Credit_Data_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "125fad1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Modules\n",
    "from warnings import simplefilter\n",
    "simplefilter(action='ignore', category=FutureWarning)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import f_oneway\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "# Display all of the columns\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "c1a8a1dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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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>Occupation</th>\n",
       "      <th>Annual_Income</th>\n",
       "      <th>Num_Bank_Accounts</th>\n",
       "      <th>Num_Credit_Card</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Num_of_Loan</th>\n",
       "      <th>Delay_from_due_date</th>\n",
       "      <th>Num_of_Delayed_Payment</th>\n",
       "      <th>Changed_Credit_Limit</th>\n",
       "      <th>Num_Credit_Inquiries</th>\n",
       "      <th>Credit_Mix</th>\n",
       "      <th>Outstanding_Debt</th>\n",
       "      <th>Credit_Utilization_Ratio</th>\n",
       "      <th>Credit_History_Age</th>\n",
       "      <th>Payment_of_Min_Amount</th>\n",
       "      <th>Total_EMI_per_month</th>\n",
       "      <th>Amount_invested_monthly</th>\n",
       "      <th>Payment_Behaviour</th>\n",
       "      <th>Monthly_Balance</th>\n",
       "      <th>Credit_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>_______</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5.42</td>\n",
       "      <td>2</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>24.46</td>\n",
       "      <td>26.6</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>104.29</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>470.69</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7.42</td>\n",
       "      <td>2</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>38.55</td>\n",
       "      <td>26.7</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>40.39</td>\n",
       "      <td>High_spent_Large_value_payments</td>\n",
       "      <td>484.59</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5.42</td>\n",
       "      <td>2</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>39.18</td>\n",
       "      <td>26.8</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>99.31</td>\n",
       "      <td>Low_spent_Medium_value_payments</td>\n",
       "      <td>465.68</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6.42</td>\n",
       "      <td>2</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>34.98</td>\n",
       "      <td>26.9</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>130.12</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>444.87</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5.42</td>\n",
       "      <td>2</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>33.38</td>\n",
       "      <td>27.0</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>43.48</td>\n",
       "      <td>High_spent_Large_value_payments</td>\n",
       "      <td>481.51</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Occupation  Annual_Income  Num_Bank_Accounts  Num_Credit_Card  \\\n",
       "0   28    _______       34847.84                  2                4   \n",
       "1   28    Teacher       34847.84                  2                4   \n",
       "2   28    Teacher       34847.84                  2                4   \n",
       "3   28    Teacher       34847.84                  2                4   \n",
       "4   28    Teacher       34847.84                  2                4   \n",
       "\n",
       "   Interest_Rate  Num_of_Loan  Delay_from_due_date  Num_of_Delayed_Payment  \\\n",
       "0              6            1                    3                       4   \n",
       "1              6            1                    7                       1   \n",
       "2              6            1                    3                       3   \n",
       "3              6            1                    3                       1   \n",
       "4              6            1                    3                       0   \n",
       "\n",
       "   Changed_Credit_Limit  Num_Credit_Inquiries Credit_Mix  Outstanding_Debt  \\\n",
       "0                  5.42                     2       Good            605.03   \n",
       "1                  7.42                     2       Good            605.03   \n",
       "2                  5.42                     2       Good            605.03   \n",
       "3                  6.42                     2       Good            605.03   \n",
       "4                  5.42                     2       Good            605.03   \n",
       "\n",
       "   Credit_Utilization_Ratio  Credit_History_Age Payment_of_Min_Amount  \\\n",
       "0                     24.46                26.6                    No   \n",
       "1                     38.55                26.7                    No   \n",
       "2                     39.18                26.8                    No   \n",
       "3                     34.98                26.9                    No   \n",
       "4                     33.38                27.0                    No   \n",
       "\n",
       "   Total_EMI_per_month  Amount_invested_monthly  \\\n",
       "0                18.82                   104.29   \n",
       "1                18.82                    40.39   \n",
       "2                18.82                    99.31   \n",
       "3                18.82                   130.12   \n",
       "4                18.82                    43.48   \n",
       "\n",
       "                 Payment_Behaviour  Monthly_Balance Credit_Score  \n",
       "0   Low_spent_Small_value_payments           470.69     Standard  \n",
       "1  High_spent_Large_value_payments           484.59         Good  \n",
       "2  Low_spent_Medium_value_payments           465.68         Good  \n",
       "3   Low_spent_Small_value_payments           444.87         Good  \n",
       "4  High_spent_Large_value_payments           481.51         Good  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV file into a Pandas DataFrame\n",
    "credit_df = pd.read_csv(\n",
    "    Path('Resources/cleaned_data_v2.csv')\n",
    ")\n",
    "credit_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "89c21ef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split target column from dataset\n",
    "y = credit_df['Credit_Score']\n",
    "columns_to_drop = ['Credit_Score', 'Occupation', 'Num_of_Loan', 'Credit_Utilization_Ratio', 'Credit_History_Age',\n",
    "                  'Payment_of_Min_Amount', 'Total_EMI_per_month', 'Amount_invested_monthly', 'Payment_Behaviour', 'Credit_Mix']\n",
    "X = credit_df.drop(columns=columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "e346a6d4",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Annual_Income</th>\n",
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       "      <th>Interest_Rate</th>\n",
       "      <th>Delay_from_due_date</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>3</td>\n",
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       "      <td>6.42</td>\n",
       "      <td>2</td>\n",
       "      <td>605.03</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>481.51</td>\n",
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      "text/plain": [
       "   Age  Annual_Income  Num_Bank_Accounts  Num_Credit_Card  Interest_Rate  \\\n",
       "0   28       34847.84                  2                4              6   \n",
       "1   28       34847.84                  2                4              6   \n",
       "2   28       34847.84                  2                4              6   \n",
       "3   28       34847.84                  2                4              6   \n",
       "4   28       34847.84                  2                4              6   \n",
       "\n",
       "   Delay_from_due_date  Num_of_Delayed_Payment  Changed_Credit_Limit  \\\n",
       "0                    3                       4                  5.42   \n",
       "1                    7                       1                  7.42   \n",
       "2                    3                       3                  5.42   \n",
       "3                    3                       1                  6.42   \n",
       "4                    3                       0                  5.42   \n",
       "\n",
       "   Num_Credit_Inquiries  Outstanding_Debt  Monthly_Balance  \n",
       "0                     2            605.03           470.69  \n",
       "1                     2            605.03           484.59  \n",
       "2                     2            605.03           465.68  \n",
       "3                     2            605.03           444.87  \n",
       "4                     2            605.03           481.51  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the data\n",
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "f412695b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the categorical variables using get_dummies\n",
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "d28bf133",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Age</th>\n",
       "      <th>Annual_Income</th>\n",
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       "      <th>Num_Credit_Card</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Delay_from_due_date</th>\n",
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       "    <tr>\n",
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       "      <td>28</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "      <td>6.42</td>\n",
       "      <td>2</td>\n",
       "      <td>605.03</td>\n",
       "      <td>444.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>34847.84</td>\n",
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       "    </tr>\n",
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      ],
      "text/plain": [
       "   Age  Annual_Income  Num_Bank_Accounts  Num_Credit_Card  Interest_Rate  \\\n",
       "0   28       34847.84                  2                4              6   \n",
       "1   28       34847.84                  2                4              6   \n",
       "2   28       34847.84                  2                4              6   \n",
       "3   28       34847.84                  2                4              6   \n",
       "4   28       34847.84                  2                4              6   \n",
       "\n",
       "   Delay_from_due_date  Num_of_Delayed_Payment  Changed_Credit_Limit  \\\n",
       "0                    3                       4                  5.42   \n",
       "1                    7                       1                  7.42   \n",
       "2                    3                       3                  5.42   \n",
       "3                    3                       1                  6.42   \n",
       "4                    3                       0                  5.42   \n",
       "\n",
       "   Num_Credit_Inquiries  Outstanding_Debt  Monthly_Balance  \n",
       "0                     2            605.03           470.69  \n",
       "1                     2            605.03           484.59  \n",
       "2                     2            605.03           465.68  \n",
       "3                     2            605.03           444.87  \n",
       "4                     2            605.03           481.51  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the data\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "60f40414",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.075, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "b1049dec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32692, 11)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "cff83a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2651, 11)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "83fcc192",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "# Fit the Standard Scaler with the training data\n",
    "X_scaler = scaler.fit(X_train)\n",
    "# Scale the training data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "001f4906",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "f7c333d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=2)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=2)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "6fa5db6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Poor', 'Standard', 'Poor', ..., 'Poor', 'Good', 'Good'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create predictions\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "\n",
    "# Review the predictions\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "b8ffdb82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[481  17 260]\n",
      " [  8 576 228]\n",
      " [ 65  62 954]]\n"
     ]
    }
   ],
   "source": [
    "# Print confusion matrix\n",
    "cm = confusion_matrix(y_pred,y_test)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "f1b72485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the confusion matrix as a heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, cmap='Blues',xticklabels=['Good', 'Poor', 'Standard'], yticklabels=['Good', 'Poor', 'Standard'], fmt='g')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix for KNN Classification Model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "959d53a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Good       0.87      0.63      0.73       758\n",
      "        Poor       0.88      0.71      0.79       812\n",
      "    Standard       0.66      0.88      0.76      1081\n",
      "\n",
      "    accuracy                           0.76      2651\n",
      "   macro avg       0.80      0.74      0.76      2651\n",
      "weighted avg       0.79      0.76      0.76      2651\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print classification report\n",
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "f947e4cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7585816672953603\n"
     ]
    }
   ],
   "source": [
    "#Check performance using accuracy\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "b99fb46c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Pearson Correlation\n",
    "plt.figure(figsize=(12,10))\n",
    "cor = X.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Oranges)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "f592b173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results for Age: F-Statistic = 222.49647862730416, p-value = 9.425679632875477e-97\n",
      "ANOVA results for Annual_Income: F-Statistic = 321.8802555801691, p-value = 2.9319454542528316e-139\n",
      "ANOVA results for Num_Bank_Accounts: F-Statistic = 2645.3360672247873, p-value = 0.0\n",
      "ANOVA results for Num_Credit_Card: F-Statistic = 3060.9345282192085, p-value = 0.0\n",
      "ANOVA results for Interest_Rate: F-Statistic = 4280.399446118644, p-value = 0.0\n",
      "ANOVA results for Delay_from_due_date: F-Statistic = 3904.0272830244303, p-value = 0.0\n",
      "ANOVA results for Num_of_Delayed_Payment: F-Statistic = 2558.995526464156, p-value = 0.0\n",
      "ANOVA results for Changed_Credit_Limit: F-Statistic = 847.8388888185214, p-value = 0.0\n",
      "ANOVA results for Num_Credit_Inquiries: F-Statistic = 3338.161787024902, p-value = 0.0\n",
      "ANOVA results for Outstanding_Debt: F-Statistic = 2937.6355945956993, p-value = 0.0\n",
      "ANOVA results for Monthly_Balance: F-Statistic = 0.031242265360288443, p-value = 0.9692407579474553\n"
     ]
    }
   ],
   "source": [
    "# Separate the numerical features based on the target variable categories\n",
    "grouped_data = [X[y == category] for category in ['Good', 'Standard', 'Poor']]\n",
    "# Perform ANOVA for each feature\n",
    "for feature in X.columns:\n",
    "    f_statistic, p_value = f_oneway(*[data[feature] for data in grouped_data])\n",
    "    print(f\"ANOVA results for {feature}: F-Statistic = {f_statistic}, p-value = {p_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "9b8b6f6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Results:\n",
      "                        sum_sq       df            F  PR(>F)\n",
      "C(Credit_Score)   29913.854301      2.0  2645.336067     0.0\n",
      "Residual         199814.992147  35340.0          NaN     NaN\n",
      "\n",
      "Tukey HSD Results:\n",
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1  group2  meandiff p-adj  lower   upper  reject\n",
      "-----------------------------------------------------\n",
      "  Good     Poor   2.6927   0.0  2.6044   2.781   True\n",
      "  Good Standard   1.7688   0.0  1.6931  1.8446   True\n",
      "  Poor Standard  -0.9239   0.0 -0.9965 -0.8513   True\n",
      "-----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 'data' is the DataFrame containing the numerical features and the categorical target variable\n",
    "data = pd.concat([X, y], axis=1)\n",
    "# Perform ANOVA to get the F-statistic and p-value\n",
    "anova = sm.formula.ols('Num_Bank_Accounts ~ C(Credit_Score)', data=data).fit()\n",
    "anova_table = sm.stats.anova_lm(anova, typ=2)\n",
    "\n",
    "# Perform Tukey HSD post-hoc test\n",
    "tukey_results = pairwise_tukeyhsd(data['Num_Bank_Accounts'], data['Credit_Score'])\n",
    "\n",
    "# Print ANOVA results\n",
    "print(\"ANOVA Results:\")\n",
    "print(anova_table)\n",
    "\n",
    "# Print Tukey HSD results\n",
    "print(\"\\nTukey HSD Results:\")\n",
    "print(tukey_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "77f9fdf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Results:\n",
      "                       sum_sq       df         F    PR(>F)\n",
      "C(Credit_Score)  9.821351e+47      2.0  0.031242  0.969241\n",
      "Residual         5.554760e+53  35340.0       NaN       NaN\n",
      "\n",
      "Tukey HSD Results:\n",
      "                        Multiple Comparison of Means - Tukey HSD, FWER=0.05                         \n",
      "====================================================================================================\n",
      "group1  group2          meandiff        p-adj           lower                  upper          reject\n",
      "----------------------------------------------------------------------------------------------------\n",
      "  Good     Poor    5.05555664648229e+21 0.9964 -1.4214116385067379e+23 1.5225227714363833e+23  False\n",
      "  Good Standard  -7.345784741104835e+21 0.9898 -1.3364664564774389e+23  1.189550761655342e+23  False\n",
      "  Poor Standard -1.2401341387587126e+22 0.9687 -1.3340685319716457e+23 1.0860417042199031e+23  False\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Perform ANOVA to get the F-statistic and p-value\n",
    "anova = sm.formula.ols('Monthly_Balance ~ C(Credit_Score)', data=data).fit()\n",
    "anova_table = sm.stats.anova_lm(anova, typ=2)\n",
    "\n",
    "# Perform Tukey HSD post-hoc test\n",
    "tukey_results = pairwise_tukeyhsd(data['Monthly_Balance'], data['Credit_Score'])\n",
    "\n",
    "# Print ANOVA results\n",
    "print(\"ANOVA Results:\")\n",
    "print(anova_table)\n",
    "\n",
    "# Print Tukey HSD results\n",
    "print(\"\\nTukey HSD Results:\")\n",
    "print(tukey_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "5bb544db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Results:\n",
      "                        sum_sq       df            F  PR(>F)\n",
      "C(Credit_Score)   20207.635818      2.0  3060.934528     0.0\n",
      "Residual         116653.564986  35340.0          NaN     NaN\n",
      "\n",
      "Tukey HSD Results:\n",
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1  group2  meandiff p-adj  lower   upper  reject\n",
      "-----------------------------------------------------\n",
      "  Good     Poor   2.2486   0.0  2.1811  2.3161   True\n",
      "  Good Standard   1.1109   0.0   1.053  1.1688   True\n",
      "  Poor Standard  -1.1377   0.0 -1.1932 -1.0822   True\n",
      "-----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Perform ANOVA to get the F-statistic and p-value\n",
    "anova = sm.formula.ols('Num_Credit_Card ~ C(Credit_Score)', data=data).fit()\n",
    "anova_table = sm.stats.anova_lm(anova, typ=2)\n",
    "\n",
    "# Perform Tukey HSD post-hoc test\n",
    "tukey_results = pairwise_tukeyhsd(data['Num_Credit_Card'], data['Credit_Score'])\n",
    "\n",
    "# Print ANOVA results\n",
    "print(\"ANOVA Results:\")\n",
    "print(anova_table)\n",
    "\n",
    "# Print Tukey HSD results\n",
    "print(\"\\nTukey HSD Results:\")\n",
    "print(tukey_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "700de9e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Results:\n",
      "                       sum_sq       df            F  PR(>F)\n",
      "C(Credit_Score)  4.609547e+05      2.0  4280.399446     0.0\n",
      "Residual         1.902876e+06  35340.0          NaN     NaN\n",
      "\n",
      "Tukey HSD Results:\n",
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1  group2  meandiff p-adj  lower   upper  reject\n",
      "-----------------------------------------------------\n",
      "  Good     Poor  10.7309   0.0 10.4584 11.0033   True\n",
      "  Good Standard   5.2049   0.0  4.9712  5.4387   True\n",
      "  Poor Standard   -5.526   0.0 -5.7499  -5.302   True\n",
      "-----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Perform ANOVA to get the F-statistic and p-value\n",
    "anova = sm.formula.ols('Interest_Rate ~ C(Credit_Score)', data=data).fit()\n",
    "anova_table = sm.stats.anova_lm(anova, typ=2)\n",
    "\n",
    "# Perform Tukey HSD post-hoc test\n",
    "tukey_results = pairwise_tukeyhsd(data['Interest_Rate'], data['Credit_Score'])\n",
    "\n",
    "# Print ANOVA results\n",
    "print(\"ANOVA Results:\")\n",
    "print(anova_table)\n",
    "\n",
    "# Print Tukey HSD results\n",
    "print(\"\\nTukey HSD Results:\")\n",
    "print(tukey_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "0c6a9fb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Results:\n",
      "                       sum_sq       df           F        PR(>F)\n",
      "C(Credit_Score)  4.747461e+04      2.0  222.496479  9.425680e-97\n",
      "Residual         3.770291e+06  35340.0         NaN           NaN\n",
      "\n",
      "Tukey HSD Results:\n",
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1  group2  meandiff p-adj  lower   upper  reject\n",
      "-----------------------------------------------------\n",
      "  Good     Poor  -3.4401   0.0 -3.8236 -3.0566   True\n",
      "  Good Standard   -1.635   0.0 -1.9641  -1.306   True\n",
      "  Poor Standard   1.8051   0.0  1.4899  2.1204   True\n",
      "-----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Perform ANOVA to get the F-statistic and p-value\n",
    "anova = sm.formula.ols('Age ~ C(Credit_Score)', data=data).fit()\n",
    "anova_table = sm.stats.anova_lm(anova, typ=2)\n",
    "\n",
    "# Perform Tukey HSD post-hoc test\n",
    "tukey_results = pairwise_tukeyhsd(data['Age'], data['Credit_Score'])\n",
    "\n",
    "# Print ANOVA results\n",
    "print(\"ANOVA Results:\")\n",
    "print(anova_table)\n",
    "\n",
    "# Print Tukey HSD results\n",
    "print(\"\\nTukey HSD Results:\")\n",
    "print(tukey_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "7a531d86",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box plot to visualize Age across Credit Score categories\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(x='Credit_Score', y='Age', data=data)\n",
    "plt.xlabel('Credit Score Category')\n",
    "plt.ylabel('Age')\n",
    "plt.title('Differences in Age Across Credit Score Categories')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "3ba080eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box plot to visualize Number of Bank Accounts across Credit Score categories\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(x='Credit_Score', y='Num_Bank_Accounts', data=data)\n",
    "plt.xlabel('Credit Score Category')\n",
    "plt.ylabel('Number of Bank Accounts')\n",
    "plt.title('Differences in Number of Bank Accounts Across Credit Score Categories')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "c511f749",
   "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>Occupation</th>\n",
       "      <th>Annual_Income</th>\n",
       "      <th>Num_Bank_Accounts</th>\n",
       "      <th>Num_Credit_Card</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Num_of_Loan</th>\n",
       "      <th>Delay_from_due_date</th>\n",
       "      <th>Num_of_Delayed_Payment</th>\n",
       "      <th>Changed_Credit_Limit</th>\n",
       "      <th>Num_Credit_Inquiries</th>\n",
       "      <th>Credit_Mix</th>\n",
       "      <th>Outstanding_Debt</th>\n",
       "      <th>Credit_Utilization_Ratio</th>\n",
       "      <th>Credit_History_Age</th>\n",
       "      <th>Payment_of_Min_Amount</th>\n",
       "      <th>Total_EMI_per_month</th>\n",
       "      <th>Amount_invested_monthly</th>\n",
       "      <th>Payment_Behaviour</th>\n",
       "      <th>Monthly_Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>_______</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>25.93</td>\n",
       "      <td>27.2</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>39.68</td>\n",
       "      <td>High_spent_Large_value_payments</td>\n",
       "      <td>485.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>30.12</td>\n",
       "      <td>27.3</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>251.63</td>\n",
       "      <td>Low_spent_Large_value_payments</td>\n",
       "      <td>303.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>143162.64</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3</td>\n",
       "      <td>Good</td>\n",
       "      <td>1303.01</td>\n",
       "      <td>35.69</td>\n",
       "      <td>18.5</td>\n",
       "      <td>No</td>\n",
       "      <td>246.99</td>\n",
       "      <td>453.62</td>\n",
       "      <td>Low_spent_Large_value_payments</td>\n",
       "      <td>788.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>30689.89</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Good</td>\n",
       "      <td>632.46</td>\n",
       "      <td>39.38</td>\n",
       "      <td>17.9</td>\n",
       "      <td>No</td>\n",
       "      <td>16.42</td>\n",
       "      <td>69.11</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>425.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>30689.89</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Good</td>\n",
       "      <td>632.46</td>\n",
       "      <td>36.82</td>\n",
       "      <td>18.1</td>\n",
       "      <td>No</td>\n",
       "      <td>16.42</td>\n",
       "      <td>191.89</td>\n",
       "      <td>Low_spent_Medium_value_payments</td>\n",
       "      <td>332.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age    Occupation  Annual_Income  Num_Bank_Accounts  Num_Credit_Card  \\\n",
       "0   28       _______       34847.84                  2                4   \n",
       "1   28       Teacher       34847.84                  2                4   \n",
       "2   35      Engineer      143162.64                  1                5   \n",
       "3   55  Entrepreneur       30689.89                  2                5   \n",
       "4   55  Entrepreneur       30689.89                  2                5   \n",
       "\n",
       "   Interest_Rate  Num_of_Loan  Delay_from_due_date  Num_of_Delayed_Payment  \\\n",
       "0              6            1                    3                       1   \n",
       "1              6            1                    3                       3   \n",
       "2              8            3                    6                       3   \n",
       "3              4            1                    5                       6   \n",
       "4              4            1                    5                       6   \n",
       "\n",
       "   Changed_Credit_Limit  Num_Credit_Inquiries Credit_Mix  Outstanding_Debt  \\\n",
       "0                  5.42                     5       Good            605.03   \n",
       "1                  5.42                     5       Good            605.03   \n",
       "2                  2.10                     3       Good           1303.01   \n",
       "3                  1.99                     4       Good            632.46   \n",
       "4                  1.99                     4       Good            632.46   \n",
       "\n",
       "   Credit_Utilization_Ratio  Credit_History_Age Payment_of_Min_Amount  \\\n",
       "0                     25.93                27.2                    No   \n",
       "1                     30.12                27.3                    No   \n",
       "2                     35.69                18.5                    No   \n",
       "3                     39.38                17.9                    No   \n",
       "4                     36.82                18.1                    No   \n",
       "\n",
       "   Total_EMI_per_month  Amount_invested_monthly  \\\n",
       "0                18.82                    39.68   \n",
       "1                18.82                   251.63   \n",
       "2               246.99                   453.62   \n",
       "3                16.42                    69.11   \n",
       "4                16.42                   191.89   \n",
       "\n",
       "                  Payment_Behaviour  Monthly_Balance  \n",
       "0   High_spent_Large_value_payments           485.30  \n",
       "1    Low_spent_Large_value_payments           303.36  \n",
       "2    Low_spent_Large_value_payments           788.11  \n",
       "3  High_spent_Medium_value_payments           425.73  \n",
       "4   Low_spent_Medium_value_payments           332.95  "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV file into a Pandas DataFrame\n",
    "test_df = pd.read_csv(\n",
    "    Path('Resources/cleaned_data_test.csv')\n",
    ")\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "aa0c5baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_drop = ['Occupation', 'Num_of_Loan', 'Credit_Utilization_Ratio', 'Credit_History_Age',\n",
    "                  'Payment_of_Min_Amount', 'Total_EMI_per_month', 'Amount_invested_monthly', 'Payment_Behaviour', 'Credit_Mix']\n",
    "X2 = test_df.drop(columns=columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "bc75de0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the categorical variables using get_dummies\n",
    "X2 = pd.get_dummies(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "b1cca909",
   "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>Annual_Income</th>\n",
       "      <th>Num_Bank_Accounts</th>\n",
       "      <th>Num_Credit_Card</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Delay_from_due_date</th>\n",
       "      <th>Num_of_Delayed_Payment</th>\n",
       "      <th>Changed_Credit_Limit</th>\n",
       "      <th>Num_Credit_Inquiries</th>\n",
       "      <th>Outstanding_Debt</th>\n",
       "      <th>Monthly_Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5</td>\n",
       "      <td>605.03</td>\n",
       "      <td>485.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5</td>\n",
       "      <td>605.03</td>\n",
       "      <td>303.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>143162.64</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3</td>\n",
       "      <td>1303.01</td>\n",
       "      <td>788.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>30689.89</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>4</td>\n",
       "      <td>632.46</td>\n",
       "      <td>425.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>30689.89</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>4</td>\n",
       "      <td>632.46</td>\n",
       "      <td>332.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual_Income  Num_Bank_Accounts  Num_Credit_Card  Interest_Rate  \\\n",
       "0   28       34847.84                  2                4              6   \n",
       "1   28       34847.84                  2                4              6   \n",
       "2   35      143162.64                  1                5              8   \n",
       "3   55       30689.89                  2                5              4   \n",
       "4   55       30689.89                  2                5              4   \n",
       "\n",
       "   Delay_from_due_date  Num_of_Delayed_Payment  Changed_Credit_Limit  \\\n",
       "0                    3                       1                  5.42   \n",
       "1                    3                       3                  5.42   \n",
       "2                    6                       3                  2.10   \n",
       "3                    5                       6                  1.99   \n",
       "4                    5                       6                  1.99   \n",
       "\n",
       "   Num_Credit_Inquiries  Outstanding_Debt  Monthly_Balance  \n",
       "0                     5            605.03           485.30  \n",
       "1                     5            605.03           303.36  \n",
       "2                     3           1303.01           788.11  \n",
       "3                     4            632.46           425.73  \n",
       "4                     4            632.46           332.95  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the data\n",
    "X2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "1d1a4014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17711, 11)"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "96a33459",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-fit the scaler on the original dataset\n",
    "X_scaler.fit(X_train, y_train)\n",
    "# Transform the new dataset X2 \n",
    "X2_scaled = X_scaler.transform(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "88578c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17711"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X2_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "f72f53f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict on the 2nd dataset X2\n",
    "y_pred_2 = model.predict(X2_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "2f7fd772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Good', 'Good', 'Poor', ..., 'Poor', 'Poor', 'Poor'], dtype=object)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the predictions\n",
    "y_pred_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "6dc12b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Credit_Score\n",
       "Standard    1442\n",
       "Poor         655\n",
       "Good         554\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "28869571",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'Standard': 7180, 'Poor': 5385, 'Good': 5146})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "distribution = Counter(y_pred_2)\n",
    "print(distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "56840a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17711"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "cdef6d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate value counts for y_test\n",
    "value_counts = np.unique(y_test, return_counts=True)\n",
    "\n",
    "# Calculate percentages\n",
    "total_samples = len(y_test)\n",
    "percentages = value_counts[1] / total_samples * 100\n",
    "\n",
    "# Sort the percentages in ascending order\n",
    "sorted_percentages = sorted(zip(*value_counts, percentages), key=lambda x: x[1])\n",
    "\n",
    "# Unzip the sorted percentages\n",
    "values, counts, percentages = zip(*sorted_percentages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "c6f5a8b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate value counts for y_test\n",
    "value_counts = np.unique(y_pred_2, return_counts=True)\n",
    "\n",
    "# Calculate percentages\n",
    "total_samples = len(y_pred_2)\n",
    "percentages = value_counts[1] / total_samples * 100\n",
    "\n",
    "# Sort the percentages in ascending order\n",
    "sorted_percentages = sorted(zip(*value_counts, percentages), key=lambda x: x[1])\n",
    "\n",
    "# Unzip the sorted percentages\n",
    "values, counts, percentages = zip(*sorted_percentages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "2c04d24c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          y_test (%)  Predictions on New Test Data (%)\n",
      "Standard        54.0                              41.0\n",
      "Poor            25.0                              30.0\n",
      "Good            21.0                              29.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate class distribution percentages for y_test\n",
    "y_test_class_distribution = (pd.Series(y_test).value_counts(normalize=True) * 100).round()\n",
    "\n",
    "# Calculate class distribution percentages for the new predictions\n",
    "predictions_class_distribution = (pd.Series(y_pred_2).value_counts(normalize=True) * 100).round()\n",
    "\n",
    "# Create a DataFrame to display the class distribution percentages in a grid\n",
    "df = pd.DataFrame({\n",
    "    'y_test (%)': y_test_class_distribution,\n",
    "    'Predictions on New Test Data (%)': predictions_class_distribution\n",
    "}).fillna(0)  # Fill NaN values with 0\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "c5d0019c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the categories and corresponding colors\n",
    "categories = ['Poor', 'Standard', 'Good']\n",
    "colors = {'Poor': 'green', 'Standard': '#FF8C00', 'Good': 'blue'}\n",
    "\n",
    "# Data for the y_test and y_pred_2 charts (values and percentages)\n",
    "values_y_test = [655, 1442, 554]\n",
    "percentages_y_test = [25, 54, 21]\n",
    "\n",
    "values_y_pred_2 = [5388, 7180, 5148]\n",
    "percentages_y_pred_2 = [30, 41, 29]\n",
    "\n",
    "# Create the first bar chart\n",
    "plt.subplot(1, 2, 1)\n",
    "bars_chart1 = plt.bar(categories, percentages_y_test, alpha=1.0)\n",
    "for bar, category in zip(bars_chart1, categories):\n",
    "    bar.set_color(colors[category])\n",
    "plt.xlabel('Target Variable (y_test)')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Distribution of Credit Score - Original Dataset')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "f661a9ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a90nncR62atUqjBw5EhEREVrtt27dgo2NjWbc0NAQkydPxuTJk3Hv3j38+uuvmDFjBnr37o3k5GTUrl1b8w163LhxJa6rfv36Za6rNHrZk7969SqmTJkCa2trjBkzpkzLGBgYoGPHjvjyyy8BQHPopCx7r+Vx7tw5nDp1SqttzZo1sLS0RJs2bQD882MbADh9+rTWfJs3by7Wn1qtLnNtPXr0wJ49ezRhW2TlypUwMzOrkMvWzM3N0bFjR0RHR2vVVVhYiFWrVqFOnTrFDg2UxahRo+Ds7IypU6fi2rVrJc4THR39xH769euH27dvo6CgAO3atSs2FJ3UKto5WL16tdby33//PfLz85+4nvJ8bvLy8kr9j6Po63lRqAYFBSE7OxvLly8vtb+iy1FXrVql1b5hwwZkZWU98XJV4J/tdPbsWTRs2LDE7fS4kHd1ddWa18vL64nre1wdZXm/HlYVf8sPHjzA6NGjkZ+fr3XBgkqlKvaf2unTp4sdCn1cLSX1sXXr1lI/9wBgY2ODoUOHYty4cbhz5w4uX74MMzMzBAQE4OTJk/D29i5x+xXtzDzNtqn0PfmzZ89qjjOlpqYiJiYGUVFRMDAwwMaNGx979nzp0qXYs2cP+vbti7p16yI7OxvLli0DAPTs2RPAP3sK7u7u2LRpE3r06AFbW1vY29trgri8XF1dMWDAAISHh8PFxQWrVq3Crl278PHHH2uu4W7fvj0aN26MKVOmID8/H7Vr18bGjRtx8ODBYv15eXkhOjoaS5YsQdu2bVGrVi2t3w08LCwsTHOsdfbs2bC1tcXq1auxdetWzJs3D9bW1jq9pkdFRkYiMDAQAQEBmDJlCoyNjbF48WKcPXsWa9euLfevjgHA2toamzZtQr9+/dC6dWuMHz8ePj4+mmOVq1atwqlTpzB48ODH9jNs2DCsXr0affr0wcSJE9GhQwcYGRnhr7/+wt69ezFw4EAMGjQITZs2xSuvvIIFCxbAyMgIPXv2xNmzZ/Hpp58WOwRUkqJg+/jjjxEUFAQDAwN4e3vD2Ni42LxpaWmoV68enn/+efTs2RNubm7IzMzEvn37sHDhQjRt2lTzul566SVERUXhzTffxPnz5xEQEIDCwkIcOXIETZs2xbBhwxAYGIjevXtj2rRpSE9PR+fOnTVX17Ru3RojRox4Yv0ffvghdu3aBV9fX7z99tto3LgxsrOzcfnyZWzbtg1Lly6tkt+BlPX9qsy/5atXryIuLg6FhYVIS0vDyZMnsWzZMly5cgWfffYZevXqpZm3X79++M9//oOwsDD4+fnh/Pnz+PDDD1G/fn2tnYPH1dKvXz8sX74cTZo0gbe3N44fP45PPvmk2Pbu37+/5ndCDg4OuHLlChYsWAB3d3d4enoCABYuXIguXbqga9eueOutt1CvXj1kZGTg4sWL+PnnnzXnbxo2bAhTU1OsXr0aTZs2hYWFBVxdXR/7n7lGuU/VllHRFShFg7GxsTg6Ooqfn59EREQUu4ZWpPgVL7GxsTJo0CBxd3cXtVotdnZ24ufnJ5s3b9Za7tdff5XWrVuLWq0u8Tr5v//++4nrEvm/a5J//PFHad68uRgbG0u9evVk/vz5xZa/cOGC9OrVS6ysrMTBwUEmTJggW7duLXYVx507d2To0KFiY2MjKpWqTNfJ9+/fX6ytrcXY2FhatmxZ7Ix60dUiP/zwg1Z70dUGZTkDX3SdvLm5uZiamkqnTp3k559/LrG/slxdUyQlJUWmTZsmzZs3FzMzM1Gr1eLh4SFjxoyRM2fOaOYr6XrrInl5efLpp59Ky5YtxcTERCwsLKRJkyYyZswYSUpK0syXk5Mj77zzjjg6OoqJiYl06tRJYmNjn3idfNGyo0ePFgcHB837Utp18jk5OfLpp59KUFCQ1K1bV9RqtZiYmEjTpk1l6tSpcvv2ba35Hzx4ILNnzxZPT08xNjYWOzs76d69uxw+fFhrnmnTpom7u7sYGRmJi4uLvPXWW6VeJ1+Sv//+W95++22pX7++GBkZia2trbRt21ZmzpwpmZmZJS7ztEp638ryfj3t33JJHr2e3cDAQGrXri1t27aV0NDQEq8wysnJkSlTpsgzzzwjJiYm0qZNG/npp58kODi42FVwpdVy9+5dGTVqlDg6OoqZmZl06dJFYmJixM/PT/z8/DTLf/bZZ+Lr6yv29vZibGwsdevWlVGjRsnly5eLvY7XXntN83sHBwcH8fX1lTlz5mjNt3btWmnSpIkYGRmV6yoklcgTLnEhIqIai3ehJCJSMIY8EZGCMeSJiBSMIU9EpGAMeSIiBWPIExEpmN5ua1BVCgsLcf36dVhaWur0Ix8iIhFBRkbGE+9LVB0pPuSvX79e7K6ORES6SE5O1vsT5cpL8SFfdIOk5OTkMv3cnYjoUenp6XBzc9O64VpNofiQLzpEY2VlxZAnoqdSEw/51qyDS0REVC4MeSIiBWPIExEpGEOeiEjBGPJERArGkCciUjCGPBGRgjHkiYgUjCFPRKRgDHkiIgVjyBMRKRhDnohIwRjyREQKxpAnIlIwhjwRkYIp/n7yVE2lXwUe3NJ3FdWXqT1gVVffVZACMOSp6qVfBZY1Bgqy9V1J9WVgArx2nkFPT42Ha6jqPbjFgH+Sgmx+06EKwZAnIlIwhjwRkYJVm5CPjIyESqVCaGiopk1EEB4eDldXV5iamsLf3x/nzp3TX5FERDVMtQj5+Ph4fP311/D29tZqnzdvHubPn49FixYhPj4ezs7OCAwMREZGhp4qJSKqWfQe8pmZmXj55ZfxzTffoHbt2pp2EcGCBQswc+ZMDB48GC1atMCKFStw//59rFmzRo8VExHVHHoP+XHjxqFv377o2bOnVvulS5eQkpKCXr16adrUajX8/Pxw+PDhqi6TiKhG0ut18uvWrcOJEycQHx9fbFpKSgoAwMnJSavdyckJV65cKbXPnJwc5OTkaMbT09MrqFoioppHb3vyycnJmDhxIlatWgUTE5NS51OpVFrjIlKs7WGRkZGwtrbWDG5ubhVWMxFRTaO3kD9+/DhSU1PRtm1bGBoawtDQEPv378d///tfGBoaavbgi/boi6Smphbbu3/Y9OnTkZaWphmSk5Mr9XUQEVVnejtc06NHD5w5c0ar7dVXX0WTJk0wbdo0NGjQAM7Ozti1axdat24NAMjNzcX+/fvx8ccfl9qvWq2GWq2u1NqJiGoKvYW8paUlWrRoodVmbm4OOzs7TXtoaCgiIiLg6ekJT09PREREwMzMDMOHD9dHyURENU61vkHZ1KlT8eDBA4wdOxZ3795Fx44dsXPnTlhaWuq7NCKiGkElIqLvIipTeno6rK2tkZaWBisrK32XQwBw8wSwqq2+q6j+XjkOOLXRdxWEmp0jer9OnoiIKg9DnohIwRjyREQKxpAnIlIwhjwRkYIx5ImIFIwhT0SkYAx5IiIFY8gTESkYQ56ISMEY8kRECsaQJyJSMIY8EZGCMeSJiBSMIU9EpGAMeSIiBWPIExEpGEOeiEjBGPJERArGkCciUjCGPBGRgjHkiYgUjCFPRKRgDHkiIgVjyBMRKRhDnohIwRjyREQKxpAnIlIwhjwRkYIx5ImIFIwhT0SkYAx5IiIFY8gTESkYQ56ISMH0GvJLliyBt7c3rKysYGVlBR8fH/zyyy+a6SEhIVCpVFpDp06d9FgxEVHNYqjPldepUwcfffQRPDw8AAArVqzAwIEDcfLkSTRv3hwA8OyzzyIqKkqzjLGxsV5qJSKqifQa8v3799canzt3LpYsWYK4uDhNyKvVajg7O+ujPCKiGq/aHJMvKCjAunXrkJWVBR8fH037vn374OjoiEaNGuH1119HamqqHqskIqpZ9LonDwBnzpyBj48PsrOzYWFhgY0bN6JZs2YAgKCgIDz//PNwd3fHpUuX8P7776N79+44fvw41Gp1if3l5OQgJydHM56enl4lr4OIqDrSe8g3btwYCQkJuHfvHjZs2IDg4GDs378fzZo1w4svvqiZr0WLFmjXrh3c3d2xdetWDB48uMT+IiMj8cEHH1RV+URE1ZreD9cYGxvDw8MD7dq1Q2RkJFq2bImFCxeWOK+Liwvc3d2RlJRUan/Tp09HWlqaZkhOTq6s0omIqj2978k/SkS0Drc87Pbt20hOToaLi0upy6vV6lIP5RAR/dvoNeRnzJiBoKAguLm5ISMjA+vWrcO+ffuwfft2ZGZmIjw8HEOGDIGLiwsuX76MGTNmwN7eHoMGDdJn2URENYZeQ/7mzZsYMWIEbty4AWtra3h7e2P79u0IDAzEgwcPcObMGaxcuRL37t2Di4sLAgICsH79elhaWuqzbCKiGkOvIf/dd9+VOs3U1BQ7duyowmqIiJRH7ydeiYio8jDkiYgUjCFPRKRgDHkiIgVjyBMRKRhDnohIwRjyREQKxpAnIlIwhjwRkYIx5ImIFIwhT0SkYAx5IiIFY8gTESkYQ56ISMEY8kRECsaQJyJSMIY8EZGCMeSJiBSMIU9EpGAMeSIiBWPIExEpGEOeiEjBGPJERArGkCciUjCGPBGRgjHkiYgUjCFPRKRgDHkiIgVjyBMRKRhDnohIwRjyREQKxpAnIlIwhjwRkYIx5ImIFEyvIb9kyRJ4e3vDysoKVlZW8PHxwS+//KKZLiIIDw+Hq6srTE1N4e/vj3PnzumxYiKimkWvIV+nTh189NFHOHbsGI4dO4bu3btj4MCBmiCfN28e5s+fj0WLFiE+Ph7Ozs4IDAxERkaGPssmIqox9Bry/fv3R58+fdCoUSM0atQIc+fOhYWFBeLi4iAiWLBgAWbOnInBgwejRYsWWLFiBe7fv481a9bos2wiohrDUN8FFCkoKMAPP/yArKws+Pj44NKlS0hJSUGvXr0086jVavj5+eHw4cMYM2ZMif3k5OQgJydHM56enl7uWq6mXcWt+7fK/yL+JezN7FHXuq6+yyCiMtB7yJ85cwY+Pj7Izs6GhYUFNm7ciGbNmuHw4cMAACcnJ635nZyccOXKlVL7i4yMxAcffKBzPVfTrqLxosbIzs/WuQ+lMzE0wfnx5xn0RDWA3q+uady4MRISEhAXF4e33noLwcHB+O233zTTVSqV1vwiUqztYdOnT0daWppmSE5OLlc9t+7fYsA/QXZ+Nr/pENUQet+TNzY2hoeHBwCgXbt2iI+Px8KFCzFt2jQAQEpKClxcXDTzp6amFtu7f5harYZara7coomIagi978k/SkSQk5OD+vXrw9nZGbt27dJMy83Nxf79++Hr66vHComIag697snPmDEDQUFBcHNzQ0ZGBtatW4d9+/Zh+/btUKlUCA0NRUREBDw9PeHp6YmIiAiYmZlh+PDh+iybiKjG0GvI37x5EyNGjMCNGzdgbW0Nb29vbN++HYGBgQCAqVOn4sGDBxg7dizu3r2Ljh07YufOnbC0tNRn2URENYZeQ/6777577HSVSoXw8HCEh4dXTUFERApT7Y7JExFRxdH71TVEVDmuXgVu8UrXUtnbA3X/BT/1YMgTKdDVq0DjxkA2f/JRKhMT4Px55Qc9D9cQKdCtWwz4J8nO/nd802HIExEpGEOeiEjBGPJERArGkCciUjCGPBGRgjHkiYgUjCFPRKRgDHkiIgVjyBMRKRhDnohIwRjyREQKxpAnIlIwhjwRkYIx5ImIFIwhT0SkYAx5IiIFY8gTESkYQ56ISMEY8kRECsaQJyJSMIY8EZGCMeSJiBSMIU9EpGAMeSIiBWPIExEpGEOeiEjBGPJERArGkCciUjCGPBGRgjHkiYgUTOeQ/+OPPzBr1iy89NJLSE1NBQBs374d586dK3MfkZGRaN++PSwtLeHo6IjnnnsO58+f15onJCQEKpVKa+jUqZOuZRMR/avoFPL79++Hl5cXjhw5gujoaGRmZgIATp8+jbCwsHL1M27cOMTFxWHXrl3Iz89Hr169kJWVpTXfs88+ixs3bmiGbdu26VI2EdG/jqEuC7333nuYM2cOJk+eDEtLS017QEAAFi5cWOZ+tm/frjUeFRUFR0dHHD9+HN26ddO0q9VqODs761IqEdG/mk578mfOnMGgQYOKtTs4OOD27ds6F5OWlgYAsLW11Wrft28fHB0d0ahRI7z++uuaw0MlycnJQXp6utZARPRvpVPI29jY4MaNG8XaT548iWeeeUanQkQEkydPRpcuXdCiRQtNe1BQEFavXo09e/bgs88+Q3x8PLp3746cnJwS+4mMjIS1tbVmcHNz06keIiIl0Cnkhw8fjmnTpiElJQUqlQqFhYU4dOgQpkyZgpEjR+pUyPjx43H69GmsXbtWq/3FF19E37590aJFC/Tv3x+//PILLly4gK1bt5bYz/Tp05GWlqYZkpOTdaqHiEgJdDomP3fuXISEhOCZZ56BiKBZs2YoKCjA8OHDMWvWrHL3N2HCBGzevBkHDhxAnTp1Hjuvi4sL3N3dkZSUVOJ0tVoNtVpd7hqIiJRIp5A3MjLC6tWr8eGHH+LkyZMoLCxE69at4enpWa5+RAQTJkzAxo0bsW/fPtSvX/+Jy9y+fRvJyclwcXHRpXQion8VnUK+SMOGDdGwYUOdlx83bhzWrFmDTZs2wdLSEikpKQAAa2trmJqaIjMzE+Hh4RgyZAhcXFxw+fJlzJgxA/b29iWe+CUiIm06hfzkyZNLbFepVDAxMYGHhwcGDhxY7CqZRy1ZsgQA4O/vr9UeFRWFkJAQGBgY4MyZM1i5ciXu3bsHFxcXBAQEYP369VqXbhIRUcl0CvmTJ0/ixIkTKCgoQOPGjSEiSEpKgoGBAZo0aYLFixfjnXfewcGDB9GsWbNS+xGRx67H1NQUO3bs0KVEIiKCjlfXDBw4ED179sT169dx/PhxnDhxAteuXUNgYCBeeuklXLt2Dd26dcOkSZMqul4iIioHnUL+k08+wX/+8x9YWVlp2qysrBAeHo558+bBzMwMs2fPxvHjxyusUCIiKj+dQj4tLa3EX53+/fffml+Y2tjYIDc39+mqIyKip6Lz4ZrXXnsNGzduxF9//YVr165h48aNGDVqFJ577jkAwNGjR9GoUaOKrJWIiMpJpxOvX331FSZNmoRhw4YhPz//n44MDREcHIzPP/8cANCkSRN8++23FVcpERGVm04hb2FhgW+++Qaff/45/vzzT4gIGjZsCAsLC808rVq1qqgaiYhIR0/1YygLCwt4e3tXVC1ERFTBdA75+Ph4/PDDD7h69WqxE6zR0dFPXRgRET09nU68rlu3Dp07d8Zvv/2GjRs3Ii8vD7/99hv27NkDa2vriq6RiIh0pFPIR0RE4PPPP8eWLVtgbGyMhQsXIjExES+88ALq1q1b0TUSEZGOdAr5P/74A3379gXwz619s7KyoFKpMGnSJHz99dcVWiAREelOp5C3tbVFRkYGAOCZZ57B2bNnAQD37t3D/fv3K646IiJ6KjqdeO3atSt27doFLy8vvPDCC5g4cSL27NmDXbt2oUePHhVdIxER6UinkF+0aBGys7MB/PO4PSMjIxw8eBCDBw/G+++/X6EFEhGR7nQK+YfvE1+rVi1MnToVU6dOrbCiiIioYuh0TN7AwKDEG5Tdvn0bBgYGT10UERFVDJ1CvrSHfeTk5MDY2PipCiIioopTrsM1//3vfwH885i/b7/9VuteNQUFBThw4ACaNGlSsRUSEZHOyhXyRXeYFBEsXbpU69CMsbEx6tWrh6VLl1ZshUREpLNyhfylS5cAAAEBAYiOjkbt2rUrpSgiIqoYOl1ds3fv3oqug4iIKoFOIV9QUIDly5dj9+7dSE1NRWFhodb0PXv2VEhxRET0dHQK+YkTJ2L58uXo27cvWrRoAZVKVdF1ERFRBdAp5NetW4fvv/8effr0qeh6iIioAul0nbyxsTE8PDwquhYiIqpgOoX8O++8g4ULF5b6oygiIqoedDpcc/DgQezduxe//PILmjdvDiMjI63pfPwfEVH1oFPI29jYYNCgQRVdCxERVTCdQj4qKqqi6yAiokqg0zF5AMjPz8evv/6Kr776SvOUqOvXryMzM7PCiiMioqej0578lStX8Oyzz+Lq1avIyclBYGAgLC0tMW/ePGRnZ/P+NURE1YROe/ITJ05Eu3btcPfuXZiammraBw0ahN27d1dYcURE9HR0vrrm0KFDxe4d7+7ujmvXrlVIYURE9PR02pMvLCxEQUFBsfa//voLlpaWT10UERFVDJ1CPjAwEAsWLNCMq1QqZGZmIiwsrFy3OoiMjET79u1haWkJR0dHPPfcczh//rzWPCKC8PBwuLq6wtTUFP7+/jh37pwuZRMR/evoFPKff/459u/fj2bNmiE7OxvDhw9HvXr1cO3aNXz88cdl7mf//v0YN24c4uLisGvXLuTn56NXr17IysrSzDNv3jzMnz8fixYtQnx8PJydnREYGKi5ooeIiEqn0zF5V1dXJCQkYN26dTh+/DgKCwsxatQovPzyy1onYp9k+/btWuNRUVFwdHTE8ePH0a1bN4gIFixYgJkzZ2Lw4MEAgBUrVsDJyQlr1qzBmDFjdCmfiOhfQ6eQBwBTU1O8+uqrePXVVyusmLS0NACAra0tgH+eRJWSkoJevXpp5lGr1fDz88Phw4dLDPmcnBzk5ORoxtPT0yusPiKimkanwzWRkZFYtmxZsfZly5aV63DNw0QEkydPRpcuXdCiRQsAQEpKCgDAyclJa14nJyfNtJJqs7a21gxubm461UNEpAQ6hfxXX32FJk2aFGtv3ry5zj+EGj9+PE6fPo21a9cWm/boQ0lEpNQHlUyfPh1paWmaITk5Wad6iIiUQKfDNSkpKXBxcSnW7uDggBs3bpS7vwkTJmDz5s04cOAA6tSpo2l3dnYucX2pqanF9u6LqNVqqNXqctdARKREOu3Ju7m54dChQ8XaDx06BFdX1zL3IyIYP348oqOjsWfPHtSvX19rev369eHs7Ixdu3Zp2nJzc7F//374+vrqUjoR0b+KTnvyo0ePRmhoKPLy8tC9e3cAwO7duzF16lS88847Ze5n3LhxWLNmDTZt2gRLS0vNcXZra2uYmppCpVIhNDQUERER8PT0hKenJyIiImBmZobhw4frUjoR0b+KTiE/depU3LlzB2PHjkVubi4AwMTEBNOmTcP06dPL3M+SJUsAAP7+/lrtUVFRCAkJ0azrwYMHGDt2LO7evYuOHTti586d/GUtEVEZlDvkCwoKcPDgQUybNg3vv/8+EhMTYWpqCk9Pz3IfCy/L4wNVKhXCw8MRHh5e3lKJiP71yh3yBgYG6N27NxITE1G/fn20b9++MuoiIqIKoNOJVy8vL/z5558VXQsREVUwnUJ+7ty5mDJlCrZs2YIbN24gPT1dayAioupBpxOvzz77LABgwIABWj9KKvqRUkm3ISYioqqnU8jv3bu3ousgIqJKoFPI+/n5VXQdRERUCXQ6Jg8AMTExeOWVV+Dr66t55N///vc/HDx4sMKKIyKip6NTyG/YsAG9e/eGqakpTpw4obm1b0ZGBiIiIiq0QCIi0p1OIT9nzhwsXboU33zzDYyMjDTtvr6+OHHiRIUVR0RET0enkD9//jy6detWrN3Kygr37t172pqIiKiC6BTyLi4uuHjxYrH2gwcPokGDBk9dFBERVQydQn7MmDGYOHEijhw5ApVKhevXr2P16tWYMmUKxo4dW9E1EhGRjnS+C2V6ejoCAgKQnZ2Nbt26Qa1WY8qUKRg/fnxF10hERDoqV8jfv38f7777Ln766Sfk5eWhf//+mvvHN2vWDBYWFpVSJBER6aZcIR8WFobly5fj5ZdfhqmpKdasWYPCwkL88MMPlVUfERE9hXKFfHR0NL777jsMGzYMAPDyyy+jc+fOKCgogIGBQaUUSEREuivXidfk5GR07dpVM96hQwcYGhri+vXrFV4YERE9vXKFfEFBAYyNjbXaDA0NkZ+fX6FFERFRxSjX4RoRQUhIiNZj/rKzs/Hmm2/C3Nxc0xYdHV1xFRIRkc7KFfLBwcHF2l555ZUKK4aIiCpWuUI+KiqqsuogIqJKoPOthomIqPpjyBMRKRhDnohIwRjyREQKxpAnIlIwhjwRkYIx5ImIFIwhT0SkYAx5IiIFY8gTESkYQ56ISMEY8kRECsaQJyJSML2G/IEDB9C/f3+4urpCpVLhp59+0poeEhIClUqlNXTq1Ek/xRIR1UB6DfmsrCy0bNkSixYtKnWeZ599Fjdu3NAM27Ztq8IKiYhqtnLdT76iBQUFISgo6LHzqNVqODs7V1FFRETKUu2Pye/btw+Ojo5o1KgRXn/9daSmpj52/pycHKSnp2sNRET/VtU65IOCgrB69Wrs2bMHn332GeLj49G9e3fk5OSUukxkZCSsra01g5ubWxVWTERUvej1cM2TvPjii5p/t2jRAu3atYO7uzu2bt2KwYMHl7jM9OnTMXnyZM14eno6g56I/rWqdcg/ysXFBe7u7khKSip1HrVaDbVaXYVVERFVX9X6cM2jbt++jeTkZLi4uOi7FCKiGkGve/KZmZm4ePGiZvzSpUtISEiAra0tbG1tER4ejiFDhsDFxQWXL1/GjBkzYG9vj0GDBumxaiKimkOvIX/s2DEEBARoxouOpQcHB2PJkiU4c+YMVq5ciXv37sHFxQUBAQFYv349LC0t9VUyEVGNoteQ9/f3h4iUOn3Hjh1VWA0RkfLUqGPyRERUPgx5IiIFY8gTESkYQ56ISMEY8kRECsaQJyJSMIY8EZGCMeSJiBSMIU9EpGAMeSIiBWPIExEpGEOeiEjBGPJERArGkCciUjCGPBGRgjHkiYgUjCFPRKRgDHkiIgVjyBMRKRhDnohIwRjyREQKxpAnIlIwhjwRkYIx5ImIFIwhT0SkYAx5IiIFY8gTESkYQ56ISMEY8kRECsaQJyJSMIY8EZGCMeSJiBSMIU9EpGB6DfkDBw6gf//+cHV1hUqlwk8//aQ1XUQQHh4OV1dXmJqawt/fH+fOndNPsURENZBeQz4rKwstW7bEokWLSpw+b948zJ8/H4sWLUJ8fDycnZ0RGBiIjIyMKq6UiKhmMtTnyoOCghAUFFTiNBHBggULMHPmTAwePBgAsGLFCjg5OWHNmjUYM2ZMVZZKRFQjVdtj8pcuXUJKSgp69eqlaVOr1fDz88Phw4f1WBkRUc2h1z35x0lJSQEAODk5abU7OTnhypUrpS6Xk5ODnJwczXh6enrlFEhEVANU2z35IiqVSmtcRIq1PSwyMhLW1taawc3NrbJLJCKqtqptyDs7OwP4vz36IqmpqcX27h82ffp0pKWlaYbk5ORKrZOIqDqrtiFfv359ODs7Y9euXZq23Nxc7N+/H76+vqUup1arYWVlpTUQEf1b6fWYfGZmJi5evKgZv3TpEhISEmBra4u6desiNDQUERER8PT0hKenJyIiImBmZobhw4frsWoioppDryF/7NgxBAQEaMYnT54MAAgODsby5csxdepUPHjwAGPHjsXdu3fRsWNH7Ny5E5aWlvoqmYioRtFryPv7+0NESp2uUqkQHh6O8PDwqiuKiEhBqu0xeSIienoMeSIiBWPIExEpGEOeiEjBGPJERArGkCciUjCGPBGRgjHkiYgUjCFPRKRgDHkiIgVjyBMRKRhDnohIwRjyREQKxpAnIlIwhjwRkYIx5ImIFIwhT0SkYAx5IiIFY8gTESkYQ56ISMEY8kRECsaQJyJSMIY8EZGCMeSJiBSMIU9EpGAMeSIiBWPIExEpGEOeiEjBGPJERArGkCciUjCGPBGRgjHkiYgUjCFPRKRgDHkiIgWr1iEfHh4OlUqlNTg7O+u7LCKiGsNQ3wU8SfPmzfHrr79qxg0MDPRYDRFRzVLtQ97Q0JB770REOqrWh2sAICkpCa6urqhfvz6GDRuGP//8U98lERHVGNV6T75jx45YuXIlGjVqhJs3b2LOnDnw9fXFuXPnYGdnV+IyOTk5yMnJ0Yynp6dXVblERNVOtd6TDwoKwpAhQ+Dl5YWePXti69atAIAVK1aUukxkZCSsra01g5ubW1WVS0RU7VTrkH+Uubk5vLy8kJSUVOo806dPR1pammZITk6uwgqJiKqXan245lE5OTlITExE165dS51HrVZDrVZXYVVERNVXtd6TnzJlCvbv349Lly7hyJEjGDp0KNLT0xEcHKzv0oiIaoRqvSf/119/4aWXXsKtW7fg4OCATp06IS4uDu7u7voujYioRqjWIb9u3Tp9l0BEVKNV68M1RET0dBjyREQKxpAnIlIwhjwRkYIx5ImIFIwhT0SkYAx5IiIFY8gTESkYQ56ISMEY8kRECsaQJyJSMIY8EZGCMeSJiBSMIU9EpGAMeSIiBWPIExEpGEOeiEjBGPJERArGkCciUjCGPBGRgjHkiYgUjCFPRKRgDHkiIgVjyBMRKRhDnohIwRjyREQKxpAnIlIwhjwRkYIx5ImIFIwhT0SkYAx5IiIFY8gTESkYQ56ISMEY8kREClYjQn7x4sWoX78+TExM0LZtW8TExOi7JCKiGqHah/z69esRGhqKmTNn4uTJk+jatSuCgoJw9epVfZdGRFTtVfuQnz9/PkaNGoXRo0ejadOmWLBgAdzc3LBkyRJ9l0ZEVO1V65DPzc3F8ePH0atXL632Xr164fDhw3qqioio5jDUdwGPc+vWLRQUFMDJyUmr3cnJCSkpKSUuk5OTg5ycHM14WloaACA9Pb1M68zMyASydSz4XyQzIxPp5mXbpsVwG5dNRiZgqts2zsys4FoUKjMTKEs0FOWHiFRyRRWvWod8EZVKpTUuIsXaikRGRuKDDz4o1u7m5lYptf1b+X3kp+8SlG8Wt3Fl8yvnJs7IyIC1tXXlFFNJqnXI29vbw8DAoNhee2pqarG9+yLTp0/H5MmTNeOFhYW4c+cO7OzsSv2PoTpLT0+Hm5sbkpOTYWVlpe9yFInbuPLV9G0sIsjIyICrq6u+Sym3ah3yxsbGaNu2LXbt2oVBgwZp2nft2oWBAweWuIxarYZardZqs7Gxqcwyq4SVlVWN/OOoSbiNK19N3sY1bQ++SLUOeQCYPHkyRowYgXbt2sHHxwdff/01rl69ijfffFPfpRERVXvVPuRffPFF3L59Gx9++CFu3LiBFi1aYNu2bXB3d9d3aURE1V61D3kAGDt2LMaOHavvMvRCrVYjLCys2CEoqjjcxpWP21h/VFITrwkiIqIyqdY/hiIioqfDkCciUjCGPFEpLl++DJVKhYSEhBrVtxKpVCr89NNP+i6jRmLIV6GQkBCoVCqoVCoYGRmhQYMGmDJlCrKysvRdWrWRmpqKMWPGoG7dulCr1XB2dkbv3r0RGxsLgH/s+pSSkoKJEyfCw8MDJiYmcHJyQpcuXbB06VLcv39f3+VRKWrE1TVK8uyzzyIqKgp5eXmIiYnB6NGjkZWVVSl31czNzYWxsXGF91uZhgwZgry8PKxYsQINGjTAzZs3sXv3bty5c0ffpemkJr4HJfnzzz/RuXNn2NjYICIiAl5eXsjPz8eFCxewbNkyuLq6YsCAAfouk0oiVGWCg4Nl4MCBWm2jR48WZ2dnyc7OlgkTJoiDg4Oo1Wrp3LmzHD16VGveffv2Sfv27cXY2FicnZ1l2rRpkpeXp5nu5+cn48aNk0mTJomdnZ1069atKl5Whbl7964AkH379pU43d3dXQBoBnd3dxERuXjxogwYMEAcHR3F3Nxc2rVrJ7t27Sq27Ny5c+XVV18VCwsLcXNzk6+++kprniNHjkirVq1ErVZL27ZtJTo6WgDIyZMnRUQkPz9fXnvtNalXr56YmJhIo0aNZMGCBVp9FL3HERER4uLioqnxSX1Xd71795Y6depIZmZmidMLCwtFROTKlSsyYMAAMTc3F0tLS3n++eclJSVFa97FixdLgwYNxMjISBo1aiQrV67Umn7hwgXp2rWrqNVqadq0qezcuVMAyMaNGyvltSkdQ74KlRTyEyZMEDs7O3n77bfF1dVVtm3bJufOnZPg4GCpXbu23L59W0RE/vrrLzEzM5OxY8dKYmKibNy4Uezt7SUsLEzTl5+fn1hYWMi7774rv//+uyQmJlbhq3t6eXl5YmFhIaGhoZKdnV1sempqqgCQqKgouXHjhqSmpoqISEJCgixdulROnz4tFy5ckJkzZ4qJiYlcuXJFs6y7u7vY2trKl19+KUlJSRIZGSm1atXSbKPMzExxcHCQF198Uc6ePSs///yzNGjQQCuIc3NzZfbs2XL06FH5888/ZdWqVWJmZibr16/XrCc4OFgsLCxkxIgRcvbsWTlz5kyZ+q7Obt26JSqVSiIjIx87X2FhobRu3Vq6dOkix44dk7i4OGnTpo34+flp5omOjhYjIyP58ssv5fz58/LZZ5+JgYGB7NmzR0RECgoKpEWLFuLv7y8nT56U/fv3S+vWrRnyT4EhX4UeDfkjR46InZ2dDB06VIyMjGT16tWaabm5ueLq6irz5s0TEZEZM2ZI48aNNXtMIiJffvmlWFhYSEFBgYj8E/KtWrWqmhdTSX788UepXbu2mJiYiK+vr0yfPl1OnTqlmV7WP/ZmzZrJF198oRl3d3eXV155RTNeWFgojo6OsmTJEhER+eqrr8TW1laysrI08yxZsuSJQTx27FgZMmSIZjw4OFicnJwkJydH06Zr39VFXFycAJDo6Gitdjs7OzE3Nxdzc3OZOnWq7Ny5UwwMDOTq1auaec6dOycANN9KfX195fXXX9fq5/nnn5c+ffqIiMiOHTvEwMBAkpOTNdN/+eUXhvxT4InXKrZlyxZYWFjAxMQEPj4+6NatGyZMmIC8vDx07txZM5+RkRE6dOiAxMREAEBiYiJ8fHy07qTZuXNnZGZm4q+//tK0tWvXrupeTCUYMmQIrl+/js2bN6N3797Yt28f2rRpg+XLl5e6TFZWFqZOnYpmzZrBxsYGFhYW+P3334s9ItLb21vzb5VKBWdnZ6SmpgL4Z/u2bNkSZmZmmnl8fHyKrWvp0qVo164dHBwcYGFhgW+++abYery8vLSOw5e17+ru0bu4Hj16FAkJCWjevDlycnKQmJgINzc3rdt6F70nD3+OH/6cA/98jh+eXrduXdSpU0czvSZuq+qEIV/FAgICkJCQgPPnzyM7OxvR0dGau9s97r75UsI99OX//1j54XZzc/PKLL9KmJiYIDAwELNnz8bhw4cREhKCsLCwUud/9913sWHDBsydOxcxMTFISEiAl5cXcnNzteYzMjLSGlepVCgsLARQtodBfP/995g0aRJee+017Ny5EwkJCXj11VeLrefR96AsfVdnHh4eUKlU+P3337XaGzRoAA8PD5iamgIo/TkPj7Y/6XP+qJp4i/DqhCFfxczNzeHh4QF3d3dN6Hh4eMDY2BgHDx7UzJeXl4djx46hadOmAP7ZIzp8+LDWH8Hhw4dhaWmJZ555pmpfRBVr1qyZ5jJTIyMjFBQUaE2PiYlBSEgIBg0aBC8vLzg7O+Py5cvlXsepU6fw4MEDTVtcXFyx9fj6+mLs2LFo3bo1PDw88Mcff1RI39WZnZ0dAgMDsWjRosde7tusWTNcvXoVycnJmrbffvsNaWlpms9x06ZNtT7nwD+f44c/51evXsX169c104sunyXdMOSrAXNzc7z11lt49913sX37dvz22294/fXXcf/+fYwaNQrAPzdpS05OxoQJE/D7779j06ZNCAsLw+TJk1GrljLextu3b6N79+5YtWoVTp8+jUuXLuGHH37AvHnzNM8PqFevHnbv3o2UlBTcvXsXwD//SUZHRyMhIQGnTp3C8OHDNXvoZTV8+HDUqlULo0aNwm+//YZt27bh008/1ZrHw8MDx44dw44dO3DhwgW8//77iI+Pr5C+q7vFixcjPz8f7dq1w/r165GYmIjz589j1apV+P3332FgYICePXvC29sbL7/8Mk6cOIGjR49i5MiR8PPz0xxGfPfdd7F8+XIsXboUSUlJmD9/PqKjozFlyhQAQM+ePdG4cWOMHDkSp06dQkxMDGbOnKnPl17z6etkwL9RSVfXFHnw4IFMmDBB7O3tn+oSyokTJ1biK6hc2dnZ8t5770mbNm3E2tpazMzMpHHjxjJr1iy5f/++iIhs3rxZPDw8xNDQUHN54qVLlyQgIEBMTU3Fzc1NFi1aVGxbuLu7y+eff661vpYtW2pdnRQbGystW7YUY2NjadWqlWzYsEHr5Gh2draEhISItbW12NjYyFtvvSXvvfeetGzZUtNHae/xk/quCa5fvy7jx4+X+vXri5GRkVhYWEiHDh3kk08+0ZxUrohLKM+fPy9dunQRY2NjadSokWzfvp0nXp8C70JJRKRgyvieT0REJWLIExEpGEOeiEjBGPJERArGkCciUjCGPBGRgjHkiYgUjCFPRKRgDHmqkfbt2weVSoV79+6VeRl/f3+EhoY+dp569ephwYIFT1UbAHz33Xfo1avXU/ejD+Hh4WjVqlWF9Td06FDMnz+/wvqj8mHIV2NFz4MtbQgJCdFbbU8Kw9zcXNjb22POnDklTo+MjIS9vX2xOziWla+vL27cuKG5g2d1kpOTg9mzZ+P999/XdymV7ptvvkHXrl1Ru3Zt1K5dGz179sTRo0e15pk9ezbmzp2L9PR0PVX578aQr8Zu3LihGRYsWAArKyuttoULF5arP10DVRfGxsZ45ZVXsHz58hJvHxsVFYURI0bo9PzTvLw8GBsbw9nZuVrehnbDhg2wsLBA165d9VZDXl5elaxn3759eOmll7B3717Exsaibt266NWrF65du6aZx9vbG/Xq1cPq1aurpCbSxpCvxpydnTWDtbW15kEXzs7OMDIywptvvok6derAzMwMXl5eWLt2rdby/v7+GD9+PCZPngx7e3sEBgYCADZv3gxPT0+YmpoiICAAK1asKHbo4/Dhw+jWrRtMTU3h5uaGt99+W3ObWX9/f1y5cgWTJk3SfKsoyahRo/DHH3/gwIEDWu0xMTFISkrCqFGjEB8fj8DAQNjb28Pa2hp+fn44ceKE1vwqlQpLly7FwIEDYW5ujjlz5hQ7XHP79m289NJLj90eAJCfn4/x48fDxsYGdnZ2mDVr1mPv956WloY33ngDjo6OsLKyQvfu3XHq1KlS5weAdevWaT3U+sCBAzAyMkJKSorWfO+88w66dev22L4AYPny5bCxscFPP/2ERo0aae63//AtfYsOsSxbtgwNGjSAWq2GiJSp/o8++ghOTk6wtLTEqFGjkJ2d/cSaiqxevRpjx45Fq1at0KRJE3zzzTcoLCzE7t27teYbMGBAie8HVT6GfA2VnZ2Ntm3bYsuWLTh79izeeOMNjBgxAkeOHNGab8WKFTA0NMShQ4fw1Vdf4fLlyxg6dCiee+45JCQkYMyYMcVu5XrmzBn07t0bgwcPxunTp7F+/XocPHgQ48ePBwBER0ejTp06+PDDDzXfKkri5eWF9u3bIyoqSqt92bJl6NChA1q0aIGMjAwEBwcjJiYGcXFx8PT0RJ8+fZCRkaG1TFhYGAYOHIgzZ87gtddee+rtceTIEfz3v//F559/jm+//bbE+kUEffv2RUpKCrZt24bjx4+jTZs26NGjB+7cuVPiMsA//4k9/ISubt26oUGDBvjf//6nacvPz8eqVavw6quvltrPw+7fv4+5c+dixYoVOHToENLT0zFs2DCteS5evIjvv/8eGzZsQEJCAgA8sf7vv/8eYWFhmDt3Lo4dOwYXFxcsXry4TDWVVmdeXh5sbW212jt06ICjR48iJydH575JR/q8BSaVXVRUlFhbWz92nj59+sg777yjGS/pma/Tpk2TFi1aaLXNnDlTAMjdu3dFRGTEiBHyxhtvaM0TExMjtWrVkgcPHohIybfuLcmSJUvE3NxcMjIyREQkIyNDzM3N5auvvipx/vz8fLG0tJSff/5Z0wZAQkNDtebbu3evVs0lKWl7NG3aVOs5udOmTZOmTZtqxh9+Xbt37xYrK6tiDxVv2LBhqfXfvXtXAMiBAwe02j/++GOt9fz0009iYWEhmZmZpdZfJCoqSgBIXFycpi0xMVEAyJEjR0REJCwsTIyMjDQPNy9r/T4+PvLmm29qTe/YsaPW7ZPLY+zYsdKwYUPN56TIqVOnBIBcvnxZp35Jd9yTr6EKCgowd+5ceHt7w87ODhYWFti5c2ex540++szX8+fPo3379lptHTp00Bo/fvw4li9fDgsLC83Qu3dvFBYW4tKlS+Wq86WXXkJhYSHWr18PAFi/fj1ERLMXmpqaijfffBONGjWCtbU1rK2tkZmZ+cTX8aiybo9OnTppHV7y8fFBUlJSsadNFW2HzMxMTX9Fw6VLl0p9IlTR059MTEy02kNCQnDx4kXNE6GWLVuGF154ocyPazQ0NNTaBk2aNNF6dioAuLu7w8HBoVz1Fz07+GG6PlN13rx5WLt2LaKjo4u9/qJHBN6/f1+nvkl3hvougHTz2Wef4fPPP8eCBQvg5eUFc3NzhIaGlul5o6U9K7ZIYWEhxowZg7fffrvYeuvWrVuuOq2trTF06FBERUVh1KhRiIqKwtChQ2FlZQXgn/D7+++/sWDBAri7u0OtVsPHx+eJr+NRZd0e5VFYWAgXFxfs27ev2DQbG5sSl7Gzs4NKpdI8taqIo6Mj+vfvj6ioKDRo0ADbtm0rsd/HKencx+Oe76tL/br69NNPERERgV9//VXrgelFig4PPfyfEFUNhnwNFRMTg4EDB+KVV14B8M8fdFJSkuZZmaVp0qQJtm3bptV27NgxrfE2bdrg3Llz8PDwKLUfY2PjEvd+SzJq1Cj4+/tjy5YtOHToECIiIrRex+LFi9GnTx8AQHJyMm7dulWmfh9W1u3x6LNVi84DGBgYFOuzTZs2SElJgaGhIerVq1emOoyNjdGsWTP89ttvxa6THz16NIYNG4Y6deqgYcOG6Ny5c5lfX35+Po4dO6b51nX+/Hncu3cPTZo0KXWZstTftGlTxMXFYeTIkZq28j5/9pNPPsGcOXOwY8eOUr9xnT17FnXq1IG9vX25+qanx8M1NZSHhwd27dqFw4cPIzExEWPGjCl29UZJxowZg99//x3Tpk3DhQsX8P3332P58uUA/m+vcNq0aYiNjcW4ceOQkJCApKQkbN68GRMmTND0U69ePRw4cADXrl17Yij7+fnBw8MDI0eOhIeHh9YVJR4eHvjf//6HxMREHDlyBC+//LLmq31lbI/k5GRMnjwZ58+fx9q1a/HFF19g4sSJJfbZs2dP+Pj44LnnnsOOHTtw+fJlHD58GLNmzSr2H+PDevfuXexh1UXt1tbWmDNnTplPuBYxMjLChAkTcOTIEZw4cQKvvvoqOnXqVOxQW3nrnzhxIpYtW4Zly5bhwoULCAsLw7lz58pc17x58zBr1iwsW7YM9erVQ0pKClJSUpCZmak1X0xMTI39cViNp+dzAlRGj554vX37tgwcOFAsLCzE0dFRZs2aJSNHjtR6vmhpz3zdtGmTeHh4iFqtFn9/f1myZIkA0DpZdvToUQkMDBQLCwsxNzcXb29vmTt3rmZ6bGyseHt7i1qtlrJ8jCIiIgSAREREaLWfOHFC2rVrJ2q1Wjw9PeWHH34odlIXJTzf89ETr2XdHmPHjpU333xTrKyspHbt2vLee+9pnYh9dN3p6ekyYcIEcXV1FSMjI3Fzc5OXX35Zrl69WuprTUxMFFNTU7l3716xae+//74YGBjI9evXn7jNihS99xs2bJAGDRqIsbGxdO/eXeskZlhYWIknS8tS/9y5c8Xe3l4sLCwkODhYpk6dWuYTr+7u7gKg2PDws3MfPHggVlZWEhsbW+bXTBWHz3glzJ07F0uXLtW67pqezgsvvIDWrVtj+vTpWu2vv/46bt68ic2bN5e5r+XLlyM0NLRct3CoTr788kts2rQJO3fu1Hcp/0o8Jv8vtHjxYrRv3x52dnY4dOgQPvnkE8018FQxPvnkE60gT0tLQ3x8PFavXo1NmzbpsbKqZ2RkhC+++ELfZfxrcU/+X2jSpElYv3497ty5g7p162LEiBGYPn06DA35f35l8ff3x9GjRzFmzBh8/vnnWtOCgoIQExNT4nIzZsyAq6ur3vbkLSwsSp32yy+/6PXWDVQ2DHkiPbt27Zrm+vpH2draFvv1aFW6ePFiqdOeeeYZnU6SU9ViyBMRKRgvoSQiUjCGPBGRgjHkiYgUjCFPRKRgDHkiIgVjyBMRKRhDnohIwRjyREQK9v8Ajfj99JBcEOMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the second bar chart\n",
    "plt.subplot(1, 2, 2)\n",
    "bars_chart2 = plt.bar(categories, percentages_y_pred_2, alpha=1.0)\n",
    "for bar, category in zip(bars_chart2, categories):\n",
    "    bar.set_color(colors[category])\n",
    "plt.xlabel('Target Variable (y_pred_2)')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Distribution of Credit Score - Test Dataset')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76dd6c2e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}