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    "# Structure of the notebook \n",
    "\n",
    "### I. Feature engineering.\n",
    "\n",
    "#### 1) Difference between off-peak prices in December and preceding January.\n",
    "#### 2). Getting time related features.\n",
    "#### 3). Getting consumption based features.\n",
    "\n",
    "### II. Choosing the evaluation metric and treatment of outliers..\n",
    "\n",
    "#### 1). The choice of the evaluation metric.\n",
    "#### 2). Handling outliers.\n",
    "\n",
    "###  III. Correlation based feature selection.\n",
    "\n",
    "###  IV. Tuning and training the Random Forest model.\n",
    "\n",
    "#### 1). The reasons why the Random Forest model is a good choice for our case.\n",
    "#### 2). Tuning and training the model.\n",
    "#### 3). Evaluating of the model.\n",
    "\n",
    "### V. Model interpretation using SHAP\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "qLHsMX9iKUj4"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
    "from sklearn.feature_selection import SelectKBest, mutual_info_classif\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report\n",
    "from feature_engine.encoding import OneHotEncoder\n",
    "from feature_engine.encoding import OrdinalEncoder\n",
    "from feature_engine.encoding import CountFrequencyEncoder\n",
    "from feature_engine.encoding import MeanEncoder\n",
    "from feature_engine.encoding import PRatioEncoder\n",
    "from feature_engine.selection import DropConstantFeatures, DropDuplicateFeatures, SmartCorrelatedSelection\n",
    "from feature_engine.wrappers import SklearnTransformerWrapper\n",
    "import optuna\n",
    "import shap\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "iTGEPqJpKepD"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('clean_data_after_eda.csv')\n",
    "df[\"date_activ\"] = pd.to_datetime(df[\"date_activ\"], format='%Y-%m-%d')\n",
    "df[\"date_end\"] = pd.to_datetime(df[\"date_end\"], format='%Y-%m-%d')\n",
    "df[\"date_modif_prod\"] = pd.to_datetime(df[\"date_modif_prod\"], format='%Y-%m-%d')\n",
    "df[\"date_renewal\"] = pd.to_datetime(df[\"date_renewal\"], format='%Y-%m-%d')"
   ]
  },
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       "   Unnamed: 0                                id  cons_12m  cons_gas_12m  \\\n",
       "0           0  24011ae4ebbe3035111d65fa7c15bc57         0         54946   \n",
       "1           1  d29c2c54acc38ff3c0614d0a653813dd      4660             0   \n",
       "2           2  764c75f661154dac3a6c254cd082ea7d       544             0   \n",
       "\n",
       "   cons_last_month date_activ   date_end date_modif_prod date_renewal  \\\n",
       "0                0 2013-06-15 2016-06-15      2015-11-01   2015-06-23   \n",
       "1                0 2009-08-21 2016-08-30      2009-08-21   2015-08-31   \n",
       "2                0 2010-04-16 2016-04-16      2010-04-16   2015-04-17   \n",
       "\n",
       "   forecast_cons_12m  ...  churn  price_off_peak_var  price_peak_var  \\\n",
       "0               0.00  ...      1            0.124787        0.100749   \n",
       "1             189.95  ...      0            0.149609        0.007124   \n",
       "2              47.96  ...      0            0.170512        0.088421   \n",
       "\n",
       "   price_mid_peak_var  price_off_peak_fix  price_peak_fix price_mid_peak_fix  \\\n",
       "0             0.06653           40.942265        22.35201           14.90134   \n",
       "1             0.00000           44.311375         0.00000            0.00000   \n",
       "2             0.00000           44.385450         0.00000            0.00000   \n",
       "\n",
       "   forcasted_price  previous_price  price_sens  \n",
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    "df.head(3)"
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       "                                 id  cons_12m  cons_gas_12m  cons_last_month  \\\n",
       "0  24011ae4ebbe3035111d65fa7c15bc57         0         54946                0   \n",
       "1  d29c2c54acc38ff3c0614d0a653813dd      4660             0                0   \n",
       "2  764c75f661154dac3a6c254cd082ea7d       544             0                0   \n",
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       "4  149d57cf92fc41cf94415803a877cb4b      4425             0              526   \n",
       "\n",
       "  date_activ   date_end date_modif_prod date_renewal  forecast_cons_12m  \\\n",
       "0 2013-06-15 2016-06-15      2015-11-01   2015-06-23               0.00   \n",
       "1 2009-08-21 2016-08-30      2009-08-21   2015-08-31             189.95   \n",
       "2 2010-04-16 2016-04-16      2010-04-16   2015-04-17              47.96   \n",
       "3 2010-03-30 2016-03-30      2010-03-30   2015-03-31             240.04   \n",
       "4 2010-01-13 2016-03-07      2010-01-13   2015-03-09             445.75   \n",
       "\n",
       "   forecast_cons_year  ...  churn  price_off_peak_var  price_peak_var  \\\n",
       "0                   0  ...      1            0.124787        0.100749   \n",
       "1                   0  ...      0            0.149609        0.007124   \n",
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       "3                   0  ...      0            0.151210        0.000000   \n",
       "4                 526  ...      0            0.124174        0.103638   \n",
       "\n",
       "   price_mid_peak_var  price_off_peak_fix price_peak_fix  price_mid_peak_fix  \\\n",
       "0            0.066530           40.942265      22.352010           14.901340   \n",
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       "\n",
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    "id": "ZdHM5Q1MOPj6"
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   "source": [
    "### I. Feature engineering\n",
    "\n",
    "#### 1). Difference between off-peak prices in December and preceding January\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "id": "Q03l5QsROoi-",
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       "      <th>price_off_peak_fix</th>\n",
       "      <th>price_peak_fix</th>\n",
       "      <th>price_mid_peak_fix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>0.151367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>0.151367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>0.151367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>0.149626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>0.149626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id price_date  price_off_peak_var  \\\n",
       "0  038af19179925da21a25619c5a24b745 2015-01-01            0.151367   \n",
       "1  038af19179925da21a25619c5a24b745 2015-02-01            0.151367   \n",
       "2  038af19179925da21a25619c5a24b745 2015-03-01            0.151367   \n",
       "3  038af19179925da21a25619c5a24b745 2015-04-01            0.149626   \n",
       "4  038af19179925da21a25619c5a24b745 2015-05-01            0.149626   \n",
       "\n",
       "   price_peak_var  price_mid_peak_var  price_off_peak_fix  price_peak_fix  \\\n",
       "0             0.0                 0.0           44.266931             0.0   \n",
       "1             0.0                 0.0           44.266931             0.0   \n",
       "2             0.0                 0.0           44.266931             0.0   \n",
       "3             0.0                 0.0           44.266931             0.0   \n",
       "4             0.0                 0.0           44.266931             0.0   \n",
       "\n",
       "   price_mid_peak_fix  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_df = pd.read_csv('price_data.csv')\n",
    "price_df[\"price_date\"] = pd.to_datetime(price_df[\"price_date\"], format='%Y-%m-%d')\n",
    "price_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z1XVCviFOvg1",
    "outputId": "64477797-8a7f-48b8-a40f-41e8c1a3527a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14542 entries, 0 to 14541\n",
      "Data columns (total 34 columns):\n",
      " #   Column                          Non-Null Count  Dtype         \n",
      "---  ------                          --------------  -----         \n",
      " 0   id                              14542 non-null  object        \n",
      " 1   cons_12m                        14542 non-null  int64         \n",
      " 2   cons_gas_12m                    14542 non-null  int64         \n",
      " 3   cons_last_month                 14542 non-null  int64         \n",
      " 4   date_activ                      14542 non-null  datetime64[ns]\n",
      " 5   date_end                        14542 non-null  datetime64[ns]\n",
      " 6   date_modif_prod                 14542 non-null  datetime64[ns]\n",
      " 7   date_renewal                    14542 non-null  datetime64[ns]\n",
      " 8   forecast_cons_12m               14542 non-null  float64       \n",
      " 9   forecast_cons_year              14542 non-null  int64         \n",
      " 10  forecast_discount_energy        14542 non-null  float64       \n",
      " 11  forecast_meter_rent_12m         14542 non-null  float64       \n",
      " 12  forecast_price_energy_off_peak  14542 non-null  float64       \n",
      " 13  forecast_price_energy_peak      14542 non-null  float64       \n",
      " 14  forecast_price_pow_off_peak     14542 non-null  float64       \n",
      " 15  has_gas                         14542 non-null  object        \n",
      " 16  imp_cons                        14542 non-null  float64       \n",
      " 17  margin_gross_pow_ele            14542 non-null  float64       \n",
      " 18  margin_net_pow_ele              14542 non-null  float64       \n",
      " 19  nb_prod_act                     14542 non-null  int64         \n",
      " 20  net_margin                      14542 non-null  float64       \n",
      " 21  num_years_antig                 14542 non-null  int64         \n",
      " 22  origin_up                       14542 non-null  object        \n",
      " 23  pow_max                         14542 non-null  float64       \n",
      " 24  churn                           14542 non-null  int64         \n",
      " 25  price_off_peak_var              14542 non-null  float64       \n",
      " 26  price_peak_var                  14542 non-null  float64       \n",
      " 27  price_mid_peak_var              14542 non-null  float64       \n",
      " 28  price_off_peak_fix              14542 non-null  float64       \n",
      " 29  price_peak_fix                  14542 non-null  float64       \n",
      " 30  price_mid_peak_fix              14542 non-null  float64       \n",
      " 31  forcasted_price                 14542 non-null  float64       \n",
      " 32  previous_price                  14542 non-null  float64       \n",
      " 33  price_sens                      14425 non-null  float64       \n",
      "dtypes: datetime64[ns](4), float64(20), int64(7), object(3)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 250
    },
    "id": "spYE4CkiO1p2",
    "outputId": "3a08bb19-4913-4fd1-bba8-2615081068b9",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>offpeak_diff_dec_january_energy</th>\n",
       "      <th>offpeak_diff_dec_january_power</th>\n",
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       "      <td>0.050443</td>\n",
       "      <td>1.500000</td>\n",
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      "text/plain": [
       "                                 id  offpeak_diff_dec_january_energy  \\\n",
       "0  0002203ffbb812588b632b9e628cc38d                        -0.006192   \n",
       "1  0004351ebdd665e6ee664792efc4fd13                        -0.004104   \n",
       "2  0010bcc39e42b3c2131ed2ce55246e3c                         0.050443   \n",
       "3  0010ee3855fdea87602a5b7aba8e42de                        -0.010018   \n",
       "4  00114d74e963e47177db89bc70108537                        -0.003994   \n",
       "\n",
       "   offpeak_diff_dec_january_power  \n",
       "0                        0.162916  \n",
       "1                        0.177779  \n",
       "2                        1.500000  \n",
       "3                        0.162916  \n",
       "4                       -0.000001  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group off-peak prices by companies and month\n",
    "monthly_price_by_id = price_df.groupby(['id', 'price_date']).agg({'price_off_peak_var': 'mean', 'price_off_peak_fix': 'mean'}).reset_index()\n",
    "\n",
    "# Get january and december prices\n",
    "jan_prices = monthly_price_by_id.groupby('id').first().reset_index()\n",
    "dec_prices = monthly_price_by_id.groupby('id').last().reset_index()\n",
    "\n",
    "# Calculate the difference\n",
    "diff = pd.merge(dec_prices.rename(columns={'price_off_peak_var': 'dec_1', 'price_off_peak_fix': 'dec_2'}), jan_prices.drop(columns='price_date'), on='id')\n",
    "diff['offpeak_diff_dec_january_energy'] = diff['dec_1'] - diff['price_off_peak_var']\n",
    "diff['offpeak_diff_dec_january_power'] = diff['dec_2'] - diff['price_off_peak_fix']\n",
    "diff = diff[['id', 'offpeak_diff_dec_january_energy','offpeak_diff_dec_january_power']]\n",
    "diff.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploiting the difference between prices to improve the model’s predictive power."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qCFY-89WO-c1"
   },
   "source": [
    "We can exploit this feature by generating a new categorical feature that indicates if the price increased, decreased or did not change during the last year. there is two reasons why this is a useful thing to do:\n",
    "\n",
    "- Categorical variables are generally more easy to interpret.\n",
    "- We can encode this feature using powerful techniques that can add more predictive power to our model such as mean encoding which creates a monotonic relationship between the variable and the target. This monotonic relationship implies that if the value of one variable increases/decreases so does the value of the other variable. ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "T24OhwifPF9U"
   },
   "outputs": [],
   "source": [
    "# A function to create the new feature \"price_change\"\n",
    "\n",
    "def price_change(x):\n",
    "    if x > 0:\n",
    "        return 'increase'\n",
    "    elif x < 0:\n",
    "        return 'decrease'\n",
    "    else:\n",
    "        return 'stable'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "id": "XllQRe5OPODe",
    "outputId": "bc64aa00-661a-425b-8a48-06d534a03bce"
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <th>cons_last_month</th>\n",
       "      <th>date_activ</th>\n",
       "      <th>date_end</th>\n",
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       "      <th>forecast_cons_year</th>\n",
       "      <th>...</th>\n",
       "      <th>price_peak_var</th>\n",
       "      <th>price_mid_peak_var</th>\n",
       "      <th>price_off_peak_fix</th>\n",
       "      <th>price_peak_fix</th>\n",
       "      <th>price_mid_peak_fix</th>\n",
       "      <th>forcasted_price</th>\n",
       "      <th>previous_price</th>\n",
       "      <th>price_sens</th>\n",
       "      <th>offpeak_diff_dec_january_energy</th>\n",
       "      <th>offpeak_diff_dec_january_power</th>\n",
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       "      <td>24011ae4ebbe3035111d65fa7c15bc57</td>\n",
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       "      <td>0</td>\n",
       "      <td>2013-06-15</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.100749</td>\n",
       "      <td>0.066530</td>\n",
       "      <td>40.942265</td>\n",
       "      <td>22.352010</td>\n",
       "      <td>14.901340</td>\n",
       "      <td>0.106312</td>\n",
       "      <td>0.112768</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>d29c2c54acc38ff3c0614d0a653813dd</td>\n",
       "      <td>4660</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-08-21</td>\n",
       "      <td>2016-08-30</td>\n",
       "      <td>2009-08-21</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>189.95</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>44.311375</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.078366</td>\n",
       "      <td>13.640956</td>\n",
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       "      <td>544</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-04-16</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>2010-04-16</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>47.96</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.385450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.126847</td>\n",
       "      <td>0.129466</td>\n",
       "      <td>45.058910</td>\n",
       "      <td>-0.004670</td>\n",
       "      <td>0.177779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bba03439a292a1e166f80264c16191cb</td>\n",
       "      <td>1584</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-03-30</td>\n",
       "      <td>2016-03-30</td>\n",
       "      <td>2010-03-30</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>240.04</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>-0.004547</td>\n",
       "      <td>0.177779</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149d57cf92fc41cf94415803a877cb4b</td>\n",
       "      <td>4425</td>\n",
       "      <td>0</td>\n",
       "      <td>526</td>\n",
       "      <td>2010-01-13</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>2010-01-13</td>\n",
       "      <td>2015-03-09</td>\n",
       "      <td>445.75</td>\n",
       "      <td>526</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103638</td>\n",
       "      <td>0.072865</td>\n",
       "      <td>40.688156</td>\n",
       "      <td>24.412893</td>\n",
       "      <td>16.275263</td>\n",
       "      <td>0.108457</td>\n",
       "      <td>0.113906</td>\n",
       "      <td>18.799168</td>\n",
       "      <td>-0.006192</td>\n",
       "      <td>0.162916</td>\n",
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       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id  cons_12m  cons_gas_12m  cons_last_month  \\\n",
       "0  24011ae4ebbe3035111d65fa7c15bc57         0         54946                0   \n",
       "1  d29c2c54acc38ff3c0614d0a653813dd      4660             0                0   \n",
       "2  764c75f661154dac3a6c254cd082ea7d       544             0                0   \n",
       "3  bba03439a292a1e166f80264c16191cb      1584             0                0   \n",
       "4  149d57cf92fc41cf94415803a877cb4b      4425             0              526   \n",
       "\n",
       "  date_activ   date_end date_modif_prod date_renewal  forecast_cons_12m  \\\n",
       "0 2013-06-15 2016-06-15      2015-11-01   2015-06-23               0.00   \n",
       "1 2009-08-21 2016-08-30      2009-08-21   2015-08-31             189.95   \n",
       "2 2010-04-16 2016-04-16      2010-04-16   2015-04-17              47.96   \n",
       "3 2010-03-30 2016-03-30      2010-03-30   2015-03-31             240.04   \n",
       "4 2010-01-13 2016-03-07      2010-01-13   2015-03-09             445.75   \n",
       "\n",
       "   forecast_cons_year  ...  price_peak_var  price_mid_peak_var  \\\n",
       "0                   0  ...        0.100749            0.066530   \n",
       "1                   0  ...        0.007124            0.000000   \n",
       "2                   0  ...        0.088421            0.000000   \n",
       "3                   0  ...        0.000000            0.000000   \n",
       "4                 526  ...        0.103638            0.072865   \n",
       "\n",
       "   price_off_peak_fix  price_peak_fix  price_mid_peak_fix forcasted_price  \\\n",
       "0           40.942265       22.352010           14.901340        0.106312   \n",
       "1           44.311375        0.000000            0.000000        0.072855   \n",
       "2           44.385450        0.000000            0.000000        0.126847   \n",
       "3           44.400265        0.000000            0.000000        0.073347   \n",
       "4           40.688156       24.412893           16.275263        0.108457   \n",
       "\n",
       "   previous_price  price_sens  offpeak_diff_dec_january_energy  \\\n",
       "0        0.112768         NaN                         0.020057   \n",
       "1        0.078366   13.640956                        -0.003767   \n",
       "2        0.129466   45.058910                        -0.004670   \n",
       "3        0.075605   28.408609                        -0.004547   \n",
       "4        0.113906   18.799168                        -0.006192   \n",
       "\n",
       "   offpeak_diff_dec_january_power  \n",
       "0                        3.700961  \n",
       "1                        0.177779  \n",
       "2                        0.177779  \n",
       "3                        0.177779  \n",
       "4                        0.162916  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging with the main dataset\n",
    "new_df_ft = df.merge(diff, how='left', left_on='id', right_on='id')\n",
    "new_df_ft.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "-YuT6KJEPXw3"
   },
   "outputs": [],
   "source": [
    "# Creating the new categorical features\n",
    "new_df_ft['price_change_energy'] = new_df_ft['offpeak_diff_dec_january_energy'].apply(price_change)\n",
    "new_df_ft['price_change_power'] = new_df_ft['offpeak_diff_dec_january_power'].apply(price_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "zRJxViDdPYuJ"
   },
   "outputs": [],
   "source": [
    "# Dropping the first feature \n",
    "new_df_ft = new_df_ft.drop(['offpeak_diff_dec_january_energy','offpeak_diff_dec_january_power'], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kxR2CYQoP5RE"
   },
   "source": [
    "#### 2). Getting time related features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "_AC_vcDfPzOz"
   },
   "outputs": [],
   "source": [
    "# Getting the activation year \n",
    "new_df_ft['activ_year'] = new_df_ft['date_activ'].dt.year\n",
    "\n",
    "# Getting the activation month \n",
    "new_df_ft['activ_month'] = new_df_ft['date_activ'].dt.month\n",
    "\n",
    "# Getting the ending year\n",
    "new_df_ft['end_year'] = new_df_ft['date_end'].dt.year\n",
    "\n",
    "# Getting the ending month \n",
    "new_df_ft['end_month'] = new_df_ft['date_end'].dt.month\n",
    "\n",
    "# Getting the year of the last modification of the product \n",
    "new_df_ft['modif_prod_year'] = new_df_ft['date_modif_prod'].dt.year\n",
    "\n",
    "# Getting the month of the last modification of the product \n",
    "new_df_ft['modif_prod_month'] = new_df_ft['date_modif_prod'].dt.month\n",
    "\n",
    "# Getting the renewal year\n",
    "new_df_ft['renewal_year'] = new_df_ft['date_renewal'].dt.year\n",
    "\n",
    "# Getting the renewal month \n",
    "new_df_ft['renewal_month'] = new_df_ft['date_renewal'].dt.month\n",
    "\n",
    "# Difference between activation and renewal in days \n",
    "new_df_ft['diff_act_renew'] = (new_df_ft['date_renewal'] - new_df_ft['date_activ']).dt.days\n",
    "\n",
    "# Difference between activation and end in days\n",
    "new_df_ft['diff_act_end'] = (new_df_ft['date_end'] - new_df_ft['date_activ']).dt.days\n",
    "\n",
    "# Difference between activation and production modification.\n",
    "new_df_ft['diff_act_modif'] = (new_df_ft['date_modif_prod'] - new_df_ft['date_activ']).dt.days\n",
    "\n",
    "# Difference between end and production modification.\n",
    "new_df_ft['diff_end_modif'] = (new_df_ft['date_end'] - new_df_ft['date_modif_prod']).dt.days\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2_MGI8QUQJCz"
   },
   "source": [
    "#### 3). Getting consumption based features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "KUF3q_jxQKRW"
   },
   "outputs": [],
   "source": [
    "# Getting the average consumption per month for the past 12 months.\n",
    "new_df_ft['avrg_month_cons'] = new_df_ft['cons_12m']/12\n",
    "\n",
    "# Getting the average consumption per month of gaz for the past 12 months.\n",
    "new_df_ft['avrg_month_cons_gaz'] = new_df_ft['cons_gas_12m']/12\n",
    "\n",
    "# Getting the average forcasted consumption per month for the next 12 months.\n",
    "new_df_ft['forcast_avrg_month_cons'] = new_df_ft['forecast_cons_12m']/12\n",
    "\n",
    "# Getting the ratio of the last month consumption to the last 12m consumption\n",
    "new_df_ft['ratio_last_month_last12m_cons'] = new_df_ft['cons_last_month']/new_df_ft['cons_12m']\n",
    "\n",
    "# Getting the ratio of the last month consumption to the the average consumption per month for the past 12 months.\n",
    "new_df_ft['ratio_last_month_avg_cons'] = new_df_ft['cons_last_month']/new_df_ft['avrg_month_cons']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II. Choosing the evaluation metric and treatment of outliers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "35BMK3XfYVQp"
   },
   "source": [
    "#### 1). The choice of the evaluation metric.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sdORblLiYddX"
   },
   "source": [
    "Exploring the distribution of the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "5v5sCHUCYi9e",
    "outputId": "5c44902a-f560-4758-c9bd-cf1ebe15146c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking if the target variable is balanced or not.\n",
    "sns.countplot(x='churn', data=new_df_ft)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y-yuK9MaYv7U"
   },
   "source": [
    "We can see that our target variable is imbalanced, which means that we need to use the \"f-1\" score as an evaluation metric. This will help us get an accurate idea about the performance of our model.\n",
    "Using \"accuracy\" as a metric will result in choosing the wrong model. The \"why\" behind this statement can be explained by the example of a dummy model that always predicts the most common class. This model will be considered as a good model in an evaluation process where we use \"accuracy\" as a metric. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2). Handling outliers :\n",
    "\n",
    "The way we handle outliers depends on the context. For example, in the case of  datasets where we have an important number of numerical features with a lot of outliers (like in our case), removing the outliers will result in losing a lot of information and capping them will increase the risk of distorting the distribution of the variable or the relationship between variables. \n",
    "So the right thing to do in this case is to use models that are robust to outliers such as the Random Forest model.  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting numerical features\n",
    "cols_num = [col for col in new_df_ft.columns if new_df_ft[col].dtype in ['int64', 'float64']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "boxplot of feature cons_12m\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature cons_gas_12m\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tdtttt2n79u1Vx6iFw4cPKyKqjlFLV1xxRdURKmVbp512WtUxamH16tW64YYbqo4xp7JP15wn6cdTxnuybcfY3mC7abs5Pj5echwAeHVxmTM12++XtDoi/iAbf1jS2yPio7P9/NDQUDSbzdLyACditln7zp07Fz0HMBfbuyJiaLZ9Zc/k/1vS66eMB7JtAIBFUHbJPyHpAttvtP0aSR+UtLXkYwKFmjlrZxaPXlLqH14jYsL2RyV9Q9JSSXdGxO4yjwkAOK7s1TWKiPsl3V/2cYAyMXtHr+KKVwBIGCUPAAmj5AEgYZQ8ACSs1Iuh8rI9LulHVecA5nCOpJ9UHQKYxRsion+2HbUqeaDObDfnuqoQqCtO1wBAwih5AEgYJQ90b1PVAYC8OCcPAAljJg8ACaPkASBhlDwAJIySB4CEUfIAkLD/B3c91D1U3C0ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature cons_last_month\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature forecast_cons_12m\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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QPv5tRGwDjszMXa0rS1peHnjggTm3pU5W9Wb72yPiKIDM/BFwb0Rc0MK6pGXlhBNOmHNb6mRVL21d2fw0e2b+DLiyJRVJy9CDDz4457bUyaoGyWzHVbosJml+69evJ6KxYnVEcN55Pqal7rGQm+0fjYhXlNdHadx0l7QIBgcH9wuSDRs2tLkiqbqqQXIp8DTwOeA6GvNsfbBVRUnLkVPHq1tVnWvr8TLf1usz89WZ+ZHMfLzFtUnLxvDw8LNBMj097QOJ6ipVR229NiLuBO4q26dHxCdbWpm0jMys1z7DBxLVTape2roKeBMwAZCZdwDntKooabnxgUR1s8qz/2bmfQc0TS1yLdKy5fBfdbOqQXJfRLwWyLJu+x9TLnNJqu+cc86Zc1vqZFWD5A9ojNJaBYwDv46jtqRF88tf7r/g6FNPPdWmSqSFm/ehwojoAT6Wmb+3BPVIy9LNN9+83/a3vvWtNlUiLdy8PZLMnAJeFhGHLUE90rI0PT0957bUyapOc3IP8I8RsZXGeu0AZOZHW1KVJKlrVL1H8gPgq+X4f9H0krQIHP6rblZ1PZL/BhARv1q2f9HKoqTlZsWK/f9N19PT06ZKpIWr+mT7qyLidmAPsCcidkbEK1tbmrR83H///ftt/+QnP2lTJdLCVb20NQR8KDNflpkvA/6IxrK7khbB6tWr59yWOlnVIDkiM78xs5GZ3wSOaElF0jJ0ySWX7Ld96aWXtqkSaeGqBsk9EfFfImJ1ef1nGiO5JC2CAydpdNJGdZM5gyQiri0fbwZWAl8qr+OAf9/a0qTlY8eOHfttb9++vU2VSAs336itMyPiJcAg8EYggJnVd6KVhUmSusN8l7b+FtgBnAKMAreV953lfU4RcVJEfCMi7oyIPRFxWWk/NiJGIuL75f2Y0h4RsTkixiJiV0Sc0fRdg+X470fEYFP7mRGxu5yzOWbWK5W6SF9f337bxx13XJsqkRZuziDJzM2Z+S+BazLz5U2vNZn58grfPwn8UWaeCpwNfDAiTgUuB3Zk5sk0gurycvybgZPLayPwKWgED3Al8BrgLODKmfApx7y36byBir+71DEeeuih/badRl7dpOpSu+9/Pl+emfdn5nfK55/TmHp+FXA+MFwOGwYuKJ/PB7Zkw7eBoyPixTQW1RrJzIcz8xFgBBgo+47MzG9nY53SLU3fJUlaApUXtqorIlYDvwHcAhyfmTNPYD0AzMwHsQpoXkBrb2mbq33vLO2SpCWyJEFSplb5IvCHmflY877Sk8hZT1y8n78xIkYjYnTfvn2t/FHS8+JcW+pmLQ+SiHgBjRD5bGZ+qTQ/WC5LUd5nLhCPAyc1nX5iaZur/cRZ2veTmUOZ2Z+Z/StXrqz/S0mL7MDgOOGEE9pUibRwLQ2SMoLqauCuA6ac30pjSDHl/ctN7RvK6K2zgUfLJbCbgPMi4phyk/084Kay77GIOLv8rA1N3yV1jV27du23fccdd7SpEmnhqq5H8ny9Dvi3wO6I+G5p+wjwl8DnI+Ji4MfAhWXfDcBbgDHgCeA9AJn5cET8GY3hxwB/mpkPl88fAD4DHA7cWF6SpCXS0iDJzP/DwR9cPHeW45ODrAWfmdcA18zSPgq8qkaZkqQalmzUliTp0GSQSJJqMUgkSbUYJFIHOHCp3QO3pU7m/61SBzhwrlHnHlU3MUgkSbUYJFIHmJqamnNb6mQGiSSpFoNEklSLQSJJqsUgkSTVYpBIkmoxSCRJtRgkkqRaDBJJUi0GiSSpFoNEklSLQSJJqsUgkSTVYpBIkmoxSCRJtRgkkqRaDBJJUi0GiSSpFoNEklSLQSJJqsUgkSTVYpBIkmoxSCRJtfS2uwAtbx//+McZGxtrdxkd6bLLLmt3CW21du1aLr300naXoQrskUiSaonMbHcNS6q/vz9HR0fbXYa0nze84Q3/rO2b3/zmktchHUxE7MzM/tn22SORJNVikEgd4MDeh70RdRODRJJUi0EidYjTTz+d008/3d6Iuo7Df9vAIa+azcz/E8t92K/+uU4fCm2QtMHY2Bjf/d5dTL3o2HaXog6y4unGCMqd9zzY5krUSXqeeLjdJczLIGmTqRcdy5OnvKXdZUjqcIfffUO7S5jXIREkETEAfAzoAT6dmX/Z5pLmND4+Ts8Tj3bF/yCS2qvniQnGxyfbXcacuj5IIqIH+ASwHtgL3BYRWzPzzvZWNo+pSXqemGh3Feok01ON9xU97a1DnWWqs0MEDoEgAc4CxjLzHoCIuA44H+jYIHn961/vzfZifHycJ598st1ldISZ/w6Hv/CwNlfSGQ4//HBWrVrV7jI6wtq1a9tdwpwOhSBZBdzXtL0XeE3zARGxEdgI8NKXvnTpKjuITh59sdQcwfac8fFxAP/yLDp9pJKecygEybwycwgYgsZcW20uR038i0LqfofCA4njwElN2yeWNknSEjgUguQ24OSIWBMRhwEXAVvbXJMkLRtdf2krMycj4hLgJhrDf6/JzD1tLkuSlo2uDxKAzLwB8KEMSWqDQ+HSliSpjQwSSVItBokkqRaDRJJUS2Qur+fzImIf8ON21yEdxHHAT9tdhDSLl2Xmytl2LLsgkTpZRIxmZn+765AWwktbkqRaDBJJUi0GidRZhtpdgLRQ3iORJNVij0SSVItBIkmqxSCRJNVikEiSajFIJEm1/H8x2E/4Cd0OwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature forecast_cons_year\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature forecast_discount_energy\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature forecast_meter_rent_12m\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature forecast_price_energy_off_peak\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature forecast_price_energy_peak\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature forecast_price_pow_off_peak\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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1TfP3kyxvvl8OPDjdsVW1sarGq2p8bGysZamSpNnM9hjH5wMvAJ6b5HW7fXUQsP9sJ08S4OPA1j2Wf74eOAf4UPN+3RzrliQtwGzz/J8HnAEcDPzebu0PA+e3OP8pwJvpLQb3zabt39EL/auTnAfcC5zVvmRJ0kLN9hjH64DrkpxUVbfN9eTNGkAzXQw+ba7nkyQtjrZj/q9NclCSfZPckmQyyZs6rUyS1Jm24f9bzSydM4B7gFXAv+2qKElSt9qG/77N++nAZ6vqRx3VI0nqg7aren4hyd3A48AFScaAH3dXliSpS616/lV1IXAyMF5VPwEepbdEgyRpCLXt+QMcBvxmkt3n939qkeuRJPVBq/BPchGwGjgBuBH4HeArGP6SNJTaXvB9Pb15+Q9U1dvoPdnruZ1VJUnqVNvwf7yqfgrsSnIQvbV4juiuLElSl9qO+U8kORi4DNgCPALM+Y5fSdLS0PYZvn/UfLw0yU3AQVX17e7KkiR1qe16/q9N8lyAqroH+L9JXtNhXZKkDrUd879o97t6q2oncFEnFUmSOtc2/Kfbby73CEiSlpC24T+R5OIkxzavi+ld+JUkDaG24b8WeBL4DHAVvXV93tlVUZKkbrWd7fMocGGSA5vPkqQh1na2z8lJ7gK2NtsvTvLXnVYmSepM22GfjwD/GngIoKq+Bbyiq6IkSd1qG/5U1ff2aHpqkWuRJPVJ2+ma30tyMlBJ9gXW0QwBSZKGT9ue/zvoze5ZAewAXoKzfSRpaM3a80+yD7C+qs7uQz2SpD6YtedfVU8BRyXZrw/1SJL6oO2Y/3bgq0mup/f8XgCq6uK9HZTkcuAM4MGqemHTdgi9m8VWAvcAZ1XVD+dcuSRp3tqO+X8XuKHZ/zm7vWbzSeC392i7ELilqo4Dbmm2JUl91PYO3/cDJHl2s/1Iy+O+lGTlHs1n0nseMMAmYDPwnjbnkyQtjrZ3+L4wye3AncCdSbYkecE8f3NZVd3ffH4AWDbP80iS5qntsM9G4F1VdVRVHQW8m94jHRekqgqomb5PsibJRJKJycnJhf6cJKnRNvwPrKpbpzaqajNw4Dx/8/tJlgM07w/OtGNVbayq8aoaHxsbm+fPSZL21Db8tyd5X5KVzevf05sBNB/XA+c0n88BrpvneSRJ87TX8E9yRfPxy8AYcG3zOhQ4d7aTJ/k0cBvwvCT3JTkP+BDwqiTfAX6z2ZYk9dFss31+Lclh9HrorwTCz8boM9vJq+oPZvjqtNYVSpIW3Wzhfym9ufjHABO7tU/9ETimo7okSR3a67BPVf23qjoeuLyqjtntdXRVGfySNKRaXfCtqgu6LkSS1D+tH+YiSfrFYfhL0ggy/CVpBBn+kjSCDH9JGkGGvySNIMNfkkaQ4S9JI8jwl6QRZPhL0ggy/CVpBBn+kjSCDH9JGkGGvySNIMNfkkaQ4S9JI8jwl6QRZPhL0ggy/CVpBBn+kjSCDH9JGkGGv7QAl112GatXr+byyy8fdCnSnAws/JP8dpL/k2RbkgsHVYe0EFdeeSUAn/rUpwZciTQ3Awn/JPsAfwX8DnAC8AdJThhELdJ8XXbZZU/btvevYTKonv/LgG1Vtb2qngSuAs4cUC3SvEz1+qfY+9cwGVT4rwC+t9v2fU3b0yRZk2QiycTk5GTfipOkX3RL+oJvVW2sqvGqGh8bGxt0OZL0C2NQ4b8DOGK37cObNmlonH322U/bfstb3jKgSqS5G1T4fx04LsnRSfYD3gBcP6BapHk5//zzn7Z97rnnDqgSae4GEv5VtQv4Y+B/AluBq6vqzkHUIi3EVO/fXr+GTapq0DW0Mj4+XhMTE4MuQ5KGSpItVTW+Z/uSvuArSeqG4S9JI8jwl6QRZPhL0ggamgu+SSaBewddhzSNQ4EfDLoIaQZHVdXP3SU7NOEvLVVJJqabTSEtZQ77SNIIMvwlaQQZ/tLCbRx0AdJcOeYvSSPInr8kjSDDX5JGkOEvSSPI8JekEWT4S9II+n9vFFWeKGVjOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature imp_cons\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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GDjbtP5T6TtVvVjietNGKqpUgWQD8SNKDknaMf6Z53B7gIuD2iPgD4P/QuIz1hogIZvBlWZI2SqpKqg4PD8/U15rNCE/aaEXWSpBsBv4U+I80JnAc/0zHEDAUEY+n9r00guXFdMmK9PeltP4QsLRp/yWp71T9J4mIvogoR0R50aJF0yzbrD08aaMV2ZSDJCK+M9FnOgeNiBeAg5IuSF2XA88AO4DxJ68qwH1peQewPj29dSnwSroE9iCwWtLC9ITX6tRnViietNGK7Dc+tSXpHyPifZJe5fhLTaJxBeqt0zz2DcA30hNb+4GP0wi27ZI2AD8D/ixtuxNYC9SAY2lbIuKIpC8AT6TtPh8RR6ZZj1luVq1axc6dOxkZGfGkjVY4atyK6C7lcjmq1WreZZi9oV6v85GPfITXX3+defPmcffdd3u+LesokvZERHmidTP5znYzm6ZSqcQ555wDwDnnnOMQsUJxkJh1gHq9zqFDjedEDh8+7Ke2rFAcJGYdYOvWrYxfZh4bG6Ovry/nisymzkFi1gF27959XHvXrl05VWLWOgeJWQeQNGnbrJM5SMw6wPve975J22adzEFi1gHmzZt3XPvMM8/MqRKz1jlIzDrAo48+elz7O9+Z1qQRZrlwkJh1gJ6enknbZp3MQWLWAX75y19O2jbrZA4Ssw6wbNmySdtmncxBYtYBPve5z03aNutkDhKzDrBixQrOO+88AM4//3xWrFiRc0VmU+cgMesQ/hGiFZWDxKwD1Go1Dh8+DDQmbazVajlXZDZ1DhKzDvDFL35x0rZZJ3OQmHWAAwcOTNo262S5BomkuZJ+IOl/pvZySY9Lqkn6ZnoNL5LOTO1aWr+s6TtuSf0/lrQmp1Mxy+Qtb3nLpG2zTpb3iORG4Nmm9peBWyNiBXAU2JD6NwBHU/+taTskXQhcB7wT6AVukzR3lmo3mzEjIyOTts06WW5BImkJ8MfAHakt4IPAvWmTfuDqtLwutUnrL0/brwPuiYjXIuKnQA24eFZOwGwGrV69+rj2mjUeXFtx5Dki+SvgM8BYapeAlyNi/J9iQ8DitLwYOAiQ1r+Stn+jf4J9zAqjUqm8Mb/WGWecwfr163OuyGzqcgkSSR8CXoqIPbN4zI2SqpKqw8PDs3VYsykplUqsXbsWSaxdu5ZSqZR3SWZTltcUo+8FrpK0FngT8FbgK8ACST1p1LEEOJS2PwQsBYYk9QBvA+pN/eOa9zlORPQBfQDlcjlm/IzMMqpUKhw4cMCjESucXEYkEXFLRCyJiGU0bpY/HBEfBR4BrkmbVYD70vKO1CatfzgiIvVfl57qWg6sBL43S6dhNqNKpRJbtmzxaMQKp9NeenATcI+kLwI/AO5M/XcC/11SDThCI3yIiKclbQeeAUaA6yNidPbLNjPrXmr8w767lMvlqFareZdhZlYYkvZERHmidXn/jsTMknq9zqZNm6jX63mXYtYSB4lZh9i6dSt79+6lr68v71LMWuIgMesA9XqdXbt2ATA4OOhRiRWKg8SsA2zdupWxscZvc8fGxjwqsUJxkJh1gN27dx/XHh+dmBWBg8SsA5z4dkS/LdGKxEFi1gEuv/zySdtmncxBYtYBrr322knbZp3MQWLWAbZv335c+1vf+lZOlZi1zkFi1gEGBwePaz/00EM5VWLWOgeJWQc4caqibpy6yIrLQWJmZpk4SMzMLBMHiZmZZeIgMTOzTBwkZmaWiYPEzMwyySVIJC2V9IikZyQ9LenG1H+WpEFJz6W/C1O/JG2RVJO0V9JFTd9VSds/J6lyqmOamVl75DUiGQH+IiIuBC4Frpd0IXAzsDsiVgK7UxvgSmBl+mwEbodG8ACbgUuAi4HN4+FjZmazI5cgiYjnI+L7aflV4FlgMbAO6E+b9QNXp+V1wLZoeAxYIOk8YA0wGBFHIuIoMAj0zt6ZmJlZ7vdIJC0D/gB4HDg3Ip5Pq14Azk3Li4GDTbsNpb5T9ZuZ2SzJNUgkvQX4O+DfRsQvmtdFY46IGZsnQtJGSVVJ1eHh4Zn6WjOzrpdbkEg6g0aIfCMi/j51v5guWZH+vpT6DwFLm3ZfkvpO1X+SiOiLiHJElBctWjRzJ2Jm1uXyempLwJ3AsxHxX5tW7QDGn7yqAPc19a9PT29dCrySLoE9CKyWtDDdZF+d+szMbJb05HTc9wIfA/ZJ+mHq+yzwJWC7pA3Az4A/S+t2AmuBGnAM+DhARByR9AXgibTd5yPiyKycgZmZATkFSUT8I3Cql1Kf9I7RdL/k+lN8113AXTNXnZmZtSL3p7bMzKzYHCRmZpaJg8TMzDJxkJiZWSYOEjMzy8RBYmZmmThIzMwsEweJmZll4iAxM7NMHCRmZpaJg8TMzDJxkJiZWSYOEjMzy8RBYmZmmeT1PhIzAL761a9Sq9XyLqMj3XjjjXmXkKsVK1Zwww035F2GTYFHJGZmloka74zqLuVyOarVat5lmL3hsssuO6nv29/+9qzXYXYqkvZERHmidR6RmJlZJqfFiERSL/AVYC5wR0R8abLt8x6R+L6ATeTJJ598Y/nd7353jpVYp+mE+0WTjUgKf7Nd0lzgb4ArgCHgCUk7IuKZfCs7tVqtxg+fepbR+WflXYp1kDmAgFFgz/4Xc67GOsXcY0fyLuE3KnyQABcDtYjYDyDpHmAd0LFBcujQIRj9f8w9Vs+7lPyNjcJpMCqeGQIaw2pedZAgwZy5eVeRv9GRxv9ndLDTIUgWAweb2kPAJSduJGkjsBHg7W9/++xUdgoLFizgV7/6Va41dIrXXnuNsbGxvMuwDjRnzhzOPHNe3mV0gHksWLAg7yImdToEyZRERB/QB417JHnWcscdd+R5eDOzGXU6PLV1CFja1F6S+szMbBacDkHyBLBS0nJJ84DrgB0512Rm1jUKf2krIkYkfQp4kMZ9yrsi4umcyzIz6xqFDxKAiNgJ7My7DjOzbnQ6XNoyM7McOUjMzCwTB4mZmWXiIDEzs0xOi0kbWyVpGPhZ3nWYTeBs4J/yLsJsAr8dEYsmWtGVQWLWqSRVTzXDqlmn8qUtMzPLxEFiZmaZOEjMOktf3gWYtcr3SMzMLBOPSMzMLBMHiZmZZeIgMTOzTBwkZmaWiYPEzMwy+f+mZIEMlYw3QgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature margin_gross_pow_ele\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature margin_net_pow_ele\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature nb_prod_act\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature net_margin\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature num_years_antig\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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/JBXI8pekAln+klQgy1+SCmT5S1KBmrqSlwbYxo0bmZiYaDrGgrD3n8PoqFciBRgZGWHt2rVNx1AXLH9pHpYuXdp0BGlOLH9V5shOGnzO+UtSgSx/SSqQ5S9JBbL8JalAlr8kFcjyl6QCWf6SVCDLX5IKFJnZdIauRMQksLPpHNIMjgWeaDqEtB+/kplD0w8OTPlLC1VEjGVmq+kcUhVO+0hSgSx/SSqQ5S/N36amA0hVOecvSQVy5C9JBbL8JalAlr8kFcjyl6QCWf6SVKD/BydejZ6n+JukAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature pow_max\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature churn\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature price_off_peak_var\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature price_peak_var\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature price_mid_peak_var\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature price_off_peak_fix\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature price_peak_fix\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature price_mid_peak_fix\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature forcasted_price\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature previous_price\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature price_sens\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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V/ayqvgNMA6e213RV3d9+5nljmytJmieD3IOZmONj/zG9z9b8QhsvA35UVTNtvB1Y0ZZXAA8CVNVMkkfa/BXAnX377N/mwX3qp81x/5KkWQzyZZffnauDJnk98FBVbU1yxlzt91n2sg5YB/CSl7xkmK1I0qIyyCWyufRq4A3tUttGepfG/gQ4Jsne0FsJ7GjLO4ATANr6FwK7+uv7bPN09f1U1bVVNV5V48uXL3/u70ySBAwpYKrq0qpaWVVj9G7S31FVvwl8AXhjm7YWuKktb2pj2vo7qqpa/YL2lNmJwGrgq8DdwOr2VNrh7Rib5uGtSZKaQe7BzIffBzYm+SDwdeC6Vr8O+GSSaWA3vcCgqu5JcgNwLzADvLOqHgdI8i7gNmAJcH1V3TOv70SSDnHpnQgIYHx8vKamnuuT15J0aEmytarG960P6x6MJGmRM2AkSZ0wYCRJnTBgJEmdMGAkSZ0wYCRJnTBgJEmdMGAkSZ0wYCRJnTBgJEmdMGAkSZ0wYCRJnTBgJEmdMGAkSZ0wYCRJnTBgJEmdMGAkSZ0wYCRJnTBgJEmdMGAkSZ0wYCRJnTBgJEmdGErAJDkhyReS3JvkniTvafUXJdmS5L7299hWT5Irk0wn+UaSk/v2tbbNvy/J2r76KUm2tW2uTJL5f6eSdOga1hnMDPC+qjoJOB14Z5KTgEuA26tqNXB7GwOcB6xur3XANdALJOBy4DTgVODyvaHU5ry9b7uJeXhfkqRmKAFTVd+vqq+15X8AvgWsANYAk23aJHB+W14DbKieO4FjkrwYOBfYUlW7q+phYAsw0da9oKrurKoCNvTtS5I0D4Z+DybJGPArwF3A8VX1/bbqB8DxbXkF8GDfZttbbbb69gPUJUnzZKgBk+SfAH8B/E5V/bh/XTvzqHnoYV2SqSRTO3fu7Ppw0sB27drFxRdfzK5du4bdijSQoQVMkufRC5dPVdXnWvmH7fIW7e9Drb4DOKFv85WtNlt95QHq+6mqa6tqvKrGly9f/tzelNSByclJtm3bxoYNG4bdijSQYT1FFuA64FtV9Ud9qzYBe58EWwvc1Fe/sD1NdjrwSLuUdhtwTpJj2839c4Db2rofJzm9HevCvn1JC8auXbu49dZbqSpuueUWz2K0oAzrDObVwFuBM5P87/Z6HfAHwGuT3Aec3cYAtwD3A9PAnwHvAKiq3cAHgLvb6/2tRpvz8bbN3wG3zscbk+bS5OQkjz32GACPPfaYZzFaUNK71SGA8fHxmpqaGnYb0pMmJib46U9/+uT4iCOOYPPmzUPsSNpfkq1VNb5vfehPkUl6ekuXLp11LI0yA0YaYT/5yU9mHUujzICRRphnMFrIDBhphM3MzMw6lkaZASONsLGxsVnH0igzYKQRtn79+lnH0igzYKQRtmrVqifPWsbGxli1atVwG5IGYMBII279+vUcffTRnr1owfGRFGnErVq1iptvvnnYbUgD8wxGktQJA0aS1AkDRhpxU1NTnHnmmWzdunXYrUgDMWCkEXfZZZfxxBNPcOmllw67FWkgBow0wqamptizZw8Ae/bs8SxGC4oBI42wyy677Cljz2K0kBgw0gjbe/bydGNplBkwkqROGDCSpE4YMNIIe+973/uU8fve974hdSINzoCRRthrXvOap4xf9apXDakTaXAGjDTCPvzhDz9l/JGPfGRInUiDM2CkEfaVr3zlKeMvf/nLQ+pEGtyiDpgkE0m+nWQ6ySXD7keSDiWLNmCSLAGuBs4DTgLenOSk4XYlSYeOxfx7MKcC01V1P0CSjcAa4N6hdjWLq666is2bNw+7jZHw6KOPUlXDbmMknXHGGcNuYaiScNRRRw27jZEwMTHBu9/97mG38bQW7RkMsAJ4sG+8vdWeIsm6JFNJpnbu3DlvzUnSYpfF+q/EJG8EJqrqt9v4rcBpVfWup9tmfHy8pqam5qtF6Rkd6Gzli1/84rz3Ic0mydaqGt+3vpjPYHYAJ/SNV7aaJGkeLOaAuRtYneTEJIcDFwCbhtyTJB0yFu1N/qqaSfIu4DZgCXB9Vd0z5LYk6ZCxaAMGoKpuAW4Zdh+SdChazJfIJElDZMBII+zkk09+yviUU04ZUifS4AwYaYS95S1vmXUsjTIDRhphH/3oR58yvuqqq4bUiTQ4A0YaYQ888MCsY2mUGTDSCBsbG5t1LI0yA0YaYevXr591LI0yA0YaYatWrXryrGVsbIxVq1YNtyFpAAaMNOLWr1/P0Ucf7dmLFpxF/Ul+aTFYtWoVN99887DbkAbmGYwkqRMGjCSpEwaMJKkTBowkqROL9ieTn40kO4HvDrsP6QCOA/5+2E1IT+OlVbV836IBIy0ASaYO9Jvn0ijzEpkkqRMGjCSpEwaMtDBcO+wGpEF5D0aS1AnPYCRJnTBgJEmdMGAkSZ0wYCRJnTBgJEmd+P+pFCC0j2lPBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature activ_year\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature activ_month\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature end_year\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature end_month\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAADrCAYAAACBxJaGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMnklEQVR4nO3dbYyl5V3H8e+P3Rp2K5QSRoKD69KMoS/QpM0k9iGpCbSGoAIxRUuBgDSur6ajMaltfEGi0fgct1ut2UCBKNYmLfSBEAOiFRuROAuk2cJGJlgqKw9TMOymu31Y+PtiDrrMPs29O/e5z+z1/SQnO+c698z9y2b3N1euc537TlUhSWrLGUMHkCSNn+UvSQ2y/CWpQZa/JDXI8pekBln+ktSgjUMHWK3zzjuvtm7dOnQMSVpXdu3a9e2qmlo5vm7Kf+vWrSwsLAwdQ5LWlSTPHG3cZR9JapDlL0kNsvwlqUGWvyQ1qNfyT/KZJC8m2X3Y2B8n2ZPk60nuSXJOnxkkSUfqe+Z/B3D5irEHgEuq6qeA/wA+0XMGSdIKvZZ/VT0EvLxi7P6qOjR6+m/AhX1mkCQdaeh9/jcDnxs4gzrasWMHi4uLQ8eYCHv37gVgenp64CSTYWZmhrm5uaFjaBUGK/8kvw0cAu46zjHbgG0AW7ZsGVMyafUOHjw4dATppKTvO3kl2QrcW1WXHDZ2E/BrwGVVdWA1P2d2drb8hK8mzfz8PADbt28fOIl0dEl2VdXsyvGxz/yTXA58DPiZ1Ra/JGlt9b3V87PAw8DFSZ5N8hHgU8BZwANJHk/yV31mkCQdqdeZf1Vde5Th2/o8pyTpxPyEryQ1yPKXpAZZ/pLUIMtfkhpk+UtSgyx/SWqQ5S9JDbL8JalBlr8kNcjyl6QGWf6S1CDLX5IaZPlLUoMsf0lqkOUvSQ2y/CWpQZa/JDXI8pekBln+ktQgy1+SGmT5S1KDLH9JapDlL0kNsvwlqUGWvyQ1yPKXpAZZ/pLUoF7LP8lnkryYZPdhY+cmeSDJU6M/39pnBknSkfqe+d8BXL5i7OPAg1X1E8CDo+eSpDHqtfyr6iHg5RXDVwF3jr6+E7i6zwySpCNtHOCc51fVc6OvnwfOHyBDZzt27GBxcXHoGJowr/+bmJ+fHziJJs3MzAxzc3NDxzimIcr//1RVJaljvZ5kG7ANYMuWLWPLdTSLi4s8vvtJXt187qA5NFnO+P7yP99dT78wcBJNkg0HVi54TJ4hyv+FJBdU1XNJLgBePNaBVbUT2AkwOzt7zF8S4/Lq5nM5+PYrho4hacJt2nPf0BFOaIitnl8Gbhx9fSPwpQEySFLT+t7q+VngYeDiJM8m+QjwB8AHkjwFvH/0XJI0Rr0u+1TVtcd46bI+zytJOj4/4StJDbL8JalBlr8kNcjyl6QGWf6S1CDLX5IaZPlLUoMsf0lqkOUvSQ2y/CWpQZa/JDXI8pekBln+ktQgy1+SGmT5S1KDLH9JapDlL0kNsvwlqUGWvyQ1yPKXpAZZ/pLUIMtfkhpk+UtSgyx/SWqQ5S9JDbL8JalBlr8kNWiw8k/yG0m+kWR3ks8mOXOoLJLUmkHKP8k08FFgtqouATYAHxoiiyS1aMhln43ApiQbgc3Afw+YRZKaMkj5V9Ve4E+AbwHPAa9U1f1DZJGkFg217PNW4CrgIuBHgTcnuf4ox21LspBkYWlpadwxJem01an8k0wneU+S973+OMnzvh/4z6paqqofAHcD71l5UFXtrKrZqpqdmpo6yVNJklbauNoDk/wh8MvAE8Cro+ECHjqJ834LeFeSzcBB4DJg4SR+jiTpJKy6/IGrgYur6nunetKqeiTJ54FHgUPAY8DOU/25kqTV6VL+TwNvAk65/AGq6hbglrX4WZKkbk5Y/kl2sLy8cwB4PMmDHPYLoKo+2l88SVIfVjPzf30tfhfw5RWv1drGkSSNwwnLv6ruBEgyX1XbD38tyXxfwSRJ/emy1fPGo4zdtEY5JEljtJo1/2uBDwMXJTl82ecs4OW+gkmS+rOaNf9/ZfkSDOcBf3rY+H7g632EkiT1azVr/s8AzwDv7j+OJGkcVr3mn+QXkzyV5JUk+5LsT7Kvz3CSpH50+ZDXHwG/UFVP9hVGkjQeXXb7vGDxS9LpocvMfyHJ54Av8sZP+N691qEkSf3qUv5ns3yJh589bKxYvhyzJGkdWXX5V9Wv9Blk0u3du5cNB15h0577ho4iacJtOPASe/ceGjrGcXXZ7XNhknuSvDh6fCHJhX2GkyT1o8uyz+3A3wLXjJ5fPxr7wFqHmkTT09M8/72NHHz7FUNHkTThNu25j+np84eOcVxddvtMVdXtVXVo9LgD8N6KkrQOdSn/l5Jcn2TD6HE98FJfwSRJ/elS/jcDvwQ8z/K1fj4INP0msCStV112+zwDXNljFknSmKy6/JNcBMwBWw//vqryF4IkrTNddvt8EbgN+ArwWi9pJElj0aX8v1tVn+wtiSRpbLqU//YktwD388Zr+zy65qkkSb3qUv4/CdwAXMr/L/vU6LkkaR3pUv7XAG+rqu/3FUaSNB5d9vnvBs7pKYckaYy6zPzPAfYk+XfeuObvVk9JWme6lP8ta3niJOcAtwKXsPzewc1V9fBankOSdHRdPuH7z8d7PcnDVfXuDufeDvx9VX0wyQ8Bmzt8ryTpFHSZ+Z/Imas9MMlbgPcBNwGM3kT2jWRJGpMub/ieSHU49iJgCbg9yWNJbk3y5jXMIkk6jrUs/y42Au8EPl1V7wC+A3x85UFJtiVZSLKwtLQ07oySdNpay/JPh2OfBZ6tqkdGzz/P8i+DN6iqnVU1W1WzU1PeN0aS1spalv8Nqz2wqp4H/ivJxaOhy4An1jCLJOk4TviGb5L9HGc9v6rOHv25u+O554C7Rjt9nsYbw0jS2Jyw/KvqLIAkv8vyHbz+muUlnuuAC072xFX1ODB7st8vSTp5XZZ9rqyqv6yq/VW1r6o+DVzVVzBJUn+6lP93klw3unn7GUmuY3mXjiRpnelS/h9m+QbuL4we14zGJEnrTJfLO3wTl3kk6bTQ5QbuU8CvcuQN3G9e+1iSpD51ubbPl4B/Af4BeLWfOJKkcehS/pur6rd6SyJJGpsub/jem+SK3pJIksamS/nPA19JcjDJviT7k+zrK5gkqT9dln3ewvKnei+qqt9JsoVT+ISvJGk4XWb+fwG8C7h29Hw/8Kk1TyRJ6l2Xmf9PV9U7kzwGUFX/M7oomyRpneky8/9Bkg2MrvA52vf/Wi+pJEm96lL+nwTuAX4kye8BXwN+v5dUkqRedbm8w11JdrF845UAV1fVk70lkyT1psuaP1W1B9jTUxZJ0pgMdQN3SdKALH9JapDlL0kNsvwlqUGWvyQ1yPKXpAZZ/pLUIMtfkhpk+UtSgyx/SWqQ5S9JDRq0/JNsSPJYknuHzCFJrRl65j8PeGVQSRqzwco/yYXAzwG3DpVBklo15Mz/z4GP4d3AJGnsBin/JD8PvFhVu05w3LYkC0kWlpaWxpROkk5/Q8383wtcmeSbwN8Blyb5m5UHVdXOqpqtqtmpqalxZ5Sk09Yg5V9Vn6iqC6tqK/Ah4B+r6vohskhSi4be7SNJGkCne/j2oaq+Cnx14BiS1BRn/pLUIMtfkhpk+UtSgyx/SWqQ5S9JDbL8JalBlr8kNcjyl6QGWf6S1CDLX5IaZPlLUoMGv7bPerLhwMts2nPf0DE0Qc747j4AXjvz7IGTaJJsOPAycP7QMY7L8l+lmZmZoSNoAi0u7gdg5m2T/R9d43b+xHeG5b9Kc3NzQ0fQBJqfnwdg+/btAyeRunHNX5IaZPlLUoMsf0lqkOUvSQ2y/CWpQZa/JDXI8pekBln+ktQgy1+SGmT5S1KDLH9JapDlL0kNsvwlqUGDlH+SH0vyT0meSPKNJPND5JCkVg11SedDwG9W1aNJzgJ2JXmgqp4YKI8kNWWQmX9VPVdVj46+3g88CUwPkUWSWjT4mn+SrcA7gEeO8tq2JAtJFpaWlsaeTZJOV4OWf5IfBr4A/HpV7Vv5elXtrKrZqpqdmpoaf0BJOk0NVv5J3sRy8d9VVXcPlUOSWjTUbp8AtwFPVtWfDZFBklo21Mz/vcANwKVJHh89rhgoiyQ1Z5CtnlX1NSBDnFuSNAG7fSRJ42f5S1KDLH9JapDlL0kNsvwlqUGWvyQ1yPKXpAZZ/pLUIMtfkhpk+UtSgyx/SWrQULdx1Dq2Y8cOFhcXh44xEV7/e5if9zbUADMzM8zNzQ0dQ6tg+UunYNOmTUNHkE6K5a/OnNlJ659r/pLUIMtfkhpk+UtSgyx/SWqQ5S9JDbL8JalBlr8kNcjyl6QGpaqGzrAqSZaAZ4bOIR3FecC3hw4hHcOPV9XUysF1U/7SpEqyUFWzQ+eQunDZR5IaZPlLUoMsf+nU7Rw6gNSVa/6S1CBn/pLUIMtfkhpk+UtSgyx/SWqQ5S9JDfpfEHjBEygCxpMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature modif_prod_year\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature modif_prod_month\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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xsbZjqAeWv3QYli5d2nYEqS+WvypzZictfq75S1KBLH9JKpDlL0kFsvwlqUCWvyQVyPKXpAJZ/pJUIMtfkgoUmdl2hp5ExDTwaNs5pINYAfyk7RDSIazOzKHZOxdN+UsLVURMZOZo2zmkKlz2kaQCWf6SVCDLXzp8m9oOIFXlmr8kFciZvyQVyPKXpAJZ/pJUIMtfkgpk+UtSgf4fch6Z2ZipziAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature renewal_year\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature renewal_month\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature diff_act_renew\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature diff_act_end\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature diff_act_modif\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature diff_end_modif\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature avrg_month_cons\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature avrg_month_cons_gaz\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature forcast_avrg_month_cons\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature ratio_last_month_last12m_cons\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "boxplot of feature ratio_last_month_avg_cons\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for col in cols_num:\n",
    "    print('boxplot of feature {}'.format(col))\n",
    "    print('--------------------------------------')\n",
    "    sns.boxplot(y=col, data=new_df_ft)\n",
    "    plt.show()\n",
    "    print('--------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mtN1WYFdY8Eo"
   },
   "source": [
    "### III. Correlation based feature selection \n",
    "We will use the correlation score between features to do feature selection in order to reduce the number of continuous variables.\n",
    "The selection of features will be based on model performance which is expressed in terms of our evaluation metric \"f-1\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jRUMjjJGTsa2",
    "outputId": "e0b26bcd-32df-48a1-b995-34779c80f44b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n",
      "53\n"
     ]
    }
   ],
   "source": [
    "# Getting numerical features \n",
    "num_ftr = [col for col in new_df_ft.columns if new_df_ft[col].dtype in ['int64', 'float64']]\n",
    "\n",
    "# Getting datetime features \n",
    "\n",
    "date_ftr = [col for col in new_df_ft.columns if new_df_ft[col].dtype == 'datetime64[ns]']\n",
    "\n",
    "# Getting categorical features \n",
    "cat_ftr = [col for col in new_df_ft.columns if col not in num_ftr + date_ftr and col != 'id']\n",
    "\n",
    "print(len(num_ftr)+len(cat_ftr))\n",
    "print(len(new_df_ft.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "zsR-y6PhQYt2"
   },
   "outputs": [],
   "source": [
    "# Checking the existence of \"inf\" values\n",
    "new_df_ft.replace([np.inf, -np.inf], np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "aZsllxXnQfuw"
   },
   "outputs": [],
   "source": [
    "# Dropping \"Nan\" values that resulted from the feature engineering process\n",
    "new_df_ft = new_df_ft.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TF7910s4Qhj_",
    "outputId": "622fa74d-873d-4c65-8bed-18948fc9b00f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating an initial feature selection pipeline to eleminate correlated features\n",
    "sel_1 = SmartCorrelatedSelection(selection_method='model_performance', estimator=RandomForestClassifier(), scoring='f1', cv=5, threshold=0.8)\n",
    "sel_1.fit(new_df_ft[num_ftr], new_df_ft['churn'])\n",
    "len(sel_1.features_to_drop_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "b2ZjiUmrSgcb",
    "outputId": "151b1d3a-b69b-4f8f-f684-7adfaf10ea68"
   },
   "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>cons_12m</th>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>forecast_discount_energy</th>\n",
       "      <th>forecast_meter_rent_12m</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>net_margin</th>\n",
       "      <th>pow_max</th>\n",
       "      <th>...</th>\n",
       "      <th>previous_price</th>\n",
       "      <th>price_sens</th>\n",
       "      <th>end_year</th>\n",
       "      <th>modif_prod_month</th>\n",
       "      <th>renewal_year</th>\n",
       "      <th>renewal_month</th>\n",
       "      <th>diff_act_end</th>\n",
       "      <th>diff_act_modif</th>\n",
       "      <th>diff_end_modif</th>\n",
       "      <th>ratio_last_month_last12m_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4660</td>\n",
       "      <td>0</td>\n",
       "      <td>189.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.38</td>\n",
       "      <td>1</td>\n",
       "      <td>18.89</td>\n",
       "      <td>13.800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078366</td>\n",
       "      <td>13.640956</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>2566</td>\n",
       "      <td>0</td>\n",
       "      <td>2566</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>544</td>\n",
       "      <td>0</td>\n",
       "      <td>47.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.60</td>\n",
       "      <td>1</td>\n",
       "      <td>6.60</td>\n",
       "      <td>13.856</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129466</td>\n",
       "      <td>45.058910</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>2192</td>\n",
       "      <td>0</td>\n",
       "      <td>2192</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1584</td>\n",
       "      <td>0</td>\n",
       "      <td>240.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.22</td>\n",
       "      <td>1</td>\n",
       "      <td>25.46</td>\n",
       "      <td>13.200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075605</td>\n",
       "      <td>28.408609</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>2192</td>\n",
       "      <td>0</td>\n",
       "      <td>2192</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4425</td>\n",
       "      <td>0</td>\n",
       "      <td>445.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.73</td>\n",
       "      <td>52.32</td>\n",
       "      <td>44.91</td>\n",
       "      <td>1</td>\n",
       "      <td>47.98</td>\n",
       "      <td>19.800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113906</td>\n",
       "      <td>18.799168</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>2245</td>\n",
       "      <td>0</td>\n",
       "      <td>2245</td>\n",
       "      <td>0.118870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8302</td>\n",
       "      <td>0</td>\n",
       "      <td>796.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.12</td>\n",
       "      <td>181.21</td>\n",
       "      <td>33.12</td>\n",
       "      <td>1</td>\n",
       "      <td>118.89</td>\n",
       "      <td>13.200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128293</td>\n",
       "      <td>40.841311</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>1827</td>\n",
       "      <td>1423</td>\n",
       "      <td>404</td>\n",
       "      <td>0.240665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cons_12m  cons_gas_12m  forecast_cons_12m  forecast_discount_energy  \\\n",
       "1      4660             0             189.95                       0.0   \n",
       "2       544             0              47.96                       0.0   \n",
       "3      1584             0             240.04                       0.0   \n",
       "4      4425             0             445.75                       0.0   \n",
       "5      8302             0             796.94                       0.0   \n",
       "\n",
       "   forecast_meter_rent_12m  imp_cons  margin_gross_pow_ele  nb_prod_act  \\\n",
       "1                    16.27      0.00                 16.38            1   \n",
       "2                    38.72      0.00                 28.60            1   \n",
       "3                    19.83      0.00                 30.22            1   \n",
       "4                   131.73     52.32                 44.91            1   \n",
       "5                    30.12    181.21                 33.12            1   \n",
       "\n",
       "   net_margin  pow_max  ...  previous_price  price_sens  end_year  \\\n",
       "1       18.89   13.800  ...        0.078366   13.640956      2016   \n",
       "2        6.60   13.856  ...        0.129466   45.058910      2016   \n",
       "3       25.46   13.200  ...        0.075605   28.408609      2016   \n",
       "4       47.98   19.800  ...        0.113906   18.799168      2016   \n",
       "5      118.89   13.200  ...        0.128293   40.841311      2016   \n",
       "\n",
       "   modif_prod_month  renewal_year  renewal_month  diff_act_end  \\\n",
       "1                 8          2015              8          2566   \n",
       "2                 4          2015              4          2192   \n",
       "3                 3          2015              3          2192   \n",
       "4                 1          2015              3          2245   \n",
       "5                11          2015             12          1827   \n",
       "\n",
       "   diff_act_modif  diff_end_modif  ratio_last_month_last12m_cons  \n",
       "1               0            2566                       0.000000  \n",
       "2               0            2192                       0.000000  \n",
       "3               0            2192                       0.000000  \n",
       "4               0            2245                       0.118870  \n",
       "5            1423             404                       0.240665  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transforming our dataset\n",
    "df_1 = sel_1.transform(new_df_ft[num_ftr])\n",
    "df_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "lN6UilGWV14Q",
    "outputId": "f350f002-4e35-43cd-82f0-28c3475262db"
   },
   "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>has_gas</th>\n",
       "      <th>origin_up</th>\n",
       "      <th>price_change_energy</th>\n",
       "      <th>price_change_power</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>forecast_discount_energy</th>\n",
       "      <th>forecast_meter_rent_12m</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>...</th>\n",
       "      <th>previous_price</th>\n",
       "      <th>price_sens</th>\n",
       "      <th>end_year</th>\n",
       "      <th>modif_prod_month</th>\n",
       "      <th>renewal_year</th>\n",
       "      <th>renewal_month</th>\n",
       "      <th>diff_act_end</th>\n",
       "      <th>diff_act_modif</th>\n",
       "      <th>diff_end_modif</th>\n",
       "      <th>ratio_last_month_last12m_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>4660</td>\n",
       "      <td>0</td>\n",
       "      <td>189.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078366</td>\n",
       "      <td>13.640956</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>2566</td>\n",
       "      <td>0</td>\n",
       "      <td>2566</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>544</td>\n",
       "      <td>0</td>\n",
       "      <td>47.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129466</td>\n",
       "      <td>45.058910</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>2192</td>\n",
       "      <td>0</td>\n",
       "      <td>2192</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>1584</td>\n",
       "      <td>0</td>\n",
       "      <td>240.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075605</td>\n",
       "      <td>28.408609</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>2192</td>\n",
       "      <td>0</td>\n",
       "      <td>2192</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>4425</td>\n",
       "      <td>0</td>\n",
       "      <td>445.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.73</td>\n",
       "      <td>52.32</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113906</td>\n",
       "      <td>18.799168</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>2245</td>\n",
       "      <td>0</td>\n",
       "      <td>2245</td>\n",
       "      <td>0.118870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>decrease</td>\n",
       "      <td>8302</td>\n",
       "      <td>0</td>\n",
       "      <td>796.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.12</td>\n",
       "      <td>181.21</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128293</td>\n",
       "      <td>40.841311</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>1827</td>\n",
       "      <td>1423</td>\n",
       "      <td>404</td>\n",
       "      <td>0.240665</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>14537</th>\n",
       "      <td>t</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>32270</td>\n",
       "      <td>47940</td>\n",
       "      <td>4648.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072062</td>\n",
       "      <td>21.198938</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>1445</td>\n",
       "      <td>1079</td>\n",
       "      <td>366</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14538</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>7223</td>\n",
       "      <td>0</td>\n",
       "      <td>631.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.03</td>\n",
       "      <td>15.94</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101103</td>\n",
       "      <td>18.186301</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>1461</td>\n",
       "      <td>0</td>\n",
       "      <td>1461</td>\n",
       "      <td>0.025059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14539</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>1844</td>\n",
       "      <td>0</td>\n",
       "      <td>190.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.60</td>\n",
       "      <td>18.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114066</td>\n",
       "      <td>18.237929</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>1460</td>\n",
       "      <td>0</td>\n",
       "      <td>1460</td>\n",
       "      <td>0.097072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14540</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>19.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078366</td>\n",
       "      <td>12.121174</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>1461</td>\n",
       "      <td>0</td>\n",
       "      <td>1461</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14541</th>\n",
       "      <td>f</td>\n",
       "      <td>ldkssxwpmemidmecebumciepifcamkci</td>\n",
       "      <td>decrease</td>\n",
       "      <td>decrease</td>\n",
       "      <td>8730</td>\n",
       "      <td>0</td>\n",
       "      <td>762.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128003</td>\n",
       "      <td>501.481008</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>2556</td>\n",
       "      <td>0</td>\n",
       "      <td>2556</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14340 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      has_gas                         origin_up price_change_energy  \\\n",
       "1           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease   \n",
       "2           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease   \n",
       "3           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease   \n",
       "4           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease   \n",
       "5           f  lxidpiddsbxsbosboudacockeimpuepw            decrease   \n",
       "...       ...                               ...                 ...   \n",
       "14537       t  lxidpiddsbxsbosboudacockeimpuepw            decrease   \n",
       "14538       f  lxidpiddsbxsbosboudacockeimpuepw            decrease   \n",
       "14539       f  lxidpiddsbxsbosboudacockeimpuepw            decrease   \n",
       "14540       f  lxidpiddsbxsbosboudacockeimpuepw            decrease   \n",
       "14541       f  ldkssxwpmemidmecebumciepifcamkci            decrease   \n",
       "\n",
       "      price_change_power  cons_12m  cons_gas_12m  forecast_cons_12m  \\\n",
       "1               increase      4660             0             189.95   \n",
       "2               increase       544             0              47.96   \n",
       "3               increase      1584             0             240.04   \n",
       "4               increase      4425             0             445.75   \n",
       "5               decrease      8302             0             796.94   \n",
       "...                  ...       ...           ...                ...   \n",
       "14537           increase     32270         47940            4648.01   \n",
       "14538           increase      7223             0             631.69   \n",
       "14539           increase      1844             0             190.39   \n",
       "14540           increase       131             0              19.34   \n",
       "14541           decrease      8730             0             762.41   \n",
       "\n",
       "       forecast_discount_energy  forecast_meter_rent_12m  imp_cons  ...  \\\n",
       "1                           0.0                    16.27      0.00  ...   \n",
       "2                           0.0                    38.72      0.00  ...   \n",
       "3                           0.0                    19.83      0.00  ...   \n",
       "4                           0.0                   131.73     52.32  ...   \n",
       "5                           0.0                    30.12    181.21  ...   \n",
       "...                         ...                      ...       ...  ...   \n",
       "14537                       0.0                    18.57      0.00  ...   \n",
       "14538                       0.0                   144.03     15.94  ...   \n",
       "14539                       0.0                   129.60     18.05  ...   \n",
       "14540                       0.0                     7.18      0.00  ...   \n",
       "14541                       0.0                     1.07      0.00  ...   \n",
       "\n",
       "       previous_price  price_sens  end_year  modif_prod_month  renewal_year  \\\n",
       "1            0.078366   13.640956      2016                 8          2015   \n",
       "2            0.129466   45.058910      2016                 4          2015   \n",
       "3            0.075605   28.408609      2016                 3          2015   \n",
       "4            0.113906   18.799168      2016                 1          2015   \n",
       "5            0.128293   40.841311      2016                11          2015   \n",
       "...               ...         ...       ...               ...           ...   \n",
       "14537        0.072062   21.198938      2016                 5          2014   \n",
       "14538        0.101103   18.186301      2016                 8          2015   \n",
       "14539        0.114066   18.237929      2016                 2          2015   \n",
       "14540        0.078366   12.121174      2016                 8          2015   \n",
       "14541        0.128003  501.481008      2016                12          2015   \n",
       "\n",
       "       renewal_month  diff_act_end  diff_act_modif  diff_end_modif  \\\n",
       "1                  8          2566               0            2566   \n",
       "2                  4          2192               0            2192   \n",
       "3                  3          2192               0            2192   \n",
       "4                  3          2245               0            2245   \n",
       "5                 12          1827            1423             404   \n",
       "...              ...           ...             ...             ...   \n",
       "14537              5          1445            1079             366   \n",
       "14538              8          1461               0            1461   \n",
       "14539              2          1460               0            1460   \n",
       "14540              8          1461               0            1461   \n",
       "14541             12          2556               0            2556   \n",
       "\n",
       "       ratio_last_month_last12m_cons  \n",
       "1                           0.000000  \n",
       "2                           0.000000  \n",
       "3                           0.000000  \n",
       "4                           0.118870  \n",
       "5                           0.240665  \n",
       "...                              ...  \n",
       "14537                       0.000000  \n",
       "14538                       0.025059  \n",
       "14539                       0.097072  \n",
       "14540                       0.000000  \n",
       "14541                       0.000000  \n",
       "\n",
       "[14340 rows x 27 columns]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenating the dataframes without the \"id\" and datetime columns\n",
    "df_full = pd.concat([new_df_ft[cat_ftr], df_1], axis=1)\n",
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m_nRvgb6ayLD",
    "outputId": "eb84ff3a-5ef4-49dd-b5d8-bac19b5cb718"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "5        1\n",
       "        ..\n",
       "14537    0\n",
       "14538    1\n",
       "14539    1\n",
       "14540    0\n",
       "14541    0\n",
       "Name: churn, Length: 14340, dtype: int64"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the target 'churn'\n",
    "y = df_full.pop('churn')\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M7FvviRAbAom",
    "outputId": "076ea88d-5b3b-41d9-d66a-6bdb7dec95ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10038 4302 10038 4302\n"
     ]
    }
   ],
   "source": [
    "# Splitting the dataset into train and test sets\n",
    "train_X, test_X, train_y, test_y = train_test_split(df_full, y, test_size=0.30)\n",
    "print(len(train_X), len(test_X), len(train_y), len(test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5JwSfmIMcNNX",
    "outputId": "976bafb9-a887-4604-cf64-c7d8efac77f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n",
      "53\n"
     ]
    }
   ],
   "source": [
    "# Getting the names of the features after the first phase of feature selection \n",
    "# Getting numerical features \n",
    "num_ftr = [col for col in df_full.columns if df_full[col].dtype in ['int64', 'float64']]\n",
    "\n",
    "# Getting datetime features \n",
    "\n",
    "date_ftr = [col for col in df_full.columns if df_full[col].dtype == 'datetime64[ns]']\n",
    "\n",
    "# Getting categorical features \n",
    "cat_ftr = [col for col in df_full.columns if col not in num_ftr + date_ftr and col != 'id']\n",
    "\n",
    "print(len(num_ftr)+len(cat_ftr))\n",
    "print(len(new_df_ft.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IV. Tuning and training the Random Forest model.\n",
    "\n",
    "#### 1). The reasons why Random Forest is a good choice for our case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reasons why Random forest is a good choice are :\n",
    "\n",
    "- Having an important number of features (like the case of our dataset) can increase the complexity of the model which may cause the problem of overfitting the dataset. Random forest, which is a bagging algorithm will be  a good choice for our dataset because bagging algorithms tend to reduce variance (overfitting).\n",
    "\n",
    "- It is a good choice because it can handle outliers for the reason that decision trees are robust to outliers because they make decisions by asking if a variable x is >= than a certain value, and therefore the outlier will fall on one of the sides of the equation.\n",
    "\n",
    "The main disadvantage of random forest models is the large inference time which makes the model slow when doing predictions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2). Tuning and training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "244cd423cd0f40bb92770babe179697e",
      "c81a547e43a544e8bda5bae91c61daf1",
      "df66dba21afd459081b08beb99407ef9",
      "5ba5ce67a44641b89b8571c4b9b78221",
      "debf1f79445242f3b0c7626773ecaf2e",
      "5bccd4cdbd194d93aea5409df08e63a3",
      "e935a2d8406741dbae1653f10a00c9c3",
      "6eebde42e11c4cfab2253176dc3f2bdf",
      "afa173865e49403b8a15294eb77f13e2",
      "57e0e384ee5946a783cf192e354c49e4",
      "364baa7d4dd84bb0a3180615b6f3eb45"
     ]
    },
    "id": "ZwqtWV2QbToC",
    "outputId": "c14fefab-b705-46c1-b2bb-c506ed732215"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-11-07 22:24:23,896]\u001b[0m A new study created in memory with name: no-name-2ed35ba7-b223-422f-ab69-f7950ec3ee84\u001b[0m\n",
      "Progress bar is experimental (supported from v1.2.0). The interface can change in the future.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43ee79521aee4af9b8109d1ef152e83d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-11-07 22:25:46,724]\u001b[0m Trial 0 finished with value: 0.02000050001250031 and parameters: {'scalers': 'standard', 'encoders': 'PRatio', 'n_estimators': 59, 'max_depth': 6, 'k': 21}. Best is trial 0 with value: 0.02000050001250031.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:29:56,720]\u001b[0m Trial 1 finished with value: 0.08001219496421315 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 157, 'max_depth': 13, 'k': 19}. Best is trial 1 with value: 0.08001219496421315.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:34:40,034]\u001b[0m Trial 2 finished with value: 0.019491995314330338 and parameters: {'scalers': 'standard', 'encoders': 'Count', 'n_estimators': 368, 'max_depth': 8, 'k': 13}. Best is trial 1 with value: 0.08001219496421315.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:38:05,982]\u001b[0m Trial 3 finished with value: 0.0040405071040481595 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 375, 'max_depth': 8, 'k': 10}. Best is trial 1 with value: 0.08001219496421315.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:42:06,856]\u001b[0m Trial 4 finished with value: 0.04516023479961177 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 296, 'max_depth': 14, 'k': 9}. Best is trial 1 with value: 0.08001219496421315.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:43:46,343]\u001b[0m Trial 5 finished with value: 0.011872418768970494 and parameters: {'scalers': 'standard', 'encoders': 'OneHot', 'n_estimators': 173, 'max_depth': 7, 'k': 19}. Best is trial 1 with value: 0.08001219496421315.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:44:36,023]\u001b[0m Trial 6 finished with value: 0.0 and parameters: {'scalers': 'minmax', 'encoders': 'OneHot', 'n_estimators': 189, 'max_depth': 2, 'k': 15}. Best is trial 1 with value: 0.08001219496421315.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:44:54,949]\u001b[0m Trial 7 finished with value: 0.0 and parameters: {'scalers': 'robust', 'encoders': 'Ordinal', 'n_estimators': 50, 'max_depth': 2, 'k': 5}. Best is trial 1 with value: 0.08001219496421315.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:48:24,059]\u001b[0m Trial 8 finished with value: 0.027297942063936216 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 324, 'max_depth': 10, 'k': 16}. Best is trial 1 with value: 0.08001219496421315.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:48:58,245]\u001b[0m Trial 9 finished with value: 0.0 and parameters: {'scalers': 'standard', 'encoders': 'Mean', 'n_estimators': 154, 'max_depth': 2, 'k': 12}. Best is trial 1 with value: 0.08001219496421315.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:50:56,337]\u001b[0m Trial 10 finished with value: 0.12215848038837698 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 107, 'max_depth': 14, 'k': 25}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:53:10,109]\u001b[0m Trial 11 finished with value: 0.11695928527011001 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 116, 'max_depth': 14, 'k': 26}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:54:51,698]\u001b[0m Trial 12 finished with value: 0.09870864496180518 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 98, 'max_depth': 12, 'k': 26}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 22:58:36,481]\u001b[0m Trial 13 finished with value: 0.07546092982217026 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 246, 'max_depth': 11, 'k': 25}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:00:24,815]\u001b[0m Trial 14 finished with value: 0.05992097537630079 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 106, 'max_depth': 10, 'k': 23}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:02:53,016]\u001b[0m Trial 15 finished with value: 0.11266373171297536 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 116, 'max_depth': 14, 'k': 23}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:05:15,838]\u001b[0m Trial 16 finished with value: 0.022061706208883265 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 237, 'max_depth': 5, 'k': 26}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:06:00,640]\u001b[0m Trial 17 finished with value: 0.09390634381647181 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 28, 'max_depth': 12, 'k': 23}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:07:56,175]\u001b[0m Trial 18 finished with value: 0.0786826175574421 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 94, 'max_depth': 14, 'k': 19}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:10:07,674]\u001b[0m Trial 19 finished with value: 0.062054949862734896 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 134, 'max_depth': 10, 'k': 21}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:13:15,764]\u001b[0m Trial 20 finished with value: 0.09647706285357371 and parameters: {'scalers': 'robust', 'encoders': 'Ordinal', 'n_estimators': 207, 'max_depth': 12, 'k': 24}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:14:41,974]\u001b[0m Trial 21 finished with value: 0.10858109491606124 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 124, 'max_depth': 14, 'k': 22}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:15:47,513]\u001b[0m Trial 22 finished with value: 0.10886685506656094 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 82, 'max_depth': 13, 'k': 26}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:16:12,181]\u001b[0m Trial 23 finished with value: 0.11625985415430269 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 24, 'max_depth': 13, 'k': 24}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:16:40,918]\u001b[0m Trial 24 finished with value: 0.062044688969471 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 36, 'max_depth': 13, 'k': 17}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:17:28,059]\u001b[0m Trial 25 finished with value: 0.03703005843905579 and parameters: {'scalers': 'robust', 'encoders': 'OneHot', 'n_estimators': 69, 'max_depth': 11, 'k': 24}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:17:53,263]\u001b[0m Trial 26 finished with value: 0.0973557369321169 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 27, 'max_depth': 13, 'k': 21}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:18:40,213]\u001b[0m Trial 27 finished with value: 0.07653404205858913 and parameters: {'scalers': 'minmax', 'encoders': 'PRatio', 'n_estimators': 68, 'max_depth': 11, 'k': 25}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:19:45,924]\u001b[0m Trial 28 finished with value: 0.04122113964670353 and parameters: {'scalers': 'standard', 'encoders': 'PRatio', 'n_estimators': 137, 'max_depth': 9, 'k': 20}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:20:10,719]\u001b[0m Trial 29 finished with value: 0.026003158991630766 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 56, 'max_depth': 5, 'k': 22}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:22:45,833]\u001b[0m Trial 30 finished with value: 0.09316126568338072 and parameters: {'scalers': 'standard', 'encoders': 'Count', 'n_estimators': 247, 'max_depth': 12, 'k': 24}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:24:20,333]\u001b[0m Trial 31 finished with value: 0.10974882029041681 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 123, 'max_depth': 14, 'k': 23}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:26:31,086]\u001b[0m Trial 32 finished with value: 0.11043810658008321 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 160, 'max_depth': 14, 'k': 25}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:28:01,014]\u001b[0m Trial 33 finished with value: 0.1021883855657579 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 109, 'max_depth': 13, 'k': 26}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:30:11,767]\u001b[0m Trial 34 finished with value: 0.1096204786532144 and parameters: {'scalers': 'robust', 'encoders': 'PRatio', 'n_estimators': 190, 'max_depth': 14, 'k': 18}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:31:10,930]\u001b[0m Trial 35 finished with value: 0.11951040700849598 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 75, 'max_depth': 13, 'k': 22}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:32:10,102]\u001b[0m Trial 36 finished with value: 0.05373259920606606 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 81, 'max_depth': 13, 'k': 20}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:32:49,051]\u001b[0m Trial 37 finished with value: 0.087402819350508 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 48, 'max_depth': 12, 'k': 22}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:33:10,495]\u001b[0m Trial 38 finished with value: 0.06197393344046702 and parameters: {'scalers': 'standard', 'encoders': 'Mean', 'n_estimators': 20, 'max_depth': 9, 'k': 24}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:33:52,251]\u001b[0m Trial 39 finished with value: 0.02576296151782666 and parameters: {'scalers': 'robust', 'encoders': 'Ordinal', 'n_estimators': 82, 'max_depth': 7, 'k': 21}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:35:13,633]\u001b[0m Trial 40 finished with value: 0.043185322728012364 and parameters: {'scalers': 'minmax', 'encoders': 'OneHot', 'n_estimators': 148, 'max_depth': 13, 'k': 14}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:37:01,722]\u001b[0m Trial 41 finished with value: 0.11407024920445999 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 115, 'max_depth': 14, 'k': 25}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:37:46,804]\u001b[0m Trial 42 finished with value: 0.11212327723263163 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 47, 'max_depth': 13, 'k': 25}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:38:26,873]\u001b[0m Trial 43 finished with value: 0.05541263226549953 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 67, 'max_depth': 14, 'k': 10}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:40:00,389]\u001b[0m Trial 44 finished with value: 0.07648470383910537 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 94, 'max_depth': 11, 'k': 26}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:41:40,351]\u001b[0m Trial 45 finished with value: 0.023795445438638325 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 169, 'max_depth': 12, 'k': 5}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:43:05,339]\u001b[0m Trial 46 finished with value: 0.10819682223626667 and parameters: {'scalers': 'standard', 'encoders': 'Mean', 'n_estimators': 46, 'max_depth': 14, 'k': 25}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:45:33,555]\u001b[0m Trial 47 finished with value: 0.033124949802665206 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 332, 'max_depth': 13, 'k': 7}. Best is trial 10 with value: 0.12215848038837698.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:50:33,707]\u001b[0m Trial 48 finished with value: 0.12450634895656347 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 399, 'max_depth': 14, 'k': 23}. Best is trial 48 with value: 0.12450634895656347.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:51:41,141]\u001b[0m Trial 49 finished with value: 0.0 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 314, 'max_depth': 3, 'k': 23}. Best is trial 48 with value: 0.12450634895656347.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:55:29,906]\u001b[0m Trial 50 finished with value: 0.08603689910259196 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 396, 'max_depth': 12, 'k': 20}. Best is trial 48 with value: 0.12450634895656347.\u001b[0m\n",
      "\u001b[32m[I 2022-11-07 23:57:49,343]\u001b[0m Trial 51 finished with value: 0.12499983435316568 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 183, 'max_depth': 14, 'k': 24}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:01:34,075]\u001b[0m Trial 52 finished with value: 0.11100715872215643 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 268, 'max_depth': 13, 'k': 24}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:11:39,523]\u001b[0m Trial 53 finished with value: 0.11400206709405183 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 356, 'max_depth': 14, 'k': 22}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:15:40,318]\u001b[0m Trial 54 finished with value: 0.11418972138693484 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 187, 'max_depth': 13, 'k': 23}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:20:19,038]\u001b[0m Trial 55 finished with value: 0.11234842911618251 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 215, 'max_depth': 14, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:22:57,834]\u001b[0m Trial 56 finished with value: 0.04093122669996963 and parameters: {'scalers': 'minmax', 'encoders': 'OneHot', 'n_estimators': 149, 'max_depth': 12, 'k': 24}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:27:48,415]\u001b[0m Trial 57 finished with value: 0.08751418373014593 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 282, 'max_depth': 13, 'k': 22}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:30:27,497]\u001b[0m Trial 58 finished with value: 0.11032404316551223 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 136, 'max_depth': 14, 'k': 19}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:32:17,892]\u001b[0m Trial 59 finished with value: 0.0952778588489301 and parameters: {'scalers': 'minmax', 'encoders': 'PRatio', 'n_estimators': 96, 'max_depth': 11, 'k': 23}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:35:29,990]\u001b[0m Trial 60 finished with value: 0.03494684181754932 and parameters: {'scalers': 'standard', 'encoders': 'Count', 'n_estimators': 218, 'max_depth': 12, 'k': 16}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:39:03,967]\u001b[0m Trial 61 finished with value: 0.1175095397269917 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 187, 'max_depth': 13, 'k': 24}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:42:39,034]\u001b[0m Trial 62 finished with value: 0.10396139527848065 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 183, 'max_depth': 13, 'k': 24}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:47:02,469]\u001b[0m Trial 63 finished with value: 0.12492877492877492 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 199, 'max_depth': 14, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:51:09,761]\u001b[0m Trial 64 finished with value: 0.1173680228396123 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 205, 'max_depth': 14, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 00:55:43,234]\u001b[0m Trial 65 finished with value: 0.11577191340445274 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 228, 'max_depth': 14, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 01:00:38,138]\u001b[0m Trial 66 finished with value: 0.10522828026535509 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 204, 'max_depth': 14, 'k': 21}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 01:05:27,315]\u001b[0m Trial 67 finished with value: 0.119321724704744 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 198, 'max_depth': 14, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 01:10:55,870]\u001b[0m Trial 68 finished with value: 0.11235073778796667 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 256, 'max_depth': 14, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 01:14:04,225]\u001b[0m Trial 69 finished with value: 0.1021562241022966 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 173, 'max_depth': 13, 'k': 23}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 01:18:18,087]\u001b[0m Trial 70 finished with value: 0.0983348373734964 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 234, 'max_depth': 13, 'k': 22}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 01:22:29,894]\u001b[0m Trial 71 finished with value: 0.12309811654917335 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 195, 'max_depth': 14, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 01:26:55,250]\u001b[0m Trial 72 finished with value: 0.11896161450484044 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 202, 'max_depth': 14, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 01:32:20,567]\u001b[0m Trial 73 finished with value: 0.11739079655402193 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 197, 'max_depth': 14, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 01:38:12,746]\u001b[0m Trial 74 finished with value: 0.11431911049496948 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 224, 'max_depth': 14, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 12:55:16,306]\u001b[0m Trial 75 finished with value: 0.12121624046162985 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 170, 'max_depth': 14, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 12:58:47,195]\u001b[0m Trial 76 finished with value: 0.1229774433208767 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 166, 'max_depth': 14, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:02:49,643]\u001b[0m Trial 77 finished with value: 0.08387298497270598 and parameters: {'scalers': 'minmax', 'encoders': 'OneHot', 'n_estimators': 164, 'max_depth': 13, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:05:52,395]\u001b[0m Trial 78 finished with value: 0.12295939952766674 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 180, 'max_depth': 14, 'k': 24}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:07:27,278]\u001b[0m Trial 79 finished with value: 0.043370787580910175 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 175, 'max_depth': 14, 'k': 12}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:08:23,854]\u001b[0m Trial 80 finished with value: 0.026012832736104267 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 154, 'max_depth': 5, 'k': 24}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:10:22,485]\u001b[0m Trial 81 finished with value: 0.11971846845977804 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 145, 'max_depth': 14, 'k': 23}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:12:19,478]\u001b[0m Trial 82 finished with value: 0.12124659715329726 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 128, 'max_depth': 14, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:13:58,586]\u001b[0m Trial 83 finished with value: 0.11763272048987843 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 128, 'max_depth': 14, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:16:16,232]\u001b[0m Trial 84 finished with value: 0.11243584228368761 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 175, 'max_depth': 14, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:18:13,008]\u001b[0m Trial 85 finished with value: 0.09428554282479884 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 161, 'max_depth': 13, 'k': 24}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:19:53,500]\u001b[0m Trial 86 finished with value: 0.08578836111680639 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 136, 'max_depth': 12, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:20:46,792]\u001b[0m Trial 87 finished with value: 0.039686584767997184 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 105, 'max_depth': 7, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:23:12,057]\u001b[0m Trial 88 finished with value: 0.12416383395885273 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 181, 'max_depth': 14, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:24:05,313]\u001b[0m Trial 89 finished with value: 0.00797105091219782 and parameters: {'scalers': 'standard', 'encoders': 'Count', 'n_estimators': 180, 'max_depth': 4, 'k': 24}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:26:25,070]\u001b[0m Trial 90 finished with value: 0.11760317313385996 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 195, 'max_depth': 13, 'k': 24}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:28:38,382]\u001b[0m Trial 91 finished with value: 0.11606558031791676 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 167, 'max_depth': 14, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:43:19,785]\u001b[0m Trial 92 finished with value: 0.11240287691262038 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 213, 'max_depth': 14, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:45:50,615]\u001b[0m Trial 93 finished with value: 0.1179314951463877 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 155, 'max_depth': 14, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:47:40,917]\u001b[0m Trial 94 finished with value: 0.10048946333283904 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 123, 'max_depth': 13, 'k': 26}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:50:14,494]\u001b[0m Trial 95 finished with value: 0.11096815115326761 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 192, 'max_depth': 13, 'k': 25}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:52:07,649]\u001b[0m Trial 96 finished with value: 0.06929823177923725 and parameters: {'scalers': 'minmax', 'encoders': 'OneHot', 'n_estimators': 143, 'max_depth': 14, 'k': 23}. Best is trial 51 with value: 0.12499983435316568.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:54:31,081]\u001b[0m Trial 97 finished with value: 0.13111670517989565 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 180, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:56:28,274]\u001b[0m Trial 98 finished with value: 0.05653999955419834 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 185, 'max_depth': 9, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 13:59:15,140]\u001b[0m Trial 99 finished with value: 0.11229480031157509 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 209, 'max_depth': 13, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:03:20,328]\u001b[0m Trial 100 finished with value: 0.12306967417485255 and parameters: {'scalers': 'standard', 'encoders': 'Count', 'n_estimators': 248, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:08:58,860]\u001b[0m Trial 101 finished with value: 0.11031284717762942 and parameters: {'scalers': 'standard', 'encoders': 'Count', 'n_estimators': 339, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:12:48,992]\u001b[0m Trial 102 finished with value: 0.11588333330237263 and parameters: {'scalers': 'standard', 'encoders': 'Count', 'n_estimators': 242, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:17:55,964]\u001b[0m Trial 103 finished with value: 0.11741549220411811 and parameters: {'scalers': 'standard', 'encoders': 'Count', 'n_estimators': 300, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:21:06,779]\u001b[0m Trial 104 finished with value: 0.0979050726590249 and parameters: {'scalers': 'standard', 'encoders': 'Count', 'n_estimators': 222, 'max_depth': 14, 'k': 18}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:23:58,609]\u001b[0m Trial 105 finished with value: 0.10891686446178514 and parameters: {'scalers': 'standard', 'encoders': 'Count', 'n_estimators': 178, 'max_depth': 13, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:30:02,889]\u001b[0m Trial 106 finished with value: 0.11745579483174078 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 393, 'max_depth': 14, 'k': 22}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:35:45,585]\u001b[0m Trial 107 finished with value: 0.10040406764534034 and parameters: {'scalers': 'minmax', 'encoders': 'PRatio', 'n_estimators': 378, 'max_depth': 13, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:38:27,943]\u001b[0m Trial 108 finished with value: 0.11580597491812443 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 155, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:39:21,671]\u001b[0m Trial 109 finished with value: 0.03518696104387924 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 112, 'max_depth': 13, 'k': 6}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:40:52,025]\u001b[0m Trial 110 finished with value: 0.09765850470346116 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 87, 'max_depth': 14, 'k': 21}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:43:58,315]\u001b[0m Trial 111 finished with value: 0.11869383157398591 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 169, 'max_depth': 14, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:47:21,352]\u001b[0m Trial 112 finished with value: 0.11937099523752684 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 192, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:50:43,962]\u001b[0m Trial 113 finished with value: 0.1112225528567653 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 182, 'max_depth': 14, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:53:21,113]\u001b[0m Trial 114 finished with value: 0.09674865865765195 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 162, 'max_depth': 13, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:56:59,005]\u001b[0m Trial 115 finished with value: 0.11952708479907118 and parameters: {'scalers': 'minmax', 'encoders': 'PRatio', 'n_estimators': 202, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 14:58:42,137]\u001b[0m Trial 116 finished with value: 0.05501221633614946 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 129, 'max_depth': 8, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:01:41,750]\u001b[0m Trial 117 finished with value: 0.12102451588182368 and parameters: {'scalers': 'standard', 'encoders': 'Ordinal', 'n_estimators': 169, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:05:36,198]\u001b[0m Trial 118 finished with value: 0.11214362819999532 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 231, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:07:54,809]\u001b[0m Trial 119 finished with value: 0.07230339263733877 and parameters: {'scalers': 'minmax', 'encoders': 'OneHot', 'n_estimators': 147, 'max_depth': 13, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:28:11,517]\u001b[0m Trial 120 finished with value: 0.10735787388603349 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 254, 'max_depth': 13, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:30:47,274]\u001b[0m Trial 121 finished with value: 0.11427617897590245 and parameters: {'scalers': 'standard', 'encoders': 'Ordinal', 'n_estimators': 169, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:33:34,681]\u001b[0m Trial 122 finished with value: 0.11594359840666643 and parameters: {'scalers': 'standard', 'encoders': 'Ordinal', 'n_estimators': 188, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:36:10,024]\u001b[0m Trial 123 finished with value: 0.12109964464864356 and parameters: {'scalers': 'standard', 'encoders': 'Ordinal', 'n_estimators': 174, 'max_depth': 14, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:39:38,317]\u001b[0m Trial 124 finished with value: 0.11040808522510943 and parameters: {'scalers': 'standard', 'encoders': 'Ordinal', 'n_estimators': 209, 'max_depth': 14, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:42:54,766]\u001b[0m Trial 125 finished with value: 0.110427518145084 and parameters: {'scalers': 'standard', 'encoders': 'Ordinal', 'n_estimators': 178, 'max_depth': 14, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:46:09,602]\u001b[0m Trial 126 finished with value: 0.101777587071607 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 196, 'max_depth': 13, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:48:36,380]\u001b[0m Trial 127 finished with value: 0.11775375970247492 and parameters: {'scalers': 'minmax', 'encoders': 'PRatio', 'n_estimators': 141, 'max_depth': 14, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:50:12,171]\u001b[0m Trial 128 finished with value: 0.03122633315037542 and parameters: {'scalers': 'standard', 'encoders': 'Ordinal', 'n_estimators': 159, 'max_depth': 10, 'k': 14}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:53:23,428]\u001b[0m Trial 129 finished with value: 0.035433763892877806 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 350, 'max_depth': 12, 'k': 9}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:56:27,089]\u001b[0m Trial 130 finished with value: 0.1275836506896249 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 217, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 15:59:51,473]\u001b[0m Trial 131 finished with value: 0.10653437284654647 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 219, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:01:30,651]\u001b[0m Trial 132 finished with value: 0.1189576734890186 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 104, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:04:07,156]\u001b[0m Trial 133 finished with value: 0.11394654069300533 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 184, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:06:30,058]\u001b[0m Trial 134 finished with value: 0.11381725929658837 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 200, 'max_depth': 13, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:08:34,855]\u001b[0m Trial 135 finished with value: 0.11935744422493495 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 174, 'max_depth': 14, 'k': 22}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:11:04,484]\u001b[0m Trial 136 finished with value: 0.11582429785213663 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 211, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:12:38,906]\u001b[0m Trial 137 finished with value: 0.10201152158976798 and parameters: {'scalers': 'minmax', 'encoders': 'Ordinal', 'n_estimators': 120, 'max_depth': 13, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:15:21,828]\u001b[0m Trial 138 finished with value: 0.12621337221862353 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 191, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:18:53,613]\u001b[0m Trial 139 finished with value: 0.11727268624888224 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 266, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:21:26,851]\u001b[0m Trial 140 finished with value: 0.11079780024896058 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 185, 'max_depth': 13, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:24:50,160]\u001b[0m Trial 141 finished with value: 0.12401937265776723 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 193, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:28:27,877]\u001b[0m Trial 142 finished with value: 0.11561764375988219 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 193, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:31:20,292]\u001b[0m Trial 143 finished with value: 0.1187302574711124 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 204, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:34:21,916]\u001b[0m Trial 144 finished with value: 0.11738368341401897 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 217, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:37:07,451]\u001b[0m Trial 145 finished with value: 0.1227306696885065 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 187, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:39:41,739]\u001b[0m Trial 146 finished with value: 0.11952480218727209 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 194, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:41:56,091]\u001b[0m Trial 147 finished with value: 0.10334732159914817 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 180, 'max_depth': 13, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:46:34,342]\u001b[0m Trial 148 finished with value: 0.11588462110532352 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 226, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:49:42,851]\u001b[0m Trial 149 finished with value: 0.11582202141287565 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 208, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:54:32,957]\u001b[0m Trial 150 finished with value: 0.09601221746585231 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 293, 'max_depth': 13, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 16:58:03,376]\u001b[0m Trial 151 finished with value: 0.11908910977060812 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 189, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:00:59,469]\u001b[0m Trial 152 finished with value: 0.11912953349924989 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 201, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:03:13,455]\u001b[0m Trial 153 finished with value: 0.0690092972183959 and parameters: {'scalers': 'minmax', 'encoders': 'OneHot', 'n_estimators': 166, 'max_depth': 14, 'k': 22}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:06:18,833]\u001b[0m Trial 154 finished with value: 0.11629106050553292 and parameters: {'scalers': 'minmax', 'encoders': 'PRatio', 'n_estimators': 153, 'max_depth': 14, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:09:39,926]\u001b[0m Trial 155 finished with value: 0.12268906998122789 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 181, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:12:52,403]\u001b[0m Trial 156 finished with value: 0.11765835801729611 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 182, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:15:19,387]\u001b[0m Trial 157 finished with value: 0.10718826538573745 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 190, 'max_depth': 13, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:18:59,101]\u001b[0m Trial 158 finished with value: 0.1194509297768515 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 198, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:23:13,414]\u001b[0m Trial 159 finished with value: 0.09996540307825783 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 239, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:25:55,620]\u001b[0m Trial 160 finished with value: 0.1205640657313856 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 164, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:28:30,963]\u001b[0m Trial 161 finished with value: 0.12301347957839706 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 172, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:31:02,684]\u001b[0m Trial 162 finished with value: 0.11770457113696797 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 177, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:32:17,764]\u001b[0m Trial 163 finished with value: 0.02998119507443131 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 189, 'max_depth': 6, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:34:41,187]\u001b[0m Trial 164 finished with value: 0.11608848085328809 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 180, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:36:44,742]\u001b[0m Trial 165 finished with value: 0.11574245656561752 and parameters: {'scalers': 'minmax', 'encoders': 'PRatio', 'n_estimators': 160, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:37:58,423]\u001b[0m Trial 166 finished with value: 0.10883221760018506 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 92, 'max_depth': 13, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:40:31,545]\u001b[0m Trial 167 finished with value: 0.11812864882724665 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 214, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:42:56,072]\u001b[0m Trial 168 finished with value: 0.1297239668434765 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 197, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:43:42,400]\u001b[0m Trial 169 finished with value: 0.0 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 198, 'max_depth': 3, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:46:01,898]\u001b[0m Trial 170 finished with value: 0.10323149097105218 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 205, 'max_depth': 13, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:48:18,810]\u001b[0m Trial 171 finished with value: 0.12479197225713187 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 174, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:50:27,337]\u001b[0m Trial 172 finished with value: 0.11646612714385744 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 173, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:55:07,679]\u001b[0m Trial 173 finished with value: 0.11925832131702205 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 316, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:58:41,155]\u001b[0m Trial 174 finished with value: 0.11247884367186553 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 185, 'max_depth': 14, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 17:59:37,117]\u001b[0m Trial 175 finished with value: 0.11510295183019688 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 36, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:03:03,047]\u001b[0m Trial 176 finished with value: 0.12519956641381363 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 192, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:06:08,252]\u001b[0m Trial 177 finished with value: 0.11066412299826311 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 193, 'max_depth': 14, 'k': 22}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:08:18,110]\u001b[0m Trial 178 finished with value: 0.041123277762054974 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 174, 'max_depth': 14, 'k': 11}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:11:34,142]\u001b[0m Trial 179 finished with value: 0.11926688992111083 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 185, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:14:18,699]\u001b[0m Trial 180 finished with value: 0.10906888354155156 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 168, 'max_depth': 13, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:17:52,645]\u001b[0m Trial 181 finished with value: 0.12125209983560636 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 194, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:18:57,801]\u001b[0m Trial 182 finished with value: 0.09192243197837333 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 58, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:22:19,724]\u001b[0m Trial 183 finished with value: 0.1213011289499829 and parameters: {'scalers': 'robust', 'encoders': 'Mean', 'n_estimators': 179, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:26:05,311]\u001b[0m Trial 184 finished with value: 0.11350529794395148 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 201, 'max_depth': 14, 'k': 26}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:29:33,518]\u001b[0m Trial 185 finished with value: 0.11406719190898418 and parameters: {'scalers': 'robust', 'encoders': 'Count', 'n_estimators': 210, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:33:02,064]\u001b[0m Trial 186 finished with value: 0.11575743369790462 and parameters: {'scalers': 'minmax', 'encoders': 'Mean', 'n_estimators': 190, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:35:51,744]\u001b[0m Trial 187 finished with value: 0.08520072171149733 and parameters: {'scalers': 'minmax', 'encoders': 'OneHot', 'n_estimators': 183, 'max_depth': 13, 'k': 22}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:38:52,449]\u001b[0m Trial 188 finished with value: 0.05832946866713238 and parameters: {'scalers': 'minmax', 'encoders': 'PRatio', 'n_estimators': 204, 'max_depth': 14, 'k': 16}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:41:51,370]\u001b[0m Trial 189 finished with value: 0.12635613530491174 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 175, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:44:38,143]\u001b[0m Trial 190 finished with value: 0.11254736315732153 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 160, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:47:15,956]\u001b[0m Trial 191 finished with value: 0.061651180660595495 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 175, 'max_depth': 14, 'k': 17}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:50:23,548]\u001b[0m Trial 192 finished with value: 0.1027940495876023 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 188, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:53:38,086]\u001b[0m Trial 193 finished with value: 0.12399675306758728 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 197, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:56:32,202]\u001b[0m Trial 194 finished with value: 0.12106235623233294 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 169, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 18:59:50,254]\u001b[0m Trial 195 finished with value: 0.1209109046409391 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 180, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 19:03:08,841]\u001b[0m Trial 196 finished with value: 0.12458952911452267 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 197, 'max_depth': 14, 'k': 24}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 19:06:00,047]\u001b[0m Trial 197 finished with value: 0.1224192266862942 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 197, 'max_depth': 14, 'k': 23}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 19:09:18,247]\u001b[0m Trial 198 finished with value: 0.11963444291551546 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 214, 'max_depth': 14, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n",
      "\u001b[32m[I 2022-11-08 19:12:17,001]\u001b[0m Trial 199 finished with value: 0.10323014536473296 and parameters: {'scalers': 'minmax', 'encoders': 'Count', 'n_estimators': 195, 'max_depth': 13, 'k': 25}. Best is trial 97 with value: 0.13111670517989565.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# A function to fit and tune Random Forest\n",
    "\n",
    "def objective(trial):\n",
    "    \n",
    "    # Defining the scalers space \n",
    "    scalers = trial.suggest_categorical('scalers', ['minmax', 'standard', 'robust'])\n",
    "    if scalers == 'minmax':\n",
    "        scaler = MinMaxScaler()\n",
    "    elif scalers == 'robust':\n",
    "        scaler = RobustScaler()\n",
    "    else:\n",
    "        scaler = StandardScaler()\n",
    "    \n",
    "    # Defining the encoder space\n",
    "    encoders = trial.suggest_categorical('encoders', ['Ordinal', 'OneHot', 'Count', 'Mean', 'PRatio'])\n",
    "    if encoders == \"Ordinal\":\n",
    "        encoder = OrdinalEncoder()\n",
    "    elif encoders == 'OneHot':\n",
    "        encoder = OneHotEncoder()\n",
    "    elif encoders == \"Count\":\n",
    "        encoder = CountFrequencyEncoder()\n",
    "    elif encoders == 'Mean':\n",
    "        encoder = MeanEncoder()\n",
    "    else :\n",
    "        encoder = PRatioEncoder()\n",
    "    \n",
    "    # Defining the n_estimators space\n",
    "    n_estims = trial.suggest_int('n_estimators', low=20, high=400)\n",
    "    \n",
    "    # Defining the max_depth space\n",
    "    max_dep = trial.suggest_int('max_depth', low=2, high=14)\n",
    "\n",
    "    # Defining the number of features space for the second phase of feature selection using mutual inforamtion selector\n",
    "    k = trial.suggest_int('k', low=5, high=len(train_X.columns))\n",
    "        \n",
    "    # Preprocessing steps for the numerical variables\n",
    "    num_preproc = Pipeline(steps=[('scaler',scaler)])\n",
    "\n",
    "    # Preprocessing steps for the categorical variables\n",
    "    cat_preproc = Pipeline(steps=[('encoder', encoder)])\n",
    "\n",
    "    preprocessor = ColumnTransformer(transformers=[('numerical_preprocessor',num_preproc, num_ftr),\n",
    "                                               ('cat_preprocessor', cat_preproc, cat_ftr)])\n",
    "\n",
    "    model_pipe = Pipeline(steps=[(\"preprocessing\", preprocessor),\n",
    "                                 ('selector', SelectKBest(mutual_info_classif, k=k)),\n",
    "                                 (\"model\", RandomForestClassifier(n_estimators=n_estims, max_depth=max_dep, max_features=None))])\n",
    "    \n",
    "    cv_score = cross_val_score(model_pipe, train_X, train_y, cv=5, scoring ='f1').mean()\n",
    "    \n",
    "    return cv_score\n",
    "\n",
    "study_1 = optuna.create_study(direction='maximize')\n",
    "study_1.optimize(objective, n_trials=200, show_progress_bar=True)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xIq1QwdQd_Y5",
    "outputId": "c9ed616e-0551-48c8-de15-5dcd40cd4a3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'scalers': 'minmax',\n",
       " 'encoders': 'Count',\n",
       " 'n_estimators': 180,\n",
       " 'max_depth': 14,\n",
       " 'k': 24}"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "study_1.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "id": "40ga7D7xQ44V"
   },
   "outputs": [],
   "source": [
    "# Training the model with the found parameters \n",
    "\n",
    " # Preprocessing steps for the numerical variables\n",
    "num_preproc = Pipeline(steps=[('scaler',MinMaxScaler())])\n",
    "\n",
    "# Preprocessing steps for the categorical variables\n",
    "cat_preproc = Pipeline(steps=[('encoder', CountFrequencyEncoder())])\n",
    "\n",
    "preprocessor = ColumnTransformer(transformers=[('numerical_preprocessor',num_preproc, num_ftr),\n",
    "                                               ('cat_preprocessor', cat_preproc, cat_ftr)])\n",
    "\n",
    "model_pipe = Pipeline(steps=[(\"preprocessing\", preprocessor),\n",
    "                                 ('selector', SelectKBest(mutual_info_classif, k=24)),\n",
    "                                 (\"model\", RandomForestClassifier(n_estimators=180, max_depth=14, max_features=None))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xNtt-DYhRyTo",
    "outputId": "3e4758d5-dc24-49e0-c948-855a69582d5e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessing',\n",
       "                 ColumnTransformer(transformers=[('numerical_preprocessor',\n",
       "                                                  Pipeline(steps=[('scaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['cons_12m', 'cons_gas_12m',\n",
       "                                                   'forecast_cons_12m',\n",
       "                                                   'forecast_discount_energy',\n",
       "                                                   'forecast_meter_rent_12m',\n",
       "                                                   'imp_cons',\n",
       "                                                   'margin_gross_pow_ele',\n",
       "                                                   'nb_prod_act', 'net_margin',\n",
       "                                                   'pow_max',\n",
       "                                                   'price_off_peak_var',\n",
       "                                                   'price_off_peak_fix',\n",
       "                                                   'p...\n",
       "                                                   'ratio_last_month_last12m_cons']),\n",
       "                                                 ('cat_preprocessor',\n",
       "                                                  Pipeline(steps=[('encoder',\n",
       "                                                                   CountFrequencyEncoder())]),\n",
       "                                                  ['has_gas', 'origin_up',\n",
       "                                                   'price_change_energy',\n",
       "                                                   'price_change_power'])])),\n",
       "                ('selector',\n",
       "                 SelectKBest(k=24,\n",
       "                             score_func=<function mutual_info_classif at 0x000001FE454FD940>)),\n",
       "                ('model',\n",
       "                 RandomForestClassifier(max_depth=14, max_features=None,\n",
       "                                        n_estimators=180))])"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_pipe.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3). Evaluation of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fJj5pui6R00d",
    "outputId": "a20117c5-d5ec-415c-f217-d3c3f3db86b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      1.00      0.97      3876\n",
      "           1       0.99      0.36      0.53       426\n",
      "\n",
      "    accuracy                           0.94      4302\n",
      "   macro avg       0.96      0.68      0.75      4302\n",
      "weighted avg       0.94      0.94      0.92      4302\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# The classification report of the model\n",
    "\n",
    "print(classification_report(test_y, model_pipe.predict(test_X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "id": "sBaLA3RGSJJX",
    "outputId": "62c52ae2-34f4-431f-fba2-463e735818ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1fe4e0ab400>"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the confusion matrix \n",
    "\n",
    "cfm=confusion_matrix(test_y, model_pipe.predict(test_X))\n",
    "ConfusionMatrixDisplay(cfm).plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bPa0S5H1ZUjU"
   },
   "source": [
    "Given the information at hand, the model was able to predict 154 cases of churn out of 426 cases in the test set which represents 36% of the cases.\\nWe think that the performance of the model is satisfactory since it's macro F1 score is larger than 0.7 which is a decent score in the case of a very imbalanced dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cgcBfv_dUMbn",
    "outputId": "6fc48591-ccb6-4e20-ad26-b3de5aea5924"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['has_gas', 'origin_up', 'price_change_energy', 'cons_12m',\n",
      "       'forecast_cons_12m', 'forecast_discount_energy',\n",
      "       'forecast_meter_rent_12m', 'imp_cons', 'margin_gross_pow_ele',\n",
      "       'nb_prod_act', 'net_margin', 'pow_max', 'price_off_peak_var',\n",
      "       'price_off_peak_fix', 'previous_price', 'price_sens', 'end_year',\n",
      "       'modif_prod_month', 'renewal_year', 'renewal_month', 'diff_act_end',\n",
      "       'diff_act_modif', 'diff_end_modif', 'ratio_last_month_last12m_cons'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Getting the names of the selected features\n",
    "\n",
    "# The selector object\n",
    "selector = model_pipe[1]\n",
    "\n",
    "# The indices of the features\n",
    "indx = selector.get_support(indices=True)\n",
    "\n",
    "select_ftr = df_full.columns[indx]\n",
    "print(select_ftr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "xFzPlYllUhOT",
    "outputId": "c7ba257d-c542-4760-8cf7-710c4700a7a6"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>has_gas</th>\n",
       "      <th>origin_up</th>\n",
       "      <th>price_change_energy</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>forecast_discount_energy</th>\n",
       "      <th>forecast_meter_rent_12m</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>...</th>\n",
       "      <th>previous_price</th>\n",
       "      <th>price_sens</th>\n",
       "      <th>end_year</th>\n",
       "      <th>modif_prod_month</th>\n",
       "      <th>renewal_year</th>\n",
       "      <th>renewal_month</th>\n",
       "      <th>diff_act_end</th>\n",
       "      <th>diff_act_modif</th>\n",
       "      <th>diff_end_modif</th>\n",
       "      <th>ratio_last_month_last12m_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>4660</td>\n",
       "      <td>189.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.38</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078366</td>\n",
       "      <td>13.640956</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>2566</td>\n",
       "      <td>0</td>\n",
       "      <td>2566</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>544</td>\n",
       "      <td>47.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.60</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129466</td>\n",
       "      <td>45.058910</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>2192</td>\n",
       "      <td>0</td>\n",
       "      <td>2192</td>\n",
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       "    <tr>\n",
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       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>1584</td>\n",
       "      <td>240.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.22</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075605</td>\n",
       "      <td>28.408609</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>2192</td>\n",
       "      <td>0</td>\n",
       "      <td>2192</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>4425</td>\n",
       "      <td>445.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.73</td>\n",
       "      <td>52.32</td>\n",
       "      <td>44.91</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113906</td>\n",
       "      <td>18.799168</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>2245</td>\n",
       "      <td>0</td>\n",
       "      <td>2245</td>\n",
       "      <td>0.118870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>8302</td>\n",
       "      <td>796.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.12</td>\n",
       "      <td>181.21</td>\n",
       "      <td>33.12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128293</td>\n",
       "      <td>40.841311</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>1827</td>\n",
       "      <td>1423</td>\n",
       "      <td>404</td>\n",
       "      <td>0.240665</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",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>14537</th>\n",
       "      <td>t</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>32270</td>\n",
       "      <td>4648.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.88</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072062</td>\n",
       "      <td>21.198938</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>1445</td>\n",
       "      <td>1079</td>\n",
       "      <td>366</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14538</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>7223</td>\n",
       "      <td>631.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.03</td>\n",
       "      <td>15.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101103</td>\n",
       "      <td>18.186301</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>1461</td>\n",
       "      <td>0</td>\n",
       "      <td>1461</td>\n",
       "      <td>0.025059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14539</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>1844</td>\n",
       "      <td>190.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.60</td>\n",
       "      <td>18.05</td>\n",
       "      <td>39.84</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114066</td>\n",
       "      <td>18.237929</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>1460</td>\n",
       "      <td>0</td>\n",
       "      <td>1460</td>\n",
       "      <td>0.097072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14540</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>131</td>\n",
       "      <td>19.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.08</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078366</td>\n",
       "      <td>12.121174</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>1461</td>\n",
       "      <td>0</td>\n",
       "      <td>1461</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14541</th>\n",
       "      <td>f</td>\n",
       "      <td>ldkssxwpmemidmecebumciepifcamkci</td>\n",
       "      <td>decrease</td>\n",
       "      <td>8730</td>\n",
       "      <td>762.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.84</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128003</td>\n",
       "      <td>501.481008</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>2556</td>\n",
       "      <td>0</td>\n",
       "      <td>2556</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14340 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      has_gas                         origin_up price_change_energy  cons_12m  \\\n",
       "1           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease      4660   \n",
       "2           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease       544   \n",
       "3           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease      1584   \n",
       "4           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease      4425   \n",
       "5           f  lxidpiddsbxsbosboudacockeimpuepw            decrease      8302   \n",
       "...       ...                               ...                 ...       ...   \n",
       "14537       t  lxidpiddsbxsbosboudacockeimpuepw            decrease     32270   \n",
       "14538       f  lxidpiddsbxsbosboudacockeimpuepw            decrease      7223   \n",
       "14539       f  lxidpiddsbxsbosboudacockeimpuepw            decrease      1844   \n",
       "14540       f  lxidpiddsbxsbosboudacockeimpuepw            decrease       131   \n",
       "14541       f  ldkssxwpmemidmecebumciepifcamkci            decrease      8730   \n",
       "\n",
       "       forecast_cons_12m  forecast_discount_energy  forecast_meter_rent_12m  \\\n",
       "1                 189.95                       0.0                    16.27   \n",
       "2                  47.96                       0.0                    38.72   \n",
       "3                 240.04                       0.0                    19.83   \n",
       "4                 445.75                       0.0                   131.73   \n",
       "5                 796.94                       0.0                    30.12   \n",
       "...                  ...                       ...                      ...   \n",
       "14537            4648.01                       0.0                    18.57   \n",
       "14538             631.69                       0.0                   144.03   \n",
       "14539             190.39                       0.0                   129.60   \n",
       "14540              19.34                       0.0                     7.18   \n",
       "14541             762.41                       0.0                     1.07   \n",
       "\n",
       "       imp_cons  margin_gross_pow_ele  nb_prod_act  ...  previous_price  \\\n",
       "1          0.00                 16.38            1  ...        0.078366   \n",
       "2          0.00                 28.60            1  ...        0.129466   \n",
       "3          0.00                 30.22            1  ...        0.075605   \n",
       "4         52.32                 44.91            1  ...        0.113906   \n",
       "5        181.21                 33.12            1  ...        0.128293   \n",
       "...         ...                   ...          ...  ...             ...   \n",
       "14537      0.00                 27.88            2  ...        0.072062   \n",
       "14538     15.94                  0.00            1  ...        0.101103   \n",
       "14539     18.05                 39.84            1  ...        0.114066   \n",
       "14540      0.00                 13.08            1  ...        0.078366   \n",
       "14541      0.00                 11.84            1  ...        0.128003   \n",
       "\n",
       "       price_sens  end_year  modif_prod_month  renewal_year  renewal_month  \\\n",
       "1       13.640956      2016                 8          2015              8   \n",
       "2       45.058910      2016                 4          2015              4   \n",
       "3       28.408609      2016                 3          2015              3   \n",
       "4       18.799168      2016                 1          2015              3   \n",
       "5       40.841311      2016                11          2015             12   \n",
       "...           ...       ...               ...           ...            ...   \n",
       "14537   21.198938      2016                 5          2014              5   \n",
       "14538   18.186301      2016                 8          2015              8   \n",
       "14539   18.237929      2016                 2          2015              2   \n",
       "14540   12.121174      2016                 8          2015              8   \n",
       "14541  501.481008      2016                12          2015             12   \n",
       "\n",
       "       diff_act_end  diff_act_modif  diff_end_modif  \\\n",
       "1              2566               0            2566   \n",
       "2              2192               0            2192   \n",
       "3              2192               0            2192   \n",
       "4              2245               0            2245   \n",
       "5              1827            1423             404   \n",
       "...             ...             ...             ...   \n",
       "14537          1445            1079             366   \n",
       "14538          1461               0            1461   \n",
       "14539          1460               0            1460   \n",
       "14540          1461               0            1461   \n",
       "14541          2556               0            2556   \n",
       "\n",
       "       ratio_last_month_last12m_cons  \n",
       "1                           0.000000  \n",
       "2                           0.000000  \n",
       "3                           0.000000  \n",
       "4                           0.118870  \n",
       "5                           0.240665  \n",
       "...                              ...  \n",
       "14537                       0.000000  \n",
       "14538                       0.025059  \n",
       "14539                       0.097072  \n",
       "14540                       0.000000  \n",
       "14541                       0.000000  \n",
       "\n",
       "[14340 rows x 24 columns]"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The new dataframe with the selected features \n",
    "final_df = df_full[select_ftr]\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i71uhlrWZnUQ",
    "outputId": "999fe4c0-8c44-4665-eb1b-c9bd1faba370"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=14, max_features=None, n_estimators=180)"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the trained model \n",
    "clf = model_pipe[-1]\n",
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m3AvBV0kkgP9",
    "outputId": "74931944-266d-4a0c-993d-345f1d6d86ec"
   },
   "outputs": [],
   "source": [
    "# Saving the model\n",
    "filename = 'best_model_PowerCo.sav'\n",
    "pickle.dump(clf, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the study \n",
    "pickle.dump(study_1, open('optuna_study_PowerCo.sav', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "id": "uKKFGXD77Fl9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "# # Getting the names of the features after the second phase of feature selection \n",
    "# Getting numerical features \n",
    "num_ftr = [col for col in final_df.columns if final_df[col].dtype in ['int64', 'float64']]\n",
    "\n",
    "# Getting categorical features \n",
    "cat_ftr = [col for col in final_df.columns if col not in num_ftr + date_ftr and col != 'id']\n",
    "\n",
    "print(len(num_ftr)+len(cat_ftr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V. Model interpretation using SHAP\n",
    "SHAP is a technique used to interpret machine learning models. It explains a prediction by calculating what are known as Shapley values, which were developed in Game Theory. These values represent the contributions of the different explanatory variables to the prediction in question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the full dataframe with the selected features.\n",
    "df_int = new_df_ft[select_ftr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>has_gas</th>\n",
       "      <th>origin_up</th>\n",
       "      <th>price_change_energy</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>forecast_discount_energy</th>\n",
       "      <th>forecast_meter_rent_12m</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>...</th>\n",
       "      <th>previous_price</th>\n",
       "      <th>price_sens</th>\n",
       "      <th>end_year</th>\n",
       "      <th>modif_prod_month</th>\n",
       "      <th>renewal_year</th>\n",
       "      <th>renewal_month</th>\n",
       "      <th>diff_act_end</th>\n",
       "      <th>diff_act_modif</th>\n",
       "      <th>diff_end_modif</th>\n",
       "      <th>ratio_last_month_last12m_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7835</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>62623</td>\n",
       "      <td>5636.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.60</td>\n",
       "      <td>405.53</td>\n",
       "      <td>13.76</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113567</td>\n",
       "      <td>18.190599</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>1461</td>\n",
       "      <td>1401</td>\n",
       "      <td>60</td>\n",
       "      <td>0.073535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7058</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>24673</td>\n",
       "      <td>3650.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.83</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.073848</td>\n",
       "      <td>41.060672</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>2557</td>\n",
       "      <td>0</td>\n",
       "      <td>2557</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>f</td>\n",
       "      <td>ldkssxwpmemidmecebumciepifcamkci</td>\n",
       "      <td>decrease</td>\n",
       "      <td>2339</td>\n",
       "      <td>227.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.77</td>\n",
       "      <td>21.80</td>\n",
       "      <td>22.44</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112147</td>\n",
       "      <td>17.713324</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>2557</td>\n",
       "      <td>2163</td>\n",
       "      <td>394</td>\n",
       "      <td>0.097050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11893</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>5973</td>\n",
       "      <td>388.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>67.84</td>\n",
       "      <td>24.62</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074933</td>\n",
       "      <td>24.044035</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>1461</td>\n",
       "      <td>1059</td>\n",
       "      <td>402</td>\n",
       "      <td>0.178302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3007</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>2059</td>\n",
       "      <td>260.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.34</td>\n",
       "      <td>18.33</td>\n",
       "      <td>14.53</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127125</td>\n",
       "      <td>-2716.204844</td>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>1481</td>\n",
       "      <td>750</td>\n",
       "      <td>731</td>\n",
       "      <td>0.073822</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>1034</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>1251</td>\n",
       "      <td>119.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.53</td>\n",
       "      <td>7.33</td>\n",
       "      <td>49.44</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114066</td>\n",
       "      <td>18.394221</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>1461</td>\n",
       "      <td>0</td>\n",
       "      <td>1461</td>\n",
       "      <td>0.062350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9554</th>\n",
       "      <td>t</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>44785</td>\n",
       "      <td>4509.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.31</td>\n",
       "      <td>328.55</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112963</td>\n",
       "      <td>19.230034</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>2192</td>\n",
       "      <td>1437</td>\n",
       "      <td>755</td>\n",
       "      <td>0.076008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5079</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>3819</td>\n",
       "      <td>573.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075014</td>\n",
       "      <td>17.281933</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>2191</td>\n",
       "      <td>2144</td>\n",
       "      <td>47</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10947</th>\n",
       "      <td>f</td>\n",
       "      <td>ldkssxwpmemidmecebumciepifcamkci</td>\n",
       "      <td>decrease</td>\n",
       "      <td>42144</td>\n",
       "      <td>3860.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.37</td>\n",
       "      <td>44.32</td>\n",
       "      <td>21.16</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112523</td>\n",
       "      <td>18.525477</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>2557</td>\n",
       "      <td>0</td>\n",
       "      <td>2557</td>\n",
       "      <td>0.011698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6441</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>1225</td>\n",
       "      <td>179.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.56</td>\n",
       "      <td>28.85</td>\n",
       "      <td>12.36</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.073711</td>\n",
       "      <td>21.852782</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>1826</td>\n",
       "      <td>1384</td>\n",
       "      <td>442</td>\n",
       "      <td>0.164082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10038 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      has_gas                         origin_up price_change_energy  cons_12m  \\\n",
       "7835        f  lxidpiddsbxsbosboudacockeimpuepw            decrease     62623   \n",
       "7058        f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease     24673   \n",
       "935         f  ldkssxwpmemidmecebumciepifcamkci            decrease      2339   \n",
       "11893       f  lxidpiddsbxsbosboudacockeimpuepw            decrease      5973   \n",
       "3007        f  lxidpiddsbxsbosboudacockeimpuepw            decrease      2059   \n",
       "...       ...                               ...                 ...       ...   \n",
       "1034        f  lxidpiddsbxsbosboudacockeimpuepw            decrease      1251   \n",
       "9554        t  lxidpiddsbxsbosboudacockeimpuepw            decrease     44785   \n",
       "5079        f  lxidpiddsbxsbosboudacockeimpuepw            decrease      3819   \n",
       "10947       f  ldkssxwpmemidmecebumciepifcamkci            decrease     42144   \n",
       "6441        f  lxidpiddsbxsbosboudacockeimpuepw            decrease      1225   \n",
       "\n",
       "       forecast_cons_12m  forecast_discount_energy  forecast_meter_rent_12m  \\\n",
       "7835             5636.69                       0.0                   129.60   \n",
       "7058             3650.39                       0.0                     0.00   \n",
       "935               227.88                       0.0                   131.77   \n",
       "11893             388.97                       0.0                     0.00   \n",
       "3007              260.77                       0.0                    14.34   \n",
       "...                  ...                       ...                      ...   \n",
       "1034              119.55                       0.0                   146.53   \n",
       "9554             4509.22                       0.0                   130.31   \n",
       "5079              573.00                       0.0                    18.72   \n",
       "10947            3860.98                       0.0                   131.37   \n",
       "6441              179.65                       0.0                    16.56   \n",
       "\n",
       "       imp_cons  margin_gross_pow_ele  nb_prod_act  ...  previous_price  \\\n",
       "7835     405.53                 13.76            1  ...        0.113567   \n",
       "7058       0.00                  4.83            1  ...        0.073848   \n",
       "935       21.80                 22.44            1  ...        0.112147   \n",
       "11893     67.84                 24.62            1  ...        0.074933   \n",
       "3007      18.33                 14.53            1  ...        0.127125   \n",
       "...         ...                   ...          ...  ...             ...   \n",
       "1034       7.33                 49.44            1  ...        0.114066   \n",
       "9554     328.55                  1.59            2  ...        0.112963   \n",
       "5079       0.00                 33.12            1  ...        0.075014   \n",
       "10947     44.32                 21.16            1  ...        0.112523   \n",
       "6441      28.85                 12.36            1  ...        0.073711   \n",
       "\n",
       "        price_sens  end_year  modif_prod_month  renewal_year  renewal_month  \\\n",
       "7835     18.190599      2016                 1          2015              3   \n",
       "7058     41.060672      2016                11          2015             12   \n",
       "935      17.713324      2016                 7          2015              8   \n",
       "11893    24.044035      2016                11          2015             12   \n",
       "3007  -2716.204844      2016                 9          2015              9   \n",
       "...            ...       ...               ...           ...            ...   \n",
       "1034     18.394221      2016                 2          2015              2   \n",
       "9554     19.230034      2016                11          2015             12   \n",
       "5079     17.281933      2016                12          2015              2   \n",
       "10947    18.525477      2016                 6          2015              6   \n",
       "6441     21.852782      2016                 6          2015              9   \n",
       "\n",
       "       diff_act_end  diff_act_modif  diff_end_modif  \\\n",
       "7835           1461            1401              60   \n",
       "7058           2557               0            2557   \n",
       "935            2557            2163             394   \n",
       "11893          1461            1059             402   \n",
       "3007           1481             750             731   \n",
       "...             ...             ...             ...   \n",
       "1034           1461               0            1461   \n",
       "9554           2192            1437             755   \n",
       "5079           2191            2144              47   \n",
       "10947          2557               0            2557   \n",
       "6441           1826            1384             442   \n",
       "\n",
       "       ratio_last_month_last12m_cons  \n",
       "7835                        0.073535  \n",
       "7058                        0.000000  \n",
       "935                         0.097050  \n",
       "11893                       0.178302  \n",
       "3007                        0.073822  \n",
       "...                              ...  \n",
       "1034                        0.062350  \n",
       "9554                        0.076008  \n",
       "5079                        0.000000  \n",
       "10947                       0.011698  \n",
       "6441                        0.164082  \n",
       "\n",
       "[10038 rows x 24 columns]"
      ]
     },
     "execution_count": 559,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the train_X and test_X with the selected features \n",
    "new_train_X = train_X[select_ftr]\n",
    "new_test_X = test_X[select_ftr]\n",
    "new_train_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>has_gas</th>\n",
       "      <th>origin_up</th>\n",
       "      <th>price_change_energy</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>forecast_discount_energy</th>\n",
       "      <th>forecast_meter_rent_12m</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>...</th>\n",
       "      <th>previous_price</th>\n",
       "      <th>price_sens</th>\n",
       "      <th>end_year</th>\n",
       "      <th>modif_prod_month</th>\n",
       "      <th>renewal_year</th>\n",
       "      <th>renewal_month</th>\n",
       "      <th>diff_act_end</th>\n",
       "      <th>diff_act_modif</th>\n",
       "      <th>diff_end_modif</th>\n",
       "      <th>ratio_last_month_last12m_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>0.002291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027148</td>\n",
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       "      <td>0.043722</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.351456</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
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       "      <td>0.636364</td>\n",
       "      <td>0.451526</td>\n",
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       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.064608</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.580630</td>\n",
       "      <td>0.050598</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.359498</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.458148</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033088</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080664</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.339073</td>\n",
       "      <td>0.050468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.359498</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.458148</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>decrease</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219803</td>\n",
       "      <td>0.003478</td>\n",
       "      <td>0.119875</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.510846</td>\n",
       "      <td>0.050393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.372539</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.469379</td>\n",
       "      <td>0.093135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>0.001337</td>\n",
       "      <td>0.009613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050258</td>\n",
       "      <td>0.012046</td>\n",
       "      <td>0.088405</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.575366</td>\n",
       "      <td>0.050565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269685</td>\n",
       "      <td>0.334539</td>\n",
       "      <td>0.079254</td>\n",
       "      <td>0.188562</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>14537</th>\n",
       "      <td>t</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>0.005199</td>\n",
       "      <td>0.056066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030986</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074418</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>...</td>\n",
       "      <td>0.323184</td>\n",
       "      <td>0.050411</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.175689</td>\n",
       "      <td>0.256781</td>\n",
       "      <td>0.071202</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14538</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>0.001164</td>\n",
       "      <td>0.007620</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.240326</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.453424</td>\n",
       "      <td>0.050388</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.179626</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.303242</td>\n",
       "      <td>0.019634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14539</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.216249</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.106342</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511562</td>\n",
       "      <td>0.050388</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.179380</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.076056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14540</th>\n",
       "      <td>f</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>decrease</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011980</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034914</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.351456</td>\n",
       "      <td>0.050341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.179626</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.303242</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14541</th>\n",
       "      <td>f</td>\n",
       "      <td>ldkssxwpmemidmecebumciepifcamkci</td>\n",
       "      <td>decrease</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>0.009196</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001785</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031604</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.574066</td>\n",
       "      <td>0.054158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.449065</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.535283</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14340 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      has_gas                         origin_up price_change_energy  cons_12m  \\\n",
       "1           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease  0.000751   \n",
       "2           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease  0.000087   \n",
       "3           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease  0.000255   \n",
       "4           f  kamkkxfxxuwbdslkwifmmcsiusiuosws            decrease  0.000713   \n",
       "5           f  lxidpiddsbxsbosboudacockeimpuepw            decrease  0.001337   \n",
       "...       ...                               ...                 ...       ...   \n",
       "14537       t  lxidpiddsbxsbosboudacockeimpuepw            decrease  0.005199   \n",
       "14538       f  lxidpiddsbxsbosboudacockeimpuepw            decrease  0.001164   \n",
       "14539       f  lxidpiddsbxsbosboudacockeimpuepw            decrease  0.000297   \n",
       "14540       f  lxidpiddsbxsbosboudacockeimpuepw            decrease  0.000021   \n",
       "14541       f  ldkssxwpmemidmecebumciepifcamkci            decrease  0.001406   \n",
       "\n",
       "       forecast_cons_12m  forecast_discount_energy  forecast_meter_rent_12m  \\\n",
       "1               0.002291                       0.0                 0.027148   \n",
       "2               0.000579                       0.0                 0.064608   \n",
       "3               0.002895                       0.0                 0.033088   \n",
       "4               0.005377                       0.0                 0.219803   \n",
       "5               0.009613                       0.0                 0.050258   \n",
       "...                  ...                       ...                      ...   \n",
       "14537           0.056066                       0.0                 0.030986   \n",
       "14538           0.007620                       0.0                 0.240326   \n",
       "14539           0.002297                       0.0                 0.216249   \n",
       "14540           0.000233                       0.0                 0.011980   \n",
       "14541           0.009196                       0.0                 0.001785   \n",
       "\n",
       "       imp_cons  margin_gross_pow_ele  nb_prod_act  ...  previous_price  \\\n",
       "1      0.000000              0.043722     0.000000  ...        0.351456   \n",
       "2      0.000000              0.076340     0.000000  ...        0.580630   \n",
       "3      0.000000              0.080664     0.000000  ...        0.339073   \n",
       "4      0.003478              0.119875     0.000000  ...        0.510846   \n",
       "5      0.012046              0.088405     0.000000  ...        0.575366   \n",
       "...         ...                   ...          ...  ...             ...   \n",
       "14537  0.000000              0.074418     0.032258  ...        0.323184   \n",
       "14538  0.001060              0.000000     0.000000  ...        0.453424   \n",
       "14539  0.001200              0.106342     0.000000  ...        0.511562   \n",
       "14540  0.000000              0.034914     0.000000  ...        0.351456   \n",
       "14541  0.000000              0.031604     0.000000  ...        0.574066   \n",
       "\n",
       "       price_sens  end_year  modif_prod_month  renewal_year  renewal_month  \\\n",
       "1        0.050352       0.0          0.636364      0.666667       0.636364   \n",
       "2        0.050598       0.0          0.272727      0.666667       0.272727   \n",
       "3        0.050468       0.0          0.181818      0.666667       0.181818   \n",
       "4        0.050393       0.0          0.000000      0.666667       0.181818   \n",
       "5        0.050565       0.0          0.909091      0.666667       1.000000   \n",
       "...           ...       ...               ...           ...            ...   \n",
       "14537    0.050411       0.0          0.363636      0.333333       0.363636   \n",
       "14538    0.050388       0.0          0.636364      0.666667       0.636364   \n",
       "14539    0.050388       0.0          0.090909      0.666667       0.090909   \n",
       "14540    0.050341       0.0          0.636364      0.666667       0.636364   \n",
       "14541    0.054158       0.0          1.000000      0.666667       1.000000   \n",
       "\n",
       "       diff_act_end  diff_act_modif  diff_end_modif  \\\n",
       "1          0.451526        0.012884        0.537402   \n",
       "2          0.359498        0.012884        0.458148   \n",
       "3          0.359498        0.012884        0.458148   \n",
       "4          0.372539        0.012884        0.469379   \n",
       "5          0.269685        0.334539        0.079254   \n",
       "...             ...             ...             ...   \n",
       "14537      0.175689        0.256781        0.071202   \n",
       "14538      0.179626        0.012884        0.303242   \n",
       "14539      0.179380        0.012884        0.303030   \n",
       "14540      0.179626        0.012884        0.303242   \n",
       "14541      0.449065        0.012884        0.535283   \n",
       "\n",
       "       ratio_last_month_last12m_cons  \n",
       "1                           0.000000  \n",
       "2                           0.000000  \n",
       "3                           0.000000  \n",
       "4                           0.093135  \n",
       "5                           0.188562  \n",
       "...                              ...  \n",
       "14537                       0.000000  \n",
       "14538                       0.019634  \n",
       "14539                       0.076056  \n",
       "14540                       0.000000  \n",
       "14541                       0.000000  \n",
       "\n",
       "[14340 rows x 24 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling numerical features \n",
    "scaler = SklearnTransformerWrapper(MinMaxScaler())\n",
    "df_int = scaler.fit_transform(df_int)\n",
    "df_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>has_gas</th>\n",
       "      <th>origin_up</th>\n",
       "      <th>price_change_energy</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>forecast_discount_energy</th>\n",
       "      <th>forecast_meter_rent_12m</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>...</th>\n",
       "      <th>previous_price</th>\n",
       "      <th>price_sens</th>\n",
       "      <th>end_year</th>\n",
       "      <th>modif_prod_month</th>\n",
       "      <th>renewal_year</th>\n",
       "      <th>renewal_month</th>\n",
       "      <th>diff_act_end</th>\n",
       "      <th>diff_act_modif</th>\n",
       "      <th>diff_end_modif</th>\n",
       "      <th>ratio_last_month_last12m_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11728</td>\n",
       "      <td>4272</td>\n",
       "      <td>13702</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>0.002291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027148</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043722</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.351456</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.451526</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.537402</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11728</td>\n",
       "      <td>4272</td>\n",
       "      <td>13702</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.064608</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.580630</td>\n",
       "      <td>0.050598</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.359498</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.458148</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11728</td>\n",
       "      <td>4272</td>\n",
       "      <td>13702</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033088</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080664</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.339073</td>\n",
       "      <td>0.050468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.359498</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.458148</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11728</td>\n",
       "      <td>4272</td>\n",
       "      <td>13702</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219803</td>\n",
       "      <td>0.003478</td>\n",
       "      <td>0.119875</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.510846</td>\n",
       "      <td>0.050393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.372539</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.469379</td>\n",
       "      <td>0.093135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11728</td>\n",
       "      <td>7009</td>\n",
       "      <td>13702</td>\n",
       "      <td>0.001337</td>\n",
       "      <td>0.009613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050258</td>\n",
       "      <td>0.012046</td>\n",
       "      <td>0.088405</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.575366</td>\n",
       "      <td>0.050565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269685</td>\n",
       "      <td>0.334539</td>\n",
       "      <td>0.079254</td>\n",
       "      <td>0.188562</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>14537</th>\n",
       "      <td>2612</td>\n",
       "      <td>7009</td>\n",
       "      <td>13702</td>\n",
       "      <td>0.005199</td>\n",
       "      <td>0.056066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030986</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074418</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>...</td>\n",
       "      <td>0.323184</td>\n",
       "      <td>0.050411</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.175689</td>\n",
       "      <td>0.256781</td>\n",
       "      <td>0.071202</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14538</th>\n",
       "      <td>11728</td>\n",
       "      <td>7009</td>\n",
       "      <td>13702</td>\n",
       "      <td>0.001164</td>\n",
       "      <td>0.007620</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.240326</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.453424</td>\n",
       "      <td>0.050388</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.179626</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.303242</td>\n",
       "      <td>0.019634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14539</th>\n",
       "      <td>11728</td>\n",
       "      <td>7009</td>\n",
       "      <td>13702</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.216249</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.106342</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511562</td>\n",
       "      <td>0.050388</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.179380</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.076056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14540</th>\n",
       "      <td>11728</td>\n",
       "      <td>7009</td>\n",
       "      <td>13702</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011980</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034914</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.351456</td>\n",
       "      <td>0.050341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.179626</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.303242</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14541</th>\n",
       "      <td>11728</td>\n",
       "      <td>3056</td>\n",
       "      <td>13702</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>0.009196</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001785</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031604</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.574066</td>\n",
       "      <td>0.054158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.449065</td>\n",
       "      <td>0.012884</td>\n",
       "      <td>0.535283</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14340 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       has_gas  origin_up  price_change_energy  cons_12m  forecast_cons_12m  \\\n",
       "1        11728       4272                13702  0.000751           0.002291   \n",
       "2        11728       4272                13702  0.000087           0.000579   \n",
       "3        11728       4272                13702  0.000255           0.002895   \n",
       "4        11728       4272                13702  0.000713           0.005377   \n",
       "5        11728       7009                13702  0.001337           0.009613   \n",
       "...        ...        ...                  ...       ...                ...   \n",
       "14537     2612       7009                13702  0.005199           0.056066   \n",
       "14538    11728       7009                13702  0.001164           0.007620   \n",
       "14539    11728       7009                13702  0.000297           0.002297   \n",
       "14540    11728       7009                13702  0.000021           0.000233   \n",
       "14541    11728       3056                13702  0.001406           0.009196   \n",
       "\n",
       "       forecast_discount_energy  forecast_meter_rent_12m  imp_cons  \\\n",
       "1                           0.0                 0.027148  0.000000   \n",
       "2                           0.0                 0.064608  0.000000   \n",
       "3                           0.0                 0.033088  0.000000   \n",
       "4                           0.0                 0.219803  0.003478   \n",
       "5                           0.0                 0.050258  0.012046   \n",
       "...                         ...                      ...       ...   \n",
       "14537                       0.0                 0.030986  0.000000   \n",
       "14538                       0.0                 0.240326  0.001060   \n",
       "14539                       0.0                 0.216249  0.001200   \n",
       "14540                       0.0                 0.011980  0.000000   \n",
       "14541                       0.0                 0.001785  0.000000   \n",
       "\n",
       "       margin_gross_pow_ele  nb_prod_act  ...  previous_price  price_sens  \\\n",
       "1                  0.043722     0.000000  ...        0.351456    0.050352   \n",
       "2                  0.076340     0.000000  ...        0.580630    0.050598   \n",
       "3                  0.080664     0.000000  ...        0.339073    0.050468   \n",
       "4                  0.119875     0.000000  ...        0.510846    0.050393   \n",
       "5                  0.088405     0.000000  ...        0.575366    0.050565   \n",
       "...                     ...          ...  ...             ...         ...   \n",
       "14537              0.074418     0.032258  ...        0.323184    0.050411   \n",
       "14538              0.000000     0.000000  ...        0.453424    0.050388   \n",
       "14539              0.106342     0.000000  ...        0.511562    0.050388   \n",
       "14540              0.034914     0.000000  ...        0.351456    0.050341   \n",
       "14541              0.031604     0.000000  ...        0.574066    0.054158   \n",
       "\n",
       "       end_year  modif_prod_month  renewal_year  renewal_month  diff_act_end  \\\n",
       "1           0.0          0.636364      0.666667       0.636364      0.451526   \n",
       "2           0.0          0.272727      0.666667       0.272727      0.359498   \n",
       "3           0.0          0.181818      0.666667       0.181818      0.359498   \n",
       "4           0.0          0.000000      0.666667       0.181818      0.372539   \n",
       "5           0.0          0.909091      0.666667       1.000000      0.269685   \n",
       "...         ...               ...           ...            ...           ...   \n",
       "14537       0.0          0.363636      0.333333       0.363636      0.175689   \n",
       "14538       0.0          0.636364      0.666667       0.636364      0.179626   \n",
       "14539       0.0          0.090909      0.666667       0.090909      0.179380   \n",
       "14540       0.0          0.636364      0.666667       0.636364      0.179626   \n",
       "14541       0.0          1.000000      0.666667       1.000000      0.449065   \n",
       "\n",
       "       diff_act_modif  diff_end_modif  ratio_last_month_last12m_cons  \n",
       "1            0.012884        0.537402                       0.000000  \n",
       "2            0.012884        0.458148                       0.000000  \n",
       "3            0.012884        0.458148                       0.000000  \n",
       "4            0.012884        0.469379                       0.093135  \n",
       "5            0.334539        0.079254                       0.188562  \n",
       "...               ...             ...                            ...  \n",
       "14537        0.256781        0.071202                       0.000000  \n",
       "14538        0.012884        0.303242                       0.019634  \n",
       "14539        0.012884        0.303030                       0.076056  \n",
       "14540        0.012884        0.303242                       0.000000  \n",
       "14541        0.012884        0.535283                       0.000000  \n",
       "\n",
       "[14340 rows x 24 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding categorical features \n",
    "enc = CountFrequencyEncoder()\n",
    "df_int = enc.fit_transform(df_int)\n",
    "df_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=14, max_features=None, n_estimators=180)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the classifier\n",
    "model = pickle.load(open('best_model_PowerCo.sav', 'rb'))\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x799.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The code to get shap values \n",
    "explainer = shap.TreeExplainer(model)\n",
    "shap_values = explainer.shap_values(df_int)\n",
    "\n",
    "# Plotting feature importance \n",
    "shap.summary_plot(shap_values[0], df_int, max_display=26, plot_type='bar')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the visual above  that the three most important features for the model are \"pow_max\" which indicates the subscribed power, the net margin and \"end_year\" which indicates the registered year of the end of the contract.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x799.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigating the global effect of each feature on the model output(pushing it down or up).\n",
    "shap.summary_plot(shap_values[0], df_int, max_display=26)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this visual we can conclude that:\n",
    "\n",
    "- High subscribed power increases the chances of the clients to churn (impacts negatively the output of the model, pushing it towards zero) this may be a signal that large clients are not satisfied with  the prices that they are paying given the large quantities they are buying, which means that there are other companies giving them better prices. Another signal that the company is suffering from an exodus of it's big clients is that higher values of the feature forcast_cons_12m push the model to predict a churning client.\n",
    "\n",
    "- The churn is mainly driven by price related variables, since the decrease in the price energy during the previous year expressed using the feature \"price_change_energy\" decreases the chances of the clients leaving the company (this feature is categorical and was encoded using \"counting\" the category \"decrease\" has the highest value this why we see in the visual that large values of this feature impact the output of model negatively).\n",
    "\n",
    "- The clients that have a gas contract with the company are less likely to churn (according to the encoding of this feature \"f\" means high value and \"t\" means small value).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'has_gas': {'f': 11728, 't': 2612},\n",
       " 'origin_up': {'lxidpiddsbxsbosboudacockeimpuepw': 7009,\n",
       "  'kamkkxfxxuwbdslkwifmmcsiusiuosws': 4272,\n",
       "  'ldkssxwpmemidmecebumciepifcamkci': 3056,\n",
       "  'Rare': 3},\n",
       " 'price_change_energy': {'decrease': 13702, 'increase': 625, 'stable': 13}}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The encoding of categorical features that may help us interpret the model.\n",
    "enc.encoder_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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