trees1.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, mean_absolute_error, mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.tree import export_graphviz\n",
    "import graphviz\n",
    "import plotly.graph_objs as go\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>person_age</th>\n",
       "      <th>person_income</th>\n",
       "      <th>person_home_ownership</th>\n",
       "      <th>person_emp_length</th>\n",
       "      <th>loan_intent</th>\n",
       "      <th>loan_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>loan_int_rate</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>loan_percent_income</th>\n",
       "      <th>cb_person_default_on_file</th>\n",
       "      <th>cb_person_cred_hist_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>59000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>123.0</td>\n",
       "      <td>PERSONAL</td>\n",
       "      <td>D</td>\n",
       "      <td>35000</td>\n",
       "      <td>16.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0.59</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>9600</td>\n",
       "      <td>OWN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>B</td>\n",
       "      <td>1000</td>\n",
       "      <td>11.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>9600</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>5500</td>\n",
       "      <td>12.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.57</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>65500</td>\n",
       "      <td>RENT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>35000</td>\n",
       "      <td>15.23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.53</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>54400</td>\n",
       "      <td>RENT</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>35000</td>\n",
       "      <td>14.27</td>\n",
       "      <td>1</td>\n",
       "      <td>0.55</td>\n",
       "      <td>Y</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   person_age  person_income person_home_ownership  person_emp_length   \n",
       "0          22          59000                  RENT              123.0  \\\n",
       "1          21           9600                   OWN                5.0   \n",
       "2          25           9600              MORTGAGE                1.0   \n",
       "3          23          65500                  RENT                4.0   \n",
       "4          24          54400                  RENT                8.0   \n",
       "\n",
       "  loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status   \n",
       "0    PERSONAL          D      35000          16.02            1  \\\n",
       "1   EDUCATION          B       1000          11.14            0   \n",
       "2     MEDICAL          C       5500          12.87            1   \n",
       "3     MEDICAL          C      35000          15.23            1   \n",
       "4     MEDICAL          C      35000          14.27            1   \n",
       "\n",
       "   loan_percent_income cb_person_default_on_file  cb_person_cred_hist_length  \n",
       "0                 0.59                         Y                           3  \n",
       "1                 0.10                         N                           2  \n",
       "2                 0.57                         N                           3  \n",
       "3                 0.53                         N                           2  \n",
       "4                 0.55                         Y                           4  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data\n",
    "df = pd.read_csv(\"credit_risk_dataset.csv\")\n",
    "# Print the first 5 rows of the dataframe.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "person_home_ownership: 4\n",
      "loan_intent: 6\n",
      "loan_grade: 7\n",
      "cb_person_default_on_file: 2\n"
     ]
    }
   ],
   "source": [
    "# Print categorical columns\n",
    "for column in df.columns:\n",
    "    if df[column].dtype == 'object':\n",
    "        print(f\"{column}: {len(df[column].unique())}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table id=\"T_80fd0\">\n",
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_80fd0_level0_col0\" class=\"col_heading level0 col0\" >count</th>\n",
       "      <th id=\"T_80fd0_level0_col1\" class=\"col_heading level0 col1\" >mean</th>\n",
       "      <th id=\"T_80fd0_level0_col2\" class=\"col_heading level0 col2\" >std</th>\n",
       "      <th id=\"T_80fd0_level0_col3\" class=\"col_heading level0 col3\" >min</th>\n",
       "      <th id=\"T_80fd0_level0_col4\" class=\"col_heading level0 col4\" >25%</th>\n",
       "      <th id=\"T_80fd0_level0_col5\" class=\"col_heading level0 col5\" >50%</th>\n",
       "      <th id=\"T_80fd0_level0_col6\" class=\"col_heading level0 col6\" >75%</th>\n",
       "      <th id=\"T_80fd0_level0_col7\" class=\"col_heading level0 col7\" >max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_80fd0_level0_row0\" class=\"row_heading level0 row0\" >person_age</th>\n",
       "      <td id=\"T_80fd0_row0_col0\" class=\"data row0 col0\" >32581.000000</td>\n",
       "      <td id=\"T_80fd0_row0_col1\" class=\"data row0 col1\" >27.734600</td>\n",
       "      <td id=\"T_80fd0_row0_col2\" class=\"data row0 col2\" >6.348078</td>\n",
       "      <td id=\"T_80fd0_row0_col3\" class=\"data row0 col3\" >20.000000</td>\n",
       "      <td id=\"T_80fd0_row0_col4\" class=\"data row0 col4\" >23.000000</td>\n",
       "      <td id=\"T_80fd0_row0_col5\" class=\"data row0 col5\" >26.000000</td>\n",
       "      <td id=\"T_80fd0_row0_col6\" class=\"data row0 col6\" >30.000000</td>\n",
       "      <td id=\"T_80fd0_row0_col7\" class=\"data row0 col7\" >144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80fd0_level0_row1\" class=\"row_heading level0 row1\" >person_income</th>\n",
       "      <td id=\"T_80fd0_row1_col0\" class=\"data row1 col0\" >32581.000000</td>\n",
       "      <td id=\"T_80fd0_row1_col1\" class=\"data row1 col1\" >66074.848470</td>\n",
       "      <td id=\"T_80fd0_row1_col2\" class=\"data row1 col2\" >61983.119168</td>\n",
       "      <td id=\"T_80fd0_row1_col3\" class=\"data row1 col3\" >4000.000000</td>\n",
       "      <td id=\"T_80fd0_row1_col4\" class=\"data row1 col4\" >38500.000000</td>\n",
       "      <td id=\"T_80fd0_row1_col5\" class=\"data row1 col5\" >55000.000000</td>\n",
       "      <td id=\"T_80fd0_row1_col6\" class=\"data row1 col6\" >79200.000000</td>\n",
       "      <td id=\"T_80fd0_row1_col7\" class=\"data row1 col7\" >6000000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80fd0_level0_row2\" class=\"row_heading level0 row2\" >person_emp_length</th>\n",
       "      <td id=\"T_80fd0_row2_col0\" class=\"data row2 col0\" >31686.000000</td>\n",
       "      <td id=\"T_80fd0_row2_col1\" class=\"data row2 col1\" >4.789686</td>\n",
       "      <td id=\"T_80fd0_row2_col2\" class=\"data row2 col2\" >4.142630</td>\n",
       "      <td id=\"T_80fd0_row2_col3\" class=\"data row2 col3\" >0.000000</td>\n",
       "      <td id=\"T_80fd0_row2_col4\" class=\"data row2 col4\" >2.000000</td>\n",
       "      <td id=\"T_80fd0_row2_col5\" class=\"data row2 col5\" >4.000000</td>\n",
       "      <td id=\"T_80fd0_row2_col6\" class=\"data row2 col6\" >7.000000</td>\n",
       "      <td id=\"T_80fd0_row2_col7\" class=\"data row2 col7\" >123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80fd0_level0_row3\" class=\"row_heading level0 row3\" >loan_amnt</th>\n",
       "      <td id=\"T_80fd0_row3_col0\" class=\"data row3 col0\" >32581.000000</td>\n",
       "      <td id=\"T_80fd0_row3_col1\" class=\"data row3 col1\" >9589.371106</td>\n",
       "      <td id=\"T_80fd0_row3_col2\" class=\"data row3 col2\" >6322.086646</td>\n",
       "      <td id=\"T_80fd0_row3_col3\" class=\"data row3 col3\" >500.000000</td>\n",
       "      <td id=\"T_80fd0_row3_col4\" class=\"data row3 col4\" >5000.000000</td>\n",
       "      <td id=\"T_80fd0_row3_col5\" class=\"data row3 col5\" >8000.000000</td>\n",
       "      <td id=\"T_80fd0_row3_col6\" class=\"data row3 col6\" >12200.000000</td>\n",
       "      <td id=\"T_80fd0_row3_col7\" class=\"data row3 col7\" >35000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80fd0_level0_row4\" class=\"row_heading level0 row4\" >loan_int_rate</th>\n",
       "      <td id=\"T_80fd0_row4_col0\" class=\"data row4 col0\" >29465.000000</td>\n",
       "      <td id=\"T_80fd0_row4_col1\" class=\"data row4 col1\" >11.011695</td>\n",
       "      <td id=\"T_80fd0_row4_col2\" class=\"data row4 col2\" >3.240459</td>\n",
       "      <td id=\"T_80fd0_row4_col3\" class=\"data row4 col3\" >5.420000</td>\n",
       "      <td id=\"T_80fd0_row4_col4\" class=\"data row4 col4\" >7.900000</td>\n",
       "      <td id=\"T_80fd0_row4_col5\" class=\"data row4 col5\" >10.990000</td>\n",
       "      <td id=\"T_80fd0_row4_col6\" class=\"data row4 col6\" >13.470000</td>\n",
       "      <td id=\"T_80fd0_row4_col7\" class=\"data row4 col7\" >23.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80fd0_level0_row5\" class=\"row_heading level0 row5\" >loan_status</th>\n",
       "      <td id=\"T_80fd0_row5_col0\" class=\"data row5 col0\" >32581.000000</td>\n",
       "      <td id=\"T_80fd0_row5_col1\" class=\"data row5 col1\" >0.218164</td>\n",
       "      <td id=\"T_80fd0_row5_col2\" class=\"data row5 col2\" >0.413006</td>\n",
       "      <td id=\"T_80fd0_row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
       "      <td id=\"T_80fd0_row5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
       "      <td id=\"T_80fd0_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
       "      <td id=\"T_80fd0_row5_col6\" class=\"data row5 col6\" >0.000000</td>\n",
       "      <td id=\"T_80fd0_row5_col7\" class=\"data row5 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80fd0_level0_row6\" class=\"row_heading level0 row6\" >loan_percent_income</th>\n",
       "      <td id=\"T_80fd0_row6_col0\" class=\"data row6 col0\" >32581.000000</td>\n",
       "      <td id=\"T_80fd0_row6_col1\" class=\"data row6 col1\" >0.170203</td>\n",
       "      <td id=\"T_80fd0_row6_col2\" class=\"data row6 col2\" >0.106782</td>\n",
       "      <td id=\"T_80fd0_row6_col3\" class=\"data row6 col3\" >0.000000</td>\n",
       "      <td id=\"T_80fd0_row6_col4\" class=\"data row6 col4\" >0.090000</td>\n",
       "      <td id=\"T_80fd0_row6_col5\" class=\"data row6 col5\" >0.150000</td>\n",
       "      <td id=\"T_80fd0_row6_col6\" class=\"data row6 col6\" >0.230000</td>\n",
       "      <td id=\"T_80fd0_row6_col7\" class=\"data row6 col7\" >0.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80fd0_level0_row7\" class=\"row_heading level0 row7\" >cb_person_cred_hist_length</th>\n",
       "      <td id=\"T_80fd0_row7_col0\" class=\"data row7 col0\" >32581.000000</td>\n",
       "      <td id=\"T_80fd0_row7_col1\" class=\"data row7 col1\" >5.804211</td>\n",
       "      <td id=\"T_80fd0_row7_col2\" class=\"data row7 col2\" >4.055001</td>\n",
       "      <td id=\"T_80fd0_row7_col3\" class=\"data row7 col3\" >2.000000</td>\n",
       "      <td id=\"T_80fd0_row7_col4\" class=\"data row7 col4\" >3.000000</td>\n",
       "      <td id=\"T_80fd0_row7_col5\" class=\"data row7 col5\" >4.000000</td>\n",
       "      <td id=\"T_80fd0_row7_col6\" class=\"data row7 col6\" >8.000000</td>\n",
       "      <td id=\"T_80fd0_row7_col7\" class=\"data row7 col7\" >30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1254bd840>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print numerical columns\n",
    "num = df.describe().T\n",
    "# Print descriptive statistics of numerical columns\n",
    "num.style.background_gradient(cmap='Oranges')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print the number of missing values in each column\n",
    "df.hist(bins=50, figsize=(20,15), density = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_55120\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_55120_level0_col0\" class=\"col_heading level0 col0\" >count</th>\n",
       "      <th id=\"T_55120_level0_col1\" class=\"col_heading level0 col1\" >unique</th>\n",
       "      <th id=\"T_55120_level0_col2\" class=\"col_heading level0 col2\" >top</th>\n",
       "      <th id=\"T_55120_level0_col3\" class=\"col_heading level0 col3\" >freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_55120_level0_row0\" class=\"row_heading level0 row0\" >person_home_ownership</th>\n",
       "      <td id=\"T_55120_row0_col0\" class=\"data row0 col0\" >32581</td>\n",
       "      <td id=\"T_55120_row0_col1\" class=\"data row0 col1\" >4</td>\n",
       "      <td id=\"T_55120_row0_col2\" class=\"data row0 col2\" >RENT</td>\n",
       "      <td id=\"T_55120_row0_col3\" class=\"data row0 col3\" >16446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_55120_level0_row1\" class=\"row_heading level0 row1\" >loan_intent</th>\n",
       "      <td id=\"T_55120_row1_col0\" class=\"data row1 col0\" >32581</td>\n",
       "      <td id=\"T_55120_row1_col1\" class=\"data row1 col1\" >6</td>\n",
       "      <td id=\"T_55120_row1_col2\" class=\"data row1 col2\" >EDUCATION</td>\n",
       "      <td id=\"T_55120_row1_col3\" class=\"data row1 col3\" >6453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_55120_level0_row2\" class=\"row_heading level0 row2\" >loan_grade</th>\n",
       "      <td id=\"T_55120_row2_col0\" class=\"data row2 col0\" >32581</td>\n",
       "      <td id=\"T_55120_row2_col1\" class=\"data row2 col1\" >7</td>\n",
       "      <td id=\"T_55120_row2_col2\" class=\"data row2 col2\" >A</td>\n",
       "      <td id=\"T_55120_row2_col3\" class=\"data row2 col3\" >10777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_55120_level0_row3\" class=\"row_heading level0 row3\" >cb_person_default_on_file</th>\n",
       "      <td id=\"T_55120_row3_col0\" class=\"data row3 col0\" >32581</td>\n",
       "      <td id=\"T_55120_row3_col1\" class=\"data row3 col1\" >2</td>\n",
       "      <td id=\"T_55120_row3_col2\" class=\"data row3 col2\" >N</td>\n",
       "      <td id=\"T_55120_row3_col3\" class=\"data row3 col3\" >26836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1255edc30>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print categorical variables\n",
    "cat = df.describe(include=\"object\").T\n",
    "cat.style.background_gradient(cmap='Oranges')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print histogram of categorical variables\n",
    "for var in cat.index:\n",
    "    sns.histplot(df[var], bins=50, edgecolor='white')\n",
    "    plt.xticks(rotation=75)\n",
    "    plt.title(var)\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "person_age                       0\n",
       "person_income                    0\n",
       "person_home_ownership            0\n",
       "person_emp_length              895\n",
       "loan_intent                      0\n",
       "loan_grade                       0\n",
       "loan_amnt                        0\n",
       "loan_int_rate                 3116\n",
       "loan_status                      0\n",
       "loan_percent_income              0\n",
       "cb_person_default_on_file        0\n",
       "cb_person_cred_hist_length       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the number of missing values in each column\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop missing values\n",
    "df.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 28638 entries, 0 to 32580\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   person_age                  28638 non-null  int64  \n",
      " 1   person_income               28638 non-null  int64  \n",
      " 2   person_home_ownership       28638 non-null  object \n",
      " 3   person_emp_length           28638 non-null  float64\n",
      " 4   loan_intent                 28638 non-null  object \n",
      " 5   loan_grade                  28638 non-null  object \n",
      " 6   loan_amnt                   28638 non-null  int64  \n",
      " 7   loan_int_rate               28638 non-null  float64\n",
      " 8   loan_status                 28638 non-null  int64  \n",
      " 9   loan_percent_income         28638 non-null  float64\n",
      " 10  cb_person_default_on_file   28638 non-null  object \n",
      " 11  cb_person_cred_hist_length  28638 non-null  int64  \n",
      "dtypes: float64(3), int64(5), object(4)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5c/3Hf/yHDh48qI0bN6qystJ9HapJkyYKCQlRYmKi+vTpo9GjR2vp0qWqqKjQuHHjNGTIkFrPBAQAWJ+pU9e3b9+u+++//5LxRx55RM8884wSEhKq/bpt27apR48ekn68KXjcuHF65513FBQUpMGDB2vRokW69tpra90HU9cBwBy1ff+1zH1WZiKsAMAc9fI+KwDA1YmwAgBYHmEFALA8wgoAYHmEFQDA8ggrAIDlEVYAAMsjrAAAlkdYAQAsj7ACAFgeYQUAsDzCCgBgeYQVAMDyCCsAgOURVgAAyyOsAACWR1gBACyPsAIAWB5hBQCwPMIKAGB5hBUAwPIIKwCA5RFWAADLI6wAAJZHWAEALI+wAgBYHmEFALA8wgoAYHmEFQDA8ggrAIDlEVYAAMsjrAAAlkdYAQAsj7ACAFgeYQUAsDzCCgBgeYQVAMDyCCsAgOURVgAAyyOsAACWR1gBACyPsAIAWB5hBQCwPMIKAGB5hBUAwPIIKwCA5ZkaVjt37lT//v0VGxsrh8Oht99+26NuGIZmz56t5s2bKzw8XMnJyfriiy889jlz5oxSU1MVERGhyMhIjRo1SiUlJXX4UwAAAs3UsCotLVWHDh30yiuvVFufP3++Fi1apKVLlyo7O1vXXHONevfurbKyMvc+qampOnr0qLKysrRx40bt3LlTaWlpdfUjAADqgmERkoz169e7X1dVVRkxMTHGCy+84B4rKioyQkNDjbVr1xqGYRiffPKJIcnIyclx75OZmWk4HA7j5MmTtf7eLpfLkGS4XC7ffxAAQK3V9v3Xstesjh8/roKCAiUnJ7vHnE6nunXrpr1790qS9u7dq8jISHXu3Nm9T3JysoKCgpSdnV3jscvLy1VcXOyxAQCsy7JhVVBQIEmKjo72GI+OjnbXCgoKFBUV5VEPDg5WkyZN3PtUZ968eXI6ne4tLi7Oz90DAPzJsmEVSDNnzpTL5XJveXl5ZrcEALgMy4ZVTEyMJKmwsNBjvLCw0F2LiYnR6dOnPeoXLlzQmTNn3PtUJzQ0VBERER4bAMC6LBtWCQkJiomJ0ZYtW9xjxcXFys7OVlJSkiQpKSlJRUVFOnDggHufrVu3qqqqSt26davzngEAgRFs5jcvKSnRl19+6X59/PhxHT58WE2aNFF8fLwmTpyoOXPmqHXr1kpISNCsWbMUGxurgQMHSpISExPVp08fjR49WkuXLlVFRYXGjRunIUOGKDY21qSfCgDgb6aG1f79+3X//fe7X0+ePFmS9Mgjj2jlypWaPn26SktLlZaWpqKiIt19993atGmTwsLC3F+zZs0ajRs3Tr169VJQUJAGDx6sRYsW1fnPAgAIHIdhGIbZTZituLhYTqdTLpeL61cAUIdq+/5r2WtWAABcRFgBACyPsAIAWB5hBQCwPMIKAGB5hBUAwPIIKwCA5XkdVrt27dKwYcOUlJSkkydPSpL++te/6v333/dbcwAASF6G1T/+8Q/17t1b4eHhOnTokMrLyyVJLpdLc+fO9WuDAAB4FVZz5szR0qVLtXz5cjVs2NA9ftddd+ngwYN+aw4AAMnLsDp27JjuvffeS8adTqeKiop87QkAAA9ehVVMTIzHaukXvf/++7rxxht9bgoAgJ/yKqxGjx6tCRMmKDs7Ww6HQ6dOndKaNWs0depUPfHEE/7uEQBwlfPqESFPPfWUqqqq1KtXL507d0733nuvQkNDNXXqVKWnp/u7RwDAVc6nR4ScP39eX375pUpKStS2bVtde+21/uytzvCIEAAwR23ff316+GJISIjatm3ryyEAAPhZXoXVoEGD5HA4Lhl3OBwKCwtTq1atlJKSoltuucXnBgEA8GqChdPp1NatW3Xw4EE5HA45HA4dOnRIW7du1YULF/S3v/1NHTp00O7du/3dLwDgKuTVJ6uYmBilpKTo5ZdfVlDQj3lXVVWlCRMmqHHjxlq3bp3GjBmjGTNmsPwSAMBnXk2wuP7667V7927dfPPNHuOff/65unfvru+++05HjhzRPffcY4ubhJlgAQDmqO37r1enAS9cuKDPPvvskvHPPvtMlZWVkqSwsLBqr2sBAHClvDoNOHz4cI0aNUr/9V//pS5dukiScnJyNHfuXI0YMUKStGPHDrVr185/nQIArlpehdWLL76o6OhozZ8/X4WFhZKk6OhoTZo0STNmzJAkPfDAA+rTp4//OgUAXLV8uilY+vF8oyRbX+vhmhUAmKNObgqW7B1SAAB78Dqs/v73v+uNN95Qbm6uzp8/71HjmVYAAH/yajbgokWLNHLkSEVHR+vQoUPq2rWrmjZtqq+//lp9+/b1d48AgKucV2H16quvatmyZVq8eLFCQkI0ffp0ZWVlafz48XK5XP7uEQBwlfMqrHJzc9W9e3dJUnh4uM6ePSvpxynta9eu9V93AADIhycFnzlzRpIUHx+vffv2SZKOHz8uHycXAgBwCa/CqmfPntqwYYMkaeTIkZo0aZJ++ctf6uGHH9agQYP82iAAAF7dZ1VVVaWqqioFB/84mXDdunXas2ePWrdurccff1whISF+bzSQuM8KAMxR2/dfn28Kvpwnn3xSzz77rJo1axaob+EXhBUAmCOgC9nW1urVq90rXAAIjD179ujhhx/Wnj17zG4FCJiAhhWTLYDAKisr04IFC1RYWKgFCxaorKzM7JaAgAhoWAEIrDVr1uj777+XJH3//ffKyMgwuSMgMAgrwKby8/OVkZHhPoNhGIYyMjKUn59vcmeA/xFWgA0ZhqGFCxfWOM4peNQ3hBVgQ7m5ucrJyXE/mfuiyspK5eTkKDc316TOgMAIaFgNGzaMqeBAAMTHx6tLly5q0KCBx3iDBg3UtWtXxcfHm9QZEBhe32dVVFSkDz74QKdPn1ZVVZVH7eKj7e2C+6xgR/n5+XrkkUc8Pl0FBwdr1apVuuGGG0zsDKi9gD588Z133lFqaqpKSkoUEREhh8PhrjkcDtuFFWBHLVq0UEpKilavXi3DMORwOJSSkkJQoV7y6jTglClT9Nhjj6mkpERFRUX617/+5d4uLnALIPBSU1PVtGlTSVKzZs2UkpJickdAYHgVVidPntT48ePVqFEjf/cD4AqEhYVp8uTJio6O1qRJkxQWFmZ2S0BAeBVWvXv31v79+/3dCwAA1fIqrB566CFNmzZNzzzzjP7xj39ow4YNHpu/VFZWatasWUpISFB4eLhuuukmPffccx73kBiGodmzZ6t58+YKDw9XcnKyvvjiC7/1AFgZyy3hauHVBIvRo0dLkp599tlLag6H45J7P7z1/PPPa8mSJVq1apXatWun/fv3a+TIkXI6nRo/frwkaf78+Vq0aJFWrVqlhIQEzZo1S71799Ynn3zCKRHUe9Utt/TYY4+Z3BXgfwF9RIiv+vXrp+joaL3++uvuscGDBys8PNw9Ayo2NlZTpkzR1KlTJUkul0vR0dFauXKlhgwZUqvvw9R12FFNU9dXrlypFi1amNgZUHuWeESIr7p3764tW7bo888/lyR9+OGHev/999W3b19J0vHjx1VQUKDk5GT31zidTnXr1k179+6t8bjl5eUqLi722AA7YbklXG28DqsdO3aof//+atWqlVq1aqUBAwZo165d/uxNTz31lIYMGaI2bdqoYcOG6tixoyZOnKjU1FRJUkFBgSQpOjra4+uio6PdterMmzdPTqfTvcXFxfm1byDQWG4JVxuvwmr16tVKTk5Wo0aNNH78eI0fP17h4eHq1auXXx9R8MYbb2jNmjXKyMjQwYMHtWrVKv3pT3/SqlWrfDruzJkz5XK53FteXp6fOgbqBsst4Wrj1TWrxMREpaWladKkSR7jCxYs0PLly/Xpp5/6pbm4uDg99dRTGjt2rHtszpw5Wr16tT777DN9/fXXuummm3To0CHdfvvt7n3uu+8+3X777dWeJqkO16xgRyy3hPogoNesvv76a/Xv3/+S8QEDBuj48ePeHLJa586dU1CQZ4sNGjRwr0WYkJCgmJgYbdmyxV0vLi5Wdna2kpKS/NYHYEUtWrRQ27ZtPcbatm1LUKFe8mrqelxcnLZs2aJWrVp5jL/33nt+vf7Tv39//eEPf1B8fLzatWunQ4cOacGCBe6puQ6HQxMnTtScOXPUunVr99T12NhYDRw40G99AFaUn5+vo0ePeowdPXpU+fn5zAZEveNVWE2ZMkXjx4/X4cOH1b17d0nS7t27tXLlylqfequNxYsXa9asWXryySd1+vRpxcbG6vHHH9fs2bPd+0yfPl2lpaVKS0tTUVGR7r77bm3atIl7rFCvXZz199NFpC9auHCh5s+fX20NsCuv77Nav369/vznP7uvTyUmJmratGn61a9+5dcG6wLXrGA3J06c0COPPFJjfdWqVfrFL35Rhx0B3gnoI0IkadCgQRo0aJC3Xw7ABxdnAx48eNBjgkWDBg3UqVMnZgOi3vFqgkVeXp7y8/Pdrz/44ANNnDhRy5Yt81tjAGrmcDg0YcKEam/+nTBhAqcAUe94FVYpKSnatm2bJLlXkPjggw/0//7f/6t2vUAA/teiRQu1a9fOY6xdu3bMBkS95FVYffzxx+rataukH2/cbd++vfbs2aM1a9Zo5cqV/uwPQA3y8/P1ySefeIx98sknHmc9gPrCq7CqqKhQaGiopB+nqw8YMECS1KZNG3377bf+6w5AtVgbEFcbr8KqXbt2Wrp0qXbt2qWsrCz16dNHknTq1Cn3I7YBBA5rA+Jq41VYPf/883rttdfUo0cPDR06VB06dJAkbdiwwX16EEDgsDYgrjZXfJ+VYRjKy8vTddddpwsXLui6665z17755hs1atRIUVFRfm80kLjPCnbE2oCoDwK2NqBhGGrVqpUKCgo8gkqSWrZsabugAuyqRYsWSklJ8RhLSUkhqFAvXXFYBQUFqXXr1u5HaQMwT79+/TxeP/TQQyZ1AgSWV9es/vjHP2ratGn6+OOP/d0PgCswZ84cj9d/+MMfTOoECCyvllsaMWKEzp07pw4dOigkJETh4eEe9TNnzvilOQA1279/v44cOeIx9tFHH2n//v3q3LmzSV0BgeFVWL300kt+bgPAlaiqqqpxtZhnn31Wb7/99iXPggPszKuwutxqzwACLzs7W8XFxdXWeAAp6iOv/+n11Vdf6Xe/+52GDh2q06dPS5IyMzMveRgcAP/r1q1bjdN8nU6nunXrVscdAYHlVVjt2LFD7du3V3Z2tt566y2VlJRIkj788EM9/fTTfm0QwKWCgoI8HkL6U08//TSnAFHvePUn+qmnntKcOXOUlZWlkJAQ93jPnj21b98+vzUHoGadO3dW+/btPcZuu+023XHHHSZ1BASOV2F15MiRah+8GBUVpe+++87npgDUznPPPefxmkf0oL7yKqwiIyOrXV390KFD3D0P1KF//vOfl30N1BdehdWQIUM0Y8YMFRQUyOFwqKqqSrt379bUqVM1YsQIf/cIoAZPPPHEZV8D9YVXYTV37ly1adNGcXFxKikpUdu2bXXPPfeoe/fu+t3vfufvHgFUY+nSpbpw4YLH2IULF7R06VKTOgIC54pXXf+pvLw8HTlyRKWlperYsaNatWrlz97qDKuuw24qKir0y1/+ssZ6VlaWGjZsWIcdAd4J2KrrF73++uvq27evBg0apGHDhmngwIH67//+b28PB+AKLF682Kc6YDdehdXs2bM1YcIE9e/fX2+++abefPNN9e/fX5MmTarx3g8A/jNmzBif6oDdeHUa8Prrr9eiRYs0dOhQj/G1a9cqPT3ddtPXOQ0Iu1mwYIE2bNhQY33AgAGaPHlyHXYEeCegpwErKiqqXdW5U6dOl1zwBeB/6enpPtUBu/EqrIYPH64lS5ZcMr5s2TKlpqb63BSAy2vYsKGGDBlSbS0lJYXJFah3vDoNmJ6err/85S+Ki4vTnXfeKenHVaBzc3M1YsQIj78oCxYs8F+3AcJpQNjV/fffr5/+FXY4HNq2bZuJHQFXprbvv149IuTjjz92rz/21VdfSZKaNWumZs2aeTw92OFweHN4ALWQn58vh8NxSVjl5+erRYsWJnYG+J9XYcW/3ABzGYahhQsX6t9PjFwcnz9/Pv9YRL3CcwQAG8rNzVVOTk61YZWTk6Pc3FyTOgMCg7ACbCg+Pr7G8/sRERGKj4+v446AwCKsABsqLy+/7GPty8vL67gjILAIK8CGpk2b5lMdsBvCCrChF154wac6YDeEFWBDDRo08KkO2A1hBdgQq67jakNYATaUlpbmUx2wG8IKsKFnn33WpzpgN4QVYEPPPfecT3XAbggrwIaCgy+/UtrP1QG7IawAG9q4caNPdcBuCCvAhvr161fjQrUOh0P9+vWr446AwCKsABuqrKy8ZBHbiwzDUGVlZR13BAQWYQXY0Lx583yqA3Zj+bA6efKkhg0bpqZNmyo8PFzt27fX/v373XXDMDR79mw1b95c4eHhSk5O1hdffGFix0DgzZw506c6YDeWDqt//etfuuuuu9SwYUNlZmbqk08+0Z///Gddd9117n3mz5+vRYsWaenSpcrOztY111yj3r17q6yszMTOgcCqqqryqQ7YjcOo6cS3BTz11FPavXu3du3aVW3dMAzFxsZqypQpmjp1qiTJ5XIpOjpaK1eu1JAhQ2r1fYqLi+V0OuVyuWp8RhBgJenp6Tpy5EiN9fbt27PkEmyhtu+/lv5ktWHDBnXu3Fn/+Z//qaioKHXs2FHLly93148fP66CggIlJye7x5xOp7p166a9e/ea0TJQJ1h1HVcbS4fV119/rSVLlqh169bavHmznnjiCY0fP16rVq2SJBUUFEiSoqOjPb4uOjraXavOxQfX/XQD7KSiosKnOmA3lg6rqqoq3XHHHZo7d646duyotLQ0jR49WkuXLvXpuPPmzZPT6XRvcXFxfuoYqBsjRozwqQ7YjaXDqnnz5mrbtq3HWGJionJzcyVJMTExkqTCwkKPfQoLC9216sycOVMul8u95eXl+blzILD+8pe/+FQH7MbSYXXXXXfp2LFjHmOff/65fvGLX0iSEhISFBMToy1btrjrxcXFys7OVlJSUo3HDQ0NVUREhMcG2ElYWJhPdcBuLB1WkyZN0r59+zR37lx9+eWXysjI0LJlyzR27FhJPy4rM3HiRM2ZM0cbNmzQkSNHNGLECMXGxmrgwIHmNg8EEA9fxNXG0mHVpUsXrV+/XmvXrtWtt96q5557Ti+99JJSU1Pd+0yfPl3p6elKS0tTly5dVFJSok2bNvEvS9Rrw4YN86kO2I2l77OqK9xnBbvp3bu3ysvLa6yHhoZq8+bNddgR4J16cZ8VgOqtW7fOpzpgN4QVYENBQZf/q/tzdcBu+BMN2FBKSopPdcBuCCvAhjIyMnyqA3ZDWAE2VNNTgmtbB+yGsAJsiKnruNoQVoANrV692qc6YDeEFWBDFy5c8KkO2A1hBdjQww8/7FMdsBvCCrChv/3tbz7VAbshrAAbqqys9KkO2A1hBdgQpwFxtSGsABt68cUXfaoDdkNYATY0YcIEn+qA3RBWgA2x3BKuNoQVYEOXe5ZVbeqA3RBWgA2NHDnSpzpgN4QVYEMrVqzwqQ7YDWEFALA8wgqwIU4D4mpDWAE2xGlAXG0IK8CGSkpKfKoDdkNYATaUnp7uUx2wG8IKsKHFixf7VAfshrACbCg4ONinOmA3hBVgQ0888YRPdcBuCCvAhpYsWeJTHbAbwgqwoYKCAp/qgN04DMMwzG7CbMXFxXI6nXK5XIqIiDC7HeBn9ejR42f32b59e8D7AHxV2/dfPlkBNtSpUyef6oDdEFaADR04cMCnOmA3hBVgQ1OnTvWpDtgNYQXY0Llz53yqA3ZDWAE29Oqrr/pUB+yGsAJs6Mknn/SpDtgNYQXYUEVFhU91wG4IK8CGli9f7lMdsBvCCrCh0aNH+1QH7IawAmzo22+/9akO2A1hBdjQxo0bfaoDdkNYATbUr18/n+qA3RBWgA2dOnXKpzpgN4QVYEMHDx70qQ7YDWEF2NAdd9zhUx2wG8IKsKHCwkKf6oDdEFaADZ08edKnOmA3tgqrP/7xj3I4HJo4caJ7rKysTGPHjlXTpk117bXXavDgwfyrEgDqGduEVU5Ojl577TXddtttHuOTJk3SO++8ozfffFM7duzQqVOn9Otf/9qkLgEAgRBsdgO1UVJSotTUVC1fvlxz5sxxj7tcLr3++uvKyMhQz549JUkrVqxQYmKi9u3bpzvvvNOsluFHhmGorKzM7DZs54cffjC7BUsICwuTw+Ewuw34yBZhNXbsWD300ENKTk72CKsDBw6ooqJCycnJ7rE2bdooPj5ee/furTGsysvLVV5e7n5dXFwcuObhs7KyMvXt29fsNmyH39mPMjMzFR4ebnYb8JHlw2rdunU6ePCgcnJyLqkVFBQoJCREkZGRHuPR0dEqKCio8Zjz5s3T73//e3+3CgAIEEuHVV5eniZMmKCsrCyFhYX57bgzZ87U5MmT3a+Li4sVFxfnt+PDv8LCwpSZmWl2G5ZU3acnflee/PneAfNYOqwOHDig06dPe9zgWFlZqZ07d+rll1/W5s2bdf78eRUVFXl8uiosLFRMTEyNxw0NDVVoaGggW4cfORwOTuPUIDMz0yOwtm/fbl4zQABZOqx69eqlI0eOeIyNHDlSbdq00YwZMxQXF6eGDRtqy5YtGjx4sCTp2LFjys3NVVJSkhktA6bhExXqM0uHVePGjXXrrbd6jF1zzTVq2rSpe3zUqFGaPHmymjRpooiICKWnpyspKYmZgABQj1g6rGrjxRdfVFBQkAYPHqzy8nL17t1br776qtltAQD8yGEYhmF2E2YrLi6W0+mUy+VSRESE2e0AtfbDDz+4r1kxRRt2VNv3X9usYAEAuHoRVgAAyyOsAACWR1gBACyPsAIAWB5hBQCwPMIKAGB5hBUAwPIIKwCA5RFWAADLI6wAAJZHWAEALI+wAgBYHmEFALA8wgoAYHmEFQDA8ggrAIDlEVYAAMsjrAAAlhdsdgP4P4ZhqKyszOw2YCM//fPCnx1cqbCwMDkcDrPbqBXCykLKysrUt29fs9uATQ0aNMjsFmAzmZmZCg8PN7uNWuE0IADA8vhkZVEltw+VEcT/HvwMw5CqLvz430HBkk1O6cA8jqoLuvbwWrPbuGK8G1qUERQsNWhodhuwhRCzG4CNGGY34CVOAwIALI+wAgBYHmEFALA8wgoAYHmEFQDA8ggrAIDlEVYAAMvjPisLMYyf3AFRWWFeIwDqr5+8t3i851gcYWUh5eXl7v9u/OE6EzsBcDUoLy9Xo0aNzG6jVjgNCACwPD5ZWUhoaKj7v892GMJySwD8r7LCfebmp+85VkdYWYjHc2UaNCSsAASUXZ5lJXEaEABgA4QVAMDyCCsAgOVxzcqiHFUXbPvcGdQhHr6IK+S4+OfFZggri7LjkzwBIFA4DQgAsDw+WVlIWFiYMjMzzW4DNlJWVqZBgwZJktavX6+wsDCTO4Kd2OnPC2FlIQ6HQ+Hh4Wa3AZsKCwvjzw/qLU4DAgAsz9JhNW/ePHXp0kWNGzdWVFSUBg4cqGPHjnnsU1ZWprFjx6pp06a69tprNXjwYBUWFprUMQAgECwdVjt27NDYsWO1b98+ZWVlqaKiQg888IBKS0vd+0yaNEnvvPOO3nzzTe3YsUOnTp3Sr3/9axO7BgD4m6WvWW3atMnj9cqVKxUVFaUDBw7o3nvvlcvl0uuvv66MjAz17NlTkrRixQolJiZq3759uvPOO81oGwDgZ5YOq3/ncrkkSU2aNJEkHThwQBUVFUpOTnbv06ZNG8XHx2vv3r2EVT1hGIbKysrMbsOSfvp74XdUvbCwMFst2Irq2SasqqqqNHHiRN1111269dZbJUkFBQUKCQlRZGSkx77R0dEqKCio8Vjl5eUeDzosLi4OSM/wj7KyMvXt29fsNizv4hR2eMrMzGSWZD1g6WtWPzV27Fh9/PHHWrfO9yfozps3T06n073FxcX5oUMAQKDY4pPVuHHjtHHjRu3cuVMtWrRwj8fExOj8+fMqKiry+HRVWFiomJiYGo83c+ZMTZ482f26uLiYwLIwbpaumWEY7rMEoaGhnO6qhp1ufEXNLB1WhmEoPT1d69ev1/bt25WQkOBR79Spkxo2bKgtW7Zo8ODBkqRjx44pNzdXSUlJNR43NDTUVk/IvNpxs/TlNWrUyOwWgICzdFiNHTtWGRkZ+p//+R81btzYfR3K6XQqPDxcTqdTo0aN0uTJk9WkSRNFREQoPT1dSUlJTK4AgHrEYRiGZZ9EUdMpjRUrVujRRx+V9OPF9ylTpmjt2rUqLy9X79699eqrr172NOC/Ky4ultPplMvlUkREhD9aBwDUQm3ffy0dVnWFsAIAc9T2/dc2swEBAFcvwgoAYHmEFQDA8ggrAIDlEVYAAMsjrAAAlkdYAQAsj7ACAFgeYQUAsDzCCgBgeZZeyLauXFxxiocwAkDduvi++3Mr/xFWks6ePStJPNMKAExy9uxZOZ3OGussZCupqqpKp06dUuPGjXl4HWzn4sND8/LyWIgZtmMYhs6ePavY2FgFBdV8ZYqwAmyOpwbgasAECwCA5RFWAADLI6wAmwsNDdXTTz+t0NBQs1sBAoZrVgAAy+OTFQDA8ggrAIDlEVYAAMsjrAAAlkdYAQAsj7ACAFgeYQUAsDzCCgBgef8filCDRPSYLwsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Finding the outliers\n",
    "def plot_comparison(data):\n",
    "    plt.figure(figsize=(10, 5)) # set the size of the figure\n",
    "    plt.subplot(1, 2, 1) # 1st plot in the figure\n",
    "    sns.boxplot(y=data[var],orient= 'y',  ) # boxplot of the variable in the 1st plot\n",
    "    plt.title('Outliers') # title of the 1st plot\n",
    "plt.show()\n",
    "\n",
    "# Visualize outliers\n",
    "for var in ['person_age', 'person_income', 'person_emp_length']:\n",
    "    plot_comparison(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       person_age  person_income person_home_ownership  person_emp_length   \n",
      "1              21           9600                   OWN                5.0  \\\n",
      "2              25           9600              MORTGAGE                1.0   \n",
      "3              23          65500                  RENT                4.0   \n",
      "4              24          54400                  RENT                8.0   \n",
      "5              21           9900                   OWN                2.0   \n",
      "...           ...            ...                   ...                ...   \n",
      "32574          52          65004                  RENT                4.0   \n",
      "32575          52          64500                  RENT                0.0   \n",
      "32576          57          53000              MORTGAGE                1.0   \n",
      "32577          54         120000              MORTGAGE                4.0   \n",
      "32579          56         150000              MORTGAGE                5.0   \n",
      "\n",
      "      loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status   \n",
      "1       EDUCATION          B       1000          11.14            0  \\\n",
      "2         MEDICAL          C       5500          12.87            1   \n",
      "3         MEDICAL          C      35000          15.23            1   \n",
      "4         MEDICAL          C      35000          14.27            1   \n",
      "5         VENTURE          A       2500           7.14            1   \n",
      "...           ...        ...        ...            ...          ...   \n",
      "32574    PERSONAL          D      20000          15.58            1   \n",
      "32575   EDUCATION          B       5000          11.26            0   \n",
      "32576    PERSONAL          C       5800          13.16            0   \n",
      "32577    PERSONAL          A      17625           7.49            0   \n",
      "32579    PERSONAL          B      15000          11.48            0   \n",
      "\n",
      "       loan_percent_income cb_person_default_on_file   \n",
      "1                     0.10                         N  \\\n",
      "2                     0.57                         N   \n",
      "3                     0.53                         N   \n",
      "4                     0.55                         Y   \n",
      "5                     0.25                         N   \n",
      "...                    ...                       ...   \n",
      "32574                 0.31                         Y   \n",
      "32575                 0.08                         N   \n",
      "32576                 0.11                         N   \n",
      "32577                 0.15                         N   \n",
      "32579                 0.10                         N   \n",
      "\n",
      "       cb_person_cred_hist_length  \n",
      "1                               2  \n",
      "2                               3  \n",
      "3                               2  \n",
      "4                               4  \n",
      "5                               2  \n",
      "...                           ...  \n",
      "32574                          19  \n",
      "32575                          20  \n",
      "32576                          30  \n",
      "32577                          19  \n",
      "32579                          26  \n",
      "\n",
      "[28264 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "def remove_outliers(data, vars, multiplier= 1.5):\n",
    "    \"\"\"\n",
    "    Remove outliers from the given DataFrame using the IQR method.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : DataFrame\n",
    "        The input dataset.\n",
    "    vars : list\n",
    "        A list of column names for which to remove outliers.\n",
    "    multiplier : float, optional\n",
    "        A factor to adjust the IQR range, by default 1.5.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    DataFrame\n",
    "        A new DataFrame with outliers removed.\n",
    "    \"\"\"\n",
    "    data_clean = data.copy() # Create a copy of the input DataFrame to avoid modifying the original data\n",
    "    for var in vars:  # Iterate through each variable/column name provided in the 'vars' list\n",
    "        # Calculate the first quartile (Q1) and third quartile (Q3) for the current variable\n",
    "        Q1 = data_clean[var].quantile(0.10)\n",
    "        Q3 = data_clean[var].quantile(0.90)\n",
    "\n",
    "        # Calculate the interquartile range (IQR)\n",
    "        IQR = Q3 - Q1\n",
    "\n",
    "        # Calculate the lower and upper bounds for outlier detection\n",
    "        lower_bound = Q1 - multiplier * IQR\n",
    "        upper_bound = Q3 + multiplier * IQR\n",
    "\n",
    "        # Filter out the rows containing outliers for the current variable\n",
    "        data_clean = data_clean[(data_clean[var] >= lower_bound) & (data_clean[var] <= upper_bound)]\n",
    "\n",
    "    return data_clean  # Return the cleaned DataFrame with outliers removed\n",
    "\n",
    "# Remove outliers from continuous variables\n",
    "df_data_clean = remove_outliers(df, [\"person_age\", \"person_income\", \"person_emp_length\"]) \n",
    "print(remove_outliers(df, [\"person_age\", \"person_income\", \"person_emp_length\"]))      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove outliers from the given DataFrame using the z_score method.\n",
    "def remove_outliers_z_score(data, vars, threshold = 3): # threshold = 3 means that we will remove the values that are more than 3 standard deviations away from the mean\n",
    "    data_clean = data.copy() # Create a copy of the input DataFrame to avoid modifying the original data\n",
    "    for var in vars: # Iterate through each variable/column name provided in the 'vars' list\n",
    "        z_scores = np.abs(stats.zscore(data_clean[var])) # Calculate the z-scores for the current variable\n",
    "        data_clean = data_clean[z_scores < threshold] # Filter out the rows containing outliers for the current variable\n",
    "    return data_clean # Return the cleaned DataFrame with outliers removed\n",
    "df_data_clean2 = remove_outliers_z_score(df, [\"person_age\", \"person_income\", \"person_emp_length\"]) # Remove outliers from continuous variables\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove outliers using median absolute deviation (MAD)\n",
    "def remove_outliers_mad(data, vars, thereshold = 3.5):  # 3.5 is the default value\n",
    "    data_clean = data.copy() # Create a copy of the input DataFrame to avoid modifying the original data\n",
    "    for var in vars: # Iterate through each variable/column name provided in the 'vars' list\n",
    "        median = np.median(data_clean[var]) # Calculate the median for the current variable\n",
    "        mad = np.median(np.abs(data_clean[var]-median)) # Calculate the median absolute deviation (MAD) for the current variable\n",
    "        if mad ==0: # If the MAD is 0, then the variable is constant and we can't remove any outliers\n",
    "            continue # Skip to the next variable\n",
    "        mad_z_scores = np.abs((data_clean[var]-median)/mad) # Calculate the z-scores for the current variable\n",
    "        data_clean = data_clean[mad_z_scores < thereshold] # Filter out the rows containing outliers for the current variable\n",
    "    return data_clean  # Return the cleaned DataFrame with outliers removed\n",
    "\n",
    "df_data_clean3 = remove_outliers_mad(df, [\"person_age\", \"person_income\", \"person_emp_length\"]) # Remove outliers from continuous variables   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Plot the comparison of the original data and the data with outliers removed\n",
    "def plot_comparison(data, data_iqr, data_z, data_mad, var):\n",
    "    plt.figure(figsize=(15, 5))\n",
    "\n",
    "    plt.subplot(1, 4, 1)\n",
    "    sns.boxplot(y=data[var])\n",
    "    plt.title('Original Data')\n",
    "\n",
    "    plt.subplot(1, 4, 2)\n",
    "    sns.boxplot(y=data_iqr[var])\n",
    "    plt.title('IQR Method')\n",
    "\n",
    "    plt.subplot(1, 4, 3)\n",
    "    sns.boxplot(y=data_z[var])\n",
    "    plt.title('Z-score Method')\n",
    "\n",
    "    plt.subplot(1, 4, 4)\n",
    "    sns.boxplot(y=data_mad[var])\n",
    "    plt.title('MAD Method')\n",
    "\n",
    "    plt.suptitle(f'Outlier Removal Comparison for {var}')\n",
    "    plt.show()\n",
    "\n",
    "# Remove outliers using IQR method\n",
    "training_data_iqr = remove_outliers(df,['person_age', 'person_income', 'person_emp_length'], multiplier=1.5) \n",
    "\n",
    "# Remove outliers using Z-score method\n",
    "training_data_z = remove_outliers_z_score(df, ['person_age', 'person_income', 'person_emp_length'], threshold=3)\n",
    "\n",
    "# Remove outliers using MAD method\n",
    "training_data_mad = remove_outliers_mad(df, ['person_age', 'person_income', 'person_emp_length'], thereshold=3.5)\n",
    "\n",
    "# Plot comparison for each continuous variable\n",
    "for var in ['person_age', 'person_income', 'person_emp_length']:\n",
    "    plot_comparison(df, training_data_iqr, training_data_z, training_data_mad, var)\n",
    "\n",
    "print(training_data_iqr['person_age'].min())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data when outliers are removed\n",
    "df_data_clean.hist(bins=50, figsize=(20,15), density = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_data_clean2.hist(bins=50, figsize=(20,15), density = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data_clean3.hist(bins=50, figsize=(20,15), density = True)\n",
    "plt.show()\n",
    "df_data_clean3['person_age'].min() "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Predicted attribute (Prognozuojamas atributas) - loan_grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chose the predictive attribute loan_grade\n",
    "X = df_data_clean.drop(\"loan_grade\", axis=1) # Drop the target variable loan_grade\n",
    "y = df_data_clean[\"loan_grade\"] # Target variable is loan_grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the target variable loan_grade as an integer using LabelEncoder \n",
    "le = LabelEncoder() # Create a label encoder object\n",
    "y = le.fit_transform(y) # Fit the label encoder and transform the target variable"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3-4 Split the data into train and test sets (Duomenų rinkinio padalijimas į mokymo ir testavimo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22611, 11) (5653, 11) (22611,) (5653,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into training and test sets (80% training and 20% test) (Duomenų rinkinio padalijimas į mokymo ir testavimo rinkinius)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Creating a decision tree model (Sprendimų medžio sudarymas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classifier using Gini Index\n",
      " DecisionTreeClassifier(random_state=42)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Creating categorical columns (Kategorinių stulpelių sudarymas)\n",
    "le = LabelEncoder() # Sukuriamas LabelEncoder objektas\n",
    "X_train = X_train.apply(le.fit_transform) # Pritaikomas LabelEncoder objektas ir transformuojami duomenys\n",
    "X_test = X_test.apply(le.fit_transform) # Pritaikomas LabelEncoder objektas ir transformuojami duomenys\n",
    "# Sukuriamas Decision Tree modelis su Gini impurity\n",
    "clf= DecisionTreeClassifier(criterion=\"gini\", random_state=42) # Sukuriamas Decision Tree modelis su Gini impurity \n",
    "clf.fit(X_train, y_train) # Mokymosi algoritmas\n",
    "print(\"Decision Tree Classifier using Gini Index\\n\", clf) # Spausdinamas Decision Tree modelis su Gini impurity"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Decision tree visualization  (Sprendimų medžio vizualizacija) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'decision_tree1.pdf'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_classes = np.unique(y) # Get the unique classes in the target variable (Gauti unikalias reikšmes)\n",
    "str_unique_classes = [str(x) for x in unique_classes] # Convert the unique classes to string (Konvertuoti unikalias klasifikatorių reikšmes į string tipo reikšmes)\n",
    "dot_data = export_graphviz(clf, out_file=None, feature_names=X.columns, class_names = str_unique_classes , filled=True, rounded=True, special_characters=True) # Export the decision tree to a dot file\n",
    "graph = graphviz.Source(dot_data) # Create a graph from dot file\n",
    "graph.render(\"decision_tree1\")  # Render the graph as a PDF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" plt.figure(figsize =(20,10))\\nplot_tree(clf, filled= True, rounded= True, class_names= ['A', 'B', 'C', 'D', 'E', 'F', 'G'], feature_names= X.columns)\\nplt.show() \""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" plt.figure(figsize =(20,10))\n",
    "plot_tree(clf, filled= True, rounded= True, class_names= ['A', 'B', 'C', 'D', 'E', 'F', 'G'], feature_names= X.columns)\n",
    "plt.show() \"\"\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Testing of decision tree model (Sprendimų medžio testavimas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 77.2687 \n",
      "Mean Absolute Error:  0.2485\n",
      "Mean Squared Error:  0.2917\n",
      "Root Squared Error:  0.4838\n",
      "determination coeficient:  0.7843\n",
      "Confusion Matrix:\n",
      " [[1791   38    0    0    0    0    0]\n",
      " [   1 1267  506   68    0    0    0]\n",
      " [   0   58  894  178    4    0    0]\n",
      " [   0    3  181  354   88    0    0]\n",
      " [   0    0   20   85   59    6    0]\n",
      " [   0    0    0   13   24    3    0]\n",
      " [   0    0    0    2    8    2    0]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Z7RLYEUmy2sUKP/74I5qamuDp6Wmy3dPTE99++61CVdmW5uZmLF26FGFhYRgyZIjS5bRSVFSE0NBQ1NXVoU+fPtizZw+CgoKULquVd955BydPnkR+fr7SpUgKCQnBzp07ERAQgMuXL2PNmjUYN24cTp061ePuh3PhwgVs374diYmJ+Nvf/ob8/HwsWbIEarUasbGxSpfXSlfDhEFEv1vx8fE4depUjz1PEBAQgMLCQlRXV+Pf//43YmNjcfjw4R4VRqWlpXj88ceRlZXV4++sGRkZKf7zsGHDEBISAj8/P7z77ruIi4tTsLLWmpubERwcjHXr1gEARo4ciVOnTiEtLa1HBhFJs9qpuX79+sHe3r7VXUrLy8sl76RJHZOQkIB9+/bh008/xS233KJ0OW1Sq9W4/fbbMWrUKKSkpGD48OHi3Wl7ioKCAlRUVOCOO+6Ag4MDHBwccPjwYWzZsgUODg5oampSusR2ubq64o9//CPOnz+vdCmteHt7t/qDIzAwsMdOJXJqTprVBpFarcaoUaOQnZ0tbmtubkZ2dnaPPU9gDQRBQEJCAvbs2YOcnByz7xyqpObm5h53m+zJkyejqKgIhYWF4iM4OBgxMTEoLCyEvb290iW2q6amBt999x28vb2VLqWVsLCwVl8rOHv2LPz8/BSqSBqDSJpVT80lJiYiNjYWwcHBGDNmDDZt2oTa2lrMnz9f6dJM1NTUmPxVefHiRRQWFsLNzQ2+vr4KVtZafHw8MjIy8MEHH8DFxQUGgwEAoNPp4OTkpHB1/2flypWIjIyEr68vrl+/joyMDBw6dAgHDhxQujQTLi4urc6vOTs7w93dvcedd3vyyScxbdo0+Pn5oaysDKtXr4a9vT3mzp2rdGmtLFu2DHfddRfWrVuHBx54AMePH8eOHTuwY8cOpUujzrDgCjxFbN26VfD19RXUarUwZswY4ejRo0qX1Mqnn34qAGj1iI2NVbq0VtqqE23ctltpCxYsEPz8/AS1Wi30799fmDx5snDw4EGly+qQnrp8e86cOYK3t7egVquFP/zhD8KcOXOE8+fPK11Wuz766CNhyJAhgkajEQYNGiTs2LFD6ZJaaVm+fenSJeHatWudfly6dMmml2/zVuFERDJpuVX4999/3+VbhQ8YMIC3CiciIpKDVZ8jIiKyBgK/RySJQURE1A1sPUy6glNzRESkKHZEREQy49ScNAYREZHMGETSGERERDJjEEmziXNE9fX1SE5O7nGXd7mZtdQJWE+t1lInYD21WkudgHXVSu2ziS+0tnxprKd/2cta6gSsp1ZrqROwnlqtpU6g59faUt+5c+e6dCuN69evY+DAgT32c3YVp+aIiGTGqTlpNjE1R0REpnJzczFt2jTo9XqoVCrs3bu31ZjTp0/jz3/+M3Q6HZydnTF69GiTW2nU1dUhPj4e7u7u6NOnD2bPnt3q1jslJSWIiopC79694eHhgeXLl+PGjRtm1drtHVFzczPKysrg4uIClUplkWMajUaT/+2prKVOwHpqtZY6Aeup1VrqBOSpVRAEXL9+HXq9HnZ2lvlbXYmOqLa2FsOHD8eCBQswa9asVvu/++47jB07FnFxcVizZg20Wi2Ki4tNbuC4bNkyfPzxx3jvvfeg0+mQkJCAWbNm4YsvvgAANDU1ISoqCl5eXjhy5AguX76Mhx56CL169RJvWtgR3X6O6L///S98fHy68y2JiMxWWlra5ZtCtpwj+vbbb7t8jmjQoEGdPkekUqmwZ88ezJgxQ9wWHR2NXr164a233mrzNdXV1ejfvz8yMjJw3333AQC+/fZbBAYGIi8vD3feeSf279+PqVOnoqysDJ6engCAtLQ0rFixAleuXIFare5Qfd3eEbX8yygtLbWKk27u7u5Kl9Ah5rbCRCStK8Ehl5s7P41GA41GY/Zxmpub8fHHH+Opp55CREQEvvzyS/j7+2PlypViWBUUFKCxsRHh4eHi6wYNGgRfX18xiPLy8jB06FAxhAAgIiICixcvRnFxMUaOHNmhero9iFqm47RarVUEkaWmD4nIuljyv31LTc3dPJu0evVqJCcnm328iooK1NTUYP369XjuuefwwgsvIDMzE7NmzcKnn36KCRMmwGAwQK1Ww9XV1eS1np6e4g0zDQaDSQi17G/Z11FcNUdEJDNLBdHNM0md6YaAXzoiAJg+fTqWLVsGABgxYgSOHDmCtLQ0TJgwodO1dgZXzRERWYmWmaSWR2eDqF+/fnBwcEBQUJDJ9sDAQHHVnJeXFxoaGlBVVWUypry8HF5eXuKYm1fRtTxvGdMRDCIiIpm1dERdeViSWq3G6NGjcebMGZPtZ8+ehZ+fHwBg1KhR6NWrF7Kzs8X9Z86cQUlJCUJDQwEAoaGhKCoqQkVFhTgmKysLWq22VchJ4dQcEZHMlFi+XVNTg/Pnz4vPL168iMLCQri5ucHX1xfLly/HnDlzMH78eEyaNAmZmZn46KOPcOjQIQCATqdDXFwcEhMT4ebmBq1Wi8ceewyhoaG48847AQBTpkxBUFAQHnzwQaSmpsJgMOCZZ55BfHy8Wd0ag4iIyAadOHECkyZNEp8nJiYCAGJjY7Fz507MnDkTaWlpSElJwZIlSxAQEID3338fY8eOFV+zceNG2NnZYfbs2aivr0dERAReeeUVcb+9vT327duHxYsXIzQ0FM7OzoiNjcXatWvNqrXbv0fU068NdbOOroNXWmNjo9IlENkUS/yOavl9V1RU1OXvEQ0dOtRqfm+aix0REZHMeK05aQwiIiKZMYikcdUcEREpih0REZHM2BFJYxAREcmMQSSNU3NERKQodkRERDJjRySNQURE1A1sPUy6olNTc9u2bcOAAQPg6OiIkJAQHD9+3NJ1ERHR74TZQbR7924kJiZi9erVOHnyJIYPH46IiAiTi94REdH/6WkXPe1pzA6iDRs2YOHChZg/fz6CgoKQlpaG3r1744033pCjPiIiq8cgkmZWEDU0NKCgoMDk1rF2dnYIDw9HXl5em6+pr6+H0Wg0eRAREbUwK4h+/PFHNDU1tXlr2PZuC5uSkgKdTic+br7VLRGRrWNHJE327xGtXLkS1dXV4qO0tFTutyQi6lEYRNLMWr7dr18/2Nvbt3lr2PZuC6vRaDp9O1siIrJ9ZnVEarUao0aNMrl1bHNzM7Kzs8VbxxIRkSl2RNLM/kJrYmIiYmNjERwcjDFjxmDTpk2ora3F/Pnz5aiPiMjq8coK0swOojlz5uDKlStISkqCwWDAiBEjkJmZ2WoBAxER/YJBJK1Tl/hJSEhAQkKCpWshIqLfIV5rjohIZuyIpDGIiIhkxiCSxvsRERGRotgRERHJjB2RNAYREZHMGETSODVHRESKYkdERCQzdkTSGERERDJjEEnj1BwRESmKHRERkczYEUljEBERyYxBJI1Tc0REpCgGERGRzJS4H1Fubi6mTZsGvV4PlUqFvXv3tjv2r3/9K1QqFTZt2mSyvbKyEjExMdBqtXB1dUVcXBxqampMxnz99dcYN24cHB0d4ePjg9TUVLNrVWxqTqfTKfXWZvn444+VLqFDtm7dqnQJHXb48GGlS+iQn3/+WekSyEYoMTVXW1uL4cOHY8GCBZg1a1a74/bs2YOjR49Cr9e32hcTE4PLly8jKysLjY2NmD9/PhYtWoSMjAwAgNFoxJQpUxAeHo60tDQUFRVhwYIFcHV1xaJFizpcK88RERF1g+4+zxMZGYnIyEjJMT/88AMee+wxHDhwAFFRUSb7Tp8+jczMTOTn5yM4OBjAL3/w3nvvvXjppZeg1+uRnp6OhoYGvPHGG1Cr1Rg8eDAKCwuxYcMGs4KIU3NERFbCaDSaPOrr6zt9rObmZjz44INYvnw5Bg8e3Gp/Xl4eXF1dxRACgPDwcNjZ2eHYsWPimPHjx0OtVotjIiIicObMGVy7dq3DtTCIiIhkZqlzRD4+PtDpdOIjJSWl0zW98MILcHBwwJIlS9rcbzAY4OHhYbLNwcEBbm5uMBgM4pib787d8rxlTEdwao6ISGaWOkdUWloKrVYrbtdoNJ06XkFBATZv3oyTJ09CpVJ1ui5LYUdERGQltFqtyaOzQfTZZ5+hoqICvr6+cHBwgIODAy5duoQnnngCAwYMAAB4eXmhoqLC5HU3btxAZWUlvLy8xDHl5eUmY1qet4zpCAYREZHMlFi+LeXBBx/E119/jcLCQvGh1+uxfPlyHDhwAAAQGhqKqqoqFBQUiK/LyclBc3MzQkJCxDG5ublobGwUx2RlZSEgIAB9+/btcD2cmiMikpkSy7drampw/vx58fnFixdRWFgINzc3+Pr6wt3d3WR8r1694OXlhYCAAABAYGAg7rnnHixcuBBpaWlobGxEQkICoqOjxaXe8+bNw5o1axAXF4cVK1bg1KlT2Lx5MzZu3GhWrQwiIiIbdOLECUyaNEl8npiYCACIjY3Fzp07O3SM9PR0JCQkYPLkybCzs8Ps2bOxZcsWcb9Op8PBgwcRHx+PUaNGoV+/fkhKSjJr6TbAICIikp0SHdHEiRPNet3333/fapubm5v45dX2DBs2DJ999pm55ZlgEBERyYwXPZXGxQpERKQodkRERDJjRySNQUREJDMGkTROzRERkaLYERERyYwdkTQGERGRzBhE0hhEREQyYxBJM/sckTm3nyUiIvotZgdRy+1nt23bJkc9REQ2p6dd9LSnMXtqriO3nyUiov/DqTlpsp8jqq+vN7mdrdFolPstiYjIisj+PaKUlBSTW9v6+PjI/ZZERD0Kp+akyR5EK1euRHV1tfgoLS2V+y2JiHoUBpE02afmNBpNp29nS0REto/fIyIikhkXK0gzO4h+6/azRERkikEkzewgssTtZ4mIiFqYHUTm3n6WiIhsv6vpCp4jIiKSGafmpDGIiIhkxiCSxhvjERGRotgRERHJjB2RNAYREZHMGETSODVHRESKYkdERCQzdkTSGERERDJjEEnj1BwRESmKHRERkczYEUljEBERyYxBJI1Tc0RENig3NxfTpk2DXq+HSqXC3r17xX2NjY1YsWIFhg4dCmdnZ+j1ejz00EMoKyszOUZlZSViYmKg1Wrh6uqKuLg41NTUmIz5+uuvMW7cODg6OsLHxwepqalm18ogIiKSmRJ3aK2trcXw4cOxbdu2Vvt++uknnDx5EqtWrcLJkyfxn//8B2fOnMGf//xnk3ExMTEoLi5GVlYW9u3bh9zcXCxatEjcbzQaMWXKFPj5+aGgoAAvvvgikpOTsWPHDrNq5dQcEZHMlJiai4yMRGRkZJv7dDodsrKyTLa9/PLLGDNmDEpKSuDr64vTp08jMzMT+fn5CA4OBgBs3boV9957L1566SXo9Xqkp6ejoaEBb7zxBtRqNQYPHozCwkJs2LDBJLB+C4PoN8yePVvpEjpkxYoVSpfQYV999ZXSJXTIzz//rHQJRCaMRqPJc41GA41GY5FjV1dXQ6VSwdXVFQCQl5cHV1dXMYQAIDw8HHZ2djh27BhmzpyJvLw8jB8/Hmq1WhwTERGBF154AdeuXUPfvn079N6cmiMikpmlpuZ8fHyg0+nER0pKikXqq6urw4oVKzB37lxotVoAgMFggIeHh8k4BwcHuLm5wWAwiGM8PT1NxrQ8bxnTEeyIiIhkZqmpudLSUjEoAFikG2psbMQDDzwAQRCwffv2Lh+vMxhEREQys1QQabVakyDqqpYQunTpEnJyckyO7eXlhYqKCpPxN27cQGVlJby8vMQx5eXlJmNanreM6QhOzRER/Q61hNC5c+fwySefwN3d3WR/aGgoqqqqUFBQIG7LyclBc3MzQkJCxDG5ublobGwUx2RlZSEgIKDD54cABhERkeyUWL5dU1ODwsJCFBYWAgAuXryIwsJClJSUoLGxEffddx9OnDiB9PR0NDU1wWAwwGAwoKGhAQAQGBiIe+65BwsXLsTx48fxxRdfICEhAdHR0dDr9QCAefPmQa1WIy4uDsXFxdi9ezc2b96MxMREs2rl1BwRkcyUWL594sQJTJo0SXzeEg6xsbFITk7Ghx9+CAAYMWKEyes+/fRTTJw4EQCQnp6OhIQETJ48GXZ2dpg9eza2bNkijtXpdDh48CDi4+MxatQo9OvXD0lJSWYt3QYYRERENmnixImSAdaRcHNzc0NGRobkmGHDhuGzzz4zu75fYxAREcmM15qTxiAiIpIZg0gaFysQEZGi2BEREcmMHZE0BhERUTew9TDpCk7NERGRotgRERHJjFNz0hhEREQyYxBJ49QcEREpih0REZHM2BFJM6sjSklJwejRo+Hi4gIPDw/MmDEDZ86ckas2IiKboMRFT62JWUF0+PBhxMfH4+jRo8jKykJjYyOmTJmC2tpaueojIrJ6DCJpZk3NZWZmmjzfuXMnPDw8UFBQgPHjx1u0MCIi+n3o0jmi6upqAL9cobU99fX1qK+vF58bjcauvCURkdXhOSJpnV4119zcjKVLlyIsLAxDhgxpd1xKSgp0Op348PHx6exbEhFZJU7NSet0EMXHx+PUqVN45513JMetXLkS1dXV4qO0tLSzb0lERDaoU1NzCQkJ2LdvH3Jzc3HLLbdIjtVoNNBoNJ0qjojIFnBqTppZQSQIAh577DHs2bMHhw4dgr+/v1x1ERHZDAaRNLOCKD4+HhkZGfjggw/g4uICg8EA4Jf7ljs5OclSIBER2TazzhFt374d1dXVmDhxIry9vcXH7t275aqPiMjqcbGCNLOn5oiIyDycmpPGi54SEZGieNFTIiKZsSOSxiAiIpIZg0gap+aIiEhR7IiIiGTGjkgag4iISGYMImkMIiIimTGIpPEcERERKYodERGRzNgRSWMQERF1A1sPk67g1BwRkQ3Kzc3FtGnToNfroVKpsHfvXpP9giAgKSkJ3t7ecHJyQnh4OM6dO2cyprKyEjExMdBqtXB1dUVcXBxqampMxnz99dcYN24cHB0d4ePjg9TUVLNrZRAREclMiYue1tbWYvjw4di2bVub+1NTU7FlyxakpaXh2LFjcHZ2RkREBOrq6sQxMTExKC4uRlZWlngPukWLFon7jUYjpkyZAj8/PxQUFODFF19EcnIyduzYYVatnJojIpKZEueIIiMjERkZ2e7xNm3ahGeeeQbTp08HAPzzn/+Ep6cn9u7di+joaJw+fRqZmZnIz89HcHAwAGDr1q2499578dJLL0Gv1yM9PR0NDQ144403oFarMXjwYBQWFmLDhg0mgfVb2BEREVkJo9Fo8qivr+/UcS5evAiDwYDw8HBxm06nQ0hICPLy8gAAeXl5cHV1FUMIAMLDw2FnZ4djx46JY8aPHw+1Wi2OiYiIwJkzZ3Dt2rUO18OO6Df8uk3tycxthZXk7e2tdAkdcv36daVL6LCb5+2pZ7FUR+Tj42OyffXq1UhOTjb7eC03NfX09DTZ7unpKe4zGAzw8PAw2e/g4AA3NzeTMTffqbvlmAaDAX379u1QPQwiIiKZWSqISktLodVqxe0ajabLtfUEnJojIrISWq3W5NHZIPLy8gIAlJeXm2wvLy8X93l5eaGiosJk/40bN1BZWWkypq1j/Po9OoJBREQks552q3B/f394eXkhOztb3GY0GnHs2DGEhoYCAEJDQ1FVVYWCggJxTE5ODpqbmxESEiKOyc3NRWNjozgmKysLAQEBHZ6WAxhERESyUyKIampqUFhYiMLCQgC/LFAoLCxESUkJVCoVli5diueeew4ffvghioqK8NBDD0Gv12PGjBkAgMDAQNxzzz1YuHAhjh8/ji+++AIJCQmIjo6GXq8HAMybNw9qtRpxcXEoLi7G7t27sXnzZiQmJppVK88RERHZoBMnTmDSpEni85ZwiI2Nxc6dO/HUU0+htrYWixYtQlVVFcaOHYvMzEw4OjqKr0lPT0dCQgImT54MOzs7zJ49G1u2bBH363Q6HDx4EPHx8Rg1ahT69euHpKQks5ZuA4BK6ObrThiNRuh0uu58y98Fa1mJBlhPrWfPnlW6hA7jqjnLq66uNlkY0Bktv+/Wrl1r8gveXHV1dUhKSrJITT0ROyIiIpnxoqfSGERERDJjEEnjYgUiIlIUOyIiIpmxI5LGICIikhmDSBqn5oiISFHsiIiIZMaOSBqDiIhIZgwiaZyaIyIiRbEjIiKSGTsiaWZ1RNu3b8ewYcPES5CHhoZi//79ctVGRGQTetrVt3sas4Lolltuwfr161FQUIATJ07g7rvvxvTp01FcXCxXfUREZOPMmpqbNm2ayfPnn38e27dvx9GjRzF48GCLFkZEZCs4NSet0+eImpqa8N5776G2tla8kRIREbXGIJJmdhAVFRUhNDQUdXV16NOnD/bs2YOgoKB2x9fX16O+vl58bjQaO1cpERHZJLOXbwcEBKCwsBDHjh3D4sWLERsbi2+++abd8SkpKdDpdOLDx8enSwUTEVkjLlRon9lBpFarcfvtt2PUqFFISUnB8OHDsXnz5nbHr1y5EtXV1eKjtLS0SwUTEVkbrpqT1uXvETU3N5tMvd1Mo9FAo9F09W2IiKwWzxFJMyuIVq5cicjISPj6+uL69evIyMjAoUOHcODAAbnqIyIiG2dWEFVUVOChhx7C5cuXodPpMGzYMBw4cAB/+tOf5KqPiMjqsSOSZlYQvf7663LVQURksxhE0njRUyIiUhQvekpEJDN2RNIYREREMmMQSePUHBERKYodERGRzNgRSWMQERHJjEEkjVNzRESkKHZEREQyY0ckjUFERCQzBpE0Ts0REcmsu6++3dTUhFWrVsHf3x9OTk647bbb8Oyzz5ocRxAEJCUlwdvbG05OTggPD8e5c+dMjlNZWYmYmBhotVq4uroiLi4ONTU1FvmZ/BqDiIjIxrzwwgvYvn07Xn75ZZw+fRovvPACUlNTsXXrVnFMamoqtmzZgrS0NBw7dgzOzs6IiIhAXV2dOCYmJgbFxcXIysrCvn37kJubi0WLFlm8Xk7NERHJrLun5o4cOYLp06cjKioKADBgwAC8/fbbOH78uHi8TZs24ZlnnsH06dMBAP/85z/h6emJvXv3Ijo6GqdPn0ZmZiby8/MRHBwMANi6dSvuvfdevPTSS9Dr9Z3+PDdjR0REJDNLTc0ZjUaTR3v3grvrrruQnZ2Ns2fPAgC++uorfP7554iMjAQAXLx4EQaDAeHh4eJrdDodQkJCkJeXBwDIy8uDq6urGEIAEB4eDjs7Oxw7dsyiPx92REREVsLHx8fk+erVq5GcnNxq3NNPPw2j0YhBgwbB3t4eTU1NeP755xETEwMAMBgMAABPT0+T13l6eor7DAYDPDw8TPY7ODjAzc1NHGMpDCIbUV5ernQJHXb9+nWlS+iQGzduKF0C2QhLTc2VlpZCq9WK29u7+/W7776L9PR0ZGRkYPDgwSgsLMTSpUuh1+sRGxvb6TrkwiAiIpKZpYJIq9WaBFF7li9fjqeffhrR0dEAgKFDh+LSpUtISUlBbGwsvLy8APzyB6y3t7f4uvLycowYMQIA4OXlhYqKCpPj3rhxA5WVleLrLYXniIiIbMxPP/0EOzvTX+/29vZobm4GAPj7+8PLywvZ2dnifqPRiGPHjiE0NBQAEBoaiqqqKhQUFIhjcnJy0NzcjJCQEIvWy46IiEhm3b1qbtq0aXj++efh6+uLwYMH48svv8SGDRuwYMECAIBKpcLSpUvx3HPPYeDAgfD398eqVaug1+sxY8YMAEBgYCDuueceLFy4EGlpaWhsbERCQgKio6MtumIOYBAREcmuu4No69atWLVqFf7f//t/qKiogF6vx6OPPoqkpCRxzFNPPYXa2losWrQIVVVVGDt2LDIzM+Ho6CiOSU9PR0JCAiZPngw7OzvMnj0bW7Zs6fTnaI9K6OZrRxiNRuh0uu58y9+Fm9vwnqx3795Kl9Ah1rRY4ddfQiTLqK6u7tD5GCktv+8effTRdhcWdER9fT1effVVi9TUE7EjIiLqBrZ+vbiuYBAREcmMFz2VZj3zOUREZJPYERERyYwdkTQGERGRzBhE0hhEREQyYxBJ4zkiIiJSFDsiIiKZsSOSxiAiIpIZg0gap+aIiEhR7IiIiGTGjkgag4iISGYMImldmppbv369eDlxIiKizuh0R5Sfn49XX30Vw4YNs2Q9REQ2hx2RtE51RDU1NYiJicFrr72Gvn37WromIiKb0hJEXXnYsk4FUXx8PKKiohAeHv6bY+vr62E0Gk0eRERELcyemnvnnXdw8uRJ5Ofnd2h8SkoK1qxZY3ZhRES2glNz0szqiEpLS/H4448jPT3d5HayUlauXInq6mrxUVpa2qlCiYisFafmpJnVERUUFKCiogJ33HGHuK2pqQm5ubl4+eWXUV9fD3t7e5PXaDSaLt0il4iIbJtZQTR58mQUFRWZbJs/fz4GDRqEFStWtAohIiLi1NxvMSuIXFxcMGTIEJNtzs7OcHd3b7WdiIh+wSCSxisrEBHJjEEkrctBdOjQIQuUQUREv1fsiIiIZMaOSBqDiIhIZgwiabwfERERKYodERFRN7D1rqYrGERERDLj1Jw0Ts0REZGi2BEREcmMHZE0BhERkcwYRNI4NUdEZIN++OEH/OUvf4G7uzucnJwwdOhQnDhxQtwvCAKSkpLg7e0NJycnhIeH49y5cybHqKysRExMDLRaLVxdXREXF4eamhqL18ogIiKSWXffBuLatWsICwtDr169sH//fnzzzTf4xz/+YXJH7dTUVGzZsgVpaWk4duwYnJ2dERERgbq6OnFMTEwMiouLkZWVhX379iE3NxeLFi2y2M+lhUro5p7PaDRCp9N151v+LtjZWc/fFL1791a6hA65ceOG0iV02K9/eZBlVFdXQ6vVdukYLb/vZs6ciV69enX6OI2NjdizZ0+Ha3r66afxxRdf4LPPPmtzvyAI0Ov1eOKJJ/Dkk08C+OXzenp6YufOnYiOjsbp06cRFBSE/Px8BAcHAwAyMzNx77334r///S/0en2nP8/NrOe3FxHR75zRaDR51NfXtznuww8/RHBwMO6//354eHhg5MiReO2118T9Fy9ehMFgQHh4uLhNp9MhJCQEeXl5AIC8vDy4urqKIQQA4eHhsLOzw7Fjxyz6uRhEREQys9TUnI+PD3Q6nfhISUlp8/0uXLiA7du3Y+DAgThw4AAWL16MJUuWYNeuXQAAg8EAAPD09DR5naenp7jPYDDAw8PDZL+DgwPc3NzEMZbCVXNERDKz1Kq50tJSk6m59u5+3dzcjODgYKxbtw4AMHLkSJw6dQppaWmIjY3tdB1yYRDZiObmZqVL6LDa2lqlS+gQazrvRj2bpYJIq9V26ByRt7c3goKCTLYFBgbi/fffBwB4eXkBAMrLy+Ht7S2OKS8vx4gRI8QxFRUVJse4ceMGKisrxddbCv9LIyKyMWFhYThz5ozJtrNnz8LPzw8A4O/vDy8vL2RnZ4v7jUYjjh07htDQUABAaGgoqqqqUFBQII7JyclBc3MzQkJCLFovOyIiIpl19xdaly1bhrvuugvr1q3DAw88gOPHj2PHjh3YsWMHAEClUmHp0qV47rnnMHDgQPj7+2PVqlXQ6/WYMWMGgF86qHvuuQcLFy5EWloaGhsbkZCQgOjoaIuumAMYREREsuvuIBo9ejT27NmDlStXYu3atfD398emTZsQExMjjnnqqadQW1uLRYsWoaqqCmPHjkVmZiYcHR3FMenp6UhISMDkyZNhZ2eH2bNnY8uWLZ3+HO3h94io26lUKqVL6BBrOkfU1NSkdAk2x5LfI5o6dWqXv0e0b98+i9TUE7EjIiKSGa81J41BREQkMwaRNOuZeyAiIpvEjoiISGbsiKQxiIiIZMYgksapOSIiUhQ7IiIimbEjksYgIiKSGYNIGqfmiIhIUeyIiIhkxo5IGoOIiEhmDCJpDCIiom5g62HSFWadI0pOToZKpTJ5DBo0SK7aiIjod8Dsjmjw4MH45JNP/u8ADmyqiIikcGpOmtkp4uDgYPHbxBIR2TIGkTSzl2+fO3cOer0et956K2JiYlBSUiJHXURE9DthVkcUEhKCnTt3IiAgAJcvX8aaNWswbtw4nDp1Ci4uLm2+pr6+HvX19eJzo9HYtYqJiKwMOyJpZgVRZGSk+M/Dhg1DSEgI/Pz88O677yIuLq7N16SkpGDNmjVdq5KIyIoxiKR16coKrq6u+OMf/4jz58+3O2blypWorq4WH6WlpV15SyIisjFdCqKamhp899138Pb2bneMRqOBVqs1eRAR/Z60dERdedgys4LoySefxOHDh/H999/jyJEjmDlzJuzt7TF37ly56iMisnoMImlmnSP673//i7lz5+Lq1avo378/xo4di6NHj6J///5y1UdERDbOrCB655135KqDiMhmcbGCNF4WgYhIZgwiabwfERERKYodERGRzNgRSWMQERHJjEEkjUFERCQzBpE0niMiIiJFsSMiIpIZOyJp7IiIiGSm9JUV1q9fD5VKhaVLl4rb6urqEB8fD3d3d/Tp0wezZ89GeXm5yetKSkoQFRWF3r17w8PDA8uXL8eNGze6VEtbGERERDYsPz8fr776KoYNG2ayfdmyZfjoo4/w3nvv4fDhwygrK8OsWbPE/U1NTYiKikJDQwOOHDmCXbt2YefOnUhKSrJ4jQwiIiKZKdUR1dTUICYmBq+99hr69u0rbq+ursbrr7+ODRs24O6778aoUaPw5ptv4siRIzh69CgA4ODBg/jmm2/wr3/9CyNGjEBkZCSeffZZbNu2DQ0NDRb5ubRgEBERycxSQWQ0Gk0ev77paFvi4+MRFRWF8PBwk+0FBQVobGw02T5o0CD4+voiLy8PAJCXl4ehQ4fC09NTHBMREQGj0Yji4mJL/WgAMIiIiKyGj48PdDqd+EhJSWl37DvvvIOTJ0+2OcZgMECtVsPV1dVku6enJwwGgzjm1yHUsr9lnyVx1RwRkcwstWqutLTU5J5uGo2mzfGlpaV4/PHHkZWVBUdHx06/b3dhR0REJDNLTc3dfJPR9oKooKAAFRUVuOOOO+Dg4AAHBwccPnwYW7ZsgYODAzw9PdHQ0ICqqiqT15WXl8PLywsA4OXl1WoVXcvzljGWwo6Iup21fCeiublZ6RKIOmXy5MkoKioy2TZ//nwMGjQIK1asgI+PD3r16oXs7GzMnj0bAHDmzBmUlJQgNDQUABAaGornn38eFRUV8PDwAABkZWVBq9UiKCjIovUyiIiIukF3/gHm4uKCIUOGmGxzdnaGu7u7uD0uLg6JiYlwc3ODVqvFY489htDQUNx5550AgClTpiAoKAgPPvggUlNTYTAY8MwzzyA+Pr7dTqyzGERERDLriVdW2LhxI+zs7DB79mzU19cjIiICr7zyirjf3t4e+/btw+LFixEaGgpnZ2fExsZi7dq1Fq9FJXTzPInRaIROp+vOtyTqFJVKpXQJHWYt053WpLq62mRhQGe0/L4bOXIk7O3tO32cpqYmfPnllxapqSfiYgUiIlIUp+aIiGTWE6fmehIGERGRzBhE0jg1R0REimJHREQkM3ZE0hhEREQyYxBJ49QcEREpih0REZHM2BFJYxAREcmMQSSNU3NERKQodkRERDJjRySNQUREJDMGkTROzRERkaLYERERyYwdkTSzO6IffvgBf/nLX+Du7g4nJycMHToUJ06ckKM2IiKbYKlbhdsqszqia9euISwsDJMmTcL+/fvRv39/nDt3Dn379pWrPiIiq8eOSJpZQfTCCy/Ax8cHb775prjN39/f4kUREdHvh1lTcx9++CGCg4Nx//33w8PDAyNHjsRrr70m+Zr6+noYjUaTBxHR7wmn5qSZFUQXLlzA9u3bMXDgQBw4cACLFy/GkiVLsGvXrnZfk5KSAp1OJz58fHy6XDQRkTVhEElTCWZ8QrVajeDgYBw5ckTctmTJEuTn5yMvL6/N19TX16O+vl58bjQaGUZkFVQqldIldJit/6JSQnV1NbRabZeOYTQaodPpcPvtt8Pe3r7Tx2lqasL58+ctUlNPZNY5Im9vbwQFBZlsCwwMxPvvv9/uazQaDTQaTeeqIyKyAVysIM2sIAoLC8OZM2dMtp09exZ+fn4WLYqIyJYwiKSZdY5o2bJlOHr0KNatW4fz588jIyMDO3bsQHx8vFz1ERGRjTMriEaPHo09e/bg7bffxpAhQ/Dss89i06ZNiImJkas+IiKbwIUK7TP7Ej9Tp07F1KlT5aiFiMgmcWpOGi96SkREiuJFT4mIZMaOSBqDiIhIZgwiaZyaIyIiRbEjIiKSGTsiaQwiIiKZMYikcWqOiEhm3X3R05SUFIwePRouLi7w8PDAjBkzWl0Vp66uDvHx8XB3d0efPn0we/ZslJeXm4wpKSlBVFQUevfuDQ8PDyxfvhw3btzo8s/jZgwiIiIbc/jwYcTHx+Po0aPIyspCY2MjpkyZgtraWnHMsmXL8NFHH+G9997D4cOHUVZWhlmzZon7m5qaEBUVhYaGBhw5cgS7du3Czp07kZSUZPF6zbr6tiW0XI2WqKfj1bd/3yx59e0//OEPsLPr/N/9zc3N+OGHHzpd05UrV+Dh4YHDhw9j/PjxqK6uRv/+/ZGRkYH77rsPAPDtt98iMDAQeXl5uPPOO7F//35MnToVZWVl8PT0BACkpaVhxYoVuHLlCtRqdac/z83YERERycxSU3M332T017fYkVJdXQ0AcHNzAwAUFBSgsbER4eHh4phBgwbB19dXvKVPXl4ehg4dKoYQAERERMBoNKK4uNgiP5cWDCIiIivh4+NjcqPRlJSU33xNc3Mzli5dirCwMAwZMgQAYDAYoFar4erqajLW09MTBoNBHPPrEGrZ37LPkrhqjohIZpZaNVdaWmoyNdeRe73Fx8fj1KlT+Pzzzzv9/nJjEBERycxSQaTVas06R5SQkIB9+/YhNzcXt9xyi7jdy8sLDQ0NqKqqMumKysvL4eXlJY45fvy4yfFaVtW1jLGUbp+a40lVshaWmNfvrgdZnjX/XAVBQEJCAvbs2YOcnBz4+/ub7B81ahR69eqF7OxscduZM2dQUlKC0NBQAEBoaCiKiopQUVEhjsnKyoJWq211p+6u6vaO6Pr16939lkREZrt+/brFVvh29xda4+PjkZGRgQ8++AAuLi7iOR2dTgcnJyfodDrExcUhMTERbm5u0Gq1eOyxxxAaGoo777wTADBlyhQEBQXhwQcfRGpqKgwGA5555hnEx8d3aErQHN2+fLu5uRllZWVwcXGx2PJYo9EIHx+fVvOnPY211AlYT63WUidgPbVaS52APLUKgoDr169Dr9d3acl1S306nQ4eHh5dXr5dUVHR4eXb7f1uffPNN/Hwww8D+OULrU888QTefvtt1NfXIyIiAq+88orJtNulS5ewePFiHDp0CM7OzoiNjcX69evh4GDZHqbbg0gOLf+yLbHuX07WUidgPbVaS52A9dRqLXUCPb9WpYLI2nCxAhGRzHitOWkMIiIimTGIpNnEF1o1Gg1Wr15t8RNolmYtdQLWU6u11AlYT63WUidgXbVS+2ziHBERUU/Uco7I3d29y+eIrl69ynNERETUOZyak8YgIiKSGYNImk2cIyIiIuvFjoiIqBvYelfTFQwiIiKZdTWEbD3EODVHRESKYkdERCQzdkTSGERERDJjEEnj1BwRESmKHRERkczYEUljEBERyYxBJI1Tc0REpCh2REREMmNHJI1BREQkMwaRNE7NERGRotgRERHJjB2RNAYREZHMGETSGERERDJjEEnjOSIiIlIUOyIiIpmxI5LGICIikhmDSBqn5oiISFHsiIiIZMaOSBqDiIhIZgwiaZyaIyIiRbEjIiKSGTsiaeyIiIhkJghClx+dsW3bNgwYMACOjo4ICQnB8ePHLfzJLINBRERkg3bv3o3ExESsXr0aJ0+exPDhwxEREYGKigqlS2tFJdh6z0dEpBCj0QidTgcAUKlUnT5Oy6/p6upqaLXaDr0mJCQEo0ePxssvvwwAaG5uho+PDx577DE8/fTTna5FDuyIiIi6QXdOyzU0NKCgoADh4eHiNjs7O4SHhyMvL8+SH8siuFiBiMhKGI1Gk+cajQYajabVuB9//BFNTU3w9PQ02e7p6Ylvv/1W1ho7gx0REZFM1Go1vLy8LHKsPn36wMfHBzqdTnykpKRY5NhKY0dERCQTR0dHXLx4EQ0NDV0+liAIrc4ztdUNAUC/fv1gb2+P8vJyk+3l5eUWC0ZLYhAREcnI0dERjo6O3fqearUao0aNQnZ2NmbMmAHgl8UK2dnZSEhI6NZaOoJBRERkgxITExEbG4vg4GCMGTMGmzZtQm1tLebPn690aa0wiIiIbNCcOXNw5coVJCUlwWAwYMSIEcjMzGy1gKEn4PeIiIhIUVw1R0REimIQERGRohhERESkKAYREREpikFERESKYhAREZGiGERERKQoBhERESmKQURERIpiEBERkaIYREREpCgGERERKer/A+5wq78NR7cYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prognozuojame testavimo duomenis \n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Apskaiciuojmas  modelio tikslumas\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy * 100 :.4f} \")\n",
    "\n",
    "# Apskaiciuojamos paklaidos (mae, mse, rse)\n",
    "mae = mean_absolute_error(y_test, y_pred) \n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rse = np.sqrt(mse_rf)\n",
    "rmse_rf= r2_score (y_test, y_pred)\n",
    "#determination coeficient\n",
    "\n",
    "print(f\"Mean Absolute Error: {mae: .4f}\")\n",
    "print(f\"Mean Squared Error: {mse: .4f}\")\n",
    "print(f\"Root Squared Error: {rse: .4f}\")\n",
    "print(f\"determination coeficient: {rmse_rf: .4f}\")\n",
    "\n",
    "\n",
    "# Apskaiciuojama confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(f\"Confusion Matrix:\\n {conf_matrix}\")\n",
    "\n",
    "# Plot confusion matrix\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"g\")\n",
    "plt.title(\"Confusion Matrix\", fontsize=16)\n",
    "plt.ylabel(\"Actual\", fontsize=14)\n",
    "plt.xlabel(\"Predicted\",fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "plt.matshow(conf_matrix, cmap=plt.cm.gray)\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "row_sums = conf_matrix.sum(axis=1, keepdims=True)\n",
    "norm_conf_mx = conf_matrix / row_sums\n",
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Keičiant maksimalų medžio gylį, eksperimentiniu būdu išmatuokite skirtingų gylių (3-4\n",
    "#### variacijos) medžių formavimo trukmę bei gaunamą tikslumą, t.y. medžio auginimas stabdomas nuo tam tikro gylio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for max_depth = 1: 63.2584 \n",
      "Accuracy for max_depth = 2: 78.7900 \n",
      "Accuracy for max_depth = 3: 88.6609 \n",
      "Accuracy for max_depth = 4: 89.3862 \n",
      "Accuracy for max_depth = 5: 89.7930 \n",
      "Accuracy for max_depth = 6: 89.4923 \n",
      "Accuracy for max_depth = 7: 88.2186 \n",
      "Accuracy for max_depth = 8: 87.9002 \n",
      "Accuracy for max_depth = 9: 87.8118 \n"
     ]
    }
   ],
   "source": [
    "for max_depth in range(1, 10):\n",
    "    clf_depth = DecisionTreeClassifier(criterion=\"gini\", random_state=42, max_depth=max_depth) # Sukuriamas Decision Tree modelis su Gini impurity\n",
    "    clf_depth.fit(X_train, y_train) # Mokymosi algoritmas\n",
    "    y_pred = clf_depth.predict(X_test) # Prognozuojame testavimo duomenis\n",
    "    accuracy = accuracy_score(y_test, y_pred) # Apskaiciuojmas  modelio tikslumas\n",
    "    print(f\"Accuracy for max_depth = {max_depth}: {accuracy * 100 :.4f} \") # Spausdinamas modelio tikslumas\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9. Creating Random Forest model (Suformuoti random forest modelį, su 5 medziais)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Random Forest): 86.6619 \n",
      "Mean Absolute Error (Random Forest):  0.1626\n",
      "Mean Squared Error (Random Forest):  0.2340\n",
      "Root Mean Squared Error (Random Forest):  0.4838\n",
      "Determiation coefficient (Random Forest):  0.8269\n",
      "Confusion Matrix (Random Forest):\n",
      "[[1788   41    0    0    0    0    0]\n",
      " [  32 1810    0    0    0    0    0]\n",
      " [   0  180  922   32    0    0    0]\n",
      " [   0   47  200  379    0    0    0]\n",
      " [   0   10   24  136    0    0    0]\n",
      " [   0    5    0   35    0    0    0]\n",
      " [   0    0    0   12    0    0    0]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Parenkamas maksimalus medžio gylis pagal geriausius rezultatus, pvz., 5\n",
    "best_depth = 5\n",
    "\n",
    "# Sukuriamas RandomForestClassifier su 5 medžiais \n",
    "rf_clf = RandomForestClassifier(n_estimators=5, max_depth=best_depth, random_state=42)\n",
    "\n",
    "# Apmokomas atsitiktinis miškas su duomenimis\n",
    "rf_clf.fit(X_train, y_train)\n",
    "\n",
    "# Testavimo duomenys\n",
    "y_pred_rf = rf_clf.predict(X_test)\n",
    "\n",
    "# Apskaiciuotas atsitiktinio miško tikslumas\n",
    "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
    "print(f\"Accuracy (Random Forest): {accuracy_rf * 100 :.4f} \")\n",
    "\n",
    "# Apskaiciuotos paklaidos (mae, mse, rmse)\n",
    "mae_rf = mean_absolute_error(y_test, y_pred_rf)\n",
    "mse_rf = mean_squared_error(y_test, y_pred_rf)\n",
    "rmse_rf = np.sqrt(mse_rf)\n",
    "#determination coefficient\n",
    "rse_rf = r2_score (y_test, y_pred_rf)\n",
    "print(f\"Mean Absolute Error (Random Forest): {mae_rf: .4f}\")\n",
    "print(f\"Mean Squared Error (Random Forest): {mse_rf: .4f}\")\n",
    "print(f\"Root Mean Squared Error (Random Forest): {rmse_rf: .4f}\")\n",
    "print(f\"Determiation coefficient (Random Forest): {rse_rf: .4f}\")\n",
    "\n",
    "\n",
    "# Apskaiciuota susimaišymo matricą\n",
    "confusion_matrix_rf = confusion_matrix(y_test, y_pred_rf)\n",
    "print(f\"Confusion Matrix (Random Forest):\\n{confusion_matrix_rf}\")\n",
    "\n",
    "# Plot confusion matrix\n",
    "sns.heatmap(confusion_matrix_rf, annot=True, fmt=\"g\")\n",
    "plt.title(\"Confusion Matrix (Random Forest)\", fontsize=16)\n",
    "plt.ylabel(\"Prediction\", fontsize=14)\n",
    "plt.xlabel(\"Actual\",fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "row_sums = confusion_matrix_rf.sum(axis=1, keepdims=True)\n",
    "norm_conf_mx = confusion_matrix_rf / row_sums\n",
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10. Testavimas su random forest dydžiu [3-9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of decision trees : 1, Accuracy: 71.3780 \n",
      "Number of decision trees : 2, Accuracy: 85.0345 \n",
      "Number of decision trees : 3, Accuracy: 84.6099 \n",
      "Number of decision trees : 4, Accuracy: 87.2457 \n",
      "Number of decision trees : 5, Accuracy: 86.6619 \n",
      "Number of decision trees : 6, Accuracy: 86.1843 \n",
      "Number of decision trees : 7, Accuracy: 84.5746 \n",
      "Number of decision trees : 8, Accuracy: 85.2645 \n",
      "Number of decision trees : 9, Accuracy: 85.3529 \n"
     ]
    }
   ],
   "source": [
    "for n_trees in range(1, 10):\n",
    "    rf_trees = RandomForestClassifier(n_estimators=n_trees, criterion=\"gini\", max_depth = best_depth, random_state=42) # Sukuriamas Random Forest su n_trees medžiais\n",
    "    rf_trees.fit(X_train, y_train)  # Apmokomi duomenys\n",
    "    y_pred_trees = rf_trees.predict(X_test) # Testavimo duomenys\n",
    "    accuracy_trees = accuracy_score(y_test, y_pred_trees) # Apskaiciuojamas tikslumas\n",
    "    print(f\"Number of decision trees : {n_trees}, Accuracy: {accuracy_trees * 100 :.4f} \")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11. Comparison for Decision trees and Random forest models (Rezultatų palyginimas Decision trees ir Random forest modelių) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decission Tree Accuracy : 89.4923 \n",
      "Random Forest Accuracy : 87.2457 \n"
     ]
    }
   ],
   "source": [
    "print(f\"Decission Tree Accuracy : {accuracy * 100 :.4f} \") # Spausdinamas Decision Tree tikslumas\n",
    "print(f\"Random Forest Accuracy : {accuracy_trees * 100 :.4f} \") # Spausdinamas Random Forest tikslumas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
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            "linecolor": "white",
            "ticks": ""
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           "ticks": "",
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           "zerolinewidth": 2
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         "text": "Modelių tikslumo palyginimas"
        },
        "xaxis": {
         "title": {
          "text": "Modeliai"
         }
        },
        "yaxis": {
         "title": {
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   "source": [
    "\n",
    "# Tikslumo metrikos\n",
    "dt_accuracy = accuracy\n",
    "rf_accuracy = accuracy_trees\n",
    "\n",
    "# Bar chart su modelių tikslumo metrikomis\n",
    "trace1 = go.Bar(\n",
    "    x=['Decision Tree', 'Random Forest'],\n",
    "    y=[dt_accuracy, rf_accuracy],\n",
    "    text=[f'{dt_accuracy:.2f}', f'{rf_accuracy:.2f}'],\n",
    "    textposition='auto',\n",
    "    marker=dict(color=['#1f77b4', '#ff7f0e'])\n",
    ")\n",
    "\n",
    "data = [trace1]\n",
    "layout = go.Layout(\n",
    "    title='Modelių tikslumo palyginimas',\n",
    "    xaxis=dict(title='Modeliai'),\n",
    "    yaxis=dict(title='Tikslumas')\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "fig.show()\n"
   ]
  }
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