CS_512_project_Cleaning_Primitive_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "86b2dd98",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1718b33",
   "metadata": {},
   "source": [
    "#### merged_feature_data is here : https://github.com/YZouzou/CS512-project/blob/main/cleaned_data/merged_feature_data.csv#df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd54eaa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_excel(\"C:/Users/suuser/merged_feature_data.xlsx\",index_col=[0])\n",
    "\n",
    "path = 'cleaned_data/merged_feature_data.csv'\n",
    "df = pd.read_csv(path, parse_dates=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b4282de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>consumer_credits</th>\n",
       "      <th>consumer_price_index</th>\n",
       "      <th>WTI_oil_price</th>\n",
       "      <th>brent_oil_price</th>\n",
       "      <th>current_account_deficit</th>\n",
       "      <th>exports_usd</th>\n",
       "      <th>exports_tl</th>\n",
       "      <th>...</th>\n",
       "      <th>unemployment_20-24</th>\n",
       "      <th>unemployment_25-29</th>\n",
       "      <th>unemployment_30-34</th>\n",
       "      <th>unemployment_35-39</th>\n",
       "      <th>unemployment_40-44</th>\n",
       "      <th>unemployment_45-49</th>\n",
       "      <th>unemployment_50-54</th>\n",
       "      <th>unemployment_55-59</th>\n",
       "      <th>unemployment_60-64</th>\n",
       "      <th>unemployment_65+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>1.731919e+07</td>\n",
       "      <td>114.49</td>\n",
       "      <td>46.84</td>\n",
       "      <td>44.51</td>\n",
       "      <td>-1445</td>\n",
       "      <td>4997279724</td>\n",
       "      <td>6791013684</td>\n",
       "      <td>...</td>\n",
       "      <td>22.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>2005</td>\n",
       "      <td>2</td>\n",
       "      <td>1.832184e+07</td>\n",
       "      <td>114.51</td>\n",
       "      <td>48.15</td>\n",
       "      <td>45.48</td>\n",
       "      <td>-2302</td>\n",
       "      <td>5651741252</td>\n",
       "      <td>7449079145</td>\n",
       "      <td>...</td>\n",
       "      <td>23.9</td>\n",
       "      <td>14.7</td>\n",
       "      <td>10.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  year  month  consumer_credits  consumer_price_index  \\\n",
       "0 2005-01-01  2005      1      1.731919e+07                114.49   \n",
       "1 2005-02-01  2005      2      1.832184e+07                114.51   \n",
       "\n",
       "   WTI_oil_price  brent_oil_price  current_account_deficit  exports_usd  \\\n",
       "0          46.84            44.51                    -1445   4997279724   \n",
       "1          48.15            45.48                    -2302   5651741252   \n",
       "\n",
       "   exports_tl  ...  unemployment_20-24  unemployment_25-29  \\\n",
       "0  6791013684  ...                22.7                14.6   \n",
       "1  7449079145  ...                23.9                14.7   \n",
       "\n",
       "   unemployment_30-34  unemployment_35-39  unemployment_40-44  \\\n",
       "0                10.7                 8.9                 7.8   \n",
       "1                10.8                 9.5                 7.8   \n",
       "\n",
       "   unemployment_45-49  unemployment_50-54  unemployment_55-59  \\\n",
       "0                 7.4                 6.8                 4.3   \n",
       "1                 7.6                 6.8                 5.1   \n",
       "\n",
       "   unemployment_60-64  unemployment_65+  \n",
       "0                 3.4               0.7  \n",
       "1                 3.4               1.1  \n",
       "\n",
       "[2 rows x 42 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e59b8b",
   "metadata": {},
   "source": [
    "#### ExchangeRates is here: https://github.com/YZouzou/CS512-project/blob/main/data/Central_Bank/ExchangeRates.xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84782bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_exchange_rate = pd.read_excel(\"C:/Users/suuser/Desktop/ExchangeRates.xlsx\")\n",
    "\n",
    "path = 'data/Central_Bank/ExchangeRates.xlsx'\n",
    "df_exchange_rate = pd.read_excel(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09135efb",
   "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>Tarih</th>\n",
       "      <th>TP DK USD A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01</td>\n",
       "      <td>1.350042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-02</td>\n",
       "      <td>1.310155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Tarih  TP DK USD A\n",
       "0  2005-01     1.350042\n",
       "1  2005-02     1.310155"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exchange_rate.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c59178fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ExchangeRate'] = df_exchange_rate['TP DK USD A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f133c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a1f6ccb",
   "metadata": {},
   "source": [
    "### Conversion from TL to USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90886775",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Consumer credits are measured in 1000 TL\n",
    "df_copy['consumer_credits'] = 1000 * df_copy['consumer_credits'] / df_copy['ExchangeRate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21adee39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Money supply is measured in 1000 TL\n",
    "df_copy['money_supply'] = df_copy['money_supply'] / df_copy['ExchangeRate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "15d5e5f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy['retail_sales'] = df_copy['retail_sales'] / df_copy['ExchangeRate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fce49388",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GDP is measured in 100 TL\n",
    "df_copy['gdp'] = 1000 * df_copy['gdp']/df_copy['ExchangeRate']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d166fd",
   "metadata": {},
   "source": [
    "### Dropping some columns and converting current_account_deficit  from Millions USD to USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "694cb4d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy['current_account_deficit'] = df_copy['current_account_deficit'] * 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5133620a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy.drop(['WTI_oil_price'], inplace = True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e80ed2b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy.drop(['gold_price_cum'], inplace = True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c4bb4c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy.drop(['unemployment_15-19','unemployment_20-24','unemployment_25-29','unemployment_35-39','unemployment_40-44','unemployment_45-49','unemployment_50-54','unemployment_55-59','unemployment_60-64','unemployment_65+'], inplace = True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e55eb03",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy.drop(['interest_rates_mortgage','exports_tl','export_unit_value_index_tl','imports_tl','import_unit_value_index_tl'], inplace = True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ae68bbd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>consumer_credits</th>\n",
       "      <th>consumer_price_index</th>\n",
       "      <th>brent_oil_price</th>\n",
       "      <th>current_account_deficit</th>\n",
       "      <th>exports_usd</th>\n",
       "      <th>imports_usd</th>\n",
       "      <th>export_unit_value_index_usd</th>\n",
       "      <th>...</th>\n",
       "      <th>MIG_NDCOG</th>\n",
       "      <th>MIG_ING</th>\n",
       "      <th>MIG_DCOG</th>\n",
       "      <th>MIG_CAG</th>\n",
       "      <th>interest_rates_personal</th>\n",
       "      <th>money_supply</th>\n",
       "      <th>real_effective_echange_rates</th>\n",
       "      <th>retail_sales</th>\n",
       "      <th>unemployment_30-34</th>\n",
       "      <th>ExchangeRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>1.282863e+10</td>\n",
       "      <td>114.49</td>\n",
       "      <td>44.51</td>\n",
       "      <td>-1445000000</td>\n",
       "      <td>4997279724</td>\n",
       "      <td>7219679862</td>\n",
       "      <td>86.8</td>\n",
       "      <td>...</td>\n",
       "      <td>55.5</td>\n",
       "      <td>48.3</td>\n",
       "      <td>50.3</td>\n",
       "      <td>36.1</td>\n",
       "      <td>29.6850</td>\n",
       "      <td>1.992981e+07</td>\n",
       "      <td>107.61</td>\n",
       "      <td>1.379450e+06</td>\n",
       "      <td>10.7</td>\n",
       "      <td>1.350042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>2005</td>\n",
       "      <td>2</td>\n",
       "      <td>1.398448e+10</td>\n",
       "      <td>114.51</td>\n",
       "      <td>45.48</td>\n",
       "      <td>-2302000000</td>\n",
       "      <td>5651741252</td>\n",
       "      <td>8323736797</td>\n",
       "      <td>86.5</td>\n",
       "      <td>...</td>\n",
       "      <td>54.2</td>\n",
       "      <td>50.7</td>\n",
       "      <td>50.1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>26.2175</td>\n",
       "      <td>2.151616e+07</td>\n",
       "      <td>109.93</td>\n",
       "      <td>5.414810e+05</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1.310155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  year  month  consumer_credits  consumer_price_index  \\\n",
       "0 2005-01-01  2005      1      1.282863e+10                114.49   \n",
       "1 2005-02-01  2005      2      1.398448e+10                114.51   \n",
       "\n",
       "   brent_oil_price  current_account_deficit  exports_usd  imports_usd  \\\n",
       "0            44.51              -1445000000   4997279724   7219679862   \n",
       "1            45.48              -2302000000   5651741252   8323736797   \n",
       "\n",
       "   export_unit_value_index_usd  ...  MIG_NDCOG  MIG_ING  MIG_DCOG  MIG_CAG  \\\n",
       "0                         86.8  ...       55.5     48.3      50.3     36.1   \n",
       "1                         86.5  ...       54.2     50.7      50.1     42.0   \n",
       "\n",
       "   interest_rates_personal  money_supply  real_effective_echange_rates  \\\n",
       "0                  29.6850  1.992981e+07                        107.61   \n",
       "1                  26.2175  2.151616e+07                        109.93   \n",
       "\n",
       "   retail_sales  unemployment_30-34  ExchangeRate  \n",
       "0  1.379450e+06                10.7      1.350042  \n",
       "1  5.414810e+05                10.8      1.310155  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "337d75a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Excluding 2009-01 last row\n",
    "df_copy= df_copy.iloc[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "89289bfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Excluding 2005-2008\n",
    "df_copy = df_copy[48:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2eeae240",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>consumer_credits</th>\n",
       "      <th>consumer_price_index</th>\n",
       "      <th>brent_oil_price</th>\n",
       "      <th>current_account_deficit</th>\n",
       "      <th>exports_usd</th>\n",
       "      <th>imports_usd</th>\n",
       "      <th>export_unit_value_index_usd</th>\n",
       "      <th>...</th>\n",
       "      <th>MIG_NDCOG</th>\n",
       "      <th>MIG_ING</th>\n",
       "      <th>MIG_DCOG</th>\n",
       "      <th>MIG_CAG</th>\n",
       "      <th>interest_rates_personal</th>\n",
       "      <th>money_supply</th>\n",
       "      <th>real_effective_echange_rates</th>\n",
       "      <th>retail_sales</th>\n",
       "      <th>unemployment_30-34</th>\n",
       "      <th>ExchangeRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>5.074031e+10</td>\n",
       "      <td>160.90</td>\n",
       "      <td>43.44</td>\n",
       "      <td>-330000000</td>\n",
       "      <td>7884493524</td>\n",
       "      <td>9281137649</td>\n",
       "      <td>95.1</td>\n",
       "      <td>...</td>\n",
       "      <td>60.4</td>\n",
       "      <td>49.3</td>\n",
       "      <td>49.2</td>\n",
       "      <td>31.8</td>\n",
       "      <td>22.8580</td>\n",
       "      <td>5.106825e+07</td>\n",
       "      <td>95.56</td>\n",
       "      <td>3.945135e+07</td>\n",
       "      <td>13.8</td>\n",
       "      <td>1.589052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2009-02-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>4.863385e+10</td>\n",
       "      <td>160.35</td>\n",
       "      <td>43.32</td>\n",
       "      <td>-150000000</td>\n",
       "      <td>8435115834</td>\n",
       "      <td>9074663754</td>\n",
       "      <td>90.9</td>\n",
       "      <td>...</td>\n",
       "      <td>56.2</td>\n",
       "      <td>47.7</td>\n",
       "      <td>52.6</td>\n",
       "      <td>34.7</td>\n",
       "      <td>21.4725</td>\n",
       "      <td>5.232834e+07</td>\n",
       "      <td>93.34</td>\n",
       "      <td>1.199247e+07</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1.652360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  year  month  consumer_credits  consumer_price_index  \\\n",
       "48 2009-01-01  2009      1      5.074031e+10                160.90   \n",
       "49 2009-02-01  2009      2      4.863385e+10                160.35   \n",
       "\n",
       "    brent_oil_price  current_account_deficit  exports_usd  imports_usd  \\\n",
       "48            43.44               -330000000   7884493524   9281137649   \n",
       "49            43.32               -150000000   8435115834   9074663754   \n",
       "\n",
       "    export_unit_value_index_usd  ...  MIG_NDCOG  MIG_ING  MIG_DCOG  MIG_CAG  \\\n",
       "48                         95.1  ...       60.4     49.3      49.2     31.8   \n",
       "49                         90.9  ...       56.2     47.7      52.6     34.7   \n",
       "\n",
       "    interest_rates_personal  money_supply  real_effective_echange_rates  \\\n",
       "48                  22.8580  5.106825e+07                         95.56   \n",
       "49                  21.4725  5.232834e+07                         93.34   \n",
       "\n",
       "    retail_sales  unemployment_30-34  ExchangeRate  \n",
       "48  3.945135e+07                13.8      1.589052  \n",
       "49  1.199247e+07                14.9      1.652360  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "08e4b769",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy.to_csv('processed_data/feature_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fc44109f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = df_copy[['consumer_credits', 'consumer_price_index',\n",
    "       'brent_oil_price', 'current_account_deficit', 'exports_usd',\n",
    "       'imports_usd', 'export_unit_value_index_usd', 'gdp', 'gold_price_lon',\n",
    "       'import_unit_value_index_usd', 'B', 'C', 'MIG_NRG_X_E', 'MIG_NDCOG',\n",
    "       'MIG_ING', 'MIG_DCOG', 'MIG_CAG', 'interest_rates_personal',\n",
    "       'money_supply', 'real_effective_echange_rates', 'retail_sales',\n",
    "       'unemployment_30-34']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "71a89eb8",
   "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>consumer_credits</th>\n",
       "      <th>consumer_price_index</th>\n",
       "      <th>brent_oil_price</th>\n",
       "      <th>current_account_deficit</th>\n",
       "      <th>exports_usd</th>\n",
       "      <th>imports_usd</th>\n",
       "      <th>export_unit_value_index_usd</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gold_price_lon</th>\n",
       "      <th>import_unit_value_index_usd</th>\n",
       "      <th>...</th>\n",
       "      <th>MIG_NRG_X_E</th>\n",
       "      <th>MIG_NDCOG</th>\n",
       "      <th>MIG_ING</th>\n",
       "      <th>MIG_DCOG</th>\n",
       "      <th>MIG_CAG</th>\n",
       "      <th>interest_rates_personal</th>\n",
       "      <th>money_supply</th>\n",
       "      <th>real_effective_echange_rates</th>\n",
       "      <th>retail_sales</th>\n",
       "      <th>unemployment_30-34</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.074031e+10</td>\n",
       "      <td>160.90</td>\n",
       "      <td>43.44</td>\n",
       "      <td>-330000000</td>\n",
       "      <td>7884493524</td>\n",
       "      <td>9281137649</td>\n",
       "      <td>95.10</td>\n",
       "      <td>1.540084e+11</td>\n",
       "      <td>870.15</td>\n",
       "      <td>92.40</td>\n",
       "      <td>...</td>\n",
       "      <td>82.4</td>\n",
       "      <td>60.4</td>\n",
       "      <td>49.3</td>\n",
       "      <td>49.2</td>\n",
       "      <td>31.8</td>\n",
       "      <td>22.8580</td>\n",
       "      <td>5.106825e+07</td>\n",
       "      <td>95.56</td>\n",
       "      <td>3.945135e+07</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4.863385e+10</td>\n",
       "      <td>160.35</td>\n",
       "      <td>43.32</td>\n",
       "      <td>-150000000</td>\n",
       "      <td>8435115834</td>\n",
       "      <td>9074663754</td>\n",
       "      <td>90.90</td>\n",
       "      <td>1.384701e+11</td>\n",
       "      <td>947.38</td>\n",
       "      <td>90.60</td>\n",
       "      <td>...</td>\n",
       "      <td>73.1</td>\n",
       "      <td>56.2</td>\n",
       "      <td>47.7</td>\n",
       "      <td>52.6</td>\n",
       "      <td>34.7</td>\n",
       "      <td>21.4725</td>\n",
       "      <td>5.232834e+07</td>\n",
       "      <td>93.34</td>\n",
       "      <td>1.199247e+07</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4.710121e+10</td>\n",
       "      <td>162.12</td>\n",
       "      <td>46.54</td>\n",
       "      <td>-1080000000</td>\n",
       "      <td>8155485081</td>\n",
       "      <td>10522038241</td>\n",
       "      <td>89.80</td>\n",
       "      <td>1.248889e+11</td>\n",
       "      <td>935.50</td>\n",
       "      <td>90.20</td>\n",
       "      <td>...</td>\n",
       "      <td>77.8</td>\n",
       "      <td>62.1</td>\n",
       "      <td>54.9</td>\n",
       "      <td>62.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>21.8000</td>\n",
       "      <td>5.154123e+07</td>\n",
       "      <td>91.39</td>\n",
       "      <td>2.469501e+07</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5.054903e+10</td>\n",
       "      <td>162.15</td>\n",
       "      <td>50.18</td>\n",
       "      <td>-1384000000</td>\n",
       "      <td>7561696283</td>\n",
       "      <td>10120319396</td>\n",
       "      <td>91.70</td>\n",
       "      <td>1.389538e+11</td>\n",
       "      <td>894.33</td>\n",
       "      <td>89.60</td>\n",
       "      <td>...</td>\n",
       "      <td>75.1</td>\n",
       "      <td>61.9</td>\n",
       "      <td>57.3</td>\n",
       "      <td>62.4</td>\n",
       "      <td>43.0</td>\n",
       "      <td>19.0125</td>\n",
       "      <td>5.481556e+07</td>\n",
       "      <td>96.10</td>\n",
       "      <td>2.976991e+07</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>5.280357e+10</td>\n",
       "      <td>163.19</td>\n",
       "      <td>57.30</td>\n",
       "      <td>-1361000000</td>\n",
       "      <td>7346407528</td>\n",
       "      <td>10867698195</td>\n",
       "      <td>93.80</td>\n",
       "      <td>1.501058e+11</td>\n",
       "      <td>942.94</td>\n",
       "      <td>86.40</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>63.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.9</td>\n",
       "      <td>48.6</td>\n",
       "      <td>20.0160</td>\n",
       "      <td>5.771973e+07</td>\n",
       "      <td>97.79</td>\n",
       "      <td>9.979813e+06</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>7.051407e+10</td>\n",
       "      <td>367.66</td>\n",
       "      <td>72.53</td>\n",
       "      <td>2704000000</td>\n",
       "      <td>12331984010</td>\n",
       "      <td>14803525725</td>\n",
       "      <td>94.06</td>\n",
       "      <td>1.728699e+11</td>\n",
       "      <td>1202.88</td>\n",
       "      <td>92.41</td>\n",
       "      <td>...</td>\n",
       "      <td>126.1</td>\n",
       "      <td>99.4</td>\n",
       "      <td>98.4</td>\n",
       "      <td>97.3</td>\n",
       "      <td>95.4</td>\n",
       "      <td>28.8220</td>\n",
       "      <td>1.033303e+08</td>\n",
       "      <td>55.47</td>\n",
       "      <td>1.031588e+07</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>6.289481e+10</td>\n",
       "      <td>390.84</td>\n",
       "      <td>78.89</td>\n",
       "      <td>2818000000</td>\n",
       "      <td>14397835420</td>\n",
       "      <td>16326538057</td>\n",
       "      <td>93.69</td>\n",
       "      <td>1.628352e+11</td>\n",
       "      <td>1196.70</td>\n",
       "      <td>92.18</td>\n",
       "      <td>...</td>\n",
       "      <td>116.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>111.9</td>\n",
       "      <td>127.6</td>\n",
       "      <td>115.0</td>\n",
       "      <td>36.4075</td>\n",
       "      <td>8.773088e+07</td>\n",
       "      <td>53.04</td>\n",
       "      <td>6.334990e+06</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>6.732884e+10</td>\n",
       "      <td>401.27</td>\n",
       "      <td>81.03</td>\n",
       "      <td>3930000000</td>\n",
       "      <td>15676860082</td>\n",
       "      <td>16174148502</td>\n",
       "      <td>92.84</td>\n",
       "      <td>1.764037e+11</td>\n",
       "      <td>1221.31</td>\n",
       "      <td>93.38</td>\n",
       "      <td>...</td>\n",
       "      <td>111.6</td>\n",
       "      <td>128.6</td>\n",
       "      <td>113.3</td>\n",
       "      <td>116.5</td>\n",
       "      <td>125.2</td>\n",
       "      <td>38.7200</td>\n",
       "      <td>8.843063e+07</td>\n",
       "      <td>59.06</td>\n",
       "      <td>1.513423e+07</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>7.241899e+10</td>\n",
       "      <td>395.48</td>\n",
       "      <td>64.75</td>\n",
       "      <td>2408000000</td>\n",
       "      <td>15491509931</td>\n",
       "      <td>16163973421</td>\n",
       "      <td>92.46</td>\n",
       "      <td>1.917709e+11</td>\n",
       "      <td>1221.42</td>\n",
       "      <td>92.75</td>\n",
       "      <td>...</td>\n",
       "      <td>111.4</td>\n",
       "      <td>124.0</td>\n",
       "      <td>108.4</td>\n",
       "      <td>125.3</td>\n",
       "      <td>120.9</td>\n",
       "      <td>36.0980</td>\n",
       "      <td>9.121423e+07</td>\n",
       "      <td>63.56</td>\n",
       "      <td>2.816625e+07</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>7.248719e+10</td>\n",
       "      <td>393.88</td>\n",
       "      <td>57.36</td>\n",
       "      <td>-543000000</td>\n",
       "      <td>13810281310</td>\n",
       "      <td>16554303252</td>\n",
       "      <td>92.37</td>\n",
       "      <td>1.936147e+11</td>\n",
       "      <td>1253.95</td>\n",
       "      <td>92.07</td>\n",
       "      <td>...</td>\n",
       "      <td>117.6</td>\n",
       "      <td>121.3</td>\n",
       "      <td>105.0</td>\n",
       "      <td>116.6</td>\n",
       "      <td>137.2</td>\n",
       "      <td>33.5850</td>\n",
       "      <td>9.605435e+07</td>\n",
       "      <td>63.96</td>\n",
       "      <td>4.145840e+07</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     consumer_credits  consumer_price_index  brent_oil_price  \\\n",
       "48       5.074031e+10                160.90            43.44   \n",
       "49       4.863385e+10                160.35            43.32   \n",
       "50       4.710121e+10                162.12            46.54   \n",
       "51       5.054903e+10                162.15            50.18   \n",
       "52       5.280357e+10                163.19            57.30   \n",
       "..                ...                   ...              ...   \n",
       "163      7.051407e+10                367.66            72.53   \n",
       "164      6.289481e+10                390.84            78.89   \n",
       "165      6.732884e+10                401.27            81.03   \n",
       "166      7.241899e+10                395.48            64.75   \n",
       "167      7.248719e+10                393.88            57.36   \n",
       "\n",
       "     current_account_deficit  exports_usd  imports_usd  \\\n",
       "48                -330000000   7884493524   9281137649   \n",
       "49                -150000000   8435115834   9074663754   \n",
       "50               -1080000000   8155485081  10522038241   \n",
       "51               -1384000000   7561696283  10120319396   \n",
       "52               -1361000000   7346407528  10867698195   \n",
       "..                       ...          ...          ...   \n",
       "163               2704000000  12331984010  14803525725   \n",
       "164               2818000000  14397835420  16326538057   \n",
       "165               3930000000  15676860082  16174148502   \n",
       "166               2408000000  15491509931  16163973421   \n",
       "167               -543000000  13810281310  16554303252   \n",
       "\n",
       "     export_unit_value_index_usd           gdp  gold_price_lon  \\\n",
       "48                         95.10  1.540084e+11          870.15   \n",
       "49                         90.90  1.384701e+11          947.38   \n",
       "50                         89.80  1.248889e+11          935.50   \n",
       "51                         91.70  1.389538e+11          894.33   \n",
       "52                         93.80  1.501058e+11          942.94   \n",
       "..                           ...           ...             ...   \n",
       "163                        94.06  1.728699e+11         1202.88   \n",
       "164                        93.69  1.628352e+11         1196.70   \n",
       "165                        92.84  1.764037e+11         1221.31   \n",
       "166                        92.46  1.917709e+11         1221.42   \n",
       "167                        92.37  1.936147e+11         1253.95   \n",
       "\n",
       "     import_unit_value_index_usd  ...  MIG_NRG_X_E  MIG_NDCOG  MIG_ING  \\\n",
       "48                         92.40  ...         82.4       60.4     49.3   \n",
       "49                         90.60  ...         73.1       56.2     47.7   \n",
       "50                         90.20  ...         77.8       62.1     54.9   \n",
       "51                         89.60  ...         75.1       61.9     57.3   \n",
       "52                         86.40  ...         77.0       63.8     60.0   \n",
       "..                           ...  ...          ...        ...      ...   \n",
       "163                        92.41  ...        126.1       99.4     98.4   \n",
       "164                        92.18  ...        116.0      116.0    111.9   \n",
       "165                        93.38  ...        111.6      128.6    113.3   \n",
       "166                        92.75  ...        111.4      124.0    108.4   \n",
       "167                        92.07  ...        117.6      121.3    105.0   \n",
       "\n",
       "     MIG_DCOG  MIG_CAG  interest_rates_personal  money_supply  \\\n",
       "48       49.2     31.8                  22.8580  5.106825e+07   \n",
       "49       52.6     34.7                  21.4725  5.232834e+07   \n",
       "50       62.0     41.0                  21.8000  5.154123e+07   \n",
       "51       62.4     43.0                  19.0125  5.481556e+07   \n",
       "52       70.9     48.6                  20.0160  5.771973e+07   \n",
       "..        ...      ...                      ...           ...   \n",
       "163      97.3     95.4                  28.8220  1.033303e+08   \n",
       "164     127.6    115.0                  36.4075  8.773088e+07   \n",
       "165     116.5    125.2                  38.7200  8.843063e+07   \n",
       "166     125.3    120.9                  36.0980  9.121423e+07   \n",
       "167     116.6    137.2                  33.5850  9.605435e+07   \n",
       "\n",
       "     real_effective_echange_rates  retail_sales  unemployment_30-34  \n",
       "48                          95.56  3.945135e+07                13.8  \n",
       "49                          93.34  1.199247e+07                14.9  \n",
       "50                          91.39  2.469501e+07                14.5  \n",
       "51                          96.10  2.976991e+07                13.0  \n",
       "52                          97.79  9.979813e+06                11.4  \n",
       "..                            ...           ...                 ...  \n",
       "163                         55.47  1.031588e+07                 9.8  \n",
       "164                         53.04  6.334990e+06                10.4  \n",
       "165                         59.06  1.513423e+07                10.4  \n",
       "166                         63.56  2.816625e+07                11.4  \n",
       "167                         63.96  4.145840e+07                12.5  \n",
       "\n",
       "[120 rows x 22 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f2b9ae7",
   "metadata": {},
   "source": [
    "### Outlier Detection and Trimming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "7a37a95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "stats = df_features.describe()\n",
    "mean = stats.loc['mean', :]\n",
    "std = stats.loc['std', :]\n",
    "lower_bound = mean - 2*std\n",
    "upper_bound = mean + 2*std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "c87187d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = list(df_features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "942d546a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_non_outliers = df_features.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "ce239032",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\suuser\\AppData\\Local\\Temp\\ipykernel_6068\\1512860943.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_non_outliers.iloc[i][cl] = lower_bound[index]\n",
      "C:\\Users\\suuser\\AppData\\Local\\Temp\\ipykernel_6068\\1512860943.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_non_outliers.iloc[i][cl] = upper_bound[index]\n"
     ]
    }
   ],
   "source": [
    "count_of_outliers = 0\n",
    "for index,cl in enumerate(columns):\n",
    "    for i in range(len(df_non_outliers)):\n",
    "        if df_non_outliers.iloc[i][cl] >= upper_bound[index]:\n",
    "            df_non_outliers.iloc[i][cl] = upper_bound[index]\n",
    "            count_of_outliers += 1\n",
    "        if df_non_outliers.iloc[i][cl] <= lower_bound[index]:\n",
    "            df_non_outliers.iloc[i][cl] = lower_bound[index]\n",
    "            count_of_outliers += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "1352c675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_of_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "57fe5a7a",
   "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>consumer_credits</th>\n",
       "      <th>consumer_price_index</th>\n",
       "      <th>brent_oil_price</th>\n",
       "      <th>current_account_deficit</th>\n",
       "      <th>exports_usd</th>\n",
       "      <th>imports_usd</th>\n",
       "      <th>export_unit_value_index_usd</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gold_price_lon</th>\n",
       "      <th>import_unit_value_index_usd</th>\n",
       "      <th>...</th>\n",
       "      <th>MIG_NRG_X_E</th>\n",
       "      <th>MIG_NDCOG</th>\n",
       "      <th>MIG_ING</th>\n",
       "      <th>MIG_DCOG</th>\n",
       "      <th>MIG_CAG</th>\n",
       "      <th>interest_rates_personal</th>\n",
       "      <th>money_supply</th>\n",
       "      <th>real_effective_echange_rates</th>\n",
       "      <th>retail_sales</th>\n",
       "      <th>unemployment_30-34</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.074031e+07</td>\n",
       "      <td>160.90</td>\n",
       "      <td>43.44</td>\n",
       "      <td>-330000000</td>\n",
       "      <td>7884493524</td>\n",
       "      <td>9281137649</td>\n",
       "      <td>95.10</td>\n",
       "      <td>1.540084e+08</td>\n",
       "      <td>870.15</td>\n",
       "      <td>92.40</td>\n",
       "      <td>...</td>\n",
       "      <td>82.4</td>\n",
       "      <td>60.4</td>\n",
       "      <td>49.3</td>\n",
       "      <td>49.2</td>\n",
       "      <td>31.8</td>\n",
       "      <td>22.8580</td>\n",
       "      <td>5.106825e+07</td>\n",
       "      <td>95.56</td>\n",
       "      <td>3.945135e+07</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4.863385e+07</td>\n",
       "      <td>160.35</td>\n",
       "      <td>43.32</td>\n",
       "      <td>-150000000</td>\n",
       "      <td>8435115834</td>\n",
       "      <td>9074663754</td>\n",
       "      <td>90.90</td>\n",
       "      <td>1.384701e+08</td>\n",
       "      <td>947.38</td>\n",
       "      <td>90.60</td>\n",
       "      <td>...</td>\n",
       "      <td>73.1</td>\n",
       "      <td>56.2</td>\n",
       "      <td>47.7</td>\n",
       "      <td>52.6</td>\n",
       "      <td>34.7</td>\n",
       "      <td>21.4725</td>\n",
       "      <td>5.232834e+07</td>\n",
       "      <td>93.34</td>\n",
       "      <td>1.199247e+07</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4.710121e+07</td>\n",
       "      <td>162.12</td>\n",
       "      <td>46.54</td>\n",
       "      <td>-1080000000</td>\n",
       "      <td>8155485081</td>\n",
       "      <td>10522038241</td>\n",
       "      <td>89.80</td>\n",
       "      <td>1.248889e+08</td>\n",
       "      <td>935.50</td>\n",
       "      <td>90.20</td>\n",
       "      <td>...</td>\n",
       "      <td>77.8</td>\n",
       "      <td>62.1</td>\n",
       "      <td>54.9</td>\n",
       "      <td>62.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>21.8000</td>\n",
       "      <td>5.154123e+07</td>\n",
       "      <td>91.39</td>\n",
       "      <td>2.469501e+07</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5.054903e+07</td>\n",
       "      <td>162.15</td>\n",
       "      <td>50.18</td>\n",
       "      <td>-1384000000</td>\n",
       "      <td>7561696283</td>\n",
       "      <td>10120319396</td>\n",
       "      <td>91.70</td>\n",
       "      <td>1.389538e+08</td>\n",
       "      <td>894.33</td>\n",
       "      <td>89.60</td>\n",
       "      <td>...</td>\n",
       "      <td>75.1</td>\n",
       "      <td>61.9</td>\n",
       "      <td>57.3</td>\n",
       "      <td>62.4</td>\n",
       "      <td>43.0</td>\n",
       "      <td>19.0125</td>\n",
       "      <td>5.481556e+07</td>\n",
       "      <td>96.10</td>\n",
       "      <td>2.976991e+07</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>5.280357e+07</td>\n",
       "      <td>163.19</td>\n",
       "      <td>57.30</td>\n",
       "      <td>-1361000000</td>\n",
       "      <td>7346407528</td>\n",
       "      <td>10867698195</td>\n",
       "      <td>93.80</td>\n",
       "      <td>1.501058e+08</td>\n",
       "      <td>942.94</td>\n",
       "      <td>86.40</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>63.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.9</td>\n",
       "      <td>48.6</td>\n",
       "      <td>20.0160</td>\n",
       "      <td>5.771973e+07</td>\n",
       "      <td>97.79</td>\n",
       "      <td>9.979813e+06</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>7.051407e+07</td>\n",
       "      <td>367.66</td>\n",
       "      <td>72.53</td>\n",
       "      <td>2704000000</td>\n",
       "      <td>12331984010</td>\n",
       "      <td>14803525725</td>\n",
       "      <td>94.06</td>\n",
       "      <td>1.728699e+08</td>\n",
       "      <td>1202.88</td>\n",
       "      <td>92.41</td>\n",
       "      <td>...</td>\n",
       "      <td>126.1</td>\n",
       "      <td>99.4</td>\n",
       "      <td>98.4</td>\n",
       "      <td>97.3</td>\n",
       "      <td>95.4</td>\n",
       "      <td>28.8220</td>\n",
       "      <td>1.033303e+08</td>\n",
       "      <td>55.47</td>\n",
       "      <td>1.031588e+07</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>6.289481e+07</td>\n",
       "      <td>390.84</td>\n",
       "      <td>78.89</td>\n",
       "      <td>2818000000</td>\n",
       "      <td>14397835420</td>\n",
       "      <td>16326538057</td>\n",
       "      <td>93.69</td>\n",
       "      <td>1.628352e+08</td>\n",
       "      <td>1196.70</td>\n",
       "      <td>92.18</td>\n",
       "      <td>...</td>\n",
       "      <td>116.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>111.9</td>\n",
       "      <td>127.6</td>\n",
       "      <td>115.0</td>\n",
       "      <td>36.4075</td>\n",
       "      <td>8.773088e+07</td>\n",
       "      <td>53.04</td>\n",
       "      <td>6.334990e+06</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>6.732884e+07</td>\n",
       "      <td>401.27</td>\n",
       "      <td>81.03</td>\n",
       "      <td>3930000000</td>\n",
       "      <td>15676860082</td>\n",
       "      <td>16174148502</td>\n",
       "      <td>92.84</td>\n",
       "      <td>1.764037e+08</td>\n",
       "      <td>1221.31</td>\n",
       "      <td>93.38</td>\n",
       "      <td>...</td>\n",
       "      <td>111.6</td>\n",
       "      <td>128.6</td>\n",
       "      <td>113.3</td>\n",
       "      <td>116.5</td>\n",
       "      <td>125.2</td>\n",
       "      <td>38.7200</td>\n",
       "      <td>8.843063e+07</td>\n",
       "      <td>59.06</td>\n",
       "      <td>1.513423e+07</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>7.241899e+07</td>\n",
       "      <td>395.48</td>\n",
       "      <td>64.75</td>\n",
       "      <td>2408000000</td>\n",
       "      <td>15491509931</td>\n",
       "      <td>16163973421</td>\n",
       "      <td>92.46</td>\n",
       "      <td>1.917709e+08</td>\n",
       "      <td>1221.42</td>\n",
       "      <td>92.75</td>\n",
       "      <td>...</td>\n",
       "      <td>111.4</td>\n",
       "      <td>124.0</td>\n",
       "      <td>108.4</td>\n",
       "      <td>125.3</td>\n",
       "      <td>120.9</td>\n",
       "      <td>36.0980</td>\n",
       "      <td>9.121423e+07</td>\n",
       "      <td>63.56</td>\n",
       "      <td>2.816625e+07</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>7.248719e+07</td>\n",
       "      <td>393.88</td>\n",
       "      <td>57.36</td>\n",
       "      <td>-543000000</td>\n",
       "      <td>13810281310</td>\n",
       "      <td>16554303252</td>\n",
       "      <td>92.37</td>\n",
       "      <td>1.936147e+08</td>\n",
       "      <td>1253.95</td>\n",
       "      <td>92.07</td>\n",
       "      <td>...</td>\n",
       "      <td>117.6</td>\n",
       "      <td>121.3</td>\n",
       "      <td>105.0</td>\n",
       "      <td>116.6</td>\n",
       "      <td>137.2</td>\n",
       "      <td>33.5850</td>\n",
       "      <td>9.605435e+07</td>\n",
       "      <td>63.96</td>\n",
       "      <td>4.145840e+07</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     consumer_credits  consumer_price_index  brent_oil_price  \\\n",
       "48       5.074031e+07                160.90            43.44   \n",
       "49       4.863385e+07                160.35            43.32   \n",
       "50       4.710121e+07                162.12            46.54   \n",
       "51       5.054903e+07                162.15            50.18   \n",
       "52       5.280357e+07                163.19            57.30   \n",
       "..                ...                   ...              ...   \n",
       "163      7.051407e+07                367.66            72.53   \n",
       "164      6.289481e+07                390.84            78.89   \n",
       "165      6.732884e+07                401.27            81.03   \n",
       "166      7.241899e+07                395.48            64.75   \n",
       "167      7.248719e+07                393.88            57.36   \n",
       "\n",
       "     current_account_deficit  exports_usd  imports_usd  \\\n",
       "48                -330000000   7884493524   9281137649   \n",
       "49                -150000000   8435115834   9074663754   \n",
       "50               -1080000000   8155485081  10522038241   \n",
       "51               -1384000000   7561696283  10120319396   \n",
       "52               -1361000000   7346407528  10867698195   \n",
       "..                       ...          ...          ...   \n",
       "163               2704000000  12331984010  14803525725   \n",
       "164               2818000000  14397835420  16326538057   \n",
       "165               3930000000  15676860082  16174148502   \n",
       "166               2408000000  15491509931  16163973421   \n",
       "167               -543000000  13810281310  16554303252   \n",
       "\n",
       "     export_unit_value_index_usd           gdp  gold_price_lon  \\\n",
       "48                         95.10  1.540084e+08          870.15   \n",
       "49                         90.90  1.384701e+08          947.38   \n",
       "50                         89.80  1.248889e+08          935.50   \n",
       "51                         91.70  1.389538e+08          894.33   \n",
       "52                         93.80  1.501058e+08          942.94   \n",
       "..                           ...           ...             ...   \n",
       "163                        94.06  1.728699e+08         1202.88   \n",
       "164                        93.69  1.628352e+08         1196.70   \n",
       "165                        92.84  1.764037e+08         1221.31   \n",
       "166                        92.46  1.917709e+08         1221.42   \n",
       "167                        92.37  1.936147e+08         1253.95   \n",
       "\n",
       "     import_unit_value_index_usd  ...  MIG_NRG_X_E  MIG_NDCOG  MIG_ING  \\\n",
       "48                         92.40  ...         82.4       60.4     49.3   \n",
       "49                         90.60  ...         73.1       56.2     47.7   \n",
       "50                         90.20  ...         77.8       62.1     54.9   \n",
       "51                         89.60  ...         75.1       61.9     57.3   \n",
       "52                         86.40  ...         77.0       63.8     60.0   \n",
       "..                           ...  ...          ...        ...      ...   \n",
       "163                        92.41  ...        126.1       99.4     98.4   \n",
       "164                        92.18  ...        116.0      116.0    111.9   \n",
       "165                        93.38  ...        111.6      128.6    113.3   \n",
       "166                        92.75  ...        111.4      124.0    108.4   \n",
       "167                        92.07  ...        117.6      121.3    105.0   \n",
       "\n",
       "     MIG_DCOG  MIG_CAG  interest_rates_personal  money_supply  \\\n",
       "48       49.2     31.8                  22.8580  5.106825e+07   \n",
       "49       52.6     34.7                  21.4725  5.232834e+07   \n",
       "50       62.0     41.0                  21.8000  5.154123e+07   \n",
       "51       62.4     43.0                  19.0125  5.481556e+07   \n",
       "52       70.9     48.6                  20.0160  5.771973e+07   \n",
       "..        ...      ...                      ...           ...   \n",
       "163      97.3     95.4                  28.8220  1.033303e+08   \n",
       "164     127.6    115.0                  36.4075  8.773088e+07   \n",
       "165     116.5    125.2                  38.7200  8.843063e+07   \n",
       "166     125.3    120.9                  36.0980  9.121423e+07   \n",
       "167     116.6    137.2                  33.5850  9.605435e+07   \n",
       "\n",
       "     real_effective_echange_rates  retail_sales  unemployment_30-34  \n",
       "48                          95.56  3.945135e+07                13.8  \n",
       "49                          93.34  1.199247e+07                14.9  \n",
       "50                          91.39  2.469501e+07                14.5  \n",
       "51                          96.10  2.976991e+07                13.0  \n",
       "52                          97.79  9.979813e+06                11.4  \n",
       "..                            ...           ...                 ...  \n",
       "163                         55.47  1.031588e+07                 9.8  \n",
       "164                         53.04  6.334990e+06                10.4  \n",
       "165                         59.06  1.513423e+07                10.4  \n",
       "166                         63.56  2.816625e+07                11.4  \n",
       "167                         63.96  4.145840e+07                12.5  \n",
       "\n",
       "[120 rows x 22 columns]"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_non_outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf5e28e",
   "metadata": {},
   "source": [
    "### Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "387fc9a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(df_non_outliers)\n",
    "scaled_df = scaler.transform(df_non_outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "fce62f16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 22)"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "eb806d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_non_outliers_scaled = pd.DataFrame(scaled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "a6a072da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting columns names from integer values to feature names to be consistent\n",
    "df_non_outliers_scaled.columns = df_non_outliers.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "ebd2d0ec",
   "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>consumer_credits</th>\n",
       "      <th>consumer_price_index</th>\n",
       "      <th>brent_oil_price</th>\n",
       "      <th>current_account_deficit</th>\n",
       "      <th>exports_usd</th>\n",
       "      <th>imports_usd</th>\n",
       "      <th>export_unit_value_index_usd</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gold_price_lon</th>\n",
       "      <th>import_unit_value_index_usd</th>\n",
       "      <th>...</th>\n",
       "      <th>MIG_NRG_X_E</th>\n",
       "      <th>MIG_NDCOG</th>\n",
       "      <th>MIG_ING</th>\n",
       "      <th>MIG_DCOG</th>\n",
       "      <th>MIG_CAG</th>\n",
       "      <th>interest_rates_personal</th>\n",
       "      <th>money_supply</th>\n",
       "      <th>real_effective_echange_rates</th>\n",
       "      <th>retail_sales</th>\n",
       "      <th>unemployment_30-34</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.203284</td>\n",
       "      <td>-1.339231</td>\n",
       "      <td>-1.353997</td>\n",
       "      <td>1.221772</td>\n",
       "      <td>-2.141114</td>\n",
       "      <td>-2.739417</td>\n",
       "      <td>-0.828772</td>\n",
       "      <td>-2.096867</td>\n",
       "      <td>-2.092608</td>\n",
       "      <td>-0.588712</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.227747</td>\n",
       "      <td>-1.759637</td>\n",
       "      <td>-2.323153</td>\n",
       "      <td>-2.636450</td>\n",
       "      <td>-2.043352</td>\n",
       "      <td>1.249452</td>\n",
       "      <td>-2.579183</td>\n",
       "      <td>0.873110</td>\n",
       "      <td>0.996852</td>\n",
       "      <td>2.718712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.316329</td>\n",
       "      <td>-1.348308</td>\n",
       "      <td>-1.358531</td>\n",
       "      <td>1.298369</td>\n",
       "      <td>-1.845113</td>\n",
       "      <td>-2.804266</td>\n",
       "      <td>-1.415554</td>\n",
       "      <td>-2.695456</td>\n",
       "      <td>-1.720218</td>\n",
       "      <td>-0.750302</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.983658</td>\n",
       "      <td>-1.996549</td>\n",
       "      <td>-2.412643</td>\n",
       "      <td>-2.433604</td>\n",
       "      <td>-1.933180</td>\n",
       "      <td>0.944193</td>\n",
       "      <td>-2.507623</td>\n",
       "      <td>0.670596</td>\n",
       "      <td>-0.522569</td>\n",
       "      <td>3.432172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.398580</td>\n",
       "      <td>-1.319099</td>\n",
       "      <td>-1.236879</td>\n",
       "      <td>0.902618</td>\n",
       "      <td>-1.995435</td>\n",
       "      <td>-2.349681</td>\n",
       "      <td>-1.569235</td>\n",
       "      <td>-3.218655</td>\n",
       "      <td>-1.777501</td>\n",
       "      <td>-0.786211</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.601638</td>\n",
       "      <td>-1.663744</td>\n",
       "      <td>-2.009938</td>\n",
       "      <td>-1.872795</td>\n",
       "      <td>-1.693839</td>\n",
       "      <td>1.016349</td>\n",
       "      <td>-2.552323</td>\n",
       "      <td>0.492713</td>\n",
       "      <td>0.180319</td>\n",
       "      <td>3.172732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.213549</td>\n",
       "      <td>-1.318604</td>\n",
       "      <td>-1.099359</td>\n",
       "      <td>0.773254</td>\n",
       "      <td>-2.314641</td>\n",
       "      <td>-2.475851</td>\n",
       "      <td>-1.303786</td>\n",
       "      <td>-2.676823</td>\n",
       "      <td>-1.976016</td>\n",
       "      <td>-0.840074</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821097</td>\n",
       "      <td>-1.675025</td>\n",
       "      <td>-1.875703</td>\n",
       "      <td>-1.848930</td>\n",
       "      <td>-1.617858</td>\n",
       "      <td>0.402196</td>\n",
       "      <td>-2.366376</td>\n",
       "      <td>0.922369</td>\n",
       "      <td>0.461135</td>\n",
       "      <td>2.199833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.092557</td>\n",
       "      <td>-1.301442</td>\n",
       "      <td>-0.830364</td>\n",
       "      <td>0.783041</td>\n",
       "      <td>-2.430375</td>\n",
       "      <td>-2.241118</td>\n",
       "      <td>-1.010395</td>\n",
       "      <td>-2.247210</td>\n",
       "      <td>-1.741627</td>\n",
       "      <td>-1.127346</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.666663</td>\n",
       "      <td>-1.567851</td>\n",
       "      <td>-1.724688</td>\n",
       "      <td>-1.341816</td>\n",
       "      <td>-1.405111</td>\n",
       "      <td>0.623291</td>\n",
       "      <td>-2.201450</td>\n",
       "      <td>1.076535</td>\n",
       "      <td>-0.633938</td>\n",
       "      <td>1.162074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>-1.142105</td>\n",
       "      <td>2.072720</td>\n",
       "      <td>-0.254972</td>\n",
       "      <td>2.512856</td>\n",
       "      <td>0.249743</td>\n",
       "      <td>-1.004973</td>\n",
       "      <td>-0.974070</td>\n",
       "      <td>-1.370257</td>\n",
       "      <td>-0.488242</td>\n",
       "      <td>-0.587814</td>\n",
       "      <td>...</td>\n",
       "      <td>2.324225</td>\n",
       "      <td>0.440262</td>\n",
       "      <td>0.423074</td>\n",
       "      <td>0.233223</td>\n",
       "      <td>0.372845</td>\n",
       "      <td>2.563463</td>\n",
       "      <td>0.388743</td>\n",
       "      <td>-2.783985</td>\n",
       "      <td>-0.615342</td>\n",
       "      <td>0.124315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>-1.551001</td>\n",
       "      <td>2.455236</td>\n",
       "      <td>-0.014690</td>\n",
       "      <td>2.561368</td>\n",
       "      <td>1.360291</td>\n",
       "      <td>-0.526633</td>\n",
       "      <td>-1.025763</td>\n",
       "      <td>-1.756830</td>\n",
       "      <td>-0.518041</td>\n",
       "      <td>-0.608462</td>\n",
       "      <td>...</td>\n",
       "      <td>1.503289</td>\n",
       "      <td>1.376629</td>\n",
       "      <td>1.178146</td>\n",
       "      <td>2.040938</td>\n",
       "      <td>1.117460</td>\n",
       "      <td>4.234730</td>\n",
       "      <td>-0.497138</td>\n",
       "      <td>-3.005654</td>\n",
       "      <td>-0.835622</td>\n",
       "      <td>0.513475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>-1.313044</td>\n",
       "      <td>2.627352</td>\n",
       "      <td>0.066159</td>\n",
       "      <td>3.034567</td>\n",
       "      <td>2.047862</td>\n",
       "      <td>-0.574494</td>\n",
       "      <td>-1.144516</td>\n",
       "      <td>-1.234123</td>\n",
       "      <td>-0.399376</td>\n",
       "      <td>-0.500735</td>\n",
       "      <td>...</td>\n",
       "      <td>1.145653</td>\n",
       "      <td>2.087365</td>\n",
       "      <td>1.256450</td>\n",
       "      <td>1.378706</td>\n",
       "      <td>1.504963</td>\n",
       "      <td>4.744229</td>\n",
       "      <td>-0.457400</td>\n",
       "      <td>-2.456497</td>\n",
       "      <td>-0.348721</td>\n",
       "      <td>0.513475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>-1.039876</td>\n",
       "      <td>2.531806</td>\n",
       "      <td>-0.548902</td>\n",
       "      <td>2.386897</td>\n",
       "      <td>1.948223</td>\n",
       "      <td>-0.577690</td>\n",
       "      <td>-1.197606</td>\n",
       "      <td>-0.642122</td>\n",
       "      <td>-0.398846</td>\n",
       "      <td>-0.557292</td>\n",
       "      <td>...</td>\n",
       "      <td>1.129397</td>\n",
       "      <td>1.827890</td>\n",
       "      <td>0.982387</td>\n",
       "      <td>1.903719</td>\n",
       "      <td>1.341604</td>\n",
       "      <td>4.166540</td>\n",
       "      <td>-0.299321</td>\n",
       "      <td>-2.045998</td>\n",
       "      <td>0.372397</td>\n",
       "      <td>1.162074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>-1.036216</td>\n",
       "      <td>2.505402</td>\n",
       "      <td>-0.828097</td>\n",
       "      <td>1.131132</td>\n",
       "      <td>1.044437</td>\n",
       "      <td>-0.455097</td>\n",
       "      <td>-1.210180</td>\n",
       "      <td>-0.571092</td>\n",
       "      <td>-0.241992</td>\n",
       "      <td>-0.618337</td>\n",
       "      <td>...</td>\n",
       "      <td>1.633338</td>\n",
       "      <td>1.675590</td>\n",
       "      <td>0.792220</td>\n",
       "      <td>1.384672</td>\n",
       "      <td>1.960850</td>\n",
       "      <td>3.612866</td>\n",
       "      <td>-0.024454</td>\n",
       "      <td>-2.009509</td>\n",
       "      <td>1.107910</td>\n",
       "      <td>1.875533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     consumer_credits  consumer_price_index  brent_oil_price  \\\n",
       "0           -2.203284             -1.339231        -1.353997   \n",
       "1           -2.316329             -1.348308        -1.358531   \n",
       "2           -2.398580             -1.319099        -1.236879   \n",
       "3           -2.213549             -1.318604        -1.099359   \n",
       "4           -2.092557             -1.301442        -0.830364   \n",
       "..                ...                   ...              ...   \n",
       "115         -1.142105              2.072720        -0.254972   \n",
       "116         -1.551001              2.455236        -0.014690   \n",
       "117         -1.313044              2.627352         0.066159   \n",
       "118         -1.039876              2.531806        -0.548902   \n",
       "119         -1.036216              2.505402        -0.828097   \n",
       "\n",
       "     current_account_deficit  exports_usd  imports_usd  \\\n",
       "0                   1.221772    -2.141114    -2.739417   \n",
       "1                   1.298369    -1.845113    -2.804266   \n",
       "2                   0.902618    -1.995435    -2.349681   \n",
       "3                   0.773254    -2.314641    -2.475851   \n",
       "4                   0.783041    -2.430375    -2.241118   \n",
       "..                       ...          ...          ...   \n",
       "115                 2.512856     0.249743    -1.004973   \n",
       "116                 2.561368     1.360291    -0.526633   \n",
       "117                 3.034567     2.047862    -0.574494   \n",
       "118                 2.386897     1.948223    -0.577690   \n",
       "119                 1.131132     1.044437    -0.455097   \n",
       "\n",
       "     export_unit_value_index_usd       gdp  gold_price_lon  \\\n",
       "0                      -0.828772 -2.096867       -2.092608   \n",
       "1                      -1.415554 -2.695456       -1.720218   \n",
       "2                      -1.569235 -3.218655       -1.777501   \n",
       "3                      -1.303786 -2.676823       -1.976016   \n",
       "4                      -1.010395 -2.247210       -1.741627   \n",
       "..                           ...       ...             ...   \n",
       "115                    -0.974070 -1.370257       -0.488242   \n",
       "116                    -1.025763 -1.756830       -0.518041   \n",
       "117                    -1.144516 -1.234123       -0.399376   \n",
       "118                    -1.197606 -0.642122       -0.398846   \n",
       "119                    -1.210180 -0.571092       -0.241992   \n",
       "\n",
       "     import_unit_value_index_usd  ...  MIG_NRG_X_E  MIG_NDCOG   MIG_ING  \\\n",
       "0                      -0.588712  ...    -1.227747  -1.759637 -2.323153   \n",
       "1                      -0.750302  ...    -1.983658  -1.996549 -2.412643   \n",
       "2                      -0.786211  ...    -1.601638  -1.663744 -2.009938   \n",
       "3                      -0.840074  ...    -1.821097  -1.675025 -1.875703   \n",
       "4                      -1.127346  ...    -1.666663  -1.567851 -1.724688   \n",
       "..                           ...  ...          ...        ...       ...   \n",
       "115                    -0.587814  ...     2.324225   0.440262  0.423074   \n",
       "116                    -0.608462  ...     1.503289   1.376629  1.178146   \n",
       "117                    -0.500735  ...     1.145653   2.087365  1.256450   \n",
       "118                    -0.557292  ...     1.129397   1.827890  0.982387   \n",
       "119                    -0.618337  ...     1.633338   1.675590  0.792220   \n",
       "\n",
       "     MIG_DCOG   MIG_CAG  interest_rates_personal  money_supply  \\\n",
       "0   -2.636450 -2.043352                 1.249452     -2.579183   \n",
       "1   -2.433604 -1.933180                 0.944193     -2.507623   \n",
       "2   -1.872795 -1.693839                 1.016349     -2.552323   \n",
       "3   -1.848930 -1.617858                 0.402196     -2.366376   \n",
       "4   -1.341816 -1.405111                 0.623291     -2.201450   \n",
       "..        ...       ...                      ...           ...   \n",
       "115  0.233223  0.372845                 2.563463      0.388743   \n",
       "116  2.040938  1.117460                 4.234730     -0.497138   \n",
       "117  1.378706  1.504963                 4.744229     -0.457400   \n",
       "118  1.903719  1.341604                 4.166540     -0.299321   \n",
       "119  1.384672  1.960850                 3.612866     -0.024454   \n",
       "\n",
       "     real_effective_echange_rates  retail_sales  unemployment_30-34  \n",
       "0                        0.873110      0.996852            2.718712  \n",
       "1                        0.670596     -0.522569            3.432172  \n",
       "2                        0.492713      0.180319            3.172732  \n",
       "3                        0.922369      0.461135            2.199833  \n",
       "4                        1.076535     -0.633938            1.162074  \n",
       "..                            ...           ...                 ...  \n",
       "115                     -2.783985     -0.615342            0.124315  \n",
       "116                     -3.005654     -0.835622            0.513475  \n",
       "117                     -2.456497     -0.348721            0.513475  \n",
       "118                     -2.045998      0.372397            1.162074  \n",
       "119                     -2.009509      1.107910            1.875533  \n",
       "\n",
       "[120 rows x 22 columns]"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_non_outliers_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8721bb3e",
   "metadata": {},
   "source": [
    "## Forming Y values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d131efef",
   "metadata": {},
   "source": [
    "#### merged_y_data is here : https://github.com/YZouzou/CS512-project/blob/main/cleaned_data/merged_y_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3e3fe7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_Y = pd.read_excel(\"C:/Users/suuser/Yvariables.xlsx\",index_col=[0])\n",
    "\n",
    "path = 'cleaned_data/merged_y_data.csv'\n",
    "df_Y = pd.read_csv(path, parse_dates=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4057b41c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>ticker</th>\n",
       "      <th>firm</th>\n",
       "      <th>marketcap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>ALYAG</td>\n",
       "      <td>ALTINYAĞ</td>\n",
       "      <td>1.077940e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>AEFES</td>\n",
       "      <td>ANADOLU EFES</td>\n",
       "      <td>4.995000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>BANVT</td>\n",
       "      <td>BANVİT</td>\n",
       "      <td>1.370000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>CCOLA</td>\n",
       "      <td>COCA COLA İÇECEK</td>\n",
       "      <td>1.958655e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>ERSU</td>\n",
       "      <td>ERSU GIDA</td>\n",
       "      <td>1.116000e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  year  month ticker              firm     marketcap\n",
       "0 2009-01-01  2009      1  ALYAG          ALTINYAĞ  1.077940e+04\n",
       "1 2009-01-01  2009      1  AEFES      ANADOLU EFES  4.995000e+06\n",
       "2 2009-01-01  2009      1  BANVT           BANVİT   1.370000e+05\n",
       "3 2009-01-01  2009      1  CCOLA  COCA COLA İÇECEK  1.958655e+06\n",
       "4 2009-01-01  2009      1   ERSU         ERSU GIDA  1.116000e+04"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8513687c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting market caps to USD\n",
    "# Market caps are measured in 1000 TL\n",
    "df_Y = df_Y.merge(df_copy[['date', 'ExchangeRate']], on='date')\n",
    "\n",
    "df_Y['marketcap'] = 1000 * df_Y['marketcap'] / df_Y['ExchangeRate']\n",
    "df_Y = df_Y.drop(columns=['ExchangeRate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3762264c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving processed Y data\n",
    "df_Y.to_csv('processed_data/target_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "75d86b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot = pd.get_dummies(df_Y['ticker'])\n",
    "df_Y = df_Y.drop('ticker',axis = 1)\n",
    "df_Y = df_Y.join(one_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a2835e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>firm</th>\n",
       "      <th>marketcap</th>\n",
       "      <th>ADANA</th>\n",
       "      <th>ADBGR</th>\n",
       "      <th>ADEL</th>\n",
       "      <th>ADNAC</th>\n",
       "      <th>AEFES</th>\n",
       "      <th>...</th>\n",
       "      <th>VAKBN</th>\n",
       "      <th>VAKFN</th>\n",
       "      <th>VAKKO</th>\n",
       "      <th>VESBE</th>\n",
       "      <th>VESTL</th>\n",
       "      <th>VKGYO</th>\n",
       "      <th>VKING</th>\n",
       "      <th>YKBNK</th>\n",
       "      <th>YKGYO</th>\n",
       "      <th>ZOREN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>ALTINYAĞ</td>\n",
       "      <td>6.783538e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>ANADOLU EFES</td>\n",
       "      <td>3.143383e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 220 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  year  month          firm     marketcap  ADANA  ADBGR  ADEL  \\\n",
       "0 2009-01-01  2009      1      ALTINYAĞ  6.783538e+06      0      0     0   \n",
       "1 2009-01-01  2009      1  ANADOLU EFES  3.143383e+09      0      0     0   \n",
       "\n",
       "   ADNAC  AEFES  ...  VAKBN  VAKFN  VAKKO  VESBE  VESTL  VKGYO  VKING  YKBNK  \\\n",
       "0      0      0  ...      0      0      0      0      0      0      0      0   \n",
       "1      0      1  ...      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   YKGYO  ZOREN  \n",
       "0      0      0  \n",
       "1      0      0  \n",
       "\n",
       "[2 rows x 220 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Y.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee2a8ef",
   "metadata": {},
   "source": [
    "## Lag effect conversion and Aligning Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "58054569",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking dates between 2009-2018\n",
    "df_Y_1 = df_Y[:-2580]\n",
    "listedate = list(df_Y_1['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "942a9e66",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Shifting 6 months for lag effect\n",
    "df_Y_2 = df_Y[1290:-1290]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "c684cde7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>firm</th>\n",
       "      <th>marketcap</th>\n",
       "      <th>ADANA</th>\n",
       "      <th>ADBGR</th>\n",
       "      <th>ADEL</th>\n",
       "      <th>ADNAC</th>\n",
       "      <th>AEFES</th>\n",
       "      <th>...</th>\n",
       "      <th>VAKBN</th>\n",
       "      <th>VAKFN</th>\n",
       "      <th>VAKKO</th>\n",
       "      <th>VESBE</th>\n",
       "      <th>VESTL</th>\n",
       "      <th>VKGYO</th>\n",
       "      <th>VKING</th>\n",
       "      <th>YKBNK</th>\n",
       "      <th>YKGYO</th>\n",
       "      <th>ZOREN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>ALTINYAĞ</td>\n",
       "      <td>1.686008e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>ANADOLU EFES</td>\n",
       "      <td>7.110000e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>BANVİT</td>\n",
       "      <td>2.960698e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>COCA COLA İÇECEK</td>\n",
       "      <td>2.238463e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>ERSU GIDA</td>\n",
       "      <td>2.016000e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27085</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>LINK BILGISAYAR</td>\n",
       "      <td>5.219500e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27086</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>LOGO YAZILIM</td>\n",
       "      <td>1.042500e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27087</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>NETAS TELEKOM.</td>\n",
       "      <td>4.475671e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27088</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>PLASTIKKART</td>\n",
       "      <td>6.620250e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27089</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>ASELSAN</td>\n",
       "      <td>2.052000e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25800 rows × 220 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  year  month              firm     marketcap  ADANA  ADBGR  \\\n",
       "1290   2009-07-01  2009      7          ALTINYAĞ  1.686008e+04      0      0   \n",
       "1291   2009-07-01  2009      7      ANADOLU EFES  7.110000e+06      0      0   \n",
       "1292   2009-07-01  2009      7           BANVİT   2.960698e+05      0      0   \n",
       "1293   2009-07-01  2009      7  COCA COLA İÇECEK  2.238463e+06      0      0   \n",
       "1294   2009-07-01  2009      7         ERSU GIDA  2.016000e+04      0      0   \n",
       "...           ...   ...    ...               ...           ...    ...    ...   \n",
       "27085  2019-06-01  2019      6   LINK BILGISAYAR  5.219500e+04      0      0   \n",
       "27086  2019-06-01  2019      6      LOGO YAZILIM  1.042500e+06      0      0   \n",
       "27087  2019-06-01  2019      6    NETAS TELEKOM.  4.475671e+05      0      0   \n",
       "27088  2019-06-01  2019      6       PLASTIKKART  6.620250e+04      0      0   \n",
       "27089  2019-06-01  2019      6           ASELSAN  2.052000e+07      0      0   \n",
       "\n",
       "       ADEL  ADNAC  AEFES  ...  VAKBN  VAKFN  VAKKO  VESBE  VESTL  VKGYO  \\\n",
       "1290      0      0      0  ...      0      0      0      0      0      0   \n",
       "1291      0      0      1  ...      0      0      0      0      0      0   \n",
       "1292      0      0      0  ...      0      0      0      0      0      0   \n",
       "1293      0      0      0  ...      0      0      0      0      0      0   \n",
       "1294      0      0      0  ...      0      0      0      0      0      0   \n",
       "...     ...    ...    ...  ...    ...    ...    ...    ...    ...    ...   \n",
       "27085     0      0      0  ...      0      0      0      0      0      0   \n",
       "27086     0      0      0  ...      0      0      0      0      0      0   \n",
       "27087     0      0      0  ...      0      0      0      0      0      0   \n",
       "27088     0      0      0  ...      0      0      0      0      0      0   \n",
       "27089     0      0      0  ...      0      0      0      0      0      0   \n",
       "\n",
       "       VKING  YKBNK  YKGYO  ZOREN  \n",
       "1290       0      0      0      0  \n",
       "1291       0      0      0      0  \n",
       "1292       0      0      0      0  \n",
       "1293       0      0      0      0  \n",
       "1294       0      0      0      0  \n",
       "...      ...    ...    ...    ...  \n",
       "27085      0      0      0      0  \n",
       "27086      0      0      0      0  \n",
       "27087      0      0      0      0  \n",
       "27088      0      0      0      0  \n",
       "27089      0      0      0      0  \n",
       "\n",
       "[25800 rows x 220 columns]"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Y_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "e46cbe72",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\suuser\\AppData\\Local\\Temp\\ipykernel_6068\\3795674812.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_Y_2['date'] = listedate\n"
     ]
    }
   ],
   "source": [
    "# Making date columns values starting from 2009-01 ending with 2018-01\n",
    "df_Y_2['date'] = listedate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "e13f2bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>firm</th>\n",
       "      <th>marketcap</th>\n",
       "      <th>ADANA</th>\n",
       "      <th>ADBGR</th>\n",
       "      <th>ADEL</th>\n",
       "      <th>ADNAC</th>\n",
       "      <th>AEFES</th>\n",
       "      <th>...</th>\n",
       "      <th>VAKBN</th>\n",
       "      <th>VAKFN</th>\n",
       "      <th>VAKKO</th>\n",
       "      <th>VESBE</th>\n",
       "      <th>VESTL</th>\n",
       "      <th>VKGYO</th>\n",
       "      <th>VKING</th>\n",
       "      <th>YKBNK</th>\n",
       "      <th>YKGYO</th>\n",
       "      <th>ZOREN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>ALTINYAĞ</td>\n",
       "      <td>1.686008e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>ANADOLU EFES</td>\n",
       "      <td>7.110000e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>BANVİT</td>\n",
       "      <td>2.960698e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>COCA COLA İÇECEK</td>\n",
       "      <td>2.238463e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>ERSU GIDA</td>\n",
       "      <td>2.016000e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27085</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>LINK BILGISAYAR</td>\n",
       "      <td>5.219500e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27086</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>LOGO YAZILIM</td>\n",
       "      <td>1.042500e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27087</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>NETAS TELEKOM.</td>\n",
       "      <td>4.475671e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27088</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>PLASTIKKART</td>\n",
       "      <td>6.620250e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27089</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>ASELSAN</td>\n",
       "      <td>2.052000e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25800 rows × 220 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  year  month              firm     marketcap  ADANA  ADBGR  \\\n",
       "1290   2009-01-01  2009      7          ALTINYAĞ  1.686008e+04      0      0   \n",
       "1291   2009-01-01  2009      7      ANADOLU EFES  7.110000e+06      0      0   \n",
       "1292   2009-01-01  2009      7           BANVİT   2.960698e+05      0      0   \n",
       "1293   2009-01-01  2009      7  COCA COLA İÇECEK  2.238463e+06      0      0   \n",
       "1294   2009-01-01  2009      7         ERSU GIDA  2.016000e+04      0      0   \n",
       "...           ...   ...    ...               ...           ...    ...    ...   \n",
       "27085  2018-12-01  2019      6   LINK BILGISAYAR  5.219500e+04      0      0   \n",
       "27086  2018-12-01  2019      6      LOGO YAZILIM  1.042500e+06      0      0   \n",
       "27087  2018-12-01  2019      6    NETAS TELEKOM.  4.475671e+05      0      0   \n",
       "27088  2018-12-01  2019      6       PLASTIKKART  6.620250e+04      0      0   \n",
       "27089  2018-12-01  2019      6           ASELSAN  2.052000e+07      0      0   \n",
       "\n",
       "       ADEL  ADNAC  AEFES  ...  VAKBN  VAKFN  VAKKO  VESBE  VESTL  VKGYO  \\\n",
       "1290      0      0      0  ...      0      0      0      0      0      0   \n",
       "1291      0      0      1  ...      0      0      0      0      0      0   \n",
       "1292      0      0      0  ...      0      0      0      0      0      0   \n",
       "1293      0      0      0  ...      0      0      0      0      0      0   \n",
       "1294      0      0      0  ...      0      0      0      0      0      0   \n",
       "...     ...    ...    ...  ...    ...    ...    ...    ...    ...    ...   \n",
       "27085     0      0      0  ...      0      0      0      0      0      0   \n",
       "27086     0      0      0  ...      0      0      0      0      0      0   \n",
       "27087     0      0      0  ...      0      0      0      0      0      0   \n",
       "27088     0      0      0  ...      0      0      0      0      0      0   \n",
       "27089     0      0      0  ...      0      0      0      0      0      0   \n",
       "\n",
       "       VKING  YKBNK  YKGYO  ZOREN  \n",
       "1290       0      0      0      0  \n",
       "1291       0      0      0      0  \n",
       "1292       0      0      0      0  \n",
       "1293       0      0      0      0  \n",
       "1294       0      0      0      0  \n",
       "...      ...    ...    ...    ...  \n",
       "27085      0      0      0      0  \n",
       "27086      0      0      0      0  \n",
       "27087      0      0      0      0  \n",
       "27088      0      0      0      0  \n",
       "27089      0      0      0      0  \n",
       "\n",
       "[25800 rows x 220 columns]"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Y_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "6c97076a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = []\n",
    "for i in range(len(df_Y_2)):\n",
    "    if df_Y_2.iloc[i]['date'] not in dates:\n",
    "        dates.append(df_Y_2.iloc[i]['date'])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "48749d2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_non_outliers_scaled['date'] = dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "bc731c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = dfYY.merge(df_non_outliers_scaled, on='date', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "ee4b3937",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop(['date','year','month','firm'],axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "c971854c",
   "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>marketcap</th>\n",
       "      <th>ADANA</th>\n",
       "      <th>ADBGR</th>\n",
       "      <th>ADEL</th>\n",
       "      <th>ADNAC</th>\n",
       "      <th>AEFES</th>\n",
       "      <th>AFYON</th>\n",
       "      <th>AGYO</th>\n",
       "      <th>AKBNK</th>\n",
       "      <th>AKCNS</th>\n",
       "      <th>...</th>\n",
       "      <th>MIG_NRG_X_E</th>\n",
       "      <th>MIG_NDCOG</th>\n",
       "      <th>MIG_ING</th>\n",
       "      <th>MIG_DCOG</th>\n",
       "      <th>MIG_CAG</th>\n",
       "      <th>interest_rates_personal</th>\n",
       "      <th>money_supply</th>\n",
       "      <th>real_effective_echange_rates</th>\n",
       "      <th>retail_sales</th>\n",
       "      <th>unemployment_30-34</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.686008e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.227747</td>\n",
       "      <td>-1.759637</td>\n",
       "      <td>-2.323153</td>\n",
       "      <td>-2.636450</td>\n",
       "      <td>-2.043352</td>\n",
       "      <td>1.249452</td>\n",
       "      <td>-2.579183</td>\n",
       "      <td>0.873110</td>\n",
       "      <td>0.996852</td>\n",
       "      <td>2.718712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.110000e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.227747</td>\n",
       "      <td>-1.759637</td>\n",
       "      <td>-2.323153</td>\n",
       "      <td>-2.636450</td>\n",
       "      <td>-2.043352</td>\n",
       "      <td>1.249452</td>\n",
       "      <td>-2.579183</td>\n",
       "      <td>0.873110</td>\n",
       "      <td>0.996852</td>\n",
       "      <td>2.718712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.960698e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.227747</td>\n",
       "      <td>-1.759637</td>\n",
       "      <td>-2.323153</td>\n",
       "      <td>-2.636450</td>\n",
       "      <td>-2.043352</td>\n",
       "      <td>1.249452</td>\n",
       "      <td>-2.579183</td>\n",
       "      <td>0.873110</td>\n",
       "      <td>0.996852</td>\n",
       "      <td>2.718712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.238463e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.227747</td>\n",
       "      <td>-1.759637</td>\n",
       "      <td>-2.323153</td>\n",
       "      <td>-2.636450</td>\n",
       "      <td>-2.043352</td>\n",
       "      <td>1.249452</td>\n",
       "      <td>-2.579183</td>\n",
       "      <td>0.873110</td>\n",
       "      <td>0.996852</td>\n",
       "      <td>2.718712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.016000e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.227747</td>\n",
       "      <td>-1.759637</td>\n",
       "      <td>-2.323153</td>\n",
       "      <td>-2.636450</td>\n",
       "      <td>-2.043352</td>\n",
       "      <td>1.249452</td>\n",
       "      <td>-2.579183</td>\n",
       "      <td>0.873110</td>\n",
       "      <td>0.996852</td>\n",
       "      <td>2.718712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25795</th>\n",
       "      <td>5.219500e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.633338</td>\n",
       "      <td>1.675590</td>\n",
       "      <td>0.792220</td>\n",
       "      <td>1.384672</td>\n",
       "      <td>1.960850</td>\n",
       "      <td>3.612866</td>\n",
       "      <td>-0.024454</td>\n",
       "      <td>-2.009509</td>\n",
       "      <td>1.107910</td>\n",
       "      <td>1.875533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25796</th>\n",
       "      <td>1.042500e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.633338</td>\n",
       "      <td>1.675590</td>\n",
       "      <td>0.792220</td>\n",
       "      <td>1.384672</td>\n",
       "      <td>1.960850</td>\n",
       "      <td>3.612866</td>\n",
       "      <td>-0.024454</td>\n",
       "      <td>-2.009509</td>\n",
       "      <td>1.107910</td>\n",
       "      <td>1.875533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25797</th>\n",
       "      <td>4.475671e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.633338</td>\n",
       "      <td>1.675590</td>\n",
       "      <td>0.792220</td>\n",
       "      <td>1.384672</td>\n",
       "      <td>1.960850</td>\n",
       "      <td>3.612866</td>\n",
       "      <td>-0.024454</td>\n",
       "      <td>-2.009509</td>\n",
       "      <td>1.107910</td>\n",
       "      <td>1.875533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25798</th>\n",
       "      <td>6.620250e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.633338</td>\n",
       "      <td>1.675590</td>\n",
       "      <td>0.792220</td>\n",
       "      <td>1.384672</td>\n",
       "      <td>1.960850</td>\n",
       "      <td>3.612866</td>\n",
       "      <td>-0.024454</td>\n",
       "      <td>-2.009509</td>\n",
       "      <td>1.107910</td>\n",
       "      <td>1.875533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25799</th>\n",
       "      <td>2.052000e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.633338</td>\n",
       "      <td>1.675590</td>\n",
       "      <td>0.792220</td>\n",
       "      <td>1.384672</td>\n",
       "      <td>1.960850</td>\n",
       "      <td>3.612866</td>\n",
       "      <td>-0.024454</td>\n",
       "      <td>-2.009509</td>\n",
       "      <td>1.107910</td>\n",
       "      <td>1.875533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25800 rows × 238 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          marketcap  ADANA  ADBGR  ADEL  ADNAC  AEFES  AFYON  AGYO  AKBNK  \\\n",
       "0      1.686008e+04      0      0     0      0      0      0     0      0   \n",
       "1      7.110000e+06      0      0     0      0      1      0     0      0   \n",
       "2      2.960698e+05      0      0     0      0      0      0     0      0   \n",
       "3      2.238463e+06      0      0     0      0      0      0     0      0   \n",
       "4      2.016000e+04      0      0     0      0      0      0     0      0   \n",
       "...             ...    ...    ...   ...    ...    ...    ...   ...    ...   \n",
       "25795  5.219500e+04      0      0     0      0      0      0     0      0   \n",
       "25796  1.042500e+06      0      0     0      0      0      0     0      0   \n",
       "25797  4.475671e+05      0      0     0      0      0      0     0      0   \n",
       "25798  6.620250e+04      0      0     0      0      0      0     0      0   \n",
       "25799  2.052000e+07      0      0     0      0      0      0     0      0   \n",
       "\n",
       "       AKCNS  ...  MIG_NRG_X_E  MIG_NDCOG   MIG_ING  MIG_DCOG   MIG_CAG  \\\n",
       "0          0  ...    -1.227747  -1.759637 -2.323153 -2.636450 -2.043352   \n",
       "1          0  ...    -1.227747  -1.759637 -2.323153 -2.636450 -2.043352   \n",
       "2          0  ...    -1.227747  -1.759637 -2.323153 -2.636450 -2.043352   \n",
       "3          0  ...    -1.227747  -1.759637 -2.323153 -2.636450 -2.043352   \n",
       "4          0  ...    -1.227747  -1.759637 -2.323153 -2.636450 -2.043352   \n",
       "...      ...  ...          ...        ...       ...       ...       ...   \n",
       "25795      0  ...     1.633338   1.675590  0.792220  1.384672  1.960850   \n",
       "25796      0  ...     1.633338   1.675590  0.792220  1.384672  1.960850   \n",
       "25797      0  ...     1.633338   1.675590  0.792220  1.384672  1.960850   \n",
       "25798      0  ...     1.633338   1.675590  0.792220  1.384672  1.960850   \n",
       "25799      0  ...     1.633338   1.675590  0.792220  1.384672  1.960850   \n",
       "\n",
       "       interest_rates_personal  money_supply  real_effective_echange_rates  \\\n",
       "0                     1.249452     -2.579183                      0.873110   \n",
       "1                     1.249452     -2.579183                      0.873110   \n",
       "2                     1.249452     -2.579183                      0.873110   \n",
       "3                     1.249452     -2.579183                      0.873110   \n",
       "4                     1.249452     -2.579183                      0.873110   \n",
       "...                        ...           ...                           ...   \n",
       "25795                 3.612866     -0.024454                     -2.009509   \n",
       "25796                 3.612866     -0.024454                     -2.009509   \n",
       "25797                 3.612866     -0.024454                     -2.009509   \n",
       "25798                 3.612866     -0.024454                     -2.009509   \n",
       "25799                 3.612866     -0.024454                     -2.009509   \n",
       "\n",
       "       retail_sales  unemployment_30-34  \n",
       "0          0.996852            2.718712  \n",
       "1          0.996852            2.718712  \n",
       "2          0.996852            2.718712  \n",
       "3          0.996852            2.718712  \n",
       "4          0.996852            2.718712  \n",
       "...             ...                 ...  \n",
       "25795      1.107910            1.875533  \n",
       "25796      1.107910            1.875533  \n",
       "25797      1.107910            1.875533  \n",
       "25798      1.107910            1.875533  \n",
       "25799      1.107910            1.875533  \n",
       "\n",
       "[25800 rows x 238 columns]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37f0d41",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "4b34f659",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = final_df.drop(columns='marketcap')\n",
    "y = final_df['marketcap']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "b950f9fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "8f3124ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "4f818569",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2965800415681.8545\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "446458a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9028061816273911"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "c2910f12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2965800415681.8545"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "mse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "41c75ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1722149.9399535032"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "\n",
    "rmse = sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "e19167f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784047.5780643439"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "fb7be859",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              Coefficients\n",
      "ADANA                        -1.743238e+06\n",
      "ADBGR                        -1.887961e+06\n",
      "ADEL                         -1.843468e+06\n",
      "ADNAC                        -2.077288e+06\n",
      "AEFES                         1.020793e+07\n",
      "...                                    ...\n",
      "interest_rates_personal      -1.531761e+05\n",
      "money_supply                  3.006510e+05\n",
      "real_effective_echange_rates  4.508123e+03\n",
      "retail_sales                 -9.343393e+03\n",
      "unemployment_30-34            5.953997e+04\n",
      "\n",
      "[237 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "cdf = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficients'])\n",
    "print(cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "c3d60d5a",
   "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>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>unemployment_30-34</th>\n",
       "      <td>59539.972110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retail_sales</th>\n",
       "      <td>-9343.393274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>real_effective_echange_rates</th>\n",
       "      <td>4508.122897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>money_supply</th>\n",
       "      <td>300650.954737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interest_rates_personal</th>\n",
       "      <td>-153176.101994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIG_CAG</th>\n",
       "      <td>116961.466362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIG_DCOG</th>\n",
       "      <td>-109680.206074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIG_ING</th>\n",
       "      <td>167828.921039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIG_NDCOG</th>\n",
       "      <td>53244.818279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIG_NRG_X_E</th>\n",
       "      <td>74639.587379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-311451.781476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-61391.760773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>import_unit_value_index_usd</th>\n",
       "      <td>130095.113162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gold_price_lon</th>\n",
       "      <td>83560.934697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdp</th>\n",
       "      <td>35853.295896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>export_unit_value_index_usd</th>\n",
       "      <td>-422041.745182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imports_usd</th>\n",
       "      <td>265046.486115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exports_usd</th>\n",
       "      <td>39845.721593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current_account_deficit</th>\n",
       "      <td>188257.300263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brent_oil_price</th>\n",
       "      <td>134758.540026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consumer_price_index</th>\n",
       "      <td>241871.052231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Coefficients\n",
       "unemployment_30-34             59539.972110\n",
       "retail_sales                   -9343.393274\n",
       "real_effective_echange_rates    4508.122897\n",
       "money_supply                  300650.954737\n",
       "interest_rates_personal      -153176.101994\n",
       "MIG_CAG                       116961.466362\n",
       "MIG_DCOG                     -109680.206074\n",
       "MIG_ING                       167828.921039\n",
       "MIG_NDCOG                      53244.818279\n",
       "MIG_NRG_X_E                    74639.587379\n",
       "C                            -311451.781476\n",
       "B                             -61391.760773\n",
       "import_unit_value_index_usd   130095.113162\n",
       "gold_price_lon                 83560.934697\n",
       "gdp                            35853.295896\n",
       "export_unit_value_index_usd  -422041.745182\n",
       "imports_usd                   265046.486115\n",
       "exports_usd                    39845.721593\n",
       "current_account_deficit       188257.300263\n",
       "brent_oil_price               134758.540026\n",
       "consumer_price_index          241871.052231"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf[:-22:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "87ad9c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GARAN    3.106540e+07\n",
       "AKBNK    2.897648e+07\n",
       "KCHOL    2.502621e+07\n",
       "TCELL    2.253687e+07\n",
       "ISCTR    2.183680e+07\n",
       "             ...     \n",
       "MERKO   -2.187878e+06\n",
       "ATLAS   -2.196453e+06\n",
       "OYAYO   -2.204303e+06\n",
       "METUR   -2.204988e+06\n",
       "EMKEL   -2.217796e+06\n",
       "Name: Coefficients, Length: 237, dtype: float64"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf['Coefficients'].sort_values(ascending=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}