Credit Scoring.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Credit Utilization Ratio</th>\n",
       "      <th>Payment History</th>\n",
       "      <th>Number of Credit Accounts</th>\n",
       "      <th>Loan Amount</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Loan Term</th>\n",
       "      <th>Type of Loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>Master</td>\n",
       "      <td>Employed</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2685.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4675000</td>\n",
       "      <td>2.65</td>\n",
       "      <td>48</td>\n",
       "      <td>Personal Loan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>High School</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2371.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3619000</td>\n",
       "      <td>5.19</td>\n",
       "      <td>60</td>\n",
       "      <td>Auto Loan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>Female</td>\n",
       "      <td>Single</td>\n",
       "      <td>Master</td>\n",
       "      <td>Employed</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2771.0</td>\n",
       "      <td>6</td>\n",
       "      <td>957000</td>\n",
       "      <td>2.76</td>\n",
       "      <td>12</td>\n",
       "      <td>Auto Loan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Female</td>\n",
       "      <td>Married</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1371.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4731000</td>\n",
       "      <td>6.57</td>\n",
       "      <td>60</td>\n",
       "      <td>Auto Loan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Self-Employed</td>\n",
       "      <td>0.99</td>\n",
       "      <td>828.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3289000</td>\n",
       "      <td>6.28</td>\n",
       "      <td>36</td>\n",
       "      <td>Personal Loan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender Marital Status Education Level Employment Status  \\\n",
       "0   60    Male        Married          Master          Employed   \n",
       "1   25    Male        Married     High School        Unemployed   \n",
       "2   30  Female         Single          Master          Employed   \n",
       "3   58  Female        Married             PhD        Unemployed   \n",
       "4   32    Male        Married        Bachelor     Self-Employed   \n",
       "\n",
       "   Credit Utilization Ratio  Payment History  Number of Credit Accounts  \\\n",
       "0                      0.22           2685.0                          2   \n",
       "1                      0.20           2371.0                          9   \n",
       "2                      0.22           2771.0                          6   \n",
       "3                      0.12           1371.0                          2   \n",
       "4                      0.99            828.0                          2   \n",
       "\n",
       "   Loan Amount  Interest Rate  Loan Term   Type of Loan  \n",
       "0      4675000           2.65         48  Personal Loan  \n",
       "1      3619000           5.19         60      Auto Loan  \n",
       "2       957000           2.76         12      Auto Loan  \n",
       "3      4731000           6.57         60      Auto Loan  \n",
       "4      3289000           6.28         36  Personal Loan  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('credit_scoring.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Age                        1000 non-null   int64  \n",
      " 1   Gender                     1000 non-null   object \n",
      " 2   Marital Status             1000 non-null   object \n",
      " 3   Education Level            1000 non-null   object \n",
      " 4   Employment Status          1000 non-null   object \n",
      " 5   Credit Utilization Ratio   1000 non-null   float64\n",
      " 6   Payment History            1000 non-null   float64\n",
      " 7   Number of Credit Accounts  1000 non-null   int64  \n",
      " 8   Loan Amount                1000 non-null   int64  \n",
      " 9   Interest Rate              1000 non-null   float64\n",
      " 10  Loan Term                  1000 non-null   int64  \n",
      " 11  Type of Loan               1000 non-null   object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 93.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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06aPCwkKbdhMmTNBTTz2ltLQ0RUVFSZKSk5OVnp6u1atX68svvyy2hv79++vo0aP6+uuvlZKSolatWqlz5846efKkJOmTTz5RXFycXnnlFW3fvl3BwcGaP39+ab4Mf8iuQwveffddSZdvenC1+Ph4xcbGysXFRWvWrNHs2bN19uxZhYaGql+/fpo0aZK1raOjo7788kuNGDFCkZGRqlatmoYMGWJz3VkAAAAz+etf/6qJEyfq119/lSRt2rRJiYmJWr9+vbVNv379bNZZtGiRatSoob1799qcOD9mzBj17dvXpm21atX0wQcfXPMypd999522bt2qo0ePWjsG33jjDS1fvlz/+te/9Nhjj2n27NkaNmyYhg0bJkmaPn261qxZU669snYNsoZhXHd5aGioNmzY8IfbqVWrllauXFlaZQGo4ri0mn2Y6exuoKzVqFFDPXr0UEJCggzDUI8ePRQQEGDTZv/+/ZoyZYq2bNmi48ePW3tiMzMzbYJs69ati2y/adOm173W/o4dO5Sbmyt/f3+b+efPn7cOX0hLSytyt9XIyEitW7fu5g72FlSIk70AoKLg0mr2w2XVAFtDhw7VqFGjJEnz5s0rsjwmJka1atXSwoULFRISosLCQjVp0kT5+fk27Yq7EsEfXZ0gNzdXwcHBNj3AV/j4+Nz4QZQxgiwAXIVLq9lHeVxWjZ52+6G3vWSio6OVn58vi8ViHdt6xYkTJ5Senq6FCxfq3nvvlaQilzC9Fa1atVJ2dracnJwUHh5ebJuIiAht2bJFgwcPts774YcfSq2GG0GQBYBicGm1yoWedvuit71kHB0dlZaWZn1+NV9fX/n7++v9999XcHCwMjMzNWHChFLbd5cuXRQZGanevXtr5syZatCggQ4fPqyvvvpKffr0UevWrfXUU08pNjZWrVu3Vvv27bVkyRLt2bNHderUKbU6/ghBFgBQ6dHTbj8V8SYmOVmHTLMfLy+vYuc7ODgoMTFRTz75pJo0aaKGDRtqzpw5RU6gLymLxaKVK1fqhRde0COPPKJjx44pKChIHTp0sN5BdcCAATp48KDGjRunCxcuqF+/fhoxYoRWrVpVKjXcCIIsAKDKoKe9agsICJC7u4e2LCq/29S7u3sUOUnrev7ojl3Lly+3Pu/SpYv27t1rs/zqE+nDw8OLPbH+Wvs4dOiQzXT16tU1Z84czZkz55r1PP/883r++edt5r322mvXbF/aCLIAAKBKCAsL0759aeU6VprxwWWLIAsAAKqMsLAwgmUlYtc7ewEAAAAlRZAFAACAKRFkAQAAYEoEWQAAAJgSQRYAAACmRJAFAACAKRFkAQAAYEpcRxYAAFQZmZmZpr8hgsVi0eeff67evXuX2jbj4uK0fPlypaamlto2ywNBFgAAVAmZmZmKaNRQ585fKLd9eri7KW1f+k2F2WPHjmnKlCn66quvdOTIEfn6+qp58+aaMmWK2rdvr6ysLPn6+pZh1eZBkAUAAFXC8ePHde78BX30WAtFBHuW+f7SsnL11/dTdfz48ZsKsv369VN+fr4WL16sOnXq6MiRI0pOTtaJEyckSUFBQWVVsukQZAEAQJUSEeypVuHe9i6jWKdPn9a3336r9evX67777pMk1apVS3fddZe1zdVDCw4dOqTatWvrs88+09y5c7VlyxbVr19fCxYsUGRkpHWdhQsXatq0aTpx4oSioqJ07733atq0aTp9+vQ1a/nggw80a9YsZWRkKDw8XE8++aSeeOKJMjv2kuBkLwAAgArC09NTnp6eWr58ufLy8m54vRdeeEHPPvusUlNT1aBBAw0cOFAXL16UJG3atEmPP/64nnrqKaWmpuqBBx7Qyy+/fN3tLVmyRFOmTNHLL7+stLQ0vfLKK5o8ebIWL158S8dX2giyAAAAFYSTk5MSEhK0ePFi+fj4qH379nr++ee1c+fO66737LPPqkePHmrQoIGmTp2qX3/9VQcOHJAkzZ07V926ddOzzz6rBg0a6IknnlC3bt2uu70XX3xRs2bNUt++fVW7dm317dtXTz/9tN57771SO9bSQJAFAACoQPr166fDhw9rxYoVio6O1vr169WqVSslJCRcc51mzZpZnwcHB0uSjh49KklKT0+3GZogqcj01c6ePauDBw9q2LBh1h5iT09PTZ8+XQcPHryFIyt9jJEFAACoYNzc3PTAAw/ogQce0OTJk/Xoo4/qxRdfVGxsbLHtnZ2drc8tFoskqbCwsET7zs3NlXR5XG3btm1tljk6OpZom2WFIAsAAFDBNW7cWMuXLy/Rug0bNtS2bdts5v3v9NVq1qypkJAQ/fLLLxo0aFCJ9lleCLIAAAAVxIkTJ9S/f38NHTpUzZo1U/Xq1bV9+3bNnDlTvXr1KtE2R48erQ4dOujNN99UTEyM1q5dq6+//trac1ucqVOn6sknn5S3t7eio6OVl5en7du369SpUxo7dmxJD6/UEWQBAECVkpaVW2H34+npqbZt2+qtt97SwYMHVVBQoNDQUA0fPlzPP/98iepo3769FixYoKlTp2rSpEmKiorS008/rXfeeeea6zz66KPy8PDQ66+/rueee07VqlVT06ZNNWbMmBLVUFYIsgAAoEoICAiQh7ub/vp+arnt08PdTQEBATfc3tXVVTNmzNCMGTOu2cYwDOvz8PBwm2lJ8vHxKTJv+PDhGj58uM10vXr1rNNxcXGKi4uzWecvf/mL/vKXv9xw7fZAkAUAAFVCWFiY0val6/jx4+W2z4CAgJu6q1dZeeONN/TAAw+oWrVq+vrrr7V48WLNnz/f3mXdMoIsAACoMsLCwipEsCxvW7du1cyZM/X777+rTp06mjNnjh599FF7l3XLCLIAAACV3CeffGLvEsoEN0QAAACAKRFkAQAAYEoEWQAAAJgSQRYAAACmRJAFAACAKRFkAQAAYEpcfgsAAFQZmZmZleKGCBaLRZ9//rl69+5d6tsuax07dlSLFi00e/bsW94WQRYAAFQJmZmZahTRSOfPnS+3fbp7uGtf2r4bDrOxsbFavHixJMnJyUl+fn5q1qyZBg4cqNjYWDk4XP4xPSsrS76+vmVWt1kQZAEAQJVw/PhxnT93Xh3GdZB3qHeZ7+/Mb2e0ceZGHT9+/KZ6ZaOjoxUfH69Lly7pyJEjSkpK0lNPPaV//etfWrFihZycnBQUFFSGlUuXLl2SxWKxBueKqmJXBwAAUMq8Q70VUD+gzB8lDcuurq4KCgrSbbfdplatWun555/Xv//9b3399ddKSEiQdHlowfLlyyVJ7dq10/jx4222cezYMTk7O2vjxo2SpFOnTmnw4MHy9fWVh4eHunXrpv3791vbJyQkyMfHRytWrFDjxo3l6uqqzMxM5eXlafz48QoNDZWrq6vq1aunv//979b1du/erW7dusnT01M1a9bUww8/bDN04+zZsxo8eLA8PT0VHBysWbNmleg1uRaCLAAAQAXXqVMnNW/eXMuWLSuybNCgQUpMTJRhGNZ5H3/8sUJCQnTvvfdKujxkYfv27VqxYoU2b94swzDUvXt3FRQUWNc5d+6cXnvtNX3wwQfas2ePAgMDNXjwYP3zn//UnDlzlJaWpvfee0+enp6SpNOnT6tTp05q2bKltm/frqSkJB05ckQPPvigdZvPPfecNmzYoH//+9/65ptvtH79ev3444+l9rowtAAAAMAEGjVqpJ07dxaZ/+CDD2rMmDH67rvvrMF16dKlGjhwoCwWi/bv368VK1Zo06ZNateunSRpyZIlCg0N1fLly9W/f39JUkFBgebPn6/mzZtLkn7++Wd98sknWr16tbp06SJJqlOnjnW/77zzjlq2bKlXXnnFOm/RokUKDQ3Vzz//rJCQEP3973/XRx99pM6dO0uSFi9erNtvv73UXhOCLAAAgAkYhiGLxVJkfo0aNdS1a1ctWbJE9957rzIyMrR582a99957kqS0tDQ5OTmpbdu21nX8/f3VsGFDpaWlWee5uLioWbNm1unU1FQ5OjrqvvvuK7aeHTt2aN26ddYe2qsdPHhQ58+fV35+vs1+/fz81LBhw5s/+GtgaAEAAIAJpKWlqXbt2sUuGzRokP71r3+poKBAS5cuVdOmTdW0adOb2r67u7tNUHZ3d79u+9zcXMXExCg1NdXmsX//fnXo0OGm9l1SBFkAAIAKbu3atdq1a5f69etX7PJevXrpwoULSkpK0tKlSzVo0CDrsoiICF28eFFbtmyxzjtx4oTS09PVuHHja+6zadOmKiws1IYNG4pd3qpVK+3Zs0fh4eGqV6+ezaNatWqqW7eunJ2dbfZ76tQp/fzzzzd7+NfE0AIAAFClnPntTIXeT15enrKzs20uvzVjxgz17NlTgwcPLnadatWqqXfv3po8ebLS0tI0cOBA67L69eurV69eGj58uN577z1Vr15dEyZM0G233aZevXpds47w8HANGTJEQ4cO1Zw5c9S8eXP9+uuvOnr0qB588EGNHDlSCxcu1MCBAzVu3Dj5+fnpwIEDSkxM1AcffCBPT08NGzZMzz33nPz9/RUYGKgXXnihVC/pRZAFAABVQkBAgNw93LVx5sZy26e7h7sCAgJuap2kpCQFBwfLyclJvr6+at68uebMmaMhQ4ZcNwQOGjRI3bt3V4cOHYpctzY+Pl5PPfWUevbsqfz8fHXo0EErV66Us7PzdWt599139fzzz+uJJ57QiRMnFBYWpueff16SFBISok2bNmn8+PHq2rWr8vLyVKtWLUVHR1vrfP31161DEKpXr65nnnlGZ86U3h8SBFkAAFAlhIWFaV/avgp9i9qEhATrtWKv5+pLbV3RrVu3YudLkq+vr/7xj39cc3uxsbGKjY0tMt/NzU1vvvmm3nzzzWLXq1+/frGXBLvC09NTH374oT788EPrvOeee+6a7W8WQRYAAFQZYWFhNxUsUbFxshcAAABMiSALAAAAUyLIAgAAwJQIsgAAoNK61slPqLhu5j0jyAIAgErH0dFRkpSfn2/nSnCzrrxnV97D6+GqBQAAoNJxcnKSh4eHjh07Jmdn51K9CD/KTmFhoY4dOyYPDw85Of1xTCXIAgCASsdisSg4OFgZGRn69ddf7V0OboKDg4PCwsJksVj+sC1BFgAAVEouLi6qX78+wwtMxsXF5YZ70O0aZGfMmKFly5Zp3759cnd3V7t27fTaa6+pYcOG1jYXLlzQM888o8TEROXl5SkqKkrz589XzZo1rW0yMzM1YsQIrVu3Tp6enhoyZIhmzJhxQ13SAACg8nJwcJCbm5u9y0AZseuAkQ0bNmjkyJH64YcftHr1ahUUFKhr1646e/astc3TTz+tL774Qp9++qk2bNigw4cPq2/fvtblly5dUo8ePZSfn6/vv/9eixcvVkJCgqZMmWKPQwIAAEA5sWuXZVJSks10QkKCAgMDlZKSog4dOujMmTP6+9//rqVLl6pTp06SpPj4eEVEROiHH37Q3XffrW+++UZ79+7VmjVrVLNmTbVo0UIvvfSSxo8fr7i4OLm4uNjj0AAAAFDGKtQpfGfOnJEk+fn5SZJSUlJUUFCgLl26WNs0atRIYWFh2rx5syRp8+bNatq0qc1Qg6ioKOXk5GjPnj3F7icvL085OTk2DwAAAJhLhQmyhYWFGjNmjNq3b68mTZpIkrKzs+Xi4iIfHx+btjVr1lR2dra1zdUh9sryK8uKM2PGDHl7e1sfoaGhpXw0AAAAKGsVJsiOHDlSu3fvVmJiYpnva+LEiTpz5oz18dtvv5X5PgEAAFC6KsRp/aNGjdKXX36pjRs36vbbb7fODwoKUn5+vk6fPm3TK3vkyBEFBQVZ22zdutVme0eOHLEuK46rq6tcXV1L+SgAAABQnuzaI2sYhkaNGqXPP/9ca9euVe3atW2W33nnnXJ2dlZycrJ1Xnp6ujIzMxUZGSlJioyM1K5du3T06FFrm9WrV8vLy0uNGzcunwMBAABAubNrj+zIkSO1dOlS/fvf/1b16tWtY1q9vb3l7u4ub29vDRs2TGPHjpWfn5+8vLw0evRoRUZG6u6775Ykde3aVY0bN9bDDz+smTNnKjs7W5MmTdLIkSPpdQUAAKjE7Bpk3333XUlSx44dbebHx8crNjZWkvTWW2/JwcFB/fr1s7khwhWOjo768ssvNWLECEVGRqpatWoaMmSIpk2bVl6HAQAAADuwa5A1DOMP27i5uWnevHmaN2/eNdvUqlVLK1euLM3SAAAAUMFVmKsWAAAAADeDIAsAAABTIsgCAADAlAiyAAAAMCWCLAAAAEyJIAsAAABTIsgCAADAlAiyAAAAMCWCLAAAAEyJIAsAAABTIsgCAADAlAiyAAAAMCWCLAAAAEyJIAsAAABTIsgCAADAlAiyAAAAMCWCLAAAAEyJIAsAAABTIsgCAADAlAiyAAAAMCWCLAAAAEyJIAsAAABTIsgCAADAlAiyAAAAMCWCLAAAAEyJIAsAAABTIsgCAADAlAiyAAAAMCWCLAAAAEyJIAsAAABTIsgCAADAlJzsXQBgZscy9un82XP2LqNKyT2aae8SAAAVBEEWKIG8vDzJYlHq0tfsXUrVZLFcfg8AAFUaQRYoAVdXV8kw9FLfBqod4GHvcqqUjOPnNHnZz5ffAwBAlUaQBW5B96aBahXube8yqpQfD53R5GU/27sMAEAFwMleAAAAMCWCLAAAAEyJIAsAAABTYoxsOeASTeWPSzQBAFD5EWTLEJdosjMu0QQAQKVGkC1DXKLJfrhEEwAAlR9BthxwiabyxyWaAACo/DjZCwAAAKZEkAUAAIApMbQAAFBlcBWZ8sdVZFCWCLIAgEqPq8jYGVeRQRkhyAIAKj2uImM/XEUGZYkgCwCoMriKTPnjKjIoS5zsBQAAAFMiyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFOy6529Nm7cqNdff10pKSnKysrS559/rt69e1uXx8bGavHixTbrREVFKSkpyTp98uRJjR49Wl988YUcHBzUr18/vf322/L09CyvwwBQCR3L2KfzZ8/Zu4wqI/dopr1LAGBCdg2yZ8+eVfPmzTV06FD17du32DbR0dGKj4+3Tv/vvZoHDRqkrKwsrV69WgUFBXrkkUf02GOPaenSpWVaO4DKKS8vT7JYlLr0NXuXUvVYLJdffwC4QSUKsnXq1NG2bdvk7+9vM//06dNq1aqVfvnllxvaTrdu3dStW7frtnF1dVVQUFCxy9LS0pSUlKRt27apdevWkqS5c+eqe/fueuONNxQSElLsenl5eTZfljk5OTdUL4DKz9XVVTIMvdS3gWoHeNi7nCoj4/g5TV72c5HOCgC4nhIF2UOHDunSpUtF5ufl5em///3vLRd1tfXr1yswMFC+vr7q1KmTpk+fbg3Qmzdvlo+PjzXESlKXLl3k4OCgLVu2qE+fPsVuc8aMGZo6dWqp1gmgcuneNFCtwr3tXUaV8eOhM5q87Gd7lwHAZG4qyK5YscL6fNWqVfL2/r8v+UuXLik5OVnh4eGlVlx0dLT69u2r2rVr6+DBg3r++efVrVs3bd68WY6OjsrOzlZgYKDNOk5OTvLz81N2dvY1tztx4kSNHTvWOp2Tk6PQ0NBSqxsAAABl76aC7JUTsSwWi4YMGWKzzNnZWeHh4Zo1a1apFffQQw9Znzdt2lTNmjVT3bp1tX79enXu3LnE23V1deXnKwAAAJO7qSBbWFgoSapdu7a2bdumgICAMinqWurUqaOAgAAdOHBAnTt3VlBQkI4ePWrT5uLFizp58uQ1x9UCAACgcijRdWQzMjLKPcRK0n/+8x+dOHFCwcHBkqTIyEidPn1aKSkp1jZr165VYWGh2rZtW+71AQAAoPyU+PJbycnJSk5O1tGjR609tVcsWrTohraRm5urAwcOWKczMjKUmpoqPz8/+fn5aerUqerXr5+CgoJ08OBBjRs3TvXq1VNUVJQkKSIiQtHR0Ro+fLgWLFiggoICjRo1Sg899NA1r1gAAACAyqFEPbJTp05V165dlZycrOPHj+vUqVM2jxu1fft2tWzZUi1btpQkjR07Vi1bttSUKVPk6OionTt36k9/+pMaNGigYcOG6c4779S3335rM751yZIlatSokTp37qzu3bvrnnvu0fvvv1+SwwIAAICJlKhHdsGCBUpISNDDDz98Szvv2LGjDMO45vJVq1b94Tb8/Py4+QEAAEAVVKIe2fz8fLVr1660awEAAABuWImC7KOPPkovKAAAAOyqREMLLly4oPfff19r1qxRs2bN5OzsbLP8zTffLJXiAAAAgGspUZDduXOnWrRoIUnavXu3zTKLxXLLRQEAAAB/pERBdt26daVdBwAAAHBTSjRGFgAAALC3EvXI3n///dcdQrB27doSFwQAAADciBIF2SvjY68oKChQamqqdu/erSFDhpRGXQAAAMB1lSjIvvXWW8XOj4uLU25u7i0VBAAAANyIUh0j+9e//lWLFi0qzU0CAAAAxSrVILt582a5ubmV5iYBAACAYpVoaEHfvn1tpg3DUFZWlrZv367JkyeXSmEAAADA9ZQoyHp7e9tMOzg4qGHDhpo2bZq6du1aKoUBAAAA11OiIBsfH1/adQAAAAA3pURB9oqUlBSlpaVJku644w61bNmyVIoCAAAA/kiJguzRo0f10EMPaf369fLx8ZEknT59Wvfff78SExNVo0aN0qwRAAAAKKJEVy0YPXq0fv/9d+3Zs0cnT57UyZMntXv3buXk5OjJJ58s7RoBAACAIkrUI5uUlKQ1a9YoIiLCOq9x48aaN28eJ3sBAACgXJSoR7awsFDOzs5F5js7O6uwsPCWiwIAAAD+SImCbKdOnfTUU0/p8OHD1nn//e9/9fTTT6tz586lVhwAAABwLSUKsu+8845ycnIUHh6uunXrqm7duqpdu7ZycnI0d+7c0q4RAAAAKKJEY2RDQ0P1448/as2aNdq3b58kKSIiQl26dCnV4gAAAIBruake2bVr16px48bKycmRxWLRAw88oNGjR2v06NFq06aN7rjjDn377bdlVSsAAABgdVNBdvbs2Ro+fLi8vLyKLPP29tbf/vY3vfnmm6VWHAAAAHAtNxVkd+zYoejo6Gsu79q1q1JSUm65KAAAAOCP3FSQPXLkSLGX3brCyclJx44du+WiAAAAgD9yU0H2tttu0+7du6+5fOfOnQoODr7logAAAIA/clNBtnv37po8ebIuXLhQZNn58+f14osvqmfPnqVWHAAAAHAtN3X5rUmTJmnZsmVq0KCBRo0apYYNG0qS9u3bp3nz5unSpUt64YUXyqRQAAAA4Go3FWRr1qyp77//XiNGjNDEiRNlGIYkyWKxKCoqSvPmzVPNmjXLpFAAAADgajd9Q4RatWpp5cqVOnXqlA4cOCDDMFS/fn35+vqWRX0AAABAsUp0Zy9J8vX1VZs2bUqzFgAAAOCG3dTJXgAAAEBFQZAFAACAKRFkAQAAYEoEWQAAAJgSQRYAAACmRJAFAACAKRFkAQAAYEoEWQAAAJgSQRYAAACmRJAFAACAKRFkAQAAYEoEWQAAAJgSQRYAAACmRJAFAACAKRFkAQAAYEoEWQAAAJgSQRYAAACmRJAFAACAKRFkAQAAYEoEWQAAAJgSQRYAAACmRJAFAACAKRFkAQAAYEoEWQAAAJiSXYPsxo0bFRMTo5CQEFksFi1fvtxmuWEYmjJlioKDg+Xu7q4uXbpo//79Nm1OnjypQYMGycvLSz4+Pho2bJhyc3PL8SgAAABgD3YNsmfPnlXz5s01b968YpfPnDlTc+bM0YIFC7RlyxZVq1ZNUVFRunDhgrXNoEGDtGfPHq1evVpffvmlNm7cqMcee6y8DgEAAAB24mTPnXfr1k3dunUrdplhGJo9e7YmTZqkXr16SZL+8Y9/qGbNmlq+fLkeeughpaWlKSkpSdu2bVPr1q0lSXPnzlX37t31xhtvKCQkpNht5+XlKS8vzzqdk5NTykcGAACAslZhx8hmZGQoOztbXbp0sc7z9vZW27ZttXnzZknS5s2b5ePjYw2xktSlSxc5ODhoy5Yt19z2jBkz5O3tbX2EhoaW3YEAAACgTFTYIJudnS1Jqlmzps38mjVrWpdlZ2crMDDQZrmTk5P8/PysbYozceJEnTlzxvr47bffSrl6AAAAlDW7Di2wF1dXV7m6utq7DAAAANyCCtsjGxQUJEk6cuSIzfwjR45YlwUFBeno0aM2yy9evKiTJ09a2wAAAKByqrBBtnbt2goKClJycrJ1Xk5OjrZs2aLIyEhJUmRkpE6fPq2UlBRrm7Vr16qwsFBt27Yt95oBAABQfuw6tCA3N1cHDhywTmdkZCg1NVV+fn4KCwvTmDFjNH36dNWvX1+1a9fW5MmTFRISot69e0uSIiIiFB0dreHDh2vBggUqKCjQqFGj9NBDD13zigUAAACoHOwaZLdv367777/fOj127FhJ0pAhQ5SQkKBx48bp7Nmzeuyxx3T69Gndc889SkpKkpubm3WdJUuWaNSoUercubMcHBzUr18/zZkzp9yPBQAAAOXLrkG2Y8eOMgzjmsstFoumTZumadOmXbONn5+fli5dWhblAQAAoAKrsGNkAQAAgOshyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCU7Hr5LcDsfvzlmM6eO2vvMqqU9Oxz9i4BAFBBEGSBEsjLy5ODRRr+Ybq9S6mSHCyX3wMAQNVGkAVKwNXVVYWG1HJwS3kGedq7nColNztXP/3jJ7m6utq7FACAnRFkgVtwe5vbFVA/wN5lVCnH9x/XT//4yd5lAAAqAE72AgAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEteRBQBUGdxWuvxxW2mUJYIsAKDS47bS9sVtpVFWCLIAgEqP20rbD7eVRlkiyAJAMfgJunyV18/P3Fa6/HFbaZQlgiwAXIWfoO2Hn58B3CyCbDmgZ6f8cXIBSoqfoO2Dn58BlARBtgzRs2Nf9O7gVvATdPni52cAJUGQLUP07NgPvTsAAFR+BNlyQM9O+aN3BwCAyo87ewEAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCUCLIAAAAwpQodZOPi4mSxWGwejRo1si6/cOGCRo4cKX9/f3l6eqpfv346cuSIHSsGAABAeanQQVaS7rjjDmVlZVkf3333nXXZ008/rS+++EKffvqpNmzYoMOHD6tv3752rBYAAADlxcneBfwRJycnBQUFFZl/5swZ/f3vf9fSpUvVqVMnSVJ8fLwiIiL0ww8/6O677y7vUgEAAFCOKnyP7P79+xUSEqI6depo0KBByszMlCSlpKSooKBAXbp0sbZt1KiRwsLCtHnz5utuMy8vTzk5OTYPAAAAmEuFDrJt27ZVQkKCkpKS9O677yojI0P33nuvfv/9d2VnZ8vFxUU+Pj4269SsWVPZ2dnX3e6MGTPk7e1tfYSGhpbhUQAAAKAsVOihBd26dbM+b9asmdq2batatWrpk08+kbu7e4m3O3HiRI0dO9Y6nZOTQ5gFAAAwmQrdI/u/fHx81KBBAx04cEBBQUHKz8/X6dOnbdocOXKk2DG1V3N1dZWXl5fNAwAAAOZiqiCbm5urgwcPKjg4WHfeeaecnZ2VnJxsXZ6enq7MzExFRkbasUoAAACUhwo9tODZZ59VTEyMatWqpcOHD+vFF1+Uo6OjBg4cKG9vbw0bNkxjx46Vn5+fvLy8NHr0aEVGRnLFAgAAgCqgQgfZ//znPxo4cKBOnDihGjVq6J577tEPP/ygGjVqSJLeeustOTg4qF+/fsrLy1NUVJTmz59v56oBAABQHip0kE1MTLzucjc3N82bN0/z5s0rp4oAAABQUZhqjCwAAABwBUEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKBFkAAACYEkEWAAAApkSQBQAAgCkRZAEAAGBKlSbIzps3T+Hh4XJzc1Pbtm21detWe5cEAACAMlQpguzHH3+ssWPH6sUXX9SPP/6o5s2bKyoqSkePHrV3aQAAACgjlSLIvvnmmxo+fLgeeeQRNW7cWAsWLJCHh4cWLVpk79IAAABQRpzsXcCtys/PV0pKiiZOnGid5+DgoC5dumjz5s3FrpOXl6e8vDzr9JkzZyRJOTk5pVpbbm6uJOnEgRMqOF9QqtvG9eX89/J7mZubW+rv65XtSry39sB7WznxvlZeZfneXtmeYRilul2Yh8Uw+bt/+PBh3Xbbbfr+++8VGRlpnT9u3Dht2LBBW7ZsKbJOXFycpk6dWp5lAgCAMvLbb7/p9ttvt3cZsAPT98iWxMSJEzV27FjrdGFhoU6ePCl/f39ZLBY7Vlax5OTkKDQ0VL/99pu8vLzsXQ5KCe9r5cV7W3nx3hbPMAz9/vvvCgkJsXcpsBPTB9mAgAA5OjrqyJEjNvOPHDmioKCgYtdxdXWVq6urzTwfH5+yKtH0vLy8+OKshHhfKy/e28qL97Yob29ve5cAOzL9yV4uLi668847lZycbJ1XWFio5ORkm6EGAAAAqFxM3yMrSWPHjtWQIUPUunVr3XXXXZo9e7bOnj2rRx55xN6lAQAAoIxUiiA7YMAAHTt2TFOmTFF2drZatGihpKQk1axZ096lmZqrq6tefPHFIsMwYG68r5UX723lxXsLFM/0Vy0AAABA1WT6MbIAAAComgiyAAAAMCWCLAAAAEyJIAvYUUJCwk1fwzg2Nla9e/cuk3qu5dChQ7JYLEpNTS3T/ZTk9aiqeK0qt/DwcM2ePfuWtmGP7wqgvBFkK4HY2FhZLBY9/vjjRZaNHDlSFotFsbGxpbIvi8Wi5cuXl8q2KrNr/Qeyfv16WSwWnT59WtLlK278/PPPZV7PpUuX9Oqrr6pRo0Zyd3eXn5+f2rZtqw8++KDM942Su/LZtlgscnFxUb169TRt2jRdvHjxhtaPi4uzru/k5KSAgAB16NBBs2fPVl5eXhlXX3ld/b5YLBb5+/srOjpaO3futHdpQJVDkK0kQkNDlZiYqPPnz1vnXbhwQUuXLlVYWJgdKyteQUGBvUuoENzd3RUYGFjm+5k6dareeustvfTSS9q7d6/WrVunxx57zBqoUXFFR0crKytL+/fv1zPPPKO4uDi9/vrrN7z+HXfcoaysLGVmZmrdunXq37+/ZsyYoXbt2un3338vw8ortyvvS1ZWlpKTk+Xk5KSePXvau6xSZRjGDf/RBNgLQbaSaNWqlUJDQ7Vs2TLrvGXLliksLEwtW7a0zktKStI999wjHx8f+fv7q2fPnjp48KB1eX5+vkaNGqXg4GC5ubmpVq1amjFjhqTLP3VJUp8+fWSxWKzTkvTvf/9brVq1kpubm+rUqaOpU6fafAFaLBa9++67+tOf/qRq1arp5ZdfLqNXwlyK+3l4+vTpCgwMVPXq1fXoo49qwoQJatGiRZF133jjDQUHB8vf318jR4687h8HK1as0BNPPKH+/furdu3aat68uYYNG6Znn33W2qawsFAzZ85UvXr15OrqqrCwsCLv0y+//KL7779fHh4eat68uTZv3myz/LPPPtMdd9whV1dXhYeHa9asWTbLT506pcGDB8vX11ceHh7q1q2b9u/ff4OvVtXk6uqqoKAg1apVSyNGjFCXLl20YsUK6/JVq1YpIiJCnp6e1nB1NScnJwUFBSkkJERNmzbV6NGjtWHDBu3evVuvvfZaeR9OpXHlfQkKClKLFi00YcIE/fbbbzp27Jgkafz48WrQoIE8PDxUp04dTZ48uchn9IsvvlCbNm3k5uamgIAA9enTx2b5uXPnNHToUFWvXl1hYWF6//33bZb/9ttvevDBB+Xj4yM/Pz/16tVLhw4dumbNeXl5evLJJxUYGCg3Nzfdc8892rZtm3X5lV+Mvv76a915551ydXXVd999d4uvFFC2CLKVyNChQxUfH2+dXrRoUZG7m509e1Zjx47V9u3blZycLAcHB/Xp00eFhYWSpDlz5mjFihX65JNPlJ6eriVLllgD65UvvPj4eGVlZVmnv/32Ww0ePFhPPfWU9u7dq/fee08JCQlFQlBcXJz69OmjXbt2aejQoWX1MpjakiVL9PLLL+u1115TSkqKwsLC9O677xZpt27dOh08eFDr1q3T4sWLlZCQoISEhGtuNygoSGvXrrX+J1uciRMn6tVXX9XkyZO1d+9eLV26tMhNRV544QU9++yzSk1NVYMGDTRw4EDrHywpKSl68MEH9dBDD2nXrl2Ki4vT5MmTbeqKjY3V9u3btWLFCm3evFmGYah79+700N8Ed3d35efnS7ocdN544w19+OGH2rhxozIzM23+OLmWRo0aqVu3bjZ/+KLkcnNz9dFHH6levXry9/eXJFWvXl0JCQnau3ev3n77bS1cuFBvvfWWdZ2vvvpKffr0Uffu3fXTTz8pOTlZd911l812Z82apdatW+unn37SE088oREjRig9PV3S5V+1oqKiVL16dX377bfatGmT9Y+ZK/8+/te4ceP02WefafHixfrxxx9Vr149RUVF6eTJkzbtJkyYoFdffVVpaWlq1qxZab5UQOkzYHpDhgwxevXqZRw9etRwdXU1Dh06ZBw6dMhwc3Mzjh07ZvTq1csYMmRIseseO3bMkGTs2rXLMAzDGD16tNGpUyejsLCw2PaSjM8//9xmXufOnY1XXnnFZt6HH35oBAcH26w3ZsyYkh+kyQwZMsRwdHQ0qlWrZvNwc3MzJBmnTp0yDMMw4uPjDW9vb+t6bdu2NUaOHGmzrfbt2xvNmze32XatWrWMixcvWuf179/fGDBgwDXr2bNnjxEREWE4ODgYTZs2Nf72t78ZK1eutC7PyckxXF1djYULFxa7fkZGhiHJ+OCDD2y2KclIS0szDMMw/vKXvxgPPPCAzXrPPfec0bhxY8MwDOPnn382JBmbNm2yLj9+/Ljh7u5ufPLJJ8W+HlXdlc+2YRhGYWGhsXr1asPV1dV49tlnjfj4eEOSceDAAWv7efPmGTVr1rROv/jiizb/dq42fvx4w93dvSzLr7T+9/MtyQgODjZSUlKuuc7rr79u3HnnndbpyMhIY9CgQddsX6tWLeOvf/2rdbqwsNAIDAw03n33XcMwLn/HNmzY0Oa7Oi8vz3B3dzdWrVplrfPKv5/c3FzD2dnZWLJkibV9fn6+ERISYsycOdMwDMNYt26dIclYvnz5TbwagH3RI1uJ1KhRQz169FBCQoLi4+PVo0cPBQQE2LTZv3+/Bg4cqDp16sjLy8va25qZmSnpco9ZamqqGjZsqCeffFLffPPNH+53x44dmjZtmjw9Pa2P4cOHKysrS+fOnbO2a926dekdrAncf//9Sk1NtXn80clV6enpRXpl/ndaujzu0dHR0TodHByso0ePXnO7jRs31u7du/XDDz9o6NChOnr0qGJiYvToo49KktLS0pSXl6fOnTtft76re2eCg4MlybrftLQ0tW/f3qZ9+/bttX//fl26dElpaWlycnJS27Ztrcv9/f3VsGFDpaWlXXe/VdmXX34pT09Pubm5qVu3bhowYIDi4uIkSR4eHqpbt6617R/9O7iaYRiyWCxlUXKVcPXne+vWrYqKilK3bt3066+/SpI+/vhjtW/fXkFBQfL09NSkSZOs37OSlJqaelOfN4vFoqCgIOv7u2PHDh04cEDVq1e3fu/6+fnpwoULNsPFrjh48KAKCgpsPqPOzs666667inz+qtp3NczNyd4FoHQNHTpUo0aNkiTNmzevyPKYmBjVqlVLCxcuVEhIiAoLC9WkSRPrT1GtWrVSRkaGvv76a61Zs0YPPvigunTpon/961/X3Gdubq6mTp2qvn37Flnm5uZmfV6tWrVbPTxTqVatmurVq2cz7z//+U+pbNvZ2dlm2mKxWIeHXIuDg4PatGmjNm3aaMyYMfroo4/08MMP64UXXpC7u/tN7/dKCPqj/eLW3H///Xr33Xfl4uKikJAQOTn939d2cf8OjBu863haWppq165dqrVWJf/7+f7ggw/k7e2thQsXqkePHho0aJCmTp2qqKgoeXt7KzEx0WbM+I185q73Oc/NzdWdd96pJUuWFFmvRo0aJT0sSVXvuxrmRo9sJXNlfNSV8VNXO3HihNLT0zVp0iR17txZEREROnXqVJFteHl5acCAAVq4cKE+/vhjffbZZ9YxVM7Ozrp06ZJN+1atWik9PV316tUr8nBw4J/YzWjYsKHNyReSikyXlsaNG0u6PG66fv36cnd3V3Jycom3FxERoU2bNtnM27Rpkxo0aCBHR0dFRETo4sWL2rJli3X5lX+TV2pBUVcCU1hYmE2IvRX79u1TUlKS+vXrVyrbw+WQ6eDgoPPnz+v7779XrVq19MILL6h169aqX7++taf2imbNmt3S561Vq1bav3+/AgMDi3zvent7F2lft25dubi42HxGCwoKtG3bNj5/MDV6ZCsZR0dH689EV//0LEm+vr7y9/fX+++/r+DgYGVmZmrChAk2bd58800FBwerZcuWcnBw0KeffqqgoCDrmfXh4eFKTk5W+/bt5erqKl9fX02ZMkU9e/ZUWFiY/vznP8vBwUE7duzQ7t27NX369HI57spi9OjRGj58uFq3bq127drp448/1s6dO1WnTp1b2u6f//xntW/fXu3atVNQUJAyMjI0ceJENWjQQI0aNZKTk5PGjx+vcePGycXFRe3bt9exY8e0Z88eDRs27Ib28cwzz6hNmzZ66aWXNGDAAG3evFnvvPOO5s+fL0mqX7++evXqpeHDh+u9995T9erVNWHCBN12223q1avXLR0fru3ixYvKzs5WYWGhTpw4ofXr12v69Olq0aKFnnvuOXuXZ1p5eXnKzs6WdPlqHO+8845yc3MVExOjnJwcZWZmKjExUW3atNFXX32lzz//3Gb9F198UZ07d1bdunX10EMP6eLFi1q5cqXGjx9/Q/sfNGiQXn/9dfXq1UvTpk3T7bffrl9//VXLli3TuHHjdPvtt9u0r1atmkaMGKHnnntOfn5+CgsL08yZM3Xu3Lkb/owDFRHdZZWQl5eXvLy8isx3cHBQYmKiUlJS1KRJEz399NNFrkdZvXp1zZw5U61bt1abNm106NAhrVy50tqzOmvWLK1evVqhoaHWy3pFRUXpyy+/1DfffKM2bdro7rvv1ltvvaVatWqV/cFWMoMGDdLEiRP17LPPWod5xMbG2gzRKImoqCh98cUXiomJUYMGDTRkyBA1atRI33zzjbWXb/LkyXrmmWc0ZcoURUREaMCAATc83lK63EP0ySefKDExUU2aNNGUKVM0bdo0m5txxMfH684771TPnj0VGRkpwzC0cuXKIj+hovTs2bNHwcHBCgsLU8eOHfXJJ59o4sSJ+vbbb+Xp6Wnv8kwrKSlJwcHBCg4OVtu2bbVt2zZ9+umn6tixo/70pz/p6aef1qhRo9SiRQt9//33mjx5ss36HTt21KeffqoVK1aoRYsW6tSpk7Zu3XrD+/fw8NDGjRsVFhamvn37KiIiQsOGDdOFCxeK/f6XpFdffVX9+vXTww8/rFatWunAgQNatWqVfH19b+m1AOzJYtzogCoAdvHAAw8oKChIH374ob1LAQCgQmFoAVCBnDt3TgsWLFBUVJQcHR31z3/+U2vWrNHq1avtXRoAABUOPbJABXL+/HnFxMTop59+0oULF9SwYUNNmjSp2CtCAABQ1RFkAQAAYEqc7AUAAABTIsgCAADAlAiyAAAAMCWCLAAAAEyJIAsAAABTIsgCuGUWi0XLly+3dxmKjY1V79697V1GmUtISLDeNhoAqjKCLACr2NhYWSyWIo/o6Gh7l2bj0KFDslgsSk1NtZn/9ttvKyEhocz3X1GCOwBUddzZC4CN6OhoxcfH28xzdXW1UzU3x9vb294lAADKET2yAGy4uroqKCjI5uHr62tdvn//fnXo0EFubm5q3Lhxkdvnrl+/XhaLRadPn7bOS01NlcVi0aFDh6zzNm3apI4dO8rDw0O+vr6KiorSqVOnJElJSUm655575OPjI39/f/Xs2VMHDx60rlu7dm1JUsuWLWWxWNSxY0dJRYcW5OXl6cknn1RgYKDc3Nx0zz33aNu2bUVqTU5OVuvWreXh4aF27dopPT39ll7DDz74QBEREXJzc1OjRo00f/5867J27dpp/PjxNu2PHTsmZ2dnbdy40Vr3s88+q9tuu03VqlVT27ZttX79+luqCQAqI4IsgBtWWFiovn37ysXFRVu2bNGCBQuKhLIbkZqaqs6dO6tx48bavHmzvvvuO8XExOjSpUuSpLNnz2rs2LHavn27kpOT5eDgoD59+qiwsFCStHXrVknSmjVrlJWVpWXLlhW7n3Hjxumzzz7T4sWL9eOPP6pevXqKiorSyZMnbdq98MILmjVrlrZv3y4nJycNHTr0po/piiVLlmjKlCl6+eWXlZaWpldeeUWTJ0/W4sWLJUmDBg1SYmKirr6p4scff6yQkBDde++9kqRRo0Zp8+bNSkxM1M6dO9W/f39FR0dr//79Ja4LAColAwD+vyFDhhiOjo5GtWrVbB4vv/yyYRiGsWrVKsPJycn473//a13n66+/NiQZn3/+uWEYhrFu3TpDknHq1Clrm59++smQZGRkZBiGYRgDBw402rdvf8N1HTt2zJBk7Nq1yzAMw8jIyDAkGT/99FOR+nv16mUYhmHk5uYazs7OxpIlS6zL8/PzjZCQEGPmzJk2ta5Zs8ba5quvvjIkGefPn79mPVcf7/+qW7eusXTpUpt5L730khEZGWkYhmEcPXrUcHJyMjZu3GhdHhkZaYwfP94wDMP49ddfDUdHR5vX2DAMo3PnzsbEiRMNwzCM+Ph4w9vb+5r1AUBVwRhZADbuv/9+vfvuuzbz/Pz8JElpaWkKDQ1VSEiIdVlkZORN7yM1NVX9+/e/5vL9+/drypQp2rJli44fP27tic3MzFSTJk1uaB8HDx5UQUGB2rdvb53n7Oysu+66S2lpaTZtmzVrZn0eHBwsSTp69KjCwsJu+Jikyz3JBw8e1LBhwzR8+HDr/IsXL1rH79aoUUNdu3bVkiVLdO+99yojI0ObN2/We++9J0natWuXLl26pAYNGthsOy8vT/7+/jdVDwBUdgRZADaqVaumevXqlXh9B4fLI5aMq346LygosGnj7u5+3W3ExMSoVq1aWrhwoUJCQlRYWKgmTZooPz+/xHVdj7Ozs/W5xWKRJGt4vhm5ubmSpIULF6pt27Y2yxwdHa3PBw0apCeffFJz587V0qVL1bRpUzVt2tS6DUdHR6WkpNisI0menp43XRMAVGaMkQVwwyIiIvTbb78pKyvLOu+HH36waVOjRg1Jsmnzv5fJatasmZKTk4vdx4kTJ5Senq5Jkyapc+fOioiIsJ4EdoWLi4skWcfUFqdu3bpycXHRpk2brPMKCgq0bds2NW7c+DpHWXI1a9ZUSEiIfvnlF9WrV8/mceUENUnq1auXLly4oKSkJC1dulSDBg2yLmvZsqUuXbqko0ePFtlGUFBQmdQNAGZFjywAG3l5ecrOzraZ5+TkpICAAHXp0kUNGjTQkCFD9PrrrysnJ0cvvPCCTdt69eopNDRUcXFxevnll/Xzzz9r1qxZNm0mTpyopk2b6oknntDjjz8uFxcXrVu3Tv3795efn5/8/f31/vvvKzg4WJmZmZowYYLN+oGBgXJ3d1dSUpJuv/12ubm5Fbn0VrVq1TRixAg999xz8vPzU1hYmGbOnKlz585p2LBht/w6ZWRkFAno9evX19SpU/Xkk0/K29tb0dHRysvL0/bt23Xq1CmNHTvWWlvv3r01efJkpaWlaeDAgdZtNGjQQIMGDdLgwYM1a9YstWzZUseOHVNycrKaNWumHj163HLtAFBp2HuQLoCKY8iQIYakIo+GDRta26Snpxv33HOP4eLiYjRo0MBISkoqcvLTd999ZzRt2tRwc3Mz7r33XuPTTz+1OdnLMAxj/fr1Rrt27QxXV1fDx8fHiIqKsp4gtnr1aiMiIsJwdXU1mjVrZqxfv77IPhYuXGiEhoYaDg4Oxn333Wet/8rJXoZhGOfPnzdGjx5tBAQEGK6urkb79u2NrVu3WpffyIlpxSnuNZJkfPvtt4ZhGMaSJUuMFi1aGC4uLoavr6/RoUMHY9myZTbbWLlypSHJ6NChQ5Ht5+fnG1OmTDHCw8MNZ2dnIzg42OjTp4+xc+dOwzA42QsArrAYxlUD2QAAAACTYIwsAAAATIkgCwAAAFMiyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCUCLIAAAAwJYIsAAAATIkgCwAAAFMiyAIAAMCU/h9i5mL1DlvCRwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(data, x='Education Level', hue='Marital Status', multiple='stack', shrink=0.8)\n",
    "sns.move_legend(ax, 'upper left', bbox_to_anchor=(1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Employment Status\n",
       "Self-Employed    347\n",
       "Employed         328\n",
       "Unemployed       325\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Gender'].value_counts()\n",
    "data['Marital Status'].value_counts()\n",
    "data['Education Level'].value_counts()\n",
    "data['Employment Status'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Gender'] = data['Gender'].map({'Male': 1, 'Female': 0})\n",
    "data['Marital Status'] = data['Marital Status'].map({'Single': 0, 'Married':1, 'Divorced': 2})\n",
    "data['Education Level'] = data['Education Level'].map({'High School': 0, 'Bachelor':1, 'Master':2, 'PhD':3})\n",
    "data['Employment Status'] = data['Employment Status'].map({'Unemployed':0, 'Employed':1, \"Self-Employed\":2})\n",
    "data['Type of Loan'] = data['Type of Loan'].map({'Auto Loan': 0, 'Home Loan':1, 'Personal Loan':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Credit Utilization Ratio</th>\n",
       "      <th>Payment History</th>\n",
       "      <th>Number of Credit Accounts</th>\n",
       "      <th>Loan Amount</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Loan Term</th>\n",
       "      <th>Type of Loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2685.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4675000</td>\n",
       "      <td>2.65</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2371.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3619000</td>\n",
       "      <td>5.19</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2771.0</td>\n",
       "      <td>6</td>\n",
       "      <td>957000</td>\n",
       "      <td>2.76</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1371.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4731000</td>\n",
       "      <td>6.57</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.99</td>\n",
       "      <td>828.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3289000</td>\n",
       "      <td>6.28</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Marital Status  Education Level  Employment Status  \\\n",
       "0   60       1               1                2                  1   \n",
       "1   25       1               1                0                  0   \n",
       "2   30       0               0                2                  1   \n",
       "3   58       0               1                3                  0   \n",
       "4   32       1               1                1                  2   \n",
       "\n",
       "   Credit Utilization Ratio  Payment History  Number of Credit Accounts  \\\n",
       "0                      0.22           2685.0                          2   \n",
       "1                      0.20           2371.0                          9   \n",
       "2                      0.22           2771.0                          6   \n",
       "3                      0.12           1371.0                          2   \n",
       "4                      0.99            828.0                          2   \n",
       "\n",
       "   Loan Amount  Interest Rate  Loan Term  Type of Loan  \n",
       "0      4675000           2.65         48             2  \n",
       "1      3619000           5.19         60             0  \n",
       "2       957000           2.76         12             0  \n",
       "3      4731000           6.57         60             0  \n",
       "4      3289000           6.28         36             2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Marital Status', 'Education Level',\n",
       "       'Employment Status', 'Credit Utilization Ratio', 'Payment History',\n",
       "       'Number of Credit Accounts', 'Loan Amount', 'Interest Rate',\n",
       "       'Loan Term', 'Type of Loan'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[['Age', 'Gender', 'Marital Status', 'Education Level',\n",
    "       'Employment Status', 'Credit Utilization Ratio', 'Payment History',\n",
    "       'Number of Credit Accounts', 'Loan Amount', 'Interest Rate',\n",
    "       'Loan Term']]\n",
    "y = data['Type of Loan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[67  0 39]\n",
      " [76  0 29]\n",
      " [65  0 24]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.32      0.63      0.43       106\n",
      "           1       0.00      0.00      0.00       105\n",
      "           2       0.26      0.27      0.27        89\n",
      "\n",
      "    accuracy                           0.30       300\n",
      "   macro avg       0.19      0.30      0.23       300\n",
      "weighted avg       0.19      0.30      0.23       300\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/MLandDataScience/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/opt/anaconda3/envs/MLandDataScience/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/opt/anaconda3/envs/MLandDataScience/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test, pred))\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "MLandDataScience",
   "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": "3.11.9"
  }
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