Term_Deposit_Marketing_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Term Deposit Marketing Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Importing libraries and dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1. Importing required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "import lightgbm\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2. Importing dataset to notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r'term-deposit-marketing-2020.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "\n",
       "   contact  day month  duration  campaign   y  \n",
       "0  unknown    5   may       261         1  no  \n",
       "1  unknown    5   may       151         1  no  \n",
       "2  unknown    5   may        76         1  no  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Preprocessing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1. Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "y            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No null value in dataset. So, we can continue with transformation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2. Variable transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40000 entries, 0 to 39999\n",
      "Data columns (total 14 columns):\n",
      "age          40000 non-null int64\n",
      "job          40000 non-null object\n",
      "marital      40000 non-null object\n",
      "education    40000 non-null object\n",
      "default      40000 non-null object\n",
      "balance      40000 non-null int64\n",
      "housing      40000 non-null object\n",
      "loan         40000 non-null object\n",
      "contact      40000 non-null object\n",
      "day          40000 non-null int64\n",
      "month        40000 non-null object\n",
      "duration     40000 non-null int64\n",
      "campaign     40000 non-null int64\n",
      "y            40000 non-null object\n",
      "dtypes: int64(5), object(9)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there are categorical variables in the dataset. In order to establish a model, i will transform the variables from categorical to numeric with LabelEncoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lbe = LabelEncoder()\n",
    "df.job = lbe.fit_transform(df.job)\n",
    "df.marital = lbe.fit_transform(df.marital)\n",
    "df.education = lbe.fit_transform(df.education)\n",
    "df.default = lbe.fit_transform(df.default)\n",
    "df.housing = lbe.fit_transform(df.housing)\n",
    "df.loan = lbe.fit_transform(df.loan)\n",
    "df.contact = lbe.fit_transform(df.contact)\n",
    "df.month = lbe.fit_transform(df.month)\n",
    "df.y=lbe.fit_transform(df.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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",
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       "    }\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</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0   58    4        1          2        0     2143        1     0        2   \n",
       "1   44    9        2          1        0       29        1     0        2   \n",
       "2   33    2        1          1        0        2        1     1        2   \n",
       "\n",
       "   day  month  duration  campaign  y  \n",
       "0    5      8       261         1  0  \n",
       "1    5      8       151         1  0  \n",
       "2    5      8        76         1  0  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Establishing the model for prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1. Splitting into train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['y']\n",
    "X = df.drop('y',axis = 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will automate the process to see which model is the best option for our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2. Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression -> The accuracy of the model: 0.934125\n",
      "DecisionTreeClassifier -> The accuracy of the model: 0.919375\n",
      "KNeighborsClassifier -> The accuracy of the model: 0.9295\n",
      "SVC -> The accuracy of the model: 0.929125\n",
      "RandomForestClassifier -> The accuracy of the model: 0.937875\n",
      "AdaBoostClassifier -> The accuracy of the model: 0.933375\n",
      "LGBMClassifier -> The accuracy of the model: 0.93925\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "names = []\n",
    "models=[LogisticRegression,DecisionTreeClassifier,KNeighborsClassifier,SVC,RandomForestClassifier,AdaBoostClassifier,LGBMClassifier]\n",
    "def compML(df,algorithm):\n",
    "    model=algorithm().fit(X_train,y_train)\n",
    "    y_pred=model.predict(X_test)\n",
    "    accuracy=accuracy_score(y_test,y_pred)\n",
    "    model_name=algorithm.__name__\n",
    "    results.append(accuracy)\n",
    "    names.append(model_name)\n",
    "    print(model_name,\"-> The accuracy of the model:\",accuracy)\n",
    "for i in models:\n",
    "    compML(df,i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3. Comparison of the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,9)})\n",
    "sns.boxplot(results,names);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LGBMClassifier ve Random Forest algorithms are the best options for our dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 Model Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy rates were obtained without any changes in parameters. In this section we will try to optimize accuracy rates using model tuning methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.4.1. LGBMClassifier Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbmc_params={'n_estimators':[100,500],\n",
    "              'subsample':[0.6,0.8,1.0],\n",
    "              'max_depth':[3,4,5],\n",
    "              'learning_rate':[0.1,0.01,0.05],\n",
    "              'min_child_samples':[5,10,20]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbmc=LGBMClassifier()\n",
    "lgbmc_cv_model=GridSearchCV(lgbmc,lgbmc_params,cv=5,n_jobs=-1,verbose=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 162 candidates, totalling 810 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    5.4s\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    7.0s\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:   14.3s\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:   27.7s\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   36.3s\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   39.6s\n",
      "[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:   51.8s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  77 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 105 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 120 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 137 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done 173 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  3.2min\n",
      "[Parallel(n_jobs=-1)]: Done 213 tasks      | elapsed:  3.8min\n",
      "[Parallel(n_jobs=-1)]: Done 234 tasks      | elapsed:  4.3min\n",
      "[Parallel(n_jobs=-1)]: Done 257 tasks      | elapsed:  4.7min\n",
      "[Parallel(n_jobs=-1)]: Done 280 tasks      | elapsed:  5.2min\n",
      "[Parallel(n_jobs=-1)]: Done 305 tasks      | elapsed:  5.6min\n",
      "[Parallel(n_jobs=-1)]: Done 330 tasks      | elapsed:  6.1min\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  6.5min\n",
      "[Parallel(n_jobs=-1)]: Done 384 tasks      | elapsed:  7.1min\n",
      "[Parallel(n_jobs=-1)]: Done 413 tasks      | elapsed:  7.6min\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:  8.2min\n",
      "[Parallel(n_jobs=-1)]: Done 473 tasks      | elapsed:  8.9min\n",
      "[Parallel(n_jobs=-1)]: Done 504 tasks      | elapsed:  9.7min\n",
      "[Parallel(n_jobs=-1)]: Done 537 tasks      | elapsed: 10.5min\n",
      "[Parallel(n_jobs=-1)]: Done 570 tasks      | elapsed: 11.1min\n",
      "[Parallel(n_jobs=-1)]: Done 605 tasks      | elapsed: 11.6min\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed: 12.2min\n",
      "[Parallel(n_jobs=-1)]: Done 677 tasks      | elapsed: 13.0min\n",
      "[Parallel(n_jobs=-1)]: Done 714 tasks      | elapsed: 14.0min\n",
      "[Parallel(n_jobs=-1)]: Done 753 tasks      | elapsed: 15.0min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed: 15.9min\n",
      "[Parallel(n_jobs=-1)]: Done 810 out of 810 | elapsed: 16.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LGBMClassifier(), n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.1, 0.01, 0.05],\n",
       "                         'max_depth': [3, 4, 5],\n",
       "                         'min_child_samples': [5, 10, 20],\n",
       "                         'n_estimators': [100, 500],\n",
       "                         'subsample': [0.6, 0.8, 1.0]},\n",
       "             verbose=10)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbmc_cv_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.05,\n",
       " 'max_depth': 3,\n",
       " 'min_child_samples': 10,\n",
       " 'n_estimators': 500,\n",
       " 'subsample': 0.6}"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Best params can be obtained from following code\n",
    "lgbmc_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbmc_tuned=LGBMClassifier(learning_rate= 0.05, max_depth= 3, min_child_samples= 10, n_estimators= 500, subsample= 0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.9445\n",
      "Confusion Matrix : \n",
      " [[7333  104]\n",
      " [ 340  223]]\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97      7437\n",
      "           1       0.68      0.40      0.50       563\n",
      "\n",
      "    accuracy                           0.94      8000\n",
      "   macro avg       0.82      0.69      0.74      8000\n",
      "weighted avg       0.94      0.94      0.94      8000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lgbmc_tuned=lgbmc.fit(X_train,y_train)\n",
    "y_pred=lgbmc_tuned.predict(X_test)\n",
    "print(\"Accuracy : \", accuracy_score(y_test, y_pred))\n",
    "print(\"Confusion Matrix : \\n\",confusion_matrix(y_test, y_pred))\n",
    "print(\"Classification Report: \\n\",classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially, the accuracy rate was 0.939. Now the rate is 0.944."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.4.2. Random Forest Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_params={'n_estimators':[10,500,1000],\n",
    "           'max_depth':[2,5,8],\n",
    "           'max_features':[2,5,8],\n",
    "           'min_samples_split':[2,5,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier()\n",
    "rf_cv_model=GridSearchCV(rf,rf_params,cv=5,n_jobs=-1,verbose=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    7.8s\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   29.6s\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:   42.2s\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:  3.6min\n",
      "[Parallel(n_jobs=-1)]: Done  77 tasks      | elapsed:  4.4min\n",
      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:  5.6min\n",
      "[Parallel(n_jobs=-1)]: Done 105 tasks      | elapsed:  7.4min\n",
      "[Parallel(n_jobs=-1)]: Done 120 tasks      | elapsed:  8.9min\n",
      "[Parallel(n_jobs=-1)]: Done 137 tasks      | elapsed: 10.5min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed: 11.8min\n",
      "[Parallel(n_jobs=-1)]: Done 173 tasks      | elapsed: 13.5min\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed: 16.4min\n",
      "[Parallel(n_jobs=-1)]: Done 213 tasks      | elapsed: 18.8min\n",
      "[Parallel(n_jobs=-1)]: Done 234 tasks      | elapsed: 22.7min\n",
      "[Parallel(n_jobs=-1)]: Done 257 tasks      | elapsed: 26.7min\n",
      "[Parallel(n_jobs=-1)]: Done 280 tasks      | elapsed: 31.0min\n",
      "[Parallel(n_jobs=-1)]: Done 305 tasks      | elapsed: 33.5min\n",
      "[Parallel(n_jobs=-1)]: Done 330 tasks      | elapsed: 36.9min\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed: 42.3min\n",
      "[Parallel(n_jobs=-1)]: Done 384 tasks      | elapsed: 47.7min\n",
      "[Parallel(n_jobs=-1)]: Done 405 out of 405 | elapsed: 54.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [2, 5, 8], 'max_features': [2, 5, 8],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'n_estimators': [10, 500, 1000]},\n",
       "             verbose=10)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_cv_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 8,\n",
       " 'max_features': 5,\n",
       " 'min_samples_split': 5,\n",
       " 'n_estimators': 500}"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_tuned=LGBMClassifier(n_estimators=500 , max_depth=8, max_features=5 , min_samples_split=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.9385\n",
      "Confusion Matrix : \n",
      " [[7312  125]\n",
      " [ 367  196]]\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.98      0.97      7437\n",
      "           1       0.61      0.35      0.44       563\n",
      "\n",
      "    accuracy                           0.94      8000\n",
      "   macro avg       0.78      0.67      0.71      8000\n",
      "weighted avg       0.93      0.94      0.93      8000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_tuned=rf.fit(X_train,y_train)\n",
    "y_pred=rf_tuned.predict(X_test)\n",
    "print(\"Accuracy : \", accuracy_score(y_test, y_pred))\n",
    "print(\"Confusion Matrix : \\n\",confusion_matrix(y_test, y_pred))\n",
    "print(\"Classification Report: \\n\",classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forest algorithm might be a good option for our dataset, but this model takes more time compared to LGBM. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5. Best features "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.5.1. LGBMClassifier Best Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xyRIS4slYZBMqSPXq1Tl06BD+/v5oNBpat26Nu7u7ucsSApAsISGeVLFpQtbW1kybNs3cZQghhChEFnlighBCiJJBmpAQQgizkSYkhBDCbKQJCSGEMBtpQkIIIcxGmpAQQgizsfgmlJyczDvvvGPuMko8CW0TQhQFi/+cUNWqVZk3b16Rr0dC7R5MQtuEEEXhoU1IKcXUqVPZuHEjWq2WsLAwGjZsyPTp07lz5w4pKSmMHDkSLy8vIiIiKFOmDH/++ScpKSl8+OGHrFq1imPHjpkej4mJYcuWLVy9epXLly/j7u5OREQEBoOhwCC7K1eu0KtXLzZt2sTFixcZNmwYN2/epF69euzZs4dt27YRFRVFcnIyf//9N//88w+hoaH079//Wew/IYQQT+GhTejXX39l3759rF69mqysLLp3706FChWYOHEiderUISEhgc8++wwvLy8ALl26xE8//cSKFSsYOXIkcXFx2NjY4ObmxoABAwDYu3cvq1atwt7enl69erFhwwYqVKiQL8hu69atNGrUyFTLpEmT8PHxoUePHmzYsIHY2FjTY8ePH2fhwoWkpqbi5eVFjx49sLe3z7MtEmonhBCW5aFNaM+ePfj4+GBtbY21tTWrVq0iIyODzZs38+uvv3LgwIE8+T1ubm4AvPDCC9StW5eKFSsCUL58eW7evAmAp6cnlSpVAsDX15ddu3YxduzYfEF2aWlpeWrZsWMHn3/+OQDt2rXL02ScnJywtramYsWKlC9fntTU1HxNSELthBDCsjy0CVlZWaHRaEy39Xo9gwcPxsnJCScnJ5ydnRk2bJjpcZ1Ol2dsQbRarel7o9GIVqstMMhOKZVv3L335bKxsTF9r9FoClxOQu2EEMKyPPTsOEdHR9avX09WVhbp6en06dOHkydPMnjwYNzc3IiPj8dgMDzWSrdv305qaioZGRmsWbMGNze3AoPs7p3X2dnZFPGwdevWApNWH0RC7YQQwrI89EioXbt2HD58mJCQENN7NX///Td+fn5YWVnRqlUr7ty5k++lswdxcHDgnXfe4fr16wQGBtKmTRuqVKmSL8hOr9fnGTd69GhGjBjB0qVLadCgQb6X24QQQhQvGnW/17eKSExMDLt372by5MmPPfbHH3/ExcWFV155hSNHjvDxxx8TExPzVPXo9Xo8PT2Jj4+XULsHkNA2IUSuwnzetPjPCd2tVq1afPjhh5QqVQobGxs+/fRTc5dUYkgDEkIUhWfehEJCQggJCXmisW3btqVt27aFXJEQQghzsfjL9gghhPjfJU1ICCGE2UgTEkIIYTbShIQQQpiNNCEhhBBmI01I5CG5QUKIZ8kiPyd08OBB4uLiGD58+GOPXbp0KWXLlsXf3/+xxkmeUA7JDRJCPEsWeSR06tQprl69+kRj9+3bR6b8Ny+EEMVCoR8JFRSC5+bmxtixY7lx4wZly5Zl9OjRNG7cmIiICGxtbTly5AjJyckMGDCAdu3aERkZSVpaGrNmzSI8PJxRo0aRnJzMpUuXcHZ2ZtKkSQD51lO3bl02bdrErl27qFy5Mm3atCnszRNCCFGICr0JFRSCt2jRIoYOHYq3tzf79+9n8ODBxMXFATlRCosWLeLEiRP06tWLTp06MWjQIHbv3k3//v2JjY2lYcOGREZGkpmZiZ+fH0eOHCEpKSnfeubPn4+HhwctW7YssAFJqJ0QQliWQm9C94bgLVq0CHd3d7y9vQFo2rQp5cqV4/Tp0wC4urqi0WioV68eN27cyDefv78/Bw8eZMGCBZw+fZobN26QlpZWYNjew0ionRBCWJZCb0L3huAlJSXlC5hTSpmygnLD6O4ec7fo6Gji4uLo0qULLi4unDhxAqVUgWF7Dg4OD6xNQu2EEMKyFPqJCfeG4A0ZMgSNRsP69esB2L9/P1euXKFu3br3nUOr1ZKdnQ3kRHqHhYURGBhIRkYGx44dw2g05lvP22+/TXJyMlqt9r4hexJqJ4QQlqXQj4TuDcHr1asXTk5OjBs3jqioKHQ6HVFRUVg/4HToxo0bM2PGDKZOnUrv3r0ZN24cc+fOxdbWlmbNmqHX6wkNDc23ntq1a+Pi4sJXX32FnZ0dHTp0KOzNE0IIUYieeaidpZFQu7wkvE4I8TCF+bxpkZ8TEuYjDUgI8SxJExJCCGE20oSEEEKYjTQhIYQQZiNNSAghhNlIExJCCGE20oSEEEKYjTQhAUiYnRDCPCwy1O5ehw4dYsmSJaYIh3tFRETQsmVLQkJCnngdJT3UTsLshBDmUCya0GuvvcZrr71m7jKEEEIUsmLxclxiYiLh4eGcOXOG8PBwAgICCAsL4+DBg6ZltmzZQkhICAEBAaxdu7bAeVJSUtDr9Xm+JE9ICCHMp1gcCeUaPnw4ffv2LTAcLz09naVLl3L16lU6deqEo6MjlStXzjNe8oSEEMKyFJsmdPv2bfR6/X3D8YKDg7GysqJq1ao0bdqUAwcO4OXllWcOyRMSQgjLUmyaUEEX+747HE+r1ZruNxqN6HS6fMvb29tjb29fdEUKIYR4LMXiPSEAW1tbatSocd9wvDVr1qCU4p9//uHw4cNyIoMQQhQDxeZICODLL7+8bzhe2bJlCQkJITs7mwkTJjw06vtezefNKtF5QpIjJIQwh2LRhFJTUylbtix16tQhOjo63+OTJ082Q1X/W6QBCSHMweJfjlu7di2ffPIJgYGB5i5FCCFEIbP4IyFfX198fX3NXYYQQogiYPFHQkIIIf53SRMSQghhNtKEhBBCmI00ISGEEGYjTUgIIYTZFEoTioqKIioqqjCmAnI+FzRgwAAAkpOTeeeddwpt7v8lEkQnhCjuLPIU7Zs3b3L06FEAqlatyrx584p8ncUx1E6C6IQQxd0jHQnNnTuX4OBgAgMDmTJlCkop5s+fj7e3d75cn/r165u+j4mJISIiAoCdO3cSGBhIQEAA/fr149atW9y6dYtBgwYRFhaGu7s7o0aNQinFxIkTuXTpEgMGDECv1+Ph4QHAlStX6NevHwEBAQQHB7Nt2zYg50hszJgxhIeH4+HhwaxZswptBwkhhCg6Dz0S2rZtG4cPH2b58uVoNBqGDx/OrFmzWL16NStWrECj0RAWFkbjxo3vO0dmZibDhg3ju+++o2HDhkybNo0VK1ZQoUIFGjZsSGRkJJmZmfj5+XHkyBHGjBlDr169mDlzJnq93jTPp59+SqtWrXjzzTdJSkqiW7durFy5EoDjx4+zcOFCUlNT8fLyokePHvmumJ2SkkJKSkqe+yTUTgghzOehTSghIYGDBw8SEhICwJ07d9iwYQPdu3fnueeeA6BDhw4Yjcb7znH8+HGqVq1Kw4YNARg6dKjpsYMHD7JgwQJOnz7NjRs3SEtLo3z58gXOs2vXLiZOnAhAzZo1adKkCQcOHADAyckJa2trKlasSPny5UlNTc3XhCTUTgghLMtDm5DBYKB37968+eabQM7RxI8//khqaup/J7GyIvOuN8mVUmg0GrKzswHQ6XRoNBrT46mpqdy+fZsNGzYQFxdHly5dcHFx4cSJEwXmBt097723c/OEbGxsTPdrNJoC55FQOyGEsCwPfU+oVatWrFq1itu3b5Odnc2AAQOwtbVl8+bNpKamkpGRwYYNG0zLV6hQgZMnT6KUYtOmTQDUrl2bq1evcurUKQDmz5/P4sWL2bFjB2FhYQQGBpKRkcGxY8cwGo1YWVmZGti9tSxfvhyApKQk9u3bR9OmTR95Y+3t7alRo0aer2rVqj3yeCGEEIXroUdCHh4eHDt2jC5dumAwGGjTpg29e/dGp9PRuXNn7O3teeGFF0zLDx06lHfffZdKlSrRvHlzrl+/jo2NDV9++SUfffQRWVlZvPjii0yZMoWDBw8ybtw45s6di62tLc2aNUOv19OiRQteeOEFwsPD+fzzz01zjx49mrFjxxITEwPAxIkTqVKlShHsFiGEEM+CRj3o9a8SQK/X4+npSXx8fLELtZMgOiGEORTm86ZcMaEYkwYkhCjupAkJIYQwG2lCQgghzEaakBBCCLORJiSEEMJspAkJIYQwG2lCQgghzEaakBBCCLMplCaUmJhIeHh4YUyVz+LFi1m8eHGRzG0JJJhOCFGSWWSo3d26dev2TNZjrlA7CaYTQpRkhdaErl27xjvvvMO5c+eoXbs2kZGRrF69mu+//x6NRkOjRo34+OOPee6556hfvz7Hjx8HcoLvdu/ezeTJk/niiy/YsWMHpUqVwsvLi4EDB5piw99//31at25N+/bt2bt3L1qtlq+//pqaNWuSmJjIxIkT0Wq1NG3alL/++ovo6OjC2jQhhBBFpNDeEzp//jxjx45l3bp1XLlyhcWLFzN79myio6NZvXo1ZcqUeWCWzz///MO2bdv45ZdfWLx4MadOnSIjIyPPMpcvX8bZ2ZmVK1fi6OjIwoULycrK4qOPPuLLL79k5cqVWFndv6+mpKSg1+vzfEmonRBCmE+hHQk1aNCAmjVrAlCnTh1SU1Nxd3enQoUKAISFhTFy5Mj7jq9atSo2NjZ07doVd3d3hg0blicjKFebNm0AqFu3Lr///jsnTpygYsWKNGjQAIDOnTszadKkAtchoXZCCGFZCq0J3X0EotFosLe3zxOlrZTKkxF0b/CdlZUVy5YtY/fu3Wzbto2uXbsW+JJabmPKDa7TarUPTHW9m4TaCSGEZSnSU7Q3bdrEjRs3AFi6dClOTk5AwcF3f/75Jz179sTR0ZERI0ZQp04dzpw589B1vPzyy6SkpJjeY1q9evV9l5VQOyGEsCxFdnacra0t/fr1Izw8nKysLBo1asT48eOBgoPv/vWvf9G0aVP8/f0pU6YMr7/+Om5ubhw5cuSB67G2tmbKlCmMGDGCUqVKUbt2bUqXLl1UmyWEEKIQFftQO6PRyNSpUxk4cCBly5bl+++/Jzk5mYiIiEcab+5QOwmmE0IUN4X5vGnxnxN6mFKlSlG+fHk6d+6MTqejevXq9z0xwRJJAxJClGTFvgkB9O3bl759+5q7DCGEEI9Jrh0nhBDCbKQJCSGEMBtpQkIIIcxGmpAQQgizkSYkhBDCbKQJmZnkCQkhSjKLP0U7OTmZMWPGMG/ePDZv3szZs2d5880377v83dEQj0PyhIQQ4tmz+CZUtWpV5s2bB8Dhw4fNXI0QQojCVGRNKDExkdmzZ6PT6dDr9Xh4eFC2bFk2btwIwNy5c/n1119ZtWoV6enp6HQ6pk2bxssvv4yHhweNGzfm6NGjfPnllwwZMoS5c+eyZMkSAF544QVat27NqFGjSE1N5dKlSwQHBzN48OAH1pSSkpLnyt6A5AkJIYQZFemR0IEDB1izZg3ly5fHxcWFESNGEBMTw8iRI1mzZg2bN28mOjqa0qVL880337Bw4UI+/vhjANzc3Pj666/R6/UAvPLKK3Tt2hWATp068d133+Hv709wcDCpqam0bduW8PDwB9YjeUJCCGFZirQJ1atXj+effx7IiW9wdnYGco5kUlJSmDZtGmvWrOHs2bNs376dhg0bmsY2adLkgXP36dOHXbt28d1333Hy5EmysrJIT09/4BjJExJCCMtSpE1Ip9Plua3Vak3fX7hwgbCwMHr27ImbmxuVKlXi6NGjpscLSlW92+TJk0lKSsLf3x8vLy927tzJwy4Ibm9vj729/RNsiRBCiKJgtlO0Dx06RK1atXjjjTd47bXX2LhxIwaD4YFjtFqtKYl1x44d9OnTBx8fH86cOUNycvIjJ6wKIYSwDGY7O65169YcO3YMX19flFI4Ojpy8uTJB47JTV2tVKkS/fr146OPPqJ06dJUq1aNV1991fT+kRBCiOKh2IfaPS0JtRNCiMdTmM+bcsUEM5MGJIQoyaQJCSGEMBtpQkIIIcxGmpAQQgizkSYkhBDCbKQJCSGEMBtpQkIIIcxGmtBjkhA6IYQoPBaVJ5SYmMiMGTOIjo5+5ut+1FA7CaETQojCI0dCQgghzMaijoRynTlzhrFjx3Ljxg3Kli3L6NGjady4MSdOnODTTz8lLS2Na9eu0bdvX7p160ZUVBTJycn8/fff/PPPP4SGhtK/f/9880qonRBCWBaLbELDhw+nb9++eHt7s3//fuKYzLEAABcASURBVAYPHkxcXBzLli3jvffew9nZmaSkJAIDA+nWrRsAx48fZ+HChaSmpuLl5UWPHj3yxTZIqJ0QQlgWi2tCt2/fRq/X4+3tDUDTpk0pV64cp0+fJiIigu3btzNnzhxOnDhBWlqaaZyTkxPW1tZUrFiR8uXLk5qamq8JSaidEEJYFotrQgVd1FsphcFgYMiQIdjb2+Pu7o6vry+xsbGmZe4OwdNoNAXOI6F2QghhWSzuxARbW1tq1KjB+vXrAdi/fz9Xrlyhbt267Nixg0GDBuHl5cW2bdsAHhqEJ4QQwnJZ3JEQwJdffsm4ceOIiopCp9MRFRWFtbU177//Pt27d8fGxoYGDRpQvXr1Qguyaz5v1iPlYkj+jxBCFB4JtTNzqJ0QQhQ3EmonhBDif4I0ISGEEGYjTUgIIYTZSBMSQghhNtKEhBBCmI00ISGEEGYjTUgIIYTZPLMmFBERQUxMzH0fX7p0KW3atOGLL754qrnDw8OfuMaHkUA7IYQoXBZzxYTY2Fg+//xzWrdu/VTz7N69+4nGPUqonQTaCSFE4SqyIyGlFJ9//jnt27cnPDycc+fOAbBy5UqCg4MJCgpi1KhRZGRkMGPGDA4dOsT48ePZunUr69ato0uXLgQGBtKhQwf27dsH5BzlJCYmAjmf2PXw8MizzokTJwIQGhpaVJslhBCiEBXZkVBcXBx//vknsbGxpKamEhgYSHp6OmvWrGHJkiXY2Ngwbdo0vvvuOwYOHEhiYiIDBw7E0dGRN998k9mzZ+Pg4MDy5cuZO3cus2fPfug6x4wZQ3R0NMuWLSvwcQm1E0IIy1JkTWj37t14e3uj0+lwcHDAzc0NpRR///03Xbp0ASArK4t//etfecaVKlWKmTNnsmnTJs6cOcPu3bspVapwDtgk1E4IISxLkTWhezN9rKysMBgM+Pj4MGbMGCAnwO7eKIbbt2/TuXNnAgMDcXR0pH79+ixcuND0eO6c2dnZj12ThNoJIYRlKbL3hJydnVm3bh2ZmZncvHmT7du3A7BhwwauXr2KUopx48bxww8/5Bl39uxZNBoN7777Lk5OTmzYsMHUqCpUqMCpU6cA2LhxY4Hr1Wq1921Q9vb21KhRI89XtWrVCmuThRBCPKYia0JeXl60bNkSf39/+vfvT506dbCzs2PgwIH07t0bPz8/jEYjffv2zTOuQYMGNGzYEB8fH/z8/KhQoQLnz58H4O2332bRokUEBwdz586dAtfr6elJUFAQGRkZRbVpQgghConkCT1GLoYE2gkhhOQJmY00ICGEKFzShIQQQpiNxVwxwVxyT3qQzwsJIcSjyX2+vPfs5idR4pvQ2bNnAeQ0bSGEeEyXL1+mVq1aTzVHiW9CNWvWBODHH3+kevXqZq6mYLmfZVq4cKHFnlIuNRae4lCn1Fg4ikONkL9Og8HA5cuXefXVV5967hLfhKz//8kG1atXf+qzPIpatWrVpMZCUBxqhOJRp9RYOIpDjZC3zqc9AsolJyYIIYQwG2lCQgghzEaakBBCCLPRjhs3bpy5izA3GxsbnJycsLGxMXcp9yU1Fo7iUCMUjzqlxsJRHGqEoquzxF+2RwghhPnIy3FCCCHMRpqQEEIIsynRTWj16tX4+vri7e2dJzivqN26dQt/f3/0ej0AO3fuJCAgAG9vb6ZPn25a7ujRo4SEhNC+fXtGjx5tykk6f/48PXr0oEOHDvTv35/bt28DOfHlffv2xcfHhx49enD58uUnqm/GjBn4+fnh5+fHlClTLLLGb775Bl9fX/z8/Pj+++8tssZcX3zxBRERERZbY3h4OH5+fgQFBREUFMSBAwcsrs5NmzYREhKCj48PEydOBCxvXy5btsy0D4OCgmjevDkTJkywuDpXrVpl+vv+4osvADPvS1VCXbx4Ubm7u6vr16+r27dvq4CAAHXy5MkiX+/+/fuVv7+/atSokUpKSlLp6emqbdu26ty5cyorK0u99dZbasuWLUoppfz8/NQff/yhlFJq5MiRauHChUoppfr27atiY2OVUkrNmDFDTZkyRSml1Pjx49WcOXOUUkqtWLFCDR48+LHr27FjhwoLC1MZGRkqMzNT9erVS61evdqiakxMTFRdu3ZVWVlZKj09Xbm7u6ujR49aVI25du7cqZycnNSIESMs7metlFJGo1G1bt1aZWVlme6ztDrPnTunWrdurS5cuKAyMzNVt27d1JYtWyyqxnudOHFCtWvXTp0/f96i6kxLS1OOjo7q6tWrKisrS3Xu3FnFx8ebtcYS24RiYmLUyJEjTbdnzJihoqKiiny9o0aNUnv27FHu7u4qKSlJJSYmql69epkeX7FihYqIiFB6vV55enqa7t+zZ48KDw9XmZmZqlmzZqYnjfPnzysPDw+llFLu7u7q/PnzSimlsrKyVLNmzVRmZuZj1XfixAnTL51SOb9UUVFRFlWjUso0Rq/XKzc3N4vbj0opdf36dRUaGqq+//57NWLECIus8dSpU6p169YqPDxcBQQEqOjoaIur87vvvlOfffaZ6fbFixctrsZ7de/eXa1du9bi6kxNTVXNmzdXer1epaenq44dO5q9xhL7ctylS5eoXLmy6XaVKlVITk4u8vVOmjSJFi1aPLSOe++vXLkyycnJXL9+HVtbW6ysrPLcf+9cVlZW2Nracu3atceqr27dujRt2hTIubjrunXr0Gg0FlUjgE6nIzIyEj8/P5ydnS1uPwKMHTuWDz74AHt7+3zzWkqNKSkpODs7M3PmTBYsWMCSJUs4f/68RdX5999/YzAYePfddwkKCmLRokUWuS9z7dy5kzt37uDj42Nxddra2jJ48GB8fHxo27Yt1atXN3uNJbYJGY1GNBqN6bZSKs9tc9dxv/sLqvN+dSulKFXqyX7EJ0+e5K233uKjjz6iZs2aFlnjoEGDSEhI4MKFC5w9e9aialy2bBnPP/88zs7Opvss8WfdrFkzpkyZgp2dHQ4ODnTu3JnIyEiLqtNgMJCQkMBnn33GTz/9xMGDB0lKSrKoGu+2ZMkS3nzzTcDyfubHjh3j559/ZvPmzWzfvp1SpUqZ/W+nxDahatWq5XnT7PLly1SpUsVi6rj3/itXrlClShUcHBxITU015XjcXXeVKlW4cuUKANnZ2dy+fZvy5cs/dk179+7ljTfeYOjQoQQHB1tcjX/99RdHjx4FoEyZMnh7e5OYmGhRNa5du5YdO3YQFBREZGQkmzZtYtmyZRZVI8Dvv/9OQkKC6bZSiurVq1tUnZUqVcLZ2RkHBwdKly6Nl5cXO3futKgac2VmZrJnzx48PDwAy/v7/u2333B2dqZixYpYW1sTEhJi9r+dEtuEXFxcSEhI4Nq1a6Snp7N+/Xrc3NyeeR1NmjThzJkzppccYmNjcXNzo3r16tjY2LB3714g54wWNzc3dDodLVq0YO3atQCsXLnSVHfbtm1ZuXIlkPMk2KJFC3Q63WPVc+HCBQYMGMDUqVPx8/OzyBr1ej1jxowhMzOTzMxM4uPj6dq1q0XV+P333xMbG8uqVasYNGgQHh4ezJ8/36JqBEhNTWXKlClkZGRw69YtVqxYwYcffmhRdbq7u/Pbb7+RkpKCwWBg+/btdOjQwaJqzHX8+HFeeuklypYtC1je306DBg3YuXMnaWlpKKXYtGmT+Wt8rHe1/sf88ssvys/PT3l7e6u5c+c+03XnnpigVM4ZVAEBAcrb21tNmjRJGY1GpZRSR48eVZ06dVLt27dXH374ocrIyFBK5bwZ37NnT+Xj46PeeustdePGDaVUzhvh/fr1U76+viosLMw0/+P49NNPVdOmTVVgYKDpa9GiRRZVo1JKRUZGKh8fH+Xv768iIyMtbj/e7eeff1YjRoyw2BqnT5+uOnTooLy9vdWCBQssss5ly5aZ/lbHjx+vDAaDxdWolFJr1qxRQ4YMyXOfpdU5Z84c1b59e+Xv769Gjhyp7ty5Y9Ya5bI9QgghzKbEvhwnhBDC/KQJCSGEMBtpQkIIIcxGmpAQQgizkSYkhBDCbKQJ/Y+rX78+AQEBea7uO3r06Cee7+DBg4wdO7YQK3x84eHh/Prrr/nuT05OpmvXrg8cGxUVxYQJE5543U87/nHma9asmelK63fz8PDg0KFDT7XeWbNm8e9//5uRI0c+1TyPKj4+3nT1ayHuZmXuAkTR++GHH3BwcCiUuU6dOvVMrrH3JKpWrcqSJUvMXUaxsHz5cqZOnZrnOoZFydPTE09Pz2eyLlG8yJFQCfbXX3/x1ltvERISQlBQEMuXLwdyrnc1ceJEQkND8fX1xcfHh71793LhwgUiIyP5/fffGTlyJImJifj7+5vmu/t2VFQUffr0ISAggGHDhgE5/30HBwcTFBTEe++9l6+ZGQwGWrVqxd9//w3AnDlzcHd3Nz3+xhtvsHXrViDnP+vQ0FDc3d0ZNWoURqMRvV5Ps2bNgJxLhnz++ee0b98eX19fRo8eTWZmJgCnT58mPDycDh060LNnTy5dulTgvunatSshISEEBwfnyZu63/iTJ08SHh5OQEAAgYGBpk+OP2g/3e33338nKCiIjh078vHHH2M0Gu/7s1u0aBHBwcH4+fmZfm6Qk7sTGhpKx44d6dq1K3/88Ue+sUOGDCE5OZnRo0ezdu1aLl68yLvvvktAQAD+/v7Mnz/ftOzmzZsJCgoiICCAsLAwjh07lmc/A3luX758mbfeeovg4GCCg4P5+uuvAYiJiaFfv35AzpHstGnT6NGjBx4eHowePdq0rTExMXTo0IGOHTsyefJk/vWvf+WrX6/X4+npydixY02/u/Hx8fTt2xcvLy+GDBlimm/jxo107NiRwMBAunXrxsGDB4En+/3MNXPmTHx9fQkICGDQoEGmS9s8aLvEAzzRR25FsVGvXj3l7++f5woIV65cUVlZWcrX11cdPnxYKaVUSkqK8vHxUX/88Yfat2+fev/995XBYFBK5XzCul+/fkqpnE//9+3bVyml1K5du5Sfn59pXXffjoyMVO3btzdd7n3FihVqyJAhpttLlixRb7/9dr56IyIiVHR0tFJKqR49eihXV1d1+vRplZKSopycnFRGRobq2bOn6t+/v8rOzlZpaWnK1dVV7dmzRyUlJammTZsqpZT64YcfVI8ePVR6eroyGAxq8ODBasWKFSoyMlJ5eHioq1evKqWU6t+/v5oxY0a+OkaOHGnKRbl06ZIaMmSIMhgM9x2flZWlPD09VVxcnFIqJ26gTZs2at++fQ/dT+PHj1cZGRnKxcVF7dy5Uyml1OrVq1W9evUK/MS5u7u7+uSTT0zrcXZ2VidOnFBnzpxR/v7+6tq1a0qpnFgOV1dXdfv27QLnOHjwoGk//+c//1FK5fweBAQEqNjYWHX58mXVvHlzdeTIEaWUUnFxcapPnz559rNSKs/tGTNmqI8//lgppdTt27fVkCFDVEpKSp7fm549e6pBgwYpg8GgUlNTVevWrVVCQoI6efKkcnZ2VhcuXFBKKRUVFaXq1auXr/akpCRVr149tXHjRqWUUmPHjlXu7u4qNTVV3blzR7m6uqq9e/eqU6dOKRcXF3Xu3DmlVM6VC1xdXVVqauoT/34uX75chYWFmfZpZGSkeuuttx64XeLB5OW4EqCgl+NOnTrFuXPnGDVqlOm+O3fu8Oeff9K9e3fKlSvHkiVLSEpKIjExkeeee+6x19u0aVPT5d43b97MoUOH6NSpE5BztJWenp5vTLt27ViyZAkdO3bk8uXL+Pv7s3PnTsqVK0ebNm2wtrYGwNfXF61WS5kyZXjppZe4evUq1apVM82zc+dOgoKCKF26NIDpP/KoqChcXV1N+6NBgwYFXmq+Xbt2jBgxgoMHD+Ls7MyYMWNMVwMuaPzZs2fJyMjA29sbyHlp0Nvbm+3bt+Pk5PTQfXXixAmsrKxMV9329/d/4Htvue99Va1aFVdXVxISEtBqtVy6dIk33njDtJxGo+HcuXM0aNCgwHnS0tLYt28f//nPfwCws7MjJCSEbdu2odPpqFu3ruloxNvbG29v7wLfp8rVpk0b+vbty4ULF3BxcWHo0KHY2dnlW87d3Z1SpUpha2tLrVq1uHnzJseOHcPV1dX0c+zZsydRUVEFrken05kuEvriiy/SrFkzbG1tgZyLaN68eZOjR4/SqlUratasCWC6COrhw4eBJ/v93LZtGyEhIaZrw/Xq1YvZs2ebjrIL2i7xYNKESiiDwYCdnR2rVq0y3XflyhXs7OzYsmULkyZN4s0338TT05OXX36ZX375Jd8cuZd1z5WVlZXn8dw/VMj5o3777bfp3r07kHO14YL+QF1dXRkzZgxbt27FyckJFxcXFi9eTJkyZfD19TUtl/vkUVAd9z6eu225L408bCzkPJnExcWxc+dOEhISmDlzJjExMfcdbzAY8l3OXilFdnb2Q/fT3cs/aBvudvfl8Y1GI1ZWVhgMBpydnU0NF3IuSPugq8MbjcZ86zUajWRnZ6PVavNdyv/48ePY2dndd3saN25MfHw8CQkJ7Nq1i9DQUObNm5dvvbn/HMB/96FWq80zr1arvW/dOp0uT20FXSTz3iiC3G3Ijah+kt/Pe+fM3VcP2i7xYPKeUAlVu3ZtSpcubWpCFy5cwN/fn8OHD7Njxw7c3d3p3r07r776Khs3bjRdtl2r1Zr+6BwcHDh//jxXr15FKcWaNWvuu77WrVuzfPlybt26BcA333zDRx99lG85GxsbHB0dmTFjBq6urrRs2ZL9+/fz+++/06ZNm0fePmdnZ2JjY8nMzMRoNDJu3LgH1nevoUOHsnbtWvz8/Pjkk0+wtbXl3Llz913+5ZdfxsrKivXr1wM5Z+rFxcXh4uLySPupfv36KKXyvOf1oP+iV6xYAcD58+dJSEjA2dkZZ2dnduzYwV9//QXA1q1bCQwM5M6dO/edx9bWliZNmpje80pNTWXlypW4uLjQpEkT/vrrL06ePGmqafjw4djb25OVlcWpU6cA8mzP1KlT+fbbb/Hy8mL06NG88sorpvEP07p1axISEkzvxSxbtuyRxt2Ps7Mzv/32G0lJSQCm7KkmTZoUuO5H+f1s06YNP//8M2lpaQBER0fj6OhoOkIXj0+OhEooa2trvv32WyZNmsT8+fPJzs5m8ODBNG/enPLlyzN06FACAgLIzs7G1dWV9evXYzQaadq0KTNnzmTgwIHMmDGDrl270qlTJypXrsy///3v+546HBoaSnJyMl26dEGj0fD8888zefLkApdt164d69evp1WrVpQuXZoGDRpQrlw5bGxsHnn7unbtyj///ENISAhKKVq2bEl4eDizZs16pPHvvfceo0eP5qeffkKr1eLl5YWjoyOJiYkFLq/T6fj222+ZOHEiUVFRGAwGBgwYQKtWrUz1PGg/6XQ6Zs6cybhx4/jqq69o2LAhFStWvG99GRkZBAcHk5WVxZgxY6hduzYAEyZM4MMPP0QphZWVFbNmzXroS6lTp05lwoQJxMTEkJmZSUBAACEhIWg0GqZOncqIESMwGAzY2toyffp07OzsGD58OO+88w4ODg506NDBNFfv3r2JiIjA398fa2tr6tevj5+fH7GxsQ/d57Vr12bkyJH06dMHa2trGjZsSJkyZR467n5eeeUVPvnkEwYOHIjBYKB06dLMnj27wJcHH/X3s3Pnzly4cIHQ0FCMRiO1atVi6tSpT1yjALmKthDCIiQlJbFq1Sree+89SpUqxfr165k3b95THxEJyyZHQkIIi1CtWjUuXbpEQEAAWq0WOzs7PvvsM3OXJYqYHAkJIYQwGzkxQQghhNlIExJCCGE20oSEEEKYjTQhIYQQZiNNSAghhNlIExJCCGE2/w9HTrKBGspGwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Importance=pd.DataFrame({'Importance':lgbmc_tuned.feature_importances_*100},\n",
    "                       index=X_train.columns)\n",
    "Importance.sort_values(by='Importance', \n",
    "                      axis=0,\n",
    "                      ascending=True).plot(kind='barh',\n",
    "                                          color='r')\n",
    "plt.xlabel('Feature which should be focusing more on')\n",
    "plt.gca().legend_=None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to LGBMClassifier algorithm, 'last contact duration', 'last contact month of year' and 'last contact day of the month'  features are important."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.5.1. Random Forests Best Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Importance=pd.DataFrame({'Importance':rf_tuned.feature_importances_*100},\n",
    "                       index=X_train.columns)\n",
    "Importance.sort_values(by='Importance', \n",
    "                      axis=0,\n",
    "                      ascending=True).plot(kind='barh',\n",
    "                                          color='r')\n",
    "plt.xlabel('Feature which should be focusing more on')\n",
    "plt.gca().legend_=None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to Random Forests algorithm, 'last contact duration', 'average yearly balance', 'age' and  'last contact day of the month' features are important."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Data visualization for customer segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "\n",
       "   contact  day month  duration  campaign   y  \n",
       "0  unknown    5   may       261         1  no  \n",
       "1  unknown    5   may       151         1  no  \n",
       "2  unknown    5   may        76         1  no  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_initial=pd.read_csv(r'term-deposit-marketing-2020.csv')\n",
    "df_initial.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fP2oyeHWZ8y4mBU4IcS+FQsGgQYPYvXs3e/fuZfXq1Xz99ddERUVp17l+/TpmZmYEBgaSkJDAjz/+iL+/P35+fnh5eT11Gyq1yKlUKsaMGcPbb78NFE3bXb9+nbNnz2rXUSgU5V6HK84lUqlUDBgwgMDAQACys7O16dwGBgYoFAoA1Go1hoaGj7X9vesVt0WtVmNtba39B6NSqUhPT9euX9ze+zORatWqVeZ51KtXj3r16pXTS0IIoT98fHx44403eOmll2jWrBmtWrUiKioKLy8vTpw4QVBQEPv27cPNzY2NGzfy3nvvUVBQQHx8/DMpcpV644mjoyNRUVFkZ2dTWFjIxIkTuXjxYrnrGxoalig+xRwcHPjhhx+4efMmGo2G4OBg1q9fD8Ddu3e1AamRkZH07NnzsbYvy4svvkhmZiY///wzABEREUyZMgUAc3NzfvvtNwAOHjz4iD0hhBA1Q5MmTWjSpIl29io0NJRt27bh4eHBkiVLWLp0KcbGxkyePJl33nkHHx8fzp07x7hx457J8St1JOfi4kJCQgLDhg1DpVLRo0cPunTpUu76vXr1Yvz48axdW3Lark2bNvj5+TFmzBjUajUvv/wy48eP1y7ft28fS5cuxdLSkkWLFpXa78O2v5+JiQnLly9n3rx55OXlUbduXe1+x44dS0BAABEREfTp0+dxu0QIISqUurCgQi59PMo9AxqNhtTUVNLT0+nbty8ANjY2bNy4sdS67u7uuLu7P/N2KjR6do++nZ0dly5dqupmPLbk5GT69OnD8mFdaFRNr8kJIapWfHz8M7th41nYt28fwcHBBAcH4+rq+kz2ef85Fv/tPHToUJlZnDr9MLgQQojqy9XV9ZkVtyeld0WuOo7i7nVvMnh1e4RACCF0jU6/8aSmkwInhHhcenYFqoQnOTcpckIIoSdq1aqlvWtc32g0Gm7evFnuo1nl0bvpSiGEqKmsra1JTk4mLS2tqptSIWrVqlXmzSUPIkVOCCH0hLGxMS+88EJVN+OxVeQrAaXI6ZjqnAwuhBBP4ruQURW2b7kmJ4QQQm9JkRNCCKG3dLrIxcbG4uvrW+p7Ozu7KmhN0WvJkpOTiYyMJCAgoEraIIQQ4tHpdJETQgghnobO33hy+/Zt3n33XVJTU+nQoQMzZ87ULlu5ciUAkyZNAopGWhs2bKBJkyaEhIQQFxeHSqXCx8eHt956q9S+MzIymD59On/88QcmJiYEBATg5OTEkSNHWLZsGWq1mubNmzN79mwaNmxYZvv27t3LN998Q25uLvn5+cyfP59OnTrh6+tL/fr1SUxMZNmyZSXetSbJ4EIIUTl0vsglJyezatUqWrZsyUcffcSmTZseus3WrVsB2L59O/n5+bz77ru0a9eOV199tcR6y5cvp0WLFnzxxRdcunSJoKAgWrduTVBQEJs2bcLa2pq1a9cye/ZsVqxYUeo4arWazZs3ExYWRoMGDdi2bRtr1qzRpoHb2dmVmQD+qMngT0teCyaEqA5q9CMEr776Kq1atQLAw8ODyMjIh24TExNDfHw8P/30E1AUr37p0qVSRe7UqVMsXrwYKCpIW7Zs4ciRI3To0EFbaIYPH86aNWvKPI6BgQFffPEFhw8f5vLly8TFxWFg8L8Z4KdNBn9akmIghKgOKqrAQTUockZG/2uiRqMp8VmhUKBWq7WfCwoKgKLkbn9/f15//XUAbt26RZ06dVi+fLk2UHXy5MkYGRlpU8QBfv/99xL7Kz7m/YnfxbKzsxkyZAienp506dIFOzs7wsPDtcslGVwIIaqWzt94cvr0af766y/UajU7duygW7du2mX3pnKfP39e+yobR0dHtm7dSkFBAdnZ2bzxxhucPXuWDz74gKioKKKioujTpw+vvvoqu3fvBooK3Lhx4+jYsSPnzp0jOTkZgC1btuDg4FBm265cuYJCoWDChAnatPGyksyFEEJUDZ0fyb300kt89tlnpKWl4ejoyJAhQwgKCgJg4MCB7N+/n4EDB2Jvb0/btm0BGDFiBFevXsXb25vCwkJ8fHzKLFSTJ08mMDAQT09PjIyMCAkJoWHDhsyePRs/Pz8KCgpo2rQp8+bNK7Ntbdq04eWXX2bAgAEoFAq6d+/O6dOnK64zhBBCPBa9SwavrspKBn9ack1OCKHvHpYMrvPTlUIIIcSTkiInhBBCb+n8Nbmapt17C+U5OSGEeEZkJKfHpMAJIWo6KXJCCCH0lhS5aiS/QJ7BE0KIxyHX5HTMg5LBKzI9Vwgh9JGM5IQQQugtKXJCCCH0Vo0qclWVKC6EEKJq1KgiJ4QQomap1jeexMbGsmrVKjZu3AhAQEAAXbt2ZcOGDdja2hIfH4+FhQXLly/HzOx/N3P88ssvBAQE8H//93/s3LmTGzducPXqVa5du8bQoUN5//33UavVzJ8/n5iYGBQKBZ6enowfPx4PDw+WLVuGjY0Nn3zyCXXr1mXWrFmcOXOGr776infffZfVq1dTq1Ytfv/9d+zs7Fi8eDEmJiba40syuBBCVI5qXeTKk5CQwPz582nbti2TJk1CqVTi6+urXTZ9+nTCwsJo2bIlAJcuXSI8PJw7d+7Qt29fRo0ahVKpJCUlhZ07d5Kfn4+vry+tW7fG2dmZmJgYbGxs+H//7/9pjxkdHU2vXr0AOHPmDHv37sXS0pJhw4Zx/PhxXFxctOs+KBl8mb8bzVu2KnNZRabnCiGEPtLL6UoLCwtt7I6trS2ZmZnaZe+++y7dunXjxRdf1H7n4OCAiYkJFhYWmJmZcefOHWJjY/H29sbQ0JDatWvj4eFBTEyMtsj99ttvvPTSSxgYGHDz5k2OHTumLXK2trZYWVlhYGCAjY1NieNDUTL4oUOHSvwUh60qDMv//w4pcEII8XiqdZFTKBTcmxRUnAxuampa7jqLFy/mwIEDJCQkaL8ra/2yEsJVKhWvvPIKCQkJnDx5kq5du9KlSxf27dtHYWEhTZs2fejxoSgZ3NrausSPlZXV03SFEEKIMlTrImdubk5SUhJ5eXlkZGQ8UmCpk5MTn3zyCYGBgaUK2b0cHR3ZsWMHKpWKu3fvolQqcXBwwMjIiA4dOrBx40a6du2Ko6MjYWFhODs7P8tTE0II8QxU62tytra2ODs74+bmRrNmzejcufMjbTdo0CAiIyO1N6yUZfjw4Vy5cgUvLy8KCgrw8PCgX79+ADg7O3Pq1ClsbGxo1KgRN2/e1E5VCiGE0B2SDK4jHpZuK4QQojRJBhdCCFFjSZETQgiht6TI6RiNqrCqmyCEEHpDipyOedBzckIIIR6PFDkhhBB6S4qceCYktVwIoYtkbkzHPCgZXJdJarkQQhfJSE4IIYTekiInhBBCb0mRu09SUhKfffYZABcuXGD69OlPvK/Y2FhtxI8QQojKJ9fk7vPXX3+RlJQEQPv27Wnfvn0Vt0gIIcSTqlFFLjY2ltDQUNRqNc2aNeO5554jMTERlUrFuHHjcHd3Z+7cuSQnJzNr1ixcXV21yeO+vr7Ur1+fxMREli1bRlpaGitWrKCwsBBra2vmzJmDubk5x48fZ8GCBZiamvLCCy9U9SkLIUSNVuOmK69cucL69etp2bIl9vb2REZGEh4eTlhYGElJSQQGBtKuXTtmzpxZals7Ozv2799P48aNWbJkCevWrWPHjh10796dxYsXk5+fT0BAACtWrCAyMpJatWqV2YasrCySk5NL/Fy/fr2iT10IIWqcGjWSA3jhhRd4/vnnOXnyJLm5uURERACQk5NDYmIiderUKXfbDh06AHDu3DlSUlIYPXo0AGq1mvr163Pp0iUsLS2xsbEBwNvbm+XLl5faz/r161m1alWZx1ge4FXqTdrqwgIMjIwf/2QrUX6BSpLLhRA6p8YVueLRlVqtJjQ0FHt7ewDS09OpX78+v/zyy0O3ValUdOrUibCwMADy8vLIzs7mr7/+KpECbmhY9h/9MWPG4O3tXeK769evM2rUKC6uDuDG8yVHgJ0/XfuYZ1n5pMAJIXTRI01X3r59myNHjhAdHU1WVlZFt6lSODo6smnTJgBSU1Px9PQkJSUFQ0NDCgsf/JLkjh07cvbsWS5fvgzAl19+SUhICHZ2dqSnp5OQkADA7t27y9y+Xr16WFtbl/ixsrJ6hmcnhBACHmEkd/ToUaZOncpLL72EWq0mKSmJpUuX0qVLl8poX4Xx8/MjODgYd3d3VCoV/v7+tGjRgueff547d+7g7+/PkCFDyty2UaNGzJ8/nw8//BC1Wk3jxo0JDQ3F2NiYzz//HH9/f4yMjGjbtm0ln5UQQoh7PTQZ3Nvbm5CQEGxtbQH473//y4wZM4iMjKyUBtYUxem2y4d1oVE1nK4UQoiq8NTJ4AqFQlvgAOzt7XlIXRRCCCF0QrlFLiMjg4yMDNq1a8e6devIzs7m7t27hIeH4+joWJltFEIIIZ5IudfkHB0dUSgU2lFbaGiodplCoWDq1KkV37oaqN17C6vlIwRCCKGLyi1yxXcIiqonBU4IIZ7MQ++uVKvVrFu3jmPHjlFYWMhrr73GhAkTMDKqcY/YCSGEqGYeeuPJkiVL+OmnnxgzZgxvv/02Z86cYdGiRZXRNiHEU5LEdlHTPXQ4Fh0dTUREBMbGRVNmvXr1wtPTs8IbVlNV12RwoZsksV3UdA8dyWk0Gm2BAzAxMSnxWQghhNBVDy1ybdq0Yf78+fz5558kJSUxf/58WrduXRltE0IIIZ7KQ4vczJkzycrKYuTIkQwbNozbt28TFBRUGW2rMitXrmTlypWPvP706dO5cOFCBbZICCHEkyi3yH3wwQcAjBw5kv/+9780aNCAhg0bkpCQwJtvvsnw4cP5/vvvK62humzevHmSIC6EEDqo3BtPxo0bB8CMGTPKXH7nzh1mzpzJ0KFDK6ZlFaSwsJDg4GASExNJT0/Hzs6Ozz//nP/85z9s3boVc3Nz6tWrp82Oe+211+jTpw/nz5+nYcOGDB48mI0bN3L9+nUWLlxI165d8fX1xc/PD4DVq1dTq1Ytfv/9d+zs7Fi8eDEmJiZVecpCCFFjlVvk2rVrB0DXrl3L3bg6plmfOXMGY2NjtmzZglqtZsyYMWzYsIHt27ezfft2FAoFw4cP1xa59PR0evbsyezZs/H19eXgwYN89913bN++nfXr15fqnzNnzrB3714sLS0ZNmwYx48fx8XFpcQ6WVlZpSKLqmNfCiGErnuqJ7pHjap+tyd36dIFMzMzwsPD+eOPP7hy5QoODg44OztrU8FdXV1Rq9XabXr27AlAs2bN6Ny5MwBNmzYtM1vP1tZWmw1nY2NDZmZmqXUeNxn8QeSVX+JBJLFd1HQ17rUlhw4dYsWKFYwePRofHx9u377Nc889x507d7TrGBkZkZ+fr/1873RjeWnfxUxNTbW/3/vuz3s9bjL4g0gMj3gQKXCipnukZHB9EhMTw4ABAxg8eDD16tUjNjYWgCNHjnDnzh3y8vL44YcfKrQNkgwuhBCVo8aN5IYOHcqUKVPYvXs3xsbGdOrUiczMTMaMGcOQIUOoV68eTZs2repmCiGEeAYemgwuKseDksEfRKYrhRA12VMngwshhBDVlRQ5IYQQeqvGXZPTdWUlgz+IPEIghBDlk5FcNScFTgghyidFTgghhN6SIieEEEJvSZETQoflF6iquglCVGty44mO+WBhFKbPmVV1M4SO+C6k+r0fVghdIiM5IYQQekuKXDk2bdrEpk2bqroZQgghnoJMV5Zj5MiRVd0EIYQQT0lvitz169eZMmUKOTk5GBgYEBgYiIGBAQsWLCA3Nxdzc3NmzZpF8+bN8fX1pX79+iQmJuLh4cHt27e1CegLFy7EyspKG70zadIklEolX331FQqFgvbt2zNnzhzy8/OZPXs2iYmJqFQqxo0bh7u7OwkJCQQFBVFYWIipqSkLFiygVatWVdgzQghRc+lNkdu2bRu9evVi7NixHDt2jFOnTqFUKgkLC6Np06ZER0czY8YMvv32WwDs7OxYtWoVt27dYtCgQXz22WcYGBhw4MABtmzZwubNmwG4ceMGCxYsIDIyEisrK/z9/fnxxx85e/Ys9vb2LFq0iL///psRI0bQsWNH1q9fz9tvv82AAZS2IKkAABmYSURBVAPYvn07Z8+eLVXkJBlcCCEqh94UOScnJyZNmkR8fDzOzs44Ozvz5Zdf8v7772vX+fvvv7W/d+jQAYAGDRrQpk0bYmNjMTY25oUXXqBRo0ba9c6cOUOnTp20eW+hoaEAfPnll+Tm5hIREQFATk4OiYmJODs7M3v2bKKjo3FxcaF3796l2vo0yeDyGq+aRZK9hXg6elPkOnfuzO7duzl69Ch79uzh+++/x9ramqioKABUKhXp6ena9WvV+l+cjZeXF3v27MHY2BgPD48S+zUyMkKhUGg/37p1CwC1Wk1oaCj29vYApKenU79+fYyNjXnllVc4cuQI3377LUePHmXu3Lkl9vk0yeASrVOzSIET4unozd2VISEh7Ny5E29vb4KCgkhISCAzM5Off/4ZgIiICKZMmVLmtn369OHUqVOcOHGCfv36lVjWvn17zp49S1paGgDz58/n0KFDODo6au++TE1NxdPTk5SUFD788EMuXLjAiBEj+OCDD/j1119LHU+SwYUQonLozUjO19eXTz75hMjISAwNDQkNDaV+/frMmzePvLw86taty6JFi8rctlatWnTq1In8/Hzq1KlTYlnjxo2ZPn067777Lmq1mn/961/4+Phw9+5dgoODcXd3R6VS4e/vT4sWLZgwYQLTp0/niy++wNjYmODg4Eo4eyGEEGWRZHAd8ajJ4DJdKYQQ/yPJ4EIIIWosKXJCCCH0lt5ck9MXD0sGl0cIhBDi0clIrpqRAieEEI9OipwQQgi9JUVOCCGE3pIiJ54JSbAWQugiufFEx1TXZHBJsBZC6CIZyQkhhNBbVV7kfH19iY2NBWDatGn06dOHXbt2VcqxXVxcSE5OrrD929nZVdi+hRBCPJxOTVdu376d8+fPY2JiUtVNEUIIoQcqpMg9Tkp3sQkTJqDRaBg6dChff/01FhYW2mUqlYqQkBDi4uJQqVT4+Pjw1ltvERsbS1hYGMbGxiQnJ+Pi4sJzzz3HwYMHAVizZg0NGzbEycmJfv36cebMGerUqcPixYtLPHCtVquZP38+MTExKBQKPD09GT9+PP7+/nTp0oVhw4YBRaPOKVOmYGZmRnBwMBkZGdSqVYsZM2bQtm1bkpOT8ff3Jycnh44dO1ZE1wohhHgMFTJdWZzSHRkZyeTJkzl16hSBgYEsWbKE7du38/bbbzNjxowS24SFhQEQFRVVosABbN26FSga6W3bto1Dhw5pI3TOnTvHrFmziIiIIDw8nAYNGhAZGYmdnR27d+8GijLgXnnlFZRKJW5ubqXy3TZt2kRKSgo7d+7k+++/58CBAxw9epTBgwdr8+iuXbvGrVu36NixI1OnTsXf35/t27czZ84cPvroIwDmzJmDj48PUVFRdOrUqdz+ycrKIjk5ucSPJIMLIcSzVyEjucdN6X6YmJgY4uPj+emnn4CiFO5Lly7x0ksv0bp1a5o0aQKAubk5Tk5OADRt2pSsrCwATE1NGTRoEADe3t58/vnnJfYfGxuLt7c3hoaG1K5dGw8PD2JiYggICGDGjBkkJycTFRWFl5cX2dnZXLx4kWnTpmm3z8nJ4fbt28TFxbFkyRIAPD09CQwMLPN8HjUZvDq9wksSrIUQuqhCitzjpnTfb9OmTWzevBmAESNGaPPaXn/9daBoZFanTh3Onj2LsXHJImBoWPoPrYGBgTbdW61Wl1pHrVaX+KzRaFCpVCgUCgYNGsTu3bvZu3cv69atQ61WY2Jioj0XKJqeNTMz024LoFAoMDAoe6D8qMng1SlWRwqcEEIXVch05dOkdAOMHDmSqKgooqKiGDlyJI6OjmzdupWCggKys7N54403OHv27CO35+7duxw+fBiAyMhIevbsWWK5o6MjO3bsQKVScffuXZRKJQ4ODgD4+PiwefNmmjRpQuPGjXn++edp1aqVtsidOHGCUaOKnhHr1q0bO3fuBODAgQPk5eWV2R5JBhdCiMpRISO5p0npLsuIESO4evUq3t7eFBYW4uPjg4ODg/bRg0exb98+li5diqWlZaljDx8+nCtXruDl5UVBQQEeHh7069cPgCZNmtCkSZMSI6/Q0FCCg4NZu3YtxsbGLF26FIVCQVBQEP7+/mzZsoV27dqVShkXQghRuWpEMridnR2XLl167O00Gg2pqan4+vqya9euCn20oaxk8Oo0XSmEEFVBksGfwv79+/Hy8uLjjz+WZ/eEEKIa0qmHwSvKk4ziAFxdXXF1dX3GrRFCCFFZakSRq07uTQavTo8QCCGELpLpSh0mBU4IIZ6OFDkhhBB6S4qcEEIIvSVFTjyQJH4LIaozufFEx+haMrgkfgshqjMZyQkhhNBbNb7IrVy5kpUrV1bY/gMCAoiMjKyw/QshhChfjS9yQggh9FeVXpOLjY1l1apVbNy4ESga9XTt2pUNGzZga2tLfHw8FhYWLF++HDMzM44dO8aKFSsoLCzE2tqaOXPmYG5ujouLC25ubpw4cQIjIyP+/e9/8/XXX3P16lWmTp3KwIEDCQgIwNTUlAsXLpCdnc3777+vzZgrduTIEZYtW4ZaraZ58+bMnj2bxMREli9fro3+iYyM5Ny5cwQFBZWZVq7RaFi4cCFHjx7F0tISlUpF165dK71vhRBC6OhILiEhgbfffptdu3ZRr149lEolt27dYsmSJaxbt44dO3bQvXt3Fi9erN2mYcOGREZGYmNjw5o1a/j6668JDQ1lzZo12nWSkpLYsmUL69evJyQkhLS0NO2ymzdvEhQUxBdffIFSqaRTp07Mnj0bR0dH0tLS+PPPPwHYsWMHPj4+5aaV79+/n19//ZVdu3axfPly7Xb3kmRwIYSoHDp5d6WFhQVt27YFwNbWlszMTM6dO0dKSgqjR48GioJO69evr92mOCOuadOmWFpaYmRkVCIdHIqy4YyNjbGysqJTp06cPn1au+z8+fN06NBB+0qt4cOHs2bNGhQKBd7e3uzcuRMfHx9u3rxJx44dWbduXZlp5b///juvv/46xsbGNGjQoFR2HVSvZHBJ/BZCVGdVWuQUCgX3Jv0UFBQAYGpqWmodlUpFp06dCAsLAyAvL4/s7GztevcmhBsZlX1a9yaCq9XqEuuVlQ5eWFgIgLe3N2PHjsXExAQvLy+ActPKQ0JCSpxTWW2pTsngUuCEENVZlU5Xmpubk5SURF5eHhkZGSVGVvfr2LEjZ8+e5fLlywB8+eWXhISEPNbx9u7di0aj4dq1a5w/f57OnTuX2P+5c+dITk4GYMuWLdp08GbNmmFlZcXmzZu1Ra68tHInJyf27t1Lfn4+mZmZREdHl2qHJIMLIUTlqNKRnK2tLc7Ozri5udGsWbMSRed+jRo1Yv78+Xz44Yeo1WoaN25MaGjoYx0vNzeXwYMHk5+fz+zZszE3N9cua9iwIbNnz8bPz4+CggKaNm3KvHnztMsHDhzIgQMHaNy4MVB+WjnAhQsXcHd3p2HDhtjY2DxWG4UQQjw7NSIZHP5356aPj89jb1tYWMinn36Kq6urdnryWZNkcCGEeHySDP6UNBoNPXr0QKFQ0Ldv36pujhBCiMegk3dXVoSFCxc+0XYKhYKYmJhn3BohhBCVocYUuepCksGFEOLZkelKHSYFTgghno4UOSGEEHpLipwQQgi9JUVOPJSkgwshqiu58UTH6FoyOEg6uBCi+pKRnBBCCL2lk0UuNjYWX1/fqm7GYyl+p+X9XFxctO/DFEIIUbl0sshVR1FRUVXdBCGEEPfR2Wtyt27dYty4cfz555+88MILrFixAqVSyTfffINCocDe3p4ZM2ZQp04d7OzsuHTpElCU3B0XF8fChQtZtGgRJ06cwMDAgL59++Ln50d2drY28VulUjFu3Djc3d2JjIzk6NGjZGRkkJqayogRI7h27Ro//fQTZmZmrF27FlNTU5YuXUpMTAyZmZlYWlqydOlSGjZsqG1DRkYG/v7+XL9+HRsbG/Ly8qq4J4UQoubS2ZHcX3/9RVBQEHv37iU9PZ1NmzYRFhbGxo0bUSqV1K5du9zgUYBr165x7Ngxdu7cyaZNm/jtt9/Iy8vjq6++wt7ensjISMLDwwkLCyMpKQkoSg/48ssvWbduHQsWLKBnz54olUoAoqOjuXr1Kn/88QebN29m//79NGnShJ07d5Y47ooVK2jbti1KpZJRo0aRnp5eqm2SDC6EEJVDZ0dybdq0oXnz5gDY2Nhw584devfurY3HGT58ONOmTSt3+8aNG2NqasqIESPo3bs3U6ZMwdTUlJMnT5Kbm0tERARQlOidmJgIQKdOnahbty5169YFwMnJCSjKk8vKyqJly5ZMnTqV77//nsuXL3P27FlatGhR4rhxcXEsWbIEgC5dumjP4V4PSwZvatVYp952IungQojqSmeL3L2J2gqFgnr16pGVlaX97t7k7uLPCoVC+52RkRHff/89cXFxHDt2jBEjRrBx40bUajWhoaHY29sDkJ6eTv369VEqlSXSxe9vA8DFixf55JNPeOutt+jfvz8GBgbcn1R0f9r5vWnkxR6WDG495z+P1EeVRQqcEKK60tnpyrIcPnyYjIwMALZu3aoNKTU3NycxMRGNRsPhw4cB+PXXX3nzzTfp0qULU6dOxcbGhsuXL+Po6MimTZsASE1NxdPTk5SUlEc6/qlTp+jatSsjR46kVatWHD16FJWq5IPSTk5O2ptQzp8/z59//llqP5IMLoQQlUNnR3L3q1u3Lu+99x6+vr4UFBRgb2/PrFmzAPjkk0+YMGECDRs2pHPnzty+fZu2bdvyr3/9C3d3d2rXrk2nTp3o2bMnXbt2JTg4GHd3d1QqFf7+/rRo0YKff/75oW0YOHAgfn5+eHh4ANCuXbtSjwdMnjyZgIAA3NzcePHFF8ucrhRCCFE5akwyuK67NxncVcemK4UQQldJMrgQQogaS4qcEEIIvSVFTse0e28h6sKCqm6GEELoBSlyOkiXnpETQojqTIqcEEIIvSVFTgghhN6SIqdHJMFbCCFKqjYPg9cUT5MMLgneQghRkozkhBBC6C0pckIIIfRWjSpyK1aseKR3VN7Lzs7uiY51584dJk6c+ETbCiGEeDZqVJE7depUqdSAipKZmUl8fHylHEsIIUTZ9PbGk+vXrzNlyhRycnIwMDCgV69eXLx4kcDAQFatWsXcuXPx8/PDwcGB5ORkRo8ezeHDh0lOTsbf35+cnBw6duyo3V92djazZ88mMTERlUrFuHHjcHd3JzIykujoaDIzM0lKSuK1114jODiYuXPnkpqaysSJE/niiy9KtC0rK6tENl5xe4UQQjxbelvktm3bRq9evRg7dizHjh0jMTGRdu3a4efn98ApyDlz5uDj48PQoUPZsWMHW7ZsAeCrr77C3t6eRYsW8ffffzNixAhtETxz5gy7du3C0NAQV1dXRo4cSWBgIKNHjy5V4ODhyeDW1taoCwse+80nkuAthBAl6W2Rc3JyYtKkScTHx+Ps7Mybb77J0aNHH7pdXFwcS5YsAcDT05PAwEAATp48SW5uLhEREQDk5OSQmJgIwCuvvELdunUBaN68OZmZmdSpU6fcYzwsGfzG87Xo/Onaxz5nKXBCCFGS3ha5zp07s3v3bo4ePcqePXvYvn17qXWKo/QKCwvL/F6hUGBgUHTZUq1WExoair29PQDp6enUr18fpVKJqampdluFQsHDIvrq1atHvXr1nvzkhBBCPBK9vfEkJCSEnTt34u3tTVBQEL/++iuGhobaG0/Mzc357bffADh48KB2u27durFz504ADhw4QF5eHgCOjo5s2rQJgNTUVDw9PUlJSSn3+EZGRqWKpxBCiMqlt0XO19eX/fv34+XlhZ+fH4sWLaJHjx7MnDmTX375hbFjx/Ldd9/h7e1Nbm6udrugoCD279+Pp6cnP/74o3ba0c/Pj9zcXNzd3RkzZgz+/v60aNGi3ONbWFjQtGlTfH19K/xchRBClE2hedjcmqgUxRHuy4d1odETXpMTQoiapvhv56FDh7C2ti61XG9HckIIIYTe3nhSXbV7b+ETP0IghBCiJClyOqL4hhh5KFwIIR5d8d/M8t5mJUVOR1y5cgWAUaMkLkcIIR5XWloaLVu2LPW9FDkd0bx5cwA2bNhAs2bNqrg1uqn4gfnw8HCsrKyqujk6R/rnwaR/Hqy69o9KpSItLY127dqVuVyKnI4wMTEBoFmzZmXeIST+x8rKSvroAaR/Hkz658GqY/+UNYIrJndXCiGE0FtS5IQQQugtKXJCCCH0lmFwcHBwVTdCFDE1NcXBwaHEC59FSdJHDyb982DSPw+mj/0jr/USQgiht2S6UgghhN6SIieEEEJvSZHTEUqlkoEDB/L6668THh5e1c2pVKtWrcLNzQ03NzdCQkKAoiR2Dw8PXn/9dZYuXapdNz4+Hh8fH/r378/06dO1mX1//fUXo0aNwtXVlffff5/s7OwqOZeKtGjRIgICAgDpn3sdPnwYHx8fBgwYwNy5cwHpn3tFRUVp//tatGgRUMP6RyOq3PXr1zW9e/fW3L59W5Odna3x8PDQJCYmVnWzKsWJEyc0w4cP1+Tl5Wny8/M1o0eP1iiVSo2zs7Pmzz//1BQUFGjeeecdzdGjRzUajUbj5uamOXPmjEaj0WimTZumCQ8P12g0Gs348eM1u3bt0mg0Gs2qVas0ISEhVXNCFeTkyZMaBwcHzdSpUzV3796V/vnHn3/+qenevbsmJSVFk5+frxk5cqTm6NGj0j//yMnJ0XTp0kVz8+ZNTUFBgWbIkCGaQ4cO1aj+kZGcDjh58iSOjo6YmZnx3HPP0b9/f/bt21fVzaoUjRo1IiAgABMTE4yNjbGxseHKlSu0bNmS5s2bY2RkhIeHB/v27ePatWvk5ubyr3/9CwAfHx/27dtHQUEBp06don///iW+1xcZGRksXbqUCRMmAHD+/Hnpn3/88MMPDBw4ECsrK4yNjVm6dCm1a9eW/vmHSqVCrVZz9+5dCgsLKSwspG7dujWqf+S1XjogNTWVRo0aaT9bWlpy/vz5KmxR5bG1tdX+fuXKFfbu3cubb75Zqj9u3LhRqp8aNWrEjRs3uH37NnXr1sXIyKjE9/oiKCiIjz76iJSUFKDsf19qav9cvXoVY2NjJkyYQEpKCr169cLW1lb65x9169blgw8+YMCAAdSuXZsuXbrUuH9/ZCSnA9RqNQqFQvtZo9GU+FwTJCYm8s477/Dpp5/SvHnzMvujvH4qq7/0pf++//57mjRpgpOTk/a78vqhJvaPSqUiJiaG+fPns2XLFs6fP09SUpL0zz8SEhKIiIjgyJEjREdHY2BgwJUrV2pU/8hITgdYWVnx888/az+npaVhaWlZhS2qXKdPn2by5Ml89tlnuLm5ERcXR1pamnZ5cX9YWVmV+D49PR1LS0saNGjAnTt3UKlUGBoa6lX/7dmzh7S0NLy8vMjMzCQnJ4dr165haGioXacm90/Dhg1xcnKiQYMGAPTt25d9+/ZJ//zj+PHjODk5YWFhARRNNa5bt65G9Y+M5HRAt27diImJ4datW9y9e5cDBw7Qs2fPqm5WpUhJSWHixIksXrwYNzc3ADp27Mjly5e5evUqKpWKXbt20bNnT5o1a4apqSmnT58Giu4a69mzJ8bGxrz66qvs2bMHgB07duhN/33zzTfs2rWLqKgoJk+ejIuLC2vXrpX++Ufv3r05fvw4WVlZqFQqoqOjcXV1lf75R5s2bTh58iQ5OTloNBoOHz5c4/77kjee6AilUsnq1aspKChgyJAhjBs3rqqbVCnmzp1LREQELVq00H43YsQIWrVqxYIFC8jLy8PZ2Zlp06ahUChISEggMDCQv//+G3t7exYsWICJiQnXrl0jICCAmzdv0qRJEz7//HPq169fhWf27EVGRhIXF8fChQuJiYmR/vnHtm3b+PbbbykoKOC1114jMDCQ2NhY6Z9/rFmzhsjISIyNjWnfvj0zZ87kl19+qTH9I0VOCCGE3pLpSiGEEHpLipwQQgi9JUVOCCGE3pIiJ4QQQm9JkRNCCKG3pMgJISrEO++8w61bt6q6GaKGkyInhKgQJ06cqOomCCFFToiaaNu2bbi5ueHh4cHo0aNJSUlhy5YtuLu74+npyTvvvMPly5cBCAgIYN26ddpt7/3s4uLCypUreeONN+jduzfLli0DYNq0aQCMGTNG+2JpIaqCvLtSiBomISGBxYsXs337dpo0acK3337LW2+9hVqtZsuWLTRo0IDIyEgmTpzI7t27H7q/nJwcvvvuO27cuEG/fv0YPHgwCxYsIDIykvXr12vfKylEVZCRnBA1TExMDN27d6dJkyYAvPXWW/Tp04eBAwdqC5KPjw83btwgOTn5ofvr06cPAI0bN8bCwoLMzMyKa7wQj0mKnBA1jKGhYYmolNzcXJKSkkqtp9FoKCws1MatFCsoKCixnqmpqfb3+9cVoqpJkROihnFwcCAmJobU1FQANm/ezI8//siePXu0d0NGRERgZmZGy5YtMTc35+LFiwDcuHGDuLi4RzqOoaEhhYWFFXMSQjwiuSYnRA1jZ2eHv78/Y8eOBYqSnn/44QcOHjzImDFjUKvVNGjQgNWrV2NgYICvry9Tpkyhf//+WFtb4+jo+EjHcXV1xdfXl5UrV9K6deuKPCUhyiUpBEIIIfSWTFcKIYTQW1LkhBBC6C0pckIIIfSWFDkhhBB6S4qcEEIIvSVFTgghhN6SIieEEEJvSZETQgiht/4/zoegaT6MDyoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "sns.countplot(y='job',hue='y', data=df_initial);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='marital',hue='y', data=df_initial);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='education', hue='y',data=df_initial);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32    1909\n",
       "31    1826\n",
       "33    1770\n",
       "34    1730\n",
       "35    1721\n",
       "36    1648\n",
       "30    1565\n",
       "37    1538\n",
       "39    1380\n",
       "38    1350\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_initial.age.value_counts().head(10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}