Term Deposit Subscription Prediction.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c90bdaa7",
   "metadata": {},
   "source": [
    "# Term Deposit Subscription Prediction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3bef3dd5",
   "metadata": {},
   "source": [
    "### Importing Libraries and Setting up the Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b228e9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, average_precision_score, confusion_matrix, classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from scipy.stats import chi2_contingency\n",
    "from warnings import filterwarnings\n",
    "from xgboost import XGBClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "\n",
    "pd.pandas.set_option('display.max_columns',None)\n",
    "sns.set_theme()\n",
    "filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "58a9fc55",
   "metadata": {},
   "source": [
    "### Reading Data and Knowing the Primaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb14b779",
   "metadata": {
    "scrolled": false
   },
   "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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>education</th>\n",
       "      <th>defaulted</th>\n",
       "      <th>house_loan</th>\n",
       "      <th>personal_loan</th>\n",
       "      <th>con_medium</th>\n",
       "      <th>last_con_month</th>\n",
       "      <th>last_con_day</th>\n",
       "      <th>last_con_dur</th>\n",
       "      <th>num_con_this_camp</th>\n",
       "      <th>days_since_last_con</th>\n",
       "      <th>num_con_prev_camp</th>\n",
       "      <th>prev_camp_outcome</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cpi</th>\n",
       "      <th>cci</th>\n",
       "      <th>euribor_3m</th>\n",
       "      <th>num_employed</th>\n",
       "      <th>subscribed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job marital_status    education defaulted house_loan  \\\n",
       "0   56  housemaid        married     basic.4y        no         no   \n",
       "1   57   services        married  high.school   unknown         no   \n",
       "2   37   services        married  high.school        no        yes   \n",
       "3   40     admin.        married     basic.6y        no         no   \n",
       "4   56   services        married  high.school        no         no   \n",
       "\n",
       "  personal_loan con_medium last_con_month last_con_day  last_con_dur  \\\n",
       "0            no  telephone            may          mon           261   \n",
       "1            no  telephone            may          mon           149   \n",
       "2            no  telephone            may          mon           226   \n",
       "3            no  telephone            may          mon           151   \n",
       "4           yes  telephone            may          mon           307   \n",
       "\n",
       "   num_con_this_camp  days_since_last_con  num_con_prev_camp  \\\n",
       "0                  1                  999                  0   \n",
       "1                  1                  999                  0   \n",
       "2                  1                  999                  0   \n",
       "3                  1                  999                  0   \n",
       "4                  1                  999                  0   \n",
       "\n",
       "  prev_camp_outcome  emp_var_rate     cpi   cci  euribor_3m  num_employed  \\\n",
       "0       nonexistent           1.1  93.994 -36.4       4.857        5191.0   \n",
       "1       nonexistent           1.1  93.994 -36.4       4.857        5191.0   \n",
       "2       nonexistent           1.1  93.994 -36.4       4.857        5191.0   \n",
       "3       nonexistent           1.1  93.994 -36.4       4.857        5191.0   \n",
       "4       nonexistent           1.1  93.994 -36.4       4.857        5191.0   \n",
       "\n",
       "  subscribed  \n",
       "0         no  \n",
       "1         no  \n",
       "2         no  \n",
       "3         no  \n",
       "4         no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'Dataset/customer_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9efd5c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 21)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d8655f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   age                  41188 non-null  int64  \n",
      " 1   job                  41188 non-null  object \n",
      " 2   marital_status       41188 non-null  object \n",
      " 3   education            41188 non-null  object \n",
      " 4   defaulted            41188 non-null  object \n",
      " 5   house_loan           41188 non-null  object \n",
      " 6   personal_loan        41188 non-null  object \n",
      " 7   con_medium           41188 non-null  object \n",
      " 8   last_con_month       41188 non-null  object \n",
      " 9   last_con_day         41188 non-null  object \n",
      " 10  last_con_dur         41188 non-null  int64  \n",
      " 11  num_con_this_camp    41188 non-null  int64  \n",
      " 12  days_since_last_con  41188 non-null  int64  \n",
      " 13  num_con_prev_camp    41188 non-null  int64  \n",
      " 14  prev_camp_outcome    41188 non-null  object \n",
      " 15  emp_var_rate         41188 non-null  float64\n",
      " 16  cpi                  41188 non-null  float64\n",
      " 17  cci                  41188 non-null  float64\n",
      " 18  euribor_3m           41188 non-null  float64\n",
      " 19  num_employed         41188 non-null  float64\n",
      " 20  subscribed           41188 non-null  object \n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "66386b69",
   "metadata": {},
   "source": [
    "### Checking for Duplicated Rows and Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b18f66cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3d86743",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c0caa24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                    0\n",
       "job                    0\n",
       "marital_status         0\n",
       "education              0\n",
       "defaulted              0\n",
       "house_loan             0\n",
       "personal_loan          0\n",
       "con_medium             0\n",
       "last_con_month         0\n",
       "last_con_day           0\n",
       "last_con_dur           0\n",
       "num_con_this_camp      0\n",
       "days_since_last_con    0\n",
       "num_con_prev_camp      0\n",
       "prev_camp_outcome      0\n",
       "emp_var_rate           0\n",
       "cpi                    0\n",
       "cci                    0\n",
       "euribor_3m             0\n",
       "num_employed           0\n",
       "subscribed             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e0270f56",
   "metadata": {},
   "source": [
    "### Are there are any other abnormalities in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6505b123",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique_values(df, exclude_cols=[]):\n",
    "    for col in df.columns:\n",
    "        if col in exclude_cols:\n",
    "            continue\n",
    "        unique_vals = df[col].nunique()\n",
    "        print(f\"{col}: {unique_vals} unique values\")\n",
    "        if unique_vals < 25:\n",
    "            print()\n",
    "            print(df[col].unique())\n",
    "        print(\"\\n\", 55 * \"_\", \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69d7d08c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age: 78 unique values\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "job: 12 unique values\n",
      "\n",
      "['housemaid' 'services' 'admin.' 'blue-collar' 'technician' 'retired'\n",
      " 'management' 'unemployed' 'self-employed' 'unknown' 'entrepreneur'\n",
      " 'student']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "marital_status: 4 unique values\n",
      "\n",
      "['married' 'single' 'divorced' 'unknown']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "education: 8 unique values\n",
      "\n",
      "['basic.4y' 'high.school' 'basic.6y' 'basic.9y' 'professional.course'\n",
      " 'unknown' 'university.degree' 'illiterate']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "defaulted: 3 unique values\n",
      "\n",
      "['no' 'unknown' 'yes']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "house_loan: 3 unique values\n",
      "\n",
      "['no' 'yes' 'unknown']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "personal_loan: 3 unique values\n",
      "\n",
      "['no' 'yes' 'unknown']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "con_medium: 2 unique values\n",
      "\n",
      "['telephone' 'cellular']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "last_con_month: 10 unique values\n",
      "\n",
      "['may' 'jun' 'jul' 'aug' 'oct' 'nov' 'dec' 'mar' 'apr' 'sep']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "last_con_day: 5 unique values\n",
      "\n",
      "['mon' 'tue' 'wed' 'thu' 'fri']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "last_con_dur: 1544 unique values\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "num_con_this_camp: 42 unique values\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "days_since_last_con: 27 unique values\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "num_con_prev_camp: 8 unique values\n",
      "\n",
      "[0 1 2 3 4 5 6 7]\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "prev_camp_outcome: 3 unique values\n",
      "\n",
      "['nonexistent' 'failure' 'success']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "emp_var_rate: 10 unique values\n",
      "\n",
      "[ 1.1  1.4 -0.1 -0.2 -1.8 -2.9 -3.4 -3.  -1.7 -1.1]\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "cpi: 26 unique values\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "cci: 26 unique values\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "euribor_3m: 316 unique values\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "num_employed: 11 unique values\n",
      "\n",
      "[5191.  5228.1 5195.8 5176.3 5099.1 5076.2 5017.5 5023.5 5008.7 4991.6\n",
      " 4963.6]\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "subscribed: 2 unique values\n",
      "\n",
      "['no' 'yes']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n"
     ]
    }
   ],
   "source": [
    "unique_values(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f92b7e40",
   "metadata": {},
   "source": [
    "`emp_var_rate`, `cpi`, `cci`, `euribor_3m`, `num_employed` - All these columns represent countrywide indexes and values and they are not cusstomer specific. So, these features, though looking like discrete values based on the no. of unique values, they are continuous ones...\n",
    "\n",
    "Null values seem to have been captured as `unknown` in many cat columns. Let's dive deep into that part to know the proportion...\n",
    "\n",
    "One more thing to note here is, `prev_camp_outcome` has missing values captured in the name of `nonexistent`..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "91c53079",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_unknown_values(df):\n",
    "\n",
    "    for column_name in df.columns:\n",
    "        unique_values = df[column_name].unique().tolist()\n",
    "        if 'unknown' in unique_values:\n",
    "            values = df[column_name].value_counts()\n",
    "            unknown_counts = values['unknown']\n",
    "            total_count = len(df)\n",
    "            unknown_percentage = (unknown_counts/ total_count) * 100\n",
    "            print(values)\n",
    "            print(f\"\\nPercentage of 'unknown' values in {column_name}: {unknown_percentage:.2f}%\")\n",
    "            print(\"\\n\", 55 * \"_\", \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "84c6b557",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job\n",
      "admin.           10419\n",
      "blue-collar       9253\n",
      "technician        6739\n",
      "services          3967\n",
      "management        2924\n",
      "retired           1718\n",
      "entrepreneur      1456\n",
      "self-employed     1421\n",
      "housemaid         1060\n",
      "unemployed        1014\n",
      "student            875\n",
      "unknown            330\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Percentage of 'unknown' values in job: 0.80%\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "marital_status\n",
      "married     24921\n",
      "single      11564\n",
      "divorced     4611\n",
      "unknown        80\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Percentage of 'unknown' values in marital_status: 0.19%\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "education\n",
      "university.degree      12164\n",
      "high.school             9512\n",
      "basic.9y                6045\n",
      "professional.course     5240\n",
      "basic.4y                4176\n",
      "basic.6y                2291\n",
      "unknown                 1730\n",
      "illiterate                18\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Percentage of 'unknown' values in education: 4.20%\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "defaulted\n",
      "no         32577\n",
      "unknown     8596\n",
      "yes            3\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Percentage of 'unknown' values in defaulted: 20.88%\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "house_loan\n",
      "yes        21571\n",
      "no         18615\n",
      "unknown      990\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Percentage of 'unknown' values in house_loan: 2.40%\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "personal_loan\n",
      "no         33938\n",
      "yes         6248\n",
      "unknown      990\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Percentage of 'unknown' values in personal_loan: 2.40%\n",
      "\n",
      " _______________________________________________________ \n",
      "\n"
     ]
    }
   ],
   "source": [
    "check_unknown_values(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5885256b",
   "metadata": {},
   "source": [
    "Most of the features has negligible proportion of unknowns and only the feature named `defaulted` seems to have higher proportion of unknown values `(1/5 th of total)`... \n",
    "\n",
    "Thing to note here is, we can't impute things in `defaulted` column without knowing whether a person has really defaulted or not.... This could well be a key factor to decide whether he/she would default in future... Let's decide on `defaulted` column upon further evaluation later on...\n",
    "\n",
    "Also one good thing to note is, **target variable doesn't have any unknown values**...\n",
    "\n",
    "We will deal with these things later when we handle cat columns..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "49d92fa3",
   "metadata": {},
   "source": [
    "### Handling Numerical and Categorial Data Seperately"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b058aa22",
   "metadata": {},
   "source": [
    "#### Feature Seperation into Numerical and Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0407f6ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete Variables:\n",
      "\n",
      "['age', 'num_con_this_camp', 'days_since_last_con', 'num_con_prev_camp']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "Categorical Variables:\n",
      "\n",
      "['job', 'marital_status', 'education', 'defaulted', 'house_loan', 'personal_loan', 'con_medium', 'last_con_month', 'last_con_day', 'prev_camp_outcome', 'subscribed']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "Continuous Variables:\n",
      "\n",
      "['last_con_dur', 'emp_var_rate', 'cpi', 'cci', 'euribor_3m', 'num_employed']\n"
     ]
    }
   ],
   "source": [
    "discrete_vars = []\n",
    "categorical_vars = []\n",
    "continuous_vars = []\n",
    "\n",
    "for col in df.columns:\n",
    "    unique_values = df[col].nunique()\n",
    "    \n",
    "    if col == 'age' or col == 'num_con_this_camp' or col == 'days_since_last_con' or col == 'num_con_prev_camp':\n",
    "        discrete_vars.append(col)\n",
    "    elif df[col].dtype == 'object':\n",
    "        categorical_vars.append(col)\n",
    "    else:\n",
    "        continuous_vars.append(col)\n",
    "\n",
    "print(\"Discrete Variables:\\n\")\n",
    "print(discrete_vars)\n",
    "print(\"\\n\", 55 * \"_\", \"\\n\")\n",
    "print(\"Categorical Variables:\\n\")\n",
    "print(categorical_vars)\n",
    "print(\"\\n\", 55 * \"_\", \"\\n\")\n",
    "print(\"Continuous Variables:\\n\")\n",
    "print(continuous_vars)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "59e6ed0a",
   "metadata": {},
   "source": [
    "#### Handling Discrete Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71284894",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age :\n",
      "\n",
      "[56 57 37 40 45 59 41 24 25 29 35 54 46 50 39 30 55 49 34 52 58 32 38 44\n",
      " 42 60 53 47 51 48 33 31 43 36 28 27 26 22 23 20 21 61 19 18 70 66 76 67\n",
      " 73 88 95 77 68 75 63 80 62 65 72 82 64 71 69 78 85 79 83 81 74 17 87 91\n",
      " 86 98 94 84 92 89]\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "num_con_this_camp :\n",
      "\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 19 18 23 14 22 25 16 17 15 20 56\n",
      " 39 35 42 28 26 27 32 21 24 29 31 30 41 37 40 33 34 43]\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "days_since_last_con :\n",
      "\n",
      "[999   6   4   3   5   1   0  10   7   8   9  11   2  12  13  14  15  16\n",
      "  21  17  18  22  25  26  19  27  20]\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "num_con_prev_camp :\n",
      "\n",
      "[0 1 2 3 4 5 6 7]\n",
      "\n",
      " _______________________________________________________ \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in discrete_vars:\n",
    "    print(column, ':\\n')\n",
    "    print(df[column].unique())\n",
    "    print(\"\\n\", 55 * \"_\", \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "68569086",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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++KJOO+00DRo0SJdeeqny8/MbHW/OGAAAAIhPDof/trehgconAACiLS6ST6+//rpuu+02XXbZZXr77bd19tln64YbbtDXX3+thoYGXXnllZKk+fPn684779RLL72kxx9/PPj8V199Vffff79+//vfa9GiReratasmT56svXv3SlKzxgAAAED82lf5RPIJAIBoi/meTz6fT//4xz/0y1/+Updddpkk6be//a1WrlypL7/8Ujt27NDOnTu1YMECtWvXTv369dOePXt0//3365prrpHD4dCTTz6pSZMm6dxzz5Uk/eUvf9Hpp5+uV155RVOmTNH777/f5BgAAACIX/sqn6hsBwAg2mK+8mnTpk3asWOHzjnnnEaPP/3005oyZYpWrlypo446Su3atQseGzlypKqqqrRmzRrt2bNHmzdv1qhRo4LHbTabhg0bphUrVkhSk2MAAAAgvrHbHQAA5omL5JMk1dTU6Morr9SoUaN04YUX6qOPPpIkFRYWKi8vr9FzcnJyJEm7du1SYWGhJKlTp04HnBM41tQYAAAAiG8Oh3/ZHZVPAABEX8wvu6uqqpIk3Xzzzbr++ut144036v3339e1116rZ599VnV1dXI6nY2ek5SUJEmqr69XbW2tJB2wdC4pKUn19fWS1OQYobLZYi+3Z7VaGn1ONBaLEfwc+HMoDMP/3Fi7Ton895fIc5MSe36JPDeJ+cWzRJ4bWi5wX+ZyeeXz+YL/1wMAgMiL+eST3W6XJF155ZWaMGGCJOnII49Ufn6+nn32WSUnJ8vlcjV6TiBhlJqaquTkZEk66DkpKSmS1OQYobBYDGVmpoX03GhwOlPMDiGiHA6rUlLsIT8/Odn/oxGr1ylW4wqHRJ6blNjzS+S5ScwvniXy3NB8gZ5PkuR2e4MNyAEAQOTFfPIpNzdXktSvX79Gj/ft21effPKJRowYoXXr1jU6VlxcHHxuYLldcXGx+vTp0+icwNh5eXmHHSMUXq9PFRU1IT03kqxWi5zOFFVU1MrjSbyeB3a7VenpyXK5PKqtbTjsuQ0NHu3dW6+OHZMPeFe8rs4tSTF3nRL57y+R5yYl9vwSeW4S84tnLZmb05lChVSC2z/Z1NBA8gkAgGiK+eTTUUcdpbS0NH377bcaNmxY8PF169ape/fuGj58uF577TVVVVUpPT1dkrRs2TKlpaVpwIABcjgc6tWrl5YvXx5sOu52u7Vy5UpdeumlktTkGKFyu2P3Jt7j8cZ0fKEK/OLg9frk9foOes7u3bX68stCbdhQpoYGr9LT7Ro8OFtDhmQrJcX/I+Hz+Z8bq9cpVuMKh0Sem5TY80vkuUnML54l8tzQfPtXPjU08P0AAEA0xfxbfMnJybrqqqv0+OOP66233tLWrVv1xBNP6IsvvtDkyZN1+umnKzs7W3/4wx9UUFCgDz/8UA8++KB+/etfB/s8/frXv9azzz6rV199VRs2bNCtt96quro6XXDBBZLUrDGQGKqqGrRgwVqtWbNXDQ1eWSyGqqoa9MUXO/XCC2tUWlpndogAACACArvdSex4BwBAtMV85ZMkXXvttUpJSdFDDz2koqIi9enTR48++qiOP/54SdKcOXN011136aKLLlK7du106aWX6tprrw0+/6KLLlJlZaUefvhhlZWV6eijj9azzz6rDh06SPI3F29qDMQ/n8+n99/frNpaj7KzU3TGGd2Vk5OqdetK9cUXO1Ve7tK8eWt1/vl91aVLutnhAgCAMDIMQ3a7RQ0NXna8AwAgyuIi+SRJkydP1uTJkw96rEePHnrmmWcO+/wrr7xSV1555SGPN2cMxLfvvivRpk0VsloNnXVWL3Xs6G9AO3Bglnr0cGrRog0qKqrRggXrlJcXWqN5AAAQuwLJJyqfAACIrphfdgeEQ22tW598sl2SNHp0l2DiKSAtza6LL+6nbt0y1NDg1ezZq7VlS7kZoQIAgAhxOPxNxql8AgAgukg+oU1Yt65UDQ1eZWUl67jjcg56jsNh1Xnn9VHHjimqrHTp5z9fqLKy+ihHCgAAIsVm89/6UvkEAEB0kXxCm7BmzV5J0lFHZckwjEOel5Rk1cSJfdWunUNr1uzVpEnvqr6ed0cBAEgEgR3v2O0OAIDoIvmEhFdR4dL27VWSpAEDOjR5fkaGQ7/5zTFyOh3673936Xe/+1hery/SYQIAgAiz21l2BwCAGUg+IeEVFPirnrp2TZfT6WjWczp3TteiRf9PdrtFr722UX/5y5eRDBEAAEQBlU8AAJiD5BMSXmDJ3ZFHNl31tL/TTuuhRx4ZI0l65JFvNH/+2nCHBgAAoshup+cTAABmIPmEhFZSUqvdu2tlsRjq1y+zxc+/+OL+mjZtqCTpj3/8j5Yt2xXuEAEAQJSw2x0AAOYg+YSEtmlThSSpR48MpaTYQhrj5puH65xzequhwasrrliszZsrwhkiAACIEiqfAAAwB8knJLSdO/2Nxrt1ywh5DIvF0KOPjtHgwR21d2+dJk16VxUV9eEKEQAAREkg+UTlEwAA0UXyCQnL5/Npxw5/8qlLl/RWjZWaate//jVenTqlad26Ml199YfyeHjXFACAeBLY7Y7KJwAAoovkExJWeblLNTVuWa2GcnNTWz1eXl6a/vWvnyk11aaPP96uJ574LgxRAgCAaGG3OwAAzEHyCQkrUPWUm5sqmy083+qDBmXr3ntPkCT99a8rtHr1nrCMCwAAIm/fsjuSTwAARBPJJySsQPKpc+fWLbn7qcsuG6Dx43uoocGra69doro6d1jHBwAAkcFudwAAmIPkExJWuPo9/ZRhGPr7309Rx44pKigo1T//+X1YxwcAAJHBbncAAJiD5BMSUk1Ng/bsqZMkde6cFvbxs7NTdNddIyVJjzzyjfburQv7awAAgPCi8gkAAHOQfEJC2ry5QpLUvn2S0tLsEXmNiROP0FFHZamy0qWHHloVkdcAAADhQ+UTAADmIPmEhLRpU7mk8C+525/FYuiOO46XJD377A/aurUyYq8FAABab1/DcSqfAACIJpJPSEiBfk+dOoV/yd3+Tj21q0aP7iKXy6uHH6b6CQCAWEblEwAA5iD5hIS0a1e1JKljx+SIvo5hGJo+/ThJ0sKFG1ReXh/R1wMAAKHb1/OJ5BMAANFE8gkJp7LSpdJSfxIoKysl4q93/PF5GjAgU7W1bi1YsC7irwcAAEJjswWW3ZF8AgAgmkg+IeHk5++RJKWl2ZWSYov46xmGoSuuOEqSNHduvnw+X8RfEwAAtJzDQc8nAADMQPIJCWf16hJJkV9yt78LLzxCqak2rV9fpi++2Bm11wUAAM1H5RMAAOYg+YSE88MPgeRT5JfcBWRkOHThhf0kSc8+mx+11wUAAM0XaDjudpN8AgAgmkg+IeHsq3yKXvJJkn75yyMlSe+/v1mVla6ovjYAAGia3e5vOM5udwAARBfJJyScH37w93zKyoresjtJOvroLPXt214ul1eLF2+J6msDAICmUfkEAIA5SD4hoZSW1mnnzipJ0dnpbn+GYeicc3pJkt5448eovjYAAGhaIPlEzycAAKKL5BMSSkHBXklSZmaSkpKsUX/9c87pI0n66KNtqqpi6R0AALGE5BMAAOYg+YSEkp/vTz7l5aWZ8vpHHdVBvXo5VV/v0YcfbjUlBgAAcHD7kk8ekyMBAKBtIfmEhBKofOrUyZzkk3/pXW9JLL0DACDW2GxUPgEAYAaST0goa9aYW/kkSeee608+LVmyTdXVDabFAQAAGnM4/EvyaTgOAEB0kXxCQlm7NpB8SjUthmOO6ahu3dJVW+vWF1/sNC0OAADQWKDyyeUi+QQAQDSRfELCqKpqUHFxrSSpY8fo7nS3P8MwdNpp3SVJS5bQ9wkAgFgR6PlE5RMAANFF8gkJY/PmCklSVlaKUlPtYRnTarXIZmv5x7hxPST5d70zjLCEAgAAWomeTwAAmMNmdgBAuGzeXC5J6tOnXavHyshwyOv1yekMrYLqnHOOkMOxWFu2VKq4uF65ucnyen2tjgsAAITO4aDyCQAAM5B8QsIIVD717ZvZ6rGSk22yWAy99FK+ioqqQxqjR48MrV9fpsWLt+hXvxpA8gkAAJPR8wkAAHOQfELC2LTJn3wKR+VTQHFxjXbsqArpuZ07p2v9+jK9++4m/epXA8IWEwAAaDmLxVBysv/W1+32BhNRLeX1+nhDCQCAFiL5hIQRqHzq06e9ysrqTI5G6tXLqU8/lT79dJtqahqC2zsDAIDoslgMtW+fquzsBkn+5FNmZlpIY3k8XpWV1ZCAAgCgBUg+IWFs2bIv+fTVV4UmRyNlZSUrMzNJpaX1+vzznRo7tpvZIQEA0CZZLIasVoveeWejJKm21q2HH17R4nFyctJ06aUDZbEYJJ8AAGgBkk9ICC6XR9u3+5fH9e2bGRPJJ8MwNGBABy1duksffbSN5BMAACYrLfVXRrvd3pCX1QMAgJYLbbE7EGO2bauU1+tTWppNubmpZocT1L+/v/n5xx9vMzkSAABgsxmSJJ9P8vmoXAIAIFpIPiEhBPo99ezZToZhmBzNPkcc0V4Wi6H168t4hxUAAJNZrftufT0ekk8AAEQLySckhMBOd716hW+nu3BITbVr+PA8SdKnn243ORoAANo2i2XfG1T0bAIAIHpIPiEhbN5cLsm/w1ysGTeuhySSTwAAmC2w7E4i+QQAQDTFRfKpqKhI/fv3P+Bj0aJFkqQ1a9Zo0qRJGjJkiMaOHavnn3++0fO9Xq8eeeQRjR49WkOGDNHVV1+tbdsa9+BpagzEtsCyu1irfJKkceN6SpL+858d3OgCAGCi/SufWHYHAED0xEXyqaCgQElJSfrss8/0+eefBz/OPPNMlZaWavLkyerevbsWLlyo6667TjNnztTChQuDz581a5bmzZune+65R/Pnz5fX69VVV10ll8slSc0aA7EtsOyud+/YSz4df3wnpafbtWdPnVavLjE7HAAA2izDMGT5390vbwgBABA9NrMDaI5169apZ8+eysnJOeDYc889J7vdrrvvvls2m019+vTRli1bNHv2bE2cOFEul0vPPPOMbrzxRo0ZM0aS9NBDD2n06NFavHixzj77bC1YsOCwYyC2eb0+bd1aKSk2K5/sdqtGj+6id9/drE8+2a5Bg7LNDgkAgDbLYjHk9fqofAIAIIriovJp7dq16tOnz0GPrVy5UiNGjJDNti+PNnLkSG3evFklJSUqKChQdXW1Ro0aFTzudDo1cOBArVixolljILbt2lWt+nqPbDaLunZNNzucgxozpqsk+j4BAGC2wI53VD4BABA9cZF8Wrdunfbu3avLLrtMJ5xwgi655BL95z//kSQVFhYqLy+v0fmBCqldu3apsLBQktSpU6cDzgkca2oMxLZt2/xVT126pMtmi81v6VNP7SZJWr68UDU1DSZHAwBA2xXo+0TlEwAA0RPzy+7cbrd+/PFH9e3bV7fccovS09P19ttv6ze/+Y2effZZ1dXVyeFwNHpOUlKSJKm+vl61tbWSdNBzysv9O6Q1NUaoYjEREni3L/A5EezcWS1J6t49PXhDabEYjZqKtpRhhHecfv0y1bVrurZvr9KXXxbp9NO7hzReIv79BSTy3KTEnl8iz01ifvEskeeG0AX+X6fyCQCA6In55JPNZtPy5ctltVqVnJwsSTr66KO1fv16Pf3000pOTg42Dg8IJIxSU1ODz3G5XME/B85JSUmRpCbHCIXFYigzMy2k50aD05lidghhU1Li/7vq0ydT6en+v2OHw6qUFHvIYyYlWcMyTnKy/0esXbtUjR/fS3PmfK+lSwt14YVHhjymlFh/fz+VyHOTEnt+iTw3ifnFs0SeG1rOaiX5BABAtMV88kmS0tIOTOIcccQR+vzzz5WXl6fi4uJGxwJf5+bmyu12Bx/r3r17o3P69+8vSU2OEQqv16eKipqQnhtJVqtFTmeKKipq5fF4zQ4nLNat2yNJyslJUVVVndLTk+VyeVRbG/rytvp6jyS1epy6Ov/3X0VFrUaNytOcOd/rvfc26Y47RoQ0XiL+/QUk8tykxJ5fIs9NYn7xrCVzczpTqJBqI1h2BwBA9MV88mn9+vW6+OKL9cQTT+j4448PPr569Wr17dtXRx55pObPny+PxyOr1V+tsmzZMvXq1UtZWVnKyMhQenq6li9fHkw+VVRUKD8/X5MmTZIkDR8+/LBjhMrtjt2beI/HG9PxtURgp7suXdKC72J6vb5WvaPp84V3HI/HqxNP7CzDkNas2avt2yuVlxd6ZVwi/f39VCLPTUrs+SXy3CTmF88SeW5oOZbdAQAQfTH/Fl+fPn3Uu3dv3X333Vq5cqU2btyov/71r/rmm2/029/+VhMnTlRVVZVuu+02bdiwQYsWLdLcuXM1ZcoUSf5eT5MmTdLMmTO1ZMkSFRQUaNq0acrLy9O4ceMkqckxENu2b6+SJHXtmmFyJIfXoUOyBg/OliR9+ukOk6MBAKBtYtkdAADRF/OVTxaLRU8++aT+/ve/6w9/+IMqKio0cOBAPfvss+rXr58kac6cOZoxY4YmTJig7Oxs3XTTTZowYUJwjKlTp8rtduv2229XXV2dhg8frqefflp2u7+XT1ZWVpNjIDb5fD7t2BFIPqWbHE3Txozpqm++2a1PPtmmiy/uZ3Y4AAC0OSy7AwAg+mI++SRJHTt21F//+tdDHh80aJBefvnlQx63Wq2aPn26pk+fHvIYiE0lJXWqrXXLMKQuXWI/+XTKKV318MNf69NPd8jr9bVqJz0AANBy+yqfWIoJAEC0xPyyO+Bwtm/393vKzU2Vw2E1OZqmDR+eq/R0u0pKavXtt7vNDgcAgDaHyicAAKKP5BPiWrz0ewpwOKwaM6arJOmDD7aaHA0AAG0PPZ8AAIg+kk+Ia9u2+SufunWL/SV3AWec4d918YMPtpgcCQAAbQ+VTwAARB/JJ8S1eKt8kqSxY/3Jp2+/LVFRUbXJ0QAA0LZQ+QQAQPSRfEJcC/R8ioed7gJyc1M1ZEi2JGnJkm0mRwMAQNsSqHwi+QQAQPSQfEJc27bNX/nUrVv8VD5J+y+9o+8TAADRRPIJAIDoI/mEuBaPlU+SdMYZPSRJn3yyXfX1HpOjAQDEoz179mj69OkaOXKkhg4dqt/85jfauHFj8PiaNWs0adIkDRkyRGPHjtXzzz/f6Pler1ePPPKIRo8erSFDhujqq6/Wtm2NK3LDMUasoecTAADRR/IJcauy0qXycpek+Or5JEmDBnVUTk6qqqsb9MUXO80OBwAQh6677jpt2bJFs2fP1r///W8lJyfriiuuUG1trUpLSzV58mR1795dCxcu1HXXXaeZM2dq4cKFwefPmjVL8+bN0z333KP58+fL6/Xqqquuksvl/781HGPEIqvVf/tL5RMAANFD8glxK7DTXWZmktLT7SZH0zIWi6Hx4/3VT++8s8nkaAAA8aa8vFxdunTRvffeq0GDBqlPnz669tprVVxcrPXr12vBggWy2+26++671adPH02cOFFXXHGFZs+eLUlyuVx65plnNHXqVI0ZM0YDBgzQQw89pMLCQi1evFiSwjJGLKLyCQCA6CP5hLgVjzvd7e+ss3pJkt59d7M8Hq/J0QAA4km7du3097//Xf369ZMk7d27V3PnzlVeXp769u2rlStXasSIEbLZbMHnjBw5Ups3b1ZJSYkKCgpUXV2tUaNGBY87nU4NHDhQK1askKSwjBGL2O0OAIDoI/mEuBVIPnXpEl/9ngJOPLGz2rVzaPfuWq1YUWR2OACAOHXHHXdo1KhRevvttzVjxgylpqaqsLBQeXl5jc7LycmRJO3atUuFhYWSpE6dOh1wTuBYOMaIRVQ+AQAQfbamTwFi044dgcqn+Ew+ORxWjRvXQ6+8sl5vv71JI0d2avpJAAD8xK9+9StdfPHFevHFF3Xddddp3rx5qqurk8PhaHReUlKSJKm+vl61tbWSdNBzysvLJSksY4TKZmvZ+6OBPk6Bz4c6bhiGbDZ/8snn8wUTUc1lGMZhXyfeNXUd0Txcx/DgOoYH17H1uIbhQfIJcSuQfOrcOc3kSEJ31lm99Mor6/XOO5t0992jgje1AAA0V9++fSVJM2bM0LfffqsXXnhBycnJBzT9rq+vlySlpqYqOTlZkr9vU+DPgXNSUlIkKSxjhMJiMZSZGdr/7U7n4V83Odkmh8N/+2sYhlJSWtYzMjnZ1qzXiXeJPr9o4TqGB9cxPLiOrcc1bB2ST4hbO3fG97I7SRozpqtSU23atq1K331XosGDs80OCQAQB/bu3aulS5fqZz/7WbAnk8ViUd++fVVcXKy8vDwVFxc3ek7g69zcXLnd7uBj3bt3b3RO//79JSksY4TC6/WpoqKmRc+xWi1yOlNUUVF70D6KgeN1dW75fP7jLpdHtbUNLXqdujr/nA/1OvGuqeuI5uE6hgfXMTy4jq3HNTw8pzOlWVVhJJ8Qt3burJYkde4cv8mn1FS7xo7tprfe2qTXX99I8gkA0CwlJSW64YYbNGfOHI0ePVqS1NDQoPz8fI0dO1YdO3bU/Pnz5fF4ZLVaJUnLli1Tr169lJWVpYyMDKWnp2v58uXBxFFFRYXy8/M1adIkSdLw4cNbPUao3O7Qbu49Hu9hn+vz+YJVxh6Pr8VNx30+X7NeJ94l+vyihesYHlzH8OA6th7XsHVYtIi45PX6tGtXIPkUv8vuJGnCBP9yidde28jOOwCAZunXr59OPvlk3XvvvVqxYoXWrVunW265RRUVFbriiis0ceJEVVVV6bbbbtOGDRu0aNEizZ07V1OmTJHk79M0adIkzZw5U0uWLFFBQYGmTZumvLw8jRs3TpLCMkYsCvR54v9cAACih8onxKXdu2vV0OCVYUh5ealmh9Mqp5/eXenpdm3fXqUvvyyk8TgAoFkefPBB/f3vf9e0adNUWVmpYcOG6cUXX1Tnzp0lSXPmzNGMGTM0YcIEZWdn66abbtKECROCz586darcbrduv/121dXVafjw4Xr66adlt/v7IGVlZbV6jFhktQYqn3j3GgCAaCH5hLgU6PeUm5squ91qcjStk5Ji01ln9dLLL6/TokUbSD4BAJolIyNDd955p+68886DHh80aJBefvnlQz7farVq+vTpmj59+iHPCccYsYbKJwAAoo9ld4hLgZ3u4rnZ+P7OP9+/9O6NN35UQ4PH5GgAAEhcgeSTx0PyCQCAaCH5hLiUCM3G9zd6dBdlZ6do7946ffrpDrPDAQAgYQWW3VH5BABA9EQs+fTWW2/J5XJFani0cYHKp3hvNh5gs1l03nl9JEkLF643ORoAQKRwf2Q+Kp8AAIi+iCWfbrrpJp144om688479d1330XqZdBGBXo+JcqyO2nf0rt3392s6uoGk6MBAEQC90fmo/IJAIDoi1jy6aOPPtKvf/1rLVu2TBdffLHOPPNMPf3009q9e3ekXhJtyI4d/mV3iZR8OvbYHPXo4VRNjVvvv7/F7HAAABHA/ZH5qHwCACD6IpZ8ysvL029/+1u99957evHFFzVs2DA99dRTOvXUU3XNNddo8eLFcrvdkXp5JLhA5VOiLLuTJMMwNHGiv/pp0SKW3gFAIuL+yHxWq//2l8onAACiJyoNx4899ljdfffdeuqppzR06FB98sknmjp1qsaMGaOnnnpKHg+7e6H53G6vCgtrJCVW5ZO0b+ndRx9t1969dSZHAwCIJO6PzBGofCL5BABA9Ngi/QI7duzQ66+/rtdff11bt25V9+7ddcMNN2jMmDH65JNP9Pjjj2vDhg3629/+FulQkCCKi2vk9fpks1mUnZ1idjhh1a9fpo45pqO+/75Eb775o371q4FmhwQAiADuj8xD8gkAgOiLWPLplVde0euvv65Vq1YpKSlJ48eP14wZMzRs2LDgOf369VNpaanmz5/PzRWaLbDTXadOqcHS+URy/vl99f33JVq4cD3JJwBIMNwfmS/QcJyeTwAARE/Ekk933HGHBg8erDvvvFNnnnmm0tMPvjyqf//+uvjiiyMVBhLQzp3+ZuOdOyfWkruACRP66O67l2nZskJt2VKhHj2cZocEAAgT7o/MR+UTAADRF7Hk01tvvaW+ffvK4/HIarVKkurq6tTQ0KCMjIzgeeedd16kQkCCClQ+JVKz8f117pyu0aO76D//2aEFC9Zp+vRhTT8JABAXuD8yH7vdAQAQfRFbs9SzZ0/9+c9/1kUXXRR8bNWqVRo1apT+9re/yev1RuqlkeD27XSXmJVPknTJJf0lSS+/vI53ZgEggXB/ZL7Asjv+fwUAIHoilnx65JFH9MYbb+jss88OPjZw4EDdeOONWrBggebMmROpl0aC27HDv+wu0Xa629/Pf95TGRkObd1aqaVLd5kdDgAgTLg/Mh+VTwAARF/Ekk9vvvmmbr75Zk2ePDn4WPv27XXFFVdo2rRp+ve//x2pl0aCC1Q+xVPyyWq1yGZr/ofTmaQJE/pIkhYsWCebzRK8WQYAxC/uj8xH5RMAANEXsZ5PpaWl6tat20GP9e7dW4WFhZF6aSS4fZVPsd/zKSPDIa/XJ6czpcXPveaaoXr++TV6/fWN+uc/f6bUVJsqK+siECUAIFq4PzLf/g3HfT6fDIM3dwAAiLSIJZ969+6t999/XyeeeOIBxz766CP16NEjUi+NBFZf71FxcY2k+Oj5lJxsk8Vi6KWX8lVUVN2i5/p8PuXkpKi4uFbTpn2kOXPGU/0EAHGO+yPzBSqfJMnrlf7X9x0AAERQxJJPv/zlL3XLLbeorKxMp59+urKysrR37159/PHHevfdd/XXv/41Ui+NBLZrlz+Bk5xsVVZWssnRNF9xcU1wl76WOPLIDiou3qHFi7dEICoAQLRxf2S+/d/I8Xp9wWSUy+XR7t216tw5jWooAADCLGLJp/POO0/V1dWaNWuWFi9eHHw8MzNTd9xxB1sIIySBfk+dOrWNG8Ojjuqozz/fqW3bKrVqVZH69nWaHRIAoBW4PzLfT5NPAW+++aM2barQRRf1U/fuGWaEBgBAwopY8kmSLrvsMl166aXatGmTysrK5HQ61bt3b1ksEetzjgQXqB7q2jX2l9yFQ2qqTUcc0V4FBaWaPfs73X//SWaHBABoJe6PzLX/sjuPxyvJqt27a7RpU4Ukqby8XhLJJwAAwimiySdJMgxDvXv3jvTLoI3YudO/7C4e+j2Fy6BB2SooKNWLL+brT38aEVLz8v1ZLEZYekd5vT52CgKAEHF/ZB7DMGQYks+3r/Lpq6+Kg8c9Hv5vAwAg3CKWfNq7d69mzJihTz75RLW1tfL5Gv9HbhiG8vPzI/XySFDbt/srn7p0aTvJp27d0pWdnaLdu2v1yivr9fvfDwt5LIvFUPv2qbJaW//uusfjVVlZDQkoAGgB7o9ig9VqyO32yePxqbq6QWvW7A0e81dDAQCAcIpY8unuu+/Wxx9/rLPOOkt5eXmUkiMsAj2fOndOMzmS6DEMQyec0Emvv/6j/vnP7zR16nEhj2WxGLJaLZo3L1/FxS3bfW9/OTlpuvTSgbJYDJJPANAC3B/FBn8FsL+C99tvdzeqdqLyCQCA8ItY8uk///mPbr31Vl188cWRegm0QYGeT22p8kmSjj++k5Ys2aY1a/ZqyZKtOu64jq0ar7i4OqTd9wAArcP9UWwI9H3yeHz67rsSSVJKik21tW6STwAAREDE3m6z2+3q1q1bpIZHGxXo+dTWkk8pKTZNnny0JOnhh78yORoAQKi4P4oNgd6HLpdHVVUNkqRevfw7yrLsDgCA8ItY8umMM87QW2+9Fanh0QZVVzeorKxeUttLPknS1KnHyjCkt9/+URs2lJkdDgAgBNwfxYbAcseaGrckyTD8b/RILLsDACASIrbsbuDAgXr44Ye1bds2DR48WMnJyY2OG4ah6667rkVjbtq0Seeff77uuOMOnX/++ZKkNWvWaMaMGVq9erU6dOigK664Qr/85S+Dz/F6vXrsscf0yiuvqLKyUsOHD9ef/vSnRu86NjUGYkOg31NGhkMZGQ6To4m+vn0z9fOf99Q772zWk09+p/vuO8nskAAALRSJ+yO0XGDZXXW1v+opOdkmm82fkCL5BABA+EW04bgkrVixQitWrDjgeEtvrhoaGnTjjTeqpqYm+FhpaakmT56ssWPH6q677tI333yju+66S2lpaZo4caIkadasWZo3b57uu+8+5eXl6YEHHtBVV12lN998Uw6Ho1ljIDbs2+mu7TQb/6lrrx2id97ZrBdfLNC0aUOVm9t2rwUAxKNw3x8hNIFld4HKp5QU6359oFh2BwBAuEUs+VRQUBDW8R599FGlpzdearVgwQLZ7Xbdfffdstls6tOnj7Zs2aLZs2dr4sSJcrlceuaZZ3TjjTdqzJgxkqSHHnpIo0eP1uLFi3X22Wc3OQZiR6DfU+fObW/JXcBJJ3XWiSd20Rdf7NBjj32re+45weyQAAAtEO77I4TmYJVPViuVTwAAREpU9vetrKzUxo0b5XK55PF4Wvz8FStW6OWXX9Z9993X6PGVK1dqxIgRstn25dBGjhypzZs3q6SkRAUFBaqurtaoUaOCx51OpwYOHBh8t7GpMRA72upOd/szDEN/+pP/+/n559eouLimiWcAAGJVa++PELpA5VPj5NO+HfAAAEB4RTT5tHz5cl144YUaMWKEzjnnHK1fv15//OMfD0giHU5FRYVuuukm3X777erUqVOjY4WFhcrLy2v0WE5OjiRp165dKiwslKQDnpeTkxM81tQYiB2Bnk9tOfkkSWec0UPHHZej2lq3Zs361uxwAAAtFI77I7TOT5fdJSdbST4BABBBEVt2t3TpUl199dUaOnSobrzxRs2cOVOSNGDAAD3yyCPKzc3V5MmTmxznzjvv1NChQ3XOOecccKyurk4OR+PG00lJSZKk+vp61dbWStJBzykvL2/WGK0RaFwZSwIl5YHP8SSw7K5bt/RDXtvAzaTFYgT/HArDiN1xDMPQLbcM14UXvq25c/P1hz8cq44dU5o1TuDv3TDCE084v4/i+XuzORJ5fok8N4n5xbNYnFu47o/QOoFEU03NwSqf6PkEAEC4RSz59PDDD+u0007TP/7xD7ndbj3wwAOSpGuuuUY1NTV65ZVXmry5eu2117Ry5Uq9+eabBz2enJwsl8vV6LFAwig1NTW4g4zL5Wq0m0x9fb1SUlKaNUaoLBZDmZmx2wza6WxesiKW7NrlX2I2YEB2k9fW4bAqJcUe8mslJVljapzkZP+Panq6//t44sQBGjZspVauLNIzz+Trr389ucXjhSOeSHwfxeP3Zksk8vwSeW4S84tnsTS3cNwfofUOXHZnpecTAAARFLHk05o1a4K7tQSqJAJOPPFEPffcc02OsXDhQu3ZsyfYLDzgz3/+s9555x3l5eWpuLi40bHA17m5uXK73cHHunfv3uic/v37S1KTY4TK6/WpoiL2+vFYrRY5nSmqqKiNq3f2fD6ftm2rkCQ5nVaVllYf9Dy73ar09GS5XB7V1jaE/Hr19f7eG7EyTl2d/3u5qqpO6enJqqys0w03HKtLL31Xjz32ta666ih16JDcxCj7/v7r6txhiSec30fx+r3ZXIk8v0Sem8T84llL5uZ0pkSlQioc90dovUDyyeXyf1/Q8wkAgMiKWPIpIyNDu3fvPuixXbt2KSMjo8kxZs6cqbq6ukaPjRs3TlOnTtW5556r119/XfPnz5fH45HV6q8wWbZsmXr16qWsrCxlZGQoPT1dy5cvDyafKioqlJ+fr0mTJkmShg8fftgxWsPtjt2beI/HG9Px/VRpaV2wL0NOTuohYw/84uD1+uT1hn7z6PP5YnYcyf/3d9pp3XT00VlavXqPHn/8G/3f/41o0XjhiCcS30fx9r3ZUok8v0Sem8T84lkszS0c90dovUCiKaBxz6fY+F4BACCRROwtvtNOO00PPfSQvv/+++BjhmGosLBQTz755AHVTAeTm5urHj16NPqQpKysLOXm5mrixImqqqrSbbfdpg0bNmjRokWaO3eupkyZIsnf62nSpEmaOXOmlixZooKCAk2bNk15eXkaN26cJDU5BmLDjh3+SqeOHZOVkhKxnGlcMQxDf/zjcZKkp55ardLSuiaeAQAwWzjuj9B6P+176K98YtkdAACRErHf4v/4xz/q22+/1UUXXaSOHTtKkm644QYVFhaqU6dOuuGGG1r9GllZWZozZ45mzJihCRMmKDs7WzfddJMmTJgQPGfq1Klyu926/fbbVVdXp+HDh+vpp5+W3W5v9hgwX2Cnu86d2/ZOdz/185/31JFHdtCaNXv1z39+r1tuGW52SACAw4jG/RGa9tPKp5QUm1wu/1J5kk8AAIRfxJJP7dq10yuvvKLXXntNy5YtU1lZmTIyMnT55Zfr/PPPDzb8bqm1a9c2+nrQoEF6+eWXD3m+1WrV9OnTNX369EOe09QYMN/27SSfDsZiMXTjjcfpyis/0OzZ32vKlGOUmdl07ycAgDkidX+Eljmw8skaXG7n9bLsDgCAcIvo+iWHw6GLLrpIF110USRfBm1AoPKpa1eSTz911lm9dNRRWfrhhz164onvdOutze/9BACIPu6PzHdgzyeb6ur8lU9uN5VPAACEW8SST6+99lqT55x33nmRenkkmB07ApVPaSZHEnssFkPTpx+nK65YrKeeWq0pU45RVhbvnANALOL+KDb8tPIpKWlfw/HWbMgBAAAOLmLJp1tuueWgjxuGIavVKqvVys0Vmm3nTn/D8S5dqHw6mJ//vKeOOaajvv++RLNmfac77jje7JAAAAfB/VFs2D/5lJRklcViBB9jtzsAAMIvYsmnJUuWHPBYTU2NVq5cqaeeekqPP/54pF4aCWhf5RPJp4MxDEM33XScLr/8fT399Gpdc80gZWdT/QQAsYb7o9gQ2NlO8vd7kiSbjd3uAACIlIgln7p06XLQx4844gg1NDTonnvu0bx58yL18kggXq9Pu3YFKp9Ydnco48b10JAh2frmm916/PFvdOedo8wOCQDwE9wfxYb9K5+Sk22NHiP5BABA+FmaPiX8+vfvrx9++MGMl0Yc2r27Rg0NXlkshvLySD4dir/6aZgk6dln81VcXGNyRACAluD+KHr2Tz6lpPiTT4GeTx6PTz4fCSgAAMIp6sknl8ulf//738rKyor2SyNO7djhr3rKy0sNlsTj4E47rZuOOy5HtbVuPfbYt2aHAwBoJu6Pomv/3e4Cy+72f4ym4wAAhFfElt2NHTtWhtF4JxGv16vS0lLV19fr5ptvjtRLI8HQ76n5DMO/890vfvGu5s79QdddN0i5uVSLAUCs4P4oNhxs2d3+faA8Hp+s1qiHBQBAwopY8mnEiBEH3FxJUnp6uk499VSdcMIJkXppJJidO/3JJ/o9Nc+pp3bTsGG5WrmySI8++o3uvfdEs0MCAPwP90exoanKJ/o+AQAQXhFLPt13332RGhptzPbtVD61RKD300UXva3nnluj668fQq8sAIgR3B/FhkM1HDcMyeeTPB6vWaEBAJCQIpZ82rlzZ4vO79y5c4QiQbzbudPf86lrV5JPzXXKKV10/PF5Wr68UP/4x9f6619PMjskAIC4P4oVjSufbPs9bpHb7aXyCQCAMItqz6fDWbNmTaRCQZwLLLuj8qn5AtVPEye+pX/9a41+97shXD8AiAHcH8WGxrvd7WvuZLUacrtZdgcAQLhFLPn08MMP689//rOOOuoonXvuucrNzVVpaak++ugjvfvuu/rtb3+rLl26ROrlkUACDce7dCF50hInndRZo0Z10tKlu/Tww1/r/vtHmx0SALR53B/FhoMtu5P2VUSRfAIAILwilnx6/fXXdeqppx7Q2+DMM89UVlaWVq1apeuvvz5SL48E0dDgUVFRjSSpc2f6FrWEYRi6+eZhOu+8N/XiiwWaOnWIevZsZ3ZYANCmcX8UGw7WcHz/x+n5BABAeFmaPiU0S5cu1dlnn33QYyeffLK++uqrSL00EsiuXTXy+SSHw6KOHVPMDifunHBCZ510Umc1NHj18MNfmx0OALR53B/FhkNXPvlvjal8AgAgvCKWfMrMzNS333570GNLly5Vbm5upF4aCWTHjkpJ/n5P+98oovluummYJGnevLXasqXC5GgAoG3j/ig2NF35RPIJAIBwitiyuwsuuEBPPPGEamtrNXbsWHXo0EElJSV677339NJLL+mOO+6I1EsjgWzd6u/31L17hsmRxIZAAi7wzmxznHRSF40Z01WffLJdt932hd5++4JIhQcAaAL3R7Eh8P+p3W5p9H8qy+4AAIiMiCWfrr32WlVWVmru3Ll6+umnJUk+n08pKSmaNm2afvGLX0TqpZFAtm71V+q09eRTRoZDXq9P6enJkiSns2VLEB9//HQNHvy83nlns15/fYNasNESACCMuD+KDYEk0/5L7vyPs+wOAIBIiFjyyTAM3XLLLbr22mv1zTffqLy8XJmZmRoyZIjS09m1DM2zbZt/2V1bTz4lJ9tksRh6+eU1KiurV12dWz5fy26Mx4zpog8/3Kbf/W6Jrr9+cIQiBQAcDvdHsSErK0UOh0XdujW+5iy7AwAgMiKWfApIT09XTk6OJGnIkCFyu92RfkkkkK1b/cmnbt3advIpoLi4Rnv21Km2tkFeb8tujI86qqO+/bZE27ZV6vXXf9QJJ3SKUJQAgKZwf2SutDS7fvvbwbLZGpcCs+wOAIDIiGjy6fXXX9ff//537d69W4Zh6JVXXtGjjz4qu92uv//973I4HJF8eSSAQOUTyafWs9stmjz5KD3wwEr99787lZFh1zHHdDQ7LABoc7g/ig12+4H9E1l2BwBAZERst7t33nlHN998s0aOHKkHH3xQXq//HaQzzjhDn376qWbNmhWpl0aCaGjwaMeOaklSjx4kn8Jh0KBs3X33iZKkDz/cqp07q0yOCADaFu6PYhvL7gAAiIyIJZ+efPJJ/eIXv9D999+vcePGBR+fOHGifve73+ntt9+O1EsjQezcWS2v16ekJKuys1PNDidh3HrrSA0a1FEej08LF27Qli0VZocEAG0G90exjWV3AABERsSST5s2bdIZZ5xx0GODBw9WUVFRpF4aCWL/fk+BLZHRehaLoUsvHaAuXdJUX+/RwoXr9e23u1vcwBwA0HLcH8U2lt0BABAZEUs+ZWVlaePGjQc9tnHjRmVlZUXqpZEg9vV7YvefcEtOtunCC/tpwIBMeb3SBx9s1fz561RYWG12aACQ0Lg/im0suwMAIDIilnw688wz9cgjj+i9996Ty+WS5N9eePXq1Zo1a5bGjx8fqZdGgmCnu8iy2Sw666xeGj26i2w2Qzt2VOmFFwr06qsb6AUFABHC/VFsY9kdAACREbHd7v7whz9o3bp1+sMf/iCLxZ/juvzyy1VTU6Nhw4bp97//faReGgkikHzq3t1pciSJyzAMHX98ngYO7KDPPtuh/Py92rixXBs3lqtXL6fGjOmqrKwUs8MEgITB/VFsCyzzp/IJAIDwiljyyeFwaM6cOfriiy+0bNkylZWVKSMjQyNGjNApp5wiw6CHDw4vsOyue3eW3UVaRoZDZ57ZSyNHdtKXXxYqP3+PNm2q0ObN+Ro2LFcnndQl+G4wACB03B/FNpuNnk8AAERCxJJPV155pa666iqdeOKJOvHEEyP1MkhgVD5FX4cOyRo/vqdGjMjTp59u18aN5VqxokiFhTU655zeSk2N2D8ZANAmcH8U26h8AgAgMiLW82nVqlW8e4eQ1dd7gs2v6fkUfR06JGvChL4699zestst2ratUi+8sEbl5fVmhwYAcY37o9hms9HzCQCASIhY8mn06NF644031NDQEKmXQALbsaNKPp+UmmpTx47JZofTZvXrl6nLLhug9u2TVFHh0iuvrFd1NT/TABAq7o9im9XqvzX2eql8AgAgnCK2hiYpKUlvvPGG3n33XfXp00epqamNjhuGoeeeey5SL484ZLEYwXL3HTv8u61165Yhu93aojEQXh07pujii/vppZfWqqysXv/+93pNm3as2WEBQFzi/ii2Be4j3G6STwAAhFPEkk+FhYUaOnRo8Gufr/F/4j/9Gm2bxWKoffvU4DuOJSX+5V19+rRXZmZai8djRUN4ZWQ4dOGFR+ill9Zq9+5avfDCGt1880izwwKAuMP9UWwLLLvzell2BwBAOIU1+bR48WKNHDlSTqdT//rXv8I5NBKcxWLIarVo3rx8FRdX6803f5QkVVTU6+GHVzR7nAEDsjR+fG/6aURAZmayJk48Qi+9VKD8/L2aMWOZrr9+kNlhAUDM4/4oflgs/jfBqHwCACC8wtrz6fe//702b97c6LGnnnpKe/bsCefLIIEVF1drx44qbdvm3+nObrdox46qZn/s3Vtn8gwSW25uqk4/vbsk6c9//kIffrjV5IgAIPZxfxQ/9jUcJ/kEAEA4hTX59NNScY/HowcffFCFhYXhfBm0AWVl/mV3mZk0G481Rx/dUaNGdZLPJ/32t0tUVFRjdkgAENO4P4ofgZ5PLLsDACC8IrbbXQC9C9BSPp9PpaX+5FP79kkmR4ODOf/8vho0KFt79tRp6tSP2RUIAFqI+6PYFOg9ybI7AADCK+LJJ6Clqqsb5HZ7ZRhSu3YOs8PBQdhsFr300llKTrbq44+366mnvjc7JABoc8rKyvSnP/1JJ598so499lhdcsklWrlyZfD40qVLdf7552vw4MEaP3683n777UbPr6+v11133aVRo0Zp6NCh+uMf/6i9e/c2OiccY8QTqzVQ+UTyCQCAcCL5hJgTqHpyOh3BdyARewYO7Kh77z1BknTPPcv1/fclJkcEAG3LDTfcoK+//loPPvigFi5cqCOPPFJXXnmlfvzxR23cuFFTpkzR6NGjtWjRIl144YW66aabtHTp0uDz77zzTn3++ed69NFH9dxzz+nHH3/U1KlTg8fDMUa8CSSfPB6W3QEAEE5h3e3uUNh5DC1Bv6f4MXnyUfrww616770t+u1vl2jx4vOVmmo3OywAiAutuT/asmWLvvjiC82bN0/HHXecJOmOO+7QZ599pjfffFN79uxR//79NW3aNElSnz59lJ+frzlz5mjUqFEqKirSa6+9pieffFLDhg2TJD344IMaP368vv76aw0dOlTPPfdcq8eIN/uST1Q+AQAQTmFPPl133XVyOBovlbrmmmtktzf+hdQwDH344YfhfnkkgNJS/4519HuKfYZh6MEHT9GqVf/WunVluvPOZbr//tFmhwUAMSfc90eZmZmaPXu2jjnmmEbPNQxDFRUVWrlypU4//fRGzxk5cqRmzJghn8+nr776KvhYQK9evZSbm6sVK1Zo6NChYRkj3gQqrkk+AQAQXmFNPk2YMCGcw6GNCiy7y8wk+RQPOnZM0WOPnaqLLnpbc+fma+zYbho/vqfZYQFAzIjE/ZHT6dQpp5zS6LH3339fW7Zs0a233qpXX31VeXl5jY7n5OSotrZWpaWlKioqUmZmppKSkg44J7ALX2FhYavHCJXN1rJl94Gk0aGW6wceNwwjuKPdwdjt+5JPBzsvUK2WqG0BmrqOaB6uY3hwHcOD69h6XMPwCGvy6a9//Ws4h0MbFVh21749y+7ixZgxXXXNNYP05JPfadq0TzV0aLZyc9PMDgsAYkI07o9WrVql//u//9O4ceM0ZswY1dXVHVBpFfja5XKptrb2gOOSlJSUpPp6///D4RgjFBaLoczM0P4PcTpTDns8OdmmlJRDLw9PS3NL8iefDnZecrKtWa8T7xJ9ftHCdQwPrmN4cB1bj2vYOlHp+dRae/bs0X333afPPvtM9fX1Gj58uG6++Wb16dNHkrRmzRrNmDFDq1evVocOHXTFFVfol7/8ZfD5Xq9Xjz32mF555RVVVlZq+PDh+tOf/qRu3boFz2lqDESHz+fbr+cTlU/x5LbbRujzz3do9eo9uv76T/Tyy2ce9t1lAEB4fPjhh7rxxht17LHHaubMmZL8CSCXy9XovMDXKSkpSk5OPuC45N+9LiUlJWxjhMLr9amioqZFz7FaLXI6U1RRUXvQZuGB43V1btXWNhxynIYG/3M9Hu9Bz6ur8yenDvU68a6p64jm4TqGB9cxPLiOrcc1PDynM6VZVWFxkXy67rrr5PV6NXv2bKWlpekf//iHrrjiCi1evFh1dXWaPHmyxo4dq7vuukvffPON7rrrLqWlpWnixImSpFmzZmnevHm67777lJeXpwceeEBXXXWV3nzzTTkcDpWWljY5BqKjosKlhgavDENq1+7Ad1MRu5KSrHryydN0xhmL9Omn2/Xkk9/p2msHmx0WACS0F154QTNmzND48eP1t7/9LViJ1KlTJxUXFzc6t7i4WKmpqcrIyFBeXp7KysrkcrkaVS8VFxcrNzc3bGOEyu0O7ebe4/Ee9rk+n09e76H7OQV6wHs8Pnk83gOawvt8vma9TrxL9PlFC9cxPLiO4cF1bD2uYevE/KLF8vJydenSRffee68GDRqkPn366Nprr1VxcbHWr1+vBQsWyG636+6771afPn00ceJEXXHFFZo9e7Yk/zt0zzzzjKZOnaoxY8ZowIABeuihh1RYWKjFixdLUpNjIHpKSmolSU6ngzW1cahfv0zdffcoSdKMGV/q++9LTI4IABLXvHnzdM899+iyyy7Tgw8+2CgBNGzYMH355ZeNzl+2bJmOPfZYWSwWHXfccfJ6vcGm4ZK0adMmFRUVafjw4WEbI94EdruTdNgkFQAAaJmY/+2+Xbt2+vvf/65+/fpJkvbu3au5c+cqLy9Pffv21cqVKzVixAjZbPuKuEaOHKnNmzerpKREBQUFqq6u1qhRo4LHnU6nBg4cqBUrVkhSk2MgegLJJ3a6i1+//OWRGj++pxoavLrmmiWqqTn08gYAQGg2bdqkv/zlLzrjjDM0ZcoUlZSUaPfu3dq9e7cqKyt1+eWX67vvvtPMmTO1ceNGPfPMM3rvvfd01VVXSZJyc3N11lln6fbbb9fy5cv13Xff6YYbbtCIESM0ZMgQSQrLGPFm/ze+2PEOAIDwifnk0/7uuOMOjRo1Sm+//bZmzJih1NTUQ+7EIkm7du0K7rbSqVOnA85pajeXwBiInt27/cmnzEyajccrwzD00EMnKzc3VevXl+lPf1pqdkgAkHDef/99NTQ06IMPPtBJJ53U6GPGjBk64ogjNGvWLH366ac677zz9Morr+iBBx5o9GbcPffco1GjRun666/XlVdeqd69e+uRRx4JHg/HGPFm/8onkk8AAIRPXPR8CvjVr36liy++WC+++KKuu+46zZs376A7sQS2/K2vr1dtrT+ZcbBzysvLJR18N5f9xwhVS7cJjoZY3SYyEE9JSZ0kf7PxUJpVW/43LcNQq5pdB3o8WCyH35I52uMEWk+EOla44znU91FubpqeeOI0nX/+m3r++TU644weOuusXocdM1a/N8MlkeeXyHOTmF88S+S5XXPNNbrmmmsOe87JJ5+sk08++ZDHU1NTde+99+ree++N6BjxxGIxZBiSz0fyCQCAcIqr5FPfvn0lSTNmzNC3336rF1544aA7rQQSRqmpqUpO9lfQuFyu4J8D5wR2YmlqjFC0ZpvgaIjVbSJ37/bvbpOXl37YrZAPxW63Bj+H8vyApCT/OA5HbI0TmF9SUmg/uuGKpzlbTU+Y0F833rhLM2eu1O9//4lOPbWHunTJaHLsWP3eDJdEnl8iz01ifvEskeeG8LNaDbndPnY0AgAgjGI++bR3714tXbpUP/vZz4I9mSwWi/r27avi4mLl5eUddCcWyd+LwO12Bx/r3r17o3P69+8vSU2OEYpQtgmOhljdJtJqtSg1NUlFRf5rlpFhP+xWyIfS0OAJfg7l+QH19f5xXK7YGicwv/p6d0iNUMMVT3O3mr7xxmO1ePFmffddiS677C0tXHjOISuuYvV7M1wSeX6JPDeJ+cWzlsytudsEI/FZrRa53R4qnwAACKOYTz6VlJTohhtu0Jw5czR69GhJUkNDg/Lz8zV27Fh17NhR8+fPl8fjkdXqr+pYtmyZevXqpaysLGVkZCg9PV3Lly8PJp8qKiqUn5+vSZMmSZKGDx9+2DFCFcvbMMbiNpE//lgmj8cnm81QRoY9pOSK939T8vlat0tNYCtlr/fwWzJHe5z/fQp5vHDH09T3kcVi6MknT9Pppy/Up5/u0BNPfKspUwYdduxY/N4Mp0SeXyLPTWJ+8SyR54bwC/R9IvkEAED4xPxbfP369dPJJ5+se++9VytWrNC6det0yy23qKKiQldccYUmTpyoqqoq3XbbbdqwYYMWLVqkuXPnasqUKZL8vZ4mTZqkmTNnasmSJSooKNC0adOUl5encePGSVKTYyA68vP3SJI6dEgO9hRC/Ovbt73uvNPfnPbee79UQcFekyMCAODQ9iWfSFgCABAuMZ98kqQHH3xQo0aN0rRp03ThhReqrKxML774ojp37qysrCzNmTNHmzZt0oQJE/TYY4/ppptu0oQJE4LPnzp1qi644ALdfvvtuuSSS2S1WvX000/Lbvf3vGnOGIi8NWv8SYmsLHpzJJpf/epInXZaN9XXe3TttR/J5fKYHRIAAAcVWH5J5RMAAOET88vuJCkjI0N33nmn7rzzzoMeHzRokF5++eVDPt9qtWr69OmaPn36Ic9pagxE3po1/sqnrKzkJs5EvDEMQw8/fIpOOeXfWr16j+6/f6Vuv/14s8MCAOAALLsDACD84qLyCW1DYNkdyafElJubppkz/X3bHn30Gy1btsvkiAAAOBDL7gAACD+ST4gJXq8v2AuoQweW3SWqs8/urV/8op98Pun66z9WZaXL7JAAAGiEZXcAAIQfySfEhB07qlRd3SCr1VD79klmh4MImjHjRHXvnqGtWyt1++3/NTscAAAaYdkdAADhR/IJMWHt2lJJUseOKcGbPiSmjAyHHnvsVBmG9NJLa/X225vMDgkAgCCW3QEAEH4knxATAsmnvLxUkyNBNIwc2UnXXz9EknTjjf9RUVGNuQEBAPA/FguVTwAAhBvJJ8SEdev8yafcXJJPbcVNNw3TUUdlac+eOk2b9ql8Pm7yAQDms9no+QQAQLiRfEJMCFQ+5eammRwJoiUpyapZs8YqKcmqDz/cqueeyzc7JAAAgpVPXi/JJwAAwoXkE0zn8/mofIpTVqtFNlvoH8cc01G33z5CknT77f/V+vWlJs8IANDWBXo+ud30fAIAIFxsZgcA7NpVrbKyelmthnJyUlVcTP+fWJeR4ZDX65PTmdLqsW6+eaQWL96qzz7bocsvf0dvvnluGCIEACA0VivL7gAACDeSTzDdDz/skSQNGNBBdjvFePEgOdkmi8XQSy/lq6ioOuRxcnLSdOmlA/X442N10kkva/nyXXrwwVW64YZjwxgtAADNt2+3O5JPAACEC8knmC6QfBo8OMfkSNBSxcU12rGjqtXjdO2arpkzR+s3v1miBx5YqVNP7aqhQ/l+AABE377kE8vuAAAIF8pMYLp9yadskyOBmSZOPEIXX9xfHo9P1177kaqrG8wOCQDQBgWW3dFwHACA8CH5BNP98MNeSSSf2jrDMDRr1unq1ClNGzeW6+abP5fPx40/ACC69jUc5/8gAADCheQTTFVT06AffyyXxLI7SB06pGj27NNlsRhasGCdXnyxwOyQAABtTCD55PWy7A4AgHAh+QRTFRSUyuv1KTs7RXl5aWaHgxhw4omddeutwyVJ//d/X+j770tMjggA0JYENj9paCD5BABAuJB8gqkC/Z6OOirL5EgQS66/fojGjeuu+nqPfvWr97V7d63ZIQEA2giHwypJqq/3mBwJAACJg+QTTBVIPh19NMkn7GOxGHrssbHq3budtm+v0q9/vVguF78EAAAiLynJn3xyuah8AgAgXEg+wVT7kk8dTY4EsaZ9+yT9618/U0aGQ8uXF+qmmz6jATkAIOKofAIAIPxIPsE0Pp9P+fn+ne6ofMLBHHFEpmbPPk0Wi6F589bqvvtWmB0SACDB7at8IvkEAEC4kHyCabZurVRlpUsOh0VHHNHe7HAQo047rbseeGC0JOmhh77WnDmrTY4IAJDIqHwCACD8SD7BNIFdzPr1y5TdbjU5GsSyyy8/Urfc4t8B79Zbv9CiRRtMjggAkKiSkvy3xy6Xh+XeAACECcknmObbb3dLkoYOzTY5EsSDadOG6qqrjpYk/e53H+vjj7eZHBEAIBEFKp98PqmhgabjAACEA8knmOabb/yVT4MHk3xC0wzD0L33nqAJE/qoocGryZMXa+XKIrPDAgAkGLvdIsPw/5m+TwAAhAfJJ5jC5/MFK5+GDCH5hOaxWAw9+uipOuWUrqqpceuSS94JLt8EACAcDMPYr+8TlU8AAIQDySeYYsuWSpWV1cvhsGjAgA5mh4M44nBYNXfuOI0YkafycpcuvvhtrVtXanZYAIAEwo53AACEF8knmCJQ9XTUUVnBdxfRdlmtFlmtluCfbbbDf7Rrl6QFC87U4MEdVVJSpwsvfFtbt1aaPAsAQKJgxzsAAMLLZnYAaJu++caffKLfU9uWkeGQ1+uT05kSfGz/Px9OZmaaPvzwIo0Z87J++GGPLrjgLb3xxrnKy0trVUwWiyGLxWjVGJLk9frk9bJLEgDEIyqfAAAIL5JPMAX9niBJyck2WSyGXnopX8XFNUpOtqmuzt2ira0vvPAIlZTUavPmCp1//pt67bVzlZOTGlI8Fouh9u1Tg1VYreHxeFVWVkMCCgDikMPh/3+AyicAAMKD5BOizuv16dtv2ekO+xQX12jnziqlpNhVW9vQ4oTNlCnHaO7cfG3YUK6LLnpbr756jjIzk1sch8ViyGq1aN68fBUXV7f4+QE5OWm69NKBslgMkk8AEIeofAIAILxIPiHqNm0qV2WlS8nJVvXvn2l2OEgAmZnJ+vDDizR69EvKz9+rSy55V6+8cpYyMhwhjVdcXK0dO6rCHCUAIF4Ekk9UPgEAEB40HEfUBfo9HX10R9lsfAsiPI44IlMLF56tDh2StWpVsSZNek81NQ1mhwUAiEM0HAcAILz4zR9Rt6/ZeEeTI0GiGTgwSy+/fKYyMhxaunSXrrhiMb84AABabN+yO6/JkQAAkBhIPiHqVq4skiQdd1yuyZEgEQ0enK15836u1FSbPvlkuy699F1VVbnMDgsAEEeofAIAILxIPiGq6us9+v57f7Px447LMTkaJKrjj8/TCy+MV1qaXZ99tkPnnfemdu+uNTssAECcCCSfAg3HvV6fGhqoggIAIFQknxBVq1eXyOXyKisrWT17Os0OBwnspJO66NVXz1HHjsn67rsSnX76Qi1fXmh2WACAOPDThuMLFqzT7Nnfq67ObWZYAADELZJPiKqvviqW5K96MgzD5GiQ6IYMydabb/4/9e3bXrt2Veu8897Q3//+FcsoAACHlZTkv0V2uTzyeLzavr1KtbVuFRbWmBwZAADxieQTomrVqkDyiX5PiI4+fdpr8eLzNXFiX3k8Pv3tbyt1yimv6IMPtsjn85kdHgAgBu2/7K6yct/OqWVl9WaFBABAXCP5hKja12ycfk+InvR0u2bNGqvHHz9V2dkp+vHHcl122Xs666zX9ckn20lCAQAa2X/ZXWXlvk0rystJPgEAEAqST4ia4uIabd1aKcOQhg4l+YToMgxDF17YT8uW/ULXXjtIyclWrVxZpIsuelvnnPOGPv2UJBQAwG9f5ZNXFRUknwAAaC2ST4iawJK7/v0zlZHhMDkatFUZGQ7deecorVhxiX7zm6OVlGTVl18W6sIL39Yll7yrwsJqs0MEAJgsUPkkSSUl+3ZLZdkdAAChIfmEqPnqq8CSO/o9wXy5uWm6994Tg0koh8OixYu36Oij5+r770vMDg8AYCKr1ZDF4t8YZf/kU3m561BPAQAAh0HyCVGz/053QKzIy/MnoT78cKKOOaaj9uyp1bPP/qD8/D1mhwYAMIlhGMHqpz176oKPU/kEAEBoSD4hKjweLzvdIaYNGNBBH3xwvq6+epB8PunddzeroGCv2WEBAEzicPhvk3/a84n+gAAAtFxcJJ/Kysr0pz/9SSeffLKOPfZYXXLJJVq5cmXw+NKlS3X++edr8ODBGj9+vN5+++1Gz6+vr9ddd92lUaNGaejQofrjH/+ovXsb/1LZ1BhonYKCUtXUuJWeble/fu3NDgc4KIfDqiefPEMjR+bJ55PefnuTtm+vMjssAIAJ9u/7FODx+BotwwMAAM0TF8mnG264QV9//bUefPBBLVy4UEceeaSuvPJK/fjjj9q4caOmTJmi0aNHa9GiRbrwwgt10003aenSpcHn33nnnfr888/16KOP6rnnntOPP/6oqVOnBo83Zwy0TqDqaejQHFmtcfFthzhktVpks4X+YbVaZLH4d8Xr3z/zfxVQm+RyecyeGgAgygI73u372n//sn17pRnhAAAQ12xmB9CULVu26IsvvtC8efN03HHHSZLuuOMOffbZZ3rzzTe1Z88e9e/fX9OmTZMk9enTR/n5+ZozZ45GjRqloqIivfbaa3ryySc1bNgwSdKDDz6o8ePH6+uvv9bQoUP13HPPHXYMtF6g2fiwYfR7QvhlZDjk9frkdKaEZTyr1dC4cT20c2e1ystd+uST7Ro3rkdYxgYAxIf9K59SUqxyOpNUVFSjHTuq1LNnuomRAQAQf2I++ZSZmanZs2frmGOOCT5mGIYMw1BFRYVWrlyp008/vdFzRo4cqRkzZsjn8+mrr74KPhbQq1cv5ebmasWKFRo6dGiTYxiGEcEZtg37mo3T7wnhl5xsk8Vi6KWX8lVUVB3yOP37Z+nnP+8tyd9o9uc/76kFC9bpu+9KdMQR7dWrV7vwBQ0AiGn7J58yMhxKT7erqChQ+ZRnXmAAAMShmE8+OZ1OnXLKKY0ee//997VlyxbdeuutevXVV5WX1/gGICcnR7W1tSotLVVRUZEyMzOVlJR0wDmFhYWSpMLCwsOO0aFDhwjMrO0oL6/X2rWlkqRjj6XyCZFTXOx/RzpU2dmpjb7u3j1Dxx6bo1WrivXppzvUs6eTZDQAtBH7L7vLyHAoI8MhSa36fwYAgLYq5pNPP7Vq1Sr93//9n8aNG6cxY8aorq5ODoej0TmBr10ul2praw84LklJSUmqr/dvl9vUGKGy2WKvt1Gg31I0+y59912JJKlnT6fy8tIOG5dhGLJYQv/l3vK/aRmGWjVOIMFgsbQunnCPE8h7hDpWrM5r/3FCGS+S8Zx4YmetXr1HJSW1Wr++TAMGNJ2MDoyz/8+ZGT970ZLIc5OYXzxL5Lkh8vZPPjmdDqWl2SXR8wkAgFDEVfLpww8/1I033qhjjz1WM2fOlORPIv00QRT4OiUlRcnJyQdNINXX1yslJaVZY4TCYjGUmXnwREssCFdvnOb44Qd/1dMJJ3Rp8pokJ9uUkmIP+bXsdmvwc2vGCZTaOxyxNU5gfklJof3oxuq8HA5rcE6hzC2S80pJsWvUqE769NPt+u9/d2nw4JwmE1zJyf45HOznLJo/e9GWyHOTmF88S+S5IXJ+uuwukHyi8gkAgJaLm+TTCy+8oBkzZmj8+PH629/+FqxM6tSpk4qLixudW1xcrNTUVGVkZCgvL09lZWVyuVyNqpuKi4uVm5vbrDFC4fX6VFFRE9JzI8lqtcjpTFFFRa08Hm9UXvOzz7ZJkgYNylJp6cH78QTiqqtzq7a2IeTXamjwBD+3Zpz6ev84LldsjROYX329W16vz/R4wjlOfb1bSUm2kOYW6XkNGdJRy5fvUklJrVatKtJRR2Uddpy6OrckNfo5M+NnL1oSeW4S84tnLZmb05lChRQaCexuJ/krn1JS/LfNVD4BANBycZF8mjdvnu655x5dfvnluu222xr1XBk2bJi+/PLLRucvW7ZMxx57rCwWi4477jh5vV599dVXwZ3rNm3apKKiIg0fPrxZY4TK7Y7dm3iPxxuV+PxN3/2JvaFDs5t8TZ/PF1JSJcDrDYyjVo3j8/n+N17r4gn3OP/7FPJ4sTqv/ccJZcxIz8tut2r48Dx99tkOLVu2SwMGZB6291NgnIP9nEXrZ88MiTw3ifnFs0SeGyLnp5VP+5JPVD4BANBSMf8W36ZNm/SXv/xFZ5xxhqZMmaKSkhLt3r1bu3fvVmVlpS6//HJ99913mjlzpjZu3KhnnnlG7733nq666ipJUm5urs466yzdfvvtWr58ub777jvdcMMNGjFihIYMGSJJTY6B0G3aVKG9e+uUlGRtsloEiGVDhmTLZrNoz546llwAQBuwf/LJ6fTvdidJlZUuVVSE3hMUAIC2KOYrn95//301NDTogw8+0AcffNDo2IQJE3Tfffdp1qxZeuCBB/Tcc8+pa9eueuCBB4JVTpJ0zz336C9/+Yuuv/56SdLJJ5+s22+/PXj8iCOOaHIMhObrr/1VT0cfndWocScQb5KSrDryyEx9//0effttibp2DW1JLgAgPgTuWwxDSkuzy2IxlJxsVV2dR7t2VatPn3YmRwgAQPyI+eTTNddco2uuueaw55x88sk6+eSTD3k8NTVV9957r+69996Qx0BoAsmnY4/NMTkSoPUGD87W99/v0bp1pTr11G5KTY35f0IBACFyOv29Qjt2TAluNNGuXZLq6mpIPgEA0EIxv+wO8W1fvyeST4h/eXlpys1Nlcfj0w8/7DE7HABABLVrl6RLLumv887rE3ysffskSdLOnSy/BgCgJUg+IWJcLo9Wr/b/gk7lExLFoEEdJUnffbc72FgcAJCYunRJV7t2ScGv09L8fZ9KS+vNCgkAgLhE8gkRs2bNXtXXe9S+fZJ69XKaHQ4QFkce2UF2u0WlpfUqLKwxOxwAQBQFmpBXVdFwHACAliD5hIjZt+Qu+7Db0gPxxOGwqndvf5+PdetKTY4GABBNycmB5FODyZEAABBfSD4hYmg2jkTVr1+mJH/yiaV3ANB2JCf7N5qorKTyCQCAliD5hIgh+YRE1auXUzabofJyl4qKWHoHAG3FvmV3VD4BANASJJ/QahaLIZvN0uijpqZB69eXSZKGD8874PhPP6xWvhURP1h6BwBtE8knAABCYzM7AMQ3i8VQ+/apBySPVq0qkc8n9erVTkcc0bHZ49EaCvGiX79MrVtXpnXryjR6dBf6mgFAG0DDcQAAQkPyCa1isRiyWi2aNy9fxcXVwcc//HCrJKl9e4cefnhFk+P075+ln/+8tyR+gUd86N27nWw2Q2Vl9SourlVubqrZIQEAImxfzycqnwAAaAmSTwiL4uJq7dhRFfx67dq9kqR27ZIaPX4o2dn84o744nBY1bOnUxs2lGvTpnKSTwDQBlD5BABAaGi0g7Dz+XzatctfBdWpU5rJ0QCR06uXv+/Tpk3lJkcCAIiG5GR6PgEAEAqSTwi7qqoGVVe7ZRhSTg7VIEhcPXs6JUk7d1arrs5tcjQAgEij4TgAAKEh+YSwC1Q9ZWenyG7nWwyJq127JHXokCyfT9qypdLscAAAERbo+VRb65bb7TU5GgAA4geZAYQdS+7QlvTq5a9+2ryZpXcAzPPPf/5Tl19+eaPH1qxZo0mTJmnIkCEaO3asnn/++UbHvV6vHnnkEY0ePVpDhgzR1VdfrW3btoV9jEQSqHySqH4CAKAlSD4h7AoL/cmnvDyST0h8+/o+Vcjn85kcDYC26MUXX9TDDz/c6LHS0lJNnjxZ3bt318KFC3Xddddp5syZWrhwYfCcWbNmad68ebrnnns0f/58eb1eXXXVVXK5XGEbI9HYbBY5HDQdBwCgpUg+Iay8Xp8KC2skkXxC29C1a7psNkNVVQ0qKakzOxwAbUhRUZGuueYazZw5Uz179mx0bMGCBbLb7br77rvVp08fTZw4UVdccYVmz54tSXK5XHrmmWc0depUjRkzRgMGDNBDDz2kwsJCLV68OGxjJKKMDIckKp8AAGgJkk8Iqz176tTQ4JXdblFWVrLZ4QARZ7NZ1K1bhiR2vQMQXT/88IPsdrveeOMNDR48uNGxlStXasSIEbLZbMHHRo4cqc2bN6ukpEQFBQWqrq7WqFGjgsedTqcGDhyoFStWhG2MROR0+pNPlZVUPgEA0Fy2pk8Bmm/fkrtUWSyGydEA0dGzp1ObNlVo69ZKjRiRZ3Y4ANqIsWPHauzYsQc9VlhYqH79+jV6LCcnR5K0a9cuFRYWSpI6dep0wDmBY+EYI1Q2W8veH7VaLY0+H+q4YRituj8xDCNY+VRb62lxnLGuqeuI5uE6hgfXMTy4jq3HNQwPkk8Iq0CzcZbcoS0JVD7t2FElr5e+TwDMV1dXJ4fD0eixpKQkSVJ9fb1qa2sl6aDnlJeXh22MUFgshjIzQ7uPcDpTDns8OdmmlBR7SGMHnh9IPkmWkOOMdU1dRzQP1zE8uI7hwXVsPa5h65B8QlgFKp/Y6Q5tSXZ2ipKTraqr86ioqCaYjAIAsyQnJx/Q9Lu+vl6SlJqaquRk/9J4l8sV/HPgnJSUlLCNEQqv16eKipoWPcdqtcjpTFFFRa08Hu8hj9fVuVVbG3qvpro6tzIy/MmrwsJKlZZWhzxWLGrqOqJ5uI7hwXUMD65j63END8/pTGlWVRjJJ4RNQ4NXu3f73wUl+YS2xDAMde2arg0byrVtW6WGDcs1OyQAbVxeXp6Ki4sbPRb4Ojc3V263O/hY9+7dG53Tv3//sI0RKrc7tJt7j8d72Of6fL5WVaj6fD45nf7qr4oKV8hxxrqmriOah+sYHlzH8OA6th7XsHVYtIiwKSqqkc8npaXZlZ4eekk7EI8C1U7btlWaHAkASMOHD9dXX30lj8cTfGzZsmXq1auXsrKyNGDAAKWnp2v58uXB4xUVFcrPz9fw4cPDNkYiCiy7o+E4AADNR/IJYbP/kjvDoNk42pb9+z55PPR9AmCuiRMnqqqqSrfddps2bNigRYsWae7cuZoyZYokf5+mSZMmaebMmVqyZIkKCgo0bdo05eXlady4cWEbIxEFkk9VVaEv3wMAoK1h2R3CJtBsvFOnVJMjAaKvY8cUJSVZVV/v0Y4dVD8BMFdWVpbmzJmjGTNmaMKECcrOztZNN92kCRMmBM+ZOnWq3G63br/9dtXV1Wn48OF6+umnZbfbwzZGInI6A8knKp8AAGgukk8IG3a6Q1tmsfj7Pm3cWK4NG0Lf5QkAQnHfffcd8NigQYP08ssvH/I5VqtV06dP1/Tp0w95TjjGSDRUPgEA0HIsu0NYVFa6VFHhfweQ5BPaqsDSuw0byswNBAAQMft6PpF8AgCguUg+ISy2bvUvM+rQIVlJSVaTowHM0bVruiRp8+aKVu2mBACIXYHkU3U1y+4AAGgukk8Ii61bKyT5m40DbVV2dqpsNkO1tW6tXbvX7HAAABGwr+cTlU8AADQXySeERaDyieQT2jKr1QguO126dKfJ0QAAIoGeTwAAtBzJJ7Saz+cLJp/y8tjpDm1b587+pXf//S/JJwBIRPt6PrHsDgCA5iL5hFbbsKFMNTVuWa2GsrNTzA4HMFXnzlQ+AUAio/IJAICWI/mEVvvyy12SpNzcVFmtfEuhbQssPc3P36Py8nqTowEAhFug51N1dYN8PjaXAACgOcgUoNW+/LJQkoK9boC2LC3Nro4dkyVJK1cWmRwNACDcApVPPp9UXe02ORoAAOIDySe02vLl/sonmo0Dfj16OCVJK1aQfAKARJOSYpPFYkiSqqro+wQAQHOQfEKruFweff11sSSST0BAz54knwAgURmGofR0uyT6PgEA0Fwkn9AqP/ywRy6XR6mpNrVr5zA7HCAmBJJPK1cWyeulHwgAJBqajgMA0DIkn9AqX33lr3rq3j1DhmGYHA0QGzp1Sldaml2VlS6tXVtqdjgAgDDbV/nEsjsAAJqD5BNaZdUq/7KiQI8bAJLVamjEiDxJNB0HgEQUSD5VVlL5BABAc5B8QqvsX/kEYJ9RozpLIvkEAIkoPT2w7I7KJwAAmoPkE0JWXl6v9evLJJF8An6K5BMAJK6MDBqOAwDQEiSfELJA1VOfPu2D7wAC8Bs5spMkaf36MpWW1pkcDQAgnGg4DgBAy5B8Qsi++spf0RH4JRvAPh07pqpPn3aSpFWrik2OBgAQTgdrOL52bak+/HCrWSEBABDTSD4hZIHlRIHlRQAaCzQdX7GCpXcAkEj2JZ/8lU8NDR5dcMFbuvTSd7V+PbucAgDwU3GXfPrnP/+pyy+/vNFja9as0aRJkzRkyBCNHTtWzz//fKPjXq9XjzzyiEaPHq0hQ4bo6quv1rZt21o0Bhrzen3BZXckn4CDGz48VxJ9nwAg0fx02d2SJdtUVFQjyV8BBQAAGour5NOLL76ohx9+uNFjpaWlmjx5srp3766FCxfquuuu08yZM7Vw4cLgObNmzdK8efN0zz33aP78+fJ6vbrqqqvkcrmaPQYaW7++TBUVLqWm2jRoULbZ4QAxafhwf+XTV18Vy+PxmhwNACBc2rVLkiQVFOyVz+fT/Plrg8e2b68yKywAAGKWzewAmqOoqEh//vOftXz5cvXs2bPRsQULFshut+vuu++WzWZTnz59tGXLFs2ePVsTJ06Uy+XSM888oxtvvFFjxoyRJD300EMaPXq0Fi9erLPPPrvJMXCgQCXH0KE5stniKocJRM2AAZlKT7erqqpBBQWlOukkdoUEgEQwblwPpaTY9PXXuzV3br4WL97X62nHDpJPAAD8VFxkDX744QfZ7Xa98cYbGjx4cKNjK1eu1IgRI2Sz7cujjRw5Ups3b1ZJSYkKCgpUXV2tUaNGBY87nU4NHDhQK1asaNYYOFAg+RRYVgTgQFarRUOH5kiSVqwoNDkaAEC4dOmSruuu89+T3nLL53K791W3bttWaVZYAADErLhIPo0dO1aPPvqounXrdsCxwsJC5eXlNXosJ8f/y96uXbtUWOj/ha9Tp04HnBM41tQYOFBgpzuST8DhBX5GaDoOAInluusGKy8vVT6f/+tTT+0qiWV3AAAcTFwsuzucuro6ORyORo8lJfnX4dfX16u2tlaSDnpOeXl5s8YIVSwuR7NaLY0+h6Kioj7YTPP44/1JPcMwZLEYIY9pGP7nWiytG8diCYynmIgn3OP871PIY8XqvPYfJ5TxYnVeVqtFxx/vT2wHqgVb87MXq8Lx70osY37xK5HnBvOlpdl1++3H6/rrP5bdbtHUqUP18cfbST4BAHAQcZ98Sk5ODjYODwgkjFJTU5WcnCxJcrlcwT8HzklJSWnWGKGwWAxlZqaF9NxocDpTQn7uihW75fNJvXu3U58+WZKk5GSbUlLsIY+ZlGSVJDkc1laNY7dbg59jIZ5wjxOYX1JSaD+6sTovh8ManFMoc4u1eSUn++fgdKbotNN6SfI36d+7t1YdOoT+sxfrWvPvSjxgfvErkecGc11wwREqLq5Rjx5OHXOM/55o7946VVc3KC0t9P9HAABINHGffMrLy1NxcXGjxwJf5+bmyu12Bx/r3r17o3P69+/frDFC4fX6VFFRE9JzI8lqtcjpTFFFRW3Iu28tXvyjJP9yooqKWjmdKaqrc6u2tiHkuOrrPZIkl8vTqnEaGjzBz7EQT7jHCcyvvt4tr9dnejzhHKe+3q2kJFtIc4u1edXV+f/dqaiolcXiVd++7bRhQ7mWL9+lE0/slHA734Xj35VYxvziV0vm5nSmUCGFFrNYDF1//ZDg106nQxUVLm3fXqX+/TPNCwwAgBgT98mn4cOHa/78+fJ4PLJa/VULy5YtU69evZSVlaWMjAylp6dr+fLlweRTRUWF8vPzNWnSpGaNEar9m0/GGo/HG3J8n3++U5I0cuS+X6J9Pl9IyZAA3/8aJni9rRvH6w2Mp5iIJ9zjBPpKhDperM5r/3FCGTNW5xX4OTvuuFxt2FCupUt3auTI3Jj+t6E1WvPvSjxgfvErkeeG2NK1a7ry8/dq+/ZKkk8AAOwn7t/imzhxoqqqqnTbbbdpw4YNWrRokebOnaspU6ZI8vd6mjRpkmbOnKklS5aooKBA06ZNU15ensaNG9esMbBPba1bX3/trwobNapTE2cDkKTjjvNXUC5dygYGAJDIunXLkCRt20bfJwAA9hf3lU9ZWVmaM2eOZsyYoQkTJig7O1s33XSTJkyYEDxn6tSpcrvduv3221VXV6fhw4fr6aeflt1ub/YY8PvqqyK5XF7l5aWqVy+n2eEAcWHYMH/yafnyXQm3rAkAsE/XrumSpO3bK02OBACA2BJ3yaf77rvvgMcGDRqkl19++ZDPsVqtmj59uqZPn37Ic5oaA37//a+/cuOEEzoHd/QCcHgDBmQqLc2mykqX1q4tVb9+LMUAgETUtau/8okd7wAAaCzul90hugLLhlhyBzSfzWbRscfmSJJWrCgyORoAQKR06+avfNq2jconAAD2R/IJzVZf79FXX/l/cT7hBJJPQEsMH54nSfryy0KTIwEAREqXLv7k044dVD4BALA/kk9otq+/LlZdnUcdO6aob9/2ZocDxJXjj/cnn5YvJ/kEAIkqsOyusLBGDQ0ek6MBACB2kHxCswX6PY0a1Yl+T0ALjRiRJ8OQfvyxXEVF1WaHAwCIgOzsFCUlWeX1+rRzJ//WAwAQQPIJzfbpp9slSSee2NnkSID4065dkgYNypYkLVtG9RMAJCKLxQguvaPpOAAA+5B8QrNUVNQHe9WMHdvN5GiA+HTyyV0lScuW7TI5EgBApHTtStNxAAB+iuQTmuXTT3fI4/Gpb9/26tnTaXY4QFwaPTqQfKLyCQASVY8e/r5PmzdXmBwJAACxg+QTmuWjj7ZJkk47jaonIFSB5FN+/h6Vl9ebHA0AIBL69cuUJK1dW2pyJAAAxA6ST2iSz+cLJp9YcgeELi8vTb17t5PPJ61YUWR2OACACOjfv4MkqaBgr8mRAAAQO0g+oUn5+Xu1a1e1UlNtGjWqk9nhAHFt5Ej/zxB9nwAgMR15pL/yadOmCtXVuU2OBgCA2EDyCU1assRf9XTiiZ2VnGwzORogvgUSuEuXknwCgESUk5Oq9u2T5PX6tH59mdnhAAAQE0g+oUkffbRVEkvugHA46aTOkqSvv96tykqXydEAAEJhtVpksx38w2636sgj/UvvNmwoO+R5Foth8iwAAIgekk84rKKimuDOXGec0cPkaID416OHU716OeV2e/XFFzvNDgcA0AIZGQ55vT45nSnKzEw75MfgwTmSpE2bKuV0puiWW77QTTd9Lq/XEjynfftUElAAgDaDNVQ4rDff/FFer0/HHZej7t0zzA4HSAhjxnTVpk35+vjjbRo/vqfZ4QAAmik52SaLxdBLL+WrqKj6kOcVF/uPvfnmRpWV1Wn27O8kSf/+9zpNmjRAo0Z10aWXDpTFYsjr9UUldgAAzETyCYe1aNEGSdKECX1NjgRIHKee2k3PPpuvTz7ZbnYoAIAQFBfXaMeOqkMet9n8iwu2b6/U55/vkCQZhlRWVq8nnvhO7dsnRSVOAABiBcvucEhbt1Zq5coiGYb0//5fb7PDARLGiSd2ls1m0aZNFdq8ucLscAAAYdaxY7IkqbzcpXXrSiX538jLzk6Rx+PTxo3lZoYHAEDUkXzCIb322kZJ/l+Uc3PTTI4GSBwZGQ4NH54rSVQ/AUACSk21KzXVv8DA5fIqI8OuXr2c6tbN38Jg165DL9kDACARkXzCIb36KkvugEg59dSukqSPP95mciQAgEjIykoJ/rl//w4yDEMdO/of27mT5BMAoG0h+YSDWr16j374YY9sNovOOquX2eEACWfMmG6SpM8+26GGBo/J0QAAwi2w9E6SjjyyQ6PHCgtJPgEA2haSTziop59eLUk666ye6tAhuYmzAbTUoEEd1bFjsqqqGvTFF7vMDgcAEGbZ2f4qpw4dkpST4/9zoPKposKlPXtqTYsNAIBoI/mEA+zZU6uFC9dLkq666hiTowESk8Vi6Kyz/I38X399o8nRAADC7cgjs3TssTkaP76nDMOQJDkcVrVr55AkrV5dYmZ4AABEFcknHOCFFwpUV+fRoEEdNWJErtnhAAlrwoQ+kqS33vpRLhdL7wAgkdjtFo0d202dO6c3ejxQ/fT997vNCAsAAFOQfGrDLBZDNpul0YfP59Ozz/4gSZoy5RjZ7dYDztn/w2rlWwgI1fHH5ykvL1Xl5S52vQOANiKQfFq9eo/JkQAAED02swOAOQzDUPv2KQckj+bNW6OdO6uVk5OqK68crKSk5n2L/K+aHEALWK0WnXtub82evVqvvrpB48b1MDskAECEUfkEAGiLSD61URaLIavVonnz8lVc7N9xxe326r77VkiSjjsuR0888XWT4/Tvn6Wf/7y3JLJPQCjOO6+vZs9erffe26KamgalptrNDgkAEEH7Kp9K5PP5TI4GAIDoIPnUxhUXV2vHjipJ0qpVxdqzp06pqTYdcUT74OOHk52dGukQgbi1f2Vh4M8/rTY8/vg8deuWoW3bKvXBB1s1ceIRjY57vT55vfxyAgCJokOHJFmthioqXNqxo0p5eWmqq3PrvPPelMVi6LXXzpHDYTU7TAAAwoqGPZAk1dd7tHSpf7v3E07ozE0P0AoZGQ55vT45nSnKzExTZmaanE7/O937P5aZmaYOHdI1efLRkqTZs1erffvURsfbt0+VxUJlIQAkCqvVopwc/5t3+fl7JUlPP/2DVq0q1sqVRXrtNXZABQAkHiqfIEn68stC1da6lZmZpGOO6Wh2OEBcS062yWIx9NJL+Soq8i9rNQxDyck21dW5D1hmYbP5d0VasaJQv/vdh+rbt70kKScnTZdeOlAWi0H1EwAkkE6d0rRrV7VefXWDhg7N1j/+sa/VwaxZ3+rCC4+QQUNNAEACIfkEFRZW68svCyVJJ5/cVVYrNztAOBQX1wSXr1oshlJS7KqtbThoIumoo7L0zTe79c47mw5YegcASCzDhuXq66+L9fLL6/Tll4UqK6sPtjzIz9+rTz/doTFjupodJgAAYcOyuzbO7fbq3Xc3y+eT+vfP1BFHtDc7JKBNGjYsV4YhbdpUod27a8wOBwAQQUce2UFPPfUzSf5/9yXprrtG6bLLBkiSHn/8W9NiAwAgEkg+tXHvvbc52GT8tNO6mx0O0Ga1b5+kfv0yJUmff76THZAAIMFdeeUxuv/+0ZKkMWO66rTTumnKlEGyWAx9+ul25efvMTlCAADCh+RTG/bqq+u1ZMk2SdIZZ3RXaiqrMAEzjRzZSRaLoY0by1VQUGp2OACACLvqqqO1YsUleu65n8kwDHXvnqGf/7ynJGnBgnXmBgcAQBiRfGqjfvhhjy6//B1J0tCh2TriiEyTIwKQnZ2iUaM6SZKWLNmq8vJ6kyMCAERajx5OpaTsewPwwgv9ff9efXUjm00AABIGyac2aOfOKv3iF2+rurpBRxzRXqee2s3skAD8z4gRecrNTVVdnUfz56+Vy+UxOyQAQBSddlp3OZ0O7dpVrWXLdgUf9/l8+uKLnfrqqyITowMAIDQkn9qY4uIanXbaAm3dWqk+fdrrV7/yb+MOIDZYrYbGj+8pq9VQQUGpJkx4TbW1brPDAgBEgNVqkc3W+CMtza5zz+0tyV/9ZLEYWrBgnU455RVNmPCmzjnnDW3eXBE8n/s4AEA8IPnUhpSU1GrChDdVULBXXbum64MPLlBamt3ssAD8RHZ2iiZM6Cu73aJ33tmkiy9+W9u3V5odFgAgTDIyHPJ6fXI6U5SZmXbAx+TJgyRJb7zxo6666kNdf/3HwV6AbrdX//zn6uC57dunkoACAMQ8Oky3ET/+WK5f/OIdbd5coU6d0vTGG/9PvXq1NzssAIfQs6dTU6Yco7lz8/X55zt14okLdP31g/XrXx+lrKwUs8MDALRCcrJNFouhl17KV1FR9QHHvV6f2rVzqLS0Tq+9tiFYFdulS7pmz/5ec+euVteuaerXr4MuvdRfxU5/KABALKPyqQ1YsaJQZ531mjZvrlD37hn6+OOL1adPe7PDAtCEPn3aa9myy3TiiZ1VW+vWAw98pWOOeUGTJr2rZ5/9QatX75Hb7TU7TABAiIqLa7RjR9UBH7t2VatfP/9mMBkZdv3iF/115JEd5HQ61LVrujwen95+e5OKiw9MXAEAEIuofEpgPp9Pzzzzg/70p6VqaPBq8OCOmj//LPXv30EVFbVmhwegGY46qqPeeONcLVy4Xo8++q2+/75Eixdv1eLFWyVJDodFPXs61bt3e/Xp026/j/bKzk6RYbAUAwDi0ahRndSxY4p69Wqn1NR9t+wjRuRp+/YN+vbb3fp//6938PEvvyxUVlYybzACAGISyacEVVZWr5tv/kyvvrpRknT22b30yCOnqn37JJMjA9BSNptVF1zQTxdc0E9r15bq9dc3avnyQq1YUaiqqgatW1emdevKDnhe+/ZJGjAgUwMGdNCAAR3Uv3+mjj46S+3ahf7vgMViHNBbxGq1NPrcHF6vjyUiAHAYDodVRx2VdcDjvXo5lZ2dot27a/Xkk9/r6quH6K67vtATT3wnSTrjjO76/e+HasSIvGiHDADAIZF8ikMH++Vvfx9/vE3XX/+xdu2qltVq6K67Rum3vx0kwzCCvxzSmBKIffs3pA0YOTJNI0d2leRP4GzbVqF160q1bl2p1q7dG/zz5s3lKiur17JlhVq2rLDRuD16OHXMMVkaNKijjjnG/5GTk9pkPBaLobS0JBUW1ujHH8v1449lwc+FhdUqKalVZaVLbrc/qZSebpfT6VCXLhnq2dOpnj3bqWdPp3r18n92u90koACghQzD0Jln9tIrr6zTjh1V6t37KdXUuP93TPrgg61asmSbFiw4Uyef3NXkaAEA8CP5FGcsFkPt26cetMKguLha06d/quefz5ckHXFEpp5//ucaObLzAeempydL8t+kAIhNTTWk/anevZ3q3dup8eN7qKHBq+LiGhUWVquiokGS9M03xdq6tVJbtlRoy5YKvfXWpuBzs7KS1b17hjp3TpfT6VB6ul0ej08ul0d79tRp165q7dxZpd27a+VrYb5o5cqigz6el5f6vyWD7dSrVzv16uVU9+5O5eWlKjs7pUWVVADQlmRnp+jii/tr0aL1Ki93KTnZqkcfPVXHHNNRd965VO+9t0XXXPORPvpoovLy0uTz+fTf/+7SP//5vXr0yNCtt45QSgq/BgAAoof/deKMxeKvXpo3Lz/YZNLt9uq//92p99/fopoatwxDOvHEzjr77N5atmyHli3bEXy+YRhKTrapRw+nxo/vLYnsExDrAg1pQ5GXl6YxYzpo8uRBslgM7d1bq2++2a2vvy7SqlXFWrWqSGvX7tWePXXas6dOX3+9u8kxrVZD7do51K5dktq1S1L79knKykqRzWbIZrMEKysbGjyqr/eoosKligqXystdqqioV0WFSzU1bhUW1qiwsOaAyqzAa+TkpCo3N1WZmf7XCLzWvs8OOZ3+OPb/bLORtAKQ+LKykjV16lDV1np09tm9NGhQR0nS00+P089+tkirV+/RFVcs1siRnfTll4WN3gj4z3926Jlnxql//8zgY1VVLlVWNmjPHpfS062yWrlHBACED8mn//F6vXrsscf0yiuvqLKyUsOHD9ef/vQndevWzezQDqq4uFrbtlVq7dpSffHFTpWV1UuScnJSdMYZPdSpU5pKSg5sKm6xGEpJsSsjwxHtkAGY5GAVVFarNHx4joYPz1F9vUclJbUqLa1TeblLdXVu1dd7/pfsNpSWZle7dkkaPDhHF188QC+88IN27tyXDAv8u1Jb29CsZXRduqTrV786Rt98U6gNG0r1448V+vHHcm3cWKadO6tVVFQjj8enXbuqtWtXy3dySkuzyencl5Bq184RTFoFvm7XLknp6XYlJVllt1tkt1uCiTOr1Qh+NgxDSUlWZWbWqrKyTpJ/OWFmZpLsdmuLYwMSUbzdQyWSHj2cwTcXAjIzpUWLztOwYf/635sMxZL8PaQuu+xIvfPOj1qzZq9OPfXf+sMfjtXllw/Uo49+raef/l4NDf4dVHNzU/X446c2Wrbn8Xi1alWx8vLS1K1bRnQnCgCIeySf/mfWrFmaN2+e7rvvPuXl5emBBx7QVVddpTfffFMOR2wlampqGrRs2S4tXrxFpaX+pFNqqk0nnthZxxzTkX5OAA6qqQqqzMxkZWYmH+Z4krKzU1v9b0xGhj/5c+qpPXXqqT0POO7xeFVUVKOdO/3bje/dW6vS0nqVltb972Pfn8vL61VWVq/y8npVVfmXF1ZXu1Vd7Q4pcdXSeWRmJikrK1nZ2f6lgo0/UpWT4//crp2DnQeRsOLpHirRHG559qRJA/TFF7vUvr1D2dmpOuqoLLVvn6SePTM0b16B1q4t1X33fan77vsy+Byr1V/BWlRUowsvfFu//vVRGjCgg8rK6vX882u0bVulJOmYYzpq4sS+uvLKo5WWZm/0uhUV/mWA3I/+f/buPLzJKnH7+J2kO22xFGhxQbEIiLJKkaogVkV+ir4io45aFAQFwWEEARdwRBBFZRMUGQRERxlQYXAdxWVcB7DFXfZVVEplLS1N0yTP+0cmgchW0uRJmnw/19WL9lkO55wWenLnnPMAAA5F+CTJ4XBo7ty5Gj58uLp27SpJmjJlijp37qylS5eqR48e4a3g/7hcbj3xRJHmzv1Je/Z43oFPSrLpvPOydN55DZWQwLvwACLfie5l5ZWRkaCMjIMvZJs3z9T//d+ZvnJcLkN2u1MVFZ6PQz+vqHDqwAH/rysrXXK5DNlsnplbDofna5fLLbfbkMtlHPan0+lWWZlDhiHt3+/Q/v0O/fzz/uPWPSHBqvr1k5WVleILqho2TFbDhimqVy9JderE+32kpMQpOTlOdep4ZmcBkaq2jKGi3ZHeXEhJidfllzf2fV1eXqXyck9I36NHE519dj199tmv2r3brrPOOknPP3+FLr74NB04UKV77vmPnn/+e82Z85NfmenpCSorq9IPP+zUDz/s1HPPfa8+fc7Vjh3lvgde7NxZoVNPTdVNNzVXz55NddZZJ8lms+r33w9o9erdSkiwKS0tQU2apCslxT+4OhRPRQWA6EL4JGnNmjUqLy9XXl6e71h6erpatmypwsLCiBk4/fTTLk2atFKSVK9eklq1ylSbNg0InQDUSjXZy0qSGjRIOWo5VqtFKSnxx3xh49W2bUPdcss5Rw3DvHvl2e1OGYbnxVBFhVPl5VU6cMCpsjKHysqqZLNZVa9esnbs8Cwd9HyUa+/eSjkcbv32W7l+++3EZ2PFx1v9Qqk/Lg30PgHV+7XNZpXVKtlsVtWtm+g3G6thwxRlZ3v20qpfn03dUXO1ZQwFfxaLRU2bnqQzz6yr0lKHLr74VF188WlauHC19u6t1Dnn1NPtt5+jn37apYoKp1wuQ61aZapdu4ZyOFz6/vud+uijbdqx44CeeOKrw8r/5ZcyPfXUSj311EqlpMSpfv3kw4L6pKQ4XXZZY+XmZsvtNrR/v0MbNuzVtm37dfLJqWrRop6aNEnTWWedpIyMJJWUHNDu3XbZ7U45HG5lZ6coJ+ck1a2bKMMwtHdvpTZt2qft28uVnByntLR4NWiQopNPriOn09Cvv5bpl1/2a9u2Ml9AdtZZJ6lZswxlZaUwOxUAQozwSVJxsWez20aNGvkdb9iwoe9cJGjVqr6effYSnXFGhtas2RnyJSUAEEuOFoYda0+r+Hirb7ni0UKsqir3/zby9Wy8XlZW9b9ZU1UqLXWooqJKlZUuORxuVVa65HS65XC4Zbc7fffv3Vvp29svWKxWixo0SPbth5WcbFNysifg8oRY3iDLcsjXFlmtVr+vLZaD1x4ailX3fi/vUxSNQx6neOiTFQ/9vF27BmrSpG5Q+wOBqS1jKByZ5ynKib7gpaTkgHbtsquiokr16iWpc+dT/K7//XfPfqKnn56u2247Wz/+uEvbt5erbt1EZWQkKi/vZA0Y0FZjxnypDz7Yqk2b9unAAacveKpfP1kWi3zh/dtvb9Lbb286rF7ffFOid945/HiopKV59gYsK3PIbnf5AvyGDZN18smpSknxvGSy213/+3/bqeTkOKWkxP3vjY44VVW5VVpa+b+HazjkcLiVmZmo+vVTVKdOnBITbXI4PNfY7S7Fx1uVkGBTvXpJqlcvSQkJ/m8GeL8nFosUH29TYqJNLpdbVVVuORye3xmGYfzvjYl4xcX5vxnh/6HDlkF6/0890gyzPz7V1vjDAc/vK6cMQ77/9+PiDv0dYJXF4inHMIyj/v/u/dpi8ZZx8HeJxeL5OjU1SQcOVMrlMmSxeK71/Hno5wfb9sfjh17rPf9HJ/oU3yOpbnZ5IiFn9cs89nmr1aq0tCSVldnlch2/sSeSw1a3PcFqy4n+vcEq02azKC0tSfv326s9IzMU/XgivP++PP/WPF+npiaoTZv6YQvbLcYf/zeJQW+88YZGjhyp1atXy2o9+B//yJEjVVJSonnz5p1wmd53x4PNYvH8B1JW5qjWfx5HKyMuzqqUlPgalSN5XnhRjrnluN1GwL8kI7ld3kFFIG2L9HZ5nWj7aku7pBNrW21ql1d12hfs+hw4UCWXy/AN3P0H8JJ05JDm0DrbbFY5nW65XO7/LSk0fJ/XZjabRaeeevwNj72/M91u93G/f1arhZkPAYiUMdTxvtfBGD9JtfP/rxMtJ7jjjINLl71htIchp9NQVZXn/yfvvz1veCF5vmcOh1tVVS653fILvC0W/e9+1x/+fpvi4iwyDP1vufTB/+9sNs+DJeLjPSG40+kp27vJOgDEgszMJKWmBnc/xuqOoZj5JCkpybPBrsPh8H0uSZWVlUpOTg6oTO87waESrB8YyqEcyqEcyonMcqqzZBDVc2goguCKtDHU8b7XkfbvnHIAALGC0ZgOThUvKSnxO15SUqKsrKxwVAkAACDiMYYCAADVQfgkqUWLFkpNTdWKFSt8x0pLS7Vq1Srl5uaGsWYAAACRizEUAACoDpbdSUpISFBBQYEmTpyoevXq6ZRTTtFTTz2l7OxsdevWLdzVAwAAiEiMoQAAQHUQPv3PkCFD5HQ6NXr0aNntduXm5mrOnDmKj2fPDQAAgKNhDAUAAI6Hp90BAAAAAAAgZNjzCQAAAAAAACFD+AQAAAAAAICQIXwCAAAAAABAyBA+AQAAAAAAIGQInwAAAAAAABAyhE8AAAAAAAAIGcInAAAAAAAAhAzhU5Tau3ev/va3v6lLly5q3769brrpJhUVFfnOL1u2TNddd53atGmj7t2765133gljbU/crl27NGLECHXq1Ent2rXTnXfeqY0bN/rOr169WgUFBWrbtq3y8/P10ksvhbG2gdu8ebPatWunxYsX+47V9rbt2LFDzZs3P+zD28ba3j5JWrJkia688kq1atVKV111lf7973/7zv3yyy8aMGCA2rdvr4suukhTp06Vy+UKY22rb8WKFUf83jVv3lyXXnqppNrdPqfTqaefflqXXHKJ2rVrp1tuuUXffvut73xt/9ksKyvTww8/rIsuukgdO3bU8OHDtWvXLt/52vp74e9//7t69+7td+x43yu3261p06apc+fOatu2re644w5t27bNzGojSPhe1lwg/4bgEe3jbbPEyrjeLNH4+sFMsfBaJWwMRKW+ffsaPXr0MAoLC41NmzYZjzzyiNG6dWtj48aNxoYNG4xWrVoZkydPNjZs2GDMnj3baNmypfHf//433NWuthtvvNG4/vrrje+++87YsGGD8Ze//MW46KKLjAMHDhi7d+82zj//fOOBBx4wNmzYYLz++utGq1atjNdffz3c1T4hDofDuO6664xmzZoZixYtMgzDiIq2ffLJJ0arVq2MHTt2GCUlJb6PioqKqGjfkiVLjJYtWxovv/yysXXrVmPGjBlGixYtjK+//tpwOBxGt27djDvvvNNYu3at8cEHHxgdO3Y0nn766XBXu1oqKyv9vmclJSXG0qVLjebNmxuvv/56rW/ftGnTjAsvvND4/PPPjS1bthijRo0yzjvvPGPHjh1R8bN5++23GxdffLHxySefGOvWrTMGDRpkXHnllUZlZWWt/b3w8ssvGy1atDAKCgp8x6rzvZo+fbpx/vnnG//5z3+M1atXG7fffrvRrVs3o7KyMhzNQA3wvayZQP8NwSPax9tmiYVxvVmi9fWDmaL9tUo4ET5FoS1bthjNmjUzioqKfMfcbrdx2WWXGVOnTjUeeugh409/+pPfPcOGDTNuv/12s6sakL179xrDhg0z1q5d6zu2evVqo1mzZsZ3331nzJw507jooouMqqoq3/lJkyYZ3bp1C0d1AzZp0iTj1ltv9fvlEQ1tmzVrlnH11Vcf8Vxtb5/b7TYuueQSY8KECX7Hb7/9dmPmzJnGW2+9ZZx77rnG3r17fecWLFhgtG/fvla+UCovLzcuueQS4/777zcMw6j17bvmmmuMxx9/3Pf1/v37jWbNmhnvv/9+rf/ZXLVqldGsWTPj008/9R0rKyszOnToYCxevLjW/V4oLi42BgwYYLRt29bo3r273wvn432vKisrjXbt2hmvvPKK7/y+ffuM1q1bG2+99ZZ5jUCN8b0MXE3+DcEj2sfbZomVcb1ZovX1g5mi+bVKuLHsLgplZGRo1qxZatWqle+YxWKRxWJRaWmpioqKlJeX53dPp06dtHLlShmGYXZ1T1jdunU1adIkNWvWTJK0e/duzZs3T9nZ2WratKmKiorUsWNHxcXF+e7p1KmTtmzZop07d4ar2ieksLBQCxcu1IQJE/yOR0Pb1q5dq5ycnCOeq+3t27x5s3799VddffXVfsfnzJmjAQMGqKioSOecc47q1q3rO9epUyeVlZVp9erVZle3xmbOnKmKigrdd999klTr25eZman//Oc/+uWXX+RyubRw4UIlJCSoRYsWtf5nc8uWLZK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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_plots = len(discrete_vars)\n",
    "\n",
    "num_rows = (num_plots + 1) // 2  # Number of rows for subplots\n",
    "\n",
    "fig, axes = plt.subplots(num_rows, 2, figsize=(12, 5 * num_rows))\n",
    "\n",
    "for i, feature in enumerate(discrete_vars):\n",
    "    \n",
    "    data = df.copy()\n",
    "\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    ax = axes[row, col]\n",
    "\n",
    "    sns.histplot(data[feature], ax=ax, bins=25, kde=True, color='darkblue')\n",
    "    ax.set_xlabel(feature.replace('_', ' ').title())\n",
    "    ax.set_ylabel(\"Frequency\")\n",
    "\n",
    "# Remove any empty subplots\n",
    "if num_plots < num_rows * 2:\n",
    "    fig.delaxes(axes[num_rows-1, 1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "aa5d7b02",
   "metadata": {},
   "source": [
    "Age looks a bit right skewed because of few outliers and there seems to exist, an interval which holds the most frequent values... Except for age nothing seems to give meaningful insight... So, digging deeper..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a73dfe9b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_con_this_camp\n",
      "1     17634\n",
      "2     10568\n",
      "3      5340\n",
      "4      2650\n",
      "5      1599\n",
      "6       979\n",
      "7       629\n",
      "8       400\n",
      "9       283\n",
      "10      225\n",
      "11      177\n",
      "12      125\n",
      "13       92\n",
      "14       69\n",
      "17       58\n",
      "16       51\n",
      "15       51\n",
      "18       33\n",
      "20       30\n",
      "19       26\n",
      "21       24\n",
      "22       17\n",
      "23       16\n",
      "24       15\n",
      "27       11\n",
      "29       10\n",
      "28        8\n",
      "26        8\n",
      "25        8\n",
      "31        7\n",
      "30        7\n",
      "35        5\n",
      "32        4\n",
      "33        4\n",
      "34        3\n",
      "42        2\n",
      "40        2\n",
      "43        2\n",
      "56        1\n",
      "39        1\n",
      "41        1\n",
      "37        1\n",
      "Name: count, dtype: int64\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "days_since_last_con\n",
      "999    39661\n",
      "3        439\n",
      "6        412\n",
      "4        118\n",
      "9         64\n",
      "2         61\n",
      "7         60\n",
      "12        58\n",
      "10        52\n",
      "5         46\n",
      "13        36\n",
      "11        28\n",
      "1         26\n",
      "15        24\n",
      "14        20\n",
      "8         18\n",
      "0         15\n",
      "16        11\n",
      "17         8\n",
      "18         7\n",
      "22         3\n",
      "19         3\n",
      "21         2\n",
      "25         1\n",
      "26         1\n",
      "27         1\n",
      "20         1\n",
      "Name: count, dtype: int64\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "num_con_prev_camp\n",
      "0    35551\n",
      "1     4561\n",
      "2      754\n",
      "3      216\n",
      "4       70\n",
      "5       18\n",
      "6        5\n",
      "7        1\n",
      "Name: count, dtype: int64\n",
      "\n",
      " _______________________________________________________ \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in discrete_vars:\n",
    "    if column != 'age':\n",
    "        print(df[column].value_counts())\n",
    "        print(\"\\n\", 55 * \"_\", \"\\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "415d46cc",
   "metadata": {},
   "source": [
    "- As we can see now, in `num_con_this_camp` - `Top 5 - 6` values dominates the distribution...`Top 3` values appears in `81%` of total no. of instances...\n",
    "\n",
    "- Whereas in `days_since_last_con` - `999` seems to dominate the frequency distribution... Some people, softwares and databases still use and understand `999` or `-999` as null value in numerical columns... So, this column seems to be filled with more than `96%` of null values. So, it would be inappropriate to impute those many values... So, dropping would be ideal...\n",
    "\n",
    "- In `num_con_prev_camp` - `0` seems to dominate the frequency distribution...`86%` of customers haven't had any call at all during previous campaign... This might be because the campaign now being conducted on new set of customers for the most part. So, this feature doesn't hold any value at all... So, we would consider dropping this as well...\n",
    "\n",
    "\n",
    "All three have huge imbalance which is of a scale we can't even think of addressing... Still let's plot these against target variable and take a call..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a61aec9c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_discrete_vars(df, discrete_vars, target_var):\n",
    "    for var in discrete_vars:\n",
    "        if var != 'age':\n",
    "            fig, ax = plt.subplots(figsize=(11, 7))\n",
    "            grouped_data = df.groupby([var, target_var]).size().unstack()\n",
    "            grouped_data.plot(kind='bar', stacked=True, ax=ax)\n",
    "            ax.set_xlabel(var)\n",
    "            ax.set_ylabel('Count')\n",
    "            ax.legend(title=target_var)\n",
    "            plt.show()\n",
    "\n",
    "plot_discrete_vars(df, discrete_vars, 'subscribed')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "afb636ac",
   "metadata": {},
   "source": [
    "As we can see, these features creates huge imbalance even when plotted against target variable... Also, they visibly are not contributing much to the desirable target which is 'yes'... Let's correlate with target var and confirm..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cd048237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cOVOSLFOsknMsOXLksLr+JmFcj7tQvEyZMlY98PLy0ssvv6zt27dLundmxd/fXxkzZrSMPUuWLCpXrtxDx54+fXpVrlzZ8viWLVtUpEgR1atXzzKmrVu3KiYmRtWrV9ft27f1559/qnr16jIMw7Kfl19+We7u7vr1118t27H1dXhw3ny+fPmeeMpZAsMwLH8JflBAQICWLl2q9957TwsXLtSpU6fUvXt31ahRQ9K9M2EFCxa0OrZcXFy0fv16qzM+yXHsSU/2uiVIOKP0448/WpatWLFCNWrUsJzZ+fzzz9WuXTtdvnxZO3bs0LJlyyzTpR48vh8c04OPffXVVypQoICOHz+uTZs2KTw8XEePHk20nerVqytTpkyW72vVqiVnZ2fLsfrg+A3DUK1atSzjj42NVa1atRQdHa2dO3cqV65cKlasmAYPHqz+/ftbrtkYOHCgXn311STrTZ8+vcLDw9WwYUOdO3dOv//+uxYvXqyffvop0dgf9zO5c+dOlS5d2uqaGF9fX7300ksP7ZctmjZtqrCwMMXExOjAgQNav369Jk+erLi4OMXExFite/9rc/fuXe3evVv16tWzOs4bNGggZ+f/m5izZcsW5c6dWyVLlrT0NS4uTjVr1tTevXt19epVu+oNDAzUiRMn9Oeff0q697s+MjJSTZo0Ufr06e3q+cN+pyeoUqWKFi5cqAwZMujw4cPasGGDpk+frsuXLyc63ho0aGB1M4KEs+hJHW9btmyRg4ODqlevnuh4u3Dhgg4dOmRXT5B6mHIGIFVky5ZNmTNn1tmzZx+6zq1btxQTE6Ns2bJZ/jHNnTu31TrOzs5yc3PT9evXLcsyZ86c5Pbuf9Mk3ZsvfuLEiYfe0Sqp0HD58mUNHTpUP/zwgxwcHFSoUCGVK1dOku2fzWDPWB6cjpfwhuRxtzvNlStXomU5c+bUtWvXJN0b+9q1a5OcC37/G7EHVa9eXSNGjFB0dLS2bNkif39/+fv7a8KECTp79qx+/vln+fj4KGfOnDp37pzi4+MVFhamsLCwRNvKkCGDpRZbX4cH++Ho6PhUn4lx+/ZtXb169aFvlgYNGqR8+fJp9erVGjlypOX2xsOGDVPx4sUVFRX10Gle93uSYy+pqZhP+rpJstyyevXq1WrYsKEOHz6sv//+Wx9++KFlnT179mj48OHas2ePXFxcVKxYMcsb8Qf7/LCfswRffvmlZsyYoaioKOXKlUve3t5ycXGxOr6lxD8Hjo6OcnNzsxyrD45fSvzHgATnzp2Tg4OD5syZo+nTp+v777/XypUrlS5dOtWpU0fDhw9/6FTNn3/+WaNGjdLRo0eVOXNmFS9e3PK63T/2x/1MXr16NckL+R8cp73u3LmjkSNHatWqVYqNjVXBggXl5+cnZ2fnRK/N/cdbVFSU4uLiEh2nTk5Oyp49u9V6Fy5ceOgxeeHChUdOc31QtWrVlDt3bkVGRsrX11e//PKLLl68aBX0be354461+Ph4ffHFF1q0aJFu3bql/Pnzy8fHx/I75n4Pvg4JfXnY8WYYhsqUKZPkfs+fP//IYI+0Q6ABkGqqVKmirVu3Kjo6Osl/eJYuXaoxY8YoIiLC8g/phQsXVKBAAcs6MTExunLlitzc3Ozev6urq/z9/dWvX78kH0+fPn2iZX369NHRo0c1d+5c+fn5KX369Lp9+7aWLl1q835TYiwPSuqvqRcuXJCfn5+ke2OvVKmSOnTokGi9+/9q+6Dq1avr7t272rFjh7Zu3apPPvlEpUqVUqZMmbRt2zZt3rxZzZo1k3TvTYiDg4Pat2+f5BvQhDeGT/I6JJdt27YpLi5O5cuXf+i+u3btqq5du+rs2bP66aefNG3aNMscf1dX1yQvjN61a5eyZcuW5HVY0pOP+UlftwQtWrRQSEiIoqKitHLlSuXNm9dyTc2NGzfUqVMneXp6as2aNSpatKgcHR21adMmrV+//rHbvl9kZKRGjx6tvn37qnnz5paw9eGHH2rPnj1W6z54C+C4uDhduXIlyaCYNWtWSdK8efOSfJObEL7y5s2rYcOGaejQoTpw4IC+++47hYWFWa7pedDJkyfVvXt31alTRzNnztTLL78sBwcHLVq0SD///LNdY3dzc9PFixcTLX/UrY5tERISovXr12vixImqVKmS5Y1/xYoVH/m8nDlzKl26dIlqio+Pt6rJ1dVVhQsX1vjx45Pcjr13W3N2dlbTpk21fPlyDRw4UCtXrlTp0qVVrFgxScnb81mzZmnu3LkaPny46tWrJ1dXV0lK8s59D74OCZ+Xk9Tx5urqqkyZMmn+/PlJ7vdh12Mh7THlDECq6dixo6KiojRx4sREj124cEFz5sxRsWLFVLJkSfn7+0tSos82WLNmjeLi4lS2bFm79+/v769jx46pSJEiKlWqlOVr1apVioiISPKC5J07d6pevXoKCAiwvOlMuMNRwl9ok3reg/tN7rEkVef9fwn/888/debMGVWoUMFSw+HDh+Xl5WUZt7e3t+bOnavvv//+oePInTu3SpQooa+++kqXL1+Wv7+/0qVLp7Jly2rp0qU6ceKEatasKUnKkiWLSpQooaNHj1r199VXX9WUKVMsU+ue5HVIDrGxsZo2bZpy5cqlunXrJnr8zp07ql+/vubMmSPp3pvl1q1b6/XXX7ecWSxXrpxOnTplNfUkOjpaPXr0UERExEP3beuYH/ycDltet0dp1KiRnJyc9NNPP2ndunVq1qyZZV9Hjx5VVFSU2rZtq2LFiln2/eDxbYudO3cqa9as6tSpkyXM3Lx5Uzt37ky0nV9//dXqs47Wr1+v2NhYBQQEJNpuwtnQK1euWPXt8uXLmjRpkqKiorR7925VqlRJf/31lxwcHOTl5aWePXvKw8PjoWeE9+7dq+joaHXu3FmvvPKK5axLwhtre84CVqhQQbt379a5c+csyw4fPvzUHxS8c+dOyy3tE8LM3r17dfny5Ue+Nk5OTipTpow2bNhgtfzHH3+06ru/v7/+/fdf5cyZ06q3v/76q2bPnv3QY/JRAgMDdenSJf3yyy/auHGjVcBIzp7v3LlTxYoVU2BgoCXMnDt3TgcPHkzUmwfvSJdw84GE38v38/f3161bt2QYhlVPDh48qKlTpz4Xn9H1vOIMDYBUU7p0aX344YeaOHGijhw5oqZNm8rNzU2HDh1SeHi4oqOjLWGnWLFiatasmSZPnqzbt2+rfPny2r9/v0JDQxUQEKCqVavavf/27dtr1apVat++vTp27Cg3NzetXbtWS5cu1cCBA5N8jo+PjyIjI1WyZEnly5dPu3bt0qxZs+Tg4GCZGpXwD+qWLVvk7u4uX19fq22kxFgeFB8fr86dO6tLly66cuWKPv/8c3l4eKhx48aS7n3Q59tvv633339f77zzjjJkyKAlS5bohx9+0OTJkx85jho1amjq1KkqUqSIZfpGQECAxo8fr5deeknFixe31NGrVy917txZvXv3VuPGjRUXF6c5c+bozz//tHwI45O8DvY6efKk/vjjD0n3zoSdPn1aixcv1t9//62pU6cmOb0rY8aMKlmypEJDQ5UuXTp5enrq2LFjWrFiherXry9Jat68uRYsWKCuXbsqODhYbm5umj9/vmJiYtSqVauH1mPrmLNmzapdu3Zp+/btKleunE2v26O4urqqbt26mjFjhs6cOWP1BrNIkSLKkiWLZsyYIWdnZzk7O2v9+vWWYGbPB3z6+Pjo66+/1ujRo1WzZk2dP39e4eHhunjxYqJpSxcuXFCPHj3Upk0bHT9+XF988YUqV66c5JkHT09PNW7cWIMHD9aZM2fk7e1tuVNVwYIFVbhwYcXGxipjxozq16+fevTooVy5cum3337T/v371bZt2yTrLVmypJydnTVu3Dh17NhRd+/e1fLly7Vx40ZJsus6rXbt2ikiIkLvvvuuevToobi4OE2YMEHp0qWzeRtJ8fHx0bp16/T111/L3d1dBw4c0PTp061+9zxMcHCw2rRpo+DgYLVo0UJnz5613GksIUg0b95cCxcuVIcOHdSlSxflz59fv/32m8LCwhQUFGSpP2vWrNq3b5+2bdsmHx8fZcyY8aH7LVKkiMqUKaNRo0ZJsr5zZXL23MfHR9OmTdOsWbNUunRpnThxQjNnztTdu3cT9WbPnj0aNGiQGjVqpD179mjy5Mlq0aKF5Y6E96tevbrKly+vbt26qVu3bnJ3d9dff/2lyZMnq2rVqo+d5om0Q6ABkKq6du2qEiVKaNGiRRo1apSuXr2q/Pnzq0aNGpZ/VBOEhISoUKFCWrZsmcLCwpQnTx61bdtW3bp1e6JPnM6bN68WL16szz//XMOGDVN0dLQKFy6skJCQh37I4OjRoy3XUkhS4cKFNXz4cK1evVo7duyQdO/MRIcOHbRkyRJt2rTJcvH7/ZJ7LA+qU6eOXnrpJfXt21exsbGqWbOmBg0aZJnaV7x4cS1atEgTJkxQv379ZBiGPDw8NHXqVMvnzyQ1jnTp0lkCzf1/0Uz4a3r16tWt6qhSpYrCw8MVGhqq4OBgpUuXTiVLltSXX35pucD/SV4He02fPl3Tp0+XdG8qTI4cOVSuXDkNGTLkodcMSNKIESM0ceJEzZkzRxcuXFDOnDnVokULy3UnWbJk0cKFCzV27FiNHDlS8fHxKl26tObPn6+XX375odu1dcxdunTRtGnT9N5772nt2rU2vW6P06JFC0VGRqpChQpWNbq6umratGkaO3asPvzwQ2XOnFleXl5auHCh3nvvPe3YscPqs5QepVmzZjp9+rSWLVumr776Snnz5lX16tXVqlUrDR48WEeOHLFMx2vVqpWuX7+u7t27K3369HrjjTfUt2/fh96o4bPPPtPMmTO1ePFi/ffff8qZM6caNmyojz76SE5OTnJyctKcOXP0+eefKyQkRNeuXVPhwoU1YsSIh36YY6FChfT5558rNDRUXbt2VbZs2VS6dGktWLBAbdq00Y4dO6w+C+hR3Nzc9PXXXyskJEQDBgxQ5syZ1alTp6f+7JIBAwYoJiZGEydO1N27d1WwYEF17dpVhw8f1o8//vjIG6yUK1dOU6ZM0aRJk9StWzcVKFBAgwcPVs+ePS1T9zJlyqRFixbp888/17hx43T9+nUVKFBAvXv3VseOHS3b6tixo0aNGqV3331XX375peWs2cO0aNFCH3/8sQIDA62mCSZnz99//31duXJF8+fP19SpU5U/f341adJEDg4Omjlzpq5du2aZrti9e3ft3btXXbp0kaurqzp16qQPPvggye06Ojpq1qxZmjRpkmbOnKlLly4pb9686tChg7p3725TbUgbDsbTXF0JAACAZ8qGDRuUL18+q/B+6NAhNWrUSNOmTbM5DANmwRkaAACA58gvv/yitWvXqk+fPipSpIjOnTun6dOnq2jRolYftAo8LzhDAwAA8By5c+eOJk2apPXr1+v8+fPKnj27qlatqt69eyd5i3fA7Ag0AAAAAEyL2zYDAAAAMC0CDQAAAADTItAAAAAAMC0CDQAAAADT4rbNeGEZhqH4eO6JkZIcHR3ocSqgzymPHqcO+pzy6HHqoM/Jw9HR4aEfuns/Ag1eWA4ODrp27ZZiY+PTupTnkrOzo9zcMtPjFEafUx49Th30OeXR49RBn5NPjhyZ5eT0+EDDlDMAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBazmldAJCWnJzI9Cklobf0OGXR55RHj1MHfU559Dh1PK99jo83FB9vpHUZSXIwDOPZrAxIYYZhyMHBIa3LAAAAeObFxcUrKupWqoaaHDky2xQMOUODF5aDg4PGL9qp0+eup3UpAAAAz6yCeV3Vp3VZOTo6PJNnaQg0eKGdPnddR85cTesyAAAA8ISer8l9AAAAAF4oBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApmWaQOPp6anly5endRmPtXz5cnl6eqZ1GVZq1aqlKVOmJNv2Dh06pI0bNybb9gAAAIAnZZpAYxYNGzbUL7/8ktZlpKj3339fe/bsSesyAAAAADmndQHPm4wZMypjxoxpXQYAAADwQngmz9D8999/6tq1q/z8/FStWjVFRkZaHouPj9fMmTNVv359eXt7q0yZMurUqZNOnjwpSRo1apTq1Kljtb3r16/Lx8dHGzduVFxcnMaNG6fq1avL29tbDRo00Ndff21XfStXrtTrr7+uUqVKqWrVqgoJCdHdu3clJZ5y5unpqYiICLVv314+Pj6qUqWKQkNDrbb3888/q2XLlvL19VW1atU0YcIExcXFSZLu3r2rcePGqWrVqvLz89Nbb7311GeAvvnmG73xxhvy8fFR6dKl1apVK6szLn/99ZdatWolPz8/lS9fXj169NDZs2cl3Zu+dubMGYWGhqpNmzY27/NRY7xz544mTpyo2rVrq1SpUmrSpInWr19vee7y5ctVt25dy3+9vb3VvHlz7dy586n6AAAAAPN75gJNbGysOnXqpCtXrmjhwoWaNGmSwsPDLY/Pnz9f4eHhGjBggNavX6+pU6fq+PHjGj16tCSpefPmOnXqlHbs2GF5ztq1a5U1a1ZVrVpVX331lb777jtNmDBB69evV1BQkIYNG2a1/qMcOHBAn3zyiXr06KH169dr1KhRWrVqlWbPnv3Q54wZM0bNmjXTmjVrFBQUpClTpmj79u2SpN27d6tz584qW7asli9frk8//VSLFy/WtGnTJEkDBw7Ur7/+qvHjx2vFihV67bXX1KVLlye+huX777/XiBEj1KlTJ61bt05z585VdHS0PvnkE0lSXFyc3n//fZUvX16rV6/W3LlzdfbsWX388ceSpIiICOXLl08dO3a0+bqcx42xV69eWrlypQYPHqzVq1erTp06+vDDD/XDDz9YtvHvv/9q8eLFGjdunFasWCEXFxcNGDBAhmE8UR8AAABgHycnRzk7p96XrZ65KWdbtmzRoUOH9P333+uVV16RJH322Wdq2rSpJOmVV17RmDFjVLNmTUlSgQIF1KBBA3333XeSpOLFi6tkyZJavXq1ypUrJ0lasWKFGjduLCcnJ508eVKZMmVSwYIFlSdPHgUFBalo0aIqUqSITfWdPn1aDg4OKlCggF566SW99NJLCg8PV5YsWR76nKZNm6pJkyaSpC5duig8PFy7du1S+fLltWDBAvn6+qpfv36SJHd3d40YMUKXLl3SiRMn9O2332rlypXy8vKSJHXo0EEHDhxQeHi4atSoYV9zJWXPnl0hISFq3LixpX8tWrTQiBEjJEk3btzQlStXlCdPHhUoUEAvv/yyJk6cqEuXLkmScuTIIScnJ2XKlEnZs2e3aZ+PGuORI0e0YcMGzZgxwzKeHj166MCBA5oxY4blbFtMTIyGDx9u1Yfu3bvrwoULypMnj919AAAAgH2yZnVJ6xKS9MwFmoMHDypbtmyWMCNJXl5elutSatWqpT///FOTJk3SsWPHdOzYMR0+fFh58+a1rB8YGKiJEyfqk08+0b///qvdu3crJCREktS6dWv98MMPql69ury8vFS5cmW9/vrrypkzp031JUz9atGihQoWLKjKlSurdu3a8vb2fuhz3N3drb53dXVVTEyMZbyVK1e2erx+/fqSpHXr1kmSWrVqZfV4TEyMsmbNalO9DypfvryOHDmiqVOn6ujRozpx4oT++ecfxcfHS5KyZcumTp06aeTIkZo8ebIqVKig6tWr67XXXnui/UmPHuPatWslSWXLlk1U5xdffGG17P4+urq6SpKljwAAAEhZ167dVlxcfKrtL2tWFzk5Pf5MzTM35czBwcHy5vp+zs73stesWbPUtm1bXblyRRUrVtTw4cPVsWNHq3XfeOMNRUdH66efftLq1avl4+NjeTNcuHBh/e9//9Ps2bNVoUIFbdy4UU2bNtWKFStsqi9DhgyaP3++VqxYoZYtW+r48ePq0qWLZUpWUtKnT59oWcJUqYRxJSVhnUWLFmnlypWWrzVr1mjJkiU21fugyMhINW7cWKdOnVKZMmXUv39/DRgwwGqdPn366Mcff9RHH30kwzA0cuRIBQYGWq4TstejxvgwhmEket6j+ggAAICUFRcXr9jY1Puy1TMXaLy8vHT9+nUdOnTIsuz48eO6ceOGJGnGjBnq3r27hg0bppYtW6p06dI6fvy41RvbrFmzqm7duvr++++1fv16NW/e3PLY/Pnz9b///U+VK1dWv379FBkZqYoVK1rOFDzOpk2bFBoaqhIlSqhz586aP3++goODbX7+g9zd3RPdAnnevHl688039eqrr0qSLly4oEKFClm+li9f/sSfyTNr1iy1aNFCo0ePVuvWrVW+fHmdOnVK0r1wcPToUQ0dOlQ5c+bUO++8o8mTJ2v27Nk6cuSIDhw4kOxjTLiBwoMX+O/YsUPFihV7ov0BAADgxfHMBZqAgADL9RZ//PGH9uzZo379+snR8V6p+fPn16+//qrDhw/r6NGjmjBhgv73v/8lOnsQGBio77//XidPntTrr79uWX758mWNGDFCGzZs0JkzZ/Tzzz9r//798vPzs6m+dOnSaerUqZo7d65OnTqlvXv3auPGjTY//0GdOnXSH3/8oUmTJun48ePatGmTpk2bpho1aujVV19VzZo1NXToUP344486deqUwsLCNHPmTKspefbInz+/du3apb///lsnT57U3LlztXDhQkn37qjm5uamNWvWaMiQITpy5IiOHTumFStWKFu2bCpatKgkKXPmzDp+/LguXrz41GN0d3dXzZo1NXz4cG3cuFHHjh1TaGioNmzYkOjMGwAAAPCgZy7QODo6aubMmSpatKg6duyo999/X6+//rpy5MghSRo7dqzu3LmjwMBABQUF6eDBgxo+fLguXbpkubWwJFWsWFFubm6qU6eO1fUmH3zwgVq0aKFPP/1U9evX15AhQ/TOO+/o/ffft6m+SpUqKSQkRBEREWrUqJHeffddFSpUKNH1Hrby8vLS1KlTtXHjRjVq1EjDhw9X27Zt1bVrV0nShAkTVK9ePQ0ZMkQNGzbUypUrFRISombNmj3R/gYPHqxcuXIpKChIb775pn766SeNHTtWkrRnzx65ubkpLCxMZ86c0VtvvaVmzZrp9OnT+vLLLy03PmjTpo02btxoc+B43Bi/+OIL1alTR4MGDVLjxo31008/acqUKWrQoMETjREAAAAvDgfjOb0I4ebNm6pSpYqmTp2qSpUqpXU5eEZ99MVGHTlzNa3LAAAAeGa5F8imib1q6MqVm3Zd2/K0cuTIbNNNAZ65u5w9ratXr+r333/XunXrVKBAAVWsWDGtSwIAAACQQp67QBMXF6dBgwYpR44cmjhxohwcHGx+bsLdvx5l69atSd5tKy2MGDHisXdnS8kzVLt3737stLP69etbPvQUAAAASG7P7ZSzJ3H27NnHfq7JK6+8YldISkmXL1/W9evXH7lOnjx55OKSMh+CFB0drf/++++R62TOnFm5cuVKkf0nB6acAQAAPBpTzkzkpZdeSusS7JIjRw7LzRLSQoYMGVSoUKE02z8AAADwzN3lDAAAAABsRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFrOaV0AkJYK5nVN6xIAAACeac/6+yUHwzCMtC4CSAuGYcjBwSGtywAAAHjmxcXFKyrqluLjUy865MiRWU5Oj59QxhkavLAcHBx07dptxcXFp3UpzyUnJ0dlzepCj1MYfU559Dh10OeUR49Tx/Pa5/h4I1XDjD0INHihxcXFKzb2+fll8yyix6mDPqc8epw66HPKo8epgz6nHm4KAAAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATItAAwAAAMC0CDQAAAAATMs5rQsA0pKTE5k+pST0lh6nLPqc8uhx6qDP98THG4qPN9K6DMBUCDR4YRmGoaxZXdK6jOcePU4d9Dnl0ePU8aL3OS4uXlFRtwg1gB0INHhhOTg4aPyinTp97npalwIAgArmdVWf1mXl6OhAoAHsQKDBC+30ues6cuZqWpcBAACAJ/RiT1QFAAAAYGoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFpPFWiuX7+uI0eO6O7du4qLi0uumgAAAADAJk8UaLZu3ao333xT/v7+euONN3To0CH17t1bo0ePTu76AAAAAOCh7A40W7Zs0bvvvquMGTOqT58+MgxDklS8eHHNnz9fX375ZbIXCQAAAABJsTvQTJw4UbVr19aCBQvUrl07S6Dp0qWLOnXqpG+++SbZiwQAAACApNgdaPbv36/AwEBJkoODg9VjlStX1pkzZ5KnMgAAAAB4DLsDjaurqy5cuJDkY//++69cXV2fuigAAAAAsIXdgaZ27dqaMGGC9uzZY1nm4OCg//77TzNmzFCNGjWSsz4AAAAAeChne5/Qu3dv/fnnn3rrrbeUK1cuSVKvXr3033//KX/+/OrVq1eyFwkAAAAASbE70GTLlk3ffPONVq5cqd9//11RUVFydXVVmzZt1Lx5c7m4uKREnQAAAACQiN2BRpLSp0+vt956S2+99VZy1wMAAAAANrMp0ISGhtq8QQcHB3Xv3v2JCwIAAAAAWxFoAAAAAJiWTYHmwIEDKV0HAAAAANjtia6hkaRr167pjz/+0LVr15QzZ075+Pgoc+bMyVkbAAAAADyS3YEmPj5eY8eO1VdffaW7d+9alru4uKhr167q3LlzshYIAAAAAA9jd6CZOnWqFixYoKCgINWtW1c5c+bUxYsX9e2332rixInKnDmzWrdunRK1AgAAAIAVuwPNsmXL1LVrV33wwQeWZUWKFFH58uWVJUsWffnllwQaAAAAAKnC0d4nXLlyRX5+fkk+VrVqVV24cOGpiwIAAAAAW9gdaCpWrKh169Yl+dhvv/2mMmXKPHVRAAAAAGALm6acrVy50vL/pUuXVmhoqC5duqTXXntNuXPnVlRUlDZt2qT169dr0KBBKVUrAAAAAFhxMAzDeNxKxYsXt32DDg7av3//UxUFpJaPvtioI2eupnUZAADIvUA2TexVQ1eu3FRsbHyyb9/Z2VFubplTbPu4hz4nnxw5MsvJ6fETymw6Q7Nhw4anLggAAAAAkptNgaZAgQI2b9CGEz4AAAAAkCzsvm2zJK1du1bbtm3T3bt3LQHGMAzdunVLf/zxhzZv3pysRQIAAABAUuwONKGhoQoNDZWrq6tiY2OVLl06OTs76/Lly3J0dNSbb76ZEnUCAAAAQCJ237Z5xYoVatq0qbZt26b27durZs2a+u233xQREaHs2bPr1VdfTYk6AQAAACARuwPNuXPn9MYbb8jBwUFeXl7avXu3JMnb21tdunTRN998Y9f2PD09FRERofbt28vHx0dVqlRRaGio5fEpU6aoVq1aVs95cJmnp6eWLFmiVq1aqVSpUnrttde0a9cuLVmyRDVq1FCZMmX00Ucf6c6dOzbXFRMTo0mTJqlmzZry9fVV8+bN9euvv1oeP3LkiLp06aKAgACVLVtWwcHBOnPmjOXxNm3aaPz48fr4449Vrlw5lSlTRr1799aNGzdsrqFNmzYKCQlRr1695Ovrq2rVqmnWrFmWaX5bt25ViRIlNGvWLAUEBKh58+aKj4/XuXPn1LNnT5UrV04BAQHq0qWLjh8/bnmOp6enjhw5YrWvtm3bqk+fPjbX9vPPP6tly5aWuiZMmKC4uDhJ0tmzZ9WzZ09VrFhRJUuWVLVq1TRu3DjFx9+708fy5ctVt25dLV68WDVq1JCvr6+Cg4N17tw59enTR35+fqpWrZoiIiJs7gUAAABeTHYHmkyZMsnBwUGSVKhQIZ0+fdoSFLy8vHT69Gm7ixgzZoyaNWumNWvWKCgoSFOmTNH27dvt2saECRPUqVMnrVq1Sq6ururSpYvWr1+vWbNm6bPPPtMPP/xgV9gKCQnR4sWL1b9/f0VGRqpq1arq0qWLjh49qjNnzqhly5ZKnz695s2bpzlz5ujChQsKCgqyCixz585Vrly5FBERoXHjxmnDhg2aO3euXeP6+uuv5erqquXLl6tnz56aOnWqwsLCLI/HxcVp06ZNWrJkiUJCQnTnzh21adNGkrRw4UItWLBAbm5ueuutt3Tu3Dn5+/urYMGCioyMtGzjv//+0/bt29W8eXObatq9e7c6d+6ssmXLavny5fr000+1ePFiTZs2TZLUtWtXXb9+XV9++aW+++47dezYUbNnz9aPP/5o2cbZs2f13XffadasWZo8ebI2bNigN954QyVLltSyZctUrVo1DRs2TFeuXLG5FwAAPA+cnBzl7Jz8Xwm3v02p7fNFn5P7y1Z2X0NTqlQprVy5UpUqVVKRIkXk5OSkLVu2qGbNmjpy5IjSp09v7ybVtGlTNWnSRJLUpUsXhYeHa9euXSpfvrzN2wgMDLSctWnSpIlGjBihIUOGqHDhwvLw8NDs2bN16NAhm7Z148YNRUREaPDgwWrQoIEkqWfPnjIMQzdu3NCyZcuUKVMmjR8/3jLeyZMnq3bt2lq1apVat24tSSpWrJh69eolSSpcuLAqV65sOaNlqyJFimjYsGFycHCQu7u7jhw5ovnz5+u9996zrNOxY0cVLlxYkvTNN9/o2rVrGjdunJyd7728ISEh2rp1q5YuXaoePXqoWbNmWrlypT766CNJ0rfffqu8efOqQoUKNtW0YMEC+fr6ql+/fpIkd3d3jRgxQpcuXdKdO3fUpEkTvfbaa8qfP78kqX379goLC9M///yjOnXqSJJiY2M1ePBgubu7y8PDQ8WLF1e6dOnUoUMHSVKHDh30zTff6Pjx43Jzc3tsLxJCNgAAZpc1q4upt4976HPqsTvQdOnSRR06dNC1a9c0Y8YMNW7cWP3791dAQIB++eUXyxtWe7i7u1t97+rqqpiYGLu2UahQIcv/u7jcO4BeeeUVy7KMGTPq7t27Nm3r2LFjiomJka+vr9XyhHAyZcoUeXt7W4W33Llzq0iRIjp48KBlWdGiRa2e7+rqqmvXrtk4onsCAgKs3qz7+fkpLCzM6sxFQpiRpH379unq1auJwmB0dLRlmlnTpk0VGhqq3bt3y8/PT6tXr1aTJk3k6GhbEj548KAqV65stax+/fqW/w8KCtJ3332nv/76SydOnNA///yjixcvWqacJbj/9cmUKZMlAElShgwZJMnqNXtUL3LkyGFT7QAAPOuuXbutuLjk/0BGJydHZc3qkmLbxz30OflkzeqSfB+seb/y5csrIiJC//zzjyRpyJAhcnR01K5du9SgQQMNGDDA7mKTOqvzqGsjYmNjEy1LOBtxP1vfoD8oXbp0j3z8YbXFx8dbPfdJzlY96MFxJYQCJycny7KEN/8JjxcpUkTTp09PtK1MmTJJkgoWLCh/f39FRkYqc+bM+ueffzRlypQnrul+t27dUlBQkO7cuaMGDRqoWbNm8vHxsZy1ut+DfX7c62VLLwAAMLu4uPgU/YT5lN4+7qHPqeeJPoemePHiKl68uKR7b6ZHjhyZrEXdL126dLp586bVshMnTqTY/qR7Z3vSpUunPXv2WMYpSW+99ZYaNmwoT09PrV69Wnfv3rWElosXL+rEiRNq1apVstayZ88eq+937dqlggULKlu2bEmu7+HhYbmOKOGsRUxMjHr37q0GDRqoYcOGkqTmzZtr/PjxcnV1VZkyZazOcD2Ou7t7orrmzZunb7/9Vu+9957+/vtv/frrr8qVK5ckKSoqSpcuXXrqC/jt7QUAAACefzadwli5cqVlitPKlSsf+5WcSpcuraioKIWHh+v06dNavHhxin9wp4uLi4KCgjRp0iRt2LBBJ0+e1BdffKGDBw+qWrVqeuedd3Tz5k317dtXBw4c0F9//aUPP/xQbm5uev3115O1lh07dmjy5Mk6fvy4IiIitGjRInXq1Omh6zdu3FjZsmVTcHCw/vzzTx05ckQDBgzQ5s2b5enpaVmvfv36unXrlhYsWGDzzQASdOrUSX/88YcmTZqk48ePa9OmTZo2bZpq1KihfPnySZJWr16tM2fOaMeOHerWrZtiYmJsnvL3MPb2AgAAAM8/m87QDBgwQEuXLpWbm9tjp5Q5ODioadOmyVGbJKlChQrq0aOH5syZo8mTJ6tatWoKDg7W/Pnzk20fSenVq5ecnJw0dOhQXb9+XcWLF9esWbMs18UsXLhQ48aNs9ztrHLlyho3bpyyZs2arHXUrl1bR44cUePGjZUnTx4NHDhQ77zzzkPXd3V11cKFCzV27Fi9++67iouLU8mSJTVnzhyra5VcXFz02muvac2aNXrttdfsqsnLy0tTp07V5MmTFRYWpjx58qht27bq2rWrHB0dNXDgQM2dO1cTJ05U3rx51bBhQ+XPnz/RGRZ72dsLAAAAPP8cDBvmAZ05c0a5c+dW+vTprT5r5WEKFCiQLMW96Nq0aaMCBQpo9OjRaV1KmkupXnz0xUYdOXM1WbcJAMCTcC+QTRN71dCVKzdT5NoLZ2dHubllTrHt4x76nHxy5MicfDcFuD+gDBkyRJ06dVLFihWfvDoAAAAASAZ23xRg165dpv7MjxEjRmjFihWPXGfq1KmqVKlSitUQFhZm+RDKh/n4449TbP8Ps3v3bnXs2PGR69SvX58zRgAAAHhm2DTl7H7BwcHKkiWLhg8f/tjbGz+LLl++rOvXrz9ynTx58lg+yyYlXL16VVFRUY9cJ2fOnMqSJUuK1ZCU6Oho/ffff49cJ3PmzJa7lz0PmHIGAHhWMOXs+UCfk0+yTjm7X4YMGbR69WqtW7dO7u7uls82SeDg4KB58+bZu9lUkyNHjjT/EMZs2bI9k7cazpAhg123bwYAAADSmt2B5r///pOfn5/l+wdP8DztZ40AAAAAgK3sDjQLFixIiToAAAAAwG42fbCmrW7dupXiH3oJAAAAAAnsPkNz5swZDRs2TNu2bXvoJ7/v37//qQsDAAAAgMexO9B89tln2rVrl958803t2rVLLi4uKl26tH799VcdPHhQU6ZMSYk6AQAAACARu6ecbd++XT179tQnn3yi5s2bK0OGDOrbt6+WLVum8uXLa8OGDSlRJwAAAAAkYneguXnzpjw9PSVJRYsW1b59+yRJTk5OatWqlX7//ffkrRAAAAAAHsLuQJMnTx5dvHhRklSoUCFdvXpVFy5ckCRlz55dly5dSt4KAQAAAOAh7A401atX18SJE7V7924VKFBA+fLl05w5c3Tjxg0tW7ZMefPmTYk6AQAAACARmwJNmzZttHr1akVHRys4OFhZs2bVpEmTJEk9e/bUvHnzVL58eUVGRqpDhw4pWjAAAAAAJLDpLmdRUVHq16+fRo4cqUaNGmno0KGWMzGNGzfWSy+9pD/++EM+Pj7y9/dP0YIBAAAAIIFNgSYyMlJ///23VqxYobVr12rx4sXy9PTUm2++qTfeeEPlypVTuXLlUrpWAAAAALBi8zU0JUuW1CeffKLNmzcrNDRUL7/8skaPHq2qVauqT58+3N0MAAAAQKqz+4M1nZ2dVbt2bdWuXVtXr17Vt99+q9WrV6t9+/Z6+eWXFRgYqC5duqRErQAAAABgxe67nN0vW7Zsat26tZYsWaIFCxbIycnJcrMAAAAAAEhpdp+hud+FCxe0Zs0affvtt/r777+VP39+devWLblqAwAAAIBHsjvQ3Lx5U//73/8UGRmprVu3ysnJSXXq1FHPnj1VqVIlOTg4pESdAAAAAJCITYEmNjZWmzZtUmRkpDZu3Kg7d+7Iy8tLAwcO1BtvvKFs2bKldJ0AAAAAkIhNgaZy5cq6du2asmbNqsDAQAUGBqpEiRIpXRsAAAAAPJJNgaZkyZIKDAxU3bp1lT59+pSuCQAAAABsYlOgmTNnTkrXAQAAAAB2e6rbNgMAAABAWiLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA0yLQAAAAADAtAg0AAAAA03JO6wKAtFQwr2talwAAgCT+TQKeFIEGLyzDMNSnddm0LgMAAIu4uHjFxxtpXQZgKgQavLAcHBx07dptxcXFp3UpzyUnJ0dlzepCj1MYfU559Dh10Od74uMNAg1gJwINXmhxcfGKjX1x/+FMDfQ4ddDnlEePUwd9BmAvbgoAAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMi0ADAAAAwLQINAAAAABMyzmtCwDSkpMTmT6lJPT2We9xfLyh+HgjrcsAAABPiECDF5ZhGMqa1SWty3juPes9jouLV1TULUINAAAmRaDBC8vBwUHjF+3U6XPX07oUpJGCeV3Vp3VZOTo6EGgAADApAg1eaKfPXdeRM1fTugwAAAA8oWd7cjsAAAAAPAKBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWhM6NatW1q0aJHl+wEDBqhNmzYPXf/06dPy9PTU1q1bU6M8AAAAINUQaExozpw5Cg8Pt3n9/Pnz65dffpGfn18KVgUAAACkPue0LgD2MwzDrvWdnJyUO3fuFKoGAAAASDvP7BkaT09PRUREqH379vLx8VGVKlUUGhpqeXzKlCmqVauW1XMeXObp6aklS5aoVatWKlWqlF577TXt2rVLS5YsUY0aNVSmTBl99NFHunPnjs11xcTEaNKkSapZs6Z8fX3VvHlz/frrr5bHjxw5oi5duiggIEBly5ZVcHCwzpw5Y3m8TZs2Gj9+vD7++GOVK1dOZcqUUe/evXXjxg2b9j9lyhSFhobqzJkz8vT01OnTpy11jRkzRhUqVFDp0qXVrVs3Xbx4UVLiKWfHjx/Xu+++q7Jly8rPz0/vvvuu/vnnH5t7IEk///yzWrZsKV9fX1WrVk0TJkxQXFycJOns2bPq2bOnKlasqJIlS6patWoaN26c4uPjJUnLly9X3bp1tXjxYtWoUUO+vr4KDg7WuXPn1KdPH/n5+alatWqKiIiw6ltISIh69epl2eesWbPsDncAAAB4vjzTZ2jGjBmjTz75RCNHjtSaNWs0YcIEBQQEqHz58jZvY8KECRo1apQKFy6sAQMGqEuXLvL29tasWbN07Ngx9e7dW998880jr0G5X0hIiNavX6+hQ4eqRIkSWrZsmbp06aJVq1YpQ4YMatmypSpVqqR58+YpOjpao0ePVlBQkCIjI5UlSxZJ0ty5c9WxY0dFREToyJEj6t27t4oUKaIPPvjgsfvv2LGjbt26pbVr1yoiIkI5cuSQJO3evVvu7u766quvdOHCBfXs2VNjx47V2LFjE22jV69eKl68uJYtW6bY2FiNGTNGH3zwgb7//nuberB792517txZHTp00KhRo3TmzBn17dtXzs7O6tGjh7p27arcuXPryy+/VObMmbVhwwZ99tln8vPzU506dSTdCz3fffedZs2apX///VfdunXT77//rq5du6pbt26aM2eOhg0bptq1a8vNzU2S9PXXXyswMFDLly/XX3/9pWHDhkmSOnfubFPdwMM4OT2zf9t5rITazTyGZx09Th30OeXR49RBn1PfMx1omjZtqiZNmkiSunTpovDwcO3atcuuQBMYGGg5a9OkSRONGDFCQ4YMUeHCheXh4aHZs2fr0KFDNm3rxo0bioiI0ODBg9WgQQNJUs+ePWUYhm7cuKFly5YpU6ZMGj9+vNKnTy9Jmjx5smrXrq1Vq1apdevWkqRixYqpV69ekqTChQurcuXK2r17t001ZM6cWZkyZUo0jSx37twaOXKkHB0dVbRoUTVs2FC//fZbkts4efKkKlWqpAIFCihdunQaNWqUjh49qvj4eDk6Pv6Hb8GCBfL19VW/fv0kSe7u7hoxYoQuXbqkO3fuqEmTJnrttdeUP39+SVL79u0VFhamf/75xxJoYmNjNXjwYLm7u8vDw0PFixdXunTp1KFDB0lShw4d9M033+j48eOWQFOkSBENGzZMDg4Ocnd315EjRzR//ny99957cnBwsKl/QFKyZnVJ6xKe2vMwhmcdPU4d9Dnl0ePUQZ9TzzMdaNzd3a2+d3V1VUxMjF3bKFSokOX/XVzuHVivvPKKZVnGjBl19+5dm7Z17NgxxcTEyNfX12p5QjiZMmWKvL29LWFGuhc0ihQpooMHD1qWFS1a1Or5rq6uunbtmo0jStorr7xiFUayZcv20Kl0PXv21KhRo/TVV1/J399fVatWVaNGjWwKM5J08OBBVa5c2WpZ/fr1Lf8fFBSk7777Tn/99ZdOnDihf/75RxcvXrRMObu/5gSZMmWyBCBJypAhgyRZvTYBAQFWwcXPz09hYWG6cuWK5UwV8CSuXbutuLj4x6/4DHJyclTWrC6mHsOzjh6nDvqc8uhx6qDPySdrVhebznQ904Hm/mCQ4FHXTMTGxiZa5uyceIi2vnF/ULp06R75+MNqi4+Pt3puUuN6Wk5OTjav27p1azVo0ECbNm3Sli1bNHnyZE2fPl0rV65Urly5Hvv8pHqa4NatWwoKCtKdO3fUoEEDNWvWTD4+PpazU/d7sJ+Pe10e3G9CQLJn7EBS4uLiFRtr7n90nocxPOvoceqgzymPHqcO+px6TDu5L126dLp586bVshMnTqToPgsVKqR06dJpz549VsvfeustzZ07V56entqzZ4/VWYWLFy/qxIkTic42PY2nmV516dIljRgxQjExMWrevLnGjRun1atX68KFC9q2bZtN23B3d0/Ug3nz5unNN9/UL7/8or///lvz589XcHCwGjZsqCxZsujSpUtPfQH/g/vctWuXChYsqGzZsj3VdgEAAGBepg00pUuXVlRUlMLDw3X69GktXrxYmzdvTtF9uri4KCgoSJMmTdKGDRt08uRJffHFFzp48KCqVaumd955Rzdv3lTfvn114MAB/fXXX/rwww/l5uam119/PdnqyJQpk65evWqZAmePbNmyaePGjfrkk0+0f/9+nTp1SosXL1a6dOnk7e1t0zY6deqkP/74Q5MmTdLx48e1adMmTZs2TTVq1FC+fPkkSatXr9aZM2e0Y8cOdevWTTExMTZP7XuYHTt2aPLkyTp+/LgiIiK0aNEiderU6am2CQAAAHN7pqecPUqFChXUo0cPzZkzR5MnT1a1atUUHBys+fPnp+h+e/XqJScnJw0dOlTXr19X8eLFNWvWLMt1MQsXLtS4cePUsmVLpU+fXpUrV9a4ceOUNWvWZKuhXr16Wrp0qRo3bqyFCxfa9VxnZ2eFhYVpzJgxat++vW7fvi0vLy/NmjXL6pqWR/Hy8tLUqVM1efJkhYWFKU+ePGrbtq26du0qR0dHDRw4UHPnztXEiROVN29eNWzYUPnz5090hsVetWvX1pEjR9S4cWPlyZNHAwcO1DvvvPNU2wQAAIC5ORh8kAdMoE2bNipQoIBGjx6drNv96IuNOnLmarJuE+bhXiCbJvaqoStXbpp2nrOzs6Pc3DKbegzPOnqcOuhzyqPHqYM+J58cOTLbdFMA0045AwAAAADTTjlLbiNGjNCKFSseuc7UqVNVqVKlFKshLCxM06ZNe+Q6H3/8sd58880U2f/u3bvVsWPHR65Tv379ZD9LAgAAADwpAs3/98EHH6hdu3aPXCdPnjwpWsNbb72levXqPXKdnDlzptj+S5QooZUrVz5yncyZM6fY/h9lwYIFabJfAAAAPNsINP9fjhw50vzDGbNly5amtyDOkCGD1QeRAgAAAM86rqEBAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACmRaABAAAAYFoEGgAAAACm5ZzWBQBpqWBe17QuAWmI1x8AAPMj0OCFZRiG+rQum9ZlII3FxcUrPt5I6zIAAMATItDgheXg4KBr124rLi4+rUt5Ljk5OSprVpdnvsfx8QaBBgAAEyPQ4IUWFxev2Nhn983284AeAwCAlMRNAQAAAACYFoEGAAAAgGkRaAAAAACYFoEGAAAAgGkRaAAAAACYFoEGAAAAgGkRaAAAAACYFoEGAAAAgGkRaAAAAACYFoEGAAAAgGkRaAAAAACYFoEGAAAAgGkRaAAAAACYFoEGAAAAgGkRaAAAAACYFoEGAAAAgGkRaAAAAACYloNhGEZaFwGklbi4+LQu4bnm5ORIj1MBfU559Dh10OeUR49TB31OHo6ODnJwcHjsegQaAAAAAKbFlDMAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWgAQAAAGBaBBoAAAAApkWggenFx8dr8uTJqlq1qkqXLq333ntPp06deuj6V65cUe/evVW+fHn5+/tr+PDhun37ttU669atU8OGDeXj46OmTZtqy5YtKT2MZ15K9LlevXry9PS0+howYEBKD+WZZW+P739ep06dNGXKlESPcSwnlhJ95li2Zm+PDx06pM6dOysgIEAVK1ZUcHCwzp49a7XOokWLVLt2bfn4+KhVq1bat29fSg/jmZfcfY6Li5OPj0+iYzmpY/5FYW+P//77b7Vr105+fn6qUKGChgwZouvXr1utw+/lFGAAJjdlyhQjICDA+Omnn4z9+/cbHTt2NOrVq2dER0cnuX5QUJARGBho7N271/jtt9+MmjVrGv369bM8vmXLFqNkyZLGvHnzjMOHDxujR482vL29jcOHD6fWkJ5Jyd3nmzdvGsWLFzd++ukn4/z585ava9eupdaQnjn29tgwDCM6Otro37+/4eHhYUyePNnqMY7lpCV3nzmWE7Onx5cvXzYqV65s9OjRw/jnn3+MPXv2GK1btzZee+01486dO4ZhGMby5csNHx8fY9WqVcahQ4eMvn37Gv7+/salS5dSe2jPlOTu8+HDhw0PDw9j//79VsfyjRs3Untozwx7enzhwgWjfPnyxsCBA42jR48aO3fuNBo2bGh069bNsg6/l1MGgQamFh0dbfj5+RmLFi2yLLt69arh4+NjREZGJlp/165dhoeHh9Uvjp9//tnw9PQ0/vvvP8MwDKNjx47Ghx9+aPW8li1bGoMHD06ZQZhASvT5zz//NDw8PIyoqKiUH4AJ2NtjwzCMnTt3Gq+//rpRu3Zto1y5coneaHMsJ5YSfeZYtmZvj5cuXWr4+fkZt2/ftiw7e/as4eHhYfz222+GYRhGvXr1jLFjx1oej4mJMapXr27MmDEjBUfybEuJPq9Zs8YoU6ZMyhdvEvb2+I8//jB69uxpxMTEWJbNnTvX8PX1tXzP7+WUwZQzmNqBAwd08+ZNVaxY0bIsa9asKlGihLZv355o/R07dih37txyd3e3LPP395eDg4N27typ+Ph47dq1y2p7khQQEJDk9l4Uyd1nSfrnn3+UK1cuZcuWLeUHYAL29liSNm3apKpVq2rlypVydXW1eoxjOWnJ3WeJY/lB9va4YsWKmjZtmjJmzGhZ5uh47+3JtWvXdOnSJR0/ftxqe87OzipXrhzHcjL2Wbp3LN//e/tFZ2+PfX199cUXX8jZ2VmSdOTIEa1atUqVK1eWxO/llOSc1gUAT+O///6TJOXPn99qeZ48eSyP3e/cuXOJ1k2fPr2yZ8+uf//9V9euXdOtW7eUL18+m7b3okjuPkv3/uHMlCmTgoODtWvXLrm5uSkwMFBt27a1/CP7IrG3x5LUs2fPh26PYzlpyd1niWP5Qfb2uGDBgipYsKDVslmzZiljxowqX7685XdGUts7cOBAcpZuKsndZ0k6ePCgYmNj9e677+rAgQPKmzev2rVrpyZNmqTQKJ5tT/L7IkH9+vV1/PhxFShQQKGhoZL4vZySXrzftHiuJFxknj59eqvlGTJkUHR0dJLrP7ju/evfuXPHru29KJK7z9K9i1OvXbum+vXrKzw8XO+8844mTZr0wl58am+PH4djOWnJ3WeJY/lBT9vjBQsWaOHCherTp49y5MiRIq/Z8yC5+yzdO5ajoqLUpk0bhYeHq379+ho4cKAiIiKSfwAm8DQ9Hj9+vBYsWKCcOXOqbdu2unnzJr+XUxBnaGBqCafO7969a3UaPTo6Wi4uLkmuf/fu3UTLo6OjlSlTJmXIkMGyvQcfT2p7L4rk7rMkhYWFKTo62jKFx9PTUzdu3ND06dPVo0ePF+4v2/b2+HE4lpOW3H2WOJYf9KQ9NgxDkyZN0vTp09W1a1e1adMm0fbux7GcvH2WpG+//VZxcXHKnDmzJKl48eI6e/aswsPD1aJFixQaybPraX5flCpVSpIUGhqq6tWr6/vvv1f16tUt27vfi34sJ4cX67csnjsJp4HPnz9vtfz8+fPKmzdvovXz5cuXaN27d+8qKipKefLkUfbs2ZUpUyabt/eiSO4+S/f+QvXg9QgeHh66deuWrl69mpzlm4K9PX4cjuWkJXefJY7lBz1Jj2NiYtS3b1/NmDFDAwcO1EcfffRU23sRJHefpXtv4BPCTAIPD48XdjqUvT0+evSoNm7caLUsb968yp49u86dO8fv5RREoIGpFS9eXFmyZNHWrVsty65du6Z9+/ZZ5gTfr3z58vrvv/904sQJy7Jt27ZJksqWLSsHBweVKVPGsizB1q1bVa5cuRQaxbMvuftsGIbq1KljmVecYM+ePcqdO7fc3NxSaCTPLnt7/Dgcy0lL7j5zLCf2JD3u16+fvvvuO33++edq37691WM5c+ZUkSJFrLYXGxurHTt2PNFr9rxI7j5fu3ZN/v7+Wr58udXyPXv26NVXX032+s3A3h7/9ttvCg4OttxkQZJOnjypK1euyN3dnd/LKYgpZzC19OnTKygoSOPHj1eOHDlUoEABjRs3Tvny5VO9evUUFxeny5cvy9XVVRkzZpSvr6/KlCmjnj17atiwYbp165aGDBmipk2bWv460qFDB3Xu3FklSpRQtWrVtGzZMu3fv18hISFpPNq0kxJ9rlu3rsLDw1W0aFF5e3try5Ytmj17tgYNGpTGo00b9vbYFhzLiSV3nx0cHDiWH2Bvj5cvX661a9eqX79+8vf314ULFyzbSlinY8eOCgkJUaFChVSqVCnNmjVLd+7ceSGnQSVI7j5nzZpVFSpU0IQJE5QzZ04VKlRI//vf/7R69WrNnDkzDUeaduztcaNGjTRr1iz17dtXffr00dWrV/Xpp5/Kx8dHNWvWlMTv5RSTtneNBp5ebGysMXbsWKNChQpG6dKljffee884deqUYRiGcerUKcPDw8NYtmyZZf2LFy8aPXr0MEqXLm0EBAQYQ4cOtXyoWIIVK1YYdevWNUqVKmU0a9bMco/+F1ly9zkmJsYIDQ01ateubZQsWdKoX7++sWTJklQf17PE3h7fr2bNmok+H8UwOJaTktx95lhOzJ4ed+jQwfDw8Ejy6/7XYfbs2Ua1atUMHx8fo1WrVsa+ffvSZGzPkuTu8/Xr141Ro0YZ1atXN7y9vY0mTZoY33//fZqN71lg7++Lo0ePGp07dzbKli1r+Pv7GwMHDjSuXr1qtU1+Lyc/B8MwjLQOVQAAAADwJLiGBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAAAIBpEWgAAAAAmBaBBgAASJL4JAcAZkSgAQDgCezZs0d9+/ZVjRo15OPjozp16mjw4ME6depUmta1detWeXp6auvWrTY/5+7duxo1apQiIyMtywYMGKBatWqlRIkAkKwINAAA2GnRokV6++23denSJfXu3VthYWHq3Lmztm3bphYtWujAgQNpXaJdzp8/r3nz5ik2NtayrFu3bgoNDU3DqgDANs5pXQAAAGayc+dOhYSEqHXr1ho0aJBleUBAgOrUqaOmTZvq448/1vLly9Owyqf3yiuvpHUJAGATztAAAGCH8PBwubq6qlevXokey5EjhwYMGKDatWvr1q1biouL06JFi/TGG2/Ix8dHNWrU0Pjx4xUdHW15zoABA9SuXTsNHTpUZcqUUcOGDRUXFydPT0+FhoaqefPm8vHxsZwtOXv2rHr16iV/f3/5+vqqXbt22rdv3yNr/uGHH9SqVSv5+fnJ29tbDRo00KJFiyRJp0+fVu3atSVJAwcOtEwze3DKma1jad++vZYtW6b69evL29tbTZo00ebNm5+w2wDweJyhAQDARoZh6JdfflGtWrXk4uKS5DoNGza0/P+gQYO0atUqvffeeypXrpz27dunqVOnav/+/Zo9e7YcHBwkSTt27FCGDBk0depU3bp1S05OTpKkGTNmqHfv3ipSpIgKFCigy5cv6+2335aLi4sGDx4sFxcXzZs3T61bt1ZERITc3d0T1bNx40Z1795dbdu2VY8ePXTnzh199dVXGjFihLy9veXl5aXQ0FB98MEH6tq1q+rVq5fkuIYMGWLTWPbu3avz588rODhYWbJk0aRJk9SjRw9t3rxZ2bJle6r+A0BSCDQAANjoypUrio6OVsGCBR+77uHDhxUREaHevXurc+fOkqTKlSsrT5486tevnzZv3qzq1atLkmJjYzVixAjly5fPahvlypVThw4dLN9PmDBBUVFR+vrrr1WgQAFJUrVq1dSwYUNNmjRJkydPTrKOZs2aWU2P8/PzU0BAgLZu3SpfX195eXlJujfNrESJEk81luvXr2v58uWWKWuZMmVSUFCQfv/9d9WvX/+xfQMAezHlDAAAGyWcOYmLi3vsutu2bZMkvf7661bLX3/9dTk5OVndhSx79uyJwowkS9BIsGXLFnl5eSlv3ryKjY1VbGysHB0dVa1aNf32229J1tGpUyeNHj1aN2/e1N69e7V27VrNnDlT0r27m9nCnrHkyJHD6vqbhHHdvn3bpn0BgL04QwMAgI2yZcumzJkz6+zZsw9d59atW4qJidHVq1clSblz57Z63NnZWW5ubrp+/bplWebMmZPcVqZMmay+j4qK0okTJ1SyZMkk108qNFy+fFlDhw7VDz/8IAcHBxUqVEjlypWTZPvnztgzlgen4iVMRYuPj7dpXwBgLwINAAB2qFKlirZu3aro6GhlyJAh0eNLly7VmDFjFBwcLEm6cOGCZXqYJMXExOjKlStyc3Oze9+urq7y9/dXv379knw8ffr0iZb16dNHR48e1dy5c+Xn56f06dPr9u3bWrp0qc37Tbj2JTnHAgDJhSlnAADYoWPHjoqKitLEiRMTPXbhwgXNmTNHxYoVU926dSVJa9assVpnzZo1iouLU9myZe3et7+/v44dO6YiRYqoVKlSlq9Vq1YpIiLCMiXufjt37lS9evUUEBBgCTwJdx1LOGuS1PMe3G9yjwUAkgtnaAAAsEPp0qX14YcfauLEiTpy5IiaNm0qNzc3HTp0SOHh4YqOjtbEiRPl7u6uZs2aafLkybp9+7bKly+v/fv3KzQ0VAEBAapatard+27fvr1WrVql9u3bq2PHjnJzc9PatWu1dOlSDRw4MMnn+Pj4KDIyUiVLllS+fPm0a9cuzZo1Sw4ODpYpaq6urpLuXaPj7u4uX19fq20UK1Ys2ccCAMmFQAMAgJ26du2qEiVKaNGiRRo1apSuXr2q/Pnzq0aNGurSpYvy588vSQoJCVGhQoW0bNkyhYWFKU+ePGrbtq26desmR0f7J0nkzZtXixcv1ueff65hw4YpOjpahQsXVkhIiFq0aJHkc0aPHq2RI0dq5MiRkqTChQtr+PDhWr16tXbs2CFJypIlizp06KAlS5Zo06ZN+vXXXxNtJ7nHAgDJxcGw9YpAAAAAAHjG8CcVAAAAAKZFoAEAAABgWgQaAAAAAKZFoAEAAABgWgQaAAAAAKZFoAEAAABgWgQaAAAAAKZFoAEAAABgWgQaAAAAAKZFoAEAAABgWgQaAAAAAKZFoAEAAABgWv8PsdL8oX4uaPIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_correlation(df, discrete_vars, target_var):\n",
    "    correlations = []\n",
    "    for var in discrete_vars:\n",
    "        if var != 'age':\n",
    "            crosstab = pd.crosstab(df[var], df[target_var])\n",
    "            chi2, _, _, _ = chi2_contingency(crosstab)\n",
    "            n = crosstab.sum().sum()\n",
    "            phi = np.sqrt(chi2 / n)\n",
    "            correlations.append((var, phi))\n",
    "\n",
    "    return correlations\n",
    "\n",
    "def plot_correlation(correlations):\n",
    "    df_corr = pd.DataFrame(correlations, columns=['Variable', 'Correlation'])\n",
    "    df_corr.sort_values(by='Correlation', inplace=True)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
    "    ax.barh(df_corr['Variable'], df_corr['Correlation'])\n",
    "    ax.set_xlabel('Correlation')\n",
    "    ax.set_ylabel('Variable')\n",
    "    ax.set_title('Correlation between Discrete Variables and Target Variable')\n",
    "    plt.show()\n",
    "\n",
    "correlations = calculate_correlation(df, discrete_vars, 'subscribed')\n",
    "plot_correlation(correlations)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "52fcdcbb",
   "metadata": {},
   "source": [
    "Looks like they don't have much correlation with target variable... So, dropping seems ideal... We could have dropped them straightaway... But we wanted some solid reason to drop them and we have some reasons now to drop them... Hence dropping..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a184bf36",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = discrete_vars[1:], inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a2dd53ea",
   "metadata": {},
   "source": [
    "Let's move towards continuous variables now..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6b35bae0",
   "metadata": {},
   "source": [
    "#### Handling Continuous Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2b3781d2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['last_con_dur', 'emp_var_rate', 'cpi', 'cci', 'euribor_3m', 'num_employed']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6a4b9b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_outliers(df):\n",
    "    outliers = {}\n",
    "    for col in df.select_dtypes(include='number').columns:\n",
    "        q1 = df[col].quantile(0.25)\n",
    "        q3 = df[col].quantile(0.75)\n",
    "        iqr = q3 - q1\n",
    "        upper_bound = q3 + (1.5 * iqr)\n",
    "        lower_bound = q1 - (1.5 * iqr)\n",
    "        num_outliers = len(df[(df[col] > upper_bound) | (df[col] < lower_bound)])\n",
    "        percent_outliers = (num_outliers / len(df)) * 100\n",
    "        outliers[col] = {'count': num_outliers, 'percent': percent_outliers}\n",
    "    return outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "30e43d9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: age\n",
      "Count of outliers: 468\n",
      "Percentage of outliers: 1.14%\n",
      "\n",
      "Column: last_con_dur\n",
      "Count of outliers: 2963\n",
      "Percentage of outliers: 7.2%\n",
      "\n",
      "Column: emp_var_rate\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n",
      "Column: cpi\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n",
      "Column: cci\n",
      "Count of outliers: 446\n",
      "Percentage of outliers: 1.08%\n",
      "\n",
      "Column: euribor_3m\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n",
      "Column: num_employed\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "outliers = count_outliers(df)\n",
    "for col, info in outliers.items():\n",
    "    print(f\"Column: {col}\")\n",
    "    print(f\"Count of outliers: {info['count']}\")\n",
    "    print(f\"Percentage of outliers: {round(info['percent'], 2)}%\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "54be38e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['last_con_dur'] = df['last_con_dur'].clip(upper=df['last_con_dur'].quantile(0.92))\n",
    "df['age'] = df['age'].clip(upper=df['age'].quantile(0.98))\n",
    "df['cci'] = df['cci'].clip(upper=df['cci'].quantile(0.98))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ecc2c343",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: age\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n",
      "Column: last_con_dur\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n",
      "Column: emp_var_rate\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n",
      "Column: cpi\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n",
      "Column: cci\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n",
      "Column: euribor_3m\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n",
      "Column: num_employed\n",
      "Count of outliers: 0\n",
      "Percentage of outliers: 0.0%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "outliers = count_outliers(df)\n",
    "for col, info in outliers.items():\n",
    "    print(f\"Column: {col}\")\n",
    "    print(f\"Count of outliers: {info['count']}\")\n",
    "    print(f\"Percentage of outliers: {round(info['percent'], 2)}%\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1cd3d9fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_column_ranges(df):\n",
    "    ranges = {}\n",
    "    for column in continuous_vars:\n",
    "        column_min = df[column].min()\n",
    "        column_max = df[column].max()\n",
    "        column_range = column_max - column_min\n",
    "        ranges[column] = [column_min, column_max, column_range]\n",
    "    return ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b3198948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: last_con_dur\n",
      "Minimum: 0, Maximum: 614, Range: 614\n",
      "\n",
      "Column: emp_var_rate\n",
      "Minimum: -3.4, Maximum: 1.4, Range: 4.8\n",
      "\n",
      "Column: cpi\n",
      "Minimum: 92.201, Maximum: 94.767, Range: 2.5660000000000025\n",
      "\n",
      "Column: cci\n",
      "Minimum: -50.8, Maximum: -30.1, Range: 20.699999999999996\n",
      "\n",
      "Column: euribor_3m\n",
      "Minimum: 0.634, Maximum: 5.045, Range: 4.411\n",
      "\n",
      "Column: num_employed\n",
      "Minimum: 4963.6, Maximum: 5228.1, Range: 264.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ranges = get_column_ranges(df)\n",
    "\n",
    "for column, values in ranges.items():\n",
    "    print(f\"Column: {column}\")\n",
    "    print(f\"Minimum: {values[0]}, Maximum: {values[1]}, Range: {values[2]}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "366eb4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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aXHLJJZkwYUJ69uzZaE4/SqO6ujrr16/PiBEjcvjhh2f48OF56KGHkuhJU1u5cmWS5M0338yoUaMyZMiQfO5zn8sDDzyQRD8AAFo6ART/0uOPP57LLrssxx13XI466qjU1dVt8xOJd59v2rSpFCW2GF/84hczZ86cnHzyybngggvy9NNPv2c/2rZtmyR5++23S1Fms/ftb387/fv33+YKjyT6UQL19fX5y1/+kg0bNmTMmDGZMWNGDjnkkJxzzjlZsmSJnjSxN954I0ny9a9/PSeffHJmzZqVj3/84zn//PP1AwAA94DivS1atCjjxo1LRUVFpk2bluSdDwr/HDS9+7x9+/ZNXmNL0rt37yTJ5MmT8+STT+aOO+5Iu3bttunHux/iOnTo0OQ1Nnfz5s1LVVVV7r333vec14+m17p16yxbtiytWrVKu3btkiQHHXRQVqxYkZkzZ+pJE2vTpk2SZNSoUTn11FOTJPvvv3+WL1+eW2+9VT8AAFo4V0CxjTvuuCNjxozJJz/5ydx8880N31D37Nkza9asabTtmjVr0qFDh3Tq1KkUpTZr69evz4IFC1JfX98wVl5ent69e2fNmjXp0aPHe/YjSbp3796ktbYEc+bMSU1NTY466qj0798//fv3T5J861vfyujRo/WjRDp27NgQPr1r3333zauvvqonTezdc/rufeve1bt377z00kv6AQDQwgmgaOTOO+/MlVdemREjRmT69OmNfi4xYMCAPPLII422X7p0aSoqKlJe7q/SB23dunW5+OKLs2TJkoaxzZs3Z/ny5enVq1cGDhyYxx57LFu2bGmYX7p0aT760Y+mW7dupSi5WZs2bVruu+++zJs3r+GRJGPHjs3kyZP1owRWrFiRioqKLFu2rNH4n/70p/Tu3VtPmtiBBx6Yjh075sknn2w0Xl1dnT333DMDBw7M8uXLG36ql7zTj44dO2a//fZr6nIBAGhiUgMarFy5MldddVWOPfbYVFZWZt26dVm7dm3Wrl2bjRs35vOf/3yeeuqpTJs2Lc8//3xmzZqV+++/P6NHjy516c1Snz59MnTo0EyaNCmPPvpoqqurc+mll6a2tjZnnXVWTj/99LzxxhsZP358nnvuucydOze33XZbKisrS116s9S9e/fstddejR5J0q1bt3Tv3l0/SqBXr17ZZ599csUVV6SqqirPP/98pkyZkieeeCLnnXeenjSxdu3aZfTo0fn+97+f+fPn58UXX8wPfvCDPPzwwxk5cmSGDRuW3XbbLRdddFGeffbZLFq0KNOnT8/ZZ5+9zb2hAABofsoKhUKh1EWwfbj55ptz7bXXvufcqaeemquvvjoPPfRQpk6dmhdeeCEf/vCHM2bMmJx44olNXGnLsXHjxlxzzTVZtGhRNm7cmAEDBuTSSy/NvvvumyR56qmnMnny5Cxfvjy77bZbzj777Jx55pklrrrl6Nu3b6ZMmZLTTjstiX6Uwrp163LNNddk8eLFqa2tzQEHHJBx48ZlwIABSfSkFG699dbccccdefXVV9OrV6+MGTMmw4YNS5KsWrUqEydOTFVVVbp06ZLPfvazGTNmjKtoAQBaAAEUAAAAAEXlK0cAAAAAikoABQAAAEBRCaAAAAAAKCoBFAAAAABFJYACAAAAoKgEUAAAAAAUlQAKAAAAgKISQAH8D1/96lfTt2/fzJo1q9SlAAAANBtlhUKhUOoiALYHGzduzBFHHJE999wzmzZtyv3335+ysrJSlwUAALDDcwUUwD/Mnz8/STJ+/Pi88MILWbp0aYkrAgAAaB4EUAD/MGfOnAwZMiSDBw/OXnvtldmzZ2+zzcyZM3PMMcfkYx/7WM4444w88MAD6du3b5YtW9awTXV1dSorK1NRUZGKiopccMEFWb16dVMuBQAAYLsigAJIsmLFivzxj3/MKaeckiQ55ZRT8rvf/S7r1q1r2ObGG2/MtGnTcsIJJ+Smm25Kv379ctFFFzXaz8qVK3PGGWekpqYm3/nOdzJ58uSsXr06w4cPT01NTROuCAAAYPshgALIO1c/7bLLLjn66KOTJKeeemq2bNmSe+65J0ny5ptv5kc/+lFGjBiRcePG5Ygjjshll13WEFi968Ybb0z79u1z22235dhjj80JJ5yQ22+/PXV1dbnllluaelkAAADbBQEU0OJt3rw5v/rVrzJs2LDU1dWltrY2HTt2zKGHHpq77747W7duzRNPPJG6urocf/zxjV578sknN3q+dOnSDBo0KO3atUt9fX3q6+uz8847Z8CAAfnDH/7QlMsCAADYbrQudQEApfbggw+mpqYm99xzT8MVT//T4sWLs3HjxiTJrrvu2miuW7dujZ6//vrrue+++3Lfffdts59/fi0AAEBLIYACWrw5c+bkIx/5SCZPntxovFAo5MILL8zs2bMzatSoJElNTU322Wefhm3Wr1/f6DWdOnXK4YcfnpEjR25znNat/ZMLAAC0TD4NAS3a2rVrs3jx4owePTqHHXbYNvPHH3985s6dmwkTJqRTp0757W9/m4EDBzbML1y4sNH2gwYNynPPPZf999+/IXAqFAoZN25c9tprr+y///7FXRAAAMB2SAAFtGjz5s1LfX19TjrppPecP+WUU/Lzn/88c+fOzejRo3PDDTekffv2GTRoUB555JH87Gc/S5KUl79zS73zzz8/Z5xxRiorKzN8+PC0bds2d911VxYtWpQbbrihydYFAACwPSkrFAqFUhcBUConnHBCWrVqlfnz57/nfKFQyLBhw7J58+b8/ve/z4wZM3LXXXdl3bp16devX4499thMmTIlc+fOzYEHHpgkefrpp3Pttdfm8ccfT6FQSJ8+fXLOOefkmGOOacqlAQAAbDcEUAD/hvr6+syfPz+HHXZYevbs2TD+05/+NJMmTcqyZcvSuXPnElYIAACw/RJAAfybTjrppOy0004577zz0rVr11RXV+e6667LsGHDMmXKlFKXBwAAsN0SQAH8m1avXp3p06dn2bJlqa2tzYc+9KF8+tOfTmVlZdq0aVPq8gAAALZbAigAAAAAiqq81AUAAAAA0LwJoAAAAAAoKgEUAAAAAEUlgAIAAACgqARQAAAAABSVAAoAAACAohJAAQAAAFBUAigAAAAAikoABQAAAEBR/Re6LVcKHmrxNAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x2000 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_plots = len(continuous_vars) + 1\n",
    "\n",
    "num_rows = (num_plots + 1) // 2  # Number of rows for subplots\n",
    "\n",
    "fig, axes = plt.subplots(num_rows, 2, figsize=(12, 5 * num_rows))\n",
    "\n",
    "for i, feature in enumerate(continuous_vars + ['age']):\n",
    "    data = df.copy()\n",
    "\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    ax = axes[row, col]\n",
    "\n",
    "    sns.histplot(data[feature], ax=ax, bins=7, color='darkblue')\n",
    "    ax.set_xlabel(feature.replace('_', ' ').title())\n",
    "    ax.set_ylabel(\"Frequency\")\n",
    "\n",
    "# Remove any empty subplots\n",
    "if num_plots < num_rows * 2:\n",
    "    fig.delaxes(axes[num_rows-1, 1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "70c31b5d",
   "metadata": {},
   "source": [
    "Both the distributions as well as ranges suggest that it would be better if we do scaling... So, let's do scaling after split...\n",
    "\n",
    "Now let's dive into the most important part which is categorical variables..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "592799b1",
   "metadata": {},
   "source": [
    "#### Handling Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8b2b91bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['job',\n",
       " 'marital_status',\n",
       " 'education',\n",
       " 'defaulted',\n",
       " 'house_loan',\n",
       " 'personal_loan',\n",
       " 'con_medium',\n",
       " 'last_con_month',\n",
       " 'last_con_day',\n",
       " 'prev_camp_outcome',\n",
       " 'subscribed']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb4634a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_cols = ['education']\n",
    "\n",
    "nominal_cols = [col for col in categorical_vars if col not in ordinal_cols and col != 'subscribed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "54fca616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job :\n",
      "\n",
      "['housemaid' 'services' 'admin.' 'blue-collar' 'technician' 'retired'\n",
      " 'management' 'unemployed' 'self-employed' 'unknown' 'entrepreneur'\n",
      " 'student']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "marital_status :\n",
      "\n",
      "['married' 'single' 'divorced' 'unknown']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "education :\n",
      "\n",
      "['basic.4y' 'high.school' 'basic.6y' 'basic.9y' 'professional.course'\n",
      " 'unknown' 'university.degree' 'illiterate']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "defaulted :\n",
      "\n",
      "['no' 'unknown' 'yes']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "house_loan :\n",
      "\n",
      "['no' 'yes' 'unknown']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "personal_loan :\n",
      "\n",
      "['no' 'yes' 'unknown']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "con_medium :\n",
      "\n",
      "['telephone' 'cellular']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "last_con_month :\n",
      "\n",
      "['may' 'jun' 'jul' 'aug' 'oct' 'nov' 'dec' 'mar' 'apr' 'sep']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "last_con_day :\n",
      "\n",
      "['mon' 'tue' 'wed' 'thu' 'fri']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "prev_camp_outcome :\n",
      "\n",
      "['nonexistent' 'failure' 'success']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n",
      "subscribed :\n",
      "\n",
      "['no' 'yes']\n",
      "\n",
      " _______________________________________________________ \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in categorical_vars:\n",
    "    print(column, ':\\n')\n",
    "    print(df[column].unique())\n",
    "    print(\"\\n\", 55 * \"_\", \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "83dc1a29",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VmJRq62HkehSfAAAAAABW5eftLpPJJF9fT1sPxeYexC1OmWGtIwqhDxrFJwAAAACAVXl7uMrJiVucHtQtTndjMDipQAEvii94oCg+/T+j0ajZs2dr7dq1unz5soKDg/XOO+/o4YcftvXQAAAA7Bp5FICs4hYn2zMYnOTsbNCkDdE6ceGyrYdjM8GP+uvVZyraehh5BsWn/zdnzhytXLlSEydOVEBAgCZPnqzw8HB9/vnncnNzs/XwAAAA7BZ5FAA4nhMXLutIbIKth2EzJQrls/UQ8hTm2UlKTU3VokWL1LdvX9WvX1/ly5fXtGnTFBsbq82bN9t6eAAAAHaLPAoAAGTGyWQy5fll9vft26c2bdrom2++UenSpc3b27Vrp3LlyikiIuKeY5pMmX+DgZOTZDAYdOlqitLSjfd8DGtzd3WWj6ebUq6ny2hnvyYGJye5uzrLaDQqJ4bGtci+vHQt7Pk6SFwLe8K1sB9ZvRYGg5OcnPL2wqvZZas86k7s+e/lQbL3v80HJadfj7OD38kb+J28gd9J+8Hv5A33+zuZ1RyK2+4kxcbGSpKKFStmsb1o0aLmtnvl5OQkZ+esJbEFvN2zdYwHxd3V2dZDuCODIWcn73Etsi8vXQt7vg4S18KecC3sR05fC/zL1nnUndjz38uDZO9/mw+KPbwG8Dt5A7+TN/A7aT/4nbzB2r+Ttv+NtwNJSUmSdMuaBO7u7kpJSbHFkAAAABwCeRQAAMgMxSdJHh4ekm6sWXCzlJQUeXp62mJIAAAADoE8CgAAZIbik/6dJh4XF2exPS4uTv7+/rYYEgAAgEMgjwIAAJmh+CSpfPnyypcvn6KioszbEhMTFRMTo+DgYBuODAAAwL6RRwEAgMyw4LhurFEQFhamyMhIFSxYUMWLF9fkyZMVEBCgxo0b23p4AAAAdos8CgAAZIbi0//r27ev0tLSNGLECCUnJys4OFgLFy6Uq6urrYcGAABg18ijAADA3TiZTCaTrQcBAAAAAACA3Ik1nwAAAAAAAGA1FJ8AAAAAAABgNRSfAAAAAAAAYDUUnwAAAAAAAGA1FJ8AAAAAAABgNRSfAAAAAAAAYDUUnwAAAAAAAGA1FJ8AGzt9+rS+/PJLWw8DVjBr1iyFhobaehgO6ea/i9DQUM2aNcvGI0KG33//Xc8//7wqV66s995775b2IUOGqGPHjjYYGYC7Wb9+vQIDA7Pcf9euXYqOjs7RMRw6dEg//fRTjsaEdWX2mn+zqKgoBQYG6tSpU5J4/wZgieITYGNvv/22fv31V1sPA7Ar/F3Yr3nz5snV1VVfffWVunfvfkv78OHD+ccGYIdeeOEFbdmyJcv927dvrxMnTuToGHr06KHff/89R2PCujJ7zb9Z9erVtWXLFhUrVuwBjQ6AI3Gx9QAAAIDjSEhIUIUKFVSyZMnbtvv4+DzgEQHICg8PD3l4eNh6GHAwmb3m38zNzU1FihR5AKMC4IiY+ZRHBAYGavXq1Wrfvr2qVKmi559/Xr/99ptWr16t+vXrq0aNGurXr5+Sk5PN++zevVudOnVSzZo19fjjj2vo0KGKj483t4eGhmrhwoV64403VL16dT3++OMaO3as0tLSbHGKDqljx47asWOHPv30U4WGht52evJ/t/3222/q0KGDqlatqvr16ysiIkJXrlx50EPPFV566SWNHTvW/Pi7775TYGCgvvnmG/O2iRMnqnPnzrp8+bJGjhypOnXqqGbNmurUqdMtn96uXr1ajRo1UtWqVdWzZ08lJCQ8sHPJTf77dyFJ586d0+uvv66goCA9/vjjmjBhgtLT0yXd/laSe729BFkTGhqqHTt2aMOGDQoMDFRoaKhGjhypNm3aqFatWvrss8+47Q55UmBgoD755BN17txZVatW1VNPPaXZs2db9Pnpp5/Utm1bVa9eXU899ZQmTJhgkXdlJcaPP/6ol156SVWrVlWjRo00ffp0paamSpI2bdqkwMBAbdq0ydz/rbfe0jPPPKOEhIRbXhd//vlnvfTSS6pWrZpCQkI0ZMgQ8/tWRr+hQ4dqyJAhkqSzZ8+qf//+qlWrlh5//HH17NlTx44dM8cbMmSIhgwZovfee08hISGqVq2aevToobNnz0q68frxzz//aPbs2bxGOIj/vubHxMRoxIgRqlu3ripVqqSQkBCNGDFCSUlJkm697e5mWXmvDg0N1XvvvacXXnhBjz/+uHbs2CGTyaQPP/xQDRo0ULVq1dSiRQt99tln1j1x2NydXp9u9zv2320mk0lLly7Vs88+q6pVq6pJkyb64osvzP3Pnz+vwYMH6/HHH1fNmjXVo0cPHT9+3Nx+t9fZu40tw8KFC9WwYUNVrlxZoaGhev/992Uymaz5dDkMik95yLRp0xQeHq6NGzfKx8dHPXv21KZNmzR//nxNmDBB3333ndauXStJ2rdvnzp27KjHHntMa9as0YwZM7R3715169bN/A8+SZoxY4aCg4P12WefafDgwVq+fLnFHzfubtasWapevbqef/55ffLJJ5n2P3DggLp06aK6devqs88+U2RkpP7880917dqVF7VseOaZZ7R161bz4//9739ycnJSVFSUedtPP/2kBg0a6LXXXtPJkyc1b948rVmzRkFBQWrXrp1iYmIkSV988YVGjx6tzp07a+PGjapRo4ZWrFjxwM8pN7jd38Unn3yi4OBgff755xo0aJCWLFmiTz/91MYjzXs++eQT87XZsmWLAgICtHbtWnXq1EkrV65U3bp1bT1EwGbee+89tWzZUl9++aXCwsI0a9Ys7dy5U5L07bffqlevXqpfv77Wr1+viIgIffXVVxowYECWY/zyyy/q16+f2rZtqy+++ELvvvuuvv76aw0aNEiS9Oyzz6pFixYaM2aMEhIS9MUXX+jrr7/W5MmTlT9/fovjXLx4Ua+//rpatWqlr776SrNnz9bOnTs1adIkSTLfnjds2DANHz5c165dMxeMli9fro8++kh+fn5q27atubgk3XgvvHTpkpYvX64PP/xQf/75p6ZPny7pxutHQECAunbtyq25DuK/r/kzZ85UTEyMZs+erU2bNmno0KHasGGDVq9enWPHXL58uUaMGKEFCxYoKChI06ZN08cff6yRI0fq888/V6dOnTRq1ChyrFwss9enzCxYsMD8794vvvhCr7zyigYPHqzt27crLS1NXbt21eHDhzVnzhytWbNGRqNR4eHhSk9Pz/R1NrOx/fDDD5o3b54iIiK0efNmDRw4UB988AEF0//HbXd5SKtWrcyzCFq0aKHRo0frnXfeUalSpVSuXDktWLBAhw4dkiQtWrRIgYGBGjlypCSpTJkymjp1qlq0aKEtW7bo6aefliQ99dRT6tSpkyTp4Ycf1kcffaTffvtNL7744oM/QQdUoEABubq6ysPDQwULFsy0/8KFC/Xkk0+qZ8+ekqRSpUppypQpatiwoXbs2KHHH3/c2kPOVUJDQzV79mydOXNGxYoV09atW9WgQQNz8enEiRM6evSo8uXLpz179mj79u0qUKCAJGnAgAH67bfftGzZMk2cOFEfffSRXnjhBXXo0EGS1L17d+3Zs0cHDhyw1ek5rNv9XTRu3FivvvqqpBuvNcuWLdMff/yh1q1b23KoeU7BggXN16ZIkSJydnZWhQoV1KxZM1sPDbC5F198US1atJAk9ezZUwsXLtRvv/2m4OBgzZ8/X40aNVLv3r0lSaVLl5bJZFKfPn10+PBhlS1bNtMYc+fOVdu2bfXKK69IkkqWLKmIiAi9+uqrOnXqlEqUKKF33nlHzZo104gRI7R9+3b17t1btWrVumWsZ8+eVWpqqh566CEVL15cxYsX19y5c80fMGbcOuXj4yMfHx+tXbtWiYmJmjx5slxcbvzzYdy4cYqKitKaNWv0xhtvmPuPHj1arq6uKlOmjF544QX9/PPPkm68fjg7O8vLy8v8Xgr79t/X/KeeekrBwcHm2UolSpTQ8uXLdfDgwRw75tNPP60nnnhCknTt2jUtWbJEU6dOVf369SXd+L3/559/tHDhQnPOhdzlbq9Pmd1VkDHrqVOnTmrTpo2kGzPqk5OTlZaWpm3btumvv/7SN998o9KlS0uSxo4dqyVLlighISHT19nLly/f9bXzxIkTcnNzU/HixfXQQw/poYceUtGiRfXQQw9Z8RlzHBSf8pBHHnnE/LOnp6ckWdy/7eHhYZ5SePDgQT355JMW+5cvX14+Pj7666+/zMWnMmXKWPTx8fHR9evXrTJ+SDExMTp+/LiqV69+S9uRI0coPt2jSpUqyd/fX1u3btUTTzyhU6dOafLkyWrTpo3OnTunn376SRUqVNCFCxdkMpn0zDPPWOyfmpqqlJQUSTf+Zpo0aWLRXr16dYpPOaRUqVIWj/Pnz29+7mFbN7+3AHnZ3XKi271H1K5d29yWUXy6W4yYmBjt27fPYqZ0xqznI0eOqESJEsqXL58mTJigV199VZUqVVKvXr1uO9YKFSqoadOm6tmzp4oUKaInn3xS9evXV6NGjW7bPyYmRgkJCQoODrbYnpKSoiNHjpgflyxZUq6urrcdPxxf+/bt9cMPP+jTTz/VsWPHdPjwYZ06dUqPPvpojh3j5veUw4cPKyUlRW+99ZYMhn9v2ElLS1NqaqqSk5NZxywXutvr065du+66b3x8vM6dO6dq1apZbH/ttdck3fggP3/+/ObCkyT5+/vr7bfflpT56+zTTz9919fO5s2ba926dXr22WdVtmxZPfHEE3r22WcpPv0/ik95SMYnVTe7+YX8Zne6hctkMlkkFW5ublneF9lz8xpaRqNRzZo1M898ullWZk7hVjffelelShVVrVpV/v7+ioqK0s8//6wGDRrIaDQqX758Wr9+/S373/w3YDQaLdpu/lvB/XF2dr5l291ea26+PRjWReIP3HC3nOh2r1cZ7xk352d3i5Fxa0jLli1v6XPzIs9//PGHXFxcdPToUZ0+fVoPP/zwbcc7ZcoU9enTR7/88ov+97//adCgQapZs6aWLl1627GWLl1aH3zwwS1tXl5edx0/cgej0agePXro0KFDatq0qV544QVVqlTJfJdEdtzuvfrm95SM3/3p06fftsDF71vudafXp4zZoze7+fcos9z7dv8evllWXmfv9tpZsGBBbdy4Ubt379bWrVu1ZcsWLVu2TG+88YZef/31rJx6rsaaT7itwMDAWyrLBw4c0JUrV275VA45x9XV1WLx8CtXrujChQvmx4899pgOHz6sRx55xPxfWlqaJkyYoDNnzthiyA4vNDRU27Zt07Zt2xQSEiJJCgkJ0Q8//KCoqCg1aNBA5cqV05UrV3T9+nWL5/7DDz/U999/L+nGpzS//fabRWy+TvrByEg0bv7buXkRXACwtcDAwFveI6KjoyXdOtvpTh577DEdPXrU4n0oNjZWkyZN0tWrVyXdyNVmzJihiIgIVapUSYMHD77lgxFJ2rt3r8aPH69HH31UnTt31vz58zV+/Hht377dIu/IUK5cOZ0+fVo+Pj7mYz/00EOaMmWKeU0q5G779+/XL7/8ohkzZmjgwIFq3ry5SpYsqRMnTmTpg+fsvFc/+uijcnFx0enTpy1+73/++WctXLjwjh+iw7Hd7fUp45rf6ffIx8dHRYsWvSUH79u3ryZMmKCyZcsqISHBYoHxixcv6vHHH9eePXsyfZ3N7LXzs88+08cff6yaNWuqb9++WrNmjdq0aaOvvvrKuk+ag+AvFrfVpUsX/fXXXxozZoyOHDmiqKgoDRw4UBUrVjT/Ax05w9vbW//8849iY2MVFBSkr776Sr/99psOHz6sYcOGWcz46Nq1q2JiYhQREaEjR45o9+7deuutt3Ts2LFbbktC1oSEhCglJUWbN2+2KD59/fXXKlKkiCpWrKi6deuqQoUK6t+/v7Zv367jx49rwoQJWr9+vfkfDd27d9e3336rBQsW6NixY/roo48svnEI9+bmv4vMBAUFycnJSbNmzdKpU6f09ddfsxg5ALsSHh6uzZs3a86cOTp69Kh+/PFHjRkzRs8880yWi0+vvfaaNm3apNmzZ+vo0aPatm2bhg4dqsuXL6tIkSJKTU3V4MGDVbt2bbVu3Vpjx45VTEyMPvzww1ti5cuXTytXrtTkyZN1/PhxHTx4UF999ZVKlSolPz8/STdmNB05ckTx8fFq3ry58ufPr759+2rv3r06cuSIhgwZol9++eWevlnU29tbx44d0/nz57O8D+xD4cKF5eLioq+//lonT57U77//rn79+uncuXMW3wR2J9l5r/bx8dErr7yiGTNmaOPGjTp58qQ++eQTTZ48WUWLFs2pU4OdudvrU/ny5eXl5aX58+frxIkT+vXXX7V48WKL/bt3766lS5dq48aNOnHihJYtW6bvv/9eDRo0UEhIiCpXrqy3335b+/bt06FDh/T222+rYMGCqlSpUqavs5m9dqakpOi9997Thg0bdOrUKUVHR2vnzp23XTIlL6L4hNuqVq2aFixYoD/++EMvvvii+vXrp+rVq2vx4sXcSpTDXnnlFR08eFDNmzfXgAEDVLFiRXXp0kWdO3dWUFCQatSoYe4bFBSkBQsWaP/+/WrZsqV69eql0qVLa8mSJUw9ziY3Nzc98cQTMhgMCgoKknSj+GQ0Gs0L9Ds7O2vRokWqXLmy+vXrp+bNm2vnzp2aPXu2uWBVv359TZkyRevWrVOzZs20efNmde3a1Van5fBu/rvI7Ba6hx9+WBEREfr222/1/PPPa/Xq1Ro8ePADGikAZO7ZZ5/V1KlT9fXXX6tZs2Z699131aRJE/M3wWXFc889p2nTpum7775Ts2bNNGjQID311FOaPXu2pBvfanzq1CmNHTtW0o218vr27atZs2Zp//79FrHKlCmjWbNmafv27XrxxRfVrl07OTs768MPPzTPLOjatauWL1+uoUOHysfHR8uXL5efn5+6deum1q1b6+zZs1q0aNE9zYjv2LGjfvrpJ94fHZC/v78mTpyoH374QS+88ILefPNN+fv7q3Pnzvrjjz8y3T+779VDhw5Vp06dNGPGDD3//POaN2+e+vbtqz59+uTEacEO3e31ycfHR5MnT1ZMTIxeeOEFzZgxw7xeU4awsDD17t1bM2bMUJMmTbR27VpNmzZNtWvXlsFg0Jw5cxQQEKAuXbqoXbt2cnd314IFC+Tq6prp62xmr51t2rTRG2+8oTlz5uj5559Xv3799NRTT2nEiBG2eCrtjpOJBXoAAAAAAABgJcx8AgAAAAAAgNVQfAIAAAAAAIDVUHwCAAAAAACA1VB8AgAAAAAAgNVQfAIAAAAAAIDVUHwCAAAAAACA1VB8AuAQTCaTrYdwV9Yan72fNwAAgKMhvwIePBdbDwCAY+rYsaMk6aOPPrL6sebMmSM3NzeFh4dn2vf333/XsmXLtHPnTl28eFFFixZVSEiIunfvrocffjjHx5aYmKixY8eqTZs2Cg4OzrG4qampioyMVOXKldW8efM79gsNDdU///xjfmwwGOTt7a3HHntML7/8sl588cUcGxMAAPhXx44dtWPHDottrq6uKly4sJ555hn169dP+fPnt9HobnjQeZE1/PXXX1q4cKG2b9+uixcvqnDhwqpRo4ZeffVVVatW7Z7jHTp0SCNHjtSqVausMFoAd8LMJwB2b8aMGUpKSsq034oVK/TKK6/owoULeuutt/Thhx+qe/fu2rFjh1q3bq0DBw7k+Nj279+vjRs3ymg05mjcuLg4LV26VGlpaZn2ffrpp7V69WqtXr1aH330kSZMmKBixYrp7bff1oQJE3J0XAAA4F8VK1Y0vwevXr1aixcvVufOnbVu3Tr16NHDpjNsbJEX5bSNGzeqVatWOnbsmPr3769FixZpwIABOn/+vNq1a6fFixffc8xvvvlGu3fvtsJoAdwNM58A5Aq7du3SuHHj1KFDBw0fPty8/fHHH1fDhg314osvatiwYVq/fr0NR2kdBQsWVFBQkMW2Ro0aqUiRIlqyZIkaN26smjVr2mZwAADkYvny5bvlPTg4OFhXr17VzJkztXfv3lvaH4TckBfFxMRo+PDhatGihcaMGSOD4d95E82bN9e4ceP03nvvKTAwUE888YQNRwogK5j5BMCq1q5dq5deeklBQUGqWrWqWrRooa+//trcbjQaNW3aNIWGhqpy5coKDQ3VlClTdP36dUlSYGCgJGn27Nnmn29n4cKF8vHx0YABA25pK1iwoIYMGaIGDRro2rVrkqT09HStWLFCzZo1U9WqVVW/fn1FRkYqJSXFvN+QIUPMn14+++yzqly5slq0aKFffvlFkhQVFaVOnTpJkjp16mS+FTE9PV3z589X06ZNVbVqVQUFBemVV17R9u3bLca1Z88ede3aVTVq1FCdOnU0YMAAnT17VqdOnVKDBg0kSUOHDlVoaOi9Pen/7/XXX5e7u7vFtPKLFy8qIiJCzzzzjCpXrqzatWurT58+OnXqlKQbn5IGBgbq6NGjFrE2btyoChUq6MyZM9kaCwAAeUnlypUlSadPn5Z04xa9gQMHqm/fvgoKClKXLl0kSSkpKZo0aZKefvppVa5cWc2aNdNXX31ljjNy5Eg9+eSTSk9Pt4g/btw4Pf744+Z86b/uNS9KTk7WlClT1LhxY1WuXFk1atRQly5dtH//fvN+Q4YMUbdu3bR69Wo1bNhQVatW1SuvvKKjR4/qxx9/VLNmzVStWjW1adPmlv06duyoTz75RM8884yqV6+uV199NdOZV3PnzpWXl5dGjBhhUXjKMGjQIBUrVkzvv/++eVtgYKBmzZpl0W/WrFnmHHLWrFmaPXv2LX1TU1M1ffp0NWjQQFWrVlXTpk316aefWsT56quv9NJLL6l69ep68skn9c477yghIcHiOM8995y+/fZbNW3aVFWqVFGLFi20e/du7dmzR23atDHH3rZtm0XsgwcPqkePHqpRo4Zq1KihPn366OTJk3d9fgBHQ/EJgNWsWLFC77zzjho2bKh58+YpMjJSbm5uGjhwoGJjYyVJH374oT7++GP16dNHixYtUrt27bRw4UJ98MEHkqTVq1dLklq3bm3++b9MJpO2bNmikJAQeXp63rbPCy+8oD59+sjLy0uS9M4772jChAlq2LChPvjgA3Xo0EHLly9X7969LabI//HHH1q4cKH69u2r999/X87OznrjjTeUkJCgSpUq6Z133jHHe/fddyVJkZGRmjNnjl5++WUtWLBAY8aM0aVLl/Tmm2+abx+MiYlRWFiYOemMiIjQH3/8oW7duqlo0aLmxKhXr17mn++Vj4+Pqlatql27dpmfpx49emjr1q0aOHCgFi5cqNdff13btm0zj71Zs2Zyd3fXxo0bLWJt2LBBISEhKlasWLbGAgBAXpLxIc7N6yp9/fXX8vb21gcffKDw8HCZTCb16dNHq1atUpcuXfTBBx+oevXq6t+/vzZs2CBJatGihc6fP6+oqChzHKPRqK+//lpNmjSRq6vrLcfOTl40ePBgrVu3Tt27d9eiRYs0dOhQHTp0SG+99ZZFXrR7924tX75cQ4YM0YQJE3TkyBF1795dEyZMUI8ePTR16lSdOXNGAwcOtDje/v37NW3aNL3++uuaPHmy4uPjFRYWpri4uNuOz2g0auvWrXc9Bzc3NzVs2FC7du1SfHz8bfv8V5s2bdS6dWtJN3LMNm3aSJIGDhyoxYsXq02bNpo3b56eeuopDRkyRF988YWkG+uPDhgwQEFBQZo5c6b69OmjTZs2qWPHjkpOTjbHj42N1cSJE9WzZ0/NmDFDiYmJ6tu3rwYMGKA2bdro/fffl8lkUv/+/c37HT161Hx75Hvvvadx48bp5MmTateunS5cuJCl8wIcAbfdAbCakydPqlu3burdu7d5W/HixfXSSy9p165datKkiXbs2KHKlSurVatWkqTatWvL09NTPj4+kmSeqh4QEHDHaevx8fFKSUlRiRIlsjSuw4cP65NPPtFbb72l7t27S5KefPJJFS1aVIMHD9Yvv/yip59+WpJ0+fJlrV+/XiVLlpQkeXl5KSwsTNu3b9ezzz6rsmXLSpLKli1r/jkuLk79+/c3z4SSJHd3d73xxhv666+/FBQUpLlz56pAgQJatGiR3N3dJUlFixbVW2+9pSNHjqhChQqSpJIlS6pixYpZOq/bKVy4sPbt22cel6enp95++23VqlVL0o3p9ydOnDAX9nx9fdWoUSN99tlnevPNN+Xk5KTY2Fht375dkydPzvY4AADIjUwmk8X6jAkJCdqxY4e5kJQxA0q6sRh5RESE3NzcJElbt27Vr7/+qmnTpumFF16QJNWtW1dJSUmKjIxU06ZNVbNmTRUvXlxffPGF+dayqKgonTt3Ti1atLjtmO41L0pNTdXVq1c1YsQI8zhq166tK1euaOLEiTp//ryKFCkiSbp69aqmT5+uMmXKSJJ27NihVatWacmSJQoJCZEkHT9+XO+9954SExPl6+sr6UY+NXfuXHP+UbVqVTVs2FDLli27pVAlSZcuXdKVK1dUvHjxu479kUcekclk0pkzZ+Tn55fpuQYEBCggIEDSvznmwYMHtWnTJg0bNkyvvvqqJCkkJET//POPoqKiVLduXX3wwQdq27at+UNHSSpXrpw6dOigdevWqUOHDpKkpKQkvfvuu6pXr56kGznnlClTNG7cOHPR69q1a+rbt6+OHj2qChUqaPbs2fL09NSSJUuUL18+8/EbNmyoBQsW6O233870vABHQPEJgNUMGTJE0o1vhPv77791/Phx8yd3qampkm4UP6ZMmaL27dsrNDRU9evXV1hY2D0dx9nZWZJumZJ+JxnfTNOkSROL7U2aNNHQoUMVFRVlLj4VLFjQXHiSZE5Y7rYA+pQpUyTduMUt47x//PFHSf+e965du/T000+bC0+SVL16df3www+SZL4N7n6ZTCY5OTlJkvz9/bVs2TKZTCadOnVKx48f199//63ffvvNPC7pxiyzL774QtHR0QoODtaGDRvk7e2tRo0a5ciYAADILXbu3KlKlSpZbDMYDHriiSc0evRo83uwJD366KPmwpMkbdu2TU5OTnr66actClihoaH67LPPdOjQIVWoUEHNmzfXypUrNWrUKLm5uenLL79UqVKl7vhNb/eaF7m5uWnhwoWSpLNnz+ro0aM6duzYLbmLJOXPn99ceJJufMglyWIsBQoUkCSL4lOJEiXMhSfpxgdu1atX186dO+86ttvN7LpZxrnez8LuGTPEGzdubLE945a8n3/+WampqWratKlFe61atVS8eHHt2LHDXHySpBo1aph/zuz5kaTt27erdu3a8vDwMP8e5MuXT7Vq1dL//ve/bJ8XYG8oPgGwmhMnTuidd97Rtm3b5OrqqkcffVTly5eX9G+SEB4eLm9vb61bt06RkZGaPHmyHnvsMY0YMUJ16tTJ0nHy588vb29v87oKt3Pt2jVdv35d+fPnN9+fn/EpXgYXFxf5+fnp8uXL5m3/neqdkUTe7dvtfv/9d0VEROj333+Xp6enypYtq4ceesjivC9duqRChQpl6fzux9mzZ80FM0n67LPPzFPiCxQooAoVKsjDw8Ninzp16qhEiRLasGGDufj0wgsvWBTKAACAVKlSJUVEREi6kSO4u7urWLFi5hksN/P29rZ4fOnSJZlMJotixc3i4uJUoUIFtWjRQh988IF+/fVX1a1bV5s3bzbP0Lmde82LJOnXX3/V+PHj9ffff8vb21vly5c335J3c2Hnduclydz3Tvz9/W/ZVqhQIf3555+37e/n5ycvL69MP4zLWBfpfpYFuHTpknk8t5ORN2YUkm5WuHBhi7xRuv1zdKdbBzOO/9VXX1ms9ZWhYMGCd9wPcDQUnwBYhdFoVPfu3eXq6qpPPvlEFSpUkIuLiw4fPmyxnpDBYFCHDh3UoUMHXbhwQT///LPmzp2rN954Q1u3brX4hPBunnrqKUVFRSklJeW2RZI1a9bovffe0yeffGJOtM6dO2cxnfv69euKj4/P0rTtO7ly5YrCw8MVGBioL7/8Uo8++qgMBoN+/vlnbdq0ydzPx8dHFy9evGX/n3/+2XzL3f1KSEjQn3/+aZ6WHx0drbffflsdO3ZUt27dzIngpEmTzJ/6STeS55YtW+qjjz5Su3btdPToUb333ns5MiYAAHITb29vValSJVv7+vj4yMvLS8uWLbtt+yOPPCJJKl26tKpWraqvv/5aBoNBiYmJat68+V1j30te5OPjoz59+pjX6Hz44Yfl5OSkFStW6Ndff83Wuf3X7dZkOn/+/B0LPk5OTnrmmWf066+/6urVq7cU7qQbM7u+++471ahRw6JI898ZXxmLqt9JxuysixcvWnxgd+TIEV26dMmcN54/f16PPvqoxb7nzp2zWNcrO3x8fPTEE0+YF6G/mYsL/1xH7sGC4wCsIj4+XkePHlXr1q1VpUoV85tnxjfFZcwceuWVVzR27FhJNz5xeumll9ShQwclJibqypUrknTbbzj5r65du+rSpUuaPn36LW3nzp3TokWLVLZsWVWqVEm1a9eWJH355ZcW/b788kulp6erZs2aWT7PjOneGf7++29dunRJnTp1UtmyZc1j/+9516pVS1u3brWYyh4TE6Pu3bvrzz//vCVudsydO1fXr1/Xyy+/LOnGIqH/196dx0VdJ34cf89wI4egImZ5hImyHnigUh5kplbWZpRd6qppKpRpmZlaeYR54BESnpiaklma3dplXqvmVbqLpplXa0gKgiKHzMzvD3/M7qS74si3AX09Hw8eOd/jM5+B1d/8Xny/n7FarXr22Wft4clisdgv6f7Pq7keeugh5ebmavLkyQoLC/uvl/YDAADntGrVSufPn5fNZlPjxo3tXwcOHNBbb73lcCveX//6V23cuFGfffaZmjdvfsXgcTXvi/7xj3+osLBQTz/9tGrVqmW/yrskPF3LLW0ljhw5okOHDtkfnzx5Urt377avE3U5AwcOVH5+vl599dXL3kI4ffp0HT16VIMGDbJv8/Pz08mTJx2O27Vrl8PjP76vLHnfV7L0QYnExEQlJCSoadOm8vT0tC8+XmLHjh06ceLEf71yrbRatWqln3/+WQ0bNrT/b6BRo0ZatGiRvvrqq2saGyhPSKkAnJaRkaFFixZdsr1+/fq6/fbbVbNmTS1btkyhoaEKCAjQxo0b7b/dK1kzKSoqSgsXLlTVqlXVrFkznTx5Um+//bZatWpl/y1WQECAdu3ape3bt6tly5YO6yeUiIyM1HPPPaeZM2fq0KFDevDBBxUUFKSDBw8qNTVVhYWF9jdg9erVU/fu3ZWUlKT8/HxFRUVp3759Sk5OVuvWrdWuXbtSfw9KFkb/7rvvFBgYqLp168rPz09z5syRu7u73N3dtXbtWn3wwQcOrzsuLk6PPvqoBg4cqN69e6ugoEAzZ85UkyZNdMcdd9ij1JYtW64Yf7KysvTDDz9IuhiTTp8+rbVr1+rTTz/VoEGD7L+RbdKkiSRp/Pjxio2NVU5OjpYtW2b/qOPz58/bLxW/6aabdPvtt2vTpk2XXQgUAABcmw4dOigqKkpxcXGKi4tTWFiY9uzZo6SkJLVr187hap57771XkyZN0ueff27/hNr/5WreF/3lL3+Ru7u7pk6dqn79+qmoqEirVq3Sd999J+nKVw6Vhs1m06BBgzRs2DC5ubkpOTlZgYGBDh/O8kfh4eGaNGmSXn75ZT3++ON64okndPPNNyszM1OrVq2yf3pvyTqdkhQTE6PPPvtMTZs2Ve3atbVq1SodPXrUYdySK50+/fRTNW3aVA0aNFDXrl01depUFRQUqGHDhtqwYYPWrVun5ORkVa5cWU8//bTeeusteXh46M4779Svv/6qN9980/6e8lrExcXpscce08CBA/X444/Ly8tL7733nr7++mslJSVd09hAeUJ8AuC0Y8eO6Y033rhk+8MPP6zbb79dKSkpSkhI0MiRI+Xp6al69epp9uzZmjhxonbs2KFevXrpueeek6enp1auXKm33npL/v7+6tixo1544QX7eIMGDVJKSooGDBigzz//3L5+0h8NHjxYERERWrZsmSZOnKicnBzVqFFDMTExGjRokMN6AAkJCapdu7ZWrlyp+fPnKyQkRL1791ZcXFyprrQqcdttt6lbt272S9M//fRTpaSkaMqUKXruuedUqVIlNWzYUEuXLtWAAQO0Y8cOdezYUREREXrnnXc0bdo0DR06VH5+furQoYOGDx8uT09PeXp6qm/fvnrvvfe0fv16bd68+b8uurl+/XqtX79e0sXL1AMCAhQREaGkpCR16dLFflzr1q316quv6u2339aaNWtUtWpVtW7dWsnJyYqPj7cvgl4iJiZGW7Zs+a+fpgMAAJxnNps1b948vfnmm5o7d65Onz6t6tWrq2/fvoqPj3c4Njg4WG3bttXmzZvVtWvXUo1f2vdFtWvX1rRp05ScnKzBgwcrMDBQkZGReuedd9SrVy/t2LFD4eHh1/Rab7rpJvXr108TJ05Ufn6+br/9ds2ePdu++PZ/c9999yk8PFyLFi1SUlKSfv/9dwUHB6tly5Z69913L/kk5JdfflnFxcWaPHmy3N3dde+99+qFF17QmDFj7Md07txZH330kUaOHKmHH35YY8eO1dSpU5WcnKzFixcrOztbYWFhSkpKUqdOnSRJzz77rKpWraqlS5fqvffeU+XKldW1a1cNHTr0iutdXUmDBg20bNkyzZgxQyNGjJDNZlP9+vX11ltv6a677rqmsYHyxGQri+soAQDXnf79+8vLy0tvvfWWq6cCAAAqqJEjR+r777+/5LY2ADcWrnwCADh46623dPjwYW3atElpaWmung4AAACACo74BABw8O233+rYsWMaMWLENS+iCQAAAADcdgcAAAAAAADDlH5VXQAAAAAAAOAqEZ8AAAAAAABgGOITAAAAAAAADEN8AgAAAAAAgGG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      "text/plain": [
       "<Figure size 1200x3000 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_plots = len(categorical_vars)\n",
    "num_rows = (num_plots + 1) // 2\n",
    "\n",
    "fig, axes = plt.subplots(num_rows, 2, figsize=(12, 5 * num_rows))\n",
    "\n",
    "for i, feature in enumerate(categorical_vars):\n",
    "    \n",
    "    data = df.copy()\n",
    "\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    ax = axes[row, col]\n",
    "\n",
    "    sns.countplot(data=data, x=feature, hue='subscribed', ax=ax, palette='Blues_r')\n",
    "    ax.set_xlabel(feature.replace('_', ' ').title().replace('Con', 'Contact'))\n",
    "    ax.set_ylabel('Count')\n",
    "\n",
    "    if feature in ['job', 'education']:\n",
    "        ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "# Remove any empty subplots\n",
    "if num_plots < num_rows * 2:\n",
    "    fig.delaxes(axes[num_rows-1, 1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3b8666f8",
   "metadata": {},
   "source": [
    "For almost all values, customers who haven't subscribed seems to dominate, which shows that there's a huge imbalance in the dataset leaning towards `no`. This we would address after splitting and scaling has been done...\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "91b4a533",
   "metadata": {},
   "source": [
    "From the plots, we can observe that `last_con_day` column doesn't seem to produce variable results on different days... So, it wouldn't contribute in anyway to the model... Let's drop it...\n",
    "\n",
    "Also we can observe that defaulted has no values contributing to `subscribed - 'yes'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e01da0ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = 'last_con_day', inplace=True)\n",
    "nominal_cols.remove('last_con_day')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0f8e982f",
   "metadata": {},
   "source": [
    "#### Handling Unknowns and Encoding Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f3691c5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace('unknown', np.nan, inplace=True)\n",
    "df.replace('nonexistent', np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a4cc55be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                      0\n",
       "job                    330\n",
       "marital_status          80\n",
       "education             1730\n",
       "defaulted             8596\n",
       "house_loan             990\n",
       "personal_loan          990\n",
       "con_medium               0\n",
       "last_con_month           0\n",
       "last_con_dur             0\n",
       "prev_camp_outcome    35551\n",
       "emp_var_rate             0\n",
       "cpi                      0\n",
       "cci                      0\n",
       "euribor_3m               0\n",
       "num_employed             0\n",
       "subscribed               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e561e1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                   0.00\n",
       "job                   0.80\n",
       "marital_status        0.19\n",
       "education             4.20\n",
       "defaulted            20.88\n",
       "house_loan            2.40\n",
       "personal_loan         2.40\n",
       "con_medium            0.00\n",
       "last_con_month        0.00\n",
       "last_con_dur          0.00\n",
       "prev_camp_outcome    86.34\n",
       "emp_var_rate          0.00\n",
       "cpi                   0.00\n",
       "cci                   0.00\n",
       "euribor_3m            0.00\n",
       "num_employed          0.00\n",
       "subscribed            0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df.isnull().sum() / len(df) * 100, 2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "72eec4db",
   "metadata": {},
   "source": [
    "`prev_camp_outcome` has more than 86% null values... So, its better if we drop this column altogether"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c99ebf5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns='prev_camp_outcome', inplace=True)\n",
    "nominal_cols.remove('prev_camp_outcome')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "31a9106d",
   "metadata": {},
   "source": [
    "Let's first encode values except for NaN and then impute values for NaN..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7f569041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['job',\n",
       " 'marital_status',\n",
       " 'education',\n",
       " 'defaulted',\n",
       " 'house_loan',\n",
       " 'personal_loan',\n",
       " 'con_medium',\n",
       " 'last_con_month',\n",
       " 'last_con_day',\n",
       " 'prev_camp_outcome',\n",
       " 'subscribed']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_vars"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c6b94643",
   "metadata": {},
   "source": [
    "Let's first encode the target variable..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7a67a208",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['subscribed'].replace({'no': 0, 'yes': 1}, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a2a74c32",
   "metadata": {},
   "source": [
    "`education` column is ordinal... So, we should define the order based on some reasoning and then proceed with encoding..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "15cff2ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscribed  education          \n",
       "0           university.degree      10495\n",
       "            high.school             8481\n",
       "            basic.9y                5572\n",
       "            professional.course     4645\n",
       "            basic.4y                3748\n",
       "            basic.6y                2103\n",
       "            illiterate                14\n",
       "1           university.degree       1669\n",
       "            high.school             1031\n",
       "            professional.course      595\n",
       "            basic.9y                 473\n",
       "            basic.4y                 428\n",
       "            basic.6y                 188\n",
       "            illiterate                 4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('subscribed')['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "da19d777",
   "metadata": {},
   "outputs": [],
   "source": [
    "education_order = {\n",
    "    'university.degree': 5,\n",
    "    'high.school': 4,\n",
    "    'professional.course': 3,\n",
    "    'basic.9y': 2,\n",
    "    'basic.4y': 1,\n",
    "    'basic.6y': 1,\n",
    "    'illiterate': 0\n",
    "}\n",
    "\n",
    "df['education'] = df['education'].map(education_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3c286f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['job',\n",
       " 'marital_status',\n",
       " 'defaulted',\n",
       " 'house_loan',\n",
       " 'personal_loan',\n",
       " 'con_medium',\n",
       " 'last_con_month']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nominal_cols"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7fd54805",
   "metadata": {},
   "source": [
    "Now, let's first encode boolean cols which has `yes`, `no` values in them..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dbee9175",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'defaulted' column has got encoded\n",
      "'house_loan' column has got encoded\n",
      "'personal_loan' column has got encoded\n"
     ]
    }
   ],
   "source": [
    "encoded_cols = []\n",
    "for col in nominal_cols:\n",
    "    unique_values = df[col].unique().tolist()\n",
    "    if \"yes\" in unique_values and \"no\" in unique_values:\n",
    "        df[col] = df[col].replace({\"no\": 0, \"yes\": 1})\n",
    "        encoded_cols.append(col)\n",
    "        print(f\"'{col}' column has got encoded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "baa1d63e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['job', 'marital_status', 'con_medium', 'last_con_month']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nominal_cols = [col for col in nominal_cols if col not in encoded_cols]\n",
    "nominal_cols"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1baea3f8",
   "metadata": {},
   "source": [
    "Let's do one-hot-encoding now for all these cols..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0144b28c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job               11\n",
       "marital_status     3\n",
       "con_medium         2\n",
       "last_con_month    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[nominal_cols].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b7cc157b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40775, 16)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(subset=['job', 'marital_status'], axis=0, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "14dbc61d",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_df = pd.get_dummies(df, columns=['job', 'marital_status', 'con_medium', 'last_con_month'], drop_first=True, dtype='int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "70f88e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 40775 entries, 0 to 41187\n",
      "Data columns (total 34 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   age                     40775 non-null  int64  \n",
      " 1   education               39180 non-null  float64\n",
      " 2   defaulted               32340 non-null  float64\n",
      " 3   house_loan              39791 non-null  float64\n",
      " 4   personal_loan           39791 non-null  float64\n",
      " 5   last_con_dur            40775 non-null  int64  \n",
      " 6   emp_var_rate            40775 non-null  float64\n",
      " 7   cpi                     40775 non-null  float64\n",
      " 8   cci                     40775 non-null  float64\n",
      " 9   euribor_3m              40775 non-null  float64\n",
      " 10  num_employed            40775 non-null  float64\n",
      " 11  subscribed              40775 non-null  int64  \n",
      " 12  job_blue-collar         40775 non-null  int32  \n",
      " 13  job_entrepreneur        40775 non-null  int32  \n",
      " 14  job_housemaid           40775 non-null  int32  \n",
      " 15  job_management          40775 non-null  int32  \n",
      " 16  job_retired             40775 non-null  int32  \n",
      " 17  job_self-employed       40775 non-null  int32  \n",
      " 18  job_services            40775 non-null  int32  \n",
      " 19  job_student             40775 non-null  int32  \n",
      " 20  job_technician          40775 non-null  int32  \n",
      " 21  job_unemployed          40775 non-null  int32  \n",
      " 22  marital_status_married  40775 non-null  int32  \n",
      " 23  marital_status_single   40775 non-null  int32  \n",
      " 24  con_medium_telephone    40775 non-null  int32  \n",
      " 25  last_con_month_aug      40775 non-null  int32  \n",
      " 26  last_con_month_dec      40775 non-null  int32  \n",
      " 27  last_con_month_jul      40775 non-null  int32  \n",
      " 28  last_con_month_jun      40775 non-null  int32  \n",
      " 29  last_con_month_mar      40775 non-null  int32  \n",
      " 30  last_con_month_may      40775 non-null  int32  \n",
      " 31  last_con_month_nov      40775 non-null  int32  \n",
      " 32  last_con_month_oct      40775 non-null  int32  \n",
      " 33  last_con_month_sep      40775 non-null  int32  \n",
      "dtypes: float64(9), int32(22), int64(3)\n",
      "memory usage: 7.5 MB\n"
     ]
    }
   ],
   "source": [
    "encoded_df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7ddfaab1",
   "metadata": {},
   "source": [
    "Everything seems to have got converted into numeric values... Now, let's impute values for NaN..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7d64be2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def null_cols(df):\n",
    "    missing = df.columns[df.isnull().sum() != 0]\n",
    "    return missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "df0b5b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['education', 'defaulted', 'house_loan', 'personal_loan'], dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_impute = null_cols(encoded_df)\n",
    "cols_to_impute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cc2a053e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "imputer = KNNImputer(n_neighbors=5)\n",
    "encoded_df[cols_to_impute] = imputer.fit_transform(encoded_df[cols_to_impute])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b7710308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_cols(encoded_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c6ab1466",
   "metadata": {},
   "source": [
    "Now we have successfully handled null values..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "93609c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>defaulted</th>\n",
       "      <th>house_loan</th>\n",
       "      <th>personal_loan</th>\n",
       "      <th>last_con_dur</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cpi</th>\n",
       "      <th>cci</th>\n",
       "      <th>euribor_3m</th>\n",
       "      <th>num_employed</th>\n",
       "      <th>subscribed</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>job_housemaid</th>\n",
       "      <th>job_management</th>\n",
       "      <th>job_retired</th>\n",
       "      <th>job_self-employed</th>\n",
       "      <th>job_services</th>\n",
       "      <th>job_student</th>\n",
       "      <th>job_technician</th>\n",
       "      <th>job_unemployed</th>\n",
       "      <th>marital_status_married</th>\n",
       "      <th>marital_status_single</th>\n",
       "      <th>con_medium_telephone</th>\n",
       "      <th>last_con_month_aug</th>\n",
       "      <th>last_con_month_dec</th>\n",
       "      <th>last_con_month_jul</th>\n",
       "      <th>last_con_month_jun</th>\n",
       "      <th>last_con_month_mar</th>\n",
       "      <th>last_con_month_may</th>\n",
       "      <th>last_con_month_nov</th>\n",
       "      <th>last_con_month_oct</th>\n",
       "      <th>last_con_month_sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>261</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>307</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education  defaulted  house_loan  personal_loan  last_con_dur  \\\n",
       "0   56        1.0        0.0         0.0            0.0           261   \n",
       "1   57        4.0        0.0         0.0            0.0           149   \n",
       "2   37        4.0        0.0         1.0            0.0           226   \n",
       "3   40        1.0        0.0         0.0            0.0           151   \n",
       "4   56        4.0        0.0         0.0            1.0           307   \n",
       "\n",
       "   emp_var_rate     cpi   cci  euribor_3m  num_employed  subscribed  \\\n",
       "0           1.1  93.994 -36.4       4.857        5191.0           0   \n",
       "1           1.1  93.994 -36.4       4.857        5191.0           0   \n",
       "2           1.1  93.994 -36.4       4.857        5191.0           0   \n",
       "3           1.1  93.994 -36.4       4.857        5191.0           0   \n",
       "4           1.1  93.994 -36.4       4.857        5191.0           0   \n",
       "\n",
       "   job_blue-collar  job_entrepreneur  job_housemaid  job_management  \\\n",
       "0                0                 0              1               0   \n",
       "1                0                 0              0               0   \n",
       "2                0                 0              0               0   \n",
       "3                0                 0              0               0   \n",
       "4                0                 0              0               0   \n",
       "\n",
       "   job_retired  job_self-employed  job_services  job_student  job_technician  \\\n",
       "0            0                  0             0            0               0   \n",
       "1            0                  0             1            0               0   \n",
       "2            0                  0             1            0               0   \n",
       "3            0                  0             0            0               0   \n",
       "4            0                  0             1            0               0   \n",
       "\n",
       "   job_unemployed  marital_status_married  marital_status_single  \\\n",
       "0               0                       1                      0   \n",
       "1               0                       1                      0   \n",
       "2               0                       1                      0   \n",
       "3               0                       1                      0   \n",
       "4               0                       1                      0   \n",
       "\n",
       "   con_medium_telephone  last_con_month_aug  last_con_month_dec  \\\n",
       "0                     1                   0                   0   \n",
       "1                     1                   0                   0   \n",
       "2                     1                   0                   0   \n",
       "3                     1                   0                   0   \n",
       "4                     1                   0                   0   \n",
       "\n",
       "   last_con_month_jul  last_con_month_jun  last_con_month_mar  \\\n",
       "0                   0                   0                   0   \n",
       "1                   0                   0                   0   \n",
       "2                   0                   0                   0   \n",
       "3                   0                   0                   0   \n",
       "4                   0                   0                   0   \n",
       "\n",
       "   last_con_month_may  last_con_month_nov  last_con_month_oct  \\\n",
       "0                   1                   0                   0   \n",
       "1                   1                   0                   0   \n",
       "2                   1                   0                   0   \n",
       "3                   1                   0                   0   \n",
       "4                   1                   0                   0   \n",
       "\n",
       "   last_con_month_sep  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1d15525c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'education', 'defaulted', 'house_loan', 'personal_loan',\n",
       "       'last_con_dur', 'emp_var_rate', 'cpi', 'cci', 'euribor_3m',\n",
       "       'num_employed', 'subscribed', 'job_blue-collar', 'job_entrepreneur',\n",
       "       'job_housemaid', 'job_management', 'job_retired', 'job_self-employed',\n",
       "       'job_services', 'job_student', 'job_technician', 'job_unemployed',\n",
       "       'marital_status_married', 'marital_status_single',\n",
       "       'con_medium_telephone', 'last_con_month_aug', 'last_con_month_dec',\n",
       "       'last_con_month_jul', 'last_con_month_jun', 'last_con_month_mar',\n",
       "       'last_con_month_may', 'last_con_month_nov', 'last_con_month_oct',\n",
       "       'last_con_month_sep'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "97cdb361",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7e306c43",
   "metadata": {},
   "source": [
    "#### Dependent, Independent and Train, Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "780b74ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = encoded_df.drop('subscribed', axis=1)\n",
    "y = encoded_df['subscribed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e0aa113e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=32)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=32)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "53c7b240",
   "metadata": {},
   "source": [
    "#### Checking for range abnormality and scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "92ced4b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_column_ranges(df):\n",
    "    ranges = {}\n",
    "    for column in df.columns:\n",
    "        column_min = df[column].min()\n",
    "        column_max = df[column].max()\n",
    "        column_range = column_max - column_min\n",
    "        ranges[column] = [column_min, column_max, column_range]\n",
    "    return ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4eebf319",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: age\n",
      "Minimum: 17, Maximum: 62, Range: 45\n",
      "\n",
      "Column: education\n",
      "Minimum: 0.0, Maximum: 5.0, Range: 5.0\n",
      "\n",
      "Column: defaulted\n",
      "Minimum: 0.0, Maximum: 1.0, Range: 1.0\n",
      "\n",
      "Column: house_loan\n",
      "Minimum: 0.0, Maximum: 1.0, Range: 1.0\n",
      "\n",
      "Column: personal_loan\n",
      "Minimum: 0.0, Maximum: 1.0, Range: 1.0\n",
      "\n",
      "Column: last_con_dur\n",
      "Minimum: 0, Maximum: 614, Range: 614\n",
      "\n",
      "Column: emp_var_rate\n",
      "Minimum: -3.4, Maximum: 1.4, Range: 4.8\n",
      "\n",
      "Column: cpi\n",
      "Minimum: 92.201, Maximum: 94.767, Range: 2.5660000000000025\n",
      "\n",
      "Column: cci\n",
      "Minimum: -50.8, Maximum: -30.1, Range: 20.699999999999996\n",
      "\n",
      "Column: euribor_3m\n",
      "Minimum: 0.634, Maximum: 5.045, Range: 4.411\n",
      "\n",
      "Column: num_employed\n",
      "Minimum: 4963.6, Maximum: 5228.1, Range: 264.5\n",
      "\n",
      "Column: subscribed\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: job_blue-collar\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: job_entrepreneur\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: job_housemaid\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: job_management\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: job_retired\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: job_self-employed\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: job_services\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: job_student\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: job_technician\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: job_unemployed\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: marital_status_married\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: marital_status_single\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: con_medium_telephone\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: last_con_month_aug\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: last_con_month_dec\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: last_con_month_jul\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: last_con_month_jun\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: last_con_month_mar\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: last_con_month_may\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: last_con_month_nov\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: last_con_month_oct\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n",
      "Column: last_con_month_sep\n",
      "Minimum: 0, Maximum: 1, Range: 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ranges = get_column_ranges(encoded_df)\n",
    "\n",
    "for column, values in ranges.items():\n",
    "    print(f\"Column: {column}\")\n",
    "    print(f\"Minimum: {values[0]}, Maximum: {values[1]}, Range: {values[2]}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0694c2de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the numerical columns\n",
    "cols_to_scale = [\"age\", \"education\", \"last_con_dur\", \"emp_var_rate\", \"cpi\", \"cci\", \"euribor_3m\", \"num_employed\"]\n",
    "\n",
    "X_train_scaled = X_train.copy()\n",
    "X_test_scaled = X_test.copy()\n",
    "X_val_scaled = X_val.copy()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scaled[cols_to_scale] = scaler.fit_transform(X_train_scaled[cols_to_scale])\n",
    "\n",
    "X_test_scaled[cols_to_scale] = scaler.transform(X_test_scaled[cols_to_scale])\n",
    "\n",
    "X_val_scaled[cols_to_scale] = scaler.transform(X_val_scaled[cols_to_scale])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b5d9dda2",
   "metadata": {},
   "source": [
    "#### Checking For Imbalance and Addressing if any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "02a0efca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train.value_counts().plot.pie(autopct = '%.2f')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a7d404b0",
   "metadata": {},
   "source": [
    "There seems to exist huge imbalance in train set target variable.... Let's address it by using SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "046fd8b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE()\n",
    "X_smote, y_smote = smote.fit_resample(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "806b52f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_smote.value_counts().plot.pie(autopct = '%.2f')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "56a88432",
   "metadata": {},
   "source": [
    "#### Model Performance Comparison"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c522eaeb",
   "metadata": {},
   "source": [
    "As in this case, we need both false positive as well as false negative to be minimal, we would see F1-score which is combined metric considering both FP and FN..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8596f0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Modelling Algorithm</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Validation Accuracy</th>\n",
       "      <th>F-1 Score</th>\n",
       "      <th>ROC AUC</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.894326</td>\n",
       "      <td>0.877223</td>\n",
       "      <td>0.596067</td>\n",
       "      <td>0.840666</td>\n",
       "      <td>1.912415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.889752</td>\n",
       "      <td>0.857756</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>0.867678</td>\n",
       "      <td>3.596049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.916656</td>\n",
       "      <td>0.874617</td>\n",
       "      <td>0.613422</td>\n",
       "      <td>0.873103</td>\n",
       "      <td>4.586135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.906451</td>\n",
       "      <td>0.857449</td>\n",
       "      <td>0.590308</td>\n",
       "      <td>0.875690</td>\n",
       "      <td>5.405864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.969838</td>\n",
       "      <td>0.897915</td>\n",
       "      <td>0.578481</td>\n",
       "      <td>0.774007</td>\n",
       "      <td>7.415959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.936807</td>\n",
       "      <td>0.845953</td>\n",
       "      <td>0.543389</td>\n",
       "      <td>0.827108</td>\n",
       "      <td>9.698210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.999763</td>\n",
       "      <td>0.891324</td>\n",
       "      <td>0.574174</td>\n",
       "      <td>0.782563</td>\n",
       "      <td>10.846408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.999763</td>\n",
       "      <td>0.874310</td>\n",
       "      <td>0.498778</td>\n",
       "      <td>0.732036</td>\n",
       "      <td>12.548222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Modelling Algorithm  Train Accuracy  Validation Accuracy  F-1 Score  \\\n",
       "3          AdaBoostClassifier        0.894326             0.877223   0.596067   \n",
       "0          LogisticRegression        0.889752             0.857756   0.585714   \n",
       "4  GradientBoostingClassifier        0.916656             0.874617   0.613422   \n",
       "6                         SVC        0.906451             0.857449   0.590308   \n",
       "7               XGBClassifier        0.969838             0.897915   0.578481   \n",
       "5        KNeighborsClassifier        0.936807             0.845953   0.543389   \n",
       "2      RandomForestClassifier        0.999763             0.891324   0.574174   \n",
       "1      DecisionTreeClassifier        0.999763             0.874310   0.498778   \n",
       "\n",
       "    ROC AUC  Difference  \n",
       "3  0.840666    1.912415  \n",
       "0  0.867678    3.596049  \n",
       "4  0.873103    4.586135  \n",
       "6  0.875690    5.405864  \n",
       "7  0.774007    7.415959  \n",
       "5  0.827108    9.698210  \n",
       "2  0.782563   10.846408  \n",
       "1  0.732036   12.548222  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = [LogisticRegression(), DecisionTreeClassifier(), RandomForestClassifier(), AdaBoostClassifier(), GradientBoostingClassifier(), KNeighborsClassifier(), SVC(), XGBClassifier()]\n",
    "model_names = ['LogisticRegression', 'DecisionTreeClassifier', 'RandomForestClassifier', 'AdaBoostClassifier', 'GradientBoostingClassifier', 'KNeighborsClassifier', 'SVC', 'XGBClassifier']\n",
    "\n",
    "accuracy_train = []\n",
    "accuracy_val = []\n",
    "f1_val = []\n",
    "roc_auc_val = []\n",
    "\n",
    "for model in models:\n",
    "    \n",
    "    mod = model\n",
    "    mod.fit(X_smote, y_smote)\n",
    "    y_pred_train = mod.predict(X_smote)\n",
    "    y_pred_val = mod.predict(X_val_scaled)\n",
    "    \n",
    "    accuracy_train.append(accuracy_score(y_smote, y_pred_train))\n",
    "    accuracy_val.append(accuracy_score(y_val, y_pred_val))\n",
    "    f1_val.append(f1_score(y_val, y_pred_val))\n",
    "    roc_auc_val.append(roc_auc_score(y_val, y_pred_val))\n",
    "\n",
    "data = {'Modelling Algorithm': model_names, 'Train Accuracy': accuracy_train, 'Validation Accuracy': accuracy_val, 'F-1 Score': f1_val, 'ROC AUC': roc_auc_val}\n",
    "\n",
    "data = pd.DataFrame(data)\n",
    "\n",
    "data['Difference'] = ((np.abs(data['Train Accuracy'] - data['Validation Accuracy'])) * 100) / (data['Train Accuracy'])\n",
    "\n",
    "data.sort_values(by='Difference')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "afb493c2",
   "metadata": {},
   "source": [
    "As we can observe above, Grad Boost classifier seems not to be over optimistic and performing better when you notice all the metrics... So, Lets, choose it and tune and see whether we can improve it furthermore..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "90b69bb0",
   "metadata": {},
   "source": [
    "#### Hypeparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "627dd0ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'learning_rate': [0.1, 0.01, 0.001],\n",
    "    'max_depth': [3, 4, 5]\n",
    "}\n",
    "\n",
    "gb_classifier = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "grid_search = GridSearchCV(gb_classifier, param_grid, cv=5)\n",
    "grid_search.fit(X_smote, y_smote)\n",
    "\n",
    "best_params = grid_search.best_params_\n",
    "best_params"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9afac2f0",
   "metadata": {},
   "source": [
    "Took more than a hour to complete around 135 fits... Now let's use the best params to train the model with..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a196d0cf",
   "metadata": {},
   "source": [
    "#### Model training and accuracy check with validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1ef5f7a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(max_depth=5, n_estimators=200, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(max_depth=5, n_estimators=200, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(max_depth=5, n_estimators=200, random_state=42)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_classifier_best = GradientBoostingClassifier(random_state=42, **best_params)\n",
    "\n",
    "gb_classifier_best.fit(X_smote, y_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e15831e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = gb_classifier_best.predict(X_smote)\n",
    "y_pred_val = gb_classifier_best.predict(X_val_scaled)\n",
    "\n",
    "train_accuracy = accuracy_score(y_smote, y_pred_train)\n",
    "val_accuracy = accuracy_score(y_val, y_pred_val)\n",
    "f1 = f1_score(y_val, y_pred_val)\n",
    "roc_auc = roc_auc_score(y_val, y_pred_val)\n",
    "\n",
    "difference = ((np.abs(train_accuracy - val_accuracy) * 100) / (train_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8c58dce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9538727076591155,\n",
       " 0.8948497854077253,\n",
       " 0.6128668171557562,\n",
       " 0.8225537731499493,\n",
       " 6.18771475244715)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_accuracy, val_accuracy, f1, roc_auc, difference)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c2c4e052",
   "metadata": {},
   "source": [
    "As we can observe, here we got much better result with a dataset which is heavily imbalanced with not much overfitting or underfitting conditions..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3c3b0655",
   "metadata": {},
   "source": [
    "Let's look at few more metrics to evaluate the model now with the test set which has had no contact with train and validatation set so far..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "52e01ef2",
   "metadata": {},
   "source": [
    "#### Accuracy check with test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1ed3e3f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy Score: 0.8995708154506438\n",
      "____________________________________________________________\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      " [[6674  554]\n",
      " [ 265  662]]\n",
      "____________________________________________________________\n",
      "\n",
      "Classification Report:\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.92      0.94      7228\n",
      "           1       0.54      0.71      0.62       927\n",
      "\n",
      "    accuracy                           0.90      8155\n",
      "   macro avg       0.75      0.82      0.78      8155\n",
      "weighted avg       0.91      0.90      0.91      8155\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = gb_classifier_best.predict(X_test_scaled)\n",
    "\n",
    "print(f'\\nAccuracy Score: {accuracy_score(y_test, y_pred_test)}', end = f\"\\n{'_'*60}\\n\\n\")\n",
    "\n",
    "print(f'Confusion Matrix:\\n\\n {confusion_matrix(y_test, y_pred_test)}', end = f\"\\n{'_'*60}\\n\\n\")\n",
    "\n",
    "print(f'Classification Report:\\n\\n {classification_report(y_test, y_pred_test)}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8db7a48f",
   "metadata": {},
   "source": [
    "As we can observe above, the model has got so close to 90% accuracy on test set and has just around 6 % False Negatives and 3% False Positives which looks fine.. Except for Precision and F1 Score on the customers who have subscribed to the term deposit, every other metrics has performed decent enough which as well could be because of the imbalance in the representation of the two classes in test set or some other reason..."
   ]
  }
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