean              22.462276           21.460318      1.347788    217.987028   \n",
       "std               23.700883           27.917349      1.459808    366.742030   \n",
       "min             -525.540000         -615.660000      1.000000  -4148.990000   \n",
       "25%               11.960000           11.950000      1.000000     51.970000   \n",
       "50%               21.090000           20.970000      1.000000    119.680000   \n",
       "75%               29.640000           29.640000      1.000000    275.810000   \n",
       "max              374.640000          374.640000     32.000000  24570.650000   \n",
       "\n",
       "       num_years_antig       pow_max         churn  \n",
       "count     16096.000000  16093.000000  16096.000000  \n",
       "mean          5.030629     20.604131      0.099093  \n",
       "std           1.676101     21.772421      0.298796  \n",
       "min           1.000000      1.000000      0.000000  \n",
       "25%           4.000000     12.500000      0.000000  \n",
       "50%           5.000000     13.856000      0.000000  \n",
       "75%           6.000000     19.800000      0.000000  \n",
       "max          16.000000    500.000000      1.000000  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of negative data for consumption which is unlikely. Can mean that the data is corrupt or the negative sign has been put mistakingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Finding missing and corrupt data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "\n",
    "sns.heatmap(train.isnull())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing values (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activity_new</th>\n",
       "      <td>59.300447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign_disc_ele</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>channel_sales</th>\n",
       "      <td>26.205268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons_12m</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons_last_month</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_activ</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_end</th>\n",
       "      <td>0.012425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_first_activ</th>\n",
       "      <td>78.205765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_modif_prod</th>\n",
       "      <td>0.975398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_renewal</th>\n",
       "      <td>0.248509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_base_bill_ele</th>\n",
       "      <td>78.205765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_base_bill_year</th>\n",
       "      <td>78.205765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_bill_12m</th>\n",
       "      <td>78.205765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_cons</th>\n",
       "      <td>78.205765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_cons_year</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_discount_energy</th>\n",
       "      <td>0.782803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_meter_rent_12m</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_price_energy_p1</th>\n",
       "      <td>0.782803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_price_energy_p2</th>\n",
       "      <td>0.782803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_price_pow_p1</th>\n",
       "      <td>0.782803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_gas</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp_cons</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <td>0.080765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>margin_net_pow_ele</th>\n",
       "      <td>0.080765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb_prod_act</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>net_margin</th>\n",
       "      <td>0.093191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_years_antig</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_up</th>\n",
       "      <td>0.540507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pow_max</th>\n",
       "      <td>0.018638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Missing values (%)\n",
       "id                                  0.000000\n",
       "activity_new                       59.300447\n",
       "campaign_disc_ele                 100.000000\n",
       "channel_sales                      26.205268\n",
       "cons_12m                            0.000000\n",
       "cons_gas_12m                        0.000000\n",
       "cons_last_month                     0.000000\n",
       "date_activ                          0.000000\n",
       "date_end                            0.012425\n",
       "date_first_activ                   78.205765\n",
       "date_modif_prod                     0.975398\n",
       "date_renewal                        0.248509\n",
       "forecast_base_bill_ele             78.205765\n",
       "forecast_base_bill_year            78.205765\n",
       "forecast_bill_12m                  78.205765\n",
       "forecast_cons                      78.205765\n",
       "forecast_cons_12m                   0.000000\n",
       "forecast_cons_year                  0.000000\n",
       "forecast_discount_energy            0.782803\n",
       "forecast_meter_rent_12m             0.000000\n",
       "forecast_price_energy_p1            0.782803\n",
       "forecast_price_energy_p2            0.782803\n",
       "forecast_price_pow_p1               0.782803\n",
       "has_gas                             0.000000\n",
       "imp_cons                            0.000000\n",
       "margin_gross_pow_ele                0.080765\n",
       "margin_net_pow_ele                  0.080765\n",
       "nb_prod_act                         0.000000\n",
       "net_margin                          0.093191\n",
       "num_years_antig                     0.000000\n",
       "origin_up                           0.540507\n",
       "pow_max                             0.018638\n",
       "churn                               0.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Missing values (%)\": train.isnull().sum()/len(train.index)*100})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is too much data missing in the columns describing forecast of energy and power prices/consumption and date of first active contract. Also, origin_up and customer activity have a lot of missing data. Origin_up relates to code of the electricity campaign the customer first subscribed to, so likely irrelevant, but activity data can be useful, so will be explored later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'activity_new', 'campaign_disc_ele', 'channel_sales', 'cons_12m',\n",
       "       'cons_gas_12m', 'cons_last_month', 'date_activ', 'date_end',\n",
       "       'date_first_activ', 'date_modif_prod', 'date_renewal',\n",
       "       'forecast_base_bill_ele', 'forecast_base_bill_year',\n",
       "       'forecast_bill_12m', 'forecast_cons', 'forecast_cons_12m',\n",
       "       'forecast_cons_year', 'forecast_discount_energy',\n",
       "       'forecast_meter_rent_12m', 'forecast_price_energy_p1',\n",
       "       'forecast_price_energy_p2', 'forecast_price_pow_p1', 'has_gas',\n",
       "       'imp_cons', 'margin_gross_pow_ele', 'margin_net_pow_ele', 'nb_prod_act',\n",
       "       'net_margin', 'num_years_antig', 'origin_up', 'pow_max', 'churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['campaign_disc_ele','date_first_activ', 'date_modif_prod','date_renewal',\n",
    "            'forecast_base_bill_ele', 'forecast_base_bill_year',\n",
    "            'forecast_bill_12m', 'forecast_cons',\n",
    "           'origin_up'],axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>activity_new</th>\n",
       "      <th>channel_sales</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <th>cons_last_month</th>\n",
       "      <th>date_activ</th>\n",
       "      <th>date_end</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>forecast_cons_year</th>\n",
       "      <th>...</th>\n",
       "      <th>forecast_price_pow_p1</th>\n",
       "      <th>has_gas</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>margin_net_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>net_margin</th>\n",
       "      <th>num_years_antig</th>\n",
       "      <th>pow_max</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, activity_new, channel_sales, cons_12m, cons_gas_12m, cons_last_month, date_activ, date_end, forecast_cons_12m, forecast_cons_year, forecast_discount_energy, forecast_meter_rent_12m, forecast_price_energy_p1, forecast_price_energy_p2, forecast_price_pow_p1, has_gas, imp_cons, margin_gross_pow_ele, margin_net_pow_ele, nb_prod_act, net_margin, num_years_antig, pow_max, churn]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 24 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for duplicates\n",
    "train[train.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16096 entries, 0 to 16095\n",
      "Data columns (total 24 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   id                        16096 non-null  object \n",
      " 1   activity_new              6551 non-null   object \n",
      " 2   channel_sales             11878 non-null  object \n",
      " 3   cons_12m                  16096 non-null  int64  \n",
      " 4   cons_gas_12m              16096 non-null  int64  \n",
      " 5   cons_last_month           16096 non-null  int64  \n",
      " 6   date_activ                16096 non-null  object \n",
      " 7   date_end                  16094 non-null  object \n",
      " 8   forecast_cons_12m         16096 non-null  float64\n",
      " 9   forecast_cons_year        16096 non-null  int64  \n",
      " 10  forecast_discount_energy  15970 non-null  float64\n",
      " 11  forecast_meter_rent_12m   16096 non-null  float64\n",
      " 12  forecast_price_energy_p1  15970 non-null  float64\n",
      " 13  forecast_price_energy_p2  15970 non-null  float64\n",
      " 14  forecast_price_pow_p1     15970 non-null  float64\n",
      " 15  has_gas                   16096 non-null  object \n",
      " 16  imp_cons                  16096 non-null  float64\n",
      " 17  margin_gross_pow_ele      16083 non-null  float64\n",
      " 18  margin_net_pow_ele        16083 non-null  float64\n",
      " 19  nb_prod_act               16096 non-null  int64  \n",
      " 20  net_margin                16081 non-null  float64\n",
      " 21  num_years_antig           16096 non-null  int64  \n",
      " 22  pow_max                   16093 non-null  float64\n",
      " 23  churn                     16096 non-null  int64  \n",
      "dtypes: float64(11), int64(7), object(6)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#checking to see what types the entries are \n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train[\"date_activ\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2012-11-07'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"date_activ\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"date_activ\"] = pd.to_datetime(train[\"date_activ\"], format='%Y-%m-%d')\n",
    "train[\"date_end\"] = pd.to_datetime(train[\"date_end\"], format = \"%Y-%m-%d\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train[\"has_gas\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "has_gas\n",
       "f    13132\n",
       "t     2964\n",
       "Name: has_gas, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(\"has_gas\")[\"has_gas\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the only values are f and t, presumably, standing for false or true. Can map 0,1 instead of f and t to make it easier to use the data in the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'f' : 0, 't' : 1}\n",
    "\n",
    "train[\"has_gas\"] = train[\"has_gas\"].map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        1\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "        ..\n",
       "16091    1\n",
       "16092    0\n",
       "16093    0\n",
       "16094    0\n",
       "16095    0\n",
       "Name: has_gas, Length: 16096, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"has_gas\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondly, I will have a look at historical data of the prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>id</th>\n",
       "      <th>price_date</th>\n",
       "      <th>price_p1_var</th>\n",
       "      <th>price_p2_var</th>\n",
       "      <th>price_p3_var</th>\n",
       "      <th>price_p1_fix</th>\n",
       "      <th>price_p2_fix</th>\n",
       "      <th>price_p3_fix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>0.151367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>0.151367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>0.151367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>0.149626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>0.149626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id  price_date  price_p1_var  price_p2_var  \\\n",
       "0  038af19179925da21a25619c5a24b745  2015-01-01      0.151367           0.0   \n",
       "1  038af19179925da21a25619c5a24b745  2015-02-01      0.151367           0.0   \n",
       "2  038af19179925da21a25619c5a24b745  2015-03-01      0.151367           0.0   \n",
       "3  038af19179925da21a25619c5a24b745  2015-04-01      0.149626           0.0   \n",
       "4  038af19179925da21a25619c5a24b745  2015-05-01      0.149626           0.0   \n",
       "\n",
       "   price_p3_var  price_p1_fix  price_p2_fix  price_p3_fix  \n",
       "0           0.0     44.266931           0.0           0.0  \n",
       "1           0.0     44.266931           0.0           0.0  \n",
       "2           0.0     44.266931           0.0           0.0  \n",
       "3           0.0     44.266931           0.0           0.0  \n",
       "4           0.0     44.266931           0.0           0.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>price_p1_var</th>\n",
       "      <th>price_p2_var</th>\n",
       "      <th>price_p3_var</th>\n",
       "      <th>price_p1_fix</th>\n",
       "      <th>price_p2_fix</th>\n",
       "      <th>price_p3_fix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>191643.000000</td>\n",
       "      <td>191643.000000</td>\n",
       "      <td>191643.000000</td>\n",
       "      <td>191643.000000</td>\n",
       "      <td>191643.000000</td>\n",
       "      <td>191643.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.140991</td>\n",
       "      <td>0.054412</td>\n",
       "      <td>0.030712</td>\n",
       "      <td>43.325546</td>\n",
       "      <td>10.698201</td>\n",
       "      <td>6.455436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.025117</td>\n",
       "      <td>0.050033</td>\n",
       "      <td>0.036335</td>\n",
       "      <td>5.437952</td>\n",
       "      <td>12.856046</td>\n",
       "      <td>7.782279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.177779</td>\n",
       "      <td>-0.097752</td>\n",
       "      <td>-0.065172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.125976</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.728885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.146033</td>\n",
       "      <td>0.085483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.266930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.151635</td>\n",
       "      <td>0.101780</td>\n",
       "      <td>0.072558</td>\n",
       "      <td>44.444710</td>\n",
       "      <td>24.339581</td>\n",
       "      <td>16.226389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.280700</td>\n",
       "      <td>0.229788</td>\n",
       "      <td>0.114102</td>\n",
       "      <td>59.444710</td>\n",
       "      <td>36.490692</td>\n",
       "      <td>17.458221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price_p1_var   price_p2_var   price_p3_var   price_p1_fix  \\\n",
       "count  191643.000000  191643.000000  191643.000000  191643.000000   \n",
       "mean        0.140991       0.054412       0.030712      43.325546   \n",
       "std         0.025117       0.050033       0.036335       5.437952   \n",
       "min         0.000000       0.000000       0.000000      -0.177779   \n",
       "25%         0.125976       0.000000       0.000000      40.728885   \n",
       "50%         0.146033       0.085483       0.000000      44.266930   \n",
       "75%         0.151635       0.101780       0.072558      44.444710   \n",
       "max         0.280700       0.229788       0.114102      59.444710   \n",
       "\n",
       "        price_p2_fix   price_p3_fix  \n",
       "count  191643.000000  191643.000000  \n",
       "mean       10.698201       6.455436  \n",
       "std        12.856046       7.782279  \n",
       "min        -0.097752      -0.065172  \n",
       "25%         0.000000       0.000000  \n",
       "50%         0.000000       0.000000  \n",
       "75%        24.339581      16.226389  \n",
       "max        36.490692      17.458221  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, I will address the negative values of price_fix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price_date</th>\n",
       "      <th>price_p1_var</th>\n",
       "      <th>price_p2_var</th>\n",
       "      <th>price_p3_var</th>\n",
       "      <th>price_p1_fix</th>\n",
       "      <th>price_p2_fix</th>\n",
       "      <th>price_p3_fix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23138</th>\n",
       "      <td>951d99fe07ca94c2139f43bc37095139</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>0.125976</td>\n",
       "      <td>0.103395</td>\n",
       "      <td>0.071536</td>\n",
       "      <td>-0.162916</td>\n",
       "      <td>-0.097749</td>\n",
       "      <td>-0.065166</td>\n",
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       "    <tr>\n",
       "      <th>28350</th>\n",
       "      <td>f7bdc6fa1067cd26fd80bfb9f3fca28f</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>0.131032</td>\n",
       "      <td>0.108896</td>\n",
       "      <td>0.076955</td>\n",
       "      <td>-0.162916</td>\n",
       "      <td>-0.097749</td>\n",
       "      <td>-0.065166</td>\n",
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       "    <tr>\n",
       "      <th>98575</th>\n",
       "      <td>9b523ad5ba8aa2e524dcda5b3d54dab2</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>0.129444</td>\n",
       "      <td>0.106863</td>\n",
       "      <td>0.075004</td>\n",
       "      <td>-0.162916</td>\n",
       "      <td>-0.097749</td>\n",
       "      <td>-0.065166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113467</th>\n",
       "      <td>cfd098ee6c567eb32374c77d20571bc7</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>0.123086</td>\n",
       "      <td>0.100505</td>\n",
       "      <td>0.068646</td>\n",
       "      <td>-0.162916</td>\n",
       "      <td>-0.097749</td>\n",
       "      <td>-0.065166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118467</th>\n",
       "      <td>51d7d8a0bf6b8bd94f8c1de7942c66ea</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>0.128132</td>\n",
       "      <td>0.105996</td>\n",
       "      <td>0.074056</td>\n",
       "      <td>-0.162912</td>\n",
       "      <td>-0.097752</td>\n",
       "      <td>-0.065172</td>\n",
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       "    <tr>\n",
       "      <th>125819</th>\n",
       "      <td>decc0a647016e183ded972595cd2b9fb</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>0.124937</td>\n",
       "      <td>0.102814</td>\n",
       "      <td>0.069071</td>\n",
       "      <td>-0.162916</td>\n",
       "      <td>-0.097749</td>\n",
       "      <td>-0.065166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128761</th>\n",
       "      <td>cc214d7c05de3ee17a7691e274ac488e</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>0.124675</td>\n",
       "      <td>0.102539</td>\n",
       "      <td>0.070596</td>\n",
       "      <td>-0.162912</td>\n",
       "      <td>-0.097752</td>\n",
       "      <td>-0.065172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141011</th>\n",
       "      <td>2a4ed325054472e03cdcc9a34693be4b</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>0.167317</td>\n",
       "      <td>0.083347</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.177779</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>160827</th>\n",
       "      <td>395a6f41bbd1a0f23a64f00645264e78</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>0.121352</td>\n",
       "      <td>0.098771</td>\n",
       "      <td>0.066912</td>\n",
       "      <td>-0.162916</td>\n",
       "      <td>-0.097749</td>\n",
       "      <td>-0.065166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181811</th>\n",
       "      <td>d4a84ff4ec620151ef05bdef0cf27eab</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>0.125976</td>\n",
       "      <td>0.103395</td>\n",
       "      <td>0.071536</td>\n",
       "      <td>-0.162916</td>\n",
       "      <td>-0.097749</td>\n",
       "      <td>-0.065166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      id  price_date  price_p1_var  \\\n",
       "23138   951d99fe07ca94c2139f43bc37095139  2015-03-01      0.125976   \n",
       "28350   f7bdc6fa1067cd26fd80bfb9f3fca28f  2015-03-01      0.131032   \n",
       "98575   9b523ad5ba8aa2e524dcda5b3d54dab2  2015-02-01      0.129444   \n",
       "113467  cfd098ee6c567eb32374c77d20571bc7  2015-02-01      0.123086   \n",
       "118467  51d7d8a0bf6b8bd94f8c1de7942c66ea  2015-07-01      0.128132   \n",
       "125819  decc0a647016e183ded972595cd2b9fb  2015-03-01      0.124937   \n",
       "128761  cc214d7c05de3ee17a7691e274ac488e  2015-06-01      0.124675   \n",
       "141011  2a4ed325054472e03cdcc9a34693be4b  2015-02-01      0.167317   \n",
       "160827  395a6f41bbd1a0f23a64f00645264e78  2015-04-01      0.121352   \n",
       "181811  d4a84ff4ec620151ef05bdef0cf27eab  2015-05-01      0.125976   \n",
       "\n",
       "        price_p2_var  price_p3_var  price_p1_fix  price_p2_fix  price_p3_fix  \n",
       "23138       0.103395      0.071536     -0.162916     -0.097749     -0.065166  \n",
       "28350       0.108896      0.076955     -0.162916     -0.097749     -0.065166  \n",
       "98575       0.106863      0.075004     -0.162916     -0.097749     -0.065166  \n",
       "113467      0.100505      0.068646     -0.162916     -0.097749     -0.065166  \n",
       "118467      0.105996      0.074056     -0.162912     -0.097752     -0.065172  \n",
       "125819      0.102814      0.069071     -0.162916     -0.097749     -0.065166  \n",
       "128761      0.102539      0.070596     -0.162912     -0.097752     -0.065172  \n",
       "141011      0.083347      0.000000     -0.177779      0.000000      0.000000  \n",
       "160827      0.098771      0.066912     -0.162916     -0.097749     -0.065166  \n",
       "181811      0.103395      0.071536     -0.162916     -0.097749     -0.065166  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data[(historical_data[\"price_p1_fix\"] < 0)|(historical_data[\"price_p1_fix\"] < 0)|(historical_data[\"price_p1_fix\"] < 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are not many values but the problem is they are negtive and < 1, whereas all power prices seem to be much larger than 1. I will just drop the entries of price_fix that are <0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = historical_data[(historical_data[\"price_p1_fix\"] < 0)|(historical_data[\"price_p1_fix\"] < 0)|(historical_data[\"price_p1_fix\"] < 0)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_data.drop(index = i, axis = 0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing values (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_date</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p1_var</th>\n",
       "      <td>0.704174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p2_var</th>\n",
       "      <td>0.704174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p3_var</th>\n",
       "      <td>0.704174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p1_fix</th>\n",
       "      <td>0.704174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p2_fix</th>\n",
       "      <td>0.704174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p3_fix</th>\n",
       "      <td>0.704174</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "              Missing values (%)\n",
       "id                      0.000000\n",
       "price_date              0.000000\n",
       "price_p1_var            0.704174\n",
       "price_p2_var            0.704174\n",
       "price_p3_var            0.704174\n",
       "price_p1_fix            0.704174\n",
       "price_p2_fix            0.704174\n",
       "price_p3_fix            0.704174"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_percentage = pd.DataFrame({\"Missing values (%)\": historical_data.isnull().sum()/len(historical_data.index)*100})\n",
    "                                      \n",
    "missing_data_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_data_percentage.plot(kind=\"bar\", figsize=(18,10))\n",
    "# Set labels\n",
    "plt.xlabel(\"Variables\")\n",
    "plt.ylabel(\"Missing values (%)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like there are not many NaN values, but it looks like there are a lot of 0.0 values for historical prices of electricity and energy. I will visualise the electricity prices first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(1,3,figsize = (12,6),sharex = True,sharey = False)\n",
    "sns.set_style(\"whitegrid\")\n",
    "fig.suptitle(\"Historical prices of energy\")\n",
    "\n",
    "sns.histplot(ax = axes[0],x = historical_data[\"price_p1_var\"])\n",
    "axes[0].set_title(\"Historic price of energy in period 1 \")\n",
    "\n",
    "sns.histplot(ax = axes[1],x = historical_data[\"price_p2_var\"])\n",
    "axes[1].set_title(\"Historic price of energy in period 2 \")\n",
    "\n",
    "sns.histplot(ax = axes[2],x = historical_data[\"price_p3_var\"])\n",
    "axes[2].set_title(\"Historic price of energy in period 3 \")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a lot of NaN and 0.0 values, but apart from that the destribution for all prices mostly stays in a certain range so can inpute NaN and 0.0 values with a mean value from existing entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_p1 = (historical_data[\"price_p1_var\"].isnull()|(historical_data[\"price_p1_var\"] == 0.0))\n",
    "missing_p2 = (historical_data[\"price_p2_var\"].isnull()|(historical_data[\"price_p2_var\"] == 0.0))\n",
    "missing_p3 = (historical_data[\"price_p3_var\"].isnull()|(historical_data[\"price_p3_var\"] == 0.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second and third periods have a lot of data that is missing or is equal to 0.0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excluding the null values, the rest of the price values for all periods tended to fall within a range. Therefore can substitue null values with the period mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_p1 = (historical_data[\"price_p1_var\"].isnull()|(historical_data[\"price_p1_var\"] == 0.0))\n",
    "missing_p2 = (historical_data[\"price_p2_var\"].isnull()|(historical_data[\"price_p2_var\"] == 0.0))\n",
    "missing_p3 = (historical_data[\"price_p3_var\"].isnull()|(historical_data[\"price_p3_var\"] == 0.0))\n",
    "\n",
    "existing_p1 = ((historical_data[\"price_p1_var\"].isnull()==False)|(historical_data[\"price_p1_var\"] != 0.0))\n",
    "existing_p2 = ((historical_data[\"price_p2_var\"].isnull()==False)|(historical_data[\"price_p2_var\"] != 0.0))\n",
    "existing_p3 = ((historical_data[\"price_p3_var\"].isnull()==False)|(historical_data[\"price_p3_var\"] != 0.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_data.loc[missing_p1,\"price_p1_var\"] = historical_data.loc[existing_p1,\"price_p1_var\"].mean()\n",
    "historical_data.loc[missing_p2,\"price_p2_var\"] = historical_data.loc[existing_p2,\"price_p2_var\"].mean()\n",
    "historical_data.loc[missing_p3,\"price_p3_var\"] = historical_data.loc[existing_p3,\"price_p3_var\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Missing values (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_date</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p1_var</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p2_var</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p3_var</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p1_fix</th>\n",
       "      <td>0.704174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p2_fix</th>\n",
       "      <td>0.704174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p3_fix</th>\n",
       "      <td>0.704174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Missing values (%)\n",
       "id                      0.000000\n",
       "price_date              0.000000\n",
       "price_p1_var            0.000000\n",
       "price_p2_var            0.000000\n",
       "price_p3_var            0.000000\n",
       "price_p1_fix            0.704174\n",
       "price_p2_fix            0.704174\n",
       "price_p3_fix            0.704174"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Missing values (%)\": historical_data.isnull().sum()/len(historical_data.index)*100})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondly, I will visualise the historical prices of power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(1,3,figsize = (12,6),sharex = True,sharey = False)\n",
    "sns.set_style(\"whitegrid\")\n",
    "fig.suptitle(\"Historical prices of power\")\n",
    "\n",
    "sns.histplot(ax = axes[0],x = historical_data[\"price_p1_fix\"])\n",
    "axes[0].set_title(\"Historic price of power in period 1 \")\n",
    "\n",
    "sns.histplot(ax = axes[1],x = historical_data[\"price_p2_fix\"])\n",
    "axes[1].set_title(\"Historic price of power in period 2 \")\n",
    "\n",
    "sns.histplot(ax = axes[2],x = historical_data[\"price_p3_fix\"])\n",
    "axes[2].set_title(\"Historic price of power in period 3 \")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outside of 0.0 values prices tend to fall in one of the two categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_p1 = (historical_data[\"price_p1_fix\"].isnull()|(historical_data[\"price_p1_fix\"] == 0.0))\n",
    "missing_p2 = (historical_data[\"price_p2_fix\"].isnull()|(historical_data[\"price_p2_fix\"] == 0.0))\n",
    "missing_p3 = (historical_data[\"price_p3_fix\"].isnull()|(historical_data[\"price_p3_fix\"] == 0.0))\n",
    "\n",
    "existing_p1 = ((historical_data[\"price_p1_fix\"].isnull()==False)|(historical_data[\"price_p1_fix\"] != 0.0))\n",
    "existing_p2 = ((historical_data[\"price_p2_fix\"].isnull()==False)|(historical_data[\"price_p2_fix\"] != 0.0))\n",
    "existing_p3 = ((historical_data[\"price_p3_fix\"].isnull()==False)|(historical_data[\"price_p3_fix\"] != 0.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_data.loc[missing_p1,\"price_p1_fix\"] = historical_data.loc[existing_p1,\"price_p1_fix\"].median()\n",
    "historical_data.loc[missing_p2,\"price_p2_fix\"] = historical_data.loc[existing_p2,\"price_p2_fix\"].median()\n",
    "historical_data.loc[missing_p3,\"price_p3_fix\"] = historical_data.loc[existing_p3,\"price_p3_fix\"].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Missing values (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_date</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p1_var</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p2_var</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p3_var</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p1_fix</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p2_fix</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p3_fix</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Missing values (%)\n",
       "id                           0.0\n",
       "price_date                   0.0\n",
       "price_p1_var                 0.0\n",
       "price_p2_var                 0.0\n",
       "price_p3_var                 0.0\n",
       "price_p1_fix                 0.0\n",
       "price_p2_fix                 0.0\n",
       "price_p3_fix                 0.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Missing values (%)\": historical_data.isnull().sum()/len(historical_data.index)*100})"
   ]
  },
  {
   "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>0.0 values (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_date</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p1_var</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p2_var</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p3_var</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p1_fix</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p2_fix</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_p3_fix</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0.0 values (%)\n",
       "id                       0.0\n",
       "price_date               0.0\n",
       "price_p1_var             0.0\n",
       "price_p2_var             0.0\n",
       "price_p3_var             0.0\n",
       "price_p1_fix             0.0\n",
       "price_p2_fix             0.0\n",
       "price_p3_fix             0.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"0.0 values (%)\": historical_data[historical_data == 0.0].sum()/len(historical_data.index)*100})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Viz and EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, let's see how many customers left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.909294234592446"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"churn\"].mean()*100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 9.9% of customers churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn = train[[\"id\",\"churn\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_total = churn.groupby(churn[\"churn\"]).count()\n",
    "churn_percentage = churn_total/churn_total.sum()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_stacked_bars(dataframe, title_, size_=(18, 10), rot_=0, legend_=\"upper right\"):\n",
    "    \n",
    "    ax = dataframe.plot(kind=\"bar\",\n",
    "        stacked=True,\n",
    "        figsize=size_,\n",
    "        rot=rot_,\n",
    "        title=title_)\n",
    "    # Annotation\n",
    "    \n",
    "    autolabel(ax)\n",
    "    # Rename legend\n",
    "    plt.legend([\"Retention\", \"Churn\"], loc=legend_)\n",
    "    # Labels\n",
    "    \n",
    "    plt.ylabel(\"Company base (%)\")\n",
    "    plt.xlabel(\"Companies\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def autolabel(ax):\n",
    "    \"\"\"Attach a text label above the bar , displaying its height.\"\"\"\n",
    "    for p in ax.patches:\n",
    "        width, height = p.get_width(), p.get_height()\n",
    "        x, y = p.get_xy() \n",
    "        ax.text(x+width/2, \n",
    "            y+height/2, \n",
    "            '{:.0f} %'.format(height), \n",
    "            horizontalalignment='center', \n",
    "            verticalalignment='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stacked_bars(churn_percentage.transpose(),\"Churning status\", (5,5), legend_=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next step would be to explore if churn depends on activity. The limitation of this is the fact that activity data has almost 60% of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "activity = train[[\"id\",\"activity_new\",\"churn\"]]"
   ]
  },
  {
   "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>id</th>\n",
       "      <th>activity_new</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48ada52261e7cf58715202705a0451c9</td>\n",
       "      <td>esoiiifxdlbkcsluxmfuacbdckommixw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24011ae4ebbe3035111d65fa7c15bc57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d29c2c54acc38ff3c0614d0a653813dd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>764c75f661154dac3a6c254cd082ea7d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bba03439a292a1e166f80264c16191cb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id                      activity_new  churn\n",
       "0  48ada52261e7cf58715202705a0451c9  esoiiifxdlbkcsluxmfuacbdckommixw      0\n",
       "1  24011ae4ebbe3035111d65fa7c15bc57                               NaN      1\n",
       "2  d29c2c54acc38ff3c0614d0a653813dd                               NaN      0\n",
       "3  764c75f661154dac3a6c254cd082ea7d                               NaN      0\n",
       "4  bba03439a292a1e166f80264c16191cb                               NaN      0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "activity = activity[activity[\"activity_new\"].isnull() == False].groupby([\"activity_new\",\"churn\"])[\"id\"].count().unstack(level = 1).sort_values(by = [0],ascending = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "activity.plot(kind = \"bar\",stacked = True,figsize=(18, 10),\n",
    " width=2)\n",
    "\n",
    "plt.xlabel(\"Industry\")\n",
    "plt.ylabel(\"Number of companies\")\n",
    "plt.xticks([])\n",
    "plt.legend([\"Retained\",\"Left\"],loc = 3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does not seem like churn rate depends on customer's industry. However, the limitation of this graph is the fact that almost 40% of industry entries are missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the data it also seemed like a few customers had both negative gross margin, net margin (from energy and power), and also negative total margin(which included other products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "margin_net_pow_ele    20\n",
       "net_margin            20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[(train[\"net_margin\"]<0) & (train[\"margin_net_pow_ele\"]<0)][[\"margin_net_pow_ele\",\"net_margin\"]].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "20 customers bring negative margines to Power Co. Can drop these entries as it's unlikely power co will offer them more discount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = train[(train[\"net_margin\"]<0) & (train[\"margin_net_pow_ele\"]<0)].index # getting index of negative margins and total margins\n",
    "train.drop(i,inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next Step would be to check if sales channel is responsible for churn. I.e., does the way the company gets their customers affect the retantion rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-pastel')\n",
    "\n",
    "channel = train[['id','channel_sales','churn']]\n",
    "#Similar approach as to group by channel as the approach taken for industry above\n",
    "channel = channel[channel[\"channel_sales\"].isnull()==False]\n",
    "\n",
    "channel = channel.groupby([\"channel_sales\",\"churn\"])[\"id\"].count().unstack(level = 1).fillna(0)\n",
    "\n",
    "channel_percentage = (channel.div(channel.sum(axis = 1),axis = 0)*100).sort_values(by = [0],ascending = True)\n",
    "\n",
    "\n",
    "channel_percentage\n",
    "\n",
    "ax = channel_percentage.plot(kind = \"bar\",\n",
    "                            stacked = True,\n",
    "                            figsize = (10,6),\n",
    "                            title = \"Churn rate by sales channel\")\n",
    "plt.legend([\"Retained\",\"Left\"], loc = 1)\n",
    "\n",
    "plt.xlabel(\"Sales Channels\")\n",
    "plt.ylabel(\"% of Companies\")\n",
    "plt.xticks([])\n",
    "\n",
    "for p in ax.patches:\n",
    "    if p.get_height() == 0.0:\n",
    "        continue\n",
    "    width,height = p.get_width(),round(p.get_height(),2) #height is the percentage of churned/retained\n",
    "    x,y = p.get_xy()\n",
    "    ax.text(x + width/2,\n",
    "            y + height/2,\n",
    "           f\"{height}%\",\n",
    "           horizontalalignment='center', \n",
    "            verticalalignment='center')\n",
    "        \n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "num_years_antig column refers to the number of years a customer stayed with Power co. Can see if loyalty/recency plays any part in churn rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-pastel')\n",
    "\n",
    "duration = train[['id','num_years_antig','churn']]\n",
    "#Similar approach as to group by channel as the approach taken for channel sales above\n",
    "duration = duration[duration[\"num_years_antig\"].isnull()==False]\n",
    "\n",
    "duration = duration.groupby([\"num_years_antig\",\"churn\"])[\"id\"].count().unstack(level = 1).fillna(0)\n",
    "\n",
    "duration_percentage = (duration.div(duration.sum(axis = 1),axis = 0)*100)\n",
    "\n",
    "\n",
    "ax = duration_percentage.plot(kind = \"bar\",\n",
    "                            stacked = True,\n",
    "                            figsize = (12,6),\n",
    "                            title = \"Churn rate by years of service\",\n",
    "                             width = 0.7)\n",
    "plt.legend([\"Retained\",\"Left\"], loc = 8)\n",
    "plt.xlabel(\"Duration of service\")\n",
    "plt.ylabel(\"% of Companies\")\n",
    "\n",
    "for p in ax.patches:\n",
    "    if p.get_height() == 0.0:\n",
    "        continue\n",
    "    width,height = p.get_width(),round(p.get_height(),2) #height is the percentage of churned/retained\n",
    "    x,y = p.get_xy()\n",
    "    ax.text(x + width/2,\n",
    "            y + height/2,\n",
    "           f\"{height}%\",\n",
    "           horizontalalignment='center', \n",
    "            verticalalignment='center')\n",
    "        \n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check if the subscription for gas corresponds to more churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_gas = train.groupby([\"churn\",\"has_gas\"],as_index = False)[\"id\"].count()\n",
    "\n",
    "\n",
    "ax = sns.barplot(data=by_gas, x = \"has_gas\", y = \"id\",hue = \"churn\",palette = \"pastel\")\n",
    "\n",
    "plt.ylabel(\"Number of clients\")\n",
    "plt.xlabel(\"Subscriptio to gas, 0 = No Subscription, 1 = Subsrcibed\")\n",
    "\n",
    "total = by_gas[\"id\"].sum()\n",
    "\n",
    "for p in ax.patches:\n",
    "    if p.get_height() == 0.0:\n",
    "        continue\n",
    "    width,height = p.get_width(),round(p.get_height(),2)\n",
    "    x,y = p.get_xy()\n",
    "    ax.text(x + width/2,\n",
    "            y + height/2,\n",
    "           f\"{round((height/total),3)*100}%\",\n",
    "           horizontalalignment='center', \n",
    "            verticalalignment='center')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}