credit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metadata:\n",
    "\n",
    "This research aimed at the case of customers' default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel Sorting Smoothing Method to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.\n",
    "\n",
    "This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables:\n",
    "- X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit.\n",
    "- X2: Gender (1 = male; 2 = female).\n",
    "- X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).\n",
    "- X4: Marital status (1 = married; 2 = single; 3 = others).\n",
    "- X5: Age (year).\n",
    "- X6 - X11: History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: \n",
    "- X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. \n",
    "- The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n",
    "- X12-X17: Amount of bill statement (NT dollar): \n",
    "- X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005. \n",
    "- X18-X23: Amount of previous payment (NT dollar):\n",
    "- X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# importing Libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "import joblib\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>HIST_9-5</th>\n",
       "      <th>HIST_8-5</th>\n",
       "      <th>HIST_7-5</th>\n",
       "      <th>HIST_6-5</th>\n",
       "      <th>HIST_5-5</th>\n",
       "      <th>HIST_4-5</th>\n",
       "      <th>BILL_9-5</th>\n",
       "      <th>BILL_8-5</th>\n",
       "      <th>BILL_7-5</th>\n",
       "      <th>BILL_6-5</th>\n",
       "      <th>BILL_5-5</th>\n",
       "      <th>BILL_4-5</th>\n",
       "      <th>PAID_9-5</th>\n",
       "      <th>PAID_8-5</th>\n",
       "      <th>PAID_7-5</th>\n",
       "      <th>PAID_6-5</th>\n",
       "      <th>PAID_5-5</th>\n",
       "      <th>PAID_4-5</th>\n",
       "      <th>DEFAULT NEXT MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>3913</td>\n",
       "      <td>3102</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2682</td>\n",
       "      <td>1725</td>\n",
       "      <td>2682</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</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>29239</td>\n",
       "      <td>14027</td>\n",
       "      <td>13559</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</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>46990</td>\n",
       "      <td>48233</td>\n",
       "      <td>49291</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8617</td>\n",
       "      <td>5670</td>\n",
       "      <td>35835</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  LIMIT_BAL  GENDER  EDUCATION  MARRIAGE  AGE  HIST_9-5  HIST_8-5  \\\n",
       "0   1      20000       2          2         1   24         2         2   \n",
       "1   2     120000       2          2         2   26        -1         2   \n",
       "2   3      90000       2          2         2   34         0         0   \n",
       "3   4      50000       2          2         1   37         0         0   \n",
       "4   5      50000       1          2         1   57        -1         0   \n",
       "\n",
       "   HIST_7-5  HIST_6-5  HIST_5-5  HIST_4-5  BILL_9-5  BILL_8-5  BILL_7-5  \\\n",
       "0        -1        -1        -2        -2      3913      3102       689   \n",
       "1         0         0         0         2      2682      1725      2682   \n",
       "2         0         0         0         0     29239     14027     13559   \n",
       "3         0         0         0         0     46990     48233     49291   \n",
       "4        -1         0         0         0      8617      5670     35835   \n",
       "\n",
       "   BILL_6-5  BILL_5-5  BILL_4-5  PAID_9-5  PAID_8-5  PAID_7-5  PAID_6-5  \\\n",
       "0         0         0         0         0       689         0         0   \n",
       "1      3272      3455      3261         0      1000      1000      1000   \n",
       "2     14331     14948     15549      1518      1500      1000      1000   \n",
       "3     28314     28959     29547      2000      2019      1200      1100   \n",
       "4     20940     19146     19131      2000     36681     10000      9000   \n",
       "\n",
       "   PAID_5-5  PAID_4-5  DEFAULT NEXT MONTH  \n",
       "0         0         0                   1  \n",
       "1         0      2000                   1  \n",
       "2      1000      5000                   0  \n",
       "3      1069      1000                   0  \n",
       "4       689       679                   0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "df = pd.read_excel('Data/copy-default-dataset.xls')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30000 entries, 0 to 29999\n",
      "Data columns (total 25 columns):\n",
      " #   Column              Non-Null Count  Dtype\n",
      "---  ------              --------------  -----\n",
      " 0   ID                  30000 non-null  int64\n",
      " 1   LIMIT_BAL           30000 non-null  int64\n",
      " 2   GENDER              30000 non-null  int64\n",
      " 3   EDUCATION           30000 non-null  int64\n",
      " 4   MARRIAGE            30000 non-null  int64\n",
      " 5   AGE                 30000 non-null  int64\n",
      " 6   HIST_9-5            30000 non-null  int64\n",
      " 7   HIST_8-5            30000 non-null  int64\n",
      " 8   HIST_7-5            30000 non-null  int64\n",
      " 9   HIST_6-5            30000 non-null  int64\n",
      " 10  HIST_5-5            30000 non-null  int64\n",
      " 11  HIST_4-5            30000 non-null  int64\n",
      " 12  BILL_9-5            30000 non-null  int64\n",
      " 13  BILL_8-5            30000 non-null  int64\n",
      " 14  BILL_7-5            30000 non-null  int64\n",
      " 15  BILL_6-5            30000 non-null  int64\n",
      " 16  BILL_5-5            30000 non-null  int64\n",
      " 17  BILL_4-5            30000 non-null  int64\n",
      " 18  PAID_9-5            30000 non-null  int64\n",
      " 19  PAID_8-5            30000 non-null  int64\n",
      " 20  PAID_7-5            30000 non-null  int64\n",
      " 21  PAID_6-5            30000 non-null  int64\n",
      " 22  PAID_5-5            30000 non-null  int64\n",
      " 23  PAID_4-5            30000 non-null  int64\n",
      " 24  DEFAULT NEXT MONTH  30000 non-null  int64\n",
      "dtypes: int64(25)\n",
      "memory usage: 5.7 MB\n"
     ]
    }
   ],
   "source": [
    "df.info() #df.info(verbose=True, show_counts=True) , df.iloc[:,-7:].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>HIST_9-5</th>\n",
       "      <th>HIST_8-5</th>\n",
       "      <th>HIST_7-5</th>\n",
       "      <th>HIST_6-5</th>\n",
       "      <th>HIST_5-5</th>\n",
       "      <th>HIST_4-5</th>\n",
       "      <th>BILL_9-5</th>\n",
       "      <th>BILL_8-5</th>\n",
       "      <th>BILL_7-5</th>\n",
       "      <th>BILL_6-5</th>\n",
       "      <th>BILL_5-5</th>\n",
       "      <th>BILL_4-5</th>\n",
       "      <th>PAID_9-5</th>\n",
       "      <th>PAID_8-5</th>\n",
       "      <th>PAID_7-5</th>\n",
       "      <th>PAID_6-5</th>\n",
       "      <th>PAID_5-5</th>\n",
       "      <th>PAID_4-5</th>\n",
       "      <th>DEFAULT NEXT MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15000.500000</td>\n",
       "      <td>167484.322667</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>1.853133</td>\n",
       "      <td>1.551867</td>\n",
       "      <td>35.485500</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>-0.266200</td>\n",
       "      <td>-0.291100</td>\n",
       "      <td>51223.330900</td>\n",
       "      <td>49179.075167</td>\n",
       "      <td>4.701315e+04</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>5.921163e+03</td>\n",
       "      <td>5225.68150</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>0.221200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8660.398374</td>\n",
       "      <td>129747.661567</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>0.790349</td>\n",
       "      <td>0.521970</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>1.133187</td>\n",
       "      <td>1.149988</td>\n",
       "      <td>73635.860576</td>\n",
       "      <td>71173.768783</td>\n",
       "      <td>6.934939e+04</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>2.304087e+04</td>\n",
       "      <td>17606.96147</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.415062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-165580.000000</td>\n",
       "      <td>-69777.000000</td>\n",
       "      <td>-1.572640e+05</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7500.750000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3558.750000</td>\n",
       "      <td>2984.750000</td>\n",
       "      <td>2.666250e+03</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>390.00000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15000.500000</td>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22381.500000</td>\n",
       "      <td>21200.000000</td>\n",
       "      <td>2.008850e+04</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>1800.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22500.250000</td>\n",
       "      <td>240000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67091.000000</td>\n",
       "      <td>64006.250000</td>\n",
       "      <td>6.016475e+04</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "      <td>5006.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4505.00000</td>\n",
       "      <td>4013.250000</td>\n",
       "      <td>4031.500000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>964511.000000</td>\n",
       "      <td>983931.000000</td>\n",
       "      <td>1.664089e+06</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>896040.00000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID       LIMIT_BAL        GENDER     EDUCATION      MARRIAGE  \\\n",
       "count  30000.000000    30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean   15000.500000   167484.322667      1.603733      1.853133      1.551867   \n",
       "std     8660.398374   129747.661567      0.489129      0.790349      0.521970   \n",
       "min        1.000000    10000.000000      1.000000      0.000000      0.000000   \n",
       "25%     7500.750000    50000.000000      1.000000      1.000000      1.000000   \n",
       "50%    15000.500000   140000.000000      2.000000      2.000000      2.000000   \n",
       "75%    22500.250000   240000.000000      2.000000      2.000000      2.000000   \n",
       "max    30000.000000  1000000.000000      2.000000      6.000000      3.000000   \n",
       "\n",
       "                AGE      HIST_9-5      HIST_8-5      HIST_7-5      HIST_6-5  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      35.485500     -0.016700     -0.133767     -0.166200     -0.220667   \n",
       "std        9.217904      1.123802      1.197186      1.196868      1.169139   \n",
       "min       21.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       28.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%       34.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%       41.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max       79.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "           HIST_5-5      HIST_4-5       BILL_9-5       BILL_8-5      BILL_7-5  \\\n",
       "count  30000.000000  30000.000000   30000.000000   30000.000000  3.000000e+04   \n",
       "mean      -0.266200     -0.291100   51223.330900   49179.075167  4.701315e+04   \n",
       "std        1.133187      1.149988   73635.860576   71173.768783  6.934939e+04   \n",
       "min       -2.000000     -2.000000 -165580.000000  -69777.000000 -1.572640e+05   \n",
       "25%       -1.000000     -1.000000    3558.750000    2984.750000  2.666250e+03   \n",
       "50%        0.000000      0.000000   22381.500000   21200.000000  2.008850e+04   \n",
       "75%        0.000000      0.000000   67091.000000   64006.250000  6.016475e+04   \n",
       "max        8.000000      8.000000  964511.000000  983931.000000  1.664089e+06   \n",
       "\n",
       "            BILL_6-5       BILL_5-5       BILL_4-5       PAID_9-5  \\\n",
       "count   30000.000000   30000.000000   30000.000000   30000.000000   \n",
       "mean    43262.948967   40311.400967   38871.760400    5663.580500   \n",
       "std     64332.856134   60797.155770   59554.107537   16563.280354   \n",
       "min   -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
       "25%      2326.750000    1763.000000    1256.000000    1000.000000   \n",
       "50%     19052.000000   18104.500000   17071.000000    2100.000000   \n",
       "75%     54506.000000   50190.500000   49198.250000    5006.000000   \n",
       "max    891586.000000  927171.000000  961664.000000  873552.000000   \n",
       "\n",
       "           PAID_8-5      PAID_7-5       PAID_6-5       PAID_5-5  \\\n",
       "count  3.000000e+04   30000.00000   30000.000000   30000.000000   \n",
       "mean   5.921163e+03    5225.68150    4826.076867    4799.387633   \n",
       "std    2.304087e+04   17606.96147   15666.159744   15278.305679   \n",
       "min    0.000000e+00       0.00000       0.000000       0.000000   \n",
       "25%    8.330000e+02     390.00000     296.000000     252.500000   \n",
       "50%    2.009000e+03    1800.00000    1500.000000    1500.000000   \n",
       "75%    5.000000e+03    4505.00000    4013.250000    4031.500000   \n",
       "max    1.684259e+06  896040.00000  621000.000000  426529.000000   \n",
       "\n",
       "            PAID_4-5  DEFAULT NEXT MONTH  \n",
       "count   30000.000000        30000.000000  \n",
       "mean     5215.502567            0.221200  \n",
       "std     17777.465775            0.415062  \n",
       "min         0.000000            0.000000  \n",
       "25%       117.750000            0.000000  \n",
       "50%      1500.000000            0.000000  \n",
       "75%      4000.000000            0.000000  \n",
       "max    528666.000000            1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 30,000 distinct credit card clients.\n",
    "- The average value for the amount of credit card limit is 167,484. The standard deviation is unusually large, max value being 1M.\n",
    "- Education level is mostly graduate school and university.\n",
    "- Most of the clients are either married or single (less frequent the other status).\n",
    "- Average age is 35.5 years, with a standard deviation of 9.2.\n",
    "- As the value 0 for default payment means 'not default' and value 1 means 'default', the mean of 0.221 means that there are 22.1% of credit card contracts that will default next month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIMIT_BAL\n",
       "50000     3365\n",
       "20000     1976\n",
       "30000     1610\n",
       "80000     1567\n",
       "200000    1528\n",
       "150000    1110\n",
       "100000    1048\n",
       "180000     995\n",
       "360000     881\n",
       "60000      825\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring Credit Limits given to customers:\n",
    "df['LIMIT_BAL'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,6))\n",
    "plt.title('Amount of credit limit - Density Plot')\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.distplot(df['LIMIT_BAL'],kde=True,bins=200, color=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The credit cards having 50,000 limit have been given to 3365 numbers of customers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gender status meaning is:\n",
    "\n",
    "- 1 : male\n",
    "- 2 : female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GENDER  DEFAULT NEXT MONTH\n",
       "2       0                     14349\n",
       "1       0                      9015\n",
       "2       1                      3763\n",
       "1       1                      2873\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = df[['GENDER','DEFAULT NEXT MONTH']].value_counts()\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data\n",
    "gen = [2, 1, 2, 1]\n",
    "default_0 = [14349, 9015, 0, 0]\n",
    "default_1 = [0, 0, 3765, 2873]\n",
    "\n",
    "# Create a bar plot\n",
    "plt.bar(gen, default_0, label='Not Default', color='skyblue', width=0.4)\n",
    "plt.bar(gen, default_1, bottom=default_0, label='Default', color='orange', width=0.4)\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Default by Gender')\n",
    "plt.xticks([1, 2], ['Male', 'Female'])\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Pre-processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                    0\n",
       "LIMIT_BAL             0\n",
       "GENDER                0\n",
       "EDUCATION             0\n",
       "MARRIAGE              0\n",
       "AGE                   0\n",
       "HIST_9-5              0\n",
       "HIST_8-5              0\n",
       "HIST_7-5              0\n",
       "HIST_6-5              0\n",
       "HIST_5-5              0\n",
       "HIST_4-5              0\n",
       "BILL_9-5              0\n",
       "BILL_8-5              0\n",
       "BILL_7-5              0\n",
       "BILL_6-5              0\n",
       "BILL_5-5              0\n",
       "BILL_4-5              0\n",
       "PAID_9-5              0\n",
       "PAID_8-5              0\n",
       "PAID_7-5              0\n",
       "PAID_6-5              0\n",
       "PAID_5-5              0\n",
       "PAID_4-5              0\n",
       "DEFAULT NEXT MONTH    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No Missing Values in the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                    30000\n",
       "LIMIT_BAL                81\n",
       "GENDER                    2\n",
       "EDUCATION                 7\n",
       "MARRIAGE                  4\n",
       "AGE                      56\n",
       "HIST_9-5                 11\n",
       "HIST_8-5                 11\n",
       "HIST_7-5                 11\n",
       "HIST_6-5                 11\n",
       "HIST_5-5                 10\n",
       "HIST_4-5                 10\n",
       "BILL_9-5              22723\n",
       "BILL_8-5              22346\n",
       "BILL_7-5              22026\n",
       "BILL_6-5              21548\n",
       "BILL_5-5              21010\n",
       "BILL_4-5              20604\n",
       "PAID_9-5               7943\n",
       "PAID_8-5               7899\n",
       "PAID_7-5               7518\n",
       "PAID_6-5               6937\n",
       "PAID_5-5               6897\n",
       "PAID_4-5               6939\n",
       "DEFAULT NEXT MONTH        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_drop = ['ID', 'GENDER', 'EDUCATION', 'MARRIAGE', 'AGE', 'DEFAULT NEXT MONTH']\n",
    "X = df.drop(columns_to_drop, axis=1)\n",
    "y = df['DEFAULT NEXT MONTH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>LIMIT_BAL</th>\n",
       "      <th>HIST_9-5</th>\n",
       "      <th>HIST_8-5</th>\n",
       "      <th>HIST_7-5</th>\n",
       "      <th>HIST_6-5</th>\n",
       "      <th>HIST_5-5</th>\n",
       "      <th>HIST_4-5</th>\n",
       "      <th>BILL_9-5</th>\n",
       "      <th>BILL_8-5</th>\n",
       "      <th>BILL_7-5</th>\n",
       "      <th>BILL_6-5</th>\n",
       "      <th>BILL_5-5</th>\n",
       "      <th>BILL_4-5</th>\n",
       "      <th>PAID_9-5</th>\n",
       "      <th>PAID_8-5</th>\n",
       "      <th>PAID_7-5</th>\n",
       "      <th>PAID_6-5</th>\n",
       "      <th>PAID_5-5</th>\n",
       "      <th>PAID_4-5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>3913</td>\n",
       "      <td>3102</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2682</td>\n",
       "      <td>1725</td>\n",
       "      <td>2682</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000</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>29239</td>\n",
       "      <td>14027</td>\n",
       "      <td>13559</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000</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>46990</td>\n",
       "      <td>48233</td>\n",
       "      <td>49291</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8617</td>\n",
       "      <td>5670</td>\n",
       "      <td>35835</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LIMIT_BAL  HIST_9-5  HIST_8-5  HIST_7-5  HIST_6-5  HIST_5-5  HIST_4-5  \\\n",
       "0      20000         2         2        -1        -1        -2        -2   \n",
       "1     120000        -1         2         0         0         0         2   \n",
       "2      90000         0         0         0         0         0         0   \n",
       "3      50000         0         0         0         0         0         0   \n",
       "4      50000        -1         0        -1         0         0         0   \n",
       "\n",
       "   BILL_9-5  BILL_8-5  BILL_7-5  BILL_6-5  BILL_5-5  BILL_4-5  PAID_9-5  \\\n",
       "0      3913      3102       689         0         0         0         0   \n",
       "1      2682      1725      2682      3272      3455      3261         0   \n",
       "2     29239     14027     13559     14331     14948     15549      1518   \n",
       "3     46990     48233     49291     28314     28959     29547      2000   \n",
       "4      8617      5670     35835     20940     19146     19131      2000   \n",
       "\n",
       "   PAID_8-5  PAID_7-5  PAID_6-5  PAID_5-5  PAID_4-5  \n",
       "0       689         0         0         0         0  \n",
       "1      1000      1000      1000         0      2000  \n",
       "2      1500      1000      1000      1000      5000  \n",
       "3      2019      1200      1100      1069      1000  \n",
       "4     36681     10000      9000       689       679  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardizing the training data:\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/scaler.pkl']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dumping the scaler to use it when getting inputs from user.\n",
    "joblib.dump(scaler, \"data/scaler.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Logistic Regression</th>\n",
       "      <th>Decision Tree Classifiers</th>\n",
       "      <th>Random Forest Classifiers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.809333</td>\n",
       "      <td>0.722833</td>\n",
       "      <td>0.812333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.759657</td>\n",
       "      <td>0.601532</td>\n",
       "      <td>0.735622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.605644</td>\n",
       "      <td>0.604071</td>\n",
       "      <td>0.650467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 Score</th>\n",
       "      <td>0.623033</td>\n",
       "      <td>0.602733</td>\n",
       "      <td>0.672821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Logistic Regression  Decision Tree Classifiers  \\\n",
       "Accuracy              0.809333                   0.722833   \n",
       "Precision             0.759657                   0.601532   \n",
       "Recall                0.605644                   0.604071   \n",
       "F1 Score              0.623033                   0.602733   \n",
       "\n",
       "           Random Forest Classifiers  \n",
       "Accuracy                    0.812333  \n",
       "Precision                   0.735622  \n",
       "Recall                      0.650467  \n",
       "F1 Score                    0.672821  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = {\n",
    "    \"Logistic Regression\" : LogisticRegression(),\n",
    "    \"Decision Tree Classifiers\" : DecisionTreeClassifier(),\n",
    "    \"Random Forest Classifiers\" : RandomForestClassifier()\n",
    "}\n",
    "\n",
    "# Evaluating models and then comparing their metrics\n",
    "\n",
    "model_performance = {}\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred, average='macro')\n",
    "    recall = recall_score(y_test, y_pred, average='macro')\n",
    "    f1 = f1_score(y_test, y_pred, average='macro')\n",
    "    model_performance[name] = [accuracy, precision, recall, f1]\n",
    "\n",
    "performance_df = pd.DataFrame(model_performance, index=[\"Accuracy\", \"Precision\", \"Recall\", \"F1 Score\"])\n",
    "performance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7dtTXX3+d6e/znDp16mY2x67Q0FD5+flp3LhxNpdT5cR6Q0JC5ObmpsmTJ9t8gz9z5kydO3cu05EFc1r6GYVr15+SkqKPPvroppfZsWNHnT59OsPf/tr1dO7cWampqXr99dcz9Lly5UqGobGvFxkZKWOMevbsqcTExAzTt23bprlz50rK/rGYU65cuaLp06dbn6ekpGj69Ony9/dXrVq1JGW+3zdv3qyNGzfe9HovXryoS5cu2bSVKVNGvr6+Sk5OlpQ3jjkAdw/OOAG462V1qdy12rRpoyZNmujVV1/VoUOHVK1aNa1atUrffvutXnjhBeuZnOrVq6tbt2766KOPdO7cOTVo0EDR0dHav39/hmWOHz9ea9euVb169dS/f39VrlxZZ86c0fbt27VmzZoMv0+UE/z8/DR16lT17NlTNWvWVNeuXeXv768jR45o+fLlatiwYaYBITv8/f01YsQIjR07Vi1atNDjjz+u+Ph4ffTRR6pTp47NTfm5pUGDBipUqJDCwsI0aNAgWSwWffbZZ//pUqxevXrp008/VUREhLZs2aKHHnpISUlJWrNmjZ5//nm1bdtWjRs31jPPPKOoqCjFxsbq0UcfVf78+bVv3z4tWrRI77//fpb3z6XXPWXKFD3//POqWLGievbsqXLlyunChQuKiYnR0qVL9cYbb0jK/rGYU0qUKKG33npLhw4dUvny5bVw4ULFxsbq448/tp6Ba926tRYvXqz27durVatWOnjwoKZNm6bKlStnGgSz4/fff1ezZs3UuXNnVa5cWfny5dM333yjEydOqGvXrpLyxjEH4O5BcAKAbHBxcdHSpUs1evRoLVy4ULNnz1ZwcLDeeecdDRkyxKbvrFmz5O/vr3nz5mnJkiVq2rSpli9fnuESrICAAG3ZskWvvfaaFi9erI8++khFihTR/fffr7feeivXtuXJJ59UiRIlNH78eL3zzjtKTk5WyZIl9dBDD2U5yl12jRkzRv7+/vrwww/14osvqnDhwnr66ac1bty4LC9zy0lFihTRsmXLNGTIEI0cOVKFChVSjx491KxZM4WGht7UMl1dXfXdd9/pzTff1Pz58/X111+rSJEiatSokapUqWLtN23aNNWqVUvTp0/XK6+8onz58ik4OFg9evRQw4YN7a7nmWeeUZ06dTRhwgR9+umnOnXqlHx8fFSzZk3Nnj3bGgIcORZzQqFChTR37lwNHDhQM2bMUEBAgD788EP179/f2qd3795KSEjQ9OnT9f3336ty5cr6/PPPtWjRIsXExNzUeoOCgtStWzdFR0frs88+U758+VSxYkV9+eWX6tixo7Wfs485AHcPi+GOSAAAkIlHHnlEp0+fzvRyUgC423CPEwAAAADYQXACAAAAADsITgAAAABgB/c4AQAAAIAdnHECAAAAADsITgAAAABgx133O05paWn666+/5OvrK4vF4uxyAAAAADiJMUYXLlxQiRIl5OJy43NKd11w+uuvvzL8CCUAAACAu9fRo0d1zz333LDPXRecfH19JV3dOX5+fk6uBgAAAICznD9/XkFBQdaMcCN3XXBKvzzPz8+P4AQAAAAgW7fwMDgEAAAAANhBcAIAAAAAOwhOAAAAAGDHXXePEwAg+4wxunLlilJTU51dChzk6uqqfPny8dMbAJBDCE4AgEylpKTo+PHjunjxorNLwU3y8vJS8eLF5ebm5uxSAOC2R3ACAGSQlpamgwcPytXVVSVKlJCbmxtnLm4jxhilpKTo1KlTOnjwoMqVK2f3hx0BADdGcAIAZJCSkqK0tDQFBQXJy8vL2eXgJnh6eip//vw6fPiwUlJS5OHh4eySAOC2xtdPAIAscZbi9sbfDwByDu+oAAAAAGAHwQkAAAAA7CA4AQCQCYvFoiVLlji7DABAHkFwAgDkWb1795bFYtGzzz6bYdqAAQNksVjUu3fvbC0rJiZGFotF//zzT7b6Hz9+XI899pgD1QIA7mQEJwBAnhYUFKQFCxbo33//tbZdunRJ8+fP17333pvj60tJSZEkBQYGyt3dPceXDwC4PRGcAAB5Ws2aNRUUFKTFixdb2xYvXqx7771XNWrUsLalpaUpKipKpUuXlqenp6pVq6avvvpKknTo0CE1adJEklSoUCGbM1WPPPKIwsPD9cILL6ho0aIKDQ2VlPFSvT///FPdunVT4cKF5e3trdq1a2vz5s2SpJ07d6pJkyby9fWVn5+fatWqpV9++SU3dwsA4Bbjd5wAAHle3759NXv2bHXv3l2SNGvWLPXp00cxMTHWPlFRUfr88881bdo0lStXTj/++KN69Oghf39/NWrUSF9//bU6duyo+Ph4+fn5ydPT0zrv3Llz9dxzz2n9+vWZrj8xMVGNGzdWyZIltXTpUgUGBmr79u1KS0uTJHXv3l01atTQ1KlT5erqqtjYWOXPnz/3dggA4JYjOAEA8rwePXpoxIgROnz4sCRp/fr1WrBggTU4JScna9y4cVqzZo3q168vSbrvvvv0888/a/r06WrcuLEKFy4sSSpWrJgKFixos/xy5crp7bffznL98+fP16lTp7R161brcsqWLWudfuTIEQ0dOlQVK1a0Lg8AcGchOAEA8jx/f3+1atVKc+bMkTFGrVq1UtGiRa3T9+/fr4sXL6p58+Y286WkpNhczpeVWrVq3XB6bGysatSoYQ1N14uIiNBTTz2lzz77TCEhIerUqZPKlCmTjS0DANwuCE4AgNtC3759FR4eLkmaMmWKzbTExERJ0vLly1WyZEmbadkZ4MHb2/uG06+9rC8zY8aM0ZNPPqnly5drxYoVioyM1IIFC9S+fXu76wYA3B4YHAIAcFto0aKFUlJSdPnyZesADukqV64sd3d3HTlyRGXLlrV5BAUFSZLc3NwkSampqQ6vu2rVqoqNjdWZM2ey7FO+fHm9+OKLWrVqlTp06KDZs2c7vB4AQN7FGScAwG3B1dVVcXFx1v+/lq+vr1566SW9+OKLSktLU6NGjXTu3DmtX79efn5+CgsLU6lSpWSxWLRs2TK1bNlSnp6e8vHxyda6u3XrpnHjxqldu3aKiopS8eLFtWPHDpUoUULVq1fX0KFD9cQTT6h06dL6888/tXXrVnXs2DHH9wFuX63aRDq7hByz/P/GOrsEwCkITrnoTnqTlHijBOB8fn5+WU57/fXX5e/vr6ioKP3xxx8qWLCgatasqVdeeUWSVLJkSY0dO1bDhw9Xnz591KtXL82ZMydb63Vzc9OqVas0ZMgQtWzZUleuXFHlypU1ZcoUubq66u+//1avXr104sQJFS1aVB06dNDYsbxnAsCdxGKMMc4u4lY6f/68ChQooHPnzt3wH+CcQHACcLu6dOmSDh48qNKlS8vDw8PZ5eAm8XfMO+6kzwR8HsCdxJFs4PR7nKZMmaLg4GB5eHioXr162rJlyw37T5o0SRUqVJCnp6eCgoL04osv6tKlS7eoWgAAAAB3I6cGp4ULFyoiIkKRkZHavn27qlWrptDQUJ08eTLT/vPnz9fw4cMVGRmpuLg4zZw5UwsXLrRehgEAAAAAucGpwWnixInq37+/+vTpo8qVK2vatGny8vLSrFmzMu2/YcMGNWzYUE8++aSCg4P16KOPqlu3bjc8S5WcnKzz58/bPAAAAADAEU4LTikpKdq2bZtCQkL+V4yLi0JCQrRx48ZM52nQoIG2bdtmDUp//PGHvvvuO7Vs2TLL9URFRalAgQLWR/qwtAAAAACQXU4bVe/06dNKTU1VQECATXtAQIB+++23TOd58skndfr0aTVq1EjGGF25ckXPPvvsDS/VGzFihCIiIqzPz58/T3gCAAAA4BCnDw7hiJiYGI0bN04fffSRtm/frsWLF2v58uV6/fXXs5zH3d1dfn5+Ng8AAAAAcITTzjgVLVpUrq6uOnHihE37iRMnFBgYmOk8o0aNUs+ePfXUU09JkqpUqaKkpCQ9/fTTevXVV+XiclvlQAAAAAC3CaclDTc3N9WqVUvR0dHWtrS0NEVHR6t+/fqZznPx4sUM4Sj91+Pvsp+jAgAAAHALOe2MkyRFREQoLCxMtWvXVt26dTVp0iQlJSWpT58+kqRevXqpZMmSioqKkiS1adNGEydOVI0aNVSvXj3t379fo0aNUps2bawBCgAAAABymlODU5cuXXTq1CmNHj1aCQkJql69ulauXGkdMOLIkSM2Z5hGjhwpi8WikSNH6tixY/L391ebNm305ptvOmsTAOCu06pN5C1b1/L/G3vL1gUAwI04NThJUnh4uMLDwzOdFhMTY/M8X758ioyMVGTkrftHGwBwe9q4caMaNWqkFi1aaPny5c4uBwBwm2M0BQDAHWnmzJkaOHCgfvzxR/31119OqyMlJcVp6wYA5ByCEwDgjpOYmKiFCxfqueeeU6tWrTRnzhyb6f/3f/+nOnXqyMPDQ0WLFlX79u2t05KTkzVs2DAFBQXJ3d1dZcuW1cyZMyVJc+bMUcGCBW2WtWTJElksFuvzMWPGqHr16vrkk09UunRpeXh4SJJWrlypRo0aqWDBgipSpIhat26tAwcO2Czrzz//VLdu3VS4cGF5e3urdu3a2rx5sw4dOiQXFxf98ssvNv0nTZqkUqVKKS0t7b/uMgCAHQQnAMAd58svv1TFihVVoUIF9ejRQ7NmzbKOvrp8+XK1b99eLVu21I4dOxQdHa26deta5+3Vq5e++OILTZ48WXFxcZo+fbp8fHwcWv/+/fv19ddfa/HixYqNjZUkJSUlKSIiQr/88ouio6Pl4uKi9u3bW0NPYmKiGjdurGPHjmnp0qXauXOnXn75ZaWlpSk4OFghISGaPXu2zXpmz56t3r1783McAHALOP0eJwAActrMmTPVo0cPSVKLFi107tw5rVu3To888ojefPNNde3aVWPH/m/giWrVqkmSfv/9d3355ZdavXq1QkJCJEn33Xefw+tPSUnRp59+Kn9/f2tbx44dbfrMmjVL/v7+2rt3rx544AHNnz9fp06d0tatW1W4cGFJUtmyZa39n3rqKT377LOaOHGi3N3dtX37du3atUvffvutw/UBABzHV1QAgDtKfHy8tmzZom7dukm6OrBQly5drJfbxcbGqlmzZpnOGxsbK1dXVzVu3Pg/1VCqVCmb0CRJ+/btU7du3XTffffJz89PwcHBkq6OIJu+7ho1alhD0/XatWsnV1dXffPNN5KuXjbYpEkT63IAALmLM04AgDvKzJkzdeXKFZUoUcLaZoyRu7u7PvzwQ3l6emY5742mSZKLi0uGH1y/fPlyhn7e3t4Z2tq0aaNSpUppxowZKlGihNLS0vTAAw9YB4+wt243Nzf16tVLs2fPVocOHTR//ny9//77N5wHAJBzOOMEALhjXLlyRZ9++qkmTJig2NhY62Pnzp0qUaKEvvjiC1WtWlXR0dGZzl+lShWlpaVp3bp1mU739/fXhQsXlJSUZG1Lv4fpRv7++2/Fx8dr5MiRatasmSpVqqSzZ8/a9KlatapiY2N15syZLJfz1FNPac2aNfroo4905coVdejQwe66AQA5gzNOAIA7xrJly3T27Fn169dPBQoUsJnWsWNHzZw5U++8846aNWumMmXKqGvXrrpy5Yq+++47DRs2TMHBwQoLC1Pfvn01efJkVatWTYcPH9bJkyfVuXNn1atXT15eXnrllVc0aNAgbd68OcOIfZkpVKiQihQpoo8//ljFixfXkSNHNHz4cJs+3bp107hx49SuXTtFRUWpePHi2rFjh0qUKKH69etLkipVqqQHH3xQw4YNU9++fe2epQIA5ByCEwDAIcv/b6z9Tk4yc+ZMhYSEZAhN0tXg9Pbbb6tw4cJatGiRXn/9dY0fP15+fn56+OGHrf2mTp2qV155Rc8//7z+/vtv3XvvvXrllVckSYULF9bnn3+uoUOHasaMGWrWrJnGjBmjp59++oZ1ubi4aMGCBRo0aJAeeOABVahQQZMnT9Yjjzxi7ePm5qZVq1ZpyJAhatmypa5cuaLKlStrypQpNsvq16+fNmzYoL59+/6HPQUAcJTFXH+x9h3u/PnzKlCggM6dOyc/P79cXVerNpG5uvxbLS9/WAKQsy5duqSDBw/a/A4R8obXX39dixYt0q+//mq3L3/HvONO+kzA5wHcSRzJBtzjBADAbSAxMVG7d+/Whx9+qIEDBzq7HAC46xCcAAC4DYSHh6tWrVp65JFHuEwPAJyAe5wAALgNzJkzJ1sDUQAAcgdnnAAAAADADs44IdvqRbzu7BJyzOaJo5xdAgAAAG4jnHECAAAAADsITgAAAABgB8EJAAAAAOwgOAEAAACAHQwOAQBwyK0cKOZ2GcjFYrHom2++Ubt27XK0LwAg7+CMEwDgjtK7d29ZLBZZLBa5ubmpbNmyeu2113TlypVcW+fx48f12GOP5XhfAEDewRknAMAdp0WLFpo9e7aSk5P13XffacCAAcqfP79GjBhh0y8lJUVubm7/eX2BgYG50hcAkHdwxgkAcMdxd3dXYGCgSpUqpeeee04hISFaunSpevfurXbt2unNN99UiRIlVKFCBUnS0aNH1blzZxUsWFCFCxdW27ZtdejQIZtlzpo1S/fff7/c3d1VvHhxhYeHW6dZLBYtWbJE0tUwFh4eruLFi8vDw0OlSpVSVFRUpn0ladeuXWratKk8PT1VpEgRPf3000pMTLROT6/53XffVfHixVWkSBENGDBAly9fzvkdBwDIEsEJAHDH8/T0VEpKiiQpOjpa8fHxWr16tZYtW6bLly8rNDRUvr6++umnn7R+/Xr5+PioRYsW1nmmTp2qAQMG6Omnn9auXbu0dOlSlS1bNtN1TZ48WUuXLtWXX36p+Ph4zZs3T8HBwZn2TUpKUmhoqAoVKqStW7dq0aJFWrNmjU0ok6S1a9fqwIEDWrt2rebOnas5c+Zozpw5ObZ/AAD2cakeAOCOZYxRdHS0vv/+ew0cOFCnTp2St7e3PvnkE+slep9//rnS0tL0ySefyGKxSJJmz56tggULKiYmRo8++qjeeOMNDRkyRIMHD7Yuu06dOpmu88iRIypXrpwaNWoki8WiUqVKZVnf/PnzdenSJX366afy9vaWJH344Ydq06aN3nrrLQUEBEiSChUqpA8//FCurq6qWLGiWrVqpejoaPXv3z9H9hMAwD7OOAEA7jjLli2Tj4+PPDw89Nhjj6lLly4aM2aMJKlKlSo29zXt3LlT+/fvl6+vr3x8fOTj46PChQvr0qVLOnDggE6ePKm//vpLzZo1y9a6e/furdjYWFWoUEGDBg3SqlWrsuwbFxenatWqWUOTJDVs2FBpaWmKj4+3tt1///1ydXW1Pi9evLhOnjyZ3d0BAMgBnHECANxxmjRpoqlTp8rNzU0lSpRQvnz/++fu2pAiSYmJiapVq5bmzZuXYTn+/v5ycXHsO8aaNWvq4MGDWrFihdasWaPOnTsrJCREX3311c1tjKT8+fPbPLdYLEpLS7vp5QEAHEdwAgDccby9vbO8B+l6NWvW1MKFC1WsWDH5+fll2ic4OFjR0dFq0qRJtpbp5+enLl26qEuXLnriiSfUokULnTlzRoULF7bpV6lSJc2ZM0dJSUnWQLd+/Xq5uLhYB64A8ppb+Vtut8Lt8ntxcD4u1QMA3NW6d++uokWLqm3btvrpp5908OBBxcTEaNCgQfrzzz8lSWPGjNGECRM0efJk7du3T9u3b9cHH3yQ6fImTpyoL774Qr/99pt+//13LVq0SIGBgSpYsGCm6/bw8FBYWJh2796ttWvXauDAgerZs6f1/iYAQN7AGScAgEPutG9nvby89OOPP2rYsGHq0KGDLly4oJIlS6pZs2bWM1BhYWG6dOmS3nvvPb300ksqWrSonnjiiUyX5+vrq7ffflv79u2Tq6ur6tSpo++++y7TS/68vLz0/fffa/DgwapTp468vLzUsWNHTZw4MVe3GQDgOIsxxji7iFvp/PnzKlCggM6dO5flJRk5pVWbyFxd/q12utydk7PvtA9+QE67dOmSDh48qNKlS8vDw8PZ5eAm8XfMO+6kzwR30ucBic8EdztHsgGX6gEAAACAHQQnAAAAALCD4AQAAAAAdtxZF6kCQB5zu97XULSoj/r2biSL5YRcXf/3T0W5ciWdWBUAAM7DGScAAAAAsIPgBAAAAAB2EJwAAAAAwA6CEwAAAADYQXACAAAAADsYVQ8A4JDa00bdsnX98uzrt2xdOcliseibb75Ru3btdOjQIZUuXVo7duxQ9erVnV0aAOAmccYJAHBH6d27tywWiywWi/Lnz6/SpUvr5Zdf1qVLl5xdGgDgNsYZJwDAHadFixaaPXu2Ll++rG3btiksLEwWi0VvvfWWs0sDANym8sQZpylTpig4OFgeHh6qV6+etmzZkmXfRx55xPpN4rWPVq1a3cKKAQB5mbu7uwIDAxUUFKR27dopJCREq1evliSlpaUpKipKpUuXlqenp6pVq6avvvrKZv49e/aodevW8vPzk6+vrx566CEdOHBAkrR161Y1b95cRYsWVYECBdS4cWNt3779lm8jAODWcnpwWrhwoSIiIhQZGant27erWrVqCg0N1cmTJzPtv3jxYh0/ftz62L17t1xdXdWpU6dbXDkA4Hawe/dubdiwQW5ubpKkqKgoffrpp5o2bZr27NmjF198UT169NC6deskSceOHdPDDz8sd3d3/fDDD9q2bZv69u2rK1euSJIuXLigsLAw/fzzz9q0aZPKlSunli1b6sKFC07bRgBA7nP6pXoTJ05U//791adPH0nStGnTtHz5cs2aNUvDhw/P0L9w4cI2zxcsWCAvLy+CEwDAatmyZfLx8dGVK1eUnJwsFxcXffjhh0pOTta4ceO0Zs0a1a9fX5J033336eeff9b06dPVuHFjTZkyRQUKFNCCBQuUP39+SVL58uWty27atKnNuj7++GMVLFhQ69atU+vWrW/dRgIAbimnBqeUlBRt27ZNI0aMsLa5uLgoJCREGzduzNYyZs6cqa5du8rb2zvT6cnJyUpOTrY+P3/+/H8rGgCQ5zVp0kRTp05VUlKS3nvvPeXLl08dO3bUnj17dPHiRTVv3tymf0pKimrUqCFJio2N1UMPPWQNTdc7ceKERo4cqZiYGJ08eVKpqam6ePGijhw5kuvbBQBwHqcGp9OnTys1NVUBAQE27QEBAfrtt9/szr9lyxbt3r1bM2fOzLJPVFSUxo4d+59rBQDcPry9vVW2bFlJ0qxZs1StWjXNnDlTDzzwgCRp+fLlKlmypM087u7ukiRPT88bLjssLEx///233n//fZUqVUru7u6qX7++UlJScmFLAAB5hdMv1fsvZs6cqSpVqqhu3bpZ9hkxYoQiIiKsz8+fP6+goKBbUR4AIA9wcXHRK6+8ooiICP3+++9yd3fXkSNH1Lhx40z7V61aVXPnztXly5czPeu0fv16ffTRR2rZsqUk6ejRozp9+nSubgMAwPmcOjhE0aJF5erqqhMnTti0nzhxQoGBgTecNykpSQsWLFC/fv1u2M/d3V1+fn42DwDA3aVTp05ydXXV9OnT9dJLL+nFF1/U3LlzdeDAAW3fvl0ffPCB5s6dK0kKDw/X+fPn1bVrV/3yyy/at2+fPvvsM8XHx0uSypUrp88++0xxcXHavHmzunfvbvcsFQDg9ufUM05ubm6qVauWoqOj1a5dO0lXh4mNjo5WeHj4DeddtGiRkpOT1aNHj1tQKQAg3S/Pvu7sEhyWL18+hYeH6+2339bBgwfl7++vqKgo/fHHHypYsKBq1qypV155RZJUpEgR/fDDDxo6dKgaN24sV1dXVa9eXQ0bNpR09WqHp59+WjVr1lRQUJDGjRunl156yZmbBwC4BZx+qV5ERITCwsJUu3Zt1a1bV5MmTVJSUpJ1lL1evXqpZMmSioqKsplv5syZateunYoUKeKMsgEAedScOXMybR8+fLh1tNbBgwdr8ODBWS6jatWq+v777zOdVqNGDW3dutWm7YknnrB5boyx/n9wcLDNcwDA7cnpwalLly46deqURo8erYSEBFWvXl0rV660Dhhx5MgRubjYXlEYHx+vn3/+WatWrXJGyQAAAADuMk4PTtLV68mzujQvJiYmQ1uFChX49g4AAADALePUwSEAAAAA4HZAcAIAAAAAOwhOAAAAAGAHwQkAAAAA7CA4AQAAAIAdBCcAAAAAsIPgBAAAAAB25InfcQIA3D7G/tj3lq0r8uFZt2xdAHAnuJXv0bktr/0bwBknAMAdo3fv3rJYLBke+/fvlyT9+OOPatOmjUqUKCGLxaIlS5bYXWZqaqrGjx+vihUrytPTU4ULF1a9evX0ySef5PLWAADyEs44AQDuKC1atNDs2bNt2vz9/SVJSUlJqlatmvr27asOHTpka3ljx47V9OnT9eGHH6p27do6f/68fvnlF509ezbHa0+XkpIiNze3XFs+AMBxnHECANxR3N3dFRgYaPNwdXWVJD322GN644031L59+2wvb+nSpXr++efVqVMnlS5dWtWqVVO/fv300ksvWfukpaXp7bffVtmyZeXu7q57771Xb775pnX6rl271LRpU3l6eqpIkSJ6+umnlZiYaJ3eu3dvtWvXTm+++aZKlCihChUqSJKOHj2qzp07q2DBgipcuLDatm2rQ4cO/cc9BAC4GQQnAABuIDAwUD/88INOnTqVZZ8RI0Zo/PjxGjVqlPbu3av58+crICBA0tWzXKGhoSpUqJC2bt2qRYsWac2aNQoPD7dZRnR0tOLj47V69WotW7ZMly9fVmhoqHx9ffXTTz9p/fr18vHxUYsWLZSSkpKr2wwAyIhL9QAAd5Rly5bJx8fH+vyxxx7TokWLbnp5EydO1BNPPKHAwEDdf//9atCggdq2bavHHntMknThwgW9//77+vDDDxUWFiZJKlOmjBo1aiRJmj9/vi5duqRPP/1U3t7ekqQPP/xQbdq00VtvvWUNWN7e3vrkk0+sl+h9/vnnSktL0yeffCKLxSJJmj17tgoWLKiYmBg9+uijN71NAADHEZwAAHeUJk2aaOrUqdbn6WHlZlWuXFm7d+/Wtm3btH79eusAE71799Ynn3yiuLg4JScnq1mzZpnOHxcXp2rVqtnU0bBhQ6WlpSk+Pt4anKpUqWJzX9POnTu1f/9++fr62izv0qVLOnDgwH/aJgCA4whOAIA7ire3t8qWLZujy3RxcVGdOnVUp04dvfDCC/r888/Vs2dPvfrqq/L09MyRdVwf8BITE1WrVi3NmzcvQ9/0wS4AALcO9zgBAOCgypUrS7p6/1K5cuXk6emp6OjoTPtWqlRJO3fuVFJSkrVt/fr1cnFxsQ4CkZmaNWtq3759KlasmMqWLWvzKFCgQM5uEADALoITAOCukZiYqNjYWMXGxkqSDh48qNjYWB05ciTLeZ544gm999572rx5sw4fPqyYmBgNGDBA5cuXV8WKFeXh4aFhw4bp5Zdf1qeffqoDBw5o06ZNmjlzpiSpe/fu8vDwUFhYmHbv3q21a9dq4MCB6tmzp/Uyvcx0795dRYsWVdu2bfXTTz/p4MGDiomJ0aBBg/Tnn3/m6H4BANjHpXoAAIfktV9yd8Qvv/yiJk2aWJ9HRERIksLCwjRnzpxM5wkNDdUXX3yhqKgonTt3ToGBgWratKnGjBmjfPmu/jM6atQo5cuXT6NHj9Zff/2l4sWL69lnn5UkeXl56fvvv9fgwYNVp04deXl5qWPHjpo4ceINa/Xy8tKPP/6oYcOGqUOHDrpw4YJKliypZs2ayc/PLwf2BgDAEQQnAMAdI6vwk+6RRx6RMcahZfbv31/9+/e/YR8XFxe9+uqrevXVVzOdXqVKFf3www9Zzp9V3YGBgZo7d262awUA5B6CEwAAAO5ataeNcnYJOapNZWdXcOfiHicAAAAAsIPgBAAAAAB2EJwAAAAAwA6CEwAgA2OMro6h4NhACshbHB0IAwCQNYITACCDxMRkXUlNVWrqFWeXgv/g4sWLkqT8+fM7uRIAuP0xqh4AIIPk5Cvavv2wGtTPr0KFisjVNZ8kiy5duuTs0pANxhhdvHhRJ0+eVMGCBeXq6urskgDgtkdwAgBk6seffpck1ax5WflcXWWxSMZcdHJVcETBggUVGBjo7DIA4I5AcAIAZMoYad2Pv2vjpgPy9fWQxWLR9KkDnV0Wsil//vycaQKAHERwAgDcUEpKqv7+O0mS5OHh4eRqAABwDgaHAAAAAAA7CE4AAAAAYAfBCQAAAADsIDgBAAAAgB0EJwAAAACwg1H1gDvA2B/7OruEHBX58CxnlwAAAGCDM04AAAAAYAfBCQAAAADsIDgBAAAAgB0EJwAAAACwg+AEAAAAAHYQnAAAAADADoITAAAAANhBcAIAAAAAO5wenKZMmaLg4GB5eHioXr162rJlyw37//PPPxowYICKFy8ud3d3lS9fXt99990tqhYAAADA3SifM1e+cOFCRUREaNq0aapXr54mTZqk0NBQxcfHq1ixYhn6p6SkqHnz5ipWrJi++uorlSxZUocPH1bBggVvffG4rdWeNsrZJeSoNpWdXQEAAMCdzanBaeLEierfv7/69OkjSZo2bZqWL1+uWbNmafjw4Rn6z5o1S2fOnNGGDRuUP39+SVJwcPCtLBkAcAe5k75EaVP5mLNLyFGRD89ydgkAYMNpwSklJUXbtm3TiBEjrG0uLi4KCQnRxo0bM51n6dKlql+/vgYMGKBvv/1W/v7+evLJJzVs2DC5urpmOk9ycrKSk5Otz8+fP5+zGwIAd5F6Ea87u4ScVd7ZBQAAbhdOu8fp9OnTSk1NVUBAgE17QECAEhISMp3njz/+0FdffaXU1FR99913GjVqlCZMmKA33ngjy/VERUWpQIEC1kdQUFCObgcAAACAO5/TB4dwRFpamooVK6aPP/5YtWrVUpcuXfTqq69q2rRpWc4zYsQInTt3zvo4evToLawYAAAAwJ3AaZfqFS1aVK6urjpx4oRN+4kTJxQYGJjpPMWLF1f+/PltLsurVKmSEhISlJKSIjc3twzzuLu7y93dPWeLBwAAAHBXcdoZJzc3N9WqVUvR0dHWtrS0NEVHR6t+/fqZztOwYUPt379faWlp1rbff/9dxYsXzzQ0AQAAAEBOcOqlehEREZoxY4bmzp2ruLg4Pffcc0pKSrKOsterVy+bwSOee+45nTlzRoMHD9bvv/+u5cuXa9y4cRowYICzNgEAAADAXcCpw5F36dJFp06d0ujRo5WQkKDq1atr5cqV1gEjjhw5IheX/2W7oKAgff/993rxxRdVtWpVlSxZUoMHD9awYcOctQkAAAAA7gJODU6SFB4ervDw8EynxcTEZGirX7++Nm3alMtVAQAAAMD/3Faj6gEAAACAMxCcAAAAAMAOghMAAAAA2EFwAgAAAAA7CE4AAAAAYAfBCQAAAADsIDgBAAAAgB0EJwAAAACwg+AEAAAAAHYQnAAAAADADoITAAAAANhBcAIAAAAAOwhOAAAAAGAHwQkAAAAA7CA4AQAAAIAdBCcAAAAAsIPgBAAAAAB2EJwAAAAAwA6CEwAAAADYQXACAAAAADsITgAAAABgB8EJAAAAAOwgOAEAAACAHTcVnA4cOKCRI0eqW7duOnnypCRpxYoV2rNnT44WBwAAAAB5gcPBad26dapSpYo2b96sxYsXKzExUZK0c+dORUZG5niBAAAAAOBsDgen4cOH64033tDq1avl5uZmbW/atKk2bdqUo8UBAAAAQF7gcHDatWuX2rdvn6G9WLFiOn36dI4UBQAAAAB5icPBqWDBgjp+/HiG9h07dqhkyZI5UhQAAAAA5CUOB6euXbtq2LBhSkhIkMViUVpamtavX6+XXnpJvXr1yo0aAQAAAMCpHA5O48aNU8WKFRUUFKTExERVrlxZDz/8sBo0aKCRI0fmRo0AAAAA4FT5HOlsjFFCQoImT56s0aNHa9euXUpMTFSNGjVUrly53KoRAAAAAJzK4eBUtmxZ7dmzR+XKlVNQUFBu1QUAAAAAeYZDl+q5uLioXLly+vvvv3OrHgAAAADIcxy+x2n8+PEaOnSodu/enRv1AAAAAECe49ClepLUq1cvXbx4UdWqVZObm5s8PT1tpp85cybHigMAAACAvMDh4DRp0qRcKAMAAAAA8i6Hg1NYWFhu1AEAAAAAeZbDwUmSUlNTtWTJEsXFxUmS7r//fj3++ONydXXN0eIAAAAAIC9wODjt379fLVu21LFjx1ShQgVJUlRUlIKCgrR8+XKVKVMmx4sEAAAAAGdyeFS9QYMGqUyZMjp69Ki2b9+u7du368iRIypdurQGDRqUGzUCAAAAgFM5fMZp3bp12rRpkwoXLmxtK1KkiMaPH6+GDRvmaHEAAAAAkBc4fMbJ3d1dFy5cyNCemJgoNze3HCkKAAAAAPISh4NT69at9fTTT2vz5s0yxsgYo02bNunZZ5/V448/nhs1AgAAAIBTORycJk+erDJlyqh+/fry8PCQh4eHGjZsqLJly+r999+/qSKmTJmi4OBgeXh4qF69etqyZUuWfefMmSOLxWLz8PDwuKn1AgAAAEB2OHyPU8GCBfXtt99q//791uHIK1WqpLJly95UAQsXLlRERISmTZumevXqadKkSQoNDVV8fLyKFSuW6Tx+fn6Kj4+3PrdYLDe1bgAAAADIjpv6HSdJKlu27E2HpWtNnDhR/fv3V58+fSRJ06ZN0/LlyzVr1iwNHz4803ksFosCAwP/87oBAAAAIDscvlSvY8eOeuuttzK0v/322+rUqZNDy0pJSdG2bdsUEhLyv4JcXBQSEqKNGzdmOV9iYqJKlSqloKAgtW3bVnv27Mmyb3Jyss6fP2/zAAAAAABHOBycfvzxR7Vs2TJD+2OPPaYff/zRoWWdPn1aqampCggIsGkPCAhQQkJCpvNUqFBBs2bN0rfffqvPP/9caWlpatCggf78889M+0dFRalAgQLWR1BQkEM1AgAAAIDDwSmrYcfz589/S87m1K9fX7169VL16tXVuHFjLV68WP7+/po+fXqm/UeMGKFz585ZH0ePHs31GgEAAADcWRwOTlWqVNHChQsztC9YsECVK1d2aFlFixaVq6urTpw4YdN+4sSJbN/DlD9/ftWoUUP79+/PdLq7u7v8/PxsHgAAAADgCIcHhxg1apQ6dOigAwcOqGnTppKk6OhoffHFF1q0aJFDy3Jzc1OtWrUUHR2tdu3aSZLS0tIUHR2t8PDwbC0jNTVVu3btyvTyQQAAAADICQ4HpzZt2mjJkiUaN26cvvrqK3l6eqpq1apas2aNGjdu7HABERERCgsLU+3atVW3bl1NmjRJSUlJ1lH2evXqpZIlSyoqKkqS9Nprr+nBBx9U2bJl9c8//+idd97R4cOH9dRTTzm8bgAAAADIjpsajrxVq1Zq1apVjhTQpUsXnTp1SqNHj1ZCQoKqV6+ulStXWgeMOHLkiFxc/ndF4dmzZ9W/f38lJCSoUKFCqlWrljZs2ODwZYIAAAAAkF03/TtOknTp0iUtXLhQSUlJat68ucqVK3dTywkPD8/y0ryYmBib5++9957ee++9m1oPAAAAANyMbAeniIgIXb58WR988IGkq7/B9OCDD2rv3r3y8vLSyy+/rNWrV6t+/fq5ViwAAAAAOEO2R9VbtWqVmjdvbn0+b948HTlyRPv27dPZs2fVqVMnvfHGG7lSJAAAAAA4U7aD05EjR2zuI1q1apWeeOIJlSpVShaLRYMHD9aOHTtypUgAAAAAcKZsBycXFxcZY6zPN23apAcffND6vGDBgjp79mzOVgcAAAAAeUC2g1OlSpX0f//3f5KkPXv26MiRI2rSpIl1+uHDh60j4QEAAADAnSTbg0O8/PLL6tq1q5YvX649e/aoZcuWKl26tHX6d999p7p16+ZKkQAAAADgTNk+49S+fXt99913qlq1ql588UUtXLjQZrqXl5eef/75HC8QAAAAAJzNod9xatasmZo1a5bptMjIyBwpCAAAAADymmyfcQIAAACAuxXBCQAAAADsIDgBAAAAgB0EJwAAAACw46aC05UrV7RmzRpNnz5dFy5ckCT99ddfSkxMzNHiAAAAACAvcGhUPenqD922aNFCR44cUXJyspo3by5fX1+99dZbSk5O1rRp03KjTgAAAABwGofPOA0ePFi1a9fW2bNn5enpaW1v3769oqOjc7Q4AAAAAMgLHD7j9NNPP2nDhg1yc3OzaQ8ODtaxY8dyrDAAAAAAyCscPuOUlpam1NTUDO1//vmnfH19c6QoAAAAAMhLHA5Ojz76qCZNmmR9brFYlJiYqMjISLVs2TInawMAAACAPMHhS/UmTJig0NBQVa5cWZcuXdKTTz6pffv2qWjRovriiy9yo0YAAAAAcCqHg9M999yjnTt3auHChdq5c6cSExPVr18/de/e3WawCAAAAAC4UzgcnCQpX7586t69u7p3757T9QAAAABAnuPwPU5RUVGaNWtWhvZZs2bprbfeypGiAAAAACAvcTg4TZ8+XRUrVszQfv/99/PjtwAAAADuSA4Hp4SEBBUvXjxDu7+/v44fP54jRQEAAABAXuJwcAoKCtL69esztK9fv14lSpTIkaIAAAAAIC9xeHCI/v3764UXXtDly5fVtGlTSVJ0dLRefvllDRkyJMcLBAAAAABnczg4DR06VH///beef/55paSkSJI8PDw0bNgwjRgxIscLBAAAAABnczg4WSwWvfXWWxo1apTi4uLk6empcuXKyd3dPTfqAwAAAACnu6nfcZIkHx8f1alTJydrAQAAAIA8yeHglJSUpPHjxys6OlonT55UWlqazfQ//vgjx4oDAAAAgLzA4eD01FNPad26derZs6eKFy8ui8WSG3UBAAAAQJ7hcHBasWKFli9froYNG+ZGPQAAAACQ5zj8O06FChVS4cKFc6MWAAAAAMiTHA5Or7/+ukaPHq2LFy/mRj0AAAAAkOc4fKnehAkTdODAAQUEBCg4OFj58+e3mb59+/YcKw4AAAAA8gKHg1O7du1yoQwAAAAAyLscDk6RkZG5UQcAAAAA5FkO3+MEAAAAAHcbh884paam6r333tOXX36pI0eOKCUlxWb6mTNncqw4AAAAAMgLHD7jNHbsWE2cOFFdunTRuXPnFBERoQ4dOsjFxUVjxozJhRIBAAAAwLkcDk7z5s3TjBkzNGTIEOXLl0/dunXTJ598otGjR2vTpk25USMAAAAAOJXDwSkhIUFVqlSRJPn4+OjcuXOSpNatW2v58uU5Wx0AAAAA5AEOB6d77rlHx48flySVKVNGq1atkiRt3bpV7u7uOVsdAAAAAOQBDgen9u3bKzo6WpI0cOBAjRo1SuXKlVOvXr3Ut2/fmypiypQpCg4OloeHh+rVq6ctW7Zka74FCxbIYrHw21IAAAAAcpXDo+qNHz/e+v9dunTRvffeq40bN6pcuXJq06aNwwUsXLhQERERmjZtmurVq6dJkyYpNDRU8fHxKlasWJbzHTp0SC+99JIeeughh9cJAAAAAI74z7/jVL9+fUVERNxUaJKkiRMnqn///urTp48qV66sadOmycvLS7NmzcpyntTUVHXv3l1jx47Vfffdd7OlAwAAAEC2OHzGSZL++usv/fzzzzp58qTS0tJspg0aNCjby0lJSdG2bds0YsQIa5uLi4tCQkK0cePGLOd77bXXVKxYMfXr108//fTTDdeRnJys5ORk6/Pz589nuz4AAAAAkG4iOM2ZM0fPPPOM3NzcVKRIEVksFus0i8XiUHA6ffq0UlNTFRAQYNMeEBCg3377LdN5fv75Z82cOVOxsbHZWkdUVJTGjh2b7ZoAAAAA4HoOX6o3atQojR49WufOndOhQ4d08OBB6+OPP/7IjRqtLly4oJ49e2rGjBkqWrRotuYZMWKEzp07Z30cPXo0V2sEAAAAcOdx+IzTxYsX1bVrV7m4/Ofbo1S0aFG5urrqxIkTNu0nTpxQYGBghv4HDhzQoUOHbO6nSr9UMF++fIqPj1eZMmVs5nF3d2eYdAAAAAD/icPpp1+/flq0aFGOrNzNzU21atWyDm8uXQ1C0dHRql+/fob+FStW1K5duxQbG2t9PP7442rSpIliY2MVFBSUI3UBAAAAwLUcPuMUFRWl1q1ba+XKlapSpYry589vM33ixIkOLS8iIkJhYWGqXbu26tatq0mTJikpKUl9+vSRJPXq1UslS5ZUVFSUPDw89MADD9jMX7BgQUnK0A4AAAAAOeWmgtP333+vChUqSFKGwSEc1aVLF506dUqjR49WQkKCqlevrpUrV1oHjDhy5EiOXBYIAAAAADfL4eA0YcIEzZo1S717986xIsLDwxUeHp7ptJiYmBvOO2fOnByrAwAAAAAy4/CpHHd3dzVs2DA3agEAAACAPMnh4DR48GB98MEHuVELAAAAAORJDl+qt2XLFv3www9atmyZ7r///gyDQyxevDjHigMAAACAvMDh4FSwYEF16NAhN2oBAAAAgDzJoeB05coVNWnSRI8++mimP1ALAAAAAHcih+5xypcvn5599lklJyfnVj0AAAAAkOc4PDhE3bp1tWPHjtyoBQAAAADyJIfvcXr++ec1ZMgQ/fnnn6pVq5a8vb1tpletWjXHigMAAACAvMDh4NS1a1dJ0qBBg6xtFotFxhhZLBalpqbmXHUAAAAAkAc4HJwOHjyYG3UAAAAAQJ7lcHAqVapUbtQBAAAAAHmWw8FJkg4cOKBJkyYpLi5OklS5cmUNHjxYZcqUydHiAAAAACAvcHhUve+//16VK1fWli1bVLVqVVWtWlWbN2/W/fffr9WrV+dGjQAAAADgVA6fcRo+fLhefPFFjR8/PkP7sGHD1Lx58xwrDgAAAADyAofPOMXFxalfv34Z2vv27au9e/fmSFEAAAAAkJc4HJz8/f0VGxuboT02NlbFihXLiZoAAAAAIE9x+FK9/v376+mnn9Yff/yhBg0aSJLWr1+vt956SxERETleIAAAAAA4m8PBadSoUfL19dWECRM0YsQISVKJEiU0ZswYmx/FBQAAAIA7RbYu1Vu6dKkuX74sSbJYLHrxxRf1559/6ty5czp37pz+/PNPDR48WBaLJVeLBQAAAABnyFZwat++vf755x9Jkqurq06ePClJ8vX1la+vb64VBwAAAAB5QbaCk7+/vzZt2iRJMsZwZgkAAADAXSVb9zg9++yzatu2rSwWiywWiwIDA7Psm5qammPFAQAAAEBekK3gNGbMGHXt2lX79+/X448/rtmzZ6tgwYK5XBoAAAAA5A3ZHlWvYsWKqlChgsLCwtSxY0f5+PjkZl0AAAAAkGc49AO4xhjNmzdPx48fz616AAAAACDPcSg4ubi4qFy5cvr7779zqx4AAAAAyHMcCk6SNH78eA0dOlS7d+/OjXoAAAAAIM/J9j1O6Xr16qWLFy+qWrVqcnNzk6enp830M2fO5FhxAAAAAJAXOBycJk2alAtlAAAAAEDe5XBwCgsLy406AAAAACDPcvgeJ0k6cOCARo4cqW7duunkyZOSpBUrVmjPnj05WhwAAAAA5AUOB6d169apSpUq2rx5sxYvXqzExERJ0s6dOxUZGZnjBQIAAACAszkcnIYPH6433nhDq1evlpubm7W9adOm2rRpU44WBwAAAAB5gcPBadeuXWrfvn2G9mLFiun06dM5UhQAAAAA5CUOB6eCBQvq+PHjGdp37NihkiVL5khRAAAAAJCXOBycunbtqmHDhikhIUEWi0VpaWlav369XnrpJfXq1Ss3agQAAAAAp3I4OI0bN04VK1ZUUFCQEhMTVblyZT388MNq0KCBRo4cmRs1AgAAAIBTOfw7Tm5ubpoxY4ZGjx6tXbt2KTExUTVq1FC5cuVyoz4AAAAAcLpsB6e0tDS98847Wrp0qVJSUtSsWTNFRkbK09MzN+sDAAAAAKfL9qV6b775pl555RX5+PioZMmSev/99zVgwIDcrA0AAAAA8oRsB6dPP/1UH330kb7//nstWbJE//d//6d58+YpLS0tN+sDAAAAAKfLdnA6cuSIWrZsaX0eEhIii8Wiv/76K1cKAwAAAIC8ItvB6cqVK/Lw8LBpy58/vy5fvpzjRQEAAABAXpLtwSGMMerdu7fc3d2tbZcuXdKzzz4rb29va9vixYsdLmLKlCl65513lJCQoGrVqumDDz5Q3bp1M+27ePFijRs3Tvv379fly5dVrlw5DRkyRD179nR4vQAAAACQHdkOTmFhYRnaevTo8Z8LWLhwoSIiIjRt2jTVq1dPkyZNUmhoqOLj41WsWLEM/QsXLqxXX31VFStWlJubm5YtW6Y+ffqoWLFiCg0N/c/1AAAAAMD1sh2cZs+enSsFTJw4Uf3791efPn0kSdOmTdPy5cs1a9YsDR8+PEP/Rx55xOb54MGDNXfuXP38888EJwAAAAC5Itv3OOWGlJQUbdu2TSEhIdY2FxcXhYSEaOPGjXbnN8YoOjpa8fHxevjhhzPtk5ycrPPnz9s8AAAAAMARTg1Op0+fVmpqqgICAmzaAwIClJCQkOV8586dk4+Pj9zc3NSqVSt98MEHat68eaZ9o6KiVKBAAesjKCgoR7cBAAAAwJ3PqcHpZvn6+io2NlZbt27Vm2++qYiICMXExGTad8SIETp37pz1cfTo0VtbLAAAAIDbXrbvccoNRYsWlaurq06cOGHTfuLECQUGBmY5n4uLi8qWLStJql69uuLi4hQVFZXh/idJcnd3txkJEAAAAAAc5dQzTm5ubqpVq5aio6OtbWlpaYqOjlb9+vWzvZy0tDQlJyfnRokAAAAA4NwzTpIUERGhsLAw1a5dW3Xr1tWkSZOUlJRkHWWvV69eKlmypKKioiRdvWepdu3aKlOmjJKTk/Xdd9/ps88+09SpU525GQAAAADuYE4PTl26dNGpU6c0evRoJSQkqHr16lq5cqV1wIgjR47IxeV/J8aSkpL0/PPP688//5Snp6cqVqyozz//XF26dHHWJgAAAAC4wzk9OElSeHi4wsPDM512/aAPb7zxht54441bUBUAAAAAXHVbjqoHAAAAALcSwQkAAAAA7CA4AQAAAIAdBCcAAAAAsIPgBAAAAAB2EJwAAAAAwA6CEwAAAADYQXACAAAAADsITgAAAABgB8EJAAAAAOwgOAEAAACAHQQnAAAAALCD4AQAAAAAdhCcAAAAAMAOghMAAAAA2EFwAgAAAAA7CE4AAAAAYAfBCQAAAADsIDgBAAAAgB0EJwAAAACwg+AEAAAAAHYQnAAAAADADoITAAAAANhBcAIAAAAAOwhOAAAAAGAHwQkAAAAA7CA4AQAAAIAdBCcAAAAAsIPgBAAAAAB2EJwAAAAAwA6CEwAAAADYQXACAAAAADsITgAAAABgB8EJAAAAAOwgOAEAAACAHQQnAAAAALCD4AQAAAAAdhCcAAAAAMAOghMAAAAA2EFwAgAAAAA7CE4AAAAAYAfBCQAAAADsIDgBAAAAgB0EJwAAAACwI08EpylTpig4OFgeHh6qV6+etmzZkmXfGTNm6KGHHlKhQoVUqFAhhYSE3LA/AAAAAPxXTg9OCxcuVEREhCIjI7V9+3ZVq1ZNoaGhOnnyZKb9Y2Ji1K1bN61du1YbN25UUFCQHn30UR07duwWVw4AAADgbuH04DRx4kT1799fffr0UeXKlTVt2jR5eXlp1qxZmfafN2+enn/+eVWvXl0VK1bUJ598orS0NEVHR2faPzk5WefPn7d5AAAAAIAjnBqcUlJStG3bNoWEhFjbXFxcFBISoo0bN2ZrGRcvXtTly5dVuHDhTKdHRUWpQIEC1kdQUFCO1A4AAADg7uHU4HT69GmlpqYqICDApj0gIEAJCQnZWsawYcNUokQJm/B1rREjRujcuXPWx9GjR/9z3QAAAADuLvmcXcB/MX78eC1YsEAxMTHy8PDItI+7u7vc3d1vcWUAAAAA7iRODU5FixaVq6urTpw4YdN+4sQJBQYG3nDed999V+PHj9eaNWtUtWrV3CwTAAAAwF3OqZfqubm5qVatWjYDO6QP9FC/fv0s53v77bf1+uuva+XKlapdu/atKBUAAADAXczpl+pFREQoLCxMtWvXVt26dTVp0iQlJSWpT58+kqRevXqpZMmSioqKkiS99dZbGj16tObPn6/g4GDrvVA+Pj7y8fFx2nYAAAAAuHM5PTh16dJFp06d0ujRo5WQkKDq1atr5cqV1gEjjhw5IheX/50Ymzp1qlJSUvTEE0/YLCcyMlJjxoy5laUDAAAAuEs4PThJUnh4uMLDwzOdFhMTY/P80KFDuV8QAAAAAFzD6T+ACwAAAAB5HcEJAAAAAOwgOAEAAACAHQQnAAAAALCD4AQAAAAAdhCcAAAAAMAOghMAAAAA2EFwAgAAAAA7CE4AAAAAYAfBCQAAAADsIDgBAAAAgB0EJwAAAACwg+AEAAAAAHYQnAAAAADADoITAAAAANhBcAIAAAAAOwhOAAAAAGAHwQkAAAAA7CA4AQAAAIAdBCcAAAAAsIPgBAAAAAB2EJwAAAAAwA6CEwAAAADYQXACAAAAADsITgAAAABgB8EJAAAAAOwgOAEAAACAHQQnAAAAALCD4AQAAAAAdhCcAAAAAMAOghMAAAAA2EFwAgAAAAA7CE4AAAAAYAfBCQAAAADsIDgBAAAAgB0EJwAAAACwg+AEAAAAAHYQnAAAAADADoITAAAAANhBcAIAAAAAOwhOAAAAAGAHwQkAAAAA7CA4AQAAAIAdTg9OU6ZMUXBwsDw8PFSvXj1t2bIly7579uxRx44dFRwcLIvFokmTJt26QgEAAADctZwanBYuXKiIiAhFRkZq+/btqlatmkJDQ3Xy5MlM+1+8eFH33Xefxo8fr8DAwFtcLQAAAIC7lVOD08SJE9W/f3/16dNHlStX1rRp0+Tl5aVZs2Zl2r9OnTp655131LVrV7m7u9/iagEAAADcrZwWnFJSUrRt2zaFhIT8rxgXF4WEhGjjxo05tp7k5GSdP3/e5gEAAAAAjnBacDp9+rRSU1MVEBBg0x4QEKCEhIQcW09UVJQKFChgfQQFBeXYsgEAAADcHZw+OERuGzFihM6dO2d9HD161NklAQAAALjN5HPWiosWLSpXV1edOHHCpv3EiRM5OvCDu7s790MBAAAA+E+cdsbJzc1NtWrVUnR0tLUtLS1N0dHRql+/vrPKAgAAAIAMnHbGSZIiIiIUFham2rVrq27dupo0aZKSkpLUp08fSVKvXr1UsmRJRUVFSbo6oMTevXut/3/s2DHFxsbKx8dHZcuWddp2AAAAALizOTU4denSRadOndLo0aOVkJCg6tWra+XKldYBI44cOSIXl/+dFPvrr79Uo0YN6/N3331X7777rho3bqyYmJhbXT4AAACAu4RTg5MkhYeHKzw8PNNp14eh4OBgGWNuQVUAAAAA8D93/Kh6AAAAAPBfEZwAAAAAwA6CEwAAAADYQXACAAAAADsITgAAAABgB8EJAAAAAOwgOAEAAACAHQQnAAAAALCD4AQAAAAAdhCcAAAAAMAOghMAAAAA2EFwAgAAAAA7CE4AAAAAYAfBCQAAAADsIDgBAAAAgB0EJwAAAACwg+AEAAAAAHYQnAAAAADADoITAAAAANhBcAIAAAAAOwhOAAAAAGAHwQkAAAAA7CA4AQAAAIAdBCcAAAAAsIPgBAAAAAB2EJwAAAAAwA6CEwAAAADYQXACAAAAADsITgAAAABgB8EJAAAAAOwgOAEAAACAHQQnAAAAALCD4AQAAAAAdhCcAAAAAMAOghMAAAAA2EFwAgAAAAA7CE4AAAAAYAfBCQAAAADsIDgBAAAAgB0EJwAAAACwg+AEAAAAAHYQnAAAAADADoITAAAAANiRJ4LTlClTFBwcLA8PD9WrV09btmy5Yf9FixapYsWK8vDwUJUqVfTdd9/dokoBAAAA3I2cHpwWLlyoiIgIRUZGavv27apWrZpCQ0N18uTJTPtv2LBB3bp1U79+/bRjxw61a9dO7dq10+7du29x5QAAAADuFk4PThMnTlT//v3Vp08fVa5cWdOmTZOXl5dmzZqVaf/3339fLVq00NChQ1WpUiW9/vrrqlmzpj788MNbXDkAAACAu0U+Z648JSVF27Zt04gRI6xtLi4uCgkJ0caNGzOdZ+PGjYqIiLBpCw0N1ZIlSzLtn5ycrOTkZOvzc+fOSZLOnz//H6u37/LlZPudbiOpyVecXUKOSf33srNLyFGXklKcXUKOuhWvz1uF94G87U56L+B9IG+7k94LeB/I2+6k94Jb8T6Qvg5jjN2+Tg1Op0+fVmpqqgICAmzaAwIC9Ntvv2U6T0JCQqb9ExISMu0fFRWlsWPHZmgPCgq6yaqBvCfW2QXksPGa5+wSgNtOrLMLyGG8DwA3J9bZBeSgW/k+cOHCBRUoUOCGfZwanG6FESNG2JyhSktL05kzZ1SkSBFZLBYnVgZnOX/+vIKCgnT06FH5+fk5uxwATsJ7AQDeB2CM0YULF1SiRAm7fZ0anIoWLSpXV1edOHHCpv3EiRMKDAzMdJ7AwECH+ru7u8vd3d2mrWDBgjdfNO4Yfn5+vEkC4L0AAO8Ddzl7Z5rSOXVwCDc3N9WqVUvR0dHWtrS0NEVHR6t+/fqZzlO/fn2b/pK0evXqLPsDAAAAwH/l9Ev1IiIiFBYWptq1a6tu3bqaNGmSkpKS1KdPH0lSr169VLJkSUVFRUmSBg8erMaNG2vChAlq1aqVFixYoF9++UUff/yxMzcDAAAAwB3M6cGpS5cuOnXqlEaPHq2EhARVr15dK1eutA4AceTIEbm4/O/EWIMGDTR//nyNHDlSr7zyisqVK6clS5bogQcecNYm4Dbj7u6uyMjIDJdwAri78F4AgPcBOMJisjP2HgAAAADcxZz+A7gAAAAAkNcRnAAAAADADoITAAAAANhBcEKuCA4O1qRJk256/jlz5vB7W1n4r/sWcIQjxxvHpq2YmBhZLBb9888/ub6uMWPGqHr16hnaAgICZLFYtGTJEvXu3Vvt2rXL9VpwZ0k/fpDzbtW+zey9aMmSJSpbtqxcXV31wgsv8LkruwzuOmFhYaZt27a5uo6TJ0+apKSkbPUtVaqUee+992zaLl68aE6cOHHT6589e7aRZCQZi8ViAgMDTefOnc3hw4dvepl5hSP7FnemsLAw6/GdL18+U6xYMRMSEmJmzpxpUlNTc3RdjhxvuX1sXrvdmT1KlSqVa+vOzPbt280TTzxhihUrZtzd3U3ZsmXNU089ZeLj440xxqxdu9ZIMmfPns31Wi5cuGBOnz5tfb53714jyXzzzTfm+PHj5tKlS+aff/65JbUgZ13/eg8ODjZDhw41//777y1Zf/pxdKtl9Xrft2/fLa/l2pqy+/np+PHjJjw83JQuXdq4ubmZe+65x7Ru3dqsWbPG2udW7dvk5GRz/Phxk5aWZm0rVqyYGTZsmDl27Jg5f/78f/7cdbfgjBNyhb+/v7y8vG56fk9PTxUrVuw/1eDn56fjx4/r2LFj+vrrrxUfH69OnTr9p2Vmx+XLl3N1+f913+LO0KJFCx0/flyHDh3SihUr1KRJEw0ePFitW7fWlStXcmw9jhxvuX1svv/++zp+/Lj1IUmzZ8+2Pt+6datN/5SUlFyrZdmyZXrwwQeVnJysefPmKS4uTp9//rkKFCigUaNG5dp6s+Lj46MiRYpYnx84cECS1LZtWwUGBsrd3V0FChT4T98oG2Ny9NhC9qW/3v/44w+99957mj59uiIjI51dVq5L3+5rH6VLl76pZeXm+8H1Dh06pFq1aumHH37QO++8o127dmnlypVq0qSJBgwYcMvqSOfm5qbAwEBZLBZJUmJiok6ePKnQ0FCVKFFCvr6+OfK5K7c//+QJzk5uuPXsfWMSExNj6tSpY9zc3ExgYKAZNmyYuXz5snX6+fPnzZNPPmm8vLxMYGCgmThxomncuLEZPHiwtc+1Z5HS0tJMZGSkCQoKMm5ubqZ48eJm4MCBxhhjGjdunOHbJGOunjEqUKCATV1Lly41tWvXNu7u7qZIkSKmXbt2WW5DZvNPnjzZSDLnzp2zti1ZssTUqFHDuLu7m9KlS5sxY8bYbGtcXJxp2LChcXd3N5UqVTKrV6+2+Ybo4MGDRpJZsGCBefjhh427u7uZPXu2McaYGTNmmIoVKxp3d3dToUIFM2XKFOtyk5OTzYABA0xgYKBxd3c39957rxk3bpzd/XX9vjXGmMOHD5vHH3/ceHt7G19fX9OpUyeTkJBgnR4ZGWmqVatmPv30U1OqVCnj5+dnunTpYs6fP5/l/kPeltVrODo62kgyM2bMsLadPXvW9OvXzxQtWtT4+vqaJk2amNjYWJv5bvTayu5r+fq+xuT+sanrvq0tVaqUee2110zPnj2Nr6+vCQsLM8YY89NPP5lGjRoZDw8Pc88995iBAweaxMRE63yXLl0yQ4YMMSVKlDBeXl6mbt26Zu3atVmuNykpyRQtWjTL96D0szrXn3E6ffq06dq1qylRooTx9PQ0DzzwgJk/f77NvIsWLTIPPPCA8fDwMIULFzbNmjWz1rp27VpTp04d4+XlZQoUKGAaNGhgDh06ZLMv0/8/s/fV64+b1NRUM27cOBMcHGw8PDxM1apVzaJFi6zT0+v/7rvvTM2aNU3+/PnN2rVrTWxsrHnkkUeMj4+P8fX1NTVr1jRbt27Ncn/hv8ns9d6hQwdTo0YN6/PsHFuNGzc2AwcONEOHDjWFChUyAQEBJjIy0qbP77//bh566CHrv3mrVq3K8Dr79ddfTZMmTazHaP/+/c2FCxcy1Pvmm2+aYsWKmQIFCpixY8eay5cvm5deeskUKlTIlCxZ0syaNcvh7b6Wvc8qjRs3NgMGDDCDBw82RYoUMY888ogxxphdu3aZFi1aGG9vb1OsWDHTo0cPc+rUKet8Wb0GM3tdZfU+8dhjj5mSJUvavM+ku/as7/X79uWXXzblypUznp6epnTp0mbkyJEmJSXFOv1Gr71Dhw6Z1q1bm4IFCxovLy9TuXJls3z5cmOM7XtR+v9fvx2ZfW6y9xlJkvnoo49MmzZtjJeXl4mMjDRnzpwxTz75pClatKjx8PAwZcuWtfu3vp1wxgk2jh07ppYtW6pOnTrauXOnpk6dqpkzZ+qNN96w9omIiND69eu1dOlSrV69Wj/99JO2b9+e5TK//vpr6zdk+/bt05IlS1SlShVJ0uLFi3XPPffotddes/kW+XrLly9X+/bt1bJlS+3YsUPR0dGqW7dutrfr5MmT+uabb+Tq6ipXV1dJ0k8//aRevXpp8ODB2rt3r6ZPn645c+bozTfflCSlpqaqXbt28vLy0ubNm/Xxxx/r1VdfzXT5w4cP1+DBgxUXF6fQ0FDNmzdPo0eP1ptvvqm4uDiNGzdOo0aN0ty5cyVJkydP1tKlS/Xll18qPj5e8+bNU3BwsN39db20tDS1bdtWZ86c0bp167R69Wr98ccf6tKli02/AwcOaMmSJVq2bJmWLVumdevWafz48dnef7g9NG3aVNWqVdPixYutbZ06ddLJkye1YsUKbdu2TTVr1lSzZs105swZSY69tm6HY/Pdd99VtWrVtGPHDo0aNUoHDhxQixYt1LFjR/36669auHChfv75Z4WHh1vnCQ8P18aNG7VgwQL9+uuv6tSpk1q0aKF9+/Zluo7vv/9ep0+f1ssvv5zp9KzO6ly6dEm1atXS8uXLtXv3bj399NPq2bOntmzZIkk6fvy4unXrpr59+youLk4xMTHq0KGD9UxPu3bt1LhxY/3666/auHGjnn76aes3yNd66aWXNHv2bOsys3pfjYqK0qeffqpp06Zpz549evHFF9WjRw+tW7fOpt/w4cM1fvx4xcXFqWrVqurevbvuuecebd26Vdu2bdPw4cOVP3/+TNeBnLd7925t2LBBbm5u1jZ7x1a6uXPnytvbW5s3b9bbb7+t1157TatXr5Z09TXboUMHubm5afPmzZo2bZqGDRtmM39SUpJCQ0NVqFAhbd26VYsWLdKaNWtsXk+S9MMPP+ivv/7Sjz/+qIkTJyoyMlKtW7dWoUKFtHnzZj377LN65pln9Oeff97UPsjOZ5X07XVzc9P69es1bdo0/fPPP2ratKlq1KihX375RStXrtSJEyfUuXNnSTd+Db700kvq3LmzzVmwBg0aZKjtzJkzWrlypQYMGCBvb+8M02901tfX11dz5szR3r179f7772vGjBl67733rNNv9NobMGCAkpOT9eOPP2rXrl1666235OPjk2EdDRo0UHx8vKSr7+lZbYe9z0jpxowZo/bt22vXrl3q27evRo0apb1792rFihWKi4vT1KlTVbRo0Sy3+bbj7OSGW+9G3+K88sorpkKFCjbXwU6ZMsX4+PiY1NRUc/78eZM/f36bbyX/+ecf4+XlleUZpwkTJpjy5cvbfGtyrczucbr+m4/69eub7t27Z3sb0+9x8vb2Nl5eXtZvVQYNGmTt06xZM+tZnnSfffaZKV68uDHGmBUrVph8+fKZ48ePW6dndcZp0qRJNsspU6ZMhm/7Xn/9dVO/fn1jjDEDBw40TZs2tdnP6RzZX6tWrTKurq7myJEj1ul79uwxksyWLVuMMVe/ffby8rL5Fn/o0KGmXr16mS4fed+NXsNdunQxlSpVMsZcPdPi5+dnLl26ZNOnTJkyZvr06cYY+6+tm30t34pjU5mccbr+LFC/fv3M008/bdP2008/GRcXF/Pvv/+aw4cPG1dXV3Ps2DGbPs2aNTMjRozIdL1vvfWWkWTOnDlzw/qyc49Tq1atzJAhQ4wxxmzbts1Isp5Futbff/9tJJmYmJhMl3PtGSdjjPnmm2/M9f/EX3vcXLp0yXh5eZkNGzbY9OnXr5/p1q2bTf1Lliyx6ePr62vmzJmT5TYhZ4WFhRlXV1fj7e1t3N3djSTj4uJivvrqqxvOd+2xZczVMzCNGjWy6VOnTh0zbNgwY4wx33//vcmXL5/Na2HFihU2r7OPP/7YFCpUyOZMyvLly42Li4v1bHJYWJgpVaqUzf2WFSpUMA899JD1+ZUrV4y3t7f54osvsrXd6Y8nnnjCGGP/s0r69l57Vs6Yq/8OP/roozZtR48eNZJMfHz8DV+D6TXZu8dp8+bNRpJZvHjxDfsZY/8ep3feecfUqlXL+vxGr70qVaqYMWPGZDrt+veis2fPZjhjdv3nLnufkdLrf+GFF2z6tGnTxvTp0yfLbbrd5buFGQ23gbi4ONWvX9/mW8yGDRsqMTFRf/75p86ePavLly/bfCNdoEABVahQIctldurUSZMmTdJ9992nFi1aqGXLlmrTpo3y5cv+4RcbG6v+/fs7tC2+vr7avn27Ll++rBUrVmjevHk235Ts3LlT69evt2lLTU3VpUuXdPHiRcXHxysoKEiBgYHW6Vl9E1+7dm3r/yclJenAgQPq16+fTc1XrlxRgQIFJEm9e/dW8+bNVaFCBbVo0UKtW7fWo48+Ksmx/RUXF6egoCAFBQVZ2ypXrqyCBQsqLi5OderUkXR1tDNfX19rn+LFi+vkyZPZ25G4rRhjrK/fnTt3KjEx0ebeF0n6999/rffAOPLauh2OzWtfi9LVffDrr79q3rx51jZjjNLS0nTw4EH98ccfSk1NVfny5W3mS05OzrDfrp3/ZqSmpmrcuHH68ssvdezYMaWkpCg5Odl6X1i1atXUrFkzValSRaGhoXr00Uf1xBNPqFChQipcuLB69+6t0NBQNW/eXCEhIercubOKFy9+U7Xs379fFy9eVPPmzW3aU1JSVKNGDZu26/dpRESEnnrqKX322WcKCQlRp06dVKZMmZuqA9nTpEkTTZ06VUlJSXrvvfeUL18+dezY0Trd3rGVrmrVqjbPr329pb9mS5QoYZ1ev359m/5xcXGqVq2azZmUhg0bKi0tTfHx8QoICJAk3X///XJx+d9FTQEBAXrggQesz11dXVWkSBG7r/X07U6Xvl57n1XuvfdeSVKtWrVslrdz506tXbs20zMxBw4c0KOPPprlazC7bvb9QZIWLlyoyZMn68CBA0pMTNSVK1fk5+dnnX6j196gQYP03HPPadWqVQoJCVHHjh0z/L0dYe8zUvqxdf37w3PPPaeOHTtq+/btevTRR9WuXbtMz2jdrrhUD7kuKChI8fHx+uijj+Tp6annn39eDz/8sEM3EXp6ejq8XhcXF5UtW1aVKlVSRESEHnzwQT333HPW6YmJiRo7dqxiY2Otj127dmnfvn3y8PBwaF3X/iOSmJgoSZoxY4bNsnfv3q1NmzZJkmrWrKmDBw/q9ddf17///qvOnTvriSeekJQz++t6119GY7FYlJaWdtPLQ94VFxdnvXk6MTFRxYsXtzkOY2NjFR8fr6FDh0py7LV1Oxyb118ak5iYqGeeecZm+3fu3Kl9+/apTJkySkxMlKurq7Zt22bTJy4uTu+//36m60gPWb/99ptDtb3zzjt6//33NWzYMK1du1axsbEKDQ213rTu6uqq1atXa8WKFapcubI++OADVahQQQcPHpR0dSCMjRs3qkGDBlq4cKHKly9vfU9xVPr71PLly222e+/evfrqq69s+l6/T8eMGaM9e/aoVatW+uGHH1S5cmV98803N1UHssfb21tly5ZVtWrVNGvWLG3evFkzZ860Trd3bKW7Vf8WZLaem1l3+nanPxz9oiCz94M2bdpkeE/ct2+fHn74YbuvwewoV66cLBaLw+8PGzduVPfu3dWyZUstW7ZMO3bs0KuvvmrzN7zRa++pp57SH3/8oZ49e2rXrl2qXbu2PvjgA4dquFZ2PyNdv48fe+wxHT58WC+++KL++usvNWvWTC+99NJN15HXEJxgo1KlStq4caPNNybr16+Xr6+v7rnnHt13333Knz+/zehV586d0++//37D5Xp6eqpNmzaaPHmyYmJitHHjRu3atUvS1dFeUlNTbzh/1apVFR0d/R+27Op1+gsXLrTej1WzZk3Fx8fbvCmnP1xcXFShQgUdPXpUJ06csC7j+lG7MhMQEKASJUrojz/+yLDca0cD8vPzU5cuXTRjxgwtXLhQX3/9tfW+kxvtr2tVqlRJR48e1dGjR61te/fu1T///KPKlSvf9L7C7emHH37Qrl27rN9E16xZUwkJCcqXL1+GYzH9mnNHX1u327FZs2ZN7d27N9PXuZubm2rUqKHU1FSdPHkyw/RrzzZf69FHH1XRokX19ttvZzo9q99tWr9+vdq2basePXqoWrVquu+++zK8d1osFjVs2FBjx47Vjh075ObmZhNKatSooREjRmjDhg164IEHNH/+/JvaL5UrV5a7u7uOHDmSYbuvPUuYlfLly+vFF1/UqlWr1KFDB+s9Vch9Li4ueuWVVzRy5Ej9+++/krJ3bNmT/pq99p6464N5pUqVtHPnTiUlJVnb1q9fb/0381ax91klKzVr1tSePXsUHByc4bhPDwA3eg1m5/NK4cKFFRoaqilTptjsp3RZvT9s2LBBpUqV0quvvqratWurXLlyOnz4cIZ+N3rtBQUF6dlnn9XixYs1ZMgQzZgx44a13oi9z0g34u/vr7CwMH3++eeaNGmSPv7445uuI68hON2lzp07l+Ebl6NHj+r555/X0aNHNXDgQP3222/69ttvFRkZqYiICLm4uMjX11dhYWEaOnSo1q5dqz179qhfv35ycXHJ9CZl6eqP2c6cOVO7d+/WH3/8oc8//1yenp4qVaqUpKuX6vz44486duyYTp8+nekyIiMj9cUXXygyMlJxcXHWGx8dERQUpPbt22v06NGSpNGjR+vTTz/V2LFjtWfPHsXFxWnBggUaOXKkJKl58+YqU6aMwsLC9Ouvv2r9+vXWaVlta7qxY8cqKipKkydP1u+//65du3Zp9uzZmjhxoiRp4sSJ+uKLL/Tbb7/p999/16JFixQYGKiCBQva3V/XCgkJUZUqVdS9e3dt375dW7ZsUa9evdS4ceMMp89xZ0lOTlZCQoKOHTum7du3a9y4cWrbtq1at26tXr16Sbp6fNSvX1/t2rXTqlWrdOjQIW3YsEGvvvqqfvnlF0mOvbZux2Nz2LBh2rBhg8LDw63fLH/77bfWm9nLly+v7t27q1evXlq8eLEOHjyoLVu2KCoqSsuXL890md7e3vrkk0+0fPlyPf7441qzZo0OHTqkX375RS+//LKeffbZTOcrV66cVq9erQ0bNiguLk7PPPOMzRczmzdv1rhx4/TLL7/oyJEjWrx4sU6dOqVKlSrp4MGDGjFihDZu3KjDhw9r1apV2rdvnypVqnRT+8XX11cvvfSSXnzxRc2dO1cHDhzQ9u3b9cEHH1gHscnMv//+q/DwcMXExOjw4cNav369tm7detN14OZ06tRJrq6umjJliiT7x1Z2hISEqHz58goLC9POnTv1008/ZRgQqXv37vLw8FBYWJh2796ttWvXauDAgerZs6f1Mr1bwd5nlawMGDBAZ86cUbdu3bR161YdOHBA33//vfr06aPU1NQbvgalq59Xfv31V8XHx+v06dNZnm2fMmWKUlNTVbduXX399dfat2+f4uLiNHny5AyXP6YrV66cjhw5ogULFujAgQOaPHmyzZcm9l57L7zwgr7//nsdPHhQ27dv19q1a//T69LeZ6Qbzfftt99q//792rNnj5YtW3ZnvT848wYrOEdWPyrXr18/Y8zNDUdet25dM3z4cGufa28S/+abb0y9evWMn5+f8fb2Ng8++KDND8Bt3LjRVK1a1XrTqzGZDyf+9ddfm+rVqxs3NzdTtGhR06FDhyy3MbP509clyWzevNkYY8zKlStNgwYNjKenp/Hz8zN169Y1H3/8sbV/+nDkbm5upmLFiub//u//jCSzcuVKY8z/BofYsWNHhnXNmzfPWm+hQoXMww8/bL1Z9OOPPzbVq1c33t7exs/PzzRr1sxs3749W/vrZod8vtZ77713y38sFDnn+h/E9Pf3NyEhIWbWrFkZfgD3/PnzZuDAgaZEiRImf/78JigoyHTv3t1m0IYbvbYceS3f6mNTmQwOcf1AM8YYs2XLFtO8eXPj4+NjvL29TdWqVc2bb75pnZ6SkmJGjx5tgoODTf78+U3x4sVN+/btza+//nrD9W/dutV06NDB+Pv7W38A9+mnn7b+QOf1N2T//fffpm3btsbHx8cUK1bMjBw50vTq1ct6s/nevXtNaGiodXnly5c3H3zwgTHGmISEBNOuXTtTvHhx4+bmZkqVKmVGjx5t/Xs7OjiEMVeHl580aZKpUKGCyZ8/v/H39zehoaFm3bp1mdZvzNWfUujatat1SPoSJUqY8PDwW/ZjrHejrAYkiIqKMv7+/iYxMdHusWWMyfCzIcYY07ZtW+uw/cYYEx8fbxo1amTc3NxM+fLlzcqVK296OPJrZbburF6v9rY7XXaGI79+ncZcHXK9ffv2pmDBgsbT09NUrFjRvPDCCyYtLe2Gr0Fjrv7Id/p7iW4wHLkxxvz1119mwIABplSpUsbNzc2ULFnSPP744zbzXL9vhw4daooUKWJ8fHxMly5dzHvvvWf9LGPvtRceHm7KlClj3N3djb+/v+nZs6f1R7FvZnAIY+x/Rrq+fmOuDsBRqVIl4+npaQoXLmzatm1r/vjjjyz30+3GYsx/uIsN0NXBEEqWLKkJEyaoX79+zi4nV61fv16NGjXS/v37uRkaAADgLsKoenDYjh079Ntvv6lu3bo6d+6cXnvtNUlXf6H+TvPNN9/Ix8dH5cqV0/79+zV48GA1bNiQ0AQAAHCXITjhprz77ruKj4+Xm5ubatWqpZ9++unO+oGz/+/ChQsaNmyYjhw5oqJFiyokJEQTJkxwdlkAAAC4xbhUDwAAAADsYFQ9AAAAALCD4AQAAAAAdhCcAAAAAMAOghMAAAAA2EFwAgAAAAA7CE4AgFwXExMji8Wif/75J9vzBAcHa9KkSblWU1ZuptZbKa/XBwB3KoITANzlevfuLYvFomeffTbDtAEDBshisah37963vrA8LDg4WBaLRQsWLMgw7f7775fFYtGcOXOyvbwxY8aoevXq2erboEEDHT9+XAUKFMj28gEA/x3BCQCgoKAgLViwQP/++6+17dKlS5o/f77uvfdeJ1aWdwUFBWn27Nk2bZs2bVJCQoK8vb1zZZ2XL1+Wm5ubAgMDZbFYcmUdAIDMEZwAAKpZs6aCgoK0ePFia9vixYt17733qkaNGjZ9k5OTNWjQIBUrVkweHh5q1KiRtm7datPnu+++U/ny5eXp6akmTZro0KFDGdb5888/66GHHpKnp6eCgoI0aNAgJSUlZbvmrVu3qnnz5ipatKgKFCigxo0ba/v27TZ9LBaLPvnkE7Vv315eXl4qV66cli5d6nCtmenevbvWrVuno0ePWttmzZql7t27K1++fDZ9//nnHz311FPy9/eXn5+fmjZtqp07d0qS5syZo7Fjx2rnzp2yWCw2Z6ssFoumTp2qxx9/XN7e3nrzzTczvVRv/fr1euSRR+Tl5aVChQopNDRUZ8+elSR99dVXqlKlijw9PVWkSBGFhIQ4tJ8BAFcRnAAAkqS+ffvanEGZNWuW+vTpk6Hfyy+/rK+//lpz587V9u3bVbZsWYWGhurMmTOSpKNHj6pDhw5q06aNYmNj9dRTT2n48OE2yzhw4IBatGihjh076tdff9XChQv1888/Kzw8PNv1XrhwQWFhYfr555+1adMmlStXTi1bttSFCxds+o0dO1adO3fWr7/+qpYtW6p79+4O1ZqVgIAAhYaGau7cuZKkixcvauHCherbt2+Gvp06ddLJkye1YsUKbdu2TTVr1lSzZs105swZdenSRUOGDNH999+v48eP6/jx4+rSpYt13jFjxqh9+/batWtXpsuOjY1Vs2bNVLlyZW3cuFE///yz2rRpo9TUVB0/flzdunVT3759FRcXp5iYGHXo0EHGmGzvZwDA/2cAAHe1sLAw07ZtW3Py5Enj7u5uDh06ZA4dOmQ8PDzMqVOnTNu2bU1YWJgxxpjExESTP39+M2/ePOv8KSkppkSJEubtt982xhgzYsQIU7lyZZt1DBs2zEgyZ8+eNcYY069fP/P000/b9Pnpp5+Mi4uL+ffff40xxpQqVcq899572d6O1NRU4+vra/7v//7P2ibJjBw50vo8MTHRSDIrVqzIdq2ZSa9tyZIlpkyZMiYtLc3MnTvX1KhRwxhjTIECBczs2bOt2+Xn52cuXbpks4wyZcqY6dOnG2OMiYyMNNWqVcuwHknmhRdesGlbu3atTX3dunUzDRs2zLTObdu2GUnm0KFDWW4LACB7OOMEAJAk+fv7q1WrVpozZ45mz56tVq1aqWjRojZ9Dhw4oMuXL6thw4bWtvz586tu3bqKi4uTJMXFxalevXo289WvX9/m+c6dOzVnzhz5+PhYH6GhoUpLS9PBgwezVe+JEyfUv39/lStXTgUKFJCfn58SExN15MgRm35Vq1a1/r+3t7f8/Px08uTJbNd6I61atVJiYqJ+/PFHzZo1K9MzQjt37lRiYqKKFClis70HDx7UgQMH7K6jdu3aN5yefsYpM9WqVVOzZs1UpUoVderUSTNmzLBewgcAcEw++10AAHeLvn37Wi+XmzJlSq6tJzExUc8884wGDRqUYVp2B6MICwvT33//rffff1+lSpWSu7u76tevr5SUFJt++fPnt3lusViUlpZ288VfI1++fOrZs6ciIyO1efNmffPNNxn6JCYmqnjx4oqJickwrWDBgnbXYW+gCU9Pzyynubq6avXq1dqwYYNWrVqlDz74QK+++qo2b96s0qVL2103AOB/OOMEALBq0aKFUlJSdPnyZYWGhmaYXqZMGbm5uWn9+vXWtsuXL2vr1q2qXLmyJKlSpUrasmWLzXybNm2yeV6zZk3t3btXZcuWzfBwc3PLVq3r16/XoEGD1LJlS91///1yd3fX6dOnHdre7NRqT9++fbVu3Tq1bdtWhQoVyjC9Zs2aSkhIUL58+TJsa/oZPTc3N6Wmpjq03nRVq1ZVdHR0ltMtFosaNmyosWPHaseOHXJzc8s04AEAbozgBACwcnV1VVxcnPbu3StXV9cM0729vfXcc89p6NChWrlypfbu3av+/fvr4sWL6tevnyTp2Wef1b59+zR06FDFx8dr/vz5GX7TaNiwYdqwYYPCw8MVGxurffv26dtvv3VocIhy5crps88+U1xcnDZv3qzu3bvf8OxLZrJTqz2VKlXS6dOnMwxNni4kJET169dXu3bttGrVKh06dEgbNmzQq6++ql9++UXS1d+FOnjwoGJjY3X69GklJydne/0jRozQ1q1b9fzzz+vXX3/Vb7/9pqlTp+r06dPavHmzxo0bp19++UVHjhzR4sWLderUKVWqVMmhbQQAEJwAANfx8/OTn59fltPHjx+vjh07qmfPnqpZs6b279+v77//3nq25d5779XXX3+tJUuWqFq1apo2bZrGjRtns4yqVatq3bp1+v333/XQQw+pRo0aGj16tEqUKJHtOmfOnKmzZ8+qZs2a6tmzp3WIdEdkp9bsKFKkSJahzWKx6LvvvtPDDz+sPn36qHz58uratasOHz6sgIAASVLHjh3VokULNWnSRP7+/vriiy+yve7y5ctr1apV2rlzp+rWrav69evr22+/Vb58+eTn56cff/xRLVu2VPny5TVy5EhNmDBBjz32mMPbCAB3O4sxjEkKAAAAADfCGScAAAAAsIPgBAAAAAB2EJwAAAAAwA6CEwAAAADYQXACAAAAADsITgAAAABgB8EJAAAAAOwgOAEAAACAHQQnAAAAALCD4AQAAAAAdhCcAAAAAMCO/wd3cqbHiEeXSAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "performance_df_long = performance_df.reset_index().melt(id_vars='index', var_name='Model', value_name='Score')\n",
    "\n",
    "# Now you can plot with 'Model' and 'Metrics' on x-axis and 'Score' on y-axis\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Model', y='Score', hue='index', data=performance_df_long, palette='viridis')\n",
    "plt.title(\"Model Performance Comparison\")\n",
    "plt.xlabel(\"Model and Metrics\")\n",
    "plt.ylabel(\"Performance Score\")\n",
    "plt.legend(title='Metrics')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/Random_Forest_Classifiers.pkl']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dumping the model which perfromed well to predict the users input data.\n",
    "\n",
    "joblib.dump(models[\"Random Forest Classifiers\"], 'data/Random_Forest_Classifiers.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "eda_env",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}