IBM Anac_Version-Copy2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df38c319",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a46fb1c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge pdpbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e6291a01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "1c7a9db5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from category_encoders import OrdinalEncoder, OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, plot_confusion_matrix, classification_report, roc_curve, plot_roc_curve, roc_auc_score, balanced_accuracy_score\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier \n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.inspection import permutation_importance \n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from pdpbox.pdp import pdp_isolate, pdp_plot, pdp_interact, pdp_interact_plot\n",
    "import ipywidgets\n",
    "import shap\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore', category=FutureWarning, module='xgboost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "31398230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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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>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/jasongersing/Desktop/DS Projects/WA_Fn-UseC_-HR-Employee-Attrition 2.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "6cd537e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def wrangle(df):\n",
    "    \n",
    "    # Dropping columns with high number of NaN values\n",
    "    df.dropna(axis=1, thresh=500, inplace = True)\n",
    "        \n",
    "    # Dropping columns with unique values\n",
    "    df.drop(columns=['EmployeeNumber'], inplace=True)\n",
    "    \n",
    "    # Dropping columns with constant values\n",
    "    df.drop(columns=['EmployeeCount', 'Over18', 'StandardHours'], inplace=True)\n",
    "\n",
    "    # Clean binary encoded columns\n",
    "    df['Attrition'] = (df['Attrition'] == 'Yes').astype(int)\n",
    "    df['Gender'] = (df['Gender'] == 'Male').astype(int)\n",
    "    df['MaritalStatus'] = (df['MaritalStatus'] == 'Married').astype(int)\n",
    "    df['OverTime'] = (df['OverTime'] == 'Yes').astype(int)\n",
    "    \n",
    "    # Brute force OneHotEncoder\n",
    "    dept = ['Research & Development', 'Sales', 'Human Resources']\n",
    "    for dept in df['Department']:\n",
    "      df[dept] = (df['Department'].str.contains(dept, regex=True)).astype(int)\n",
    "\n",
    "    field = ['Life Sciences', 'Medical', 'Marketing', 'Technical Degree', 'Other', 'Human Resources']\n",
    "    for field in df['EducationField']:\n",
    "      df[field] = (df['EducationField'].str.contains(dept, regex=True)).astype(int)\n",
    "\n",
    "    role = ['Sales Executive', 'Research Scientist', 'Laboratory Technician', 'Manufactoring Director', 'Healthcare Representative', 'Manager', 'Sales Representative', 'Research Director', 'Human Resources']\n",
    "    for role in df['JobRole']:\n",
    "      df[role] = (df['JobRole'].str.contains(dept, regex=True)).astype(int)\n",
    "\n",
    "    # Brute force OrdinalEncoder\n",
    "    df['BusinessTravel'] = pd.factorize(df['BusinessTravel'])[0]\n",
    "\n",
    "    df = df.drop(columns=['Department', 'EducationField', 'JobRole'])\n",
    "    \n",
    "    \n",
    "    return df\n",
    "\n",
    "df = wrangle(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "5988c4f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>...</th>\n",
       "      <th>Marketing</th>\n",
       "      <th>Technical Degree</th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Research Scientist</th>\n",
       "      <th>Laboratory Technician</th>\n",
       "      <th>Manufacturing Director</th>\n",
       "      <th>Healthcare Representative</th>\n",
       "      <th>Manager</th>\n",
       "      <th>Sales Representative</th>\n",
       "      <th>Research Director</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
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       "      <td>1102</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>279</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1373</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1392</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>591</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Attrition  BusinessTravel  DailyRate  DistanceFromHome  Education  \\\n",
       "0   41          1               0       1102                 1          2   \n",
       "1   49          0               1        279                 8          1   \n",
       "2   37          1               0       1373                 2          2   \n",
       "3   33          0               1       1392                 3          4   \n",
       "4   27          0               0        591                 2          1   \n",
       "\n",
       "   EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  ...  \\\n",
       "0                        2       0          94               3  ...   \n",
       "1                        3       1          61               2  ...   \n",
       "2                        4       1          92               2  ...   \n",
       "3                        4       0          56               3  ...   \n",
       "4                        1       1          40               3  ...   \n",
       "\n",
       "   Marketing  Technical Degree  Sales Executive  Research Scientist  \\\n",
       "0          0                 0                0                   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",
       "   Laboratory Technician  Manufacturing Director  Healthcare Representative  \\\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",
       "   Manager  Sales Representative  Research Director  \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",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm wrangle function worked.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "9aa8e5b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Attrition'\n",
    "y = df[target]\n",
    "X = df.drop(columns=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "99654da7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "0bfa09fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "a6e03536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check\n",
    "len(X_train) + len(X_val) + len(X_test) == len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "451ada66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1470, 43), (882, 43), (294, 43), (294, 43))"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, X_train.shape, X_val.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "7825c63f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Accuracy: 0.8231292517006803\n"
     ]
    }
   ],
   "source": [
    "print('Baseline Accuracy:', y_train.value_counts(normalize=True).max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "id": "43f0d1b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 1: Logistic Regression\n",
    "model_lr = make_pipeline(\n",
    "    SimpleImputer(strategy='mean'),\n",
    "    StandardScaler(),\n",
    "    LogisticRegression()\n",
    ")\n",
    "\n",
    "model_lr.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "d3dc74aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 2: Random Forest\n",
    "model_rf = RandomForestClassifier(n_jobs=-1, random_state=42)\n",
    "\n",
    "model_rf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "b3c8e41b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14:23:47] WARNING: ../src/learner.cc:576: \n",
      "Parameters: { \"n_estimator\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
      "\n",
      "\n",
      "[14:23:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.2, max_delta_step=0,\n",
       "              max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimator=75, n_estimators=100,\n",
       "              n_jobs=-1, num_parallel_tree=1, predictor='auto', random_state=42,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 3: XGBoost (Not using a pipeline in order to use Shap later)\n",
    "\n",
    "model_xgb = XGBClassifier(random_state=42,n_estimator=75, n_jobs=-1, learning_rate=.2) \n",
    "model_xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "id": "2794c1ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (LOGR): 0.8741496598639455\n",
      "Validation Accuracy (LOGR): 0.8809523809523809\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (LOGR):', model_lr.score(X_train, y_train))\n",
    "print('Validation Accuracy (LOGR):', model_lr.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "d9f10f9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (RF): 1.0\n",
      "Validation Accuracy (RF): 0.8809523809523809\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (RF):', model_rf.score(X_train, y_train))\n",
    "print('Validation Accuracy (RF):', model_rf.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "f2d58c2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (XGB): 1.0\n",
      "Validation Accuracy (XGB): 0.8843537414965986\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (XGB):', model_xgb.score(X_train, y_train))\n",
    "print('Validation Accuracy (XGB):', model_xgb.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "1ed683fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.96      0.93       256\n",
      "           1       0.57      0.32      0.41        38\n",
      "\n",
      "    accuracy                           0.88       294\n",
      "   macro avg       0.74      0.64      0.67       294\n",
      "weighted avg       0.86      0.88      0.87       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Logistic Regression')\n",
    "print(classification_report(y_val, model_lr.predict(X_val)))\n",
    "plot_confusion_matrix(model_lr, X_val, y_val);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "68be56e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.99      0.94       256\n",
      "           1       0.71      0.13      0.22        38\n",
      "\n",
      "    accuracy                           0.88       294\n",
      "   macro avg       0.80      0.56      0.58       294\n",
      "weighted avg       0.86      0.88      0.84       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Random Forest')\n",
    "print(classification_report(y_val, model_rf.predict(X_val)))\n",
    "plot_confusion_matrix(model_rf, X_val, y_val);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "5e8a0bf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94       256\n",
      "           1       0.64      0.24      0.35        38\n",
      "\n",
      "    accuracy                           0.88       294\n",
      "   macro avg       0.77      0.61      0.64       294\n",
      "weighted avg       0.86      0.88      0.86       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('XG Boost')\n",
    "print(classification_report(y_val, model_xgb.predict(X_val)))\n",
    "plot_confusion_matrix(model_xgb, X_val, y_val);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "cd9bacda",
   "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": [
    "#Plot ROC curves for the three models\n",
    "\n",
    "lr = plot_roc_curve(model_lr,\n",
    "               X_val,\n",
    "               y_val,\n",
    "               name='logistic regression');\n",
    "\n",
    "rf = plot_roc_curve(model_rf,\n",
    "               X_val,\n",
    "               y_val,\n",
    "               ax=lr.ax_,\n",
    "               name='random forest');\n",
    "\n",
    "xgb = plot_roc_curve(model_xgb,\n",
    "               X_val,\n",
    "               y_val,\n",
    "               ax=lr.ax_,\n",
    "               name='xgboost');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "e6c91a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8071546052631577"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_val, model_lr.predict_proba(X_val)[:,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "a0d77e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7941509046052632"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_val, model_rf.predict_proba(X_val)[:,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "c92c29e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8193873355263157"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_val, model_xgb.predict_proba(X_val)[:,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "bf5cfb6f",
   "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>false_pos_rate</th>\n",
       "      <th>true_pos_rate</th>\n",
       "      <th>thresholds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.948723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.948723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.837836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.763679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.659992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.485952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.470688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.027344</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.445672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.027344</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.440857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.429563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.420934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.035156</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.388477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.035156</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.357490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.050781</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.320799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.050781</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.309704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.058594</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.277294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.058594</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.275058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.274583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.270926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.070312</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.257801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.070312</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.253734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.209847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.204191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.105469</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.186614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.105469</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.178131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.161399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.155636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.132812</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.123094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.132812</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.122682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.164062</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.083899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.164062</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>0.081364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.183594</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>0.074921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.183594</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>0.072593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.292969</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>0.036082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.292969</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.035983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.028908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.763158</td>\n",
       "      <td>0.028868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.351562</td>\n",
       "      <td>0.763158</td>\n",
       "      <td>0.022721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.351562</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.022301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.355469</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.021611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.355469</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>0.021449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.390625</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>0.018387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.390625</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.017979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.410156</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.017187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.410156</td>\n",
       "      <td>0.868421</td>\n",
       "      <td>0.017186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.492188</td>\n",
       "      <td>0.868421</td>\n",
       "      <td>0.011951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.492188</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.011932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.570312</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.008973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.570312</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.008810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.589844</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.007867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.589844</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.007846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.652344</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.005919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.652344</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.005818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.878906</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.001680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.878906</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    false_pos_rate  true_pos_rate  thresholds\n",
       "0         0.000000       0.000000    1.948723\n",
       "1         0.000000       0.026316    0.948723\n",
       "2         0.000000       0.131579    0.837836\n",
       "3         0.011719       0.131579    0.763679\n",
       "4         0.011719       0.236842    0.659992\n",
       "5         0.023438       0.236842    0.485952\n",
       "6         0.023438       0.263158    0.470688\n",
       "7         0.027344       0.263158    0.445672\n",
       "8         0.027344       0.289474    0.440857\n",
       "9         0.031250       0.289474    0.429563\n",
       "10        0.031250       0.315789    0.420934\n",
       "11        0.035156       0.315789    0.388477\n",
       "12        0.035156       0.394737    0.357490\n",
       "13        0.050781       0.394737    0.320799\n",
       "14        0.050781       0.421053    0.309704\n",
       "15        0.058594       0.421053    0.277294\n",
       "16        0.058594       0.447368    0.275058\n",
       "17        0.062500       0.447368    0.274583\n",
       "18        0.062500       0.473684    0.270926\n",
       "19        0.070312       0.473684    0.257801\n",
       "20        0.070312       0.500000    0.253734\n",
       "21        0.085938       0.500000    0.209847\n",
       "22        0.085938       0.526316    0.204191\n",
       "23        0.105469       0.526316    0.186614\n",
       "24        0.105469       0.578947    0.178131\n",
       "25        0.113281       0.578947    0.161399\n",
       "26        0.113281       0.605263    0.155636\n",
       "27        0.132812       0.605263    0.123094\n",
       "28        0.132812       0.631579    0.122682\n",
       "29        0.164062       0.631579    0.083899\n",
       "30        0.164062       0.657895    0.081364\n",
       "31        0.183594       0.657895    0.074921\n",
       "32        0.183594       0.710526    0.072593\n",
       "33        0.292969       0.710526    0.036082\n",
       "34        0.292969       0.736842    0.035983\n",
       "35        0.328125       0.736842    0.028908\n",
       "36        0.328125       0.763158    0.028868\n",
       "37        0.351562       0.763158    0.022721\n",
       "38        0.351562       0.789474    0.022301\n",
       "39        0.355469       0.789474    0.021611\n",
       "40        0.355469       0.815789    0.021449\n",
       "41        0.390625       0.815789    0.018387\n",
       "42        0.390625       0.842105    0.017979\n",
       "43        0.410156       0.842105    0.017187\n",
       "44        0.410156       0.868421    0.017186\n",
       "45        0.492188       0.868421    0.011951\n",
       "46        0.492188       0.894737    0.011932\n",
       "47        0.570312       0.894737    0.008973\n",
       "48        0.570312       0.921053    0.008810\n",
       "49        0.589844       0.921053    0.007867\n",
       "50        0.589844       0.947368    0.007846\n",
       "51        0.652344       0.947368    0.005919\n",
       "52        0.652344       0.973684    0.005818\n",
       "53        0.878906       0.973684    0.001680\n",
       "54        0.878906       1.000000    0.001638\n",
       "55        1.000000       1.000000    0.000082"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get predicted probabilities from model\n",
    "\n",
    "y_pred_prob = model_xgb.predict_proba(X_val)[:,-1]\n",
    "\n",
    "# Input true labels and probability predictions \n",
    "fpr, tpr, thresholds = roc_curve(y_val, y_pred_prob)\n",
    "\n",
    "# Put data into dictionary\n",
    "data = {'false_pos_rate': fpr,\n",
    "        'true_pos_rate': tpr,\n",
    "        'thresholds':thresholds}\n",
    "\n",
    "df_pos = pd.DataFrame(data)\n",
    "df_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "b79de55c",
   "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": [
    "# Create a plot that takes the p value where TPR(p) == TNR(p). Since TPR = 1 - FPR, we get FPR + TPR -1 for our \n",
    "# y-axis. We plot this against different thresholds and determine the minimum value. \n",
    "\n",
    "plt.scatter(thresholds,np.abs(fpr+tpr-1))\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"|FPR + TPR - 1|\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "f899e4ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.036081888"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To determine the exact minimum\n",
    "\n",
    "thresholds[np.argmin(np.abs(fpr+tpr-1))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "162aa7b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use new threshold and check validation accuracy.\n",
    "\n",
    "y_pred_new_threshold = (model_xgb.predict_proba(X_val)[:,1]>=0.036).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "141ec69a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy (XGB): 0.7074829931972789\n"
     ]
    }
   ],
   "source": [
    "print('Validation Accuracy (XGB):', accuracy_score(y_val, y_pred_new_threshold))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "28c9b9e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try a different method (Maximize Accuracy), which better accounts for unblanced datasets.\n",
    "\n",
    "threshold = []\n",
    "accuracy = []\n",
    "\n",
    "for p in np.unique(model_xgb.predict_proba(X_val)[:,1]):\n",
    "  threshold.append(p)\n",
    "  y_pred = (model_xgb.predict_proba(X_val)[:,1] >= p).astype(int)\n",
    "  accuracy.append(balanced_accuracy_score(y_val,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "3c68b613",
   "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": [
    "plt.scatter(threshold,accuracy)\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Balanced accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "54e43382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0725931"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine exact maximum\n",
    "\n",
    "threshold[np.argmax(accuracy)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "e3788356",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use new threshold and check validation accuracy.\n",
    "\n",
    "y_pred_new_threshold_2 = (model_xgb.predict_proba(X_val)[:,1]>=0.073).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "ccd53e86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy (XGB): 0.7993197278911565\n"
     ]
    }
   ],
   "source": [
    "print('Validation Accuracy (XGB):', accuracy_score(y_val, y_pred_new_threshold_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "58d9aef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Neither new threshold improves our validation accuracy score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "bd6f0dec",
   "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>false_pos_rate</th>\n",
       "      <th>true_pos_rate</th>\n",
       "      <th>thresholds</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.948723</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.948723</td>\n",
       "      <td>0.973684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>0.868421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.763679</td>\n",
       "      <td>0.868500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.659992</td>\n",
       "      <td>0.763248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.485952</td>\n",
       "      <td>0.763518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.470688</td>\n",
       "      <td>0.737215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.027344</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.445672</td>\n",
       "      <td>0.737349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.027344</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.440857</td>\n",
       "      <td>0.711052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.429563</td>\n",
       "      <td>0.711213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.420934</td>\n",
       "      <td>0.684924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.035156</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.388477</td>\n",
       "      <td>0.685113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.035156</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.357490</td>\n",
       "      <td>0.606283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.050781</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.320799</td>\n",
       "      <td>0.607390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.050781</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.309704</td>\n",
       "      <td>0.581170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.058594</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.277294</td>\n",
       "      <td>0.581905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.058594</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.275058</td>\n",
       "      <td>0.555729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.274583</td>\n",
       "      <td>0.556155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.270926</td>\n",
       "      <td>0.530014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.070312</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.257801</td>\n",
       "      <td>0.530992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.070312</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.253734</td>\n",
       "      <td>0.504920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.209847</td>\n",
       "      <td>0.507332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.204191</td>\n",
       "      <td>0.481417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.105469</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.186614</td>\n",
       "      <td>0.485284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.105469</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.178131</td>\n",
       "      <td>0.434061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.161399</td>\n",
       "      <td>0.436025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.155636</td>\n",
       "      <td>0.410670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.132812</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.123094</td>\n",
       "      <td>0.416481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.132812</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.122682</td>\n",
       "      <td>0.391629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.164062</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.083899</td>\n",
       "      <td>0.403300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.164062</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>0.081364</td>\n",
       "      <td>0.379411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.183594</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>0.074921</td>\n",
       "      <td>0.388256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.183594</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>0.072593</td>\n",
       "      <td>0.342785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.292969</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>0.036082</td>\n",
       "      <td>0.411856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.292969</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.035983</td>\n",
       "      <td>0.393805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.028908</td>\n",
       "      <td>0.420616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.763158</td>\n",
       "      <td>0.028868</td>\n",
       "      <td>0.404673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.351562</td>\n",
       "      <td>0.763158</td>\n",
       "      <td>0.022721</td>\n",
       "      <td>0.423899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.351562</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.022301</td>\n",
       "      <td>0.409777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.355469</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.021611</td>\n",
       "      <td>0.413134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.355469</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>0.021449</td>\n",
       "      <td>0.400364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.390625</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>0.018387</td>\n",
       "      <td>0.431881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.390625</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.017979</td>\n",
       "      <td>0.421330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.410156</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.017187</td>\n",
       "      <td>0.439498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.410156</td>\n",
       "      <td>0.868421</td>\n",
       "      <td>0.017186</td>\n",
       "      <td>0.430745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.492188</td>\n",
       "      <td>0.868421</td>\n",
       "      <td>0.011951</td>\n",
       "      <td>0.509472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.492188</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.503318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.570312</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.008973</td>\n",
       "      <td>0.579945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.570312</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.008810</td>\n",
       "      <td>0.575751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.589844</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.007867</td>\n",
       "      <td>0.595104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.589844</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.007846</td>\n",
       "      <td>0.592187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.652344</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.005919</td>\n",
       "      <td>0.654463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.652344</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.005818</td>\n",
       "      <td>0.652874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.878906</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.001680</td>\n",
       "      <td>0.879300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.878906</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001638</td>\n",
       "      <td>0.878906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    false_pos_rate  true_pos_rate  thresholds  distance\n",
       "0         0.000000       0.000000    1.948723  1.000000\n",
       "1         0.000000       0.026316    0.948723  0.973684\n",
       "2         0.000000       0.131579    0.837836  0.868421\n",
       "3         0.011719       0.131579    0.763679  0.868500\n",
       "4         0.011719       0.236842    0.659992  0.763248\n",
       "5         0.023438       0.236842    0.485952  0.763518\n",
       "6         0.023438       0.263158    0.470688  0.737215\n",
       "7         0.027344       0.263158    0.445672  0.737349\n",
       "8         0.027344       0.289474    0.440857  0.711052\n",
       "9         0.031250       0.289474    0.429563  0.711213\n",
       "10        0.031250       0.315789    0.420934  0.684924\n",
       "11        0.035156       0.315789    0.388477  0.685113\n",
       "12        0.035156       0.394737    0.357490  0.606283\n",
       "13        0.050781       0.394737    0.320799  0.607390\n",
       "14        0.050781       0.421053    0.309704  0.581170\n",
       "15        0.058594       0.421053    0.277294  0.581905\n",
       "16        0.058594       0.447368    0.275058  0.555729\n",
       "17        0.062500       0.447368    0.274583  0.556155\n",
       "18        0.062500       0.473684    0.270926  0.530014\n",
       "19        0.070312       0.473684    0.257801  0.530992\n",
       "20        0.070312       0.500000    0.253734  0.504920\n",
       "21        0.085938       0.500000    0.209847  0.507332\n",
       "22        0.085938       0.526316    0.204191  0.481417\n",
       "23        0.105469       0.526316    0.186614  0.485284\n",
       "24        0.105469       0.578947    0.178131  0.434061\n",
       "25        0.113281       0.578947    0.161399  0.436025\n",
       "26        0.113281       0.605263    0.155636  0.410670\n",
       "27        0.132812       0.605263    0.123094  0.416481\n",
       "28        0.132812       0.631579    0.122682  0.391629\n",
       "29        0.164062       0.631579    0.083899  0.403300\n",
       "30        0.164062       0.657895    0.081364  0.379411\n",
       "31        0.183594       0.657895    0.074921  0.388256\n",
       "32        0.183594       0.710526    0.072593  0.342785\n",
       "33        0.292969       0.710526    0.036082  0.411856\n",
       "34        0.292969       0.736842    0.035983  0.393805\n",
       "35        0.328125       0.736842    0.028908  0.420616\n",
       "36        0.328125       0.763158    0.028868  0.404673\n",
       "37        0.351562       0.763158    0.022721  0.423899\n",
       "38        0.351562       0.789474    0.022301  0.409777\n",
       "39        0.355469       0.789474    0.021611  0.413134\n",
       "40        0.355469       0.815789    0.021449  0.400364\n",
       "41        0.390625       0.815789    0.018387  0.431881\n",
       "42        0.390625       0.842105    0.017979  0.421330\n",
       "43        0.410156       0.842105    0.017187  0.439498\n",
       "44        0.410156       0.868421    0.017186  0.430745\n",
       "45        0.492188       0.868421    0.011951  0.509472\n",
       "46        0.492188       0.894737    0.011932  0.503318\n",
       "47        0.570312       0.894737    0.008973  0.579945\n",
       "48        0.570312       0.921053    0.008810  0.575751\n",
       "49        0.589844       0.921053    0.007867  0.595104\n",
       "50        0.589844       0.947368    0.007846  0.592187\n",
       "51        0.652344       0.947368    0.005919  0.654463\n",
       "52        0.652344       0.973684    0.005818  0.652874\n",
       "53        0.878906       0.973684    0.001680  0.879300\n",
       "54        0.878906       1.000000    0.001638  0.878906\n",
       "55        1.000000       1.000000    0.000082  1.000000"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To confirm above method, create a new column in our dataframe that employs the distance formula |\n",
    "# in order to find the minimum distance between the ROC curve and the top left corner (0,1). \n",
    "\n",
    "df_pos['distance'] = np.sqrt((1 - df_pos['true_pos_rate'])**2 + (0 - df_pos['false_pos_rate'])**2)\n",
    "df_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "ab4e485c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The minimum distance (0.342785) is at the same threshold value given using the Maximizing Accuracy method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "id": "639ee28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use trial and error to find a threshold that improves our validation accuracy score. \n",
    "\n",
    "y_pred_new_threshold_3 = (model_xgb.predict_proba(X_val)[:,1]>=0.82).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "id": "713c4c0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy (XGB): 0.8877551020408163\n"
     ]
    }
   ],
   "source": [
    "print('Validation Accuracy (XGB):', accuracy_score(y_val, y_pred_new_threshold_3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "c489146b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A very slight improvement in our validation accuracy score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "id": "c7b2c0c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature')"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Plot the gini importances of our features on the target.\n",
    "\n",
    "importances = model_xgb.feature_importances_\n",
    "feature_names =X_train.columns\n",
    "feat_imp= pd.Series(data=importances, index=feature_names).sort_values()\n",
    "feat_imp.tail(10).plot(kind='barh')\n",
    "plt.xlabel('Gini Importance')\n",
    "plt.ylabel('Feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "2b556e03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14:23:50] WARNING: ../src/learner.cc:576: \n",
      "Parameters: { \"n_estimator\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
      "\n",
      "\n",
      "[14:23:50] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.2, max_delta_step=0,\n",
       "              max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimator=75, n_estimators=100,\n",
       "              n_jobs=-1, num_parallel_tree=1, predictor='auto', random_state=42,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train a model (XGBoost) for permutation_importance \n",
    "\n",
    "model_perm = XGBClassifier(random_state=42,n_estimator=75, n_jobs=-1, learning_rate=.2) \n",
    "\n",
    "\n",
    "model_perm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "38faf850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8843537414965986"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_perm.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "id": "52090335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'importances_mean': array([ 0.00272109,  0.00612245, -0.00136054, -0.00680272, -0.00204082,\n",
       "         0.00612245, -0.00136054, -0.00408163,  0.00204082, -0.00204082,\n",
       "        -0.00272109,  0.        ,  0.0047619 ,  0.00408163,  0.00952381,\n",
       "         0.02857143, -0.00272109,  0.        , -0.00612245,  0.        ,\n",
       "         0.00340136,  0.00340136,  0.00272109, -0.00272109, -0.00068027,\n",
       "         0.00204082,  0.00136054, -0.00340136,  0.00340136,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ]),\n",
       " 'importances_std': array([0.00726332, 0.00396663, 0.00630858, 0.00481025, 0.00593047,\n",
       "        0.00544218, 0.00272109, 0.00136054, 0.00272109, 0.00166632,\n",
       "        0.00544218, 0.00215121, 0.00552656, 0.00254535, 0.00897341,\n",
       "        0.00346872, 0.00544218, 0.        , 0.00757519, 0.01031684,\n",
       "        0.00481025, 0.00215121, 0.00254535, 0.00136054, 0.00499896,\n",
       "        0.00272109, 0.00593047, 0.00215121, 0.00215121, 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        ]),\n",
       " 'importances': array([[ 0.0170068 , -0.00340136,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.01020408,  0.01020408,  0.00340136,  0.00680272,  0.        ],\n",
       "        [-0.00680272,  0.01020408, -0.00340136,  0.        , -0.00680272],\n",
       "        [-0.00340136,  0.        , -0.01360544, -0.00680272, -0.01020408],\n",
       "        [-0.00680272, -0.00680272, -0.00680272,  0.00680272,  0.00340136],\n",
       "        [ 0.01020408,  0.        ,  0.01360544,  0.00680272,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , -0.00680272,  0.        ],\n",
       "        [-0.00340136, -0.00340136, -0.00680272, -0.00340136, -0.00340136],\n",
       "        [ 0.        ,  0.00340136,  0.        ,  0.        ,  0.00680272],\n",
       "        [ 0.        , -0.00340136,  0.        , -0.00340136, -0.00340136],\n",
       "        [ 0.        , -0.00680272,  0.00680272, -0.00680272, -0.00680272],\n",
       "        [ 0.        ,  0.        ,  0.        , -0.00340136,  0.00340136],\n",
       "        [ 0.        ,  0.01020408,  0.00680272, -0.00340136,  0.01020408],\n",
       "        [ 0.00340136,  0.00680272,  0.        ,  0.00680272,  0.00340136],\n",
       "        [ 0.02040816,  0.02040816,  0.        ,  0.00340136,  0.00340136],\n",
       "        [ 0.03061224,  0.02380952,  0.02721088,  0.02721088,  0.03401361],\n",
       "        [ 0.00680272, -0.00680272, -0.00680272,  0.        , -0.00680272],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [-0.02040816,  0.        , -0.00680272,  0.        , -0.00340136],\n",
       "        [-0.01020408,  0.0170068 , -0.00680272, -0.00680272,  0.00680272],\n",
       "        [ 0.00680272,  0.00340136, -0.00340136,  0.01020408,  0.        ],\n",
       "        [ 0.00340136,  0.00340136,  0.        ,  0.00340136,  0.00680272],\n",
       "        [ 0.00340136,  0.        ,  0.00340136,  0.00680272,  0.        ],\n",
       "        [-0.00340136,  0.        , -0.00340136, -0.00340136, -0.00340136],\n",
       "        [ 0.        ,  0.        ,  0.00340136,  0.00340136, -0.01020408],\n",
       "        [ 0.        ,  0.00340136,  0.        ,  0.        ,  0.00680272],\n",
       "        [ 0.00340136,  0.01020408, -0.00680272, -0.00340136,  0.00340136],\n",
       "        [-0.00340136,  0.        , -0.00680272, -0.00340136, -0.00340136],\n",
       "        [ 0.00340136,  0.00680272,  0.00340136,  0.        ,  0.00340136],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])}"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Permutation_Importance as another way to determine impact of our features\n",
    "perm_imp = permutation_importance(model_perm, X_val, y_val,random_state=42)\n",
    "perm_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "b412987f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Organize data into DateFrame to better interpret\n",
    "\n",
    "data_perm = {'imp_mean':perm_imp['importances_mean'],\n",
    "             'imp_std':perm_imp['importances_std']}\n",
    "\n",
    "\n",
    "df_perm = pd.DataFrame(data_perm, index=X_val.columns).sort_values('imp_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "a6351e3a",
   "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>imp_mean</th>\n",
       "      <th>imp_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <td>-0.006803</td>\n",
       "      <td>0.004810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <td>-0.006122</td>\n",
       "      <td>0.007575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HourlyRate</th>\n",
       "      <td>-0.004082</td>\n",
       "      <td>0.001361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>-0.003401</td>\n",
       "      <td>0.002151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <td>-0.002721</td>\n",
       "      <td>0.005442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <td>-0.002721</td>\n",
       "      <td>0.001361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <td>-0.002721</td>\n",
       "      <td>0.005442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobLevel</th>\n",
       "      <td>-0.002041</td>\n",
       "      <td>0.001666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>-0.002041</td>\n",
       "      <td>0.005930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DailyRate</th>\n",
       "      <td>-0.001361</td>\n",
       "      <td>0.006309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>-0.001361</td>\n",
       "      <td>0.002721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <td>-0.000680</td>\n",
       "      <td>0.004999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medical</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Representative</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technical Degree</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Director</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Executive</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life Sciences</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Scientist</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laboratory Technician</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing Director</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaritalStatus</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare Representative</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manager</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PerformanceRating</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <td>0.001361</td>\n",
       "      <td>0.005930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <td>0.002041</td>\n",
       "      <td>0.002721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobInvolvement</th>\n",
       "      <td>0.002041</td>\n",
       "      <td>0.002721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.002721</td>\n",
       "      <td>0.007263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <td>0.002721</td>\n",
       "      <td>0.002545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.004810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research &amp; Development</th>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.002151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.002151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyRate</th>\n",
       "      <td>0.004082</td>\n",
       "      <td>0.002545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>0.004762</td>\n",
       "      <td>0.005527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <td>0.006122</td>\n",
       "      <td>0.005442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BusinessTravel</th>\n",
       "      <td>0.006122</td>\n",
       "      <td>0.003967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <td>0.009524</td>\n",
       "      <td>0.008973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverTime</th>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.003469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           imp_mean   imp_std\n",
       "DistanceFromHome          -0.006803  0.004810\n",
       "RelationshipSatisfaction  -0.006122  0.007575\n",
       "HourlyRate                -0.004082  0.001361\n",
       "Sales                     -0.003401  0.002151\n",
       "PercentSalaryHike         -0.002721  0.005442\n",
       "YearsAtCompany            -0.002721  0.001361\n",
       "JobSatisfaction           -0.002721  0.005442\n",
       "JobLevel                  -0.002041  0.001666\n",
       "Education                 -0.002041  0.005930\n",
       "DailyRate                 -0.001361  0.006309\n",
       "Gender                    -0.001361  0.002721\n",
       "YearsInCurrentRole        -0.000680  0.004999\n",
       "Medical                    0.000000  0.000000\n",
       "StockOptionLevel           0.000000  0.010317\n",
       "Marketing                  0.000000  0.000000\n",
       "Sales Representative       0.000000  0.000000\n",
       "Technical Degree           0.000000  0.000000\n",
       "Human Resources            0.000000  0.000000\n",
       "Research Director          0.000000  0.000000\n",
       "Sales Executive            0.000000  0.000000\n",
       "Life Sciences              0.000000  0.000000\n",
       "Research Scientist         0.000000  0.000000\n",
       "Laboratory Technician      0.000000  0.000000\n",
       "Manufacturing Director     0.000000  0.000000\n",
       "MaritalStatus              0.000000  0.002151\n",
       "Healthcare Representative  0.000000  0.000000\n",
       "Manager                    0.000000  0.000000\n",
       "PerformanceRating          0.000000  0.000000\n",
       "Other                      0.000000  0.000000\n",
       "YearsWithCurrManager       0.001361  0.005930\n",
       "YearsSinceLastPromotion    0.002041  0.002721\n",
       "JobInvolvement             0.002041  0.002721\n",
       "Age                        0.002721  0.007263\n",
       "WorkLifeBalance            0.002721  0.002545\n",
       "TotalWorkingYears          0.003401  0.004810\n",
       "Research & Development     0.003401  0.002151\n",
       "TrainingTimesLastYear      0.003401  0.002151\n",
       "MonthlyRate                0.004082  0.002545\n",
       "MonthlyIncome              0.004762  0.005527\n",
       "EnvironmentSatisfaction    0.006122  0.005442\n",
       "BusinessTravel             0.006122  0.003967\n",
       "NumCompaniesWorked         0.009524  0.008973\n",
       "OverTime                   0.028571  0.003469"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_perm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "2742ccfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose a feature to \n",
    "col = 'TotalWorkingYears'\n",
    "X_val_perm = X_val.copy()\n",
    "X_val_perm[col] = np.random.permutation(X_val_perm[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "540802a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.891156462585034"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_perm.score(X_val_perm, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "f7ad340b",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = 'TotalWorkingYears'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "6221d495",
   "metadata": {},
   "outputs": [],
   "source": [
    "isolate= pdp_isolate(\n",
    "    model_xgb,\n",
    "    dataset= X_val, \n",
    "    model_features=X_val.columns,\n",
    "    feature=feature\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "43dedcd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1080x684 with 2 Axes>,\n",
       " {'title_ax': <AxesSubplot:>,\n",
       "  'pdp_ax': <AxesSubplot:xlabel='TotalWorkingYears'>})"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zc4mIiIhI5tDCTUREREREJMP5+eAkIiIiIiIigaCFm4iIiIiISIbTwk1ERERERCTDaeEmIiIiIiKS4bRwExERERERyXBauImIiIiIiGS4jDkBd11d3WoABwF8IBqNfift8k8AuDAajb53hu7nEIA/iUajj83E7XncVxmA3wCoAPCZaDT602ne3h8BeEU0Gv3YDIw3I+rq6v4ewL5oNHrHqMurADRHo1EzQ/fzbQA/jEajvzvHddYA+EI0Gn3zNO+rAMCvAdRHo9GfuJcVA/g2gC1wfuHxV9Fo9OfTuR8RERERkYnKmIWbKwngi3V1dQ9Fo9Hd7GFmwCUAFkWj0fUzcWPRaPSXAH45E7c1U6LR6Gfn6H4+MIGrrQKwcTr3U1dXdxWArwEIA6hP+9LfAeiKRqOb6urqVgJ4pK6u7rFoNHpsOvcnIiIiIjIRmbZw6wXwRQB31tXVXRWNRgfSv1hXV/ddAM9Go9EvjP7c3ZJ2J4CXA5gP4PMAtgJ4EYBBAH8UjUZPuDf1kbq6us0ACgB8MRqN3ube3usBfBpAPoAeAJ+IRqOP1NXV/R2AqwAsBbAzGo2+a9RcbwQQhbMl5jSAWwB0ALgNwLK6urqnAFwVjUZ7077nAQBfTduic+bzurq6PgD/CuBVAJYA+Hw0Gv1GXV3de+FsLXxdXV3dBXC2AJUAeB7AagCfAnDIbVLq3u7qUZ/fBODD7qytAD4ajUbjo38QdXV1nwJwk/t4tgF4YzQaXe02rwSwDs5WqUVpP4M3Afgnt92O0beZdttD7uN7tTv/36S2RtbV1X0GwDsADAHY487XmOoD4DEAMQB3AXgxnJ/1X8JZ0H7b7f1bAK8F8J9wngODAA4AeF80Gu1yt949Fo1Gbx1jvI+5Hf9m1OV/DOAGAIhGo0fq6uruBfBWAP8+3uMUEREREZkpmfget38C0AXgn6fwvYXRaPRKAJ8F8E0AX4lGo5sBHAXw3rTr9Uaj0UsBvBLAv9TV1V1QV1e3wb3P10Sj0S0Abgbw07q6uhL3e1YB2DLGoi0M4FYAb3bv67MAfgHgJIAPANgfjUYvSV+0TUABgJZoNPoSAH8C4Et1dXWFo67z3wC+HY1GL4azhegKrxutq6t7KYD3AKhxH+PnAfxsjOtdB6fX5XAWvvNGXaU4Go1eEI1G/yrtexbBWai+ORqNvgjA4XOMkgOgx73eWwHcVldXV11XV/c+OIu5y93H9SyA747x/WsB/DYajV4BZ5H15Wg0OoyR3tfBWWi/DMBm934OALgYcLbejbNoQzQafUc0Gr1njC+tgPM8SjkGYPk5HqOIiIiIyIzJuIVbNBpNAngXgPfV1dW9cpLf/r/un/sBNEaj0Z1pn1emXa/eva8TAO4BEIGziFsCIOZuIfsBnF03U7s5PhqNRofGuM+XA4hFo9ED7m3eB6AJzoJnOn7h/vkEnIVcagGZev/YBQDucO+zAcDTE7jN18J5PL93H+PnAcyvq6urHHW91wD4cTQaTUSjUQtnYZjuoTFu+2oAz0Sj0efdz+vHuE66r7qzPw3gGQDXwFm03R6NRrvd63wFQKSuri5/1PcOwtniBjh9Rs8P9zaHAfyhrq7uHwD8bzQa/b3HTOcSAmDTPjfu7YuIiIiIzLqMW7gBQDQaPQqgFsD3AFSlfcnCecGcMvoFfX/a3wfPcRfpL7hD7nVz4CzALkl9ALgSzlYfwNkKOJYcnP2CPnWbeee4f8D7sfQCgLtwwqjr9o7x/andSs91uzkA/ivt8V0K4DIA7aPue2jUbYxeoIzXIv17xlrkjr6PlJB7H6NbhuDszjv6ACcD7gIfeOHjBQBEo9EEgM0APuHe9o/q6uo+7DHTuRyBs6tsylI4W91ERERERGZdRi7cAMB979dvAPxZ2sXNcBYaqKurWwrgpVO8+fe6t7ESwCvgvGcqBuBV7q6PqKurew2crVhFHrcVA3BdXV3dWvf7Xg5nt7o/eHxf+mM5H+5ufBPhbpF6CM7iFu779S5xv5wAkO/eJuC8XyzltwDeUVdXt8T9/IPu/KP9H4A319XVlbuf34QXLk5H2wbgAncW4OxdU8dyozv7pXAOBPIggLsBvD9t99SPAdgWjUb7x76JFxiCu2Cuq6t7HZzH9vtoNPp3cLZOXj7B2xnLL+DsPou6urrlAK6H8x4/EREREZFZl7ELN9fHcPZ7pf4TwJK6urrdAG4HcN8Ub7ewrq7uCTi72/2/aDS6x93F72YAP6yrq9sJ4B/gHNBkvK1LAAD3+z4M5/1wz8I56Mbro9Foh8cM/whnofgsgL+Hs/CZjHcDeK07698DaHTn6YBzsI7f1NXV7YC75c792j0APgfg3rq6uqfhHGzjTWlb9VLXuw/At+AeORFAOZwDjowrGo02u7f3A7ftGo/5t7rXuw3A26LRaDuA7wD4HYDtdXV1u+BsEXynZ4kRzwPoq6ur2w5n0f8cgGfdx/ASAHWAc2qBurq6D07idgHn4DOldXV1z7kzfjIaje6f5G2IiIiIiEyJsdZrQ4r4gbsA/Gg0Gn1gBm7rMgAviUaj/+F+fguAF0ej0bdN97bd27MAqqPRaMtM3J6IiIiISLbLtNMBSGbYA+Cv6urqboazi+QRuLsJioiIiIjI3NMWNxERERERkQyX6e9xExERERERCTwt3ERERERERDKcFm4iIiIiIiIZTgs3ERERERGRDKeFm4iIiIiISIbTwk1ERERERCTDaeEmIiIiIiKS4bRwExERERERyXBauImIiIiIiGQ4LdxEREREREQynBZuIiIiIiIiGU4LNxERERERkQynhZuIiIiIiEiG08JNREREREQkw2nhJiIiIiIikuG0cBMREREREclwWriJiIiIiIhkOC3cREREREREMpwWbiIiIiIiIhlOCzcREREREZEMp4WbiIiIiIhIhtPCTUREREREJMNp4SYiIiIiIpLhtHATERERERHJcFq4iYiIiIiIZDgt3ERERERERDKcFm4iIiIiIiIZTgs3ERERERGRDKeFm4iIiIiISIbTwk1ERERERCTDaeEmIiIiIiKS4bRwExERERERyXBauImIiIiIiGQ4LdxEREREREQynBZuIiIiIiIiGU4LNxERERERkQynhZuIiIiIiEiG08JNREREREQkw2nhJiIiIiIikuG0cBMREREREclwWriJiIiIiIhkOC3cREREREREMpwWbiIiIiIiIhlOCzcREREREZEMp4WbiIiIiIhIhtPCTUREREREJMNp4SYiIiIiIpLhtHATERERERHJcFq4iYiIiIiIZDgt3ERERERERDKcFm4iIiIiIiIZTgs3ERERERGRDKeFm4iIiIiISIbTwk1ERERERCTDaeEmIiIiIiKS4bRwExERERERyXBauImIiIiIiGQ4LdxEREREREQynBZuIiIiIiIiGU4LNxERERERkQynhZuIiIiIiEiG08JNREREREQkw2nhJiIiIiIikuG0cBMREREREclwWrh5MMYUs2cIKrXnUXsetedSfx6151F7HrXn8WN7Ldy8rWAPEGBqz6P2PGrPpf48as+j9jxqz+O79rlT+aZIJBIC8HUAmwH0A/hALBbbN+o6xQDuBXBTLBaLT3dQogR7gABLsAcIsAR7gABLsAcIuAR7gABLsAcIsAR7gABLsAcIsAR7gMma6ha3NwIojMViVwH4FIAvpn8xEolcBmAbgHXTmk5ERERERESmvHC7GsDdABCLxR4FcNmorxcA+GMAft7SllLBHiDAKtgDBFgFe4AAq2APEHAV7AECrII9QIBVsAcIsAr2AAFWwR5gsqa0qySAMgAdaZ8PRyKR3FgsNgQAsVjsYQCIRCLnvJHbb7/9ZgA3A0AikfjBLbfccheAkwAWAMgHcBjAKgCdAIYAVAI4DmARnEXncTj7pybcm6wAcBTAMgBJAKfcv7e5j7Us7TYHALQCWAKgBUAhgNK0r/e5t1tojKkEUAKgOO3rPQC6AVQDaHTvuzDt613ubVRl4GNaDKDZB4+p0BizJssek19+Tv3GmI1Z9pj88nNqcttn02Py088Jbv9sekx++Tl1uO2z6TH55eeU+ncnmx6TX35O/caYRVn2mPzycyo0xhRm2mOy1u7GOIy1dryvjSsSifw7gEdjsdj/uJ8fi8Viy8e43gMAPujn97gZYzZYa/ey5wgitedRex6151J/HrXnUXsetefxY/up7ir5MIDXAEAkErkSwDMzNlHmSbIHCDC151F7HrXnUn8etedRex615/Fd+6nuKvkzAK+MRCK/B2AAvC8SidwAoDQWi31zxqbLDKfYAwSY2vOoPY/ac6k/j9rzqD2P2vP4rv2UdpUMEmPMxnPtayqzR+151J5H7bnUn0ftedSeR+15/NheJ+D21sYeIMDUnkftedSeS/151J5H7XnUnsd37bVw8zbV3Ull+tSeR+151J5L/XnUnkftedSex3fttXDzVsYeIMDUnkftedSeS/151J5H7XnUnsd37fUeNw/GmEJrbR97jiBSex6151F7LvXnUXsetedRex4/ttcWN2+r2AMEmNrzqD2P2nOpP4/a86g9j9rz+K69Fm7eBtgDBJja86g9j9pzqT+P2vOoPY/a8/iuvRZu3lrZAwSY2vOoPY/ac6k/j9rzqD2P2vP4rr0Wbt6WsAcIMLXnUXsetedSfx6151F7HrXn8V17Ldy8tbAHCDC151F7HrXnUn8etedRex615/Fdey3cvBWyBwgwtedRex6151J/HrXnUXsetefxXXst3LyVsgcIMLXnUXsetedSfx6151F7HrXn8V17ncfNgx/P8ZAt1J5H7XnUnkv9edSeR+151J7Hj+21xc2b787xkEXUnkftedSeS/151J5H7XnUnsd37bVw8+arlXiWUXsetedRey7151F7HrXnUXse37XXws1bgj1AgCXYAwRYgj1AgCXYAwRcgj1AgCXYAwRYgj1AgCXYAwRYgj3AZGnh5m0xe4AAU3setedRey7151F7HrXnUXse37XXws1bM3uAAFN7HrXnUXsu9edRex6151F7Ht+118LNWwl7gABTex6151F7LvXnUXsetedRex7ftdfCzVsxe4AAU3setedRey7151F7HrXnUXse37XXedw8+PEcD9lC7XnUnkftudSfR+151J5H7Xn82F5b3Lz57hwPWUTtedSeR+251J9H7XnUnkfteXzXXgs3bz3sAQJM7XnUnkftudSfR+151J5H7Xl8114LN2/d7AECTO151J5H7bnUn0ftedSeR+15fNdeCzdv1ewBAkztedSeR+251J9H7XnUnkfteXzXXgs3b43sAQJM7XnUnkftudSfR+151J5H7Xl8114LN28V7AECrII9QIBVsAcIsAr2AAFXwR4gwCrYAwRYBXuAAKtgDxBgFewBJksLN2+F7AECTO151J5H7bnUn0ftedSeR+15fNde53Hz4MdzPGQLtedRex6151J/HrXnUXsetefxY3ttcfPmu3M8ZBG151F7HrXnUn8etedRex615/Fdey3cvHWxBwgwtedRex6151J/HrXnUXsetefxXXst3Lz5ahNqllF7HrXnUXsu9edRex6151F7Ht+1z2UP4ANVAFrZQwSU2vOoPY/ac6k/j9rzqD1PVrevbYgb969ef07kOjN6G0svffEa+Ky9Dk7iwRhTZq3tZM8RRGrPo/Y8Xu3d/wnmAsgZ5898AAXun/lw9qxIArAAht2/J92/21Gfp19u075ux/gY7/Jxv1ZfE05Or87s03OfR+15/NJ+AouAc30tIxcQe377i3nnXfeGrrTLQ2lfn8jH6OtO5Da8bn/0nFO9DcD59z/984lIfc/oP2f0NtoO7CmqXHveL/3w/6YUbXHztgBAxv9jlqXUnkftZ1ltQ3zMhdfG1755dW1DvBEji6/0RVg+vP/dTl+ApRZhgPM/8rH+hzve/+DHM97/QD0vr22Ipz5PjvGRvpAcb1E51vek/j6tRWXq8qLKqoW1DfFujCw29dvNuTOn/+7M8iJgOt87k7cxoRf/L3rfR1bUNsSPj/reiS4gxrtO+m143c5Eb2MqZvXF/3RvI5STuwjAqbTrnutPjPH5RK/rdb3RX7PnuO5Yl5/rdjNS24G9F1SuPY89xqRo4eYtnz1AgKk9j9pPQG1DPITxt3zlYPzFVz7G+Z//wvM3LwcwHyMLr9TiZABAL3z0P8VzGO/FW6pnXtrnE30BCkxjUZly2U0fWw5gXepr7mJzvMXj6K2YYy0qx7rO6K2aE1lUprebzJ9T+Z7xbmOyH6Nv65wfl//px1fXNsTnj3EboxcQXrc3+uvj3cZkTOeF+0zcxlQWEBN+wV40f8GyCV73XF/3um7S43oTmjXbnD55vARAC3uOIBrs7cljzzBZWrh5O8weIMDUnicw7d3fuudg/AVYLpyTdOa5f6YvvnI8bj594TXk/tkD4PR437B0y4s74SzSsln6b3Izyjj9x9sKYOA8L8a6zrm+J/0F97legI/1NdYCYqq/tT/Xdc+6fP0r/6gTQPFErovxFwGTnVUALLtsaw+y/9+djLT22utPsmcIqpVXXnPK+1qZRQs3b6sA7GYPEVBqz+O79uNs/RrvvV8FcF5wp/4814vX1FaS1MJrGEA/nAXYjL8QPHD/3Uu2vPvmwCycM804/TN2oZlN9t3762o99zn07w6P2vMceXTbok2vfwt7jEnRws2b3ufDo/Y8Gde+tiGeD6ASwDyMvQBL7Qo13taE9IVX6qPb/TNjzFu8tJs9Q5CpP4/a86g9j9rzlC5c3MOeYbK0cPM2xB4gwNSeJyPa1zbEc+G832s5gIXuxYM4exfEWdv6xZBbWJQR7YNK/XnUnkftedSeJ7egMKN+cTsROgG3t0r2AAGm9jy09rUN8VBtQ3x+bUN8E4BrAbwIQBmcN2+3AOgA0AXnQB39cBaZWbFoA4D2Q/vL2TMEmfrzqD2P2vOoPU/i6KF57BkmS1vcvB33vorMErXnmdP27gFCSgEsArASzm6QAwASOPuIellv+RVbm9gzBJn686g9j9rzqD3Pks2X+e5ontri5m0Re4AAU3ueOWlf2xAvrm2IrwRwNYCtANbAed9ZM5wta4FatAHAyZ07FrBnCDL151F7HrXnUXuepl1Pz2fPMFna4uZNi1seteeZtfZpBxlZ5f45DOfw+M2zdZ9+YoeGp3qId5kB6s+j9jxqz6P2PHbYf+21cPOm3fV41J5nRtuPc5CRbgDaRWSUFVddowUskfrzqD2P2vOoPc+SLVe0smeYLG3R8LaCPUCAqT3PtNtP4CAjvdO9j2x0+KH7tYswkfrzqD2P2vOoPc/xxx6pZs8wWdri5i3BHiDAEuwBAiwxlW/SQUamr3z5ytPsGYJM/XnUnkftedSep2zp8i72DJOlhZuITFttQ7wYQBWc962VwHnfWiecA4yIiIiIyDRp4eatAsAp9hABVQG1Z6mAR3sdZGR2dBw7Mg9AG3uOoFJ/HrXnUXsetefpPHGsdNmL2FNMjhZu3o6yBwgwtecZs70OMjL7Vl19rX5ZQaT+PGrPo/Y8as+z7LKrfPeLZh2cxNsy9gABpvY8Z9rrICNz6+gj23z3Zulsov48as+j9jxqz3Pyye2+O4eetrh50wEVeNSeJJSbl6xtiM+DDjIy50xujmXPEGTqz6P2PGrPo/Y8Jsd/7bVw86ZN2DxqP0dqG+J5AIrhHFik6s23/WwZgA3QQUbm3JLNl/vuvDLZRP151J5H7XnUnmfhpovb2TNMlhZu3pYB2M0eIqDUfhbUNsRDcBZoxXDer1blfg4AFkD/kUceLJ2/et0B0oiBdmz7wwurN15wmD1HUKk/j9rzqD2P2vOc3PlYVcXKNewxJkULN2860g+P2k+Te161IjgLs3IAC9w/Q3AWaQNw3qPWkv595ctX6d8Gkvmr12nrJpH686g9j9rzqD1PxYrVvjuHnl6ceVMjnim3r22I57jfn/rIwQsPxmPG+NbRl03283NdZ/Sfqb8n0z7sFP9M1teEbW1DvADOIm0enEVaJZzHbgAMAugD4LlbxlBfr573JGrPpf48as+j9jxqzzPU35fDnmGy9GTxVgbgJHuIgCqrbYg3wll45OKFC7E8AAXuR2Ha3/Mx9oJqquwM3N5Yb4BNXWamefsWgKltiI++rBfOe9MmfTCR040nSjBqK5zMDbXnUn8etedRex615+lqaixmzzBZxlrfHVBlThljCq21few5soG7297oBVhqEZaPkYVXAYCCrqaTpaULlwDjL2osnINnjP4Ymr1HEQwdx47kly9fOcCeI4jUnkv9edSeR+151J6nu/nUkpLqRXfV14R9c7RsbXHztgo6QIYn98TMxe5H+tav9I9zPd+SGLUA2/+7u8o233DTwdmcW8Z24P67l2x59816szSB2nOpP4/a86g9j9rzHHl026JNr38Le4xJ0cLNm34LMsqoRdp8OO+lKkm7ylhbwPoxyV32cgoK+mdiXpm8vOLiQfYMQaX2XOrPo/Y8as+j9jx5Rf5rr4Wbt0CfX2PUIq0CziKtNO0qA3AOeDHj+2dXb7xAR1oiUXsetedSfx6151F7HrXnqVy7QUeVzEJL4JyAOOu5R2JMnYS5As4RCefBeY9Z6tDxs7JIG8uJJ3dULbpwS/dc3JecTe151J5L/XnUnkftedSe59RzOysr157HHmNStHDzlpVH+klbpKV2d6Qu0sayYEM4wbrvoFN7HrXnUn8etedRex6156lcs953Wzu1cPNWyB5gukYt0irg7O44L+0qgxjjJMxsfe1t+ewZgkrtedSeS/151J5H7XnUnqevM+G79lq4eSv1vkrmmOAijbolbaK6W5p8d36NbKH2PGrPpf48as+j9jxqz9PT2lLEnmGydB43D5l8Hjd3kVaEF74nLQRnd8fUIs2XR2fUuU141J5H7bnUn0ftedSeR+15/HgetxB7AB9YxR4AAGob4qHahnhpbUN8YW1DfGNtQ/wqABEAWwFsAbAMzvvTWgE0w9mi1gGfLtoA59wm7BmCSu151J5L/XnUnkftedSe58ij2xaxZ5gs7Srpbc63ttU2xEMY2d2xHEAVRg4cAjjnRetFlp+qoGBemW8XnX6n9jxqz6X+PGrPo/Y8as9TUDrPd1s6tXDzlpjNGx9jkVbp/pna3XEIzuIxqxdpY5m/Zn0Xe4agUnsetedSfx6151F7HrXnKV+x2nfttXDzthjOLofT5i7SUu9JG71IA0bekxa4RdpYGp9+YsGSzZf57j+qbKD2PGrPpf48as+j9jxqz9O065nKBevD7DEmRQs3b81T+aa0RVrq6I6pRZpxP7RI81C18fx29gxBpfY8as+l/jxqz6P2PGrPs2DdecE4j1skEgkB+DqAzXAOfvGBWCy2L+3rrwfwWTi7+d0Wi8W+NQOzspQAaDvXFUYt0srgvCcttUgDRnZ3bIOz+6NMQHfTySIAnew5gkjtedSeS/151J5H7XnUnqe7pbmAPcNkTXWL2xsBFMZisasikciVAL4I4A0AEIlE8gB8CcDlALoBPByJRH4Vi8UaZ2DeObPsDTfmAviHxa95x6uXveHGuwB89vgv7hiqbYgbjOzuWAZnS9p8vPA9aVqkTVNve7vvT37uV2rPo/Zc6s+j9jxqz6P2PH0d/ms/1YXb1QDuBoBYLPZoJBK5LO1rmwDsi8Vi7QAQiUQeAlAD4MfTGZTg7wF8IpSbmwvgYhMKvX3DjR/bXjhvXkfRvLKWkvmVzeWLFp+oWrX6RE5unhZps2DttdefZM8QVGrPo/Zc6s+j9jxqz6P2PCuvvOYUe4bJmurCrQxnH7BjOBKJ5MZisaExvnYazm6DL3D77bffDOBmAEgkEj+45ZZb7gJwEsACAPkADsM5j1onnC1ZlQCOA1gEZwvXcQArMHLkxwoAR+Gc0ywJ4JT79zb3sZal3eYAnPeXLYFzzrNCAKWpry957TuuNzm5uTfntKI2p80AWINerHln5woAwA/yjo48jmRl1zfswoP3hPZurDDD+QDQbPJOvH/Zy//2yyca3r0u2fPy1HW/e8Vb/qhqZ8NVr+tv/OvUZfHKlf/21/lrHv5Z44M/T13Wnlv06Mc2/dFnv/jcz/55YXLgzML4w9d9ouad9932zq2DbR9MXfbEsgs++dMO2/6PXc9/O3VZY0HZ//35ymu/Wr/v/75WZofWAsAAQm0ffemH3vSxh+/48IVDp9+auu695139vkNHj1X9ae+hf0tddri0+rufqLz0J98/cs9/FcDOB4DuUN6+j172rtroYz/81Opk7ytn+zF97bnDBV+u6PrHbHpMfvk5vam7+ps/Kmr8cDY9Jr/8nP4yb+2HLg/1rMymx+Snn9O7T4Qevbes6V+y6TH55ef0Wqz/+JdyT74+mx6TX35OP8up+vzjRQv3ZNNj8svP6bjN3fmpi9/8yWx6TH75OW1LFt/34WdOxr65d9dKENYacPbQS8A5EGIznL35iq21uzEOY+3kNxRFIpF/B/BoLBb7H/fzY7FYbLn794sB/GssFnuN+/mXADwci8V+Muk7Ilr2hhv/GcDH4bxvrbeorOx7kZs/+rPOplMljfv2FHc2N1X1dZ1ePNjXt3hooH/x8ODQ4uGhocU2ObwQo05sbozpCOXmNubk5jbm5OU35hUWniwoLmksrqhonL90WeOSDeHWnLw8bbEbJf7rHy8Kv+4tvvttSDZQex6151J/HrXnUXsetec5uO134TXXvOK/62vCSfYsEzXVLW4PA3g9gP9x3+P2TNrXdgHYEIlEKgF0AbgGwBemNSXHZwFYm0xeZ0Kh3/Z2dkYBhMoWLlpYtnDRejgr6CcA9KR/00BvT87JPburE40nF/d2Jhb39/QsHurvXzw0MLB4eHBw2UBf32W9nR0lqesffuoJPIVfDYVyck6FcnMbc/LyGnPzCxrzC4saC+fNa5xXVX1y4dp1pyoWLemd00efAUoWBu8xZwq151F7LvXnUXsetedRe56Sqmrfnfx8qgu3nwF4ZSQS+T2cIye+LxKJ3ACgNBaLfTMSidwC4LdwtjzdFovFjs/MuHPn+C/uGALwt8aYO0ZtsjxW2xA/AWd3zrUAquEc2r8DgM0vKh5etXlL46rNW8Y9GEvrsSOlzYcOLO5qbV3c19W1eLCvN7WwWzzY2/ui/q6u6m4gBwBO7t6FPQ9vG2urXWNBcUljcXlFY8WSpY2LN2xszSso8M1vDCaiZffz81dccbWOtESg9jxqz6X+PGrPo/Y8as/Tun9P+cLzN7PHmJQp7SoZJMaYcmvtuOd5qG2Il8HZ93UFnH1dO+DsIztl4261GxxYnBwcWjw8PLQY1paO+rbxt9otqGpcuG59o9+22p3c+VipTkrJofY8as+l/jxqz6P2PGrP07ovvnLB+vAvgrCrZJBU4OyDrZylvibcCeC52ob4fjhvPlwL58AqnXDedDhpE91q13Lo4KLTba2L+7tOLx7o7V0y7lY7AHt+3wBjTGfaVruTmb7Vrv3gPv1jRqL2PGrPpf48as+j9jxqz9Nx9FDpgvVh9hiTooWbtwmd46G+JtwH4GBtQ/wInBNwr4ezG2UfnCNrzqgFy1d2LVi+sgvA/rG+PtDbk9O4d09VovHE4p6OjsUDPd2LB92tdsODQ0sG+vou7e3sOLPV7vDOJ7Dz7l8PhXJympzFXV5jbkG+u9Wu7OS8BVWN1WvWnZq/ZGnPWPc3G/pPd/ruxIjZQu151J5L/XnUnkftedSep7/rdD57hsnSrpIejDGF1tpJbzlzT9RdAedwn4sBDMM55GfGbNEaY6vdmffaJYeHFieHhxfC3WqXMmqrXWNeQWFjfnFxY+G8sqbWI4deOdDbe0FhaemOrTfceGt+UfHwdObrOHYkv3z5yoFpPUiZErXnUXsu9edRex6151F7nu7mU0tKqhfdpV0ls8sqAOOeT2E89TVhC6AdQHttQ7wYzjkeVsM5YEsnnHM7UHlttRvs7w817olXtZ88vmSsrXaDfX1bejs75o3+vu72tvPv+/Y3Llh18ZYvn/eSmr1TPdXBgfvvXrLl3Tcfnsr3yvSoPY/ac6k/j9rzqD2P2vMceXTbok2vfwt7jEnRws3btPc7rq8J9wDYW9sQPwRgIZzdKCsAdLsfGSmvoCC54qLNTSsu2twEYOdY12k7fqyk+dCBRfu3P/r55PDQavfi0FB//xX7dzx654HHtifyigofKy6f/9ji9edtX3vZFUdCOTlj3dQLlFQtnLPdMuVsas+j9lzqz6P2PGrPo/Y8xQuqfHXQPkALt4mY0gFGxlJfEx4EcLy2IX4SwHyMcTqBmbqvuVK5bHl35bLlB07En7+/u73tHXDeE9hXVFb+8+KKiue729ou7+/puTzRc/wViZPHsfvhB5vyi4u3l86vfGzZpgt3rLz4knFPOlk4v5K+VTKo1J5H7bnUn0ftedSeR+15CssqfNdeCzdvVXBOtj1j3H1pWwG01jbE5wFYDmAlnIXbtE8nwLD1hhtvffjOO2xfV9cVhaWl27fecGO9+x63u5LDwzj4xI7ljXt3X9GTSFw+0NNzdWtX1+tajx7Bs7F7jhQUF+8orarasWrzpY8tXn9eInWbrXvjFSuvvGbcI3rK7FF7HrXnUn8etedRex6152k7uK980YVb2GNMig5O4sEYU2atnfUTI9Y2xAvgHMRkHYACOEei9N0m3IkYHhw0ex99eF3zoQNX9HR2XD7Y13cprC0BgJy8vD0FxSU7yhYu2rGguiq+5iUvbWHPG0Snnn2yZNGFWzJ2N95spvZc6s+j9jxqz6P2PG0H9qysXHuezuOWZRbAOZjIrKqvCfcDOFzbED/m3uc6OLtR9s/F/c+lnLw8G6552b5wzcv2AbhzoLcnZ/fD2za1HTt6Rd/p05f3dCTe0tOReGfj3t3J3Y/teLagdN6O+YuX7thw1danS7RLwZxo3v1cuf5HwqH2XOrPo/Y8as+j9jxtB/bOq1x7HnuMSdHCzducnuOhviY8DKCptiHeDKAczi6US+GcRiAB57QCWSW/qHj4oldc/yyAZwHc1t3elr/nkYcubt4bf0US2Njd1vre7rbWm449/0x/XkHhzsJ583YsWL5yx3lba3ZN95QDMrbBnp489gxBpfZc6s+j9jxqz6P2PIO9/muvXSU9TPU8bjOptiFeBGfxthbOedU64WyJy2qpc5u0HT9WcuCxP2zpbDp1eX939xXDQ4MbAADGdOUXFj5RVFa+o3rN2h0bXrx1/1RPPSBn03lleNSeS/151J5H7XnUnseP53ELsQfwgVXsAeprwr31NeH9AO4H8DScxVs1gFLqYLPswP13LwGcI1de9oY3P/TyP/3wl179Z598x2Vv/JNXVK9e+6mi0nm/HRoYWNNxqvEv9j36+x/e/Z9fvOfer3/lnx++844/PvDYH5Ynh7UxbqpS7WXuqT2X+vOoPY/a86g9z5FHty1izzBZ2lXSW8a8v6y+JjwE4ETa6QTWwFnADcE5GqVvfmMwEfMWLx1zn+/F689LLF5/3u8A/A4ADu98cvHxXc9d1p1ou3ygp+eK/hPdr2o/cQzxhgca8wqLdiSTw+XJwaFFhfNKH916w3u+rt0rvY3XXmaf2nOpP4/a86g9j9rzlC5c7Ltz6Gnh5i3jDs1fXxO2ANoAtNU2xEvhnE5gBQADZ6E5SBxvxuQWFk2o/arNWxpXbd7yawC/Tg4PY/+OP6w6tX/P5T0dicv7u7teBeconehub994z9e+/NacvLzDodzclpzcvKbc/Pym/MLCloKS0qbiivnNlcuWN1evXpuY6EnCs9VE28vMU3su9edRex6151F7ntyCQt/9Il8LN2+VAJrZQ4ynvibcBSBe2xA/AGARnKNRVgDoBuC73ySkaz+0v3x1TSQxme8J5eRgw5UvObzhypccBvCT33zlC98dHhy4MPV1Ewr1hnJzW5JDQ9VD/f3n951OVqZ//37nj8FQTk5zKCe3KZSb25Kbl9eUW1DQlF9U1FJYWtY0b0FV88K165rnVVVT3/s4m6bSXmaG2nOpP4/a86g9j9rzJI4emrfkksvZY0yKFm7ejrMHmIj6mvAAgKO1DfHjcE4nsBbAQoycTsB3B+1YfsXWpuneRmFp6Y7u9rb1AAoB9BWXV/z82ps++PXU13tPd+ae2r+v6nRzU3VvZ8fC/t6e6qH+vuqhgcGFw0OD1UMD/RsGenu2wtqi9Nvdte0+wJiuUE5OU05ublNObl5LTn5eU15BYXNBcUlzUXl5U/nCxc2L1q1v8+OumTPRXqZG7bnUn0ftedSeR+15lmy+zHfnCtbCzdsiAF3sISbKPTJOM4Dm2oZ4GZzTCSyDs3DrQAbu+jmekzt3LKjeeMGx6dzG1htuvPXhO++wfV1dVxSWlm7fesON9elfL5pXNrT6kksbATSe63bajh8raTlyqLq7rbW6r6tr4UBfb/VQf3/18ODgwuTwUHV/T88a2zVcBefAMemSJhRqC+XkNuXk5jTn5OU35+bnN+UWFLb2tLddMTjQvy6/sOjZZZsu+FFOXt4QjLHGhGwoFErCwJpQyIZCOUkYY0OhkDWhUDKUk2ONMWf+Pjw0ZOLb7n9vf0/PlsLS0j9sveHGW6e7WJyJ9jI1as+l/jxqz6P2PGrP07Tr6fkVK9ewx5gULdy8+fbIm/U14U4Az9Y2xPcBWAJnN8pcAKcBZPxufnZo2Ez3NvKLiofdLWxf97zyOVQuW95duWx5N4BD411nsL8/1Hxw//z2kyeqezsS1f093QsH+/qqhwYGqoeHBquHB4eWDfb1XWKtLU//vr6u0+v373j0jdOZL6W7ve38e77+lT/Oyy/YE8rLbcrNy2/OKyxsyi8qbi4ur2guX7S4aSJbAWeivUyN2nOpP4/a86g9j9rz2GH/tdd53DwYY4qttb5+r1hKbUM8F0AVnAVcGYBeOIu4jNR2cG9h5ZoNGb/AnKzu9rb8bXd857vDg4PnpS4L5eQcWbBi1dettSEABtYaCxuChbHWGsCGYG3IAoC1IWsRAixgEepoOvVumxweOZywMZ25eXmHh4eGq21yuAov/AVN0oRCraGc3Oac3NymnLy85tz8/Kb8oqLm/KLi1vaTJ14+2NsTLpxX9uhMbL2TycnW571fqD+P2vOoPY/a83SePLasbMnyX/vpPG7a4uZtBYDd7CFmgns6gcbahvgpOAcwWQ1gMZyjUGbc6QQOP3T/oso1Gw6z55hpJfMrBwpL5z3c3d62Eu5774rKyn/34j95+++mcnv3f+fW6u72tnekbqukYv5PUu/jGx4cNKcO7JufOHl8YU/iBVsBFw4PDiwf7OvdMnorIAB0t7dtfPB7366K/OlH6oJ+lM25lK3Pe79Qfx6151F7HrXnOf7YI9Vlr38Le4xJ0cLNW4I9wExzTyfQDqC9tiFeAuc9cKvgvD+rA8AAcbwzypevzNitgdPl9d67mbqtnLw8u3TjpralGze1AYiPdxtdba0FTQf2VccfevDfk0NDa92LQ/1dXa//7Ve/tK5iyZIfbr7utfcWl1dkxakmMlk2P+/9QP151J5H7XnUnqds6XLfHMMiRQu3gKuvCXcD2FPbED8I5yiUGwCUwzmVgE4KOUtm6r13M3VbpZUL+ksrFxw7vPPJB7vb25bC2XrXn1tQ+HhyeGhJ65HDf3//d279s5KK+T9dd8VV/7viwosz9hQZIiIiItnItwfemEMV7AHmQn1NeLC+JnwcwDYAj8F5/9tCOI+f8ubNjmNH5jHuN8i23nDjrSXzK+8MGbO3ZH7lD17+gQ/++fX/7y/esnTTBR/OKyh4pqut9aadd//6V/d+/Sv/svO3d12SHNbb32aanvdc6s+j9jxqz6P2PJ0njpWyZ5gsHZzEQzYdnGSyahvi8+C8x28FCKcT0Bt2ecZrf+Dx7csOPfn4W3o7Em+w1s7LycvbXb5oyQ8vftWr7ymtXNDPmDXb6HnPpf48as+j9jxqz+PHg5Noi5u3ZewBWOprwqfra8LPA3gAzgFaSgBUw9mNbtYdfWRb9Vzcj7zQeO3XvuiK4y//wIe+XPPu9796/tLl/wRrc9qOHYk+ePu3/u/+79z60cM7n1w817NmGz3vudSfR+151J5H7XlOPrl9AXuGydJ73Lz5ZhU+W+prwv0ADtc2xI9h5HQC1QD6AXTO1v2a3BxtDibxal+2cFHf1htu/FlyePhnT99z14uaDux/e3d7243P3PubG3c//OADC9es+9HFr3rN4zoa5eTpec+l/jxqz6P2PGrPY3L8114LN2+n2ANkivqa8DCAU7UN8SY4BzBZBWApnN0nOwDM6Buelmy+vHUmb08mbqLtQzk5uOTVr38cwOOHnnpi8cHHt/9JT6L9j48998zLT+6O7ytbuOhHF7/q1b+ZV1Wt3UAmSM97LvXnUXsetedRe56Fmy5uZ88wWdpV0ltgd5UcT31N2NbXhBP1NeGdAB4EcBDOQq4aQMFM3c+x7Q8vnKnbksmZSvvVl1zaeO1NH/zqS99382sql6/4exgk208c+9tt3/v2Xfd9+xsfP/jEY0tnY9Zso+c9l/rzqD2P2vOoPc/JnY9VsWeYLG1x89bGHiCT1deEewDsq22IH4azcFsPZxHXhmkeyGT+6nUd059QpmI67UsrF/S/5O3v/mVyePiXz/zu7ktO7d/39p5E+w3P3XfPu/Y+0rCtavXaH11y/eu2azfKsel5z6X+PGrPo/Y8as9TsWK1786hp4WbNzWagPqa8CCAE7UN8UY4WynDcI5EOeXN0EN9vWpPMhPtQzk52Hzda58C8NTRZ3Yu3Lfj0Tf3JNrfdGLXcy9t3Lv7YFn1oh9d+IpX/V/FoiW90584e+h5z6X+PGrPo/Y8as8z1N/nu98g68nirQzASfYQfuEeUvVobUO8Gc5BTFYC6IJzQu9JOd14ogRAy8xOKBMx0+1XXLS5acVFm7/R3d72nafvuetVicaTb0ucPP6ph77/3Y8Wl5X/ctUll/7PusuvPDZT9+dnet5zqT+P2vOoPY/a83Q1NRazZ5gsncfNgzGm0FqrAytMUW1DvALABXAWwJPafbLj2JH88uUrB2ZpNDmH2W6fHB7Gc/fde1Hjvt1v6+/ufgWAnLzCooerVq3+0SXXv+7RnLy8wP7DpOc9l/rzqD2P2vOoPU9386klJdWL7tJ53LLLKvYAflZfE04AeATA0wDmAaic6PceuP/uJbM0lniY7fahnBxc9Mrrn3nlhz7+6S2vfcPrShdUfWtooH/Tyd27/vO3X/vSTx76/u1vazt+rGQ2Z8hUet5zqT+P2vOoPY/a8xx5dNsi9gyTpV0lvem3INPk/ibjeG1DvAXAWgCr4ew62XWu78srLh6c/elkLHPZftmmC1qWbbrgm72nO2/b+ZtfvyLReOLticaTn/z9D//rI0Xzyn618uIt/7Phypccnqt52PS851J/HrXnUXsetefJK/Jfey3cvOn8GjPEPZH3rtqG+AkAm+AchTIBYMz/cKo3XqAjLZEw2hfNKxu68q033A3g7mfvu+f8k3vib+vt7HjT7oceeNv+HY88GsrJOT08OLSksLR0x9Ybbrw1v6h4Rs8bmCn0vOdSfx6151F7HrXnqVy7wXdHldSukt60CXuG1deEOwD8AcBOACUAFgAwo6934skdvju/RrZgt7/w5a96/pUf/Fj00tf/8WvnVVV/Y6i//+KBnp5XDg8OXNjd3vae+759638+9ZtfvSgbd6dktw869edRex6151F7nlPP7Zzw23cyhba4edORfmZBfU3YAjhZ2xBvBbAGzi6UZ+0+uWBDOMGZTjKl/dKNm9qWbtz0nd985QvXDA8OXOBeHBrq77vi2HPPXHHsuWcQysk9kldYsKugpDQ+r6p614oLN8erVq465264mSxT2geV+vOoPY/a86g9T+Wa9b7b2qmFm7dC9gDZrL4mPABgd9rukwvh7D450Nfels+cLcgyrX1haen27va2dXD+e+wrKi//acXipY90tbZs6u/u3jTY13dxf3f3dZ1Np3D8+WcRysk5lltQGC8sKdlVWlW9a8UFF8WrV6/tZD+Oici09kGj/jxqz6P2PGrP09eZ8F17Ldy8lbIHCIL6mvDp2ob4DjgLtwsAzOtpbS4B0MydLJi6W5oy6twmW2+48daH77zD9nV1XVFYWrp96w031rvvcXskdZ1T+/dWHN/1XPh0a0u4v7srPNTft6mzp/sVnc1NOLHrOYRyck7kFhTsKigp3VVauSC+/PwL44vWbUjwHtXYMq190Kg/j9rzqD2P2vP0tLYUsWeYLJ3HzYPO4zb3ahvieQDWdLec2lhSteg0AN+9edTvsuW8Ms2HDpQde+6ZjadbWjb1d3dtGuzv25QcHl6e+nooJ6cxN79gV0FJSby0csGupeHzdy05L9zOnDlb2vuV+vOoPY/a86g9jx/P46Ytbt5WAdjNHiJI6mvCgwD2LNz0lpI//uaPc+BshesA0M+dLDgO3H/3ki3vvtn3h+CvXr22s3r12h0AdqQuazlyuPTYs0+HO1uaws5irj880NJz7emWZpzcE4cJ5ZzKK8iP5xeX7CqtXBBfunHTrqXh8+fs6LLZ0t6v1J9H7XnUnkfteY48um3Rpte/hT3GpGjh5k1b20ia48+2ATgC57QB58PZbbUdgG9+M+JXBfPKsnaRXLVyVVfVylWPAXgsdVnb8WMlR55+auPplqZNfV1d4cH+vk0DrS3XdLW2mMa9u/HkXb9syS0o2FVQXLKrZH5lfPH683Yt23RBcygnZ8bny+b2fqD+PGrPo/Y8as9TUDrPd1s6tXDzlmAPEGAJ9+iTTbUN8TY4Wz83wDkpuu+OBOQn89es9+1RGaeictny7sply58A8ETqsvaTJ4qPPP3keZ1NTeG+7tObBvv6NnW1tmztam0Jndq3B0/fc1drbn7BroLi4njJ/Mr4onUbdi2/4KJT013MBa19plF/HrXnUXsetecpX7Had+21cPO2GFoksJxpX18THgKwv7Yh3gggDGf3yU5oi+isaHz6iQVLNl/mu3/QZtL8JUt75i9Z+hSAp1KXdTadKjz01BPndTY1burr6to02NcX7mprvaqrrTXn1P69eOZ3d7fn5ufH84uLd5VUVO5auGZdfOXFl5yczGJO7bnUn0ftedSeR+15mnY9U7lgfZg9xqTo4CQejDGV1to29hxBNF772oa4gXPS7gvhHB6+Fdp9ckYd3f5Q2YorrvbF4fPZTrc0Fx566vH1HacaN/WdPh0e7O/bNDw4uBbuL8aMCSVyC/Lj+UXF8eKKinj16rW7Vl/youPjLebUnkv9edSeR+15gtw+OTwMm0yaZDJpbHLYDA8NGZtMOh/WmuTQkEnapLFJ616WNHZ42Fhrz1zHWovk8HAIzt+NtUm41w+lvg5rTTJ55jpIDg3lHH76qRsG+3ovHR4c/CGAzx7/xR1D7B4ToS1u3koAaOHGMWZ7d/fJltqG+EMAVgA4D8AQtFvrjOluOlkEZ4umeJhXVd130SuufxbAs6nLutvb8g8+8diGjlMnw72nT28a7OsNd7e3vau7vS23+eAB7Hog1pmbXxDPLyqKF1fM31W1avWuNZdefiyUk6P2ZJnWf6C3J+fhO+/4YF9X1+WFpaU7tt5w463uqTCyTqa1H8voF5o2mRx5sZl6MZlMYnh4OHTmhWbSAjZpksPDZ15Ipl0/lHqh6by4TIZSLzTdF6HGJpMGFqkXqQYYedEKa42FNbCATSZDzt+tsRap2wi514FzG8mQtTBA6j5gYG2oq/Ho/Jbm1oQFDM7cj/t398+z/u784t/Aue+RGawNAUhdDvfyEODOdOZrNmQBwLqfn7k9mNRlNu1rY37duRw4c59nNkYYACF3Trgzp66DkZkRcr9/5D7cr6U+P6vBmQ8L5zGd+Z6zPgfcGSxCNv1z97IzMwDGAiY5OFD4/PYdA+mPNX0GO/blANIf95m2aY8z/T6dGWyqTerroxqnf4zu735PKO12z8xgz77PkNsNad8bSvv66Ntk+zicQH/LHmQitMXNgzFmo7VWR5UkmGj72oZ4MYCNcHat1O6TM+DJ//rmKh3lamb1dCTyDj7x2LrEyRObek93bhrs6900PDi4HkAeAMCYrtz8/LgZHj5RvmzFHxasWBVf+6LLj+Tk5ekf6Tk0G8/94cFB03u6M7+3s6Ogv6c7f6Cnp3Cwvz9/aGAgf2hgoHB4aDA/OThYMDw8VJAcGi5IDg/nJ4eHC2xyuKCnI3H10MDARXB+0Tqck5e3q6C4ZCdw1gu7F75wHHlheuZzuJ/bkReW7mVnfT7OC8WRF5ojl496UWfP3P7Ii7iRuZB2/dDIC82R20kODRWEcnMHz3oxP/qF3tn3C6S9kDzHi1f3heTIDHasF5J27Bewoz7EXyycPXJs2ufOZcakf27hPDnSPjfWfdakf8C5PeNc36Q+P+vrFsa84D6N833u10du0w4N5ZncvH739kbdn3M/o+aAgXv7Jv0+zlyGkcdikme+blItDM48TpN2H2n3Y15wmQGApBn1eerrBmdaJmHMmZnMSAuk3597+dnt0z83JjV/+mOHOdPVWBj3H7eRy3CmnzFJJ0Pq+03S+Vk5n7cfP3Zz+qmBAPzh+C/uuBI+oIWbB53HjWey7Wsb4gvgnLy7GM6Wuqz8rfRc0Hll5kbv6c7cg0/sWJc4cXxT7+nO8EBvX3h4cOA8APkAAGO6c/Pzd+cXFu0qKi+PL1ixateaSy8/kldQoF2Dp2Cwvz/UnWgr6O/qKujv7i4Y7OvLH+zvKxgaGCgYGhgoGB4azB/s6S5BKCfHXTwV2mRqEZUsSCaTBTaZzLfWFthkssDaZL5N2gJYW2BtssBam2+tLYTz55m/I/XznBlJGNOHs17E4awXMxjzxarz4saMfD11HeCFL5BGvYh9wddw5kXiC168jrxogxmZwYy8AE27z1G3nbTGhMzwWC9WzQsuc184jrzQHHncIy8sU/Ml0z+HQdJg1It290Vj2gvTs17YpjabpL0gt8akv3g1qZ/FWS/aR16MmuSZF5ojLybTXqib1Avgs+7fGHP2i2nnxWfyzO0at61J/XxTCwNjTeoFsznzQjl51tcBa0zIwsAO9fXl5BUVDbrf5852ZiYgZJLu/Tg/b2OsMaGkex0YGGtCoaT7/Uh9vwm59+lc373M/dO4txcyNhTKSc1sTShkQ6HQmevBhKwJGYRycpMmZGzIhKwJhWBCIRvKyUm614cJhawJ5dic3NzkbBzxd7bo/7dz7/7v3PqR7va2d8B5u00PgC8f/8Ud2uKWDbTFjWcq7Wsb4jkAlsPZAjcM7T45JdrixvP4925dW7xmY077iWObejs7w4N9vZuGBgbOA1AAADCmNzcvf3d+UVG8qKx8V+XyFbvWXHrZYb/sPpccHkZ/T3duT0eisL+7O3+gtzd/sK+3cMjZAlUwPDhYMDQ0WJAcGspPDg0VJIeHC5JnLZ6GC2zS5ttkstBZRCULbNI6iyjrLqKStsDCOosq2AL38nxM/+0BfcaYARjTb9wPGDNgTMj5PGT6YUIDJmT6jQn1h0KhARMK9ZlQaCAUCvWbUE5/KCc0EMrJ7Q/l5PTn5Ob2h3JzB3Ly8vtz8/L6cwsK+vMKCgbyCov6C0tL+wtL5w089ov/vSntBUZfyfzKO6+96YNfn/5PIvPo3x0etedR+7nn7oJe29fZ8bLh4eEfAojqPW7Zo4c9QIBNun19TXgYwOHahngTgPVwFnGnAfTO8GxZrWj+fG1lJilZsKA7fM21pwDsBfBLwPmfzMHHd6xuO350U09nR3iwt3dTT2fHG3s6Em9vPXoYex95qC83P39PXmHRrqKy8l0Vi5fsObVvz6v6ursvG+99UcnhYfR0JAr6Tnfm93V3FQz09hYM9vUVnLX73tBQ/vDQYGFyeDg/OTTsLJqGhwuSyWS+dbY+FdhkMj9pkwVIpm11StpCa20+nM8LnL/bAgsUwNoCTG+Xs2F34TQAmD5jzIC7gOo3IdNvQqHToZxQi7t4GjAhd1GVE+oPhXIGTCjUHxpZQPWFcnMGQrl5/Tm5uf25efn9XSeOlC447/yT+YVF/flFRQMFpaX9RfPK+gtL5w0yfou/9YYbb334zjtsX1fXFYWlpdu33nBj/ZwPMUf07w6P2vOo/dzLLyoevvamD3794Lbf3bfmmlf8d31N2Dd7sWiLmwcdVZJnJtrXNsQrMXLy7m4473/zzX+gLEE+yhXbRNsP9veHDj6+fVXrsSObejs6Ng309oaHBgc2wtriUVe1MKYjFAr1zPDue4Mv3PJ01laoPhNyt0a5W6BGtjyF+kM5OQPOFqic1NangVBubl9Obt5Abl7eQG5BQV9ufsFAflFRf35xcX/hvHn9xWXlA7O9ZVHPfR6151F7HrXnaXp+55qF52/+qZ8Wbtri5q0aOqoky7Tb19eE22ob4o/AOXDJIgCVAFK/Nh+Cs5DTb7tGadn9/Hz9j4Rjou3zCgqS572k5iCAgwDuApzF3KEnH1u595GHvpQcHl7hXtUAMHlFRU8YExoYe/e9nP5QTs7AuXbfyy8u7isoLhkoLJ03UFxe0Z+tB03Rc59H7XnUnkfteVr37ylfeP5m9hiTooWbt0b2AAE2I+3d3SePAzjungOuCM6pBioAVME5J1xq161+OAu5wZm4b79afPGlrewZgmo67fMKCpIbrtx66Nhzz/zurPdFVcz/32x9X9RM03OfR+151J5H7XkWbrrIdxtmQt5XCbwK9gABVjHTN1hfE7b1NeGe+ppwc31NeG99TfgRADEAvwewE0ATnF3IqjGyqCvByFa6QGg/uK+UPUNQzUT7rTfceGvJ/Mo7c/Lyny2ZX3lnNr8vaqbpuc+j9jxqz6P2PB1HD/muvba4eStkDxBgc9Le3SJ32v1oBIDahng+nNMKlMJZwFVi5D1BQ3AOdtKPkcNtZ5X+050F7BmCaibap954DUBb2SZJz30etedRex615+nvOj2Tp2qZEzo4iQedx40nk9q7u1gWwtn6Vg5nITcfI1utB+As5rLiXCw6rwyP2nOpP4/a86g9j9rzdDefWlJSveguPx2cRLtKelvFHiDAMqa9u4tlb31NuKW+Jry/via8A8DvADwE4EkAJ+D891SFkV0sS+HTrdoH7r97CXuGoFJ7LvXnUXsetedRe54jj25bxJ5hsnz5onKOdbEHCLCMbu/+hqbL/TgFALUN8Vw4W+VKMLKAS22KT8LZKteHDN/FsqRqoc5fSKL2XOrPo/Y8as+j9jzFC6p8d45fLdy8ZcSuegHlu/b1NeEhAB3uxwkAqG2IF8BZyM3DyPvlUgc7GQQwDPeQ7e5l6X+aUV8bj3Wvk7r/aS0MC+dXarcNErXnUn8etedRex615yksq/Bdey3cvFUB0KFaObKifX1NuB/OgUzaABx23y9XjJH3y+Vi5KTgybQPO8afoz8w6u8LASx3/96JKZ7WoHVvvGLlldd0TOV7ZXrUnkv9edSeR+151J6n7eC+8kUXbmGPMSlauHk7yR4gwLKyfX1N2ALodj+aZvjmW2ob4vvhLODWwTmASg8mudvp0i2Xt8zwXDJBas+l/jxqz6P2PGrPs+iCzTqPWxZawB4gwNR+Cuprwv31NeGjALYB2A5n0VYNZxE3ofPRNe9+rnz2JpRzUXsu9edRex6151F7nrYDe+exZ5gsbXHz5rtzPGQRtZ8G9+AprQBaaxviJQCWAlgNZ/F2Gud4D+FgT0/eXMwoL6T2XOrPo/Y8as+j9jyDvf5rr/O4ecikc4kFjdrPPPeol1VwdqOcB+e9d52jr6fzyvCoPZf686g9j9rzqD2PzuOWnTLmXGIBpPYzrL4mPFRfE24E8HsAjwJogbMb5QKkbYHXeWV41J5L/XnUnkftedSeR+dxy04v2Bohc0btZ4l7gJQEgERtQ3wvgEVwtsLlA+iet3hpN3G8QFN7LvXnUXsetedRe57ShYt9dw49Ldy8DbEHCDC1nwP1NeE+OKcpOAbnHHNrSxYuXgbnoCbaVXWO5RYW6XlPpP48as+j9jxqz5NbUDjMnmGytKukt0r2AAGm9nOoviY8XF8Tbq6vCf/hie99Yz+c98DplztzrP3Qfh1hjEj9edSeR+151J4ncfSQ744qqYWbt+PsAQJM7UmOP/b7XQCehPPeN0MeJ1CWX7F1ps/tJ5Og/jxqz6P2PGrPs2TzZb47h54Wbt5898bFLKL2PIvqa8KnAOyBc/ASmSMnd+7Q+QuJ1J9H7XnUnkfteZp2PT2fPcNkTWk3qEgkUgTg+wAWwjkf1HtisVjzGNerhnP0uotisZhf3yujxS2P2vOk2u8HUAJnEd3KGyc47NCwtnASqT+P2vOoPY/a89hh/7Wf6gvjDwF4JhaL1QC4A8CnR18hEolcB+Ae+H+riXbX41F7nuPAmaNPPgfnQCW+2xfcj1Zcdc0Lfgkmc0f9edSeR+151J5nyZYrfPcL6aku3K4GcLf7998AeMUY10m6l7dN8T4yxQr2AAGm9jxn2tfXhIfgvN8tB0AhbaKAOPzQ/X7/ZZevqT+P2vOoPY/a8xx/7BHfvRXEWGvPeYVIJHITgD8fdfEpAB+NxWK7IpFICMCRWCy2fJzvPwQgPNaukrfffvvNAG4GgEQi8YNbbrnlLgAn4RwQIR/AYTgnYe6Ec2j4SjhbAhbBWXQeh/MCM+HeZAWAowCWwVk4nnL/3gZnt9CytNscgLPr1xI4JyEuBFCa9vU+93YvABCHs7tYcdrXewB0w3n/T6N734VpX08dSr0qAx/TYgDNPnhMl7r3k02PyS8/pyr3Omce01Uf/VRfXnHJq4sXVB0d6OrC6cYTJWuvvf7kgfvvXpJXXDxYvfGCjhNP7qhasCGc6Gtvy+9uaSpOfb1gXln//DXruxqffmJB1cbz27ubThb1trcXpr5eNH9+X8nCJb0tu5+fv/jiS1vbD+4r7T/dWZD6eknVwp7C+ZUDrXvjFUu3XN7SvPu58sGenrzU1+ctXtqdW1g01H5of/nyK7Y2ndy5Y4EdGjYrrrqm+fBD9y8qX77yNAB0HDsyb9XV1546+si2apObY5dsvrz12PaHF85fva5jqK83NxMeU0FZWV9/Z2dhNj0mP/2cDm67d3lRxYLebHpMfvk5DfZ05yaHh3Oy6TH55eeU+ncnmx6TX35OQ/29OQvWbUxk02Pyy8+pt61l3ZFHt9W37t21MpNe71lrd2Mcngu3sUQikZ8C+NdYLLY9EomUA3g4FotdOM51D2GchZsfGGMWWWtPsecIIrXnGa99bUN8KYBLADQBmPw/HuLpwP13V6699nq/76ngW+rPo/Y8as+j9jzHH39k/bIXXfXj+ppwkj3LRE11V8mHAbzG/furATTMzDgZqYI9QIBVsAcIsIqxLqyvCZ8AsBc60uSs6Th2RO8lJFJ/HrXnUXsetefpPHGslD3DZE315LrfAPC9SCTyEJzNgDcAQCQSuQXAvlgs9ssZmi8THGUPEGBqz3Ou9qmTcy+A/9/DmnFWXX2ttjITqT+P2vOoPY/a8yy77CrfHRhmSgu3WCzWA+AtY1z+72Nctnoq95FBlsHZwiBzT+15xm1fXxNO1jbEnwHwYjj7anfN5WDZ7ugj26or12zQLy1I1J9H7XnUnkfteU4+uX1B2ZIxD9GRsXSeLG++2e81C6k9zznb19eEB+EcaTIfQMGcTBQQJjdH7x0kUn8etedRex615zE5/muvhZs3bcLmUXsez/b1NeFuAI/DeT9czmwPFBRLNl/uu/PKZBP151F7HrXnUXuehZsubmfPMFlauHlbxh4gwNSeZ0Lt62vCbQCegXP6AJkBx7Y/vJA9Q5CpP4/a86g9j9rznNz5mO9eu2jh5k0HX+BRe54Jt6+vCR8FcBA60uSMmL96XQd7hiBTfx6151F7HrXnqVix+jR7hsnSws3bVI+8KdOn9jyTbb8HzsklK2Z+lGAZ6uvV855I/XnUnkftedSeZ6i/z3dv89DCzVsZe4AAU3ueSbWvrwkPw9llchBAyaxMFBCnG0+oH5H686g9j9rzqD1PV1NjMXuGydLCzdth9gABpvY8k25fXxPuB/AEgEI4R5uUKVh77fUn2TMEmfrzqD2P2vOoPc/KK6/x3UHwtHDztoo9QICpPc+U2tfXhLvgLN4qoH9fpuTA/XcvYc8QZOrPo/Y8as+j9jxHHt22iD3DZOmFlbcB9gABpvY8U25fXxNuAfA8dLCSKckrLh5kzxBk6s+j9jxqz6P2PHlF/muvhZs3nV+DR+15ptv+CJzdLX13qF226o0X6AhjROrPo/Y8as+j9jyVazfoqJJZSJuwedSeZ1rt62vCFkAcQDt0pMlJOfHkDi12idSfR+151J5H7XlOPbezkj3DZGnh5q2FPUCAqT3PtNu7R5p8GsAwgKJpTxQQCzaEE+wZgkz9edSeR+151J6ncs16323t1MLNWyF7gABTe54ZaV9fE+6Dc7CSEui8fBPS196mI3ISqT+P2vOoPY/a8/R1JnzXXgs3b6XsAQJM7XlmrH19TbgTwJMAFkD/5njqbmny3Xllson686g9j9rzqD1PT2uL7/YG0osobzqXGI/a88xo+/qacBOc97xpX34POqcPl/rzqD2P2vOoPY/O45addC4xHrXnmY32BwEch7PlTcahc/pwqT+P2vOoPY/a8+g8btmpjz1AgKk9z4y3d480+TyALgBlM3372aJgXlk/e4YgU38etedRex615ykonee78wVr4eYtwR4gwBLsAQIsMRs3Wl8THoLzfjcDHXxmTPPXrO9izxBk6s+j9jxqz6P2POUrVvuuvRZu3hazBwgwteeZtfb1NeFeOEeaLIOONPkCjU8/oV1JidSfR+151J5H7Xmadj2j87hloWb2AAGm9jyz2r6+JpwA8BSc97uZ2bwvv6naeH47e4YgU38etedRex6151mw7jydxy0LlbAHCDC155n19vU14ZMA9gKonu378pPuppO+OzxxNlF/HrXnUXsetefpbmkuYM8wWVq4edP5NXjUnmeu2u8H0AjAd7srzJbe9na9949I/XnUnkftedSep6/Df+21cPOmc4nxqD3PnLSvrwknATwLoBc64ToAndOHTf151J5H7XnUnkfncctOOpcYj9rzzFn7+prwIJwjTeYB8N1uCzNN5/ThUn8etedRex6159F53LJTD3uAAFN7njltX18T7oZzpMlyADlzed+Zpmj+fJ2/kEj9edSeR+151J6nsNx/7bVw89bNHiDA1J5nztvX14TbADwNoGqu7zuTlCxc0sueIcjUn0ftedSeR+15SqqqfXfycy3cvOmIdzxqz0NpX18TPg7gAOv+M0HL7ufns2cIMvXnUXsetedRe57W/XvK2TNMlhZu3hrZAwSY2vMw2+8B0AKggjgDzeKLL21lzxBk6s+j9jxqz6P2PAs3XdTGnmGytHDzVsEeIMAq2AMEWAXrjt0jTT4NYBABPJdf+8F9OromkfrzqD2P2vOoPU/H0UO+a6+FmzffneMhi6g9D7V9fU14AM7BSgoB5DNnmWv9pzsDf2RNJvXnUXsetedRe57+rtO+e32hhZs3nUuMR+156O3ra8JdcBZv8xGgI03qnD5c6s+j9jxqz6P2PDqPW3bSucR41J4nI9rX14Rb4JygewF7lrmic/pwqT+P2vOoPY/a8+g8btmpiz1AgKk9Tya1PwpnC2AgThNQUrVQ5y8kUn8etedRex615yleUOW7UzFo4ebNdyfnyyJqz5Mx7etrwhbAbgDtcE7QndUK51cOsGcIMvXnUXsetedRe57CsgrftdfCzVsgfsufodSeJ6Pa19eEhwHsBJBElh9ttHVvvII9Q5CpP4/a86g9j9rztB3c57tfBmvh5k1vGuVRe56Ma19fE+4HsB3AKTgn6K5EFv4btnTL5S3sGYJM/XnUnkftedSeZ9EFm3UetywUmIMiZCC158nI9vU14Z76mvCzALbBed/bfDiz5lIHm0HNu5/z3W8As4n686g9j9rzqD1P24G989gzTFbWvNiZRb47x0MWUXuejG5fXxPuAbCntiF+GMBSAOvg/HuWgHPibt8a7OnJY88QZOrPo/Y8as+j9jyDvf5rb6y17BkymjGm0FqbMQdqCBK15/Fb+9qGeC6ARQA2ACgC0IkMOsDKZHQcO5Jfvnyl794wnS3Un0ftedSeR+15uptPLSmpXnRXfU04yZ5lorSrpLeMOJ9VQKk9j6/a19eEh+prwsfh7EL5uHvxQgAlvKmmRuf04VJ/HrXnUXsetefx43nctKukt072AAGm9jy+bO/+1qyptiHeDOfgJWvhHMikHz55TPMWL+1mzxBk6s+j9jxqz6P2PKULF/vuHHpauHkbYg8QYGrP4+v27rnfWgG01jbEywGsAbAYzvvfOgBk7D7iuYVFvm7vd+rPo/Y8as+j9jy5BYXD7BkmS7tKeqtkDxBgas+TNe3ra8Id9TXhpwA8BOAEnKNQLkCG/vvXfmi/jjBGpP48as+j9jxqz5M4ekhHlcxCx9kDBJja82Rd+/qacBeAXbUN8YMAlsPZCmfgbIHLmN94Lr9iaxN7hiBTfx6151F7HrXnWbL5Mt+dQy8jf+OcYXz3xsUsovY8Wdu+vibcV18T3gfgQQBxAKVw3geXEadAOLlzR0aeQy8o1J9H7XnUnkfteZp2PT2fPcNkaYubNy1uedSeJ+vb19eEBwAcqW2In4BzBMoNAMoBdAHoZc1lh4YN675F/ZnUnkftedSexw77r70Wbt6ybpcxH1F7nsC0r68JDwE4UdsQb4Tz3rcNcBZyPXAWcXNqxVXXNM/1fcoI9edRex6151F7niVbrmhlzzBZWf9b9Rmwgj1AgKk9T+Da19eEk/U14WYAjwD4A5xFWzWcrXBz5vBD92ftbqp+oP48as+j9jxqz3P8sUeq2TNMlra4eUuwBwiwBHuAAEuwB2BxTyXQBqCttiFeBmA1gKVwDmCSwCyfSqB8+crTs3n7cm7qz6P2PGrPo/Y8ZUuXz/leNdOlhZuIyDjqa8KdAJ6ubYjvh7MVchWchVsCgO/O/yIiIiL+pV0lvVWwBwiwCvYAAVbBHiCT1NeEu+trwnE4R6LcB2f3yWoAeTN9Xx3HjvjuvDLZRP151J5H7XnUnqfzxLFS9gyTpS1u3o6yBwgwtedR+zHU14T7AByobYgfBbAYwHoA8+GcC65/Ju5j1dXXnpqJ25GpUX8etedRex6151l22VW+OzCMtrh5W8YeIMDUnkftz6G+JjxYXxM+CmAbgCcB5MA5EmXxdG/76CPbfPdm6Wyi/jxqz6P2PGrPc/LJ7b47h562uHlLsgcIMLXnUfsJqK8JDwNorG2In4JzKoH1cHah7MUUTyVgcnNm9eAncm7qz6P2PGrPo/Y8Jsd/7bVw86ZN2Dxqz6P2k+AeibKltiHeCuf9b2vhbIEbxCSP0Llk8+W+O69MNlF/HrXnUXsetedZuOnidvYMk6VdJb1plzEetedR+ymorwnb+ppwor4m/ASAh+EsgKsBVGKC/94e2/7wwlkcUTyoP4/a86g9j9rznNz5WBV7hsnSFjdvbewBAkztedR+muprwqcBPFvbED8AYDmc88EBzoFMhsb7vvmr13XM/nQyHvXnUXsetedRe56KFat9dw49Ldy8qRGP2vOo/Qyprwn3ANhT2xA/DOdE3uvg9E3A2ZXyLEN9vWpPpP48as+j9jxqzzPU35fDnmGytKuktzL2AAGm9jxqP8Pqa8L99TXhgwAeAPAMgEI474MrTL/e6cYTJXM/naSoP4/a86g9j9rzdDU1TvtI0HPNWOu7A6rMKWNMobW2jz1HEKk9j9rPvtqGeAhAFYANcBbK3QC6O44dyS9fvnKAOlyAZVj/3LSPrP9Fa1fTybzShUtesBV6ksyMDJP9zurU1dSYV7pw8XTbyxRkUXvf/bfX3XyqsKR60V31NWHfHElbm2e9rQKwmz1EQKk9j9rPMvd/FE21DfFmOAcvWQug+tiOhxaWL7/hOe50wXXg/ruXbHn3zYdn6eZDcM75l5v253j/H7YA+tyP0wCmu5jM+N/SPvu/319z5Yc+eYI8BqsT9efzzP98d81VH/3UkUl8SyA7TdE5Z95557fXbP2zTx+c6/vNQHM+7/ZvfmnFnrt/7ptFG6Atbp6MMWustbPxH5R4UHseteeobYiXP/M/39t60Vvfk4Tz/rcO+O9/vr727P9+f+mFb37XZBYPOXjhYmy8900MYWQxlvrodS8fTP+zviY87gFsspX+3eFRex615/Fjey3cPBhjyqy1new5gkjtedSexxhTdvO2XUkAKwCshLNwSwAYZs4VFKeefbJk0YVb+vHChdh4uyoOwFmA9QPocf/sw6iFGIBBP+2Ow6B/d3jUnkftefzYXrtKelsCwFc/1Cyi9jxqz7Okvia8G8Cu2ob4QTinElgD5/0D5zyVgIzL4OxdE8fdRbHt4N5liy7csh/O4qsLI1vFBjD2Yky//Zw5+neHR+151J7Hd+21xc2DMWaBtVZntSdQex615xmrfW1DPB/AYjgHMsmDs4DLlANosITwwq1i6Ysxg5HdTJNwFl/97p+pxdgLFmK3XXdp+WBPj577BPp3h0ftedSex4/ttcXNW6H3VWSWqD2P2vO8oH19TXgAwJHahvgJOKcQ2ACgAs5BK3rndLrZNdZCbLxdFAfhLMB6MLIQ68fZi7HU+8UmvJvpN3t79dznUXsetedRex7ftdfCzVspe4AAU3setecZt717wIoTtQ3xRgAL4CzgFsJZvHTNzXiTlr5rYvpibPTuHhYj7xfrxMiWsX6MffCO2Xq/mJ77PGrPo/Y8as/ju/baVdKDzmfFo/Y8as8zmfa1DXEDYD6c98BVw1n4dMzieMDUD2mf2irWh7HfLzaUCe8X03OfR+151J5H7Xn82F4LNw/GmI3WWp3PikDtedSeZ6rtaxviZQBWA1gKZyGUwMRPJZB+SPvctM/HktWHtNdzn0ftedSeR+15/Nheu0p689VKPMuoPY/a80ypfX1NuBPA07UN8f1wTiWwCiNbvMY6pH36wTtSuyh2Y+Q9Y6PfLxaUQ9rruc+j9jxqz6P2PL5rP6UtbpFIpAjA9+G8t+I0gPfEYrHmUdf5cwBvdz+9KxaL1U1zVgpjTLm1drZ3PZIxqD2P2vPMVPvahngBnK1vZRj/kPapLWPa9cKl5z6P2vOoPY/a8/ix/VS3uH0IwDOxWOzvIpHI2wF8GsDHU1+MRCJrAbwTwIvh/Ea3IRKJ/CwWiz093YEJFmP23zMiY1N7HrXnmZH29TXhfgAHpz9O4Oi5z6P2PGrPo/Y8vms/3mGWvVwN4G73778B8IpRXz8K4PpYLDYci8WScM475LvNka5m76vILFF7HrXnUXsu9edRex6151F7Ht+199ziFolEbgLw56MuPoWRFeppAOXpX4zFYoMAWiKRiAHwbwCejMVie0bf9u23334zgJsBIJFI/OCWW265C8BJOIe5zgdwGM77NDrh7NpTCeA4gEVwFp3H4byXI+HeZAWcReMyOCdcPeX+vc19rGVptzkAoBXOWdNb4JzLoTTt633u7V5kjHkGQAmA4rSv98B5P0g1gEb3vgvTvt7l3kZVBj6mxXCerJn+mC4yxhzJssfkl5/TUmNMdZY9Jr/8nIbd9tn0mPz0czrPGNOeZY/JLz+nEve5n02PyS8/p9S/O9n0mPzycyo2xuRl2WPyy89ppTHmD5n2mM51wJSpvsftpwD+NRaLbY9EIuUAHo7FYheOuk4hgNvgLOw+HIvFJnwC1EzixyPOZAu151F7HrXnUn8etedRex615/Fj+6m+x+1hAK8BsB3AqwE0pH/R3dL2CwD3xWKxz01rQr7D7AECTO151J5H7bnUn0ftedSeR+15fNd+qgu3bwD4XiQSeQjOZsAbACASidwCYB+cw06/FEBBJBJ5tfs9fx2LxR6Z5rwMqwD4ajWeRdSeR+151J5L/XnUnkftedSex3ftdQJuD8aYFdbao+w5gkjtedSeR+251J9H7XnUnkftefzYfqpHlQySbvYAAab2PGrPo/Zc6s+j9jxqz6P2PL5rr4Wbt2r2AAGm9jxqz6P2XOrPo/Y8as+j9jy+a6+Fm7dG9gABpvY8as+j9lzqz6P2PGrPo/Y8vmuvhZu3CvYAAVbBHiDAKtgDBFgFe4CAq2APEGAV7AECrII9QIBVsAcIsAr2AJOlhZu3QvYAAab2PGrPo/Zc6s+j9jxqz6P2PL5rr6NKejDGFFpr+9hzBJHa86g9j9pzqT+P2vOoPY/a8/ixvba4eVvFHiDA1J5H7XnUnkv9edSeR+151J7Hd+21cPPWxR4gwNSeR+151J5L/XnUnkftedSex3fttXDz5qtNqFlG7XnUnkftudSfR+151J5H7Xl8114LN29V7AECTO151J5H7bnUn0ftedSeR+15fNdeCzdvJ9kDBJja86g9j9pzqT+P2vOoPY/a8/iuvRZu3hawBwgwtedRex6151J/HrXnUXsetefxXXst3LzlswcIMLXnUXsetedSfx6151F7HrXn8V17ncfNgx/P8ZAt1J5H7XnUnkv9edSeR+151J7Hj+21xc2b787xkEXUnkftedSeS/151J5H7XnUnsd37bVw89bJHiDA1J5H7XnUnkv9edSeR+151J7Hd+21cPM2xB4gwNSeR+151J5L/XnUnkftedSex3fttXDzVskeIMDUnkftedSeS/151J5H7XnUnsd37bVw83acPUCAqT2P2vOoPZf686g9j9rzqD2P79pr4eZtEXuAAFN7HrXnUXsu9edRex6151F7Ht+118LNmxrxqD2P2vOoPZf686g9j9rzqD2P79rrPG4ejDHF1toe9hxBpPY8as+j9lzqz6P2PGrPo/Y8fmzvu5UmwQr2AAGm9jxqz6P2XOrPo/Y8as+j9jy+a6+Fm7cEe4AAS7AHCLAEe4AAS7AHCLgEe4AAS7AHCLAEe4AAS7AHCLAEe4DJ0sJNREREREQkw2nh5q2CPUCAVbAHCLAK9gABVsEeIOAq2AMEWAV7gACrYA8QYBXsAQKsgj3AZOngJB78+MbFbKH2PGrPo/Zc6s+j9jxqz6P2PH5sry1u3paxBwgwtedRex6151J/HrXnUXsetefxXXst3Lwl2QMEmNrzqD2P2nOpP4/a86g9j9rz+K69Fm7eTrEHCDC151F7HrXnUn8etedRex615/Fdey3cvPluM2oWUXsetedRey7151F7HrXnUXse37XXws1bG3uAAFN7HrXnUXsu9edRex6151F7Ht+118LNWy57gABTex6151F7LvXnUXsetedRex7ftdfCzVsZe4AAU3setedRey7151F7HrXnUXse37XXedw8GGMKrbV97DmCSO151J5H7bnUn0ftedSeR+15/NheW9y8rWIPEGBqz6P2PGrPpf48as+j9jxqz+O79lq4eRtgDxBgas+j9jxqz6X+PGrPo/Y8as/ju/ZauHlrZQ8QYGrPo/Y8as+l/jxqz6P2PGrP47v2Wrh5W8IeIMDUnkftedSeS/151J5H7XnUnsd37bVw89bCHiDA1J5H7XnUnkv9edSeR+151J7Hd+21cPNWyB4gwNSeR+151J5L/XnUnkftedSex3fttXDzVsoeIMDUnkftedSeS/151J5H7XnUnsd37XUeNw9+PMdDtlB7HrXnUXsu9edRex6151F7Hj+21xY3b747x0MWUXsetedRey7151F7HrXnUXse37XXws2br1biWUbtedSeR+251J9H7XnUnkfteXzXXgs3bwn2AAGWYA8QYAn2AAGWYA8QcAn2AAGWYA8QYAn2AAGWYA8QYAn2AJOlhZu3xewBAkztedSeR+251J9H7XnUnkfteXzXXgs3b83sAQJM7XnUnkftudSfR+151J5H7Xl8114LN28l7AECTO151J5H7bnUn0ftedSeR+15fNdeCzdvxewBAkztedSeR+251J9H7XnUnkfteXzXXudx8+DHczxkC7XnUXsetedSfx6151F7HrXn8WN7bXHz5rtzPGQRtedRex6151J/HrXnUXsetefxXXst3Lz1sAcIMLXnUXsetedSfx6151F7HrXn8V17Ldy8dbMHCDC151F7HrXnUn8etedRex615/Fdey3cvFWzBwgwtedRex6151J/HrXnUXsetefxXXst3Lw1sgcIMLXnUXsetedSfx6151F7HrXn8V17Ldy8VbAHCLAK9gABVsEeIMAq2AMEXAV7gACrYA8QYBXsAQKsgj1AgFWwB5gsLdy8FbIHCDC151F7HrXnUn8etedRex615/Fde53HzYMfz/GQLdSeR+151J5L/XnUnkftedSex4/ttcXNm+/O8ZBF1J5H7XnUnkv9edSeR+151J7Hd+21cPPWxR4gwNSeR+151J5L/XnUnkftedSex3fttXDz5qtNqFlG7XnUnkftudSfR+151J5H7Xl8114LN29V7AECTO151J5H7bnUn0ftedSeR+15fNdeCzdvJ9kDBJja86g9j9pzqT+P2vOoPY/a8/iuvRZu3hawBwgwtedRex6151J/HrXnUXsetefxXXst3LzlswcIMLXnUXsetedSfx6151F7HrXn8V17ncfNgx/P8ZAt1J5H7XnUnkv9edSeR+151J7Hj+21xc2b787xkEXUnkftedSeS/151J5H7XnUnsd37bVw89bJHiDA1J5H7XnUnkv9edSeR+151J7Hd+21cPM2xB4gwNSeR+151J5L/XnUnkftedSex3fttXDzVskeIMDUnkftedSeS/151J5H7XnUnsd37XOn8k2RSKQIwPcBLARwGsB7YrFY86jrfATAewFYAH8fi8V+Pb1RaY6zBwgwtedRex6151J/HrXnUXsetefxXfupbnH7EIBnYrFYDYA7AHw6/YuRSKQKwIcBvARABMA3IpGImc6gRIvYAwSY2vOoPY/ac6k/j9rzqD2P2vP4rv2UtrgBuBrA592//wbAZ9K/GIvFWiKRyOZYLDYUiURWA0jEYrEXnHfg9ttvvxnAzQCQSCR+cMstt9wF5yzmC+CcW+EwnCO+dMLZD7USzup4EZxF53EAKwAk3JusAHAUwDIASQCn3L+3uY+1LO02BwC0AlgCoAVAIYDStK/3ube7zhjTDqAEQHHa13sAdAOoBtDo3ndh2te73NuoysDHtBhAsw8e0zpjTDLLHpNffk7VxpjcLHtMfvk55RpjNmbZY/LTz2m5+9zPpsfkl59Tmfvcz6bH5JefU+rfnWx6TH75Oc03xnRl2WPyy89pnTHmeKY9JmvtbozD8zxukUjkJgB/PuriUwA+GovFdkUikRCAI7FYbPkY3/tRAHUA/iMWi9Wd844ylDGm2Frbw54jiNSeR+151J5L/XnUnkftedSex4/tPXeVjMVi34nFYhemfwDoADDPvco8jKxCR3/vV+GsMq+JRCLXztDMc20Fe4AAU3setedRey7151F7HrXnUXse37Wf6q6SDwN4DYDtAF4NoCH9i5FIZCOAfwHwZgCDAPrhbE70owR7gABLsAcIsAR7gABLsAcIuAR7gABLsAcIsAR7gABLsAcIsAR7gMma6sLtGwC+F4lEHoKz/+YNABCJRG4BsC8Wi/0yEonsBPAInKNK/iYWiz04EwOLiIiIiIgEjed73ILOGLPxXG8SlNmj9jxqz6P2XOrPo/Y8as+j9jx+bK+Fmwc/vnExW6g9j9rzqD2X+vOoPY/a86g9jx/bT/U8bkGyjD1AgKk9j9rzqD2X+vOoPY/a86g9j+/aa+Hmza8HVckGas+j9jxqz6X+PGrPo/Y8as/ju/ZauHk7xR4gwNSeR+151J5L/XnUnkftedSex3fttXDz5rvNqFlE7XnUnkftudSfR+151J5H7Xl8114LN29t7AECTO151J5H7bnUn0ftedSeR+15fNdeCzdvUz3XnUyf2vOoPY/ac6k/j9rzqD2P2vP4rr0Wbt7K2AMEmNrzqD2P2nOpP4/a86g9j9rz+K69zuPmwRhTaK3tY88RRGrPo/Y8as+l/jxqz6P2PGrP48f22uLmbRV7gABTex6151F7LvXnUXsetedRex7ftdfCzdsAe4AAU3setedRey7151F7HrXnUXse37XXws1bK3uAAFN7HrXnUXsu9edRex6151F7Ht+118LN2xL2AAGm9jxqz6P2XOrPo/Y8as+j9jy+a6+Fm7cW9gABpvY8as+j9lzqz6P2PGrPo/Y8vmuvhZu3QvYAAab2PGrPo/Zc6s+j9jxqz6P2PL5rr4Wbt1L2AAGm9jxqz6P2XOrPo/Y8as+j9jy+a6/zuHnw4zkesoXa86g9j9pzqT+P2vOoPY/a8/ixvba4efPdOR6yiNrzqD2P2nOpP4/a86g9j9rz+K69Fm7efLUSzzJqz6P2PGrPpf48as+j9jxqz+O79lq4eUuwBwiwBHuAAEuwBwiwBHuAgEuwBwiwBHuAAEuwBwiwBHuAAEuwB5gsLdy8LWYPEGBqz6P2PGrPpf48as+j9jxqz+O79lq4eWtmDxBgas+j9jxqz6X+PGrPo/Y8as/ju/ZauHkrYQ8QYGrPo/Y8as+l/jxqz6P2PGrP47v2Wrh5K2YPEGBqz6P2PGrPpf48as+j9jxqz+O79jqPmwc/nuMhW6g9j9rzqD2X+vOoPY/a86g9jx/ba4ubN9+d4yGLqD2P2vOoPZf686g9j9rzqD2P79pr4eathz1AgKk9j9rzqD2X+vOoPY/a86g9j+/aa+HmrZs9QICpPY/a86g9l/rzqD2P2vOoPY/v2mvh5q2aPUCAqT2P2vOoPZf686g9j9rzqD2P79pr4eatkT1AgKk9j9rzqD2X+vOoPY/a86g9j+/aa+HmrYI9QIBVsAcIsAr2AAFWwR4g4CrYAwRYBXuAAKtgDxBgFewBAqyCPcBkaeHmrZA9QICpPY/a86g9l/rzqD2P2vOoPY/v2us8bh78eI6HbKH2PGrPo/Zc6s+j9jxqz6P2PH5sry1u3nx3jocsovY8as+j9lzqz6P2PGrPo/Y8vmuvhZu3LvYAAab2PGrPo/Zc6s+j9jxqz6P2PL5rr4WbN19tQs0yas+j9jxqz6X+PGrPo/Y8as/ju/ZauHmrYg8QYGrPo/Y8as+l/jxqz6P2PGrP47v2Wrh5O8keIMDUnkftedSeS/151J5H7XnUnsd37bVw87aAPUCAqT2P2vOoPZf686g9j9rzqD2P79pr4eYtnz1AgKk9j9rzqD2X+vOoPY/a86g9j+/a6zxuHvx4jodsofY8as+j9lzqz6P2PGrPo/Y8fmyvLW7efHeOhyyi9jxqz6P2XOrPo/Y8as+j9jy+a6+Fm7dO9gABpvY8as+j9lzqz6P2PGrPo/Y8vmuvhZu3IfYAAab2PGrPo/Zc6s+j9jxqz6P2PL5rr4Wbt0r2AAGm9jxqz6P2XOrPo/Y8as+j9jy+a6+Fm7fj7AECTO151J5H7bnUn0ftedSeR+15fNdeCzdvi9gDBJja86g9j9pzqT+P2vOoPY/a8/iuvRZu3tSIR+151J5H7bnUn0ftedSeR+15fNde53HzYIwpttb2sOcIIrXnUXsetedSfx6151F7HrXn8WN73600CVawBwgwtedRex6151J/HrXnUXsetefxXXst3Lwl2AMEWII9QIAl2AMEWII9QMAl2AMEWII9QIAl2AMEWII9QIAl2ANMlhZuIiIiIiIiGU4LN28V7AECrII9QIBVsAcIsAr2AAFXwR4gwCrYAwRYBXuAAKtgDxBgFewBJksHJ/HgxzcuZgu151F7HrXnUn8etedRex615/Fje21x87aMPUCAqT2P2vOoPZf686g9j9rzqD2P79pr4eYtyR4gwNSeR+151J5L/XnUnkftedSex3fttXDzdoo9QICpPY/a86g9l/rzqD2P2vOoPY/v2mvh5s13m1GziNrzqD2P2nOpP4/a86g9j9rz+K69Fm7e2tgDBJja86g9j9pzqT+P2vOoPY/a8/iuvRZu3nLZAwSY2vOoPY/ac6k/j9rzqD2P2vP4rr0Wbt7K2AMEmNrzqD2P2nOpP4/a86g9j9rz+K69zuPmwRhTaK3tY88RRGrPo/Y8as+l/jxqz6P2PGrP48f22uLmbRV7gABTex6151F7LvXnUXsetedRex7ftdfCzdsAe4AAU3setedRey7151F7HrXnUXse37XXws1bK3uAAFN7HrXnUXsu9edRex6151F7Ht+118LN2xL2AAGm9jxqz6P2XOrPo/Y8as+j9jy+a6+Fm4fbbrvtpewZgkrtedSeR+251J9H7XnUnkftefzYXgs3bzezBwgwtedRex6151J/HrXnUXsetefxXXst3ERERERERDKcFm4iIiIiIiIZTgs3b99kDxBgas+j9jxqz6X+PGrPo/Y8as/ju/bGWsueQURERERERM5BW9xEREREREQynBZuIiIiIiIiGS6XPUCmikQiIQBfB7AZQD+AD8RisX3cqYIjEok8CaDD/fRgLBZ7H3OeIIhEIi8G8LlYLPaySCSyHsB3AVgAzwL4SCwWSzLny2aj2l8K4FcA9rpf/kYsFvsRb7rsFIlE8gDcBmA1gAIA/wjgeeh5P+vGaX8Met7PiUgkkgPgWwA2AhgG8D4ABnruz7px2pdDz/05E4lEFgJ4HMArAQzBZ897bXEb3xsBFMZisasAfArAF7njBEckEikEgFgs9jL3Q4u2WRaJRP4SwLcBFLoX/TuAT8disRo4/0N/A2u2bDdG+0sB/Hva81//A58d7wLQ6j7HXw3gq9Dzfq6M1V7P+7nzegCIxWJbAXwWzvNez/25MVZ7PffniPtLo3oAve5Fvnvea+E2vqsB3A0AsVjsUQCXcccJlM0AiiORyD2RSOS+SCRyJXugANgP4E1pn78IwIPu338D4BVzPlFwjNX+tZFIZFskEvlOJBKZR5or2/0YwGfSPh+CnvdzZbz2et7PgVgs9nOMnHh4FYBT0HN/TpyjvZ77c+MLAG4FcML93HfPey3cxleGkV31AGA4Eolo19K50QPnP67rAHwQwA/UfnbFYrH/BTCYdpGJxWKpQ86ehrMrh8yCMdpvB/DJWCx2DYADAKKUwbJcLBbrisVip90XST8B8GnoeT8nxmmv5/0cisViQ5FI5HsA/hPOz0DP/TkyRns99+dAJBJ5L4DmWCz227SLffe818JtfJ0A0n/rEYrFYkOsYQJmD4Dvx2IxG4vF9gBoBbCEPFPQpO/jPQ9AgjRHEP0sFos9nvo7gC3MYbJZJBJZAeB+AP8Vi8XuhJ73c2aM9nrez7FYLPYeAOfBec9VUdqX9NyfZaPa36Pn/px4P4BXRiKRBwBcAuAOAAvTvu6L570WbuN7GMBrAMDdVe8Z7jiB8n647ymMRCJL4Wz9PEmdKHiejEQiL3P//moADcRZgua3kUjkCvfvEThvopYZFolEFgG4B8BfxWKx29yL9byfA+O01/N+jkQikXdHIpG/dj/tgfMLi8f03J9947T/qZ77sy8Wi10Ti8VeGovFXgbgKQA3AviN35732v1sfD+DszL/PZw3LOoAGXPnOwC+G4lEHoJzpJ/3a2vnnPsLAN+KRCL5AHbB2Z1D5saHAHw1EokMAGjEyPshZGb9DYD5AD4TiURS77f6OID/0PN+1o3V/hYAX9bzfk78FMDtkUhkG4A8AH8G5/muf/Nn31jtj0L/5rP47rWOsdZ6X0tERERERERotKukiIiIiIhIhtPCTUREREREJMNp4SYiIiIiIpLhtHATERERERHJcFq4iYiIiIiIZDgt3EREZMYZY75mjHnKGPO8MWbA/ftTxpgXnFrFGPM6Y8wtHrf3XmPMd40x7zTG/Dzt8guNMdYY8860y/7FGPN3k5j1kDFm9ajL/sgY8/cTvY207ys3xhwzxrxu1OX/bYz5wmRvT0REJEXncRMRkRlnrf0IALgLogestZec4+qXTeKm7wPwpbTPr4NzMufrAPzAvawGwF9N4jZfwFr7SwC/nML3dRhjagF83RjzgLW2yxjzGgCbofOBiojINGiLm4iIzAljzHnGmAeMMU8bYx4xxlxujDkfwAcBfNAY8z5jzDJjzN3GmEeNMUdGb/Wy1p4E0GKMOc+96DoAnwHwMuMoBHAegD+MdX/uHN81xvzKGLPLGPP6UfPtNcZcmdrC515+yBjzD8aY7caY54wxL3Ivv9AY85i7JfE/jTH73Bn/D8A2AH9vjCkB8J8AbrTW9hljPmWMecIYs9MY83ljjHFv65/cx7zHnXmRe3mzMeY37n2sMMY86N7ndmPMlbPygxIRkYykhZuIiMyV7wP4D2vtxQD+HMBPAOwHcCuAW621twN4B4D/ttZeCeAiAH9mjKkadTv3AdhqjCkCsMZaux3AQThbtV4M4PfW2qGx7s8YU+DeRqu1dpO19lfu5ysA/AzA+6y1j44xe6u19gp31r9xL/segM+6WxMP4Oy9WD4O4K0AbgfwfWvtY8aY6wG8CMDlALYAWAbgncaY9QDCAF5irT0PwBEA73JvpwrA59z7eD+AX1trLwPwWQBXj1taRESyjhZuIiIy64wxpQDWW2t/CgDu4qgNwMb061lrvwDgiDHmEwC+AiAfQMmom4sB2ArgpXC2bAHAvQBeBmc3yXsmcH9/GHWbPwZwwFr70DgP4W73z2cBVBpjKgGsttbe5V5+26jH0Qbgb+Es0P7RvfgVcBaWjwN4As4uohdYa/cB+AsAHzDGfBHAVQBK024uNevvAHzCGHMngAUAvjrOrCIikoW0cBMRkbkw1v9vDEa919pduHwMwGE4C54W93rpHgBwBYBXwXl/G9w/Xwxn4XbvBO6vd9TXPgZgrTHmtePM3+f+ad3bGR5jrtEOAzhurR10P88B8GVr7SXuFrQXA/gnd9fLe9yZfwJny9+Z27bW9rp/PgzgfAC/BfA2AKmthSIiEgBauImIyKyz1nYCOGCMeRMAuO/PWgxnC9YQRhZUrwTwb9baH8PZOrYMzoIn/bba4Sy8roezFQoAHoOzu+ESa+1ej/sby3YAHwLwNfd9aV6PpwPAfmPMq92LboCzqDuX+wC82xhTaozJBfBzAH8CZ8vhA9baWwHsAfC60Y/ZfQyfB/Aua+33AHwUwKVec4qISPbQUSVFRGSuvAvArcaYOgD9AN5krR0wxmwD8D1jzCkA/wLgv4wxvQCOwlmQrRnjth4A8HJrbSsAWGuT7sFB2idwf2MOZ63dZoy5H86Wvp0TeDw3ArjNGPNPAJ7GC7fijb79XxljNsPZ9TEHzu6X3wOwFMBPjTHPuFcd7zH/J4A73VMqDLv3LyIiAWGs9foFoYiIiIxmjPksgG9Za0+6W/beaa19M3suERHJTtriJiIiMjVHANxrjBmEs6XvJvI8IiKSxbTFTUREREREJMPp4CQiIiIiIiIZTgs3ERERERGRDKeFm4iIiIiISIbTwk1ERERERCTDaeEmIiIiIiKS4f4/+IpjpVzIoLkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_plot(isolate, feature_name=feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "a55eceb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = 'Age'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "7fbe9f62",
   "metadata": {},
   "outputs": [],
   "source": [
    "isolate= pdp_isolate(\n",
    "    model_xgb,\n",
    "    dataset= X_val, \n",
    "    model_features=X_val.columns,\n",
    "    feature=feature\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "3a66f5af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1080x684 with 2 Axes>,\n",
       " {'title_ax': <AxesSubplot:>, 'pdp_ax': <AxesSubplot:xlabel='Age'>})"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_plot(isolate, feature_name=feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "5a616641",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['Age','TotalWorkingYears']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "a384fbf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "interact= pdp_interact(\n",
    "    model_xgb,\n",
    "    dataset= X_val, \n",
    "    model_features=X_val.columns,\n",
    "    features=features\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "9e1a81b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 540x684 with 3 Axes>,\n",
       " {'title_ax': <AxesSubplot:>,\n",
       "  'pdp_inter_ax': <AxesSubplot:xlabel='Age', ylabel='TotalWorkingYears'>})"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x684 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_interact_plot(interact,plot_type='grid', feature_names=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "eefa1086",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_row = X_val.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "38512fae",
   "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>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>Marketing</th>\n",
       "      <th>Technical Degree</th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Research Scientist</th>\n",
       "      <th>Laboratory Technician</th>\n",
       "      <th>Manufacturing Director</th>\n",
       "      <th>Healthcare Representative</th>\n",
       "      <th>Manager</th>\n",
       "      <th>Sales Representative</th>\n",
       "      <th>Research Director</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>1184</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  BusinessTravel  DailyRate  DistanceFromHome  Education  \\\n",
       "607   49               0       1184                11          3   \n",
       "\n",
       "     EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
       "607                        3       0          43               3         3   \n",
       "\n",
       "     ...  Marketing  Technical Degree  Sales Executive  Research Scientist  \\\n",
       "607  ...          0                 0                0                   0   \n",
       "\n",
       "     Laboratory Technician  Manufacturing Director  Healthcare Representative  \\\n",
       "607                      0                       0                          0   \n",
       "\n",
       "     Manager  Sales Representative  Research Director  \n",
       "607        0                     0                  0  \n",
       "\n",
       "[1 rows x 43 columns]"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "0360a71e",
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = shap.TreeExplainer(model_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "id": "c4c55ba8",
   "metadata": {},
   "outputs": [
    {
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": -1.8850562572479248, \"outValue\": -4.839813232421875, \"link\": \"identity\", \"featureNames\": [\"Age\", \"BusinessTravel\", \"DailyRate\", \"DistanceFromHome\", \"Education\", \"EnvironmentSatisfaction\", \"Gender\", \"HourlyRate\", \"JobInvolvement\", \"JobLevel\", \"JobSatisfaction\", \"MaritalStatus\", \"MonthlyIncome\", \"MonthlyRate\", \"NumCompaniesWorked\", \"OverTime\", \"PercentSalaryHike\", \"PerformanceRating\", \"RelationshipSatisfaction\", \"StockOptionLevel\", \"TotalWorkingYears\", \"TrainingTimesLastYear\", \"WorkLifeBalance\", \"YearsAtCompany\", \"YearsInCurrentRole\", \"YearsSinceLastPromotion\", \"YearsWithCurrManager\", \"Sales\", \"Research & Development\", \"Human Resources\", \"Life Sciences\", \"Other\", \"Medical\", \"Marketing\", \"Technical Degree\", \"Sales Executive\", \"Research Scientist\", \"Laboratory Technician\", \"Manufacturing Director\", \"Healthcare Representative\", \"Manager\", \"Sales Representative\", \"Research Director\"], \"features\": {\"0\": {\"effect\": -0.15549281239509583, \"value\": 49.0}, \"1\": {\"effect\": 0.03790835291147232, \"value\": 0.0}, \"2\": {\"effect\": -0.7705613970756531, \"value\": 1184.0}, \"3\": {\"effect\": 0.09185328334569931, \"value\": 11.0}, \"4\": {\"effect\": -0.014833135530352592, \"value\": 3.0}, \"5\": {\"effect\": -0.28455063700675964, \"value\": 3.0}, \"6\": {\"effect\": -0.11131264269351959, \"value\": 0.0}, \"7\": {\"effect\": -0.5205459594726562, \"value\": 43.0}, \"8\": {\"effect\": -0.07324550300836563, \"value\": 3.0}, \"9\": {\"effect\": -0.03772910311818123, \"value\": 3.0}, \"10\": {\"effect\": -0.15881578624248505, \"value\": 4.0}, \"11\": {\"effect\": 0.012466877698898315, \"value\": 1.0}, \"12\": {\"effect\": 0.19897477328777313, \"value\": 7654.0}, \"13\": {\"effect\": -0.23246467113494873, \"value\": 5860.0}, \"14\": {\"effect\": -0.3176736533641815, \"value\": 1.0}, \"15\": {\"effect\": -0.6537870764732361, \"value\": 0.0}, \"16\": {\"effect\": 0.24595646560192108, \"value\": 18.0}, \"18\": {\"effect\": 0.278724730014801, \"value\": 1.0}, \"19\": {\"effect\": -0.7543361186981201, \"value\": 2.0}, \"20\": {\"effect\": -0.11620279401540756, \"value\": 9.0}, \"21\": {\"effect\": -0.004001806490123272, \"value\": 3.0}, \"22\": {\"effect\": -0.18865837156772614, \"value\": 4.0}, \"23\": {\"effect\": -0.014649460092186928, \"value\": 9.0}, \"24\": {\"effect\": -0.31502872705459595, \"value\": 8.0}, \"25\": {\"effect\": 0.489921510219574, \"value\": 7.0}, \"26\": {\"effect\": -0.06397345662117004, \"value\": 7.0}, \"27\": {\"effect\": 0.09130748361349106, \"value\": 1.0}, \"28\": {\"effect\": 0.38599327206611633, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('i7Z8X171BY5B9GFPBSMOH')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x7fc3fe2448b0>"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_values = explainer.shap_values(sample_row)\n",
    "\n",
    "shap.initjs() \n",
    "shap.force_plot(\n",
    "    base_value= explainer.expected_value,\n",
    "    shap_values=shap_values,\n",
    "    features=sample_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "id": "bacf4062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 43)"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "393cd14b",
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_df = pd.DataFrame(data=shap_values[0], index=X_val.columns, columns=['shap_values'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "id": "761072dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.839756736755371"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_df['shap_values'].sum() + -1.885"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "c836199d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# waterfall plot \n",
    "\n",
    "explainer = shap.TreeExplainer(model_xgb)\n",
    "shap_values = explainer(sample_row)\n",
    "shap.plots.waterfall(shap_values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "5eb8fe72",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df[['Attrition','DailyRate', 'StockOptionLevel', 'OverTime', 'HourlyRate', 'YearsSinceLastPromotion', 'Research & Development', 'NumCompaniesWorked', 'YearsInCurrentRole', 'EnvironmentSatisfaction']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "5f21fab6",
   "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>Attrition</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>Research &amp; Development</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1102</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>279</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1373</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1392</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attrition  DailyRate  StockOptionLevel  OverTime  HourlyRate  \\\n",
       "0          1       1102                 0         1          94   \n",
       "1          0        279                 1         0          61   \n",
       "2          1       1373                 0         1          92   \n",
       "3          0       1392                 0         1          56   \n",
       "4          0        591                 1         0          40   \n",
       "\n",
       "   YearsSinceLastPromotion  Research & Development  NumCompaniesWorked  \\\n",
       "0                        0                       0                   8   \n",
       "1                        1                       1                   1   \n",
       "2                        0                       1                   6   \n",
       "3                        3                       1                   1   \n",
       "4                        2                       1                   9   \n",
       "\n",
       "   YearsInCurrentRole  EnvironmentSatisfaction  \n",
       "0                   4                        2  \n",
       "1                   7                        3  \n",
       "2                   0                        4  \n",
       "3                   7                        4  \n",
       "4                   2                        1  "
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "0212caac",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Attrition'\n",
    "y = df_new[target]\n",
    "X = df_new.drop(columns=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "3b225b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "ff1e8ea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "8fe57d70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check\n",
    "len(X_train) + len(X_val) + len(X_test) == len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "e55020b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1470, 9), (882, 9), (294, 9), (294, 9))"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, X_train.shape, X_val.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "6a51e7d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Accuracy: 0.8231292517006803\n"
     ]
    }
   ],
   "source": [
    "print('Baseline Accuracy:', y_train.value_counts(normalize=True).max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "id": "f8580d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 1: Logistic Regression\n",
    "model_lr = make_pipeline(\n",
    "    SimpleImputer(strategy='mean'),\n",
    "    StandardScaler(),\n",
    "    LogisticRegression()\n",
    ")\n",
    "\n",
    "model_lr.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "id": "70067c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 2: Random Forest\n",
    "model_rf = RandomForestClassifier(n_jobs=-1, random_state=42)\n",
    "\n",
    "model_rf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "3bf7a89e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14:23:55] WARNING: ../src/learner.cc:576: \n",
      "Parameters: { \"n_estimator\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
      "\n",
      "\n",
      "[14:23:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.2, max_delta_step=0,\n",
       "              max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimator=75, n_estimators=100,\n",
       "              n_jobs=-1, num_parallel_tree=1, predictor='auto', random_state=42,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 3: XGBoost\n",
    "\n",
    "model_xgb = XGBClassifier(random_state=42,n_estimator=75, n_jobs=-1, learning_rate=.2) \n",
    "model_xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "id": "e12ac64c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (LOGR): 0.8299319727891157\n",
      "Validation Accuracy (LOGR): 0.8741496598639455\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (LOGR):', model_lr.score(X_train, y_train))\n",
    "print('Validation Accuracy (LOGR):', model_lr.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "id": "0e9edaf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (RF): 1.0\n",
      "Validation Accuracy (RF): 0.8741496598639455\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (RF):', model_rf.score(X_train, y_train))\n",
    "print('Validation Accuracy (RF):', model_rf.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "id": "58f925fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (XGB): 1.0\n",
      "Validation Accuracy (XGB): 0.8503401360544217\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (XGB):', model_xgb.score(X_train, y_train))\n",
    "print('Validation Accuracy (XGB):', model_xgb.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "858362dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.98      0.93       256\n",
      "           1       0.56      0.13      0.21        38\n",
      "\n",
      "    accuracy                           0.87       294\n",
      "   macro avg       0.72      0.56      0.57       294\n",
      "weighted avg       0.84      0.87      0.84       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Logistic Regression')\n",
    "print(classification_report(y_val, model_lr.predict(X_val)))\n",
    "plot_confusion_matrix(model_lr, X_val, y_val);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "id": "39b445f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.97      0.93       256\n",
      "           1       0.53      0.21      0.30        38\n",
      "\n",
      "    accuracy                           0.87       294\n",
      "   macro avg       0.71      0.59      0.62       294\n",
      "weighted avg       0.85      0.87      0.85       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Random Forest')\n",
    "print(classification_report(y_val, model_rf.predict(X_val)))\n",
    "plot_confusion_matrix(model_rf, X_val, y_val);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "327394e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.95      0.92       256\n",
      "           1       0.33      0.16      0.21        38\n",
      "\n",
      "    accuracy                           0.85       294\n",
      "   macro avg       0.61      0.56      0.57       294\n",
      "weighted avg       0.81      0.85      0.83       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('XG Boost')\n",
    "print(classification_report(y_val, model_xgb.predict(X_val)))\n",
    "plot_confusion_matrix(model_xgb, X_val, y_val);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "id": "f6e166a4",
   "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": [
    "lr = plot_roc_curve(model_lr,\n",
    "               X_val,\n",
    "               y_val,\n",
    "               name='logistic regression');\n",
    "\n",
    "rf = plot_roc_curve(model_rf,\n",
    "               X_val,\n",
    "               y_val,\n",
    "               ax=lr.ax_,\n",
    "               name='random forest');\n",
    "\n",
    "xgb = plot_roc_curve(model_xgb,\n",
    "               X_val,\n",
    "               y_val,\n",
    "               ax=lr.ax_,\n",
    "               name='xgboost');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "id": "592727f3",
   "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>imp_mean</th>\n",
       "      <th>imp_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Research &amp; Development</th>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.002151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.002151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyRate</th>\n",
       "      <td>0.004082</td>\n",
       "      <td>0.002545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>0.004762</td>\n",
       "      <td>0.005527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <td>0.006122</td>\n",
       "      <td>0.005442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BusinessTravel</th>\n",
       "      <td>0.006122</td>\n",
       "      <td>0.003967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <td>0.009524</td>\n",
       "      <td>0.008973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverTime</th>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.003469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         imp_mean   imp_std\n",
       "Research & Development   0.003401  0.002151\n",
       "TrainingTimesLastYear    0.003401  0.002151\n",
       "MonthlyRate              0.004082  0.002545\n",
       "MonthlyIncome            0.004762  0.005527\n",
       "EnvironmentSatisfaction  0.006122  0.005442\n",
       "BusinessTravel           0.006122  0.003967\n",
       "NumCompaniesWorked       0.009524  0.008973\n",
       "OverTime                 0.028571  0.003469"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_perm.tail(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "c91e5b33",
   "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>Attrition</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>Research &amp; Development</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19479</td>\n",
       "      <td>5993</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>24907</td>\n",
       "      <td>5130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2396</td>\n",
       "      <td>2090</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>23159</td>\n",
       "      <td>2909</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>16632</td>\n",
       "      <td>3468</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attrition  TrainingTimesLastYear  MonthlyRate  MonthlyIncome  \\\n",
       "0          1                      0        19479           5993   \n",
       "1          0                      3        24907           5130   \n",
       "2          1                      3         2396           2090   \n",
       "3          0                      3        23159           2909   \n",
       "4          0                      3        16632           3468   \n",
       "\n",
       "   BusinessTravel  OverTime  YearsSinceLastPromotion  Research & Development  \\\n",
       "0               0         1                        0                       0   \n",
       "1               1         0                        1                       1   \n",
       "2               0         1                        0                       1   \n",
       "3               1         1                        3                       1   \n",
       "4               0         0                        2                       1   \n",
       "\n",
       "   NumCompaniesWorked  EnvironmentSatisfaction  \n",
       "0                   8                        2  \n",
       "1                   1                        3  \n",
       "2                   6                        4  \n",
       "3                   1                        4  \n",
       "4                   9                        1  "
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_newer = df[['Attrition','TrainingTimesLastYear', 'MonthlyRate', 'MonthlyIncome', 'BusinessTravel', 'OverTime', 'YearsSinceLastPromotion', 'Research & Development', 'NumCompaniesWorked',  'EnvironmentSatisfaction']]\n",
    "df_newer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "deeb4782",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Attrition'\n",
    "y = df_newer[target]\n",
    "X = df_newer.drop(columns=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "id": "9e9a9565",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "id": "48c86d35",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "af906522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check\n",
    "len(X_train) + len(X_val) + len(X_test) == len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "bc9f016a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1470, 9), (882, 9), (294, 9), (294, 9))"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, X_train.shape, X_val.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "787520ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Accuracy: 0.8231292517006803\n"
     ]
    }
   ],
   "source": [
    "print('Baseline Accuracy:', y_train.value_counts(normalize=True).max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "3157aa90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 1: Logistic Regression\n",
    "model_lr = make_pipeline(\n",
    "    SimpleImputer(strategy='mean'),\n",
    "    StandardScaler(),\n",
    "    LogisticRegression()\n",
    ")\n",
    "\n",
    "model_lr.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "id": "118d66b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 2: Random Forest\n",
    "model_rf = RandomForestClassifier(n_jobs=-1, random_state=42)\n",
    "\n",
    "model_rf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "af3f1965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14:24:13] WARNING: ../src/learner.cc:576: \n",
      "Parameters: { \"n_estimator\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
      "\n",
      "\n",
      "[14:24:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.2, max_delta_step=0,\n",
       "              max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimator=75, n_estimators=100,\n",
       "              n_jobs=-1, num_parallel_tree=1, predictor='auto', random_state=42,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 3: XGBoost\n",
    "\n",
    "model_xgb = XGBClassifier(random_state=42,n_estimator=75, n_jobs=-1, learning_rate=.2) \n",
    "model_xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "1ea0d340",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (LOGR): 0.8344671201814059\n",
      "Validation Accuracy (LOGR): 0.8741496598639455\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (LOGR):', model_lr.score(X_train, y_train))\n",
    "print('Validation Accuracy (LOGR):', model_lr.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "cc7346be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (RF): 1.0\n",
      "Validation Accuracy (RF): 0.8639455782312925\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (RF):', model_rf.score(X_train, y_train))\n",
    "print('Validation Accuracy (RF):', model_rf.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "488f3015",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (XGB): 0.9988662131519275\n",
      "Validation Accuracy (XGB): 0.8605442176870748\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (XGB):', model_xgb.score(X_train, y_train))\n",
    "print('Validation Accuracy (XGB):', model_xgb.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "id": "a5fe43c8",
   "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": [
    "lr = plot_roc_curve(model_lr,\n",
    "               X_val,\n",
    "               y_val,\n",
    "               name='logistic regression');\n",
    "\n",
    "rf = plot_roc_curve(model_rf,\n",
    "               X_val,\n",
    "               y_val,\n",
    "               ax=lr.ax_,\n",
    "               name='random forest');\n",
    "\n",
    "xgb = plot_roc_curve(model_xgb,\n",
    "               X_val,\n",
    "               y_val,\n",
    "               ax=lr.ax_,\n",
    "               name='xgboost');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "id": "42e9b7b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing features - based either on Perumutation_Importance or on Shapley Plot - has not increased our AUC values |\n",
    "# or our validation scores. They appear not to be creating noise; on the contrary, they are helping the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "id": "882c643c",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Attrition'\n",
    "y = df[target]\n",
    "X = df.drop(columns=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "44e3937f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "47151a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "88bf1192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check\n",
    "len(X_train) + len(X_val) + len(X_test) == len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "b530d7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1470, 43), (882, 43), (294, 43), (294, 43))"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All columns from wrangled DF are back\n",
    "X.shape, X_train.shape, X_val.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "660585ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Accuracy: 0.8231292517006803\n"
     ]
    }
   ],
   "source": [
    "print('Baseline Accuracy:', y_train.value_counts(normalize=True).max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "8a8d3f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 1: Logistic Regression\n",
    "model_lr = make_pipeline(\n",
    "    SimpleImputer(strategy='mean'),\n",
    "    StandardScaler(),\n",
    "    LogisticRegression()\n",
    ")\n",
    "\n",
    "model_lr.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "41fb8684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (LOGR): 0.8741496598639455\n",
      "Validation Accuracy (LOGR): 0.8877551020408163\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (LOGR):', model_lr.score(X_train, y_train))\n",
    "print('Validation Accuracy (LOGR):', model_lr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "537c8d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 2: Random Forest\n",
    "model_rf = RandomForestClassifier(n_jobs=-1, random_state=42)\n",
    "\n",
    "model_rf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "5126d732",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (RF): 1.0\n",
      "Validation Accuracy (RF): 0.8809523809523809\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (RF):', model_rf.score(X_train, y_train))\n",
    "print('Validation Accuracy (RF):', model_rf.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "id": "b9fcb94a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14:25:24] WARNING: ../src/learner.cc:576: \n",
      "Parameters: { \"n_estimator\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
      "\n",
      "\n",
      "[14:25:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.2, max_delta_step=0,\n",
       "              max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimator=75, n_estimators=100,\n",
       "              n_jobs=-1, num_parallel_tree=1, predictor='auto', random_state=42,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 3: XGBoost\n",
    "\n",
    "model_xgb = XGBClassifier(random_state=42,n_estimator=75, n_jobs=-1, learning_rate=.2) \n",
    "model_xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "70e5f0c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy (XGB): 1.0\n",
      "Validation Accuracy (XGB): 0.8571428571428571\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy (XGB):', model_xgb.score(X_train, y_train))\n",
    "print('Validation Accuracy (XGB):', model_xgb.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3ce7402",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The logistic regression model performed best on the test data. |\n",
    "# The random forest model and the XGBoost model were overfitting. "
   ]
  }
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