customer_churn_prediction.ipynb

{
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   "id": "initial_id",
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    "ExecuteTime": {
     "end_time": "2024-08-24T05:15:38.546294Z",
     "start_time": "2024-08-24T05:15:38.538195Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:15:38.625634Z",
     "start_time": "2024-08-24T05:15:38.569713Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df = pd.read_csv('Churn_Modelling.csv')\n",
    "df.head()"
   ],
   "id": "b27d5781121f2c94",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>93826.63</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
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      ]
     },
     "execution_count": 3,
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   ],
   "execution_count": 3
  },
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   },
   "cell_type": "code",
   "source": "df.drop('RowNumber',inplace=True,axis=1)",
   "id": "21c679a3e4902652",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:15:38.646927Z",
     "start_time": "2024-08-24T05:15:38.644142Z"
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   },
   "cell_type": "code",
   "source": "df.columns",
   "id": "c00df53b865c67c1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CustomerId', 'Surname', 'CreditScore', 'Geography', 'Gender', 'Age',\n",
       "       'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember',\n",
       "       'EstimatedSalary', 'Exited'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
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     "end_time": "2024-08-24T05:15:38.691536Z",
     "start_time": "2024-08-24T05:15:38.655765Z"
    }
   },
   "cell_type": "code",
   "source": "df.info()",
   "id": "9fc7f43ef349fb27",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 13 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   CustomerId       10000 non-null  int64  \n",
      " 1   Surname          10000 non-null  object \n",
      " 2   CreditScore      10000 non-null  int64  \n",
      " 3   Geography        10000 non-null  object \n",
      " 4   Gender           10000 non-null  object \n",
      " 5   Age              10000 non-null  int64  \n",
      " 6   Tenure           10000 non-null  int64  \n",
      " 7   Balance          10000 non-null  float64\n",
      " 8   NumOfProducts    10000 non-null  int64  \n",
      " 9   HasCrCard        10000 non-null  int64  \n",
      " 10  IsActiveMember   10000 non-null  int64  \n",
      " 11  EstimatedSalary  10000 non-null  float64\n",
      " 12  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(8), object(3)\n",
      "memory usage: 1015.8+ KB\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
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     "start_time": "2024-08-24T05:15:38.711591Z"
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   },
   "cell_type": "code",
   "source": "df.drop(['CustomerId','Surname','Gender','Geography'],inplace=True,axis=1)",
   "id": "84a4d5d27202a0bb",
   "outputs": [],
   "execution_count": 7
  },
  {
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    }
   },
   "cell_type": "code",
   "source": "df.info()",
   "id": "74f16b5e7ea5e8f0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 9 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   CreditScore      10000 non-null  int64  \n",
      " 1   Age              10000 non-null  int64  \n",
      " 2   Tenure           10000 non-null  int64  \n",
      " 3   Balance          10000 non-null  float64\n",
      " 4   NumOfProducts    10000 non-null  int64  \n",
      " 5   HasCrCard        10000 non-null  int64  \n",
      " 6   IsActiveMember   10000 non-null  int64  \n",
      " 7   EstimatedSalary  10000 non-null  float64\n",
      " 8   Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 703.2 KB\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
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     "end_time": "2024-08-24T05:15:38.931641Z",
     "start_time": "2024-08-24T05:15:38.927694Z"
    }
   },
   "cell_type": "code",
   "source": "df.shape",
   "id": "b68a41d64e2e5d72",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:15:38.999350Z",
     "start_time": "2024-08-24T05:15:38.992763Z"
    }
   },
   "cell_type": "code",
   "source": "df['NumOfProducts'].nunique()",
   "id": "6fc3c86944ae3140",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:15:39.305242Z",
     "start_time": "2024-08-24T05:15:39.017400Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df['Exited'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('Exited')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Number of customer churn')"
   ],
   "id": "71d92e75a20a7e7d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of customer churn')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:15:39.320177Z",
     "start_time": "2024-08-24T05:15:39.309074Z"
    }
   },
   "cell_type": "code",
   "source": "category_col = [col for col in df.columns if df[col].nunique() < 10]",
   "id": "9ea1583e5afece72",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:15:39.336368Z",
     "start_time": "2024-08-24T05:15:39.327651Z"
    }
   },
   "cell_type": "code",
   "source": "category_col",
   "id": "4e4d20e9618cf7d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NumOfProducts', 'HasCrCard', 'IsActiveMember', 'Exited']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:15:39.769222Z",
     "start_time": "2024-08-24T05:15:39.343100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def col_summary(dataframe, col_name):\n",
    "    sns.countplot(x=dataframe[col_name], data=df)\n",
    "    plt.show(block=True)\n",
    "    print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
    "                        \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
    "    print(\"##########################################\")\n",
    "\n",
    "\n",
    "for col in category_col:\n",
    "    col_summary(df,col)"
   ],
   "id": "7e97aa34758cf740",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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GqrCwUPX19friiy+c1qOjo3X+/Hnt3bu3zY4PAAC4L09XDyBJw4YNU3l5uYYOHarhw4fr6aefVufOnZ32CQwM1OHDhyVJFRUVl1w/deqUampqnNY9PT3l7+/veH5jWSzNPCC4Dc6hWTjfgNma8j3ALQJowYIFqqysVHp6urKyslRdXS0vLy+nfby8vFRbWytJv7p+7tw5x+NLPb+xAgM7NPVQ4EY6dWrn6hHQhjjfAJrCLQIoKipKklRTU6OUlBSNHTv2gvt1amtr5ePjI0ny9va+IGZqa2vVsWNHeXt7Ox7/ct3X17dJcx079r1+vK/6N3l4WPkG7GZOnDij+vqGVvncnG/305rnG8DlwWJp/IsXLgugyspKFRYW6tZbb3Vs69Wrl86fP6/g4GAdOHDggv1/uqwVEhKiysrKC9b79u0rf39/eXt7q7KyUuHh4ZKkuro6VVVVKTg4uEkz2u1qdADBPXH+zML5BtBYLrsJ+uDBg0pKStKRI0cc23bt2qWAgADFxsZq9+7djstZklRQUCCbzSZJstlsKigocKxVV1drz549stlsslqtioqKclovLCyUp6enIiIi2uDIAACAu3NZAEVFRSkyMlJpaWnat2+ftmzZouzsbD344IOKj49Xly5dlJqaqpKSEi1ZskTFxcUaN26cJGns2LHauXOnlixZopKSEqWmpqpbt25KSEiQJE2YMEHLli3T5s2bVVxcrPT0dI0fP77Jl8AAAMCVyWUB5OHhodzcXPn6+uruu+/WzJkzNXHiRE2aNMmxVlFRoTFjxugf//iHFi1apNDQUElSt27dtHDhQuXn52vcuHGqqqrSokWLZPnx9u877rhD06ZN06xZszRlyhT1799fjz76qKsOFQAAuBmX3gQdEhKinJyci66FhYVp1apVl3zu4MGDNXjw4EuuJyYmKjEx8XfPCAAArjz8MVQAAGAcAggAABinWQE0adIknTp16oLtx48f15gxY373UAAAAK2p0fcA/e///q+Ki4slSZ9++qkWL17s9Le6JOnbb79VWVlZy04IAADQwhodQD179tTSpUtlt9tlt9u1c+dOXXXVVY51i8UiPz8/ZWZmtsqgAAAALaXRAdS9e3etXLlSkpSamqqZM2eqffv2rTYYAABAa2nW2+CzsrIk/fBX2evq6mT/xe+f/+n39QAAALijZgXQhx9+qCeffFKHDh2SJNntdlksFsd/v/zyyxYdEgAAoCU1K4AyMjLUv39/5eXlcRkMAABcdpoVQIcPH9bSpUvVvXv3lp4HAACg1TXr9wDFxcU5/bV1AACAy0mzXgEaMGCA5syZow8++EBhYWFOb4eXpKSkpBYZDgAAoDU0+ybofv366dixYzp27JjT2k9/kR0AAMBdNSuAXn755ZaeAwAAoM00K4DWrVv3q+ujR49uzqcFAABoE80KoAULFjg9rq+v17Fjx+Tp6an+/fsTQAAAwK01K4D+53/+54JtZ86c0axZs9SnT5/fPRQAAEBratbb4C+mXbt2+vOf/6zly5e31KcEAABoFS0WQJK0d+9eNTQ0tOSnBAAAaHHNugQ2ceLEC97ufubMGX311VeaPHlyS8wFAADQapoVQAkJCRds8/LyUkpKim688cbfPRQAAEBralYA/fw3PZ8+fVr19fW6+uqrW2woAACA1tSsAJKkFStWaOnSpaqsrJQkBQQE6N577+XPYAAAALfXrABatGiRVq1apb/85S+KiYlRQ0ODdu7cqZycHHl5eSkxMbGl5wQAAGgxzQqg119/XZmZmRo2bJhjW9++fRUSEqLMzEwCCAAAuLVmvQ3+9OnT6tGjxwXbe/bsqePHj//emQAAAFpVswIoJiZGL774otPv/Kmvr9eyZcvUv3//FhsOAACgNTTrElhqaqr+9Kc/6aOPPlJkZKQkaffu3aqtrdXSpUtbdEAAAICW1qwACg8PV1pamqqqqnTgwAF5e3vr/fff14IFCxQREdHSMwIAALSoZl0Ce/nll5Wenq4OHTooPT1dqampmjhxolJSUvT666+39IwAAAAtqlkBtHz5cj333HO68847Hdsef/xxZWdna8mSJS02HAAAQGtoVgCdOHFC11577QXbe/bs6fjFiAAAAO6qWQEUGxurhQsXqrq62rGtpqZGixcvVkxMTIsNBwAA0BqadRP0rFmzNGXKFN18882O3wf03XffKSgoSLm5uS05HwAAQItrVgBde+21euutt7R161Z988038vT0VI8ePXTzzTfLw8OjpWcEAABoUc3+Y6heXl665ZZbWnIWAACANtGse4AAAAAuZwQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMI5LA+jIkSNKTk5WfHy8Bg0apKysLNXU1EiSSktLNXnyZEVHR+v222/Xtm3bnJ770UcfaeTIkbLZbJo0aZJKS0ud1l966SUNGjRIMTExSktLU3V1dZsdFwAAcG8uCyC73a7k5GRVV1dr9erVev755/X+++9r/vz5stvtmj59uoKCgpSfn69Ro0YpKSlJ5eXlkqTy8nJNnz5dY8aM0dq1axUQEKCHHnpIdrtdkvT2228rJydHGRkZWrFihYqKipSdne2qQwUAAG7GZQF04MABFRYWKisrS71791ZcXJySk5O1YcMGffLJJyotLVVGRobCw8M1bdo0RUdHKz8/X5K0Zs0a9evXT1OmTFHv3r2VlZWlsrIy7dixQ5K0cuVK3XfffRo6dKj69++vOXPmKD8/n1eBAACAJMnTVV84ODhYS5cuVVBQkNP206dPq6ioSNdff738/Pwc22NjY1VYWChJKioqUlxcnGPN19dXkZGRKiwsVFxcnL744gslJSU51qOjo3X+/Hnt3btXMTExjZ7RYmnmwcFtcA7NwvkGzNaU7wEuC6COHTtq0KBBjscNDQ1atWqVBg4cqIqKCnXu3Nlp/8DAQB0+fFiSfnX91KlTqqmpcVr39PSUv7+/4/mNFRjYoamHBTfSqVM7V4+ANsT5BtAULgugX8rOztaePXu0du1avfTSS/Ly8nJa9/LyUm1trSSpurr6kuvnzp1zPL7U8xvr2LHv9eNtRb/Jw8PKN2A3c+LEGdXXN7TK5+Z8u5/WPN8ALg8WS+NfvHCLAMrOztaKFSv0/PPP67rrrpO3t7eqqqqc9qmtrZWPj48kydvb+4KYqa2tVceOHeXt7e14/Mt1X1/fJs1lt6vRAQT3xPkzC+cbQGO5/PcAzZ07V8uXL1d2draGDx8uSQoJCVFlZaXTfpWVlY7LWpdaDw4Olr+/v7y9vZ3W6+rqVFVVpeDg4FY+GgAAcDlwaQDl5OTo1Vdf1d/+9jfdcccdju02m027d+92XM6SpIKCAtlsNsd6QUGBY626ulp79uyRzWaT1WpVVFSU03phYaE8PT0VERHRBkcFAADcncsCaP/+/crNzdUDDzyg2NhYVVRUOD7i4+PVpUsXpaamqqSkREuWLFFxcbHGjRsnSRo7dqx27typJUuWqKSkRKmpqerWrZsSEhIkSRMmTNCyZcu0efNmFRcXKz09XePHj2/yJTAAAHBlctk9QO+9957q6+uVl5envLw8p7WvvvpKubm5mjlzpsaMGaOwsDAtWrRIoaGhkqRu3bpp4cKFevrpp7Vo0SLFxMRo0aJFsvz4/rc77rhDZWVlmjVrlmpra/WHP/xBjz76aJsfIwAAcE8Wu53bBi+lsrLx7wLz9PzhXUF/mr9Be8uOt+5g+FURXQO0+uGROnHijOrqWuddQZxv99EW5xvA5cFikYKCGvcuMJffBA0AANDWCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcdwigGprazVy5Eht377dsa20tFSTJ09WdHS0br/9dm3bts3pOR999JFGjhwpm82mSZMmqbS01Gn9pZde0qBBgxQTE6O0tDRVV1e3ybEAAAD35/IAqqmp0SOPPKKSkhLHNrvdrunTpysoKEj5+fkaNWqUkpKSVF5eLkkqLy/X9OnTNWbMGK1du1YBAQF66KGHZLfbJUlvv/22cnJylJGRoRUrVqioqEjZ2dkuOT4AAOB+XBpA+/bt0/jx4/Xdd985bf/kk09UWlqqjIwMhYeHa9q0aYqOjlZ+fr4kac2aNerXr5+mTJmi3r17KysrS2VlZdqxY4ckaeXKlbrvvvs0dOhQ9e/fX3PmzFF+fj6vAgEAAEkuDqAdO3YoISFBr732mtP2oqIiXX/99fLz83Nsi42NVWFhoWM9Li7Osebr66vIyEgVFhaqvr5eX3zxhdN6dHS0zp8/r7179zZpPoul8R9wT005h5zvy19rnW8++ODj8vloLM/W+1b02yZMmHDR7RUVFercubPTtsDAQB0+fPg310+dOqWamhqndU9PT/n7+zue31iBgR2atD/cS6dO7Vw9AtoQ5xtAU7g0gC6lurpaXl5eTtu8vLxUW1v7m+vnzp1zPL7U8xvr2LHv9eNtRb/Jw8PKN2A3c+LEGdXXN7TK5+Z8u5/WPN8ALg8WS+NfvHDLAPL29lZVVZXTttraWvn4+DjWfxkztbW16tixo7y9vR2Pf7nu6+vbpDnsdjU6gOCeOH9m4XwDaCyXvwvsYkJCQlRZWem0rbKy0nFZ61LrwcHB8vf3l7e3t9N6XV2dqqqqFBwc3PrDAwAAt+eWAWSz2bR7927H5SxJKigokM1mc6wXFBQ41qqrq7Vnzx7ZbDZZrVZFRUU5rRcWFsrT01MRERFtdxAAAMBtuWUAxcfHq0uXLkpNTVVJSYmWLFmi4uJijRs3TpI0duxY7dy5U0uWLFFJSYlSU1PVrVs3JSQkSPrh5uply5Zp8+bNKi4uVnp6usaPH9/kS2AAAODK5JYB5OHhodzcXFVUVGjMmDH6xz/+oUWLFik0NFSS1K1bNy1cuFD5+fkaN26cqqqqtGjRIll+fP/bHXfcoWnTpmnWrFmaMmWK+vfvr0cffdSVhwQAANyI29wE/dVXXzk9DgsL06pVqy65/+DBgzV48OBLricmJioxMbHF5gMAAFcOt3wFCAAAoDURQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOJ6uHgAALgdWq0VWq8XVY0BSQ4NdDQ12V4+ByxwBBAC/wWq1yN/fTx4evGjuDurrG1RVdZYIwu9CAAHAb7BaLfLwsOqJv2/V10dPunoco/XsfLWemjBIVquFAMLvQgABQCN9ffSk9pYdd/UYAFoAr+cCAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwzhX7t8Bqamo0Z84cvfPOO/Lx8dGUKVM0ZcoUV48FALgMWK0WWa0WV48BSQ0N9lb5w7dXbADNmzdPu3bt0ooVK1ReXq7HH39coaGhGjFihKtHAwC4MavVIn9/P3l4cJHEHdTXN6iq6myLR9AVGUBnz57VmjVr9J//+Z+KjIxUZGSkSkpKtHr1agIIAPCrrFaLPDyseuLvW/X10ZOuHsdoPTtfracmDJLVaiGAGmPv3r2qq6tTTEyMY1tsbKwWL16shoYGWa1UPQDg13199KT2lh139RhoJVdkAFVUVKhTp07y8vJybAsKClJNTY2qqqoUEBDQqM9jtUr2JgZnRGiAfL2uyP+tl42woI6Of7d263K+XY/zbRbOt1maer4tTbhty2K3N/VHvPtbt26d/uM//kPvv/++Y1tpaaluvfVWbdmyRddcc40LpwMAAK52RV4L8vb2Vm1trdO2nx77+Pi4YiQAAOBGrsgACgkJ0YkTJ1RXV+fYVlFRIR8fH3Xs2PFXngkAAExwRQZQ37595enpqcLCQse2goICRUVFcQM0AAC4MgPI19dXo0ePVnp6uoqLi7V582a9+OKLmjRpkqtHAwAAbuCKvAlakqqrq5Wenq533nlH7du319SpUzV58mRXjwUAANzAFRtAAAAAl3JFXgIDAAD4NQQQAAAwDgEEAACMQwDBSW1trUaOHKnt27e7ehS0oiNHjig5OVnx8fEaNGiQsrKyVFNT4+qx0Eq+/fZbTZ06VTExMRoyZIiWLl3q6pHQRhITEzVjxgxXj+GWCCA41NTU6JFHHlFJSYmrR0ErstvtSk5OVnV1tVavXq3nn39e77//vubPn+/q0dAKGhoalJiYqE6dOunNN9/UnDlzlJeXp/Xr17t6NLSyjRs3asuWLa4ew20RQJAk7du3T+PHj9d3333n6lHQyg4cOKDCwkJlZWWpd+/eiouLU3JysjZs2ODq0dAKKisr1bdvX6Wnp6tHjx4aPHiwbrzxRhUUFLh6NLSiqqoqzZs3T1FRUa4exW0RQJAk7dixQwkJCXrttddcPQpaWXBwsJYuXaqgoCCn7adPn3bRRGhNnTt31vz589W+fXvZ7XYVFBTo008/VXx8vKtHQyt65plnNGrUKPXq1cvVo7gtT1cPAPcwYcIEV4+ANtKxY0cNGjTI8bihoUGrVq3SwIEDXTgV2sKwYcNUXl6uoUOHavjw4a4eB63k448/1meffab169crPT3d1eO4LV4BAgyXnZ2tPXv26K9//aurR0ErW7BggRYvXqwvv/xSWVlZrh4HraCmpkazZ8/WrFmz5OPj4+px3BqvAAEGy87O1ooVK/T888/ruuuuc/U4aGU/3Q9SU1OjlJQUPfbYY/Ly8nLxVGhJOTk56tevn9OrvLg4Aggw1Ny5c/XKK68oOzubyyFXsMrKShUWFurWW291bOvVq5fOnz+v06dPKyAgwIXToaVt3LhRlZWViomJkfTDrzaRpLfffluff/65K0dzOwQQYKCcnBy9+uqr+tvf/qYRI0a4ehy0ooMHDyopKUlbtmxRSEiIJGnXrl0KCAggfq5AL7/8surq6hyPn332WUlSSkqKq0ZyWwQQYJj9+/crNzdXiYmJio2NVUVFhWMtODjYhZOhNURFRSkyMlJpaWlKTU1VWVmZsrOz9eCDD7p6NLSCrl27Oj1u166dJCksLMwV47g1AggwzHvvvaf6+nrl5eUpLy/Pae2rr75y0VRoLR4eHsrNzdXcuXN19913y9fXVxMnTtSkSZNcPRrgUha73W539RAAAABtibfBAwAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAFosj59+ujf/u3fLtj+xhtvaNiwYa32dWtra5Wbm6vhw4crKipKQ4cO1VNPPaXjx4877ffee+/p//2//yebzaatW7dq2LBh6tOnj+MjMjJSI0aM0EsvvdSi802cOFELFy783Z+ntLRUW7ZsaYGJAFwKAQSgWTZs2KCPP/64zb5eXV2dpk2bpjfffFOPPPKINm3apKysLO3du1d33XWXjhw54th3wYIFuvnmm/XWW29pwIABkqS0tDRt27ZN27Zt0+bNmzVt2jTNmzdP69ata7NjaKy0tDQVFxe7egzgikYAAWiWrl27KiMjQ7W1tW3y9VatWqUvv/xSf//73zV8+HB17dpVAwcO1Isvvih/f39lZmY69v3+++8VGxurrl27ysfHR5LUoUMHBQcHKzg4WF26dNGdd96pG2+8Ue+8806bzA/AvRBAAJrl4Ycf1pEjR7Rs2bKLrh88eFB9+vTRwYMHHdsWLlyoiRMnSvrhctnEiROVl5enAQMG6KabbtK6deu0adMmDR06VHFxccrOznY8d82aNRozZswFf7Hey8tLiYmJ2rx5s06cOKFhw4aprKxMaWlpv3k5ztPTU1dddZWkHy5fzZ07V7fccouGDBmi06dP6/Dhw/rLX/6i+Ph4JSQk6KmnnnIKvnfffVfDhw9XdHS0MjIyVF9f71ibMWOGZsyY4fT1+vTpo+3bt0uSzp49q1mzZikhIUEJCQl68sknVVNToxkzZmjHjh3Kyclx/L9auXKlhg4dqqioKI0ZM0afffbZrx4XgN9GAAFolpCQECUnJ2vx4sUqLS1t1uf4/PPPVVpaqrVr1+qOO+5Qenq6Vq5cqby8PM2YMUNLly7Vnj17dPbsWe3fv19RUVEX/TyxsbGqr6/X7t27tXbtWl1zzTVKS0vT2rVrL7r/+fPn9c477+jDDz/ULbfc4tj+xhtvKDs7Wzk5OfLy8tJ9992n6upqvfzyy5o/f74++OADzZs3T5K0b98+Pfzww7r33nuVn5+vuro6FRQUNPrYn3jiCRUUFCg3N1cvvviiCgoKNH/+fM2cOVMxMTGaMmWKFi5cqD179mjevHmaPXu2/vu//1txcXF6+OGH1dDQ0IT/0wB+iQAC0GwTJ05UWFiY0+WnprDb7XriiScUFhamu+++W9XV1frzn/+siIgIjRs3ToGBgTpw4IC+//572e12XX311Rf9PB07dpQkVVVVKSAgQB4eHurQoYMCAgIc+8yePVsxMTGKiYlR//799fjjj+u+++7Tv/zLvzj2GTJkiG644Qb169dPW7du1ZEjR5Sdna0+ffroxhtv1KxZs/TKK6/ozJkzys/PV1xcnCZPnqzw8HA9+eST6ty5c6OO++TJk9q0aZNmzZql2NhYRUZGKiMjQ6GhoerQoYOuuuoq+fn5yd/fX2VlZbJYLAoNDVW3bt308MMPKzs7mwACfidPVw8A4PLl4eGh9PR0TZgwQZs3b27y8wMDA+Xn5ydJ8vb2liR169bNse7j46Pa2lpH+FRUVFz08xw9elSS5O/vf8mvlZycrD/84Q+OrxUcHCwPDw+nfbp27er49/79+9WjRw+n6LrhhhtUV1en7777Tvv371ffvn0da1dddZXT41/z7bffqr6+XpGRkY5tcXFxiouLu2Dfm2++Wdddd53++Mc/6vrrr9ctt9yiu+66S56efPsGfg9eAQLwu9xwww0aO3asMjMzVV1d7dhusVgu2Leurs7p8cV+iF/seT4+Prruuuu0e/fui86wa9cueXh46Prrr7/knIGBgQoLC1NYWJiuueaaC+JH+r8I++W/f/LTPT4//ddutzut/3Q/0cWO4+fH/vP9fouvr6/WrFmjFStWKD4+Xm+88YbGjBnj9K43AE1HAAH43VJSUnT27FmnG6J/+iF/5swZx7af3xDdVPfcc4/WrFlzwQ/+uro65eXl6dZbb3W65PV79ezZU998842qqqoc2woLC+Xp6alrr71WvXv31hdffOFYa2ho0N69ex2Pr7rqKqdj//l9Ut27d5eHh4fT/ps3b9add955wRyff/65XnjhBQ0cOFCpqanatGmTampqmnS/EYALEUAAfrdOnTopJSVFZWVljm1BQUHq0qWLli1bptLSUr3xxhv64IMPmv017r33Xg0YMEATJ07Uu+++q/Lycn322Wd64IEH9P3332vmzJktcCT/56abblL37t312GOP6auvvtInn3yiuXPnauTIkerYsaPGjx+vXbt2KS8vTwcOHNAzzzyj8vJyx/OjoqL04Ycf6uOPP9Y///lPZWRkOKKwffv2Gj16tDIzM1VcXKwvvvhCzz//vAYOHChJ8vPz0zfffKNjx47Jx8dHixYt0po1a3Tw4EFt3LhRZ8+eVZ8+fVr0eAHTEEAAWsS4ceMUExPjeGy1Wh0/4G+//XZt2rRJDz74YLM/v9VqVW5ursaPH6/58+drxIgRSklJUXh4uNauXauQkJCWOAwHDw8P5ebmSpLGjx+vRx55RLfccosyMjIkSWFhYcrLy9PGjRs1evRoVVRUaPDgwY7njxo1SsOHD9dDDz2k+++/XyNHjnS6STotLU0RERH613/9Vz3wwANKSEjQX//6V0nSXXfdpa1bt+r+++9X3759lZmZqaVLl+q2227T4sWLlZ2drfDw8BY9XsA0FvsvL2IDAABc4XgFCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHH+P84cPnDbCFugAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               NumOfProducts  Ratio\n",
      "NumOfProducts                      \n",
      "1                       5084  50.84\n",
      "2                       4590  45.90\n",
      "3                        266   2.66\n",
      "4                         60   0.60\n",
      "##########################################\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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LoLfeeks9evRQWVmZZ9nHH3+skJAQxcfH64MPPpD720R0u906dOiQ7Ha7JMlut8vhcHi2KywsVGFhoex2uyIjIxUVFeU17nA4FBUVVav7fwAAwLXLZwEUFxcnm82mP/zhDzp27Jj279+vpUuX6v7779fAgQN15swZpaWl6ejRo0pLS1NZWZkGDRokSRo1apSef/557dixQ7m5uZo5c6b69u2rNm3aeMaXL1+uAwcO6MCBA1qxYoXGjBnjq0MFAAB+xmf3ADVt2lQbNmzQ4sWLNXz4cDVp0kS/+c1vdP/998tisWjdunWaN2+etm/fro4dOyojI0ONGzeWdDGeFixYoJUrV+r06dPq1auXFi5c6Nn3hAkTVFJSouTkZAUGBmrEiBEaN26cj44UAAD4G4vb7Q+3Ivknp5OnwADUXlDQxafA7n1in3ILTv74BoABolu10NbfDdGpU+dUWdlwT4GFhdXsKTB+GSoAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIzjNwE0adIkPfzww57XR44c0ciRI2W32zV8+HAdPnzYa/19+/apf//+stvtmjp1qk6ePOkZc7vdWr58uXr27KnExEQtXbpU1dXVV+1YAACAf/OLAHrhhRe0f/9+z+vz589r0qRJSkhI0O7duxUXF6fJkyfr/PnzkqScnBzNmTNHycnJ2rZtm86cOaOUlBTP9ps2bdK+ffu0evVqrVy5Unv37tWmTZuu+nEBAAD/5PMAKi0t1dKlSxUbG+tZ9uKLL8pms2nmzJlq166d5syZoyZNmuill16SJG3ZskWDBg3SsGHDFB0draVLl2r//v3Kz8+XJG3evFnTpk1TQkKCevbsqenTp2vr1q0+OT4AAOB/gnw9gT/+8Y8aOnSoTpw44VmWnZ2t+Ph4WSwWSZLFYlG3bt2UlZWlpKQkZWdna+LEiZ71W7ZsqaioKGVnZ8tqtaqwsFDdu3f3jMfHx6ugoEAnTpxQREREjef27ZcHAAD1qKG+v9Zmvz4NoH/+8596//33tXfvXqWmpnqWFxcXq3379l7rhoaGKi8vT5IuGzKhoaEqKipScXGxJHmNh4WFSZKKiopqFUChodfV6ngAAMAPa968ia+nIKmOATRmzBitXr1azZo181p+8uRJ3X///dq9e/eP7qOiokLz5s3T3Llz1ahRI6+xsrIyWa1Wr2VWq1Uul0uSVF5efsXx8vJyz+vvjknybF9TJSXfyO2u1SYAoMDAAL/5Sx7wN6dOnVNVVcM8mGSx1PzkRY0D6O9//7tycnIkSe+9956efPJJNW7c2GudL774QgUFBTXa3+rVq9W5c2f16dPnkjGbzXZJrLhcLk8oXWk8ODjYK3ZsNpvnc0kKDg6u0dz+xe0WAQQAQD3zh++tNQ6gG2+8UevXr5fb7Zbb7dahQ4f0s5/9zDNusVjUuHFjpaWl1Wh/L7zwgpxOp+Li4iT9/0h5+eWXNWTIEDmdTq/1nU6n5/JVZGTkZcfDw8MVGRkp6eJltNatW3s+l6Tw8PCaHi4AALiG1TiA2rRpo82bN0uSUlJSNGfOHDVt2rTOX/iZZ55RZWWl5/Xy5cslSdOnT9d7772np556Sm63WxaLxRNcDzzwgCTJbrfL4XAoKSlJklRYWKjCwkLZ7XZFRkYqKipKDofDE0AOh0NRUVG1uv8HAABcu+p0D9CSJUskXTyzUllZKff3zmVFRUX96D5atWrl9bpJk4vXy9u2bavQ0FCtWLFCaWlp+s1vfqPnnntOZWVlGjRokCRp1KhRGj16tLp27arY2FilpaWpb9++atOmjWd8+fLl+vnPfy5JWrFihcaPH1+XQwUAANegOgXQP/7xDz3yyCMqLCyUJK8zNRaLRR9//PG/NammTZtq3bp1mjdvnrZv366OHTsqIyPDc89RXFycFixYoJUrV+r06dPq1auXFi5c6Nl+woQJKikpUXJysgIDAzVixAiNGzfu35oTAAC4dljc3z99UwO33367OnXqpAcffPCyl8G+f3bnp8rp5CkwALUXFHTxKbB7n9in3IKTP74BYIDoVi209XdDdOrUOVVWNtxTYGFh9fwU2HcVFRVp/fr1nktOAAAAPyV1+lUYCQkJcjgc9T0XAACAq6JOZ4C6d++u+fPn680331Tbtm29HoeXpOTk5HqZHAAAQEOo803QnTt3VklJiUpKSrzGLPwCLQAA4OfqFEDPPPNMfc8DAADgqqlTAO3Zs+cHx4cNG1aX3QIAAFwVdQqglStXer2uqqpSSUmJgoKC1KVLFwIIAAD4tToF0Ouvv37JsnPnzmnu3Lnq2LHjvz0pAACAhlSnx+Avp0mTJnrooYe0adOm+tolAABAg6i3AJKk3NxcVVc3zE93BAAAqC91ugQ2evToSx53P3funD755BN+5xYAAPB7dQqgHj16XLLMarVq+vTp+tWvfvVvTwoAAKAh1SmAvvuTns+ePauqqipdf/319TYpAACAhlSnAJKkzMxMrV+/Xk6nU5LUokULjRo1il+DAQAA/F6dAig9PV1btmzRf//3fysuLk7V1dU6dOiQVq9eLavVqkmTJtX3PAEAAOpNnQJo+/btSktL02233eZZ1qlTJ0VGRiotLY0AAgAAfq1Oj8GfPXtWN9xwwyXLb7zxRp08efLfnRMAAECDqlMAxcXFaePGjV4/86eqqkobNmxQly5d6m1yAAAADaFOl8BSUlJ077336p133lFMTIwk6aOPPpLL5dL69evrdYIAAAD1rU4B1K5dO82ePVulpaU6duyYbDab3njjDa1cuVLR0dH1PUcAAIB6VadLYM8884xSU1N13XXXKTU1VSkpKRo9erSmT5+u7du31/ccAQAA6lWdAmjTpk1asWKF/uu//suzbNasWVq2bJkyMjLqbXIAAAANoU4BdOrUKf3iF7+4ZPmNN97o+cGIAAAA/qpOARQfH69Vq1aprKzMs6yiokJPPvmk4uLi6m1yAAAADaFON0HPnTtX48ePV+/evT0/D+jLL79UWFiY1qxZU5/zAwAAqHd1CqBf/OIXevHFF/XWW2/p888/V1BQkG644Qb17t1bgYGB9T1HAACAelXnX4ZqtVr1n//5n/U5FwAAgKuiTvcAAQAA/JQRQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4Pg2gL774QhMmTFBcXJz69u2r9evXe8by8/M1btw4de3aVXfccYfefvttr23feecdDRkyRHa7XWPGjFF+fr7X+NNPP60+ffooLi5Os2fP9vrN9QAAwGw+C6Dq6mpNmjRJzZs311/+8hfNnz9fa9eu1d69e+V2uzV16lSFhYVp165dGjp0qJKTk3X8+HFJ0vHjxzV16lQlJSVp586datGihaZMmSK32y1Jevnll7V69WotWLBAmZmZys7O1rJly3x1qAAAwM/4LICcTqc6deqk1NRU3XDDDbr11lv1q1/9Sg6HQ++++67y8/O1YMECtWvXTpMnT1bXrl21a9cuSdKOHTvUuXNnjR8/Xh06dNCSJUtUUFCggwcPSpI2b96ssWPHql+/furSpYvmz5+vXbt2cRYIAABI8mEARURE6IknnlDTpk3ldrvlcDj03nvvKTExUdnZ2br55pvVuHFjz/rx8fHKysqSJGVnZyshIcEzFhwcrJiYGGVlZamqqkoffvih13jXrl114cIF5ebmXrXjAwAA/ivI1xOQpNtuu03Hjx9Xv379dPvtt2vx4sWKiIjwWic0NFRFRUWSpOLi4iuOnzlzRhUVFV7jQUFBCgkJ8WxfUxZLHQ8IAABcUUN9f63Nfv0igFauXCmn06nU1FQtWbJEZWVlslqtXutYrVa5XC5J+sHx8vJyz+srbV9ToaHX1fZQAADAD2jevImvpyDJTwIoNjZWklRRUaHp06dr+PDhl9yv43K51KhRI0mSzWa7JGZcLpeaNWsmm83mef398eDg4FrNq6TkG317XzUA1FhgYIDf/CUP+JtTp86pqqq6QfZtsdT85IXPAsjpdCorK0v9+/f3LGvfvr0uXLig8PBwHTt27JL1/3VZKzIyUk6n85LxTp06KSQkRDabTU6nU+3atZMkVVZWqrS0VOHh4bWao9utBg2ggACLAgK4zgZ8V3W1W9XV/MsDuJb5w8kFnwXQV199peTkZO3fv1+RkZGSpMOHD6tFixaKj4/Xxo0bVV5e7jnr43A4FB8fL0my2+1yOByefZWVlenIkSNKTk5WQECAYmNj5XA41KNHD0lSVlaWgoKCFB0dfZWP8soCAiwKCWmswEB+FiXwXVVV1SotPU8EAWhQPgug2NhYxcTEaPbs2UpJSVFBQYGWLVumBx54QImJiWrZsqVSUlI0ZcoUvfHGG8rJydGSJUskScOHD9eGDRuUkZGhfv36KT09Xa1bt/YEzz333KO5c+fqpptuUkREhFJTU3XXXXfV+hJYQwoIsCgwMEB/+PNb+uzEaV9PB/ALN0Zcr0X39FFAgIUAAtCgfBZAgYGBWrNmjRYuXKi7775bwcHBGj16tMaMGSOLxaI1a9Zozpw5SkpKUtu2bZWenq6oqChJUuvWrbVq1SotXrxY6enpiouLU3p6uizf3v49ePBgFRQUaO7cuXK5XBowYIBmzJjhq0P9QZ+dOK3cgpO+ngYAAEbx6U3QkZGRWr169WXH2rZtqy1btlxx21tvvVW33nrrFccnTZqkSZMm/dtzBAAA1x5uQAEAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHF8GkBff/21pk2bpsTERPXp00dLlixRRUWFJCk/P1/jxo1T165ddccdd+jtt9/22vadd97RkCFDZLfbNWbMGOXn53uNP/300+rTp4/i4uI0e/ZslZWVXbXjAgAA/s1nAeR2uzVt2jSVlZVp69atevzxx/XGG2/oiSeekNvt1tSpUxUWFqZdu3Zp6NChSk5O1vHjxyVJx48f19SpU5WUlKSdO3eqRYsWmjJlitxutyTp5Zdf1urVq7VgwQJlZmYqOztby5Yt89WhAgAAP+OzADp27JiysrK0ZMkSdejQQQkJCZo2bZr27dund999V/n5+VqwYIHatWunyZMnq2vXrtq1a5ckaceOHercubPGjx+vDh06aMmSJSooKNDBgwclSZs3b9bYsWPVr18/denSRfPnz9euXbs4CwQAACT5MIDCw8O1fv16hYWFeS0/e/assrOzdfPNN6tx48ae5fHx8crKypIkZWdnKyEhwTMWHBysmJgYZWVlqaqqSh9++KHXeNeuXXXhwgXl5uY27EEBAICfhCBffeFmzZqpT58+ntfV1dXasmWLevbsqeLiYkVERHitHxoaqqKiIkn6wfEzZ86ooqLCazwoKEghISGe7WvKYqntUQGoL7z/gGtXQ72/a7NfnwXQ9y1btkxHjhzRzp079fTTT8tqtXqNW61WuVwuSVJZWdkVx8vLyz2vr7R9TYWGXlfbwwBQD5o3b+LrKQBoIP7y/vaLAFq2bJkyMzP1+OOP66abbpLNZlNpaanXOi6XS40aNZIk2Wy2S2LG5XKpWbNmstlsntffHw8ODq7VvEpKvtG391XXu8DAAL/5nwDwN6dOnVNVVbWvp1FnvL+BK2vI97fFUvOTFz7/OUALFy7Upk2btGzZMt1+++2SpMjISDmdTq/1nE6n57LWlcbDw8MVEhIim83mNV5ZWanS0lKFh4fXam5ud8N9APhhDfn+a+gPAD/MH95/Pg2g1atX67nnntNjjz2mwYMHe5bb7XZ99NFHnstZkuRwOGS32z3jDofDM1ZWVqYjR47IbrcrICBAsbGxXuNZWVkKCgpSdHT0VTgqAADg73wWQJ9++qnWrFmjiRMnKj4+XsXFxZ6PxMREtWzZUikpKcrLy1NGRoZycnI0YsQISdLw4cN16NAhZWRkKC8vTykpKWrdurV69OghSbrnnnu0YcMGvfbaa8rJyVFqaqruuuuuWl8CAwAA1yaf3QP0f//3f6qqqtLatWu1du1ar7FPPvlEa9as0Zw5c5SUlKS2bdsqPT1dUVFRkqTWrVtr1apVWrx4sdLT0xUXF6f09HRZvr39e/DgwSooKNDcuXPlcrk0YMAAzZgx46ofIwAA8E8Wt5sr1lfidDbcTdBBQRdvkrz3iX3KLTjZMF8E+ImJbtVCW383RKdOnVNl5U/3Jmje38Clrsb722KRwsJ+IjdBAwAAXG0EEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMI5fBJDL5dKQIUN04MABz7L8/HyNGzdOXbt21R133KG3337ba5t33nlHQ4YMkd1u15gxY5Sfn+81/vTTT6tPnz6Ki4vT7NmzVVZWdlWOBQAA+D+fB1BFRYX+93//V3l5eZ5lbrdbU6dOVVhYmHbt2qWhQ4cqOTlZx48flyQdP35cU6dOVVJSknbu3KkWLVpoypQpcrvdkqSXX35Zq1ev1oIFC5SZmans7GwtW7bMJ8cHAAD8j08D6OjRo7rrrrv05Zdfei1/9913lZ+frwULFqhdu3aaPHmyunbtql27dkmSduzYoc6dO2v8+PHq0KGDlixZooKCAh08eFCStHnzZo0dO1b9+vVTly5dNH/+fO3atYuzQAAAQJKPA+jgwYPq0aOHtm3b5rU8OztbN998sxo3buxZFh8fr6ysLM94QkKCZyw4OFgxMTHKyspSVVWVPvzwQ6/xrl276sKFC8rNzW3YAwIAAD8JQb784vfcc89llxcXFysiIsJrWWhoqIqKin50/MyZM6qoqPAaDwoKUkhIiGf7mrJYarU6gHrE+w+4djXU+7s2+/VpAF1JWVmZrFar1zKr1SqXy/Wj4+Xl5Z7XV9q+pkJDr6vt1AHUg+bNm/h6CgAaiL+8v/0ygGw2m0pLS72WuVwuNWrUyDP+/ZhxuVxq1qyZbDab5/X3x4ODg2s1j5KSb/TtfdX1LjAwwG/+JwD8zalT51RVVe3radQZ72/gyhry/W2x1Pzkhc+fArucyMhIOZ1Or2VOp9NzWetK4+Hh4QoJCZHNZvMar6ysVGlpqcLDw2s1D7e74T4A/LCGfP819AeAH+YP7z+/DCC73a6PPvrIczlLkhwOh+x2u2fc4XB4xsrKynTkyBHZ7XYFBAQoNjbWazwrK0tBQUGKjo6+egcBAAD8ll8GUGJiolq2bKmUlBTl5eUpIyNDOTk5GjFihCRp+PDhOnTokDIyMpSXl6eUlBS1bt1aPXr0kHTx5uoNGzbotddeU05OjlJTU3XXXXfV+hIYAAC4NvllAAUGBmrNmjUqLi5WUlKS/vrXvyo9PV1RUVGSpNatW2vVqlXatWuXRowYodLSUqWnp8vy7e3fgwcP1uTJkzV37lyNHz9eXbp00YwZM3x5SAAAwI/4zU3Qn3zyidfrtm3basuWLVdc/9Zbb9Wtt956xfFJkyZp0qRJ9TY/AABw7fDLM0AAAAANiQACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGuWYDqKKiQrNnz1ZCQoJ69+6tjRs3+npKAADATwT5egINZenSpTp8+LAyMzN1/PhxzZo1S1FRURo4cKCvpwYAAHzsmgyg8+fPa8eOHXrqqacUExOjmJgY5eXlaevWrQQQAAC4Ni+B5ebmqrKyUnFxcZ5l8fHxys7OVnV1tQ9nBgAA/ME1eQaouLhYzZs3l9Vq9SwLCwtTRUWFSktL1aJFixrtJyBAcrsbapYXRUe1ULD1mvzPANRa27Bmns8DroF/nvH+Bv6/q/H+tlhqvu41+c4sKyvzih9Jntcul6vG+2nR4rp6ndflPHLXLQ3+NYCfmubNm/h6CvWC9zdwKX95f18D/8a6lM1muyR0/vW6UaNGvpgSAADwI9dkAEVGRurUqVOqrKz0LCsuLlajRo3UrFmzH9gSAACY4JoMoE6dOikoKEhZWVmeZQ6HQ7GxsQq4Fm4sAAAA/5ZrsgaCg4M1bNgwpaamKicnR6+99po2btyoMWPG+HpqAADAD1jc7oZ+zsk3ysrKlJqaqldeeUVNmzbVhAkTNG7cOF9PCwAA+IFrNoAAAACu5Jq8BAYAAPBDCCAAAGAcAggAABiHAILRKioqNHv2bCUkJKh3797auHGjr6cEoJ65XC4NGTJEBw4c8PVU4EeuyV+FAdTU0qVLdfjwYWVmZur48eOaNWuWoqKiNHDgQF9PDUA9qKio0O9//3vl5eX5eirwMwQQjHX+/Hnt2LFDTz31lGJiYhQTE6O8vDxt3bqVAAKuAUePHtXvf/978bAzLodLYDBWbm6uKisrFRcX51kWHx+v7OxsVVdX+3BmAOrDwYMH1aNHD23bts3XU4Ef4gwQjFVcXKzmzZvLarV6loWFhamiokKlpaVq0aKFD2cH4N91zz33+HoK8GOcAYKxysrKvOJHkue1y+XyxZQAAFcJAQRj2Wy2S0LnX68bNWrkiykBAK4SAgjGioyM1KlTp1RZWelZVlxcrEaNGqlZs2Y+nBkAoKERQDBWp06dFBQUpKysLM8yh8Oh2NhYBQTw1gCAaxl/y8NYwcHBGjZsmFJTU5WTk6PXXntNGzdu1JgxY3w9NQBAA+MpMBgtJSVFqampGjt2rJo2baqHHnpIAwYM8PW0AAANzOLmJ0QBAADDcAkMAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCMBVddttt2n37t2XLN+9e7duu+22evkahw4d0uTJk9WjRw91795d9913nz744IMabbtnzx6NHDlScXFx6t27t2bNmqXCwsJ6mde/PPzww3r44YfrdZ8AaocAAnBNefnllzV27FhFR0dr8+bNeu6553TTTTdpzJgxcjgcP7jtkiVLtGTJEt11113as2eP0tPTVVxcrN/+9rc6efLkVToCAFcDAQTgmnH27FnNnTtXDz74oP7nf/5HHTt2VLt27ZSSkqK+fftq2bJlV9z2/fffV2ZmptLT0zVy5Ei1bdtWdrtd6enpqqysVGZm5lU8EgANjV+GCsDvOBwOLV++XEeOHJHFYlH37t2VlpamiIgIXbhwQfPnz9err74ql8ulHj16aP78+YqMjNTrr7+us2fPasyYMZfsc9asWSovL5d08XLb9u3bFRoaqnfffVfz5s3TwYMH1aVLFyUkJHhtFxwcrLVr1yosLEyS5HK5tGLFCr344os6efKkIiMjNXnyZN19992SLl7iGzRokJ5//nmFhYXpL3/5ixwOhxYtWqRjx46pX79+nv0C8B3OAAHwK998840mT56sXr16ad++fdqwYYO+/PJLZWRkSJK2bt2q9957Txs3btTOnTt17tw5LV68WJKUm5urX/7yl2ratOkl+23durXat2/vef3BBx+offv22r59u3r37q3c3FzFxsZedk4333yzIiIiJEkZGRl68803tWrVKr300ksaNmyYFi5cKKfT6Vl/79692rBhgx599FGdOnVKkydP1i233KI9e/aoffv2eumll+rtzwtA3XAGCMBVN2/ePC1cuNBrWWVlpcLDw1VeXq4pU6bovvvuk8ViUZs2bTRgwADl5ORIkr766ivZbDa1atVKISEhevTRR1VaWirpYjxdLn4ux2Kx6MEHH1SjRo1qtW10dLR69uyprl27SpIeeOABpaen6/PPP/ecJfr1r3+tjh07SroYbC1atNCMGTNksVj00EMPaf/+/TWaI4CGQwABuOqmTZumAQMGeC175ZVX9Oyzzyo8PFzDhg3T008/rY8//lhHjx7VJ598om7dukmS7r77br3wwgvq3bu3EhMT1b9/fyUlJUmSQkJCdObMmRrNITQ01BM/tdm2f//++sc//qFHH31Ux44d05EjRyRJVVVVnnVatWrl+fzo0aOKjo6WxWLxLIuNjVVZWVmN5gmgYXAJDMBVFxoaqrZt23p9hIaGSpK+/vpr/frXv9a7776rmJgYzZ49W/fdd59n2w4dOuj111/XsmXLFB4erscee0zjx4+X2+1WTEyMPv/8c509e/aSr/n+++8rOTnZEx42m81rPCYmRh999NFl55uZmakVK1ZIkh5//HHNmDFDQUFBGjZsmLZt23bJ+t/ft9vt9nr9s5/97Mf+iAA0MAIIgF959dVXdf3112vdunUaO3asEhISlJ+f74mIPXv26I033tCgQYP0xz/+UevXr5fD4VBJSYn69Omj6667Tlu2bLlkv5mZmSoqKrrizcd33nmncnJyLnlU/ty5c8rMzPSc4Xnuuef0yCOPaPr06brjjjs8QfX9yPmXDh066MiRI15niD7++OPa/8EAqFdcAgPgV0JCQnT8+HH985//VOvWrfW3v/1Nr7zyiucG5W+++UZPPvmkmjdvrtatW2vv3r36+c9/rubNmyswMFCzZ89WSkqKysvLdeedd8rlcunPf/6z3nzzTT3zzDNX/LpxcXEaOXKkpkyZohkzZigxMVFFRUV64oknFBAQoIkTJ3rm98Ybb6hz5876+uuvPTdgu1yuy+538ODBWrVqldLS0vTb3/5Wr7/+uhwOh9dlMgBXHwEEwK8MGjRI7733nqZNmyaLxaLY2FjNmjVLq1atksvl0r333quioiLNmDFDp0+fVufOnbV27VoFBgZKungDcrNmzfTUU09p69atnn1s3bpVXbp0+cGvPX/+fLVr106ZmZlatGiRmjVrpl69eunxxx9X8+bNJUmLFy9WamqqBg8erMjISI0cOVKBgYH6+OOP9R//8R+X7PP666/X+vXrlZqaqqFDh6p79+4aOnToFc8YAbg6LG7ehQAAwDDcAwQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4/w/5rTVagibZPQAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           HasCrCard  Ratio\n",
      "HasCrCard                  \n",
      "1               7055  70.55\n",
      "0               2945  29.45\n",
      "##########################################\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                IsActiveMember  Ratio\n",
      "IsActiveMember                       \n",
      "1                         5151  51.51\n",
      "0                         4849  48.49\n",
      "##########################################\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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vGjZsmJKTk5Wfn6/3339fmZmZmjRpkkaNGqWKigqlpqbq2LFjSk1Nldvt1ujRoyVJkyZN0quvvqqdO3cqPz9fc+bM0bBhwxQdHW2ML1u2TAcPHtTBgwe1fPlyTZkyxV+7CgAAWhi/XQMkScuWLdPixYs1adIk2e123XvvvZo8ebIsFovWrFmj+fPna8eOHerRo4cyMzMVEhIiSYqJidGiRYuUnp6uM2fOaMiQIVq8eLHxvNOnT1dpaamSkpIUGBioCRMmaOrUqX7aSwAA0NJYvF4uNbwUl6v57gILCrpwF9i9K7KUX3T6xzcATKBn51BtfXSsysrOqaaGu8AANIzFIoWH1+8uML4MFQAAmA4BBAAATIcAAgAApkMAAQAA0yGAAACA6RBAAADAdAggAABgOgQQAAAwHQIIAACYDgEEAABMhwACAACmQwABAADTIYAAAIDpEEAAAMB0CCAAAGA6BBAAADAdAggAAJgOAQQAAEyHAAIAAKZDAAEAANMhgAAAgOkQQAAAwHQIIAAAYDoEEAAAMB0CCAAAmA4BBAAATIcAAgAApkMAAQAA0yGAAACA6RBAAADAdAggAABgOgQQAAAwHb8G0Ntvv60ePXr4/MyaNUuSdPToUU2cOFEOh0Pjx4/XkSNHfLbNysrSbbfdJofDoZkzZ+r06dPGmNfr1bJlyzR48GDFx8crLS1NdXV1V3TfAABAy+XXADp27JiGDx+uAwcOGD9PPvmkKisrlZiYqLi4OO3Zs0cxMTGaMWOGKisrJUl5eXlKSUlRUlKStm/froqKCiUnJxvPu2HDBmVlZSkjI0Pp6enau3evNmzY4K/dBAAALYxfA+iLL77QTTfdpIiICOOnXbt2euONN2Sz2TRnzhx169ZNKSkpatOmjd58801J0pYtWzR69GiNGzdOPXv2VFpamvbv36/CwkJJ0ubNmzVr1izFxcVp8ODBmj17trZu3erPXQUAAC2I3wPo+uuvv2h5bm6uYmNjZbFYJEkWi0UDBgxQTk6OMR4XF2es36lTJ0VFRSk3N1cnT55UcXGxBg4caIzHxsaqqKhIp06datb9AQAAV4cgf/1hr9erL7/8UgcOHNCaNWtUW1urUaNGadasWXI6nerevbvP+mFhYSooKJAknTp1Sh07drxovKSkRE6nU5J8xsPDwyVJJSUlF213Od/1FwA/4PgD0FANed3wWwCdOHFCbrdbVqtVK1as0N///nc9+eSTqqqqMpZ/n9VqlcfjkSRVVVVdcryqqsp4/P0xScb29RUWdm2D9wvAT9ehQxt/TwFAK+e3AOrcubMOHjyo6667ThaLRb169VJdXZ0ee+wxxcfHXxQrHo9HwcHBkiSbzfaD43a73Sd2bDab8bsk2e32Bs2xtPRbeb2N2r0fFRgYwIs8cAllZedUW8udmwAaxmKp/5sXfgsgSWrfvr3P427duqm6uloRERFyuVw+Yy6Xyzh9FRkZ+YPjERERioyMlCQ5nU516dLF+F2SIiIiGjQ/r1fNFkAALo9jD0Bz8ttF0O+//74GDRokt9ttLPvss8/Uvn17xcbG6uOPP5b3u1dAr9erw4cPy+FwSJIcDoeys7ON7YqLi1VcXCyHw6HIyEhFRUX5jGdnZysqKqpB1/8AAIDWy28BFBMTI5vNpscff1zHjx/X/v37lZaWpvvvv1+jRo1SRUWFUlNTdezYMaWmpsrtdmv06NGSpEmTJunVV1/Vzp07lZ+frzlz5mjYsGGKjo42xpctW6aDBw/q4MGDWr58uaZMmeKvXQUAAC2M306BtW3bVuvWrdNTTz2l8ePHq02bNrr77rt1//33y2KxaM2aNZo/f7527NihHj16KDMzUyEhIZIuxNOiRYuUnp6uM2fOaMiQIVq8eLHx3NOnT1dpaamSkpIUGBioCRMmaOrUqX7aUwAA0NJYvF7OtF+Ky9V8F0EHBV24CPreFVnKLzr94xsAJtCzc6i2PjpWZWXnVFPDRdAAGsZikcLD63cRNF+GCgAATIcAAgAApkMAAQAA0yGAAACA6RBAAADAdAggAABgOgQQAAAwHQIIAACYDgEEAABMhwACAACmQwABAADTIYAAAIDpEEAAAMB0CCAAAGA6BBAAADCdRgXQlClTVFFRcdHy06dPKyEh4SdPCgAAoDkF1XfF9957T3l5eZKkjz76SKtXr1ZISIjPOl9//bWKioqadoYAAABNrN4BdMMNN+ill16S1+uV1+vV4cOHdc011xjjFotFISEhSk1NbZaJAgAANJV6B1B0dLQ2b94sSUpOTlZKSoratm3bbBMDAABoLvUOoO9bsmSJJMnpdKqmpkZer9dnPCoq6qfPDAAAoJk0KoD++te/6oknnlBxcbEkyev1ymKxGP/52WefNekkAQAAmlKjAmjRokXq16+fVq1axWkwAABw1WlUAJWUlOill15SdHR0U88HAACg2TXqc4Di4uKUnZ3d1HMBAAC4Ihr1DtDAgQO1cOFC/eUvf1HXrl19boeXpKSkpCaZHAAAQHNo9EXQffr0UWlpqUpLS33GLBZLk0wMAACguTQqgF5++eWmngcAAMAV06gAeuWVVy47Pm7cuMY8LQAAwBXRqABKT0/3eVxbW6vS0lIFBQWpX79+BBAAAGjRGhVA77777kXLzp07p3nz5qlHjx4/eVIAAADNqVG3wf+QNm3a6JFHHtGGDRua6ikBAACaRZMFkCTl5+errq6uKZ8SAACgyTXqFNjkyZMvut393Llz+vzzzzV16tRGTSQxMVGhoaF6+umnJUlHjx7V/Pnz9b//+7/q3r27Fi5cqD59+hjrZ2VlacWKFXI6nbrlllu0ePFihYaGSrrw3WTLly/Xrl27VFdXpwkTJmj27NkKCGjS3gMAAFepRhXBoEGDFB8f7/MzcuRIrV27VrNnz27w873++uvav3+/8biyslKJiYmKi4vTnj17FBMToxkzZqiyslKSlJeXp5SUFCUlJWn79u2qqKhQcnKysf2GDRuUlZWljIwMpaena+/evZyaAwAAhka9A/T9T3o+e/asamtrdd111zVqAuXl5UpLS1Pfvn2NZW+88YZsNpvmzJkji8WilJQUvffee3rzzTeVkJCgLVu2aPTo0cbdZmlpaRo+fLgKCwsVHR2tzZs3a9asWYqLi5MkzZ49W88//7ymT5/eqDkCAIDWpdHnhDZt2qShQ4dq4MCBGjx4sIYMGaKMjIwGP88zzzyjO++8U927dzeW5ebmKjY21jjNZrFYNGDAAOXk5Bjj/4gbSerUqZOioqKUm5urkydPqri4WAMHDjTGY2NjVVRUpFOnTjVybwEAQGvSqHeAVq5cqS1btug3v/mNYmJiVFdXp8OHDysjI0NWq1WJiYn1ep4PP/xQ//M//6O9e/dqwYIFxnKn0+kTRJIUFhamgoICSdKpU6fUsWPHi8ZLSkrkdDolyWc8PDxc0oVvsf/n7S6Hb/UA/IfjD0BDNeR1o1EBtGPHDqWmpmrEiBHGsl69eikyMlKpqan1CqDq6mrNnz9f8+bNU3BwsM+Y2+2W1Wr1WWa1WuXxeCRJVVVVlxyvqqoyHn9/TJKxfX2FhV3boPUBNI0OHdr4ewoAWrlGBdDZs2d1/fXXX7T8hhtu0OnTp+v1HBkZGerTp4+GDh160ZjNZrsoVjwejxFKlxq32+0+sWOz2YzfJclut9drbv9QWvqtvN4GbVJvgYEBvMgDl1BWdk61tXykBoCGsVjq/+ZFowIoJiZG69ev16JFi4xby2tra7Vu3Tr169evXs/x+uuvy+VyKSYmRtL/R8pbb72lsWPHyuVy+azvcrmM01eRkZE/OB4REaHIyEhJF06jdenSxfhdkiIiIhq0n16vmi2AAFwexx6A5tSoAEpOTta9996rDz74QL1795Ykffrpp/J4PHrppZfq9Rwvv/yyampqjMfLli2TdOGOrY8++khr166V1+uVxWKR1+vV4cOH9eCDD0qSHA6HsrOzlZCQIEkqLi5WcXGxHA6HIiMjFRUVpezsbCOAsrOzFRUV1aDrfwAAQOvVqADq1q2b5s6dq/Lych0/flw2m01//vOflZ6erp49e9brOTp37uzzuE2bC6eDunbtqrCwMC1fvlypqam6++67tW3bNrndbo0ePVqSNGnSJE2ePFn9+/dX3759lZqaqmHDhik6OtoYX7ZsmX72s59JkpYvX65p06Y1ZlcBAEAr1KgAevnll/Xcc8/piSeeMO7eCggI0OzZs/Wf//mfuuuuu37SpNq2bas1a9Zo/vz52rFjh3r06KHMzEyFhIRIunAKbtGiRUpPT9eZM2c0ZMgQLV682Nh++vTpKi0tVVJSkgIDAzVhwoRGf0I1AABofSxeb8PPtI8YMUJPPPGEhg8f7rP8nXfe0ZIlS7Rv374mm6A/uVzNdxF0UNCFi6DvXZGl/KL6XTgOtHY9O4dq66NjVVZ2TjU1XAQNoGEsFik8vH4XQTfqgxDLysr085///KLlN9xww0UXJwMAALQ0jQqg2NhYvfDCC3K73cay6upqrV692rirCwAAoKVq1DVA8+bN07Rp03TLLbcYnwf0zTffKDw8XC+++GJTzg8AAKDJNSqAfv7zn+uNN97Q+++/r6+++kpBQUG6/vrrdcsttygwMLCp5wgAANCkGhVA0oWvl/jVr37VlHMBAAC4Ihr9bfAAAABXKwIIAACYDgEEAABMhwACAACmQwABAADTIYAAAIDpEEAAAMB0CCAAAGA6BBAAADAdAggAAJgOAQQAAEyHAAIAAKZDAAEAANMhgAAAgOkQQAAAwHQIIAAAYDoEEAAAMB0CCAAAmA4BBAAATIcAAgAApkMAAQAA0yGAAACA6RBAAADAdAggAABgOgQQAAAwHQIIAACYDgEEAABMx68B9PXXX2v69OmKiYnRsGHD9NJLLxljhYWFmjp1qvr3768xY8bowIEDPtt+8MEHGjt2rBwOh6ZMmaLCwkKf8Y0bN2ro0KGKiYnR3Llz5Xa7r8g+AQCAls9vAVRXV6fExER16NBB//Vf/6WFCxdq1apV2rt3r7xer2bOnKnw8HDt3r1bd955p5KSknTixAlJ0okTJzRz5kwlJCRo165dCg0N1cMPPyyv1ytJeuutt5SRkaFFixZp06ZNys3N1dKlS/21qwAAoIXxWwC5XC716tVLCxYs0PXXX69bb71Vv/jFL5Sdna2//e1vKiws1KJFi9StWzfNmDFD/fv31+7duyVJO3fuVJ8+fTRt2jTdeOONWrJkiYqKinTo0CFJ0ubNm3Xfffdp+PDh6tevnxYuXKjdu3fzLhAAAJDkxwDq2LGjVqxYobZt28rr9So7O1sfffSR4uPjlZubq5tvvlkhISHG+rGxscrJyZEk5ebmKi4uzhiz2+3q3bu3cnJyVFtbq08++cRnvH///jp//rzy8/Ov2P4BAICWK8jfE5CkESNG6MSJExo+fLhGjhypp556Sh07dvRZJywsTCUlJZIkp9N5yfGKigpVV1f7jAcFBal9+/bG9vVlsTRyhwD8ZBx/ABqqIa8bLSKA0tPT5XK5tGDBAi1ZskRut1tWq9VnHavVKo/HI0mXHa+qqjIeX2r7+goLu7ahuwKgCXTo0MbfUwDQyrWIAOrbt68kqbq6WrNnz9b48eMvul7H4/EoODhYkmSz2S6KGY/Ho3bt2slmsxmP/3ncbrc3aF6lpd/qu+uqm1xgYAAv8sAllJWdU21tnb+nAeAqY7HU/80LvwWQy+VSTk6ObrvtNmNZ9+7ddf78eUVEROj48eMXrf+P01qRkZFyuVwXjffq1Uvt27eXzWaTy+VSt27dJEk1NTUqLy9XREREg+bo9arZAgjA5XHsAWhOfrsI+u9//7uSkpJ08uRJY9mRI0cUGhqq2NhYffrpp8bpLEnKzs6Ww+GQJDkcDmVnZxtjbrdbR48elcPhUEBAgPr27esznpOTo6CgIPXs2fMK7BkAAGjp/BZAffv2Ve/evTV37lwdO3ZM+/fv19KlS/Xggw8qPj5enTp1UnJysgoKCpSZmam8vDxNmDBBkjR+/HgdPnxYmZmZKigoUHJysrp06aJBgwZJku655x6tW7dO+/btU15enhYsWKC77rqrwafAAABA6+S3AAoMDNSLL74ou92uX//610pJSdHkyZM1ZcoUY8zpdCohIUGvvfaaVq5cqaioKElSly5d9MILL2j37t2aMGGCysvLtXLlSlm+u/z7jjvu0IwZMzRv3jxNmzZN/fr102OPPeavXQUAAC2MxevlTPuluFzNdxF0UNCFi6DvXZGl/KLTzfNHgKtMz86h2vroWJWVnVNNDRdBA2gYi0UKD6/fRdB8GSoAADAdAggAAJgOAQQAAEyHAAIAAKZDAAEAANMhgAAAgOkQQAAAwHQIIAAAYDoEEAAAMB0CCAAAmA4BBAAATIcAAgAApkMAAQAA0yGAAACA6RBAAADAdAggAABgOgQQAAAwHQIIAACYDgEEAABMhwACAACmQwABAADTIYAAAIDpEEAAAMB0CCAAAGA6BBAAADAdAggAAJgOAQQAAEyHAAIAAKZDAAEAANMhgAAAgOkQQAAAwHQIIAAAYDoEEAAAMB2/BtDJkyc1a9YsxcfHa+jQoVqyZImqq6slSYWFhZo6dar69++vMWPG6MCBAz7bfvDBBxo7dqwcDoemTJmiwsJCn/GNGzdq6NChiomJ0dy5c+V2u6/YfgEAgJbNbwHk9Xo1a9Ysud1ubd26Vc8995z+/Oc/a8WKFfJ6vZo5c6bCw8O1e/du3XnnnUpKStKJEyckSSdOnNDMmTOVkJCgXbt2KTQ0VA8//LC8Xq8k6a233lJGRoYWLVqkTZs2KTc3V0uXLvXXrgIAgBYmyF9/+Pjx48rJydFf//pXhYeHS5JmzZqlZ555Rv/6r/+qwsJCbdu2TSEhIerWrZs+/PBD7d69W4888oh27typPn36aNq0aZKkJUuWaMiQITp06JAGDRqkzZs367777tPw4cMlSQsXLtT06dP12GOPyW63+2uXAZhIQIBFAQEWf08DaFHq6ryqq/P6exqS/BhAEREReumll4z4+YezZ88qNzdXN998s0JCQozlsbGxysnJkSTl5uYqLi7OGLPb7erdu7dycnIUFxenTz75RElJScZ4//79df78eeXn5ysmJqbec7Tw2gX4zdV8/AUEWHTddSEKDOQyS+D7amvrdOZMZbNFUENeN/wWQO3atdPQoUONx3V1ddqyZYsGDx4sp9Opjh07+qwfFhamkpISSbrseEVFhaqrq33Gg4KC1L59e2P7+goLu7ahuwWgCXTo0MbfU2gSj//hfX156oy/pwG0CDd0vE5P3jNUoaFt/T0VSX4MoH+2dOlSHT16VLt27dLGjRtltVp9xq1WqzwejyTJ7XZfcryqqsp4fKnt66u09Ft5m+mdusDAgFbzIg80tbKyc6qtrfP3NBrtH8f3l6fOKL/otL+nA7QozXl8Wyz1f/OiRQTQ0qVLtWnTJj333HO66aabZLPZVF5e7rOOx+NRcHCwJMlms10UMx6PR+3atZPNZjMe//N4Q6//8XrVbAEE4PI49oDWqyUc334/Qb148WJt2LBBS5cu1ciRIyVJkZGRcrlcPuu5XC7jtNalxiMiItS+fXvZbDaf8ZqaGpWXlysiIqKZ9wYAAFwN/BpAGRkZ2rZtm5599lndcccdxnKHw6FPP/3UOJ0lSdnZ2XI4HMZ4dna2MeZ2u3X06FE5HA4FBASob9++PuM5OTkKCgpSz549r8BeAQCAls5vAfTFF1/oxRdf1AMPPKDY2Fg5nU7jJz4+Xp06dVJycrIKCgqUmZmpvLw8TZgwQZI0fvx4HT58WJmZmSooKFBycrK6dOmiQYMGSZLuuecerVu3Tvv27VNeXp4WLFigu+66i1vgAQCAJD9eA/TOO++otrZWq1at0qpVq3zGPv/8c7344otKSUlRQkKCunbtqpUrVyoqKkqS1KVLF73wwgt66qmntHLlSsXExGjlypWyfHf/2x133KGioiLNmzdPHo9Ht99+ux577LErvo8AAKBlsni9LeFSpJbJ5Wq+u8CCgi7cJXLviizuEgG+07NzqLY+OlZlZedUU3P13gXG8Q1c7Eoc3xaLFB5ev7vA/H4RNAAAwJVGAAEAANMhgAAAgOkQQAAAwHQIIAAAYDoEEAAAMB0CCAAAmA4BBAAATIcAAgAApkMAAQAA0yGAAACA6RBAAADAdAggAABgOgQQAAAwHQIIAACYDgEEAABMhwACAACmQwABAADTIYAAAIDpEEAAAMB0CCAAAGA6BBAAADAdAggAAJgOAQQAAEyHAAIAAKZDAAEAANMhgAAAgOkQQAAAwHQIIAAAYDoEEAAAMB0CCAAAmA4BBAAATKdFBJDH49HYsWN18OBBY1lhYaGmTp2q/v37a8yYMTpw4IDPNh988IHGjh0rh8OhKVOmqLCw0Gd848aNGjp0qGJiYjR37ly53e4rsi8AAKDl83sAVVdX6z/+4z9UUFBgLPN6vZo5c6bCw8O1e/du3XnnnUpKStKJEyckSSdOnNDMmTOVkJCgXbt2KTQ0VA8//LC8Xq8k6a233lJGRoYWLVqkTZs2KTc3V0uXLvXL/gEAgJbHrwF07Ngx3XXXXfrmm298lv/tb39TYWGhFi1apG7dumnGjBnq37+/du/eLUnauXOn+vTpo2nTpunGG2/UkiVLVFRUpEOHDkmSNm/erPvuu0/Dhw9Xv379tHDhQu3evZt3gQAAgCQ/B9ChQ4c0aNAgbd++3Wd5bm6ubr75ZoWEhBjLYmNjlZOTY4zHxcUZY3a7Xb1791ZOTo5qa2v1ySef+Iz3799f58+fV35+fvPuEAAAuCoE+fOP33PPPT+43Ol0qmPHjj7LwsLCVFJS8qPjFRUVqq6u9hkPCgpS+/btje3ry2Jp0OoAmhDHH9B6Ndfx3ZDn9WsAXYrb7ZbVavVZZrVa5fF4fnS8qqrKeHyp7esrLOzahk4dQBPo0KGNv6cAoJm0lOO7RQaQzWZTeXm5zzKPx6Pg4GBj/J9jxuPxqF27drLZbMbjfx632+0Nmkdp6bf67rrqJhcYGNBi/kcAtDRlZedUW1vn72k0Gsc3cGnNeXxbLPV/88Lvd4H9kMjISLlcLp9lLpfLOK11qfGIiAi1b99eNpvNZ7ympkbl5eWKiIho0Dy83ub7AXB5zXn8NfcPgMtrCcdfiwwgh8OhTz/91DidJUnZ2dlyOBzGeHZ2tjHmdrt19OhRORwOBQQEqG/fvj7jOTk5CgoKUs+ePa/cTgAAgBarRQZQfHy8OnXqpOTkZBUUFCgzM1N5eXmaMGGCJGn8+PE6fPiwMjMzVVBQoOTkZHXp0kWDBg2SdOHi6nXr1mnfvn3Ky8vTggULdNdddzX4FBgAAGidWmQABQYG6sUXX5TT6VRCQoJee+01rVy5UlFRUZKkLl266IUXXtDu3bs1YcIElZeXa+XKlbJ8d/n3HXfcoRkzZmjevHmaNm2a+vXrp8cee8yfuwQAAFqQFnMR9Oeff+7zuGvXrtqyZcsl17/11lt16623XnI8MTFRiYmJTTY/AADQerTId4AAAACaEwEEAABMhwACAACmQwABAADTIYAAAIDpEEAAAMB0CCAAAGA6BBAAADAdAggAAJgOAQQAAEyHAAIAAKZDAAEAANMhgAAAgOkQQAAAwHQIIAAAYDoEEAAAMB0CCAAAmA4BBAAATIcAAgAApkMAAQAA0yGAAACA6RBAAADAdAggAABgOgQQAAAwHQIIAACYDgEEAABMhwACAACmQwABAADTIYAAAIDpEEAAAMB0CCAAAGA6BBAAADCdVhtA1dXVmjt3ruLi4nTLLbdo/fr1/p4SAABoIYL8PYHmkpaWpiNHjmjTpk06ceKEfv/73ysqKkqjRo3y99QAAICftcoAqqys1M6dO7V27Vr17t1bvXv3VkFBgbZu3UoAAQCA1nkKLD8/XzU1NYqJiTGWxcbGKjc3V3V1dX6cGQAAaAla5TtATqdTHTp0kNVqNZaFh4erurpa5eXlCg0NrdfzBARIXm9zzfKCnlGhsltb5X8NQIN1DW9n/B7QCv55xvEN/L8rcXxbLPVft1UemW632yd+JBmPPR5PvZ8nNPTaJp3XD3nirl82+98ArjYdOrTx9xSaBMc3cLGWcny3gn9jXcxms10UOv94HBwc7I8pAQCAFqRVBlBkZKTKyspUU1NjLHM6nQoODla7du0usyUAADCDVhlAvXr1UlBQkHJycoxl2dnZ6tu3rwJaw4UFAADgJ2mVNWC32zVu3DgtWLBAeXl52rdvn9avX68pU6b4e2oAAKAFsHi9zX2fk3+43W4tWLBA//3f/622bdtq+vTpmjp1qr+nBQAAWoBWG0AAAACX0ipPgQEAAFwOAQQAAEyHAAIAAKZDAMHUqqurNXfuXMXFxemWW27R+vXr/T0lAE3M4/Fo7NixOnjwoL+nghakVX4VBlBfaWlpOnLkiDZt2qQTJ07o97//vaKiojRq1Ch/Tw1AE6iurtbvfvc7FRQU+HsqaGEIIJhWZWWldu7cqbVr16p3797q3bu3CgoKtHXrVgIIaAWOHTum3/3ud+JmZ/wQToHBtPLz81VTU6OYmBhjWWxsrHJzc1VXV+fHmQFoCocOHdKgQYO0fft2f08FLRDvAMG0nE6nOnToIKvVaiwLDw9XdXW1ysvLFRoa6sfZAfip7rnnHn9PAS0Y7wDBtNxut0/8SDIeezwef0wJAHCFEEAwLZvNdlHo/ONxcHCwP6YEALhCCCCYVmRkpMrKylRTU2MsczqdCg4OVrt27fw4MwBAcyOAYFq9evVSUFCQcnJyjGXZ2dnq27evAgI4NACgNeNVHqZlt9s1btw4LViwQHl5edq3b5/Wr1+vKVOm+HtqAIBmxl1gMLXk5GQtWLBA9913n9q2batHHnlEt99+u7+nBQBoZhYvnxAFAABMhlNgAADAdAggAABgOgQQAAAwHQIIAACYDgEEAABMhwACAACmQwABAADTIYAAAIDpEEAArhojRoxQjx49fvDn4MGDP7rtnj17JElnz57VK6+80uh5vPDCC5o8eXKjtwfgf3wVBoCryty5czVmzJiLll933XWX3W7Xrl0KCQmRJG3cuFEHDx7UuHHjmmOKAK4CBBCAq8q1116riIiIBm8XGhpq/M43AAHgFBiAVuGLL75Qnz59jFNbHo9HI0eO1FNPPSXp/0+B7dmzRxkZGTp06JB69OhhrPvkk09q0KBBGjRokGbPnq3y8nLjuY8dO6ZJkybJ4XBoypQpKisru9K7B6CJEUAAWoVu3bopMTFRy5Yt09mzZ7Vy5UrV1dXpt7/9rc96Y8aM0bRp0xQTE6MDBw5Ikp599lkdOXJEa9eu1ebNm3X27Fn95je/kXQhjhITExUdHa09e/Zo5MiR2r59+xXfPwBNi1NgAK4q8+fP1+LFi32WRUVF6fXXX9eDDz6oP/3pT0pJSdE777yj9evXy263+6wbHByskJAQXXPNNYqIiJDb7daWLVu0e/du4x2htLQ0DRo0SJ9//rmKi4tVXl6uBQsWKCQkRN26ddOhQ4d0+vTpK7bPAJoeAQTgqjJr1izdfvvtPsuCgi68lFmtVi1cuFCTJ0/W+PHjFR8f/6PPV1hYqPPnz+vuu+/2WV5XV6evvvpKhYWFuv76640LqCWpb9++2r9/fxPsDQB/IYAAXFXCwsLUtWvXS47n5+crMDBQH3/8sTwej6xW62Wfr7a2VpL0hz/8wSdy/vG3tm3bdtFF09dcc00jZw+gpeAaIACtRklJiVasWKGnn35a58+f1+rVq39wPYvFYvweHR2twMBAlZeXq2vXruratavatm2rJUuWqLS0VDfeeKO++uorffvtt8Y2n332WbPvC4DmRQABuKp8++23cjqdF/1UVlZq4cKFiomJ0b/9279p7ty5yszM1LFjxy56DrvdrlOnTunvf/+72rZtq4kTJ2rBggU6ePCgjh07pjlz5ujrr79Wly5d9Mtf/lKdOnVSSkqKvvjiC+3Zs0dvvPGGH/YcQFOyePlADABXiREjRqioqOgHxx555BGtXr1ae/fu1Q033CBJmjFjhioqKvSHP/xBv/rVr5SUlKSEhAR98803mj59uk6dOqV3331XISEheuaZZ/SnP/1J58+f18CBA/X4448rOjpa0oXrhB5//HF9/PHH6tGjh+Li4nTkyBG9/PLLV2zfATQtAggAAJgOp8AAAIDpEEAAAMB0CCAAAGA6BBAAADAdAggAAJgOAQQAAEyHAAIAAKZDAAEAANMhgAAAgOkQQAAAwHQIIAAAYDr/B7MCMaPzvd6uAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Exited  Ratio\n",
      "Exited               \n",
      "0         7963  79.63\n",
      "1         2037  20.37\n",
      "##########################################\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:15:54.499334Z",
     "start_time": "2024-08-24T05:15:54.453252Z"
    }
   },
   "cell_type": "code",
   "source": [
    "gender = df[df['Exited'] == 1]['IsActiveMember'].value_counts()\n",
    "print(gender)\n",
    "gender = [gender[0] / sum(gender) * 100, gender[1] / sum(gender) * 100] # Female / Male\n",
    "print(gender)"
   ],
   "id": "73047199b45bcbaa",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IsActiveMember\n",
      "0    1302\n",
      "1     735\n",
      "Name: count, dtype: int64\n",
      "[63.91752577319587, 36.08247422680412]\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:18:18.583265Z",
     "start_time": "2024-08-24T05:18:18.532161Z"
    }
   },
   "cell_type": "code",
   "source": "df['Exited'].value_counts()",
   "id": "482a0d2533cd400f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Exited\n",
       "0    7963\n",
       "1    2037\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:18:25.532911Z",
     "start_time": "2024-08-24T05:18:25.472497Z"
    }
   },
   "cell_type": "code",
   "source": "df.describe()",
   "id": "462342a02687d770",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        CreditScore           Age        Tenure        Balance  NumOfProducts  \\\n",
       "count  10000.000000  10000.000000  10000.000000   10000.000000   10000.000000   \n",
       "mean     650.528800     38.921800      5.012800   76485.889288       1.530200   \n",
       "std       96.653299     10.487806      2.892174   62397.405202       0.581654   \n",
       "min      350.000000     18.000000      0.000000       0.000000       1.000000   \n",
       "25%      584.000000     32.000000      3.000000       0.000000       1.000000   \n",
       "50%      652.000000     37.000000      5.000000   97198.540000       1.000000   \n",
       "75%      718.000000     44.000000      7.000000  127644.240000       2.000000   \n",
       "max      850.000000     92.000000     10.000000  250898.090000       4.000000   \n",
       "\n",
       "         HasCrCard  IsActiveMember  EstimatedSalary        Exited  \n",
       "count  10000.00000    10000.000000     10000.000000  10000.000000  \n",
       "mean       0.70550        0.515100    100090.239881      0.203700  \n",
       "std        0.45584        0.499797     57510.492818      0.402769  \n",
       "min        0.00000        0.000000        11.580000      0.000000  \n",
       "25%        0.00000        0.000000     51002.110000      0.000000  \n",
       "50%        1.00000        1.000000    100193.915000      0.000000  \n",
       "75%        1.00000        1.000000    149388.247500      0.000000  \n",
       "max        1.00000        1.000000    199992.480000      1.000000  "
      ],
      "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>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>650.528800</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>96.653299</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>584.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>652.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>718.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>850.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:29:30.272223Z",
     "start_time": "2024-08-24T05:29:30.202014Z"
    }
   },
   "cell_type": "code",
   "source": "df.loc[1]",
   "id": "ee4a7f3db36b83da",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CreditScore           608.00\n",
       "Age                    41.00\n",
       "Tenure                  1.00\n",
       "Balance             83807.86\n",
       "NumOfProducts           1.00\n",
       "HasCrCard               0.00\n",
       "IsActiveMember          1.00\n",
       "EstimatedSalary    112542.58\n",
       "Exited                  0.00\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:30:03.082683Z",
     "start_time": "2024-08-24T05:30:03.017084Z"
    }
   },
   "cell_type": "code",
   "source": "df[df[\"Age\"]>25][['Exited']].count()",
   "id": "ed5a9d11c318a344",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Exited    9389\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:30:18.803718Z",
     "start_time": "2024-08-24T05:30:18.775Z"
    }
   },
   "cell_type": "code",
   "source": [
    "ge_greater_25_exited_yes= df.Exited[(df[\"Age\"] > 25) & (df[\"Exited\"] == 1)].count()\n",
    "age_less_25_exited_yes=df.Exited[(df[\"Age\"] <= 25) & (df[\"Exited\"] == 1)].count()"
   ],
   "id": "64a2fc65a5a887d4",
   "outputs": [],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:30:28.746753Z",
     "start_time": "2024-08-24T05:30:28.713250Z"
    }
   },
   "cell_type": "code",
   "source": "round(age_less_25_exited_yes)",
   "id": "898d8f43ca00a656",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:30:37.309785Z",
     "start_time": "2024-08-24T05:30:37.279642Z"
    }
   },
   "cell_type": "code",
   "source": "df[(df[\"HasCrCard\"] == 1)].count()[0]",
   "id": "432fa90c1f5a6fb3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7055"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:30:44.430671Z",
     "start_time": "2024-08-24T05:30:44.387771Z"
    }
   },
   "cell_type": "code",
   "source": "df[(df[\"Exited\"] == 1)].count()[0]",
   "id": "c67b34ecebe924f8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2037"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:31:02.874885Z",
     "start_time": "2024-08-24T05:31:02.845226Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(f'Probability of a customer being exited given they have a credit card, {round(df[(df[\"HasCrCard\"] == 1) & (df[\"Exited\"] == 1)].count()[0] / df[(df[\"Exited\"] == 1)].count()[0] *100,2)} % ')\n",
    "print(f'Probability of a customer being exited given they do not have a credit card, {round(df[(df[\"HasCrCard\"] == 0) & (df[\"Exited\"] == 1)].count()[0] / df[(df[\"Exited\"] == 1)].count()[0] *100,2)} % ')"
   ],
   "id": "1145df8e276ed24e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of a customer being exited given they have a credit card, 69.91 % \n",
      "Probability of a customer being exited given they do not have a credit card, 30.09 % \n"
     ]
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:31:14.147710Z",
     "start_time": "2024-08-24T05:31:14.124758Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(f'Probability of a customer being exited given they are activeMember, {round(df[(df[\"IsActiveMember\"] == 1) & (df[\"Exited\"] == 1)].count()[0] / df[(df[\"Exited\"] == 1)].count()[0] *100,2)} % ')\n",
    "print(f'Probability of a customer being exited given they are not active Member, {round(df[(df[\"IsActiveMember\"] == 0) & (df[\"Exited\"] == 1)].count()[0] / df[(df[\"Exited\"] == 1)].count()[0] *100,2)} % ')"
   ],
   "id": "439f567a63aaa246",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of a customer being exited given they are activeMember, 36.08 % \n",
      "Probability of a customer being exited given they are not active Member, 63.92 % \n"
     ]
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:31:28.253793Z",
     "start_time": "2024-08-24T05:31:28.191968Z"
    }
   },
   "cell_type": "code",
   "source": "df.corr()",
   "id": "62819531dd431568",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 CreditScore       Age    Tenure   Balance  NumOfProducts  \\\n",
       "CreditScore         1.000000 -0.003965  0.000842  0.006268       0.012238   \n",
       "Age                -0.003965  1.000000 -0.009997  0.028308      -0.030680   \n",
       "Tenure              0.000842 -0.009997  1.000000 -0.012254       0.013444   \n",
       "Balance             0.006268  0.028308 -0.012254  1.000000      -0.304180   \n",
       "NumOfProducts       0.012238 -0.030680  0.013444 -0.304180       1.000000   \n",
       "HasCrCard          -0.005458 -0.011721  0.022583 -0.014858       0.003183   \n",
       "IsActiveMember      0.025651  0.085472 -0.028362 -0.010084       0.009612   \n",
       "EstimatedSalary    -0.001384 -0.007201  0.007784  0.012797       0.014204   \n",
       "Exited             -0.027094  0.285323 -0.014001  0.118533      -0.047820   \n",
       "\n",
       "                 HasCrCard  IsActiveMember  EstimatedSalary    Exited  \n",
       "CreditScore      -0.005458        0.025651        -0.001384 -0.027094  \n",
       "Age              -0.011721        0.085472        -0.007201  0.285323  \n",
       "Tenure            0.022583       -0.028362         0.007784 -0.014001  \n",
       "Balance          -0.014858       -0.010084         0.012797  0.118533  \n",
       "NumOfProducts     0.003183        0.009612         0.014204 -0.047820  \n",
       "HasCrCard         1.000000       -0.011866        -0.009933 -0.007138  \n",
       "IsActiveMember   -0.011866        1.000000        -0.011421 -0.156128  \n",
       "EstimatedSalary  -0.009933       -0.011421         1.000000  0.012097  \n",
       "Exited           -0.007138       -0.156128         0.012097  1.000000  "
      ],
      "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>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003965</td>\n",
       "      <td>0.000842</td>\n",
       "      <td>0.006268</td>\n",
       "      <td>0.012238</td>\n",
       "      <td>-0.005458</td>\n",
       "      <td>0.025651</td>\n",
       "      <td>-0.001384</td>\n",
       "      <td>-0.027094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.003965</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009997</td>\n",
       "      <td>0.028308</td>\n",
       "      <td>-0.030680</td>\n",
       "      <td>-0.011721</td>\n",
       "      <td>0.085472</td>\n",
       "      <td>-0.007201</td>\n",
       "      <td>0.285323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>0.000842</td>\n",
       "      <td>-0.009997</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012254</td>\n",
       "      <td>0.013444</td>\n",
       "      <td>0.022583</td>\n",
       "      <td>-0.028362</td>\n",
       "      <td>0.007784</td>\n",
       "      <td>-0.014001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balance</th>\n",
       "      <td>0.006268</td>\n",
       "      <td>0.028308</td>\n",
       "      <td>-0.012254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.304180</td>\n",
       "      <td>-0.014858</td>\n",
       "      <td>-0.010084</td>\n",
       "      <td>0.012797</td>\n",
       "      <td>0.118533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumOfProducts</th>\n",
       "      <td>0.012238</td>\n",
       "      <td>-0.030680</td>\n",
       "      <td>0.013444</td>\n",
       "      <td>-0.304180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003183</td>\n",
       "      <td>0.009612</td>\n",
       "      <td>0.014204</td>\n",
       "      <td>-0.047820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HasCrCard</th>\n",
       "      <td>-0.005458</td>\n",
       "      <td>-0.011721</td>\n",
       "      <td>0.022583</td>\n",
       "      <td>-0.014858</td>\n",
       "      <td>0.003183</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011866</td>\n",
       "      <td>-0.009933</td>\n",
       "      <td>-0.007138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsActiveMember</th>\n",
       "      <td>0.025651</td>\n",
       "      <td>0.085472</td>\n",
       "      <td>-0.028362</td>\n",
       "      <td>-0.010084</td>\n",
       "      <td>0.009612</td>\n",
       "      <td>-0.011866</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011421</td>\n",
       "      <td>-0.156128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <td>-0.001384</td>\n",
       "      <td>-0.007201</td>\n",
       "      <td>0.007784</td>\n",
       "      <td>0.012797</td>\n",
       "      <td>0.014204</td>\n",
       "      <td>-0.009933</td>\n",
       "      <td>-0.011421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exited</th>\n",
       "      <td>-0.027094</td>\n",
       "      <td>0.285323</td>\n",
       "      <td>-0.014001</td>\n",
       "      <td>0.118533</td>\n",
       "      <td>-0.047820</td>\n",
       "      <td>-0.007138</td>\n",
       "      <td>-0.156128</td>\n",
       "      <td>0.012097</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:35:29.801795Z",
     "start_time": "2024-08-24T05:35:29.764297Z"
    }
   },
   "cell_type": "code",
   "source": [
    "y = df.iloc[:, -1].values\n",
    "x = df.iloc[:, :-1].values"
   ],
   "id": "831cddb363629850",
   "outputs": [],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:38:38.367823Z",
     "start_time": "2024-08-24T05:38:38.358912Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "print(scaler)\n",
    "X_norm = scaler.fit_transform(x)\n",
    "#X_test_norm = scaler.fit_transform(x_test)\n",
    "print(X_norm)\n",
    "print(f\"Peak to Peak range by column in Raw        X:{np.ptp(x,axis=0)}\")\n",
    "print(f\"Peak to Peak range by column in Normalized X:{np.ptp(X_norm,axis=0)}\")"
   ],
   "id": "181030f35e7211bd",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MinMaxScaler()\n",
      "[[0.538      0.32432432 0.2        ... 1.         1.         0.50673489]\n",
      " [0.516      0.31081081 0.1        ... 0.         1.         0.56270874]\n",
      " [0.304      0.32432432 0.8        ... 1.         0.         0.56965435]\n",
      " ...\n",
      " [0.718      0.24324324 0.7        ... 0.         1.         0.21039009]\n",
      " [0.844      0.32432432 0.3        ... 1.         0.         0.46442905]\n",
      " [0.884      0.13513514 0.4        ... 1.         0.         0.19091423]]\n",
      "Peak to Peak range by column in Raw        X:[5.0000000e+02 7.4000000e+01 1.0000000e+01 2.5089809e+05 3.0000000e+00\n",
      " 1.0000000e+00 1.0000000e+00 1.9998090e+05]\n",
      "Peak to Peak range by column in Normalized X:[1. 1. 1. 1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:39:51.884971Z",
     "start_time": "2024-08-24T05:39:50.872793Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.metrics import precision_recall_curve"
   ],
   "id": "2ae5dda934bc4ac3",
   "outputs": [],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:40:47.473887Z",
     "start_time": "2024-08-24T05:40:46.773120Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import imblearn\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import Pipeline\n",
    "\n",
    "over = SMOTE(sampling_strategy = 1)\n",
    "x_resam, y_resam = over.fit_resample(X_norm, y)\n",
    "Counter(y_resam)"
   ],
   "id": "50b5ea06269d1e42",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 7963, 0: 7963})"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:40:59.514101Z",
     "start_time": "2024-08-24T05:40:59.347603Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# split into train test sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(X_norm, y, test_size=0.33, random_state=1)\n",
    "x_train_re, x_test_re, y_train_re, y_test_re = train_test_split(x_resam, y_resam, test_size=0.33, random_state=1)"
   ],
   "id": "912600ce538d646a",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:41:22.003820Z",
     "start_time": "2024-08-24T05:41:21.951074Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def model(classifier,xtrain,ytrain):\n",
    "    classifier.fit(xtrain,ytrain)\n",
    "    return classifier"
   ],
   "id": "e3e0e372852041b6",
   "outputs": [],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:42:30.936093Z",
     "start_time": "2024-08-24T05:42:30.832535Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def model_evaluation(classifier,xtest,ytest):\n",
    "    y_pred=classifier.predict(xtest)\n",
    "    acc=accuracy_score(ytest,y_pred)\n",
    "    #cv = RepeatedStratifiedKFold(n_splits = 10,n_repeats = 3,random_state = 1)\n",
    "    print(\"Accuracy : \",'{0:.2%}'.format(acc))\n",
    "    #print(\"Cross Validation Score : \",'{0:.2%}'.format(cross_val_score(classifier,x_train,y_train,cv = cv,scoring = 'roc_auc').mean()))\n",
    "    print(\"ROC_AUC Score : \",'{0:.2%}'.format(roc_auc_score(ytest,y_pred)))\n",
    "    print(\"#########  Confusion Matrix  ########\")\n",
    "    cm = confusion_matrix(ytest,y_pred)\n",
    "    names = ['True Neg','False Pos','False Neg','True Pos']\n",
    "    counts = [value for value in cm.flatten()]\n",
    "    percentages = ['{0:.2%}'.format(value) for value in cm.flatten()/np.sum(cm)]\n",
    "    labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in zip(names,counts,percentages)]\n",
    "    labels = np.asarray(labels).reshape(2,2)\n",
    "    sns.heatmap(cm,annot = labels,cmap = 'Blues',fmt ='')\n",
    "\n",
    "    # Classification Report\n",
    "    print(classification_report(ytest,y_pred))\n",
    "    data= {\n",
    "        'ytest': ytest,\n",
    "        'ypred': y_pred\n",
    "    }\n",
    "\n",
    "\n",
    "\n",
    "    df2 = pd.DataFrame(data)\n",
    "    print(df2.head(20))"
   ],
   "id": "319d1ad24294a750",
   "outputs": [],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:43:18.696870Z",
     "start_time": "2024-08-24T05:43:17.629156Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier_dt = DecisionTreeClassifier(random_state = 1000,max_depth = 4,min_samples_leaf = 1)\n",
    "print(\"Decision tree Classifier model for imbalanced dataset\")\n",
    "classifier=model(classifier_dt,x_train,y_train)\n",
    "model_evaluation(classifier,x_test,y_test)"
   ],
   "id": "3091f103fad80689",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree Classifier model for imbalanced dataset\n",
      "Accuracy :  84.91%\n",
      "ROC_AUC Score :  69.13%\n",
      "#########  Confusion Matrix  ########\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.96      0.91      2609\n",
      "           1       0.75      0.42      0.54       691\n",
      "\n",
      "    accuracy                           0.85      3300\n",
      "   macro avg       0.81      0.69      0.72      3300\n",
      "weighted avg       0.84      0.85      0.83      3300\n",
      "\n",
      "    ytest  ypred\n",
      "0       0      0\n",
      "1       0      0\n",
      "2       0      0\n",
      "3       0      0\n",
      "4       0      0\n",
      "5       0      0\n",
      "6       0      0\n",
      "7       0      0\n",
      "8       0      0\n",
      "9       0      0\n",
      "10      0      0\n",
      "11      1      0\n",
      "12      0      0\n",
      "13      0      0\n",
      "14      0      0\n",
      "15      0      0\n",
      "16      0      0\n",
      "17      0      0\n",
      "18      0      0\n",
      "19      0      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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Xy5YuZuOGdazdsOWZ/0GXlCkmJoYYi5KYo6Mjjo7mfyc8PT355JNPTMHCnyIiIggODqZIkSKkS/fXL1mlSpUiKCgIgODgYEqXLm3qc3FxoWjRogQFBVG6dGmOHj2Kv7+/qb9kyZI8fvyYU6dO4efn90znoZJEKhQTG4drOifT6/QujnhmyQA8+fK+ces+Xh5unAsN51xoOKHX7jDhg8YUyPP3/xD+v2GztpLO2ZEP21Uztf1+8SY5s2Um/E6E6bh5vD0Y/l59EhISOBFyHb/COc2O41dEl5mJbUVERLD9y21MmTrjjyxETe79sajQMpAODwtjwtjR5MqVm67d3+ezdRspW648u3ftJE+eV3jw4AF2dnbkyp2bXLlzEx0dxfRpUwz/8D+rPHle4cH9+1w4f87UFnL2LBEREeR+5RV+2LObzRvXU/nNKgwbMZr1G7dy8cIFzpzRpczJws5224IFCyhVqpTZtmCB8ZcrNzc3KlX6a21XfHw8K1eupHz58oSFhZE1q3kW2N3dnevXrwM8tf/+/ftER0eb9Ts4OJApUybT/s9CAUMqdOj4RUoUyEHTGn68misrc4e3Ji4u3tQ/Z+VORvV8i3qVi5Evlycfj2xN+ZJ5OX3+2f7i3L73kGGzt5Inx19R8I6fT3Lp2m2WjGtP0Ve9qeCXj7nD3+FRVAzx8Qks2bSXssXz0K9DDV7NlZX+nWpR8bVXSSQZIvKPOTk64uLiwo7vvuXKlcvs2/ujaaGi5aLHjBkz8v2O7wiYNIHQS5c49OtBTp86RaFCRcibLx8VKlZi8MCPOHb0CCdPHGf40MFEPnqEm5vbP5rbK3nzUbFSZYYOHsixo0c4euQIw4cMpFTpMuTPX4D4+HimTZ3C9zu+48qVy2zdsglnFxfy5M7zbz8W+QdsuYahe/fuHDp0yGzr3r271TkEBARw4sQJ+vTpQ2RkpCEj4ejoaApgn9b/59U9T9v/WagkkQrt2n+a2St3Eji8FXFx8cxeuZPsnhlN/TNWfI9rOmcCh7XCzdWZwycu0bDHXO4+ePYVs8u3/Ez7RuXxzpoJeLK+ofmHC5g+sAU/rPiIiMhoNn33G4NnbAbg0rU7tO6/mEl9mzL8/fp8/8tpvtgVTMzjWJueu/y3pXV0ZMLkAKZNmczqVZ/incOHrt3fZ+7smZw6eZJX8uYzGzsrcB5TJk2gedOGpE+XnibNmtO0eQsAxk+awqTx4+jWuQNp0jhQoWIlBg0d9q/mN27iZNMx7dOkoWrV6vQf+ORKiCpVq9HDvzdTJ08kPDyMV17Jy6w583DLmNHKUSWlS6z8YE1AQADLly9nxowZFChQACcnJ+7evWs2JiYmBmdnZ+DJnUUtv/xjYmJwc3PDycnJ9Nqy/3nKXXYJiRW8k4GLn7/1QfLSKpIvO2kd0hB8+rKpbdPs9zh0/BLjF3yVjDNL2e4cDEzuKYikSM5J/OuuT48tNjvW5XmNn2v82LFjWb16NQEBAdSvXx+A+fPns2/fPj799FPTuNmzZxMcHMzixYvp1KkTfn5+9OrVy9Tfpk0bKlWqRNeuXSlZsiSLFi2iXLkn9+OJjY2lRIkSrFq1SmsYJGXJm9ODL+f3olq5QuTKnpkOTV6natmCbN0ZlNxTExExSK4bNwUGBrJmzRqmT59uChYAfH19OX78uNnNww4dOoSvr6+p/9ChQ6a+yMhITpw4ga+vL/b29hQvXtysPygoCAcHBwoVKvTMc1NJQl6IbbuPMjvfTuaPbI1nlgz8fvEGbQct4diZq8k9NRERo2S4jCskJIR58+bRrVs3SpUqRVjYX/fVKVu2LNmzZ2fw4MH06NGDXbt2ceTIESZOnAhAs2bNWLx4MQsXLqRq1arMnTsXHx8fU0ahdevWjBgxggIFCpA1a1ZGjRpFy5YtVZIQSS1UkhBJXFKXJHL6b7XZsUIDGz3TuIULFzJt2rRE+06fPs3FixcZOnQowcHB5M6dmyFDhvDGG2+YxuzZs4cJEyZw/fp1/Pz8GDt2LDlz/nV12sKFC1m2bBkxMTHUqlWLkSNHmtY3PAsFDCIpmAIGkcQldcCQq9fnNjvWpTkNbXas5KSSRCrm7ZmRqQOa82aZAkRFP2bDt4cZMedzomNimdq/GT3/uG3zn/pMWsf8tT+YtQ3oXJtXc3nSbeRKU1tGVxcm9m1CvcrFsLe34+sfj9M/YCP3Ip7vvuQiKdGtW7eYMHY0+3/5iUyZMtO1+/s0atKU4UMG8fnWzYbxZcqW45OlK5JhppKUUtNDo2xFAUMq9tnULty5/4ganWaQJWN65o96l7i4eIbM3EKhvNkZPnsrn37+i2n8/YfmT+JrWacUw9+rx+qvDpq1zxn2Dnl9PGjS62MSEhKYPeQd5o1oxbsDlryQ8xJJKgkJCfTt3ZO4+HgWLV3BzRs3GDZ4IOldXRkweCgf9OlnGnv16hU6d2hL6zbtknHGIi+OAoZUqkAeL8qVeIXc1Qdz8/aTh+WM/fhLJvZp8iRgeMWLGct3cOOW8UE6adLYM31gC9q+VY5zl8PN+tI5O9KkekmqdZzObyefPAmt/9SN7Fj8IU6ODkTH6L4K8vI6cfwYQUG/8eXXO/DJmZPChYvQsXMXli9dTI2atciQIYNp7LAhg6hZuw7Vqv/9I9/l5aUMg5Euq0ylboTf560ec03Bwp/cXF3IkN6ZHF6ZOXPxZqL7uro4UTy/N5XbTWX/kfNmffEJCTT9YD7Bp6+YtTs4pDG7HbXIy+jy5VAyZ8mCz/8tFCtQsCAnjh/j8eO/ntey/5efOXzoIL0/6Jsc05QXILkuq0zJ/nGG4c6dO6a7RP3TW6VK0rkXEcmOn0+aXtvZ2fHe25XZdeA0hV7xIj4+noFdalO7QhFu3XvI7JW7WPXFftO+1TrOSPS4UdGP+e6nk2ZtPVtX4cjvl7l192HSnZDIC+Du7sGD+w+IjIw0XW52/dp1YmNjiYh4QObMWQBY8slCGjZuQrbs2ZNzuiIv1HMFDN9++y0rV67kyJEjREdHm9qdnZ0pVqwY7du3p0YNpedSogkfNqZkoZxUbBOAX5FcJCTA7xdu8PGaPVQqlZ+5w97hQUQkn+868lzHfe/tyjSr6UfDnvOSaOYiL07xEr5kzZqVSRPGMnDwMMLDwvh0xVIAU4bhcmgoB/b/woBBQ5NzqpLUUk9iwGaeOWBYunQpgYGBdOnSBX9/f9zd3U0PrggPD+fXX39l0KBBfPDBB7Rt2zYp5yzPaVzvRvi3rkLbQUs5EXKNEyHX+GrPUe7cf/I462NnrpI/d1a6tqj0XAFDtxaVmDagOQOmbuL7X04l1fRFXhgnJycCps+kf78PqVCuFFmyuNOhUxemTpmIa3pXAHZ89w0FCxUm36uvJvNsJSmlplKCrTxzwLBkyRImT56caAYhX758lCtXjoIFCzJ27FgFDCnI9IEt6Nq8Ip2GrWDL90Gm9j+DhT+dOnedN8sUeObjfti2OhP7NmHw9M3MXb3bRrMVSX7Fipdg+7c7CQ8LI1PmzPz80z4yZ85MuvTpAdi390eqVquezLMUefGeedFjVFQUPj4+Tx3j5eXFgwfGVfeSPIZ0q0uXZhVpN3gp67/56x7iw9+vz5fzzW+UVaKgD79fuPFMx333rXJM7NuE/gEbmPnp9zads0hyunf3Lu3btOLu3Tt4eHri4ODAj3t2U7pMWeDJZZfHjx2lpN9ryTxTSWpa9Gj0zAFDzZo1GTRoEL/++iuxseaXzsXHx3P48GGGDBlC7dq1bT5JeX4FX/FicNc6TF32LT/9FoKXewbT9tWeo1R6LT8ftq3OKz4edG1RkXcblGXmCutf/pnd0jFjYAs+/fwX1n9zyOy49vap5wdD/psyZspE5KNHzJgWwOXQUDZtWM+WzRvp0KkL8OTeCw8fPiRfPpUjUjs7O9ttqcUzlyRGjRrF5MmT6dy5M3FxcWTKlMm0huHu3bs4ODjQqFEjBg8enJTzlWf0VpUSODikYXDXugzuWtesz8XPn9YDPmH4+/UZ0aM+F6/epsOQZYZLKBNT4/XCZEjvTNuG5WnbsLxZX8F6I7h07bZNz0PkRZsybQZjR4+kWZO3yJHDh4DpsyhWvATw5C6QAG4ZMybnFOUFSE2ZAVt57mdJREZGcurUKcLCwoiMjMTJyQkvLy8KFy6Ms7PzP56IniUhYqRnSYgkLqmfJZG//9c2O9aZgDo2O1Zyeu6P3MXFBT8/v6SYi4iISIqgBIORbg0tIiJiQSUJI90aWkRERKxShkFERMSCEgxGyjC8pNq8VY7I3wIN28NDswEo+qo33y/pw+2fp3Nw3RAql87/TMf9Yl5P2rxVzqytQB4vvpjXkxs/BnBy2yj6d6plStelc3Zk/czu3PgxgDVTu+DslNa0X52KRVk3vauNzljkn7lx4wb9PuxNpdfLUqNqJQImTzS7tf3/+2nfXlo0aUj50n5069yBC+fPJTru22+241u0oFnbms9W8WaFcjSsX5sjwUGm9piYGN6qW4uwsMQf9iYpk729nc221EIBw0tqw7eHyVNjsGnLX2cYZy/dZO5nu3FzdWbbx/6cPHed0i0nsHVnEGund8Uzs+vfHs/Ozo7pA1tQ4/XCZu0uzmnZMud9rt68R8U2AXw4aR3+71alW4tKAHRs8gaemV2p2CYA76yZ6NT0DdO+Q7rVZfyC7UnzAYg8g4SEBD7q05uoqEiWfrqKKVNn8MPuXcydM9Mw9uzZM/Tq0Z0q1aqzZv1GChcuQpdO7Xn00Pyhavfv32fyhPFmbbdv32b61MkETJ9Fo8ZNGT92tKlv88b1VKr8Jp6eWZPkHEVeFAUML6mo6MfcuPXAtL1Tvyx22DFs9ue0eascDx9F03vCGs6FhjNu/leEXArjtaK5Ej2Wt2dGti/oRf03ixtuGV3xtVfJnDEdvcav4czFm3yz9wRzVu3i7bqlACjwihd7Dv7OmYs32X3wdwrk8QKgVoUiXA+/R/Dpy0n7QYg8xYXz5zgSHMSYcRN59dX8vFaqND38e/PVl9sMY9etWY1vST969vqAPK/k5cN+/cngmoEvv/zCbNyMqVPMHn8NTx6L7ebmRtly5aleo6YpM/E4JoaVn66gYxdl2l42unGTkQKGVCCzWzr6dajB8DlbiXkcS+VS+dm25wjx8X/dYqNimwC+2Xsi0f1LFs7J5Rt3eKP1ZO5HRJr1HTl9mZZ9FhHz2Pzunm6uTx79G3r9DsUL5MDBwZ4SBXNw+fodQNkFSRncPTyZt+AT3D08zNojHkQYxl65HErxP27QBE+ybq8WKMCRoCBT268HD/DrwQN07f6e2b7ZsmXj3r17XLt6lRPHj5see71580YqVqqk7MJLSLeGNtKix1Sga4tKXAu7x+YdQQDk8fHg1+MXCRzWivpvFufS1VsMmr6Zn4MTr8d+9cMxvvrhWKJ9f2Yw/uTslJZOTd7gqx+OArB00z7eqVuau7/M5NjZq3yyYS81Xi/MzdsPlF2QZOfm5kaFipVMr+Pj41nz2UrKlS9vGJvF3YObN82fp3Lj+nXTXR1jYmIYM2o4g4eNIG3atGbjsmb1onWbdtSvUwNHRycmT53O48ePWfXpcj5ZuiIJzkzkxVOGIRXo2OR1Pl6zx/Ta1cWJfh1rcj38Ho395/HjobN88XFPfLwy/av3sbOzY9HoNrimdyJgybcA3Lr7kNItJpC31lDKvzOJuw8iGdKtDhMWfEWDKsU5smUEP302EN+CT39wmciLMGNaACdPnsD/gz6Gvjp16vLtN9+wZ/cuYmNj+XzLZo4fO8rjx48BWPjxXAoXLsobFSomeuw+/fqz+8ef2bPvF96sUpWtmzfxRoWK2NunoXuXjtStWY2lixcl6fmJ7agkYaQMw0uuVJFc5MiamfVf//U0yti4OIJPXWbc/K8ACD59meqvF6JV/bKmL/rnlSaNPZ+MaUvdysVo8H6gWdYB4ObtJ6+rlSvErXuPOHX+Bts+7kX99+aQJ4c7n4xtR5mWE/7hWYr8ezOmBbDq0+VMmTqD/PmNj3KvUKky7/XoSb8PexEXF0eZsuVo0LAREQ8iOHPmdzZsWMfGzV8kcuS//JmNePz4MSs/XcbCxcv4eO5s8uV7lYDps2jRpCHlyr9OkaLFkuQcxXZSUynBVpRheMnVrFCEvYfPcvfBX2sProff57TFo6rPXryJT7bM/+g9HBzsWTW5Ew2qlKBxr4/5JfjvH1I1pFsdxs//kkKveD0JXE5f5pt9JyiW3xs313/+rBGRf2Pi+LF8unwp4ycFUKPW3z9Rt2v39/npwGF27N7LwsXLePTwId45cvD9d99y/9496tepSfnSfvR878kixvKl/fhy2+eG43yxdQvlX69A1qxeBP12mDcqVsTNzY0SJUvy2+FDhvGS8mgNg5EyDC+5MsXyGNYmHDh6gUqlzB+/W+AVL9Zt//UfvcfcYa2pVr4QjXrO5aegxNdBAFQpW4A7DyIJOnWZEgVymK4/dkjzJC61I/X84MjLY/68QDasW8PkgOnUrP33DwHa/uU2jh4JZsDgobi7uxMVFcXBA/sZM34Svr4lqVf/LdPYo0eDGTKwP+s2bsHdw93sOLGxsXy6fCkLFi8FwN7O3rQAOS42jud83p9IiqGA4SVX9NXsrPnqoFnbJxt+5P133mRo93qs/uoA7zYoxys5PFj95ZNx6V0ccXF2JPyOcaW4pWrlCtGuUXl6jl1NSGgYXu4ZAIiLTzDsP6RbXQZO2wTA2UthpHVIQ+PqJcntnYVT565zz+IKDJGkdi4khIXz59GpSzf8XitFeFiYqc/D05PwsDBcM2TA2dmZ3HnyMGLYYF4rXYb8BQowY1oAXtmyU7FSZezt7cmYKZNp3xs3rgOQK3duw3t+8fkWypZ/naxZn1xiXLRYcb7a9gWeWbPy68EDdOjUOWlPWmwiFSUGbEYliZdc1iwZDPdOuHTtDg17zKVe5WIcWj+UepWL0aT3x1wNuwfAh+1qsHdl/2c6fuMaJQGYO7wVF3ZMNG2W+1cunZ/7EVH8djIUgEdRMfQat4YZg1rSuVlFuo9a+S/PVOT57dr5PXFxcSxa8DHVq1Q02wCqV6nIN9ufrPUpUrQYQ0eMYlrAJFq1aApA4McLsLd/9n8mY2NjWbFsKZ26dDO1vdfDn6tXr9C1U3veaf0uJXxL2u4EJcmoJGFkl5BC8mMufv7JPQWRFOfOwcDknoJIiuScxPlxv9E7bXas30ZWs9mxkpNKEiIiIhZSUWLAZhQwiIiIWEhNpQRb0RoGERERsUoZBhEREQtKMBgpYBAREbGgkoSRShIiIiJilTIMIiIiFpRgMFLAICIiYkElCSMFDCIiIhYULxhpDYOIiIhYpQyDiIiIBZUkjBQwiIiIWFC8YKSShIiIiFilDIOIiIgFlSSMFDCIiIhYULxgpJKEiIiIWKUMg4iIiAWVJIwUMIiIiFhQwGCkkoSIiIhYpQyDiIiIBSUYjBQwiIiIWFBJwkgBg4iIiAXFC0ZawyAiIiJWKcMgIiJiQSUJIwUMIiIiFhQvGKkkISIiIlYpwyAiImLBXikGAwUMIiIiFhQvGKkkISIiIlYpwyAiImJBV0kYKWAQERGxYK94wUABg4iIiAVlGIy0hkFERESsUoZBRETEghIMRgoYRERELNihiMGSShIiIiJilQIGERERC/Z2ttv+iZiYGBo0aMD+/ftNbePGjaNgwYJm28qVK03927Zto0aNGvj6+tKzZ09u375t6ktISGDq1KmUL1+esmXLMmXKFOLj459rTipJiIiIWEjOqySio6Pp168fZ86cMWsPCQmhX79+NGnSxNTm6uoKwJEjRxg6dCijR4+mUKFCjB8/nsGDB7NgwQIAli5dyrZt2wgMDCQ2Npb+/fvj7u5O586dn3leyjCIiIikEGfPnqVly5ZcunTJ0BcSEkKRIkXw9PQ0bS4uLgCsXLmSunXr0rhxYwoVKsSUKVPYs2cPoaGhAKxYsYLevXtTunRpypcvz0cffcSqVauea24KGERERCzY2dluex4HDhygXLlyrF271qw9IiKCGzdukCdPnkT3Cw4OpnTp0qbX2bNnx9vbm+DgYG7cuMG1a9coU6aMqb9UqVJcuXKFmzdvPvPcVJIQERGxYMunVcbExBATE2PW5ujoiKOjo2Fs69atEz1GSEgIdnZ2zJ8/nx9++IFMmTLRsWNHU3ni5s2bZM2a1Wwfd3d3rl+/TlhYGIBZv4eHBwDXr1837Pd3FDCIiIgkoQULFhAYGGjW5u/vT69evZ75GOfOncPOzo68efPSpk0bDh48yPDhw3F1daVmzZpERUUZAhBHR0diYmKIiooyvf7/PsAQyDyNAgYRERELtlzz2L17dzp27GjWllh24WkaN25M1apVyZQpEwCFChXiwoULrF69mpo1a+Lk5GT48o+JicHFxcUsOHBycjL9GTCtgXgWChhEREQs2PIqib8rPzwPOzs7U7Dwp7x58/LLL78A4OXlRXh4uFl/eHg4np6eeHl5ARAWFoaPj4/pzwCenp7PPActehQREbGQXIse/86sWbPo0KGDWdupU6fImzcvAL6+vhw6dMjUd+3aNa5du4avry9eXl54e3ub9R86dAhvb+9nXr8AyjCIiIikeFWrVmXhwoUsXryYmjVrsnfvXrZs2cKKFSsAaNWqFW3btqVkyZIUL16c8ePHU6VKFXLmzGnqnzp1KtmyZQNg2rRpdOrU6bnmoIBBRETEgi2vkrCFEiVKMGvWLGbPns2sWbPIkSMH06ZNw8/PDwA/Pz/GjBnD7NmzuXfvHhUqVGDs2LGm/Tt37sytW7fw9/cnTZo0NG/e3JCxsMYuISEhwZYn9U+5+Pkn9xREUpw7BwOtDxL5D3JO4l9331n+m82Otaa9n82OlZy0hkFERESsUklCRETEQnI+SyKlUsAgIiJi4Z8+ZTI1U0lCRERErFKGQURExIJKEkYKGERERCwoXjBSSUJERESsUoZBRETEgkoSRgoYRERELOgqCSMFDCIiIhaUYTDSGgYRERGxShkGERERC8ovGClgEBERsZDSnlaZEqgkISIiIlYpwyAiImJBCQYjBQwiIiIWdJWEkUoSIiIiYpUyDCIiIhaUYDBSwCAiImJBV0kYqSQhIiIiVinDICIiYkEJBiMFDCIiIhZ0lYRRigkYTnw3NbmnIJLi3H30OLmnIJIiZXNLm6THV73eSJ+JiIiIWJViMgwiIiIphUoSRgoYRERELNgrXjBQSUJERESsUoZBRETEgjIMRgoYRERELGgNg5FKEiIiImKVMgwiIiIWVJIwUsAgIiJiQRUJI5UkRERExCplGERERCzo8dZGChhEREQsKP1upIBBRETEghIMRgqiRERExCplGERERCxoDYORAgYRERELiheMVJIQERERq5RhEBERsaA7PRopYBAREbGgNQxGKkmIiIiIVcowiIiIWFCCwUgBg4iIiAWtYTBSSUJERESsUoZBRETEgh1KMVhSwCAiImJBJQkjBQwiIiIWFDAYaQ2DiIiIWKUMg4iIiAU7XVdpoIBBRETEgkoSRipJiIiIiFXKMIiIiFhQRcJIAYOIiIgFPXzKSCUJERERsUoZBhEREQta9GikgEFERMSCKhJGKkmIiIiIVcowiIiIWLDXw6cMFDCIiIhYUEnCSAGDiIiIBS16NNIaBhEREbFKGQYRERELunGTkQIGERERC4oXjFSSEBERSWFiYmJo0KAB+/fvN7WFhobSoUMHSpYsSb169di7d6/ZPj/99BMNGjTA19eXdu3aERoaata/bNkyKlWqhJ+fH0OGDCEyMvK55qSAQURExIK9nZ3NtucVHR1N3759OXPmjKktISGBnj174uHhwcaNG2nUqBH+/v5cvXoVgKtXr9KzZ0+aNm3Khg0byJIlCz169CAhIQGAb775hsDAQMaMGcPy5csJDg4mICDg+T6T5z4TERGRVM7Oznbb8zh79iwtW7bk0qVLZu2//PILoaGhjBkzhnz58tG9e3dKlizJxo0bAVi/fj3FihWjU6dO5M+fn4kTJ3LlyhUOHDgAwIoVK2jfvj1Vq1alRIkSjB49mo0bNz5XlkEBg4iISBKKiYkhIiLCbIuJiUl07IEDByhXrhxr1641aw8ODqZIkSKkS5fO1FaqVCmCgoJM/aVLlzb1ubi4ULRoUYKCgoiLi+Po0aNm/SVLluTx48ecOnXqmc9Dix5FREQs2PK36QULFhAYGGjW5u/vT69evQxjW7dunegxwsLCyJo1q1mbu7s7169ft9p///59oqOjzfodHBzIlCmTaf9noYBBRETEgp0NL5Po3r07HTt2NGtzdHR8rmNERkYa9nF0dDRlKp7WHxUVleh7/v/+z0IBg4iISBJydHR87gDBkpOTE3fv3jVri4mJwdnZ2dRv+eUfExODm5sbTk5OpteW/S4uLs88B61hEBERsWBnw80WvLy8CA8PN2sLDw83lRn+rt/T05NMmTLh5ORk1h8bG8vdu3fx9PR85jkoYBAREbGQnJdVJsbX15fjx4+bygsAhw4dwtfX19R/6NAhU19kZCQnTpzA19cXe3t7ihcvbtYfFBSEg4MDhQoVeuY5KGAQERGxkNIyDGXLliV79uwMHjyYM2fOsHDhQo4cOULz5s0BaNasGYcPH2bhwoWcOXOGwYMH4+PjQ7ly5YAniykXL17Mjh07OHLkCKNGjaJly5YqSYiIiKQmadKkYd68eYSFhdG0aVM+//xz5s6di7e3NwA+Pj7MmTOHjRs30rx5c+7evcvcuXNNizfr169P9+7dGTFiBJ06daJEiRL079//ueZgl/DnbaCS2fnwKOuDRP5jXBzTJPcURFKkbG5pk/T4nx2+bLNjtX7Nx2bHSk66SkJERMSCLS+rTC1UkhARERGrlGEQERGxoN+mjRQwiIiIWFBJwkhBlIiIiFilDIOIiIgF5ReMFDCIiIhYUEnCSCUJERERsUoZBhEREQv6bdpIAYOIiIgFlSSMFDCIiIhYULhgpKyLiIiIWKUMg4iIiAVVJIwUMIiIiFiwV1HCQCUJERERsUoZBhEREQsqSRgpYEih2jWry83rVw3tRUqUZPrHy63uX6eCL5PnfILva2VsNqfgwwcZ2KsLvQcMp16j5mZ9U8cNB+CjYWNt9n4iTzNx1FC+/nLr3/bPnL8Ev1Jlk3QObzesxfVrf/2cpknjQA6fnDRs2pIWrdsm6XtL0rJTScJAAUMK9t4HA6hcvbZZW9q0aZNpNn9ZOn82Fd6sTsZMmZN7KvIf1uujQXTz7wPAru+2s2blMhYsX2vqd8uY8cXMo+9AqtasC0Bc7GMO/3qAKeNGkCGjG3XqN3ohcxB5EbSGIQVLl96VLO4eZlsGtxfzj+DT55WexfNmJPc05D/O1TUD7h4euHt4kN41A2nSpDG9dvfweGHBdfr/m0fWbNmp06ARpcqU58dd37+Q95ekYWdnuy21UMDwknr4MILpE0bwdv0qNHizFF1aNeKnH3YmOjbo0H56tG/JW1XL0KFFPb7cst7UF/HgPlPGDKFpzTdo3bAG86ZPJDo66qnv/d4HA/nuq885fuS3vx1zLOgwvTq1omHVsrzXthl7d+0w69+05lPebVSDpjXfYN6MSQzw78y3T0kvizyva1ev8GaZYiz/ZD71q73BzCnjWbpwLh9072A27u2Gtdj+xRYAEhISWP7JfJrWrUr9qq8zqE9Pbly/9tzvncYhDQ5/BCzx8fGs/nQJ7zSqQ82KpfjgvY6EnP3dNHbnt9tp06wBNSu8RruWDflxtwKNlMAeO5ttqYUChpfU/JmTuXzpIhNmzGfByk0U832NmZNG8/jxY7NxcXFxjB/Wn0rVarLosy2069KTudMmcPF8CAAzJo7iYUQE0+YvZ+SkGfx+8jhzp0986nu/XqkK5Sq8yZyp44mLjTX0374VzogBvahZryHzP91AizYdmTZ+OMeCDgOw85svWbn4Y7p/MIDpC1Zw49pVjgYdstEnI2LuWPBvLFyxhubvtLE6dtO6z9jx9ZcMHzeZeUtXkcXdnY/8uxEb+9jqvgCxsY/5Yed3HPzlJypWrgrA8k8+Zu3KZfTqO5BFn64jWzZvBvR+j8jIR9y5fYvxIwfzbocufLphG/XeasKYYQO4f+/evzpnkaSgNQwp2Jyp45g3w/zLe80XO3F2SUfxkqVp1qodefLmB6BZq/Z8/cUm7t6+hadXNtP4Rw8jeHD/Hpkyu5Mtew6yZc+Bu4cnWdw9uXo5lJ9/3MX67T+Q3jUDAB8MGknPDi3p3usjU1ti3u8zkG7vNmHzulU0b93erO+LjWvwK12Ohs1bAeDtk4uQ30+xed1KipV8jS82raVxy3epXK0WAB8NG0ebJjX//QcmkojmrdqSwyfXM41dvWIJfQYONy2W7Dd4JE3rVmX/T/uoULlKovtMnzSGWQHjAYiOjsbJ2ZkWrdtRs24DEhIS2LTuM7r2+JAKbz4JIPoPG0WrxnX5bvs2ChUtTmxsLJ5ZvciW3Zu323QgX/4CODo5/vsTl38lNZUSbEUBQwrWrksPKrxZ3azNydkFgBp13+KnH3by1daNXL54njOnTwIQFx9nNj6DW0YaNGnJrMmjWb1sIeUqVKZWgyZkcHPjxNHfiI+P593G5l/W8fHxXL0cSv5CRf52bl7ZvGndoRurlsznzRp1zPpCL55n/749NK5R3tQWFxtLjpy5ATgf8jtvt+30f3N0wydXnmf8VESeTzZv72ca9+jRI8Ju3mD0kI+wt//r2yI6OprLly787X4du/tTuWoNABwdHXH38CRNmjQA3Ll9i/v37lGkWHHTeAeHtBQsXJSL58/xVpMWvF6xMv38u5Ir9ytUeLMqDRo1w/mPn3NJPgoYjBQwpGAZM2XB+29+MwoYO5STx4KpXrsB9Zu0JIu7J326J34Zl/9HQ2nQ9G1+/nEXP/2wi6+2bmTU5FnExcWR3jUDcxZ/ZtjH3TOr1fk1a9WO77/exvyZk3FJl97UHhcXR7Xa9XmnXRez8Wkcnvx1S5PGgYQEi4MZGkRsw9HRyfTnxJ5AGBcX98f/n5TXRk+aRs7cr5iNcXvKYuPMmbPgkzPxn1NHJ6dE2+Pj44mLj8fOzo5JM+Zx8vhR9v2wix927WDLhjXMWbiC/AULPf3EJEnpskojrWF4CT18GMHu77YzeMwU2v6RhXhw/4+ap8UX7+1b4QROm4C3Ty5ate/KnMWfUbJ0WX7ZuxufXHl4GPEAsMPbJxfePrmIjo7mk7nTeRwTY3UeDg5p6dlvCPv2fM/RoF9N7T65cnMl9JLpmN4+ufj5x13s+uYrAHK/ko+zp0+Ync/Vy6H//oMRscLBIS2PHj0yvX706Mk6AoAMGdzInCULt2+F45MzFz45c+GVLTvzZ0/j0sXz/+j9XF0zkCWLOyeOHTG1xcY+5vdTJ8iVOw8XL5xj3swAChctTpf3e7N87VayemXj4C/7/t2JiiQBBQwvIUdHJ5ydXdi3ewfXr13h1/37TGsdLBc9ZnDLyL4937NwdgBXL4dyNOgQ586cJl+BQuTKk5fS5SswZfRgTp88xpnTJ5k2fjiRjyJxzeD2THPxfa0M1WrX58b/3bymQdO3OXPqBMsWBnIl9CK7vv2KZQvmkDVbdgAaNn+HLes/Y+/uHVy6cI4ZE0cRGflIz5+XJFeoSDFCzpxm145vCL14gakTRmH/R/kAoGXr9nwybzb7ftjN5UsXmTJuBMeO/EbuPK885ahP1+Ld9ixZEMi+H3Zz4XwIAeNGERMdTbWadXB1dWPrxnWsWLyAa1cu8/PePVy/elXZhRTA3s52W2qhksRLKG3atAwYOYGFc6axdf1qvLJ706p9V5YvnMvZ30+ZpVPTpk3LqMmzmD9zCu+3b066dOmp3aAJdd5qCkD/4eOZN2MSg3t3wz5NGkqXr0CPPoOeaz5d/ftx4KcfTK+9snkzaspslnw8k42fLcPdMytd/ftRrXZ9AKrUqMvVy6HMCRjH45gY6jZqRtZs3qbL0ESSSqmy5WnRuh1TJ4wmjb09Ld5tx62wm6b+t9t04NGjh0ydMIpHDx9SsHBRAmYv/Ff3P3n73fY8ehjB1PEjefjwIcVKlGTm/KVkypwFgLFTZrJgznQ+XbqQzJmz0K3nB5QpX+Ffn6v8OypJGNklJKSM4vH58Kdf+y+px5HffiW7t4/pao642Fha1q/CiIkzbHor69TAxTGN9UEi/0HZ3JL2F4ydp27Z7FjVCrnb7FjJSRkGeeF+/mEXJ44F0av/MNKlS8+WdatIlz49hYuWSO6piYgAukoiMcowyAv36OFD5k6fwMGf9xIdHUWRYiV578MB5H4lX3JPLcVRhkEkcUmdYdh9+rbNjlWlYBabHSs5KWAQScEUMIgkTgHDi6eShIiIiIXUdHWDreiyyv+g4R/5M3XccNPrs7+f5IOu79KoWjl6dW7NmVMnEt1v9fJFZvuJpAZhN28wYmAfGlR/g2b1qhE4YwrR0dEAnD55nPc7vUudymV4v2Nrjh8NNtv31/0/0+HtxtSqWJoP3++k+4mkInY2/C+1UMDwH7N7x3YO/vyj6XVU5COGf+RPMd/XmLNkNUWK+zKivz9RkY/M9tv13XY+Xfzxi56uSJJKSEhgxKC+REVFMWfhCkaMD+CnH3ezZP4c7ty+Rd8eXcibLz8LVqylas069PPvanp65Y3r1xjWvzd132rMguVryJQpM0P79yaFVHlFbE4lif+QB/fv8cncGRQoXNTUtuf7b3BycqJLz77Y2dnx3gcDOPjzXn7Y+R216jciLjaWeTMm8d1Xn5Pd2ycZZy9ie5cunufE0WA2f72bLO4eAHTq7s/Hs6aSOYs7bhkz0nfQcNKkSUPuPHk5+MtPbN2whm7+fdi2ZSMFCxfl7TYdABg0chxN6lQh6PBB08Or5OWlqySMlGH4D1kUOI3qtRuQK89fVyOcOn6UoiX8THdZtLOzo0jxkpw8/iT1Ghn5iPMhZ5i5aCWFi/kmy7xFkkoWdw8CZi8wBQt/ehjxgKtXLlOgcFHTg6QA8uUvYCpLnDgWTAm/0qY+Z2cXChQszPEj5mULeTnZ2XBLLRQw/EcEHdrP0aDDtO7Yzaz99q0w3D08zdoyZ8lC+M0nd79zzeDG9PnLyftqgRc2V5EXJUMGN8q+/tddFePj49m87jNeK1OeLO7uhN+8YTb+5o3r3Lt7F4Bb4eF4eFr+7LgTZrGPvJzs7exstqUWChj+A2Kio5k9ZRw9+w3GycnZrC86Koq0aR3N2tKmdeTxY+sPnxJJbebPnsbvp0/S5f3eVK5ak5PHj/LF5g3ExsZy4Od97Nuzi8exT57XEh0VafzZcdTPjqReWsPwH7ByyXzyFypC6XLG+9OndXQy/AP3+HGMIbAQSe3mz5nOhjUrGTlhKnlfzQ/AR0NGMXvaRKZPGsOrBQrRuPnb/HboAPDkIXCGn52YGFwzZHjhcxfbSz15Adt5roDh4MGDzzy2TBk9EyCl2PP919y5dYvGNcoD8DjmyW9Ie3d/R9Wa9bh9y/ye6Xdu3SKLh4fhOCKp1cyACXy+cS1Dx0zkzWo1Te31Gjahdv2G3L1zG3cPTz6ePY1s2XMA4JE1K7dvhZsd5/atW7xaQE+aTBUUMRg8V8AwZswYzp49C/DUS4fs7Ow4efLkv5uZ2MyUwMXExcaaXi+eNxOAzj0+5GjQIdatXEJCQgJ2dnYkJCRw/GgQrdp3SabZirxYyxbN4/ON6xgxPoAq1WuZ2g//eoAvNq1j5ISpuHt4kpCQwP6f9tKoaUsAihTz5WjQb6bxUVGRnPn9JB269Xjh5yDyIjxXwLBx40b69u3L5cuXWbt2LU5OTkk1L7Ehr2zeZq9d0qUHwNsnFxkzZ2HJx7OYP2sK9Ro156utG4iKiqRytVqJHUokVblwPoQVixfwbvsuFPd9jVvhf2UMcubKzU8/7mHLhjWULV+BNSuXEfHgPrUbNAKeZB/WrFzKqmWf8EalKiz/5GOye+fAr5Syq6lBarrhkq0816JHR0dHpk+fDsDMmTOTYj7ygqVP78qYgDkcCz5Mr06tOHX8CGOnBuLski65pyaS5Pbt2UVcXBwrliygad0qZptnVi9GTZzKxrWr6NiqKaEXLzBt7iLSpXvys5HdOwdjJ89k+xdb6N7+He7fu8f4gNmmS5Tl5WZnZ7sttfhHD58KCQnhwIEDtGrVymYT0cOnRIz08CmRxCX1w6cOnLtns2OVzZvRZsdKTv/oKol8+fKRL58eRSwiIqlTKkoM2IwuqxQREbGkiMFAN24SERERq5RhEBERsaCrJIyUYUgFYmJi6N6mKcGH/7qx1sljR+jTvR2Na5Sn8zsN2f75pmc61urli5g6brhZ29nfT1Kngq/Z1qvTkwWv8fHxTBs/nKY132CAf2fu3vnrJlAXzp3Bv9M7etyvJKub168xqE8P6lYpx9sNa7H+s08THfd2w1q8WaaYYVu26Mlj3R89esSUcSNpWLMSzetX57Pli832Xzh3JvWrvk7Xti0JvXjB1H7n9i1aN6lLdJQWdr9MdJWEkTIML7mY6GgmjRrExfMhprbbt8IZ/lEP6jduyUfDxnLm9Ammjx9JFg8Pyr1R+W+Pteu77Xy6+GOq1apv1n7p/Dny5S/I2GnzTG0ODk/+6uzft4ejQYeYuWglS+fPZu2nS+jeuz8Aq5YupHWHbrrMTJLVyCEfkS1bdhatWMeF8yGMHTYQr+zZqVy1htm4BcvXEBcXb3q9Z+e3LP54DnX+uO9CwPiR/H7qBOOnziI+PoEJIweTxsGBt99tz9nfT7Fl/RpmLljKl1s2siBwBuMCZgGwZuUymrRojZOzbrf+MtG/WkbKMLzELp4P4cNubbl29bJZ+88/7CRzFg86vtebHDlzU6VGXarXbcDub7cnepy42FjmBIxjxoSRZPf2MfRfunienHnyksXdw7S5ZcwEQOjF8xQqWoJcefJSunwFLv/xm9WFc2e5diWU1ytVtek5izyPB/fvceJoMG07dccnV24qvlmNsq9X4PDB/YaxmTJnwd3DA3cPD5ycnVj+yXx6fPgR2bJ7c/fuHXZ+u51+g0dS3Pc1fP1K0d2/D2tXLgPg0oXz5MmbjwIFC1OhclUuXTwPwN27d/hx9/c0bNriRZ62SJJQwPASOxp0CN/XyjBjwQqz9lLlK9B3yBjD+IcPHyR6nMjIR5wPOcPMRSspXMzX0H/pfAg5cuZOdF9Pr+xcOn+OmJgYzv5+Ek+vbACsXraQVu27KrsgycrRyRlnZxe2f7GF2NjHXLpwnmPBv5G/4NOf97D202W4e3hQ960mAFy78iQoL1KsuGlM3vwFuBUexrWrV8iaLTvXrlwmIuIBp08dx8sru+k4jZu/o+zCy8jOhlsqoZLES6xBk5aJtmfLnsP0gByAu3dusWfHN7Tp9F6i410zuDF9/vK/fZ/Qi+dJSEjgvbbNePgwgtLlK9ClZ1/Sp3elYpUafLl5HY2rl8PTKxuTZi3i0oVzXLl8iTcqV/t3JyjyLzk5OfHhgKHMChjPxrUriYuLo06DxtRv1Oxv94mKimTTus/oN3gE9vZPfqfKnMUdgPCbN/HJ9SR4DrtxHYB7d+9QrERJSpYqw1vVK5DBLSOTZ87j3t277Nn1HUs/e7b1Q5KyaNGjkTIMqVx0dBRjh/QjcxZ36jVu/tz7x8Y+5tqVUGIfP6bvkDH0GTSKE0eCCBgzFIC0adMydd5SVm3dwdJ1X5I9hw+f/ZFdOHX8KO+1bUbX1o05fOBnW5+ayDO5eOEcr1eqwrwlqxg0Yhx7dn7Ld9u3/e34nd9+jUu6dFT+v6dWZsvuTZHivsyeNon79+5xKzycpQufrOmJffzk6a8jJ0xl8zd72LR9F4WLFmftqifZhbCbN3ivQytaN6nLN199nrQnK5KElGFIxSIfPWL0oA+4EnqRaR8vw9nZ5bmP4eCQlrVf7cHJyQkHhye3Yv1o2Dh6dW7FrbCbuHtmBf76DSz04nlCL57njcrV6PZuEzq+1xvPrNkY/lFPVmz8Gkc9sExeoEMHfuHLrRvZsO17nJydKVSkGOFhN1ixZAE16zZIdJ89O7+lao06poW9fxo6eiIjB/ahYc2KpHfNQLeeH3D8aDDp0ruaxmTKlBmA+/fusWfndyxZtZFhAz6keq16vFm9Jp1aN6VUmfJ4/PFzIymXqqlGyjCkUg8fRjC07/tcOHeWSbMX/e0ahGeRPr2rKVgAyJnnFQDCw28axv6ZXYh4cJ/Qi+cpVe4N8hcqAsDlSxf+8RxE/onTp07gkzO32RqC/AULc+PatUTHx8TEEHToVypWMZbTfHLmYvFnG9n89W42f70bX7/S2Nvb45Utu2Hsus+W07BpS5ycnTl25DfKvP4GWb2y4ZMzN6dOHLPdCUqS0RIGIwUMqVB8fDxjh/Tl2tXLBMxdQp68r/7jY108H0KTGq9z/f+uxDh35jRp0jjgnSOX2djLly5w6cI5KrxZ3bTYMSH+yWVqcXFxuh+DvHAeHp5cCb3E4z/KBgAXL5wnu3eORMefO/s7sbGPKVy0uFl7fHw8/fy7EnL2dzJnccfR0ZGf9+0hf8HCpHd1NRv74P49du34hsbN3gbA3s6ehPgnf/f1cyAvMwUMqdA32zZz5PBBPhw0kvSuGbh9K5zbt8J5cP/J09ceP37M7VvhxMXFWT1Wztyv4O2Tk5mTx3Dh3BmOBR9m5qTR1G3YlAxubmZjVy9fRKt2T66McM3ghrdPLrZ/vokfvv8GgBy5/nmWQ+SfeKNyFdI4ODBl3AhCL15g3w+7WbV0Ec3eeZe4uDhuhYebBRPnQ87inSMnjo6OZsext7fH2dmFhYEzuHzpIj/u/p7liz6mTceuhvdc99mnNGzSwpTVKFikKNu/2Ezw4V+5eP4cBf/IuEkKpxSDgQKGVGjv7h3Ex8czsn8vWjesbtrGDOkLwImjQbRuWJ2wm9etHsve3p5Rk2eTLl16PurRkdGDPqRk6XJ0++PmTH+6evkSF86dpUKV6qa2DwaOYMv6VSycM5W+Q8b8ozUUIv+Gq2sGZsxbzK3wMLq3f4e5MybTtlN33mrSgps3rtO0bhWOHfnNNP7O7Vu4WgTCf+o7aAT29mno0rYF82YG0PujIYabPz14cJ+d322nUfO3TW3+fQZy4Od9DB/YB/++A8iaSAlDUh47G/6XWtglpJD82Plw3TZVxJKLY5rknoJIipTNLa31Qf/CkdAImx2rRE5X64NeArpKQkRExIKukjBSwCAiImJB8YKRAgYRERFLihgMtOhRRERErFLAICIiYiG5rpL47rvvKFiwoNnWu3dvAE6cOEGLFi3w9fWlWbNmHDtmfhOwbdu2UaNGDXx9fenZsye3b9+22ecBChhEREQM7Oxstz2Ps2fPUrVqVfbu3Wvaxo0bx6NHj+jWrRulS5dm06ZN+Pn50b17dx49egTAkSNHGDp0KP7+/qxdu5b79+8zePBgm34mChhERERSiJCQEAoUKICnp6dpc3Nz46uvvsLJyYkBAwaQL18+hg4dSvr06fn6668BWLlyJXXr1qVx48YUKlSIKVOmsGfPHkJDQ202NwUMIiIiFpLrRo8hISHkyZPH0B4cHEypUqVMt923s7PjtddeIygoyNRfunRp0/js2bPj7e1NcHDwc87g7ylgEBERsWTDiCEmJoaIiAizLSYmxvCWCQkJnD9/nr1791K7dm1q1KjB1KlTiYmJISwsjKxZzZ9y6u7uzvXrT+7Ye/Pmzaf224IuqxQREUlCCxYsIDAw0KzN39+fXr16mbVdvXqVyMhIHB0dmTlzJpcvX2bcuHFERUWZ2v+fo6OjKfCIiop6ar8tKGAQERGxYMtnQHTv3p2OHTuatVl+uQPkyJGD/fv3kzFjRuzs7ChcuDDx8fH079+fsmXLGr78Y2JicP7jIWdOTk6J9ru42O4ZPgoYRERELNjy1tCOjo6JBgiJyZQpk9nrfPnyER0djaenJ+Hh4WZ94eHhpjKEl5dXov2enp7/fOIWtIZBREQkBfjxxx8pV64ckZGRpraTJ0+SKVMmSpUqxW+//cafz4tMSEjg8OHD+Pr6AuDr68uhQ4dM+127do1r166Z+m1BAYOIiIiF5LhKws/PDycnJ4YNG8a5c+fYs2cPU6ZMoUuXLtSpU4f79+8zfvx4zp49y/jx44mMjKRu3boAtGrViq1bt7J+/XpOnTrFgAEDqFKlCjlz5rTFxwHo8dYiKZoeby2SuKR+vPXvNx7Z7FgFvNI989gzZ84wYcIEgoKCSJ8+Pe+88w49e/bEzs6OI0eOMHLkSEJCQihYsCCjR4+mSJEipn03bdrE7NmzuXfvHhUqVGDs2LFkzpzZZuehgEEkBVPAIJK4pA4YztyItD7oGeX3st3Cw+SkkoSIiIhYpaskRERELNjyKonUQgGDiIiIBcULRipJiIiIiFXKMIiIiFhSisFAAYOIiIgFW94aOrVQSUJERESsUoZBRETEgq6SMFLAICIiYkHxgpFKEiIiImKVMgwiIiKWlGIwUMAgIiJiQVdJGClgEBERsaBFj0ZawyAiIiJWKcMgIiJiQQkGIwUMIiIiFlSSMFJJQkRERKxShkFERMRAKQZLChhEREQsqCRhpJKEiIiIWKUMg4iIiAUlGIwUMIiIiFhQScJIJQkRERGxShkGERERC3qWhJECBhEREUuKFwwUMIiIiFhQvGCkNQwiIiJilTIMIiIiFnSVhJECBhEREQta9GikkoSIiIhYpQyDiIiIJSUYDBQwiIiIWFC8YKSShIiIiFilDIOIiIgFXSVhpIBBRETEgq6SMFJJQkRERKxShkFERMSCShJGyjCIiIiIVcowiIiIWFCGwUgZBhEREbFKGQYRERELukrCSAGDiIiIBZUkjFSSEBEREauUYRAREbGgBIORAgYRERFLihgMVJIQERERq5RhEBERsaCrJIwUMIiIiFjQVRJGKkmIiIiIVcowiIiIWFCCwUgBg4iIiCVFDAYKGERERCxo0aOR1jCIiIiIVcowiIiIWNBVEkZ2CQkJCck9CREREUnZVJIQERERqxQwiIiIiFUKGERERMQqBQwiIiJilQIGERERsUoBg4iIiFilgEFERESsUsAgIiIiVilgEBEREasUMIiIiIhVChjEJDo6miFDhlC6dGkqVqzIkiVLkntKIilGTEwMDRo0YP/+/ck9FZFkoYdPicmUKVM4duwYy5cv5+rVqwwcOBBvb2/q1KmT3FMTSVbR0dH069ePM2fOJPdURJKNAgYB4NGjR6xfv55FixZRtGhRihYtypkzZ1i1apUCBvlPO3v2LP369UPP6ZP/OpUkBIBTp04RGxuLn5+fqa1UqVIEBwcTHx+fjDMTSV4HDhygXLlyrF27NrmnIpKslGEQAMLCwsicOTOOjo6mNg8PD6Kjo7l79y5ZsmRJxtmJJJ/WrVsn9xREUgRlGASAyMhIs2ABML2OiYlJjimJiEgKooBBAHBycjIEBn++dnZ2To4piYhICqKAQQDw8vLizp07xMbGmtrCwsJwdnbGzc0tGWcmIiIpgQIGAaBw4cI4ODgQFBRkajt06BDFixfH3l5/TURE/uv0TSAAuLi40LhxY0aNGsWRI0fYsWMHS5YsoV27dsk9NRERSQF0lYSYDB48mFGjRtG+fXtcXV3p1asXtWrVSu5piYhICmCXoLuRiIiIiBUqSYiIiIhVChhERETEKgUMIiIiYpUCBhEREbFKAYOIiIhYpYBBRERErFLAICIiIlYpYBARERGrFDCIiIiIVQoYRERExCoFDCIiImLV/wBQtXf25ZeInQAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:43:53.142470Z",
     "start_time": "2024-08-24T05:43:52.850630Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"Decision tree Classifier model for balanced dataset\")\n",
    "classifier=model(classifier_dt,x_train_re,y_train_re)\n",
    "model_evaluation(classifier,x_test_re,y_test_re)"
   ],
   "id": "ae5c4183bdef538a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree Classifier model for balanced dataset\n",
      "Accuracy :  73.10%\n",
      "ROC_AUC Score :  73.44%\n",
      "#########  Confusion Matrix  ########\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.85      0.75      2551\n",
      "           1       0.82      0.62      0.70      2705\n",
      "\n",
      "    accuracy                           0.73      5256\n",
      "   macro avg       0.75      0.73      0.73      5256\n",
      "weighted avg       0.75      0.73      0.73      5256\n",
      "\n",
      "    ytest  ypred\n",
      "0       0      0\n",
      "1       1      1\n",
      "2       1      1\n",
      "3       1      0\n",
      "4       1      1\n",
      "5       0      0\n",
      "6       0      1\n",
      "7       0      0\n",
      "8       1      0\n",
      "9       1      1\n",
      "10      1      0\n",
      "11      1      1\n",
      "12      1      0\n",
      "13      0      0\n",
      "14      0      0\n",
      "15      0      0\n",
      "16      0      0\n",
      "17      1      1\n",
      "18      1      1\n",
      "19      0      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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d81l+3ryJnzdvYvXKFbwf+iGZmZkULVqUL1astjnGx9f3hu0q5edH6dJlcqxzdrb9cADIysokKzMTL29v1q3/P7Zt/YNft/zMooWfsnrVV6xYtc4cDMndY8+AYf78+YSGhlqUhYSEMGTIkDyf4+rVq0RFRRESEkLLli354YcfeOutt/D396dy5cqkpqbaBCBGoxGTyWQee/Pv+r+/tg5kbkZjGO5DpoxM3Ao7m18XcTXi7XE9hZqUnMK5C5fx9SrGiagETkQlEBV7iakvd6ZahRv/Ivy3N97/msIuRl7p/Yi57M/T5ylbsgQJl5LN563g58WbLz5GdnY2h47HEVizrMV5AmvpMTOxr+TkZP7v2w3MmPXuX1mINiT9NajQOpBOiI9n6uSJlCtXnheCX+KLr1bTqHETfvn5JypUqMiVK1cwGAyUK1+ecuXLk5aWyjuzZ9zSL9h/q1ChIlcuX7boZjh+7BjJycmUr1iRX7f8wtrVK3no4Ra8MW4iK1d/zelTpzh6VI8y3+uCg4MJCwuz2IKDg2/pHJ988gnZ2dmEhIRQu3ZtXn31Vfz9/VmyZAlwfd4P6+9Nk8mEq6trjsHB31/fSjCqgOE+FHbwNPWqlaZL60CqlPNh7ps9ycz85/GZD5b9xITBj/PoQ3WoXM6bj8b3pElAJY6cjMvT+S8mXeWNOV9TofQ/6dZN2w5zJvYin73Vh9pV/GgaWJm5bz7DtVQTWVnZfLbmdxrVrcBrfVtTpZwPI/q3pVn9KuSQDBG5bc5GI66urmz68Qeio8/yx++/mQcqWg96dHd3Z/OmH5k5fSpRZ84QtnsXRyIjqVGjFpUqV6Zps+aMfn04B/bv4/Chg7w5djQp165RrFix22pbxUqVadb8IcaOfp0D+/exf98+3hzzOg2CGlK1ajWysrKYPWsGmzf9SHT0Wb5etwYXV1cqlK/wX98WuR0G+21GoxE3NzeLLafuiJs5ePAgNWpYjvuqWbMmMTExAPj6+pKQYDmOLSEhAW9vb3z/yor93TXx76+9vb3z3AYFDPehn3ccYc6ynwh9swc/LxrGwWMx7Drwz/iBd5dsZtHarYS+0YMdy0dRrpQHTwyaS+KVlDxfY/G6bWwLP25+nZWVTbdX5uPgYODXJcP5YtbzfP/7IV6bsQqAM7GX6DniU/p0fpDdK0fTxL8S63+OwJSeYb8bl/95hYxGpr49k00/bKTLE48xa8Z0Xgh+CW9vbyIPH7bZ9/3QDzlyJJJuXZ5gxLBXeLJrN7p0ewqAKdNnULp0GQYO6MvAAf2oUKEib8965z+1761pb1OmTFkGDujLS8EDqFylKu/NmQtAi5aPMChkKLPenkbnjh3Y+H/f8f4HH1LM3f0/XVNuT34+VpkTHx8fjv31BM/fTpw4QZky17vA/P39CQsLM9fFxsYSGxuLv78/vr6++Pn5WdSHhYXh5+eX5/ELAIbsnDq884FrYEh+N0HuoFqVS1HIyZGII2fNZWvmvEjYwTNMmf9dPrasYLu0KzT3nUT+B7nc4RF4pV9aa7dzRX/05G0dV716dZYsWULjxo0JDw+nZ8+ejBw5klatWrF582ZmzZrF2rVrqVq1Knv37qVXr16MHz+eunXrMmXKFIoUKcK8efMAWLBgAUuXLmXWrOsD04cPH07//v3p169fntujQY9yV1Qq68W88c/Re9RCjp05xyNNatCyUXXGffBNfjdNRMRGQVtLIiAggA8++IA5c+bw/vvvU7FiRRYsWEDVqlUBCAwMZNKkScyZM4ekpCSaNm3K5Mn/PHo8YMAALly4QEhICI6OjnTr1o2+ffveUhuUYZC7ZuSAdjzftSneHkX58/Q5Jn/0LRt+2Z/fzSrQlGEQydmdzjCUHfy13c4VNbeT3c6Vn5RhkLtmxqcbmfHpxvxuhohI7gpWgqFA0KBHERERyZUyDPcxP293Zo3sxsMNq5Gals6qH/Yw7oNvSDP982RCpbJe7P5qDB4PDDOXRX47Mcf1IiZ9tIFpC77Hu4Qb743uTqsmNUlJS+fz9TsYP3e9xaObIveKM6dPM/WtSYTv3YO7uzs9nn2Ovv2f580xo/jma9uBbw0bNeaThUvIzs5m3oehrFm9kpSUFB54sCmjx47Dw8MjH+5C7K2gjWEoCBQw3Me+mPU8ly5fo3X/d/FwL8K8Cc+SmZnFmPfWAddXl1zz/ou4ulg+D9zsuZk4/muVySdbBzJ+cEc+X399mdWFU/uSnZ1Ni76z8XQvwsIpfUhKTmHmZz/ctXsTsYesrCxCBg2kdp26rFi9ljOnTzNqxDB8fHwZOXosL7/6mnnfmJhoBvTtRc/negOwauUK1q5ZxbS3Z+FevDhTJk1g4rixvB/6UT7djdiTAgZbChjuU9Uq+NK4XkXKtxrN+YtXgOsLPk179UnGvLeOx1vUI/TNHsQlJNkcm3Ap2fx1MTcXRg/swKh31nIm9hLGQk6cv3CZt+Z/x4mo65OErN0czoOBle/OjYnY0YULCVSvUZM3xk2gSBE3ypevQKMmD7B3TxiPdnzcYpGpN8aMok279jzS6voS77//uoV27R8lqGEjAPr2f55RI17L8Toi9wONYbhPnUu4zOOD5pqDhb8Vc7s+DWj75rWZ9OEGhs+wnSv/317p3Yq4hCSWfL0dAFN6Bv3fWGIOFmpWKsljD9Xlt91Hb3YakQLJ29uHmbPfo0gRN7Kzs9m7J4w9u3cR1KiRxX47tm9jT9guhr78T9ede/Hi/PbrL5w7d47U1FS+/+5batSsebdvQe6QgjZxU0Fw2xmGS5cumeepvt2pUuXOSUpOYdO2f2a2MxgMvPj0Q/y88wgAgyd/CUDzBlVveA5Xl0K89MzDDHlreY4LWv3wycs0b1CVsENnmLfiVzvfgcjd1aHNI8TGxvDQwy1p3aadRd1nnyzgic5PWiyPHfzSYIYOfom2jzyEo6MjXt7eLP18xd1uttwh99MHvb3cUobhhx9+oHfv3gQEBPDggw/SokULGjduTGBgIL169WLTpk25n0TyxdRXOhNQoywTQtfn+ZhubRtw9VoaazeH51j/2oxVtH3+fZwLObF4et5nCxMpiGa/N4c5c+dx5MhhZr49zVx+NiqKnTu206NnL4v9Y6KjcXFxYc7ceXy6aCm+viUZ9+aYu91skbsmzxmGhQsXEhoayvPPP09ISAienp7mpTMTEhLYvXs3o0aN4uWXX6ZXr165n1DumreGdiKkZwt6jVrIoeOxeT7uydYBrPphzw2fftj/ZzQAwROW8cfnIylXyoMzsRft0maRu612nboAmNLSGP36cF4bPpJCRiObftxI9Ro1qVylinnf7Oxs3hj9Oq8OH8nDLVoCMHP2e7Rv05J9+yKoV88/X+5B7EgJBht5Dhg+++wz3n77bVq3bm1TV7lyZRo3bkz16tWZPHmyAoYC5J3Xn+KFbs3o/8YS1t0gU5ATYyEnHgqqyuyFP1qUFy3iQrumtVj9415zN8XhE9eDEK8SbgoY5J5yISGBiIhw80BGgEqVq5Cenk7y1WRKGD344/ffaPlIK4vjLl68SFxcLNWrVzeXlSxViuIlShAbE62A4T6gLglbee6SSE1NNa+KdSO+vr5cuXLlpvvI3TNmYAee79qM3qMXsnJjWO4H/Eudqn4UcnK0WOUSoLBLIZa+3Z9GdSuYy+rXLEdGRiZHT5+3R7NF7pro6LMMezmEc+fOmcsOHTpACQ8PSpTwIDs7m4MH9hMQWN/iOHd3d4xGI8eP/7Ni66VLF0lKTKR06Zv/nhS5V+U5YGjTpg2jRo1i9+7dZGRYLkmclZXFnj17GDNmDO3atbvBGeRuql7Rl9EvtGfWoh/Yuvc4vp5FzVte1KpcipNnE2yWnz534QrrNofzzutP4V+9DE0DK/PhuJ58tHwLV66m3olbEbljatepS61atRn/xhiOHzvGb79u4d1ZM3lh4IvA9bkXrl69SuXKVSyOc3JyotOTXXhn1tuE7d7F0aN/Mub1EdSr52/u2pB7m56SsJXnLokJEybw9ttvM2DAADIzMylevLh5DENiYuL1H6BOnRg9evSdbK/k0eMt6uHk5MjoFzow+oUOFnV5WejL17MoiVdScqwLnrCMGa91ZcNH18/zxbc7eeN9+y3UInK3ODo68l7oh0ybMpnezz6Nq6srPZ/7Z3KmCxcuAFDM3d3m2BGvjyF0znuMGvkaaalpNHnwQaZMn3lffUD8L9M/o61bXq0yJSWFyMhI4uPjSUlJwdnZGV9fX2rWrImLi8ttN0SrVYrY0mqVIjm706tVVh3xvd3OdXRme7udKz/d8lvu6upKYGDgnWiLiIiIFFCaGlpERMSKuiRsKWAQERGxorEotrSWhIiIiORKGQYRERErSjDYUobhPrFmzossmPicTfmDAZU4tH7CTY8t7GJk7ps9OPvz28RsmUHoGz0o4mrM03UKuxhZ+V4w536byfJZz+PiXMhc175Zbb5654XbuyERO/h67Rr8a1e32QLq1Ljpcd9u+IYBfS1nrE1PT+fd2TNp3aIZLZo1YfbMty3mpFn+xec83LQxTzzWjn0R4eZyk8nE4x3aEh+vic3uJQ4OBrtt9wsFDPeBp9o1oEPzOjbltav48fnM53P9hp05oiv1a5Xj8UGhPBo8h6A65Xn7ta55uk6/Jx/Eu4QbzZ6biZ9Pcfp3edBcN2ZgB6bM/7/bvCuR/65dh0fZ/Mvv5m3jpl8oV648z/41z0JOdu7YzqQJ42zKPwydw/qv1zFh8hQ+WvApO7ZvY9aM6cD1qaLfmfU2M995n06duzBl8kTzcWtXr6T5Qw/j7e1j/xsUuYsUMNzjShQrzNRXOrP7wCmL8gFdm/LzomGcv3g513OY0jMZNn0lew9HER55liVfb+PBwEp5uk61ir5s2fUnR0+f55ddf1Ktgi8AbZvWIi4hiYgjZ//T/Yn8Fy4uLnh5e5u3bzd8Q3Z2Ni8PG57j/vM+DGXwiy9QpkxZi/Ls7GxWfPk5Q18dRrPmD1OzVm3eGD+RlSuWc+3qVc6ejaJYsWI0atyEVq3bcOrkCQDSTSaWLV1Cv+eVabvXGAz22+4XChjucdNefZIvvt3J4RNxFuXtmtbihXFL+WDZz7me49XpX7Et4vovuHKlPHi6fRC/7j6ap+tExV2ibrXSODk5UK96ac7GXQKUXZCCJykxkYWffszLr76G0Zhzl9u2rX/w0YJPad2mrUX5xYsXuXr1KnXr/rOoVLVq1cnISOfgwQOULFmSpKQkYmNiOHTwICVLlQJg7drVNGveXNmFe5CmhralgOEe9nDDajSrX4VpH9vOSNZ92Md8/VPELZ3v40m9OPLdJHw8izJtwT8f9je7zsI1f1CulAeJ29+jpJc7n6z6ndYP1OT8xSvKLkiB8tWKL/H29qFNuxvPurd42ZcENWxkU+7u7o6TUyHOn/9nkaq4uOurtCZeuoSPjy89n+vNY+1bM2nCOIYNf5309HQ+X7qY/s8PtP/NiOQDBQz3KGejE6FvPMMr078iNS3dLuecvehHHu49izOxl1gXOgiDwZDrdS4kXiXoqalUajuWJs9MJ/FKCmMGtmfq/O/o2KIu+9aNY+sXr+NfXSv4Sf7Jzs5mzeqV9HjWdmBwXjg5OdGqTRs+eO8dzsXFceXKFd6Z+TZOTk6kp1//uXj1tRH88ts2tvyxnYdbtOTrtWt4sGkzHBwcCX6+Hx3aPMLCTz+2523JHaQuCVsKGO5RY4MfZc+hM2zadthu54w8EcfO/afo9fpn1K1ammb1q+T5OucvXl/W/JHGNbiQdI3Ik+eYN/45er3+GW9/8j2fTL7xIDORO+3ggf2cP3eO9h0eu+1zvD76DQoXKULbVg/TpuVD+AfUp5i7O0Xc3Mz7FHN3x9nZmfT0dJYtXUS/AS/w0dw5VK5chRWr17H8i885dPCAPW5J7jB1SdjSPAz3qKfa1cfXsxjxf8wGwLnQ9X/KJ1sH4t30tTyfp5CTI489XJfN2yPNy1Ofv3iFC0lX8SpR5JavM2Zge4bPXEWNir5kZGYSceQsR06dY/nsFyjm5sLlZC2BLXffH7//Rv0GQTmuOplXnp6efLJwCUmJiRidncnOzmbOe7PxK13aZt/1X6+jyQNN8fHxJXzvHl4ZNpxixYpRLyCAvXvCqFXb9qkmKVjupw96e1HAcI9q98L7ODk5ml9PebkzAGPfX3dL58nKzubjSb0YPPkLvvo+DICyJUvgVbwIkSfibuk6LRpV49KVFMIjz1KvWmnz45xOjtcTWQb0Ayj5Y//+fQQE1v9P5xgzagQdH+/Eg02bAfDDxv/Dw9OTypWrWOyXkZHB0sULmf/pQgAcDA5kZV1fFDgzI5NbXCBYpMBQwHCPOhN7yeL139mBE1EJuR7r4lwIdzcXzl24QmZmFp+u/p2JIY9zNu4SKWnpvDuqOxt+2W/zRERu1xkzsAOvz14DwLEz8RRycqRzqwDK+3kQeSKOpOSUW75PEXs4fvQoj3V8wqIsMzOTSxcv4u7uTqEbPDXxb8XdixP6/rt4+/iQeOkS06ZMZsDzA3FwsOzZXf/NOho1eQAfn+uPGNeuU5fvNqzH28eH3bt20rf/APvdmNwxSjDY0hiG/0Hd2tbn1KZp5tfjPljPus0RLJsxgO8XDOXPU+d4ftzSWzrnQ0FVuZycyt7DUQBcSzUx5K3lvDuqOwO6NiN4wjK73oPIrbhwIYFixYpZlMXFxdKqRTPCw/fm6RwhQ1+hYuXK9OvVkzGjRvBcr74817uvxT4ZGRksWbTQ4smIFweFEBMTzQv9+/BMz2ep5x/wX29H7oL8HsNgMpno2LEjO3bsMJfFxMTwwgsv4O/vT5s2bfjuu+8sjtmwYQOtW7fG39+fwYMHc/HiRXNddnY2s2bNokmTJjRq1IgZM2aQlZV1a+9JdgHJj7kGhuR3E0QKnEu7QvO7CSIFkssdzo8HTvzJbufaO/6RW9o/LS2N1157jR9//JElS5bQuHFjMjIyePLJJylTpgwjRoxg586dvPXWW6xZs4Zq1aqxb98+evXqxcSJE6lRowZTpkyhcOHCzJ8/H4DPPvuMJUuWMGvWLDIyMhgxYgR9+/ZlwIC8Z7zUJSEiImIlv7okjh07xmuvvWYz1mXLli3Exsby5Zdf4ubmRqVKlfj111/Zu3cv1apVY9myZXTo0IHOnTsDMGPGDFq2bElUVBRly5ZlyZIlDB06lKCgIACGDx/O+++/f0sBg7okRERErORXl8TOnTtp3LgxK1assCl/4IEHcPvXY7wffvghTz/9NAARERHmYACgVKlS+Pn5ERERwblz54iNjaVhw4bm+gYNGhAdHc3583lfFE0ZBhERkQKiZ8+eOZZHRUVRunRpZs2axddff02JEiUYOnQorVu3BuD8+fP4+FhOQe7p6UlcXBzx8fEAFvVeXl4AxMXF2Rx3I8owiIiIWLHnTI8mk4nk5GSLzWQy3VJ7rl27xtq1a7l8+TLz5s2jc+fODB06lP379wOQmppqs0aK0WjEZDKRmppqfv3vOuCW2qEMg4iIiBV7Ttw0f/58QkMtBzCHhIQwZMiQPJ/D0dGR4sWLM2HCBBwcHKhduza7d+/mq6++om7dujg7O9t8+JtMJlxdXS2CA2dnZ/PXAK6urnlugwIGERGROyg4OJh+/fpZlN1oxdQb8fHxwWAwWMz7UbFiRY4cOQKAr68vCQmW8+MkJCTg7e2Nr+/1OUHi4+MpU6aM+WsAb2/vPLdBXRIiIiJW7NklYTQacXNzs9huNWDw9/fn6NGjZGZmmsuOHz9O6b+mJvf39ycsLMxcFxsbS2xsLP7+/vj6+uLn52dRHxYWhp+fX57HL4ACBhERERv5PXGTtY4dO5KVlcXEiRM5ffo0n3/+Ob/99hvdu3cHoEePHnz99desXLmSyMhIRo4cSYsWLShbtqy5ftasWezYsYMdO3Ywe/Zseve+tUUB1SUhIiJipaBNDe3m5sbChQuZMGECHTt2xM/Pj3fffZfatWsDEBgYyKRJk5gzZw5JSUk0bdqUyZMnm48fMGAAFy5cICQkBEdHR7p160bfvn1vqQ2a6VGkANNMjyI5u9MzPTaZvsVu59o+6mG7nSs/KcMgIiJiRctb21LAICIiYkXxgi0NehQREZFcKcMgIiJiRV0SthQwiIiIWFG8YEtdEiIiIpIrZRhERESsqEvClgIGERERKwoYbKlLQkRERHKlDIOIiIgVJRhsKWAQERGxoi4JWwoYRERErChesKUxDCIiIpIrZRhERESsqEvClgIGERERK4oXbKlLQkRERHKlDIOIiIgVB6UYbChgEBERsaJ4wZa6JERERCRXyjCIiIhY0VMSthQwiIiIWHFQvGBDAYOIiIgVZRhsaQyDiIiI5EoZBhEREStKMNhSwCAiImLFgCIGa+qSEBERkVwpwyAiImJFT0nYUsAgIiJiRU9J2FKXhIiIiORKGQYRERErSjDYUoZBRETEioPBYLftdphMJjp27MiOHTts6q5cuULz5s1Zs2aNRfmGDRto3bo1/v7+DB48mIsXL5rrsrOzmTVrFk2aNKFRo0bMmDGDrKysW3tPbutORERE5I5IS0tj2LBhHD16NMf6mTNncv78eYuyffv2MXbsWEJCQlixYgWXL19m9OjR5vqFCxeyYcMGQkNDmTNnDuvXr2fhwoW31C4FDCIiIlYMBvttt+LYsWN0796dM2fO5Fi/e/dutm/fjre3t0X5smXL6NChA507d6ZGjRrMmDGDLVu2EBUVBcCSJUsYOnQoQUFBNGnShOHDh/P555/fUtsUMIiIiFgxGAx2227Fzp07ady4MStWrLCpM5lMvPnmm4wbNw6j0WhRFxERQVBQkPl1qVKl8PPzIyIignPnzhEbG0vDhg3N9Q0aNCA6OtomU3EzGvQoIiJixZ6DHk0mEyaTyaLMaDTafOgD9OzZ84bnmTdvHrVq1aJZs2Y2defPn8fHx8eizNPTk7i4OOLj4wEs6r28vACIi4uzOe5GFDCIiIjcQfPnzyc0NNSiLCQkhCFDhuT5HMeOHWP58uV88803OdanpqbaBCBGoxGTyURqaqr59b/rAJtA5mYUMIiIiFi53acbchIcHEy/fv0synLKLtxIdnY2b7zxBkOHDjVnBqw5OzvbfPibTCZcXV0tggNnZ2fz1wCurq55bocCBhERESv2nIbhRt0PeRUTE8PevXs5cuQIb7/9NgApKSmMHz+e7777jk8++QRfX18SEhIsjktISMDb2xtfX18A4uPjKVOmjPlrwGbw5M0oYBARESnAfH19+eGHHyzKevXqRa9evXjiiScA8Pf3JywsjC5dugAQGxtLbGws/v7++Pr64ufnR1hYmDlgCAsLw8/PL8/jF0ABg4iIiI2CtJaEk5MT5cuXtynz9PQ0Zw969OhBr169CAgIoG7dukyZMoUWLVpQtmxZc/2sWbMoWbIkALNnz6Z///631g473IuIiMh95V5brTIwMJBJkyYxZ84ckpKSaNq0KZMnTzbXDxgwgAsXLhASEoKjoyPdunWjb9++t3QNQ3Z2drad231bXAND8rsJIgXOpV2hue8k8j/I5Q7/ufvs0nC7nevzXgF2O1d+UoZBRETESkHqkigoFDCIiIhYUbxgS1NDi4iISK6UYRAREbGiLglbChhERESs3GtPSdwNChhERESsKMNgS2MYREREJFfKMIiIiFhRfsGWAgYREREr9lyt8n6hLgkRERHJlTIMIiIiVpRgsKWAQURExIqekrClLgkRERHJlTIMIiIiVpRgsKWAQURExIqekrClLgkRERHJlTIMIiIiVpRgsKWAQURExIqekrBVYAKG+R+/nt9NEClw6o/7Ib+bIFIgHZra9o6eX/31tvSeiIiISK4KTIZBRESkoFCXhC0FDCIiIlYcFC/YUJeEiIiI5EoZBhERESvKMNhSwCAiImJFYxhsqUtCREREcqUMg4iIiBV1SdhSwCAiImJFPRK21CUhIiIiuVLAICIiYsXBYLDbdjtMJhMdO3Zkx44d5rLw8HCeeeYZAgMDadeuHStXrrQ4ZuvWrXTs2BF/f3969+5NVFSURf2iRYto3rw5gYGBjBkzhpSUlFt7T27rTkRERO5jDnbcblVaWhrDhg3j6NGj5rL4+HheeOEFGjVqxNq1axk6dCiTJ0/ml19+ASAmJobBgwfTpUsXVq1ahYeHB4MGDSI7OxuAjRs3EhoayqRJk1i8eDERERHMnDnzlt8TERER+ReDwX7brTh27Bjdu3fnzJkzFuWbNm3Cy8uLYcOGUaFCBR577DE6d+7M+vXrAVi5ciV16tShf//+VK1alWnTphEdHc3OnTsBWLJkCX369KFly5bUq1ePiRMnsnr16lvKMihgEBERKSB27txJ48aNWbFihUV58+bNmTZtms3+ycnJAERERBAUFGQud3V1pXbt2oSHh5OZmcn+/fst6gMCAkhPTycyMjLPbdNTEiIiIlZud+xBTkwmEyaTyaLMaDRiNBpt9u3Zs2eO5yhTpgxlypQxv75w4QLffvstQ4YMAa53Wfj4+Fgc4+npSVxcHJcvXyYtLc2i3snJieLFixMXF5fn+1CGQURExIo9uyTmz59PgwYNLLb58+ffdttSU1MZMmQIXl5ePP300wCkpKTYBCBGoxGTyURqaqr5dU71eaUMg4iIyB0UHBxMv379LMpyyi7kxdWrVxk0aBCnTp3iiy++wNXVFQBnZ2ebD3+TyUSxYsVwdnY2v7au//v4vFDAICIiYsWeMz3eqPvhViUnJ/P8889z5swZFi9eTIUKFcx1vr6+JCQkWOyfkJBAzZo1KV68OM7OziQkJFC5cmUAMjIySExMxNvbO8/XV5eEiIiIlfyeh8FaVlYWISEhnD17lqVLl1K1alWLen9/f8LCwsyvU1JSOHToEP7+/jg4OFC3bl2L+vDwcJycnKhRo0ae26CAQUREpIBbtWoVO3bs4K233qJYsWLEx8cTHx9PYmIiAF27dmXPnj0sWLCAo0ePMnr0aMqUKUPjxo2B64MpP/30UzZt2sS+ffuYMGEC3bt3V5eEiIjIf1HQ1pLYuHEjWVlZBAcHW5Q3atSIpUuXUqZMGT744AOmTp3K3LlzCQwMZO7cueZluh977DGio6MZN24cJpOJtm3bMmLEiFtqgyH772mg8tmS3VG57yTyP2b6msP53QSRAunQ1LZ39PxTNh+z27nGtqpit3PlJ3VJiIiISK7UJSEiImLFQAHrkygAFDCIiIhYsedjlfcLBQwiIiJWFDDY0hgGERERyZUyDCIiIlYMBe25ygJAAYOIiIgVdUnYUpeEiIiI5EoZBhERESvqkbClgEFERMSKvRaNup+oS0JERERypQyDiIiIFQ16tKWAQURExIp6JGypS0JERERypQyDiIiIFQctPmVDAYOIiIgVdUnYUsAgIiJiRYMebWkMg4iIiORKGQYRERErmrjJlgIGERERK4oXbKlLQkRERHKlDIOIiIgVdUnYUsAgIiJiRfGCLXVJiIiISK6UYRAREbGiv6ZtKWAQERGxYlCfhA0FUSIiIpIrZRhERESsKL9gSwGDiIiIFT1WaUtdEiIiIlYMdtxuh8lkomPHjuzYscNcFhUVRd++fQkICODRRx/l999/tzhm69atdOzYEX9/f3r37k1UVJRF/aJFi2jevDmBgYGMGTOGlJSUW2qTAgYREZECJC0tjWHDhnH06FFzWXZ2NoMHD8bLy4vVq1fTqVMnQkJCiImJASAmJobBgwfTpUsXVq1ahYeHB4MGDSI7OxuAjRs3EhoayqRJk1i8eDERERHMnDnzltqlgEFERMSKwWC/7VYcO3aM7t27c+bMGYvy7du3ExUVxaRJk6hcuTLBwcEEBASwevVqAFauXEmdOnXo378/VatWZdq0aURHR7Nz504AlixZQp8+fWjZsiX16tVj4sSJrF69+payDAoYRERErBgMBrttt2Lnzp00btyYFStWWJRHRERQq1YtChcubC5r0KAB4eHh5vqgoCBznaurK7Vr1yY8PJzMzEz2799vUR8QEEB6ejqRkZF5bpsGPYqIiNxBJpMJk8lkUWY0GjEajTb79uzZM8dzxMfH4+PjY1Hm6elJXFxcrvWXL18mLS3Not7JyYnixYubj88LZRhERESsONhxmz9/Pg0aNLDY5s+ff0vtSUlJsQkwjEajORC5WX1qaqr59Y2OzwtlGERERKzYc6bH4OBg+vXrZ1GWU3bhZpydnUlMTLQoM5lMuLi4mOutP/xNJhPFihXD2dnZ/Nq63tXVNc9tUIZBRETkDjIajbi5uVlstxow+Pr6kpCQYFGWkJBg7ma4Ub23tzfFixfH2dnZoj4jI4PExES8vb3z3AYFDCIiIlbyex4Ga/7+/hw8eNDcvQAQFhaGv7+/uT4sLMxcl5KSwqFDh/D398fBwYG6deta1IeHh+Pk5ESNGjXy3AYFDCIiIlby6ymJG2nUqBGlSpVi9OjRHD16lAULFrBv3z66desGQNeuXdmzZw8LFizg6NGjjB49mjJlytC4cWPg+mDKTz/9lE2bNrFv3z4mTJhA9+7d1SUhIiJyP3F0dOTDDz8kPj6eLl268M033zB37lz8/PwAKFOmDB988AGrV6+mW7duJCYmMnfuXHPA8thjjxEcHMy4cePo378/9erVY8SIEbfUBkP239NA5bMlu6Ny30nkf8z0NYfzuwkiBdKhqW3v6PnXRMTa7Vxd/EvZ7Vz5SU9JiIiIWLHnUxL3CwUMIiIiVhQu2NIYBhEREcmVMgwiIiJW1CNhSwGDiIiIFQd1SthQl4SIiIjkShmGAir05WdJSjhnU16mWm36jH8/1+OnPNua58bOonytALu16fShcJZNGU6HAa9Q/5GOFnXr580A4PEXR9rteiI3M6VrbZ5sUPqG9X0+3sWuk5fuaBt+HNGc0iX+mfgmPTOLqIsprNgRxdKtZ+7oteXOUpeELQUMBVibXoOo1aSFRZmjU/7/k/2y4lNqNGxO4aLu+d0U+R82bcMR3t14FID29UrSr1kFnv5wu7k+KSX9rrRj6oZIvt93fYlgJ0cDjSt5MrlLLZJS0vlmr/2e5Ze7y6AuCRvqkijAnF2L4Fbcw2JzdSuW383C6FqEzV8uyO9myP+45LQMEpJNJCSbSE7NICs72/w6IdlEeubdmZMuOfWfdsQlpfH13hi2Hb9Im9q+d+X6IndL/v+5Krcl7dpVflj2Icf27iD1ajIlfErR8pnnqR7U1GbfUwf38uOyeVyIPUPR4p488Pgz1G91vUsh9WoyGxeH8mfYVowurtRo1IxHegykkNH5htdu22sQK98dT8DDHShbvU6O+5yJ3M+Pyz4i4ewpSvj68VDX3tRo9JC5fsf/rWL7tysxpaRQ76G2xEedpG7ztvg/3O4/vjMi1/kVd2HTyIeY8+Mx+jQrz4bwWBKvpdOwYgn6frLbvN+PI5ozd/Nx1u2JAeDFlpV4pnEZXAo5EnYqkbe+OUxsUuqNLpOjzKxs0jOzgOup7X7NKvB04zJ4F3UmIiqJqesjOXouGYD2dX0Z0roKfsVdOHsphfc2HmXz4Xg7vQtyu9QlYUsZhnvUD0s/5GLMWXq8Pp3gGZ9Qtnodvv14NpkZlmnYrKxM1syZRM3GD/HizIU8/FQ/vl84h/izpwHY8PEs0lKu0mf8e3QbNpGY40fYuOiDm167WoMHqRrYhO8Xvk9WZqZNfXLiRb6aNZZ6D7Xlhekf88Djz7B+/kzORO4H4MAfm/l11RLa9hpE34lzSEo4x+nIfXZ6Z0QsBZYvTve521mWhzEFzz5Qlo4BpRixYj895u3gQnIaH/dvgJND3j49nBwMtK7tQ9Mqnvx06DwAgx6pTN/m5Zm+4QjdQrcTcymFBX3r41rIEY8iRt5+qi4fbznJY+/+wZrd0cx8ph7urvpbLr85YLDbdr/Qd2UB9n8L32PjYssP71c+XInRxZVyNerR+NFu+JStCECTx7oT/sv/cTXpEsU8fcz7p127SkryFYq4l6C4d0mKe5fErbgnbiU8uHQuhj93b2XYgjW4FHYD4LHnh/HJmBdp/dyL5rKctOsdwvzXB7Dz+9U0eay7Rd3uH7+mQp36NGzbGQCPkqU5d+oYO79fTbkaddn949c06tCFmo0fBq4PlPxgSI///H6J5GTpH6eJupiSp337N6/I5G8OmwdLTlh3iC2jW9Csmhe/ROb8V//4TjV54/HrSwQ7F3IkNT2TxX+cZkPE9XENzz5Qlnc3HuXnv44fv/YQG4c34/HAUuyPSqKQkwPnklKJSUxl4e+nORKXTFpG1n+9bRG7U8BQgD3ctS/VGzazKCvk7AJAveZtOBL2B3t/+pYLMVHEnfoTgKwsy180rm7FqN/6cb775B1+X7uMqvUfwP/h9rgWKcrZIwfJzs5iTsgzFsdkZ2dx6VwMpSpWu2Hb3L19adb5WX5bs5RaTVpa1F2IPsPRPduZ0f+fJymyMjPxKHl9RPv5Myd48Il/AgTXIkXxKFUmr2+LyC2JTsxbsFDY6Eip4i7MfqYe/16Tz7mQIxW8Ct/wuA82HWfTwetPNKVlZBF/JY2svw73dDNSvLCRfVFJ5v0zsrI5EH2ZSt5F+GrnWX6JjOfTAUGciL/KT4fOs3p3NKnpChjym7okbClgKMAKFytu/pC19s28tzn750HqNGtD/daPU7S4B4smDM1x3w79XiaoTSeO7P6DP8P+YO9PG3hq2GSysjJxLlyE/pM/tDmmqIdXru1r/OhT7P/tR35YOhdnl39+oWZlZVKnWSuaPtHTYn9Hx+vfbg6OjmC9SGrBWDRV7kNp//rwzWlxXse/uhv+/v+rX0ZwKv6qxT43e+Li4lUTZ26QwUi7wQe/o8Fgvt6gJXupW6YYLWv60Ka2Dz2alKXXgl1Exl65yV3JnaaAwZbGMNyD0q5d5cDWn3hyyBs83K0PNRo2I+XqX79crH4hJide5PuFcyjh60ezzs/Sf/KHVKgdyNE9W/EsVYa0a1cxGAx4lCyNR8nSZJjS2PzlAjLTc38kzdHJifb9hnJk1+8WYxA8S5XlUly0+ZweJUvzZ9hWDmzdDIB36QrEnjxqcT+XzsXY4Z0Rubn0zGyKOP/zd1JhoyOeRYwAXEnNICE5DS83Z85cTOHMxRRik1J5rUM1KngVua3rJadlkHAlDf9yxc1lTg4GapUuxsn4q1T0LsyIDtXYf/Yyc348xuPvbSUuKZWmVT3/033Kf2ew43/3CwUM9yAnoxGjswuRu34jMT6O4/t2mQcqZlgNenR1K0rk7t/5cdlHXDoXw5nD+zh3+ji+FargVbo8leo1ZN3cqcQcjyT25FHWz59JemoKLkVuPH7h38rXCqBO09YkxceZyxq0foLYE3/yy1efcTHuLAf+2MwvX32Gu9f1x8yC2nVm18Y1RO76jYTo02z4eDam1BQtJyt33IGzSVQv6Ua7Or6U9yzMhCdrkfmvIHvx76d5pW0VWtTwprxnYSY9WZvA8sU5aZVxuBWLfj9NSOvKtKjhTSXvIkx8shbOTg783/44rqRk8HTjsgS3rEjpEq48XN0Lv+KuHI5RdkEKHnVJ3IMcnQrxxEuj2PzFfHZtXEdx75I07fwsW1Yu4typY3j5lbPYt/uwSfyw9EM+Hj0Qo4sr/i06ENjiUQA6DRrFxsWhfD51JA6OjlSuF0TbPiG31J5WzwZzbO8/E+a4e/vSffhkfvryE7Z/u5KiJTxp9WwwdZq2AqD2Ay25FBfN/332PhkmE4GPPIa7l2+BmJRK7m/bjl9k8R+nmfBkLbKysln8+2l8iv7zCPHC305RxNmJiZ1rUcTFiYPRSQxcuIfLqRm3fc1Fv5/CzcWJSU/WooizE+FnEunzyW4uXb0e3L/8eTivtatKcItKXEw28e4PR9l67MJ/vlf5b/L4YMz/FEN2Tp16+WDJ7qj8boLcJacPR1DCp5T5aY6szEzeebELT7060a5TWd8Ppq85nN9NECmQDk1te0fP/1Ok/YK2R2rcH11M+pNO7ro/d//B2aOH6ND/ZYwuhdm1cQ3OroUpXaVWfjdNRERuQAGD3HUPdevLxkVz+GLa62SkmyhTtRbPjJyOk9GY300TEQH0lEROFDDIXefsWpgnXhqV380QEbmh++npBnvRUxIiIiKSK2UY/gdkpJv47I1BtOsTYh5UmHg+lm8/eYfoY4dx9/ShTa9BVKoXZD5mx3er2Pn9aq5duUy56nVo13cIHiX/mY1x9w9fs23DclKvXqVSvSAeHfBKgVhJU+RWFHI0sCrkAd7613TQpdxdGN+5Jg0rehB/JY33fjjK9/vPmY9pW8eXV9pWwaeYC3tPX2L82kPEJF5fnMqjiJE3O9XkgcoeJF5LZ97PJ8yLWsm9RU9J2FKG4T6XYTKxLnQq8WdPmcuys7NZ+e543Ip70H/yXOo0a82q9yaQlHD9l+KBPzbz29qldOj/Ci9Mm49rUXe+mvWmeZa8Q9t+ZvOXC2jz3Ev0mfA+ly+c5/tcFqwSKWiMTg7MeqYeVX3/mXPE0cHAR30CycjMpmvoNj777RRvP1WXKn/tE1DOnZlP12XR76fpFroNU0Y2s56pZz5+znP+lCzmTL9PdjNtQySvP1qd1rV9bK4tBZ8mbrKlgOE+Fn/2NAvHD+HSecu/cE4fCufSuRg69H8Fr9LladqpJ6Wr1CRiy/cApF5L5pEeL1AloDEeJcvwwOPPcCE2imuXEwHYumEFD3R8mhqNHsKnbEVa9RhIfNRJsrJsV64UKYgq+xRh+YuNKOthuUbEQ9W8KOnuwusr93Mq4Rpf7TzLr38mEFjOHYB+zSuwITyWr3ae5VTCNaZuiMS7qDPFCxeiduli1C9fghEr9nM49gpbjiTwya8n6d+8Qj7coYj9KWC4j52JjKBCLX/6TphjUR597DAlK1TF6OJqLitbvQ7RRw8BENSmE/Ufub5wVOq1ZMJ+/BrvMhUoXKw4adeucu7UMWr8a1GscjXrMfDtT3BwcLwLdyXy3wVVLMGOE5foOW+HRXnDSiXYfvwiV9P+CX6HLAtn5a5oABpV9ODHg+fNddGXUmgz8zcSr6VT1sOVC8kmzl76Z12JP+OSqV26WJ6Xx5aCw2Cw33a/0BiG+1iD1k/kWJ6ceIGiJSwnEiniXoLLFxMsysJ/+T++/Xg2joUK0eP16RgMBi6djwXg2pUkFk94mcT4OCrWrU/bXoPzPJ20SH5bseNsjuVlPQoTfSmFV9tV5YmAUly6ls7cTcfYfDieoi5OuBcuhKODgQV961OjVFH2RSUx6ZvDnL+cRkKyiWIuTrgUcjCvNlnS3ZlCjg64uTiReC339Vmk4LiPPuftRhmG/0HpaWk4FipkUeboVIhMq3UoKtapz4ApHxHY8lFWvjOOxPOxpKdd/+vp+0Uf8MDjT9Nl6JvEnz3N1x9Nv2vtF7lTChsd6VzfD3dXJwYt3cs3e2N4t6c/tUsXo7DxegZtTMcarA+PZdCSvRRycuCj3oEYDLAvKonzV9IY+3gNXAs5Us7DlT7NKgBQyFG/au81DgaD3bb7hb6L/wc5FTLarEaZmZFOIaOzRZm7ly8lK1Slbe8Qinl4s++3H8zdDg8+/gzVGjxI2ep1eOz5YRzbu50rlywzFCL3moysbBKvpTPx68McjrnCot9Ps+VIAk81LENm1vVBv6t3n2V9eCwHoi8zcsV+qvq64V/WHVNGFq9+EUHjSp7sHP8ISwc24qud16e8v5p2+2tRiBQUt9QlsWvXrjzv27Bhw1tujNwdRT28iI8+ZVGWnHgJt+IeAJw6GE7REp54+pUFwGAw4FW6HNeuXMat+PWujL/r/v315QvxFC3hdRfuQOTOSLiSBtmWq8SfjL9K9ZJFuXQtnfSMLE78a+XKpJR0ElPSKenuAiRxIPoybWf9hpebkUvX0mlaxZOLV01cM2lA8L0mv/ICsbGxTJgwgV27dlG8eHF69+5N3759ATh06BDjx4/nzz//pEqVKkycOJE6deqYj92wYQPvvfce8fHxNGvWjMmTJ+Ph4WG3tt1SwDBp0iSOHTsGwM3WrDIYDBw+rEVzCqrSVWqy9ZvlpJvSzFmFs38eoEy169942zYsx93Ll0cHvApAVlYm504fp2G7LhTz8sGthCfnzhyndJWaACREnwGDwbx8tci9KiIqieAWlXAwwF8JBSr5FCE6MYXMrGwOxlymRqmi5nkZihcuRInCRmIupeLu6kRor0BCloaTkGwC4KEaXuw6cTG/bkf+i3yKGF555RX8/PxYs2YNx44dY/jw4ZQuXZqmTZsycOBAHn/8caZPn86XX35JcHAwP/74I4ULF2bfvn2MHTuWiRMnUqNGDaZMmcLo0aOZP3++3dp2S10Sq1evplWrVlSvXp2IiAgiIyNz3BQsFGzlatajmKc3G+bPJP7sKbZ+8yUxxyMJaNEeuD5Yct+vP3Dgj81ciIni/z57n3STiXoPtcVgMNC4Q1d+XbWYE/vDOHf6ON8vfJ/qDZqaMxQi96pvI2JxMMC4TjUp5+HKM43L0ryaFyt3XR8kuej30zz3QDna1fGlkncRpnStQ2TsFfadTSIpJYMiRkeGd6hKmRKudA0qTZcGpfn011P5e1Nyz0hKSiI8PJyXXnqJChUq0Lp1a5o3b862bdv47rvvcHZ2ZuTIkVSuXJmxY8dSpEgRvv/++uPwy5Yto0OHDnTu3JkaNWowY8YMtmzZQlSU/VaCvqWAwWg08s477wDw3nvv2a0Rcnc5ODjy1LBJJCde5NM3XuLAH5vp9uoEc4agWoMHad9vKL+tWcInY4K5dC6GnqOmmx/DbPzoUwS16cQ3H01n8cSXKeFTio7Bw/PzlkTs4mpaJs8vDKOidxG+fvlBej1YjteW7+NwzBUAfjhwjunfHmF4h2qsHNwERwcIWbrXfPyw5fso61GYdS8/SO+m5Xn1iwgORF/Or9uR/yA/Jm5ycXHB1dWVNWvWkJ6ezokTJ9izZw81a9YkIiKCBg0aYPhrEKXBYKB+/fqEh4cDEBERQVDQP7P1lipVCj8/PyIiIuz2ntzyY5VGo5HZs2ezc+dOuzVC7ryxn2+yeO1RsjS93nznhvsHtOhAQIsOOdYZDAaaPfkczZ58zq5tFMkPtcb8YPH6+Pmr9Pl49w33X7U7mlW7o3OsO5Vwjb6f3PhYuXfY8+EGk8mEyWSyKDMajRitVuh1dnZm3LhxTJ48mSVLlpCZmUmXLl146qmn2Lx5M1WqVLHY39PTk6NHjwJw/vx5fHx8bOrj4uLsdh+3NQ9D5cqVqVy5st0aISIicr+aP38+oaGhFmUhISEMGTLEZt/jx4/TsmVL+vXrx9GjR5k8eTIPPPAAKSkpNgGG0Wg0ByKpqak3rbcHTdwkIiJixZ5jHoODg+nXr59FmfWHO8C2bdtYtWoVW7ZswcXFhbp163Lu3Dk++ugjypYta/PhbzKZcHFxAa5nJ3Kqd3V1xV40D4OIiIg1g/02o9GIm5ubxZZTwHDgwAHKly9vDgIAatWqRUxMDL6+viQkWM51k5CQYO6GuFG9t7f3f34r/qaAQUREpADw8fHh9OnTFpmCEydOUKZMGfz9/dm7d695SoPs7Gz27NmDv78/AP7+/oSFhZmPi42NJTY21lxvD+qSuA9kpJv47I1BtOsTQvlaAQDEnvyTjYs+4HzUSbzLVKBtr0GUrlorx+OnPNs6x/LHXxxJveZtb3qd7KwsNnw8m8idv1GqYlWeHPIGRdxLAHA+6iTr582g/1sfmkf2itxp5TxceeOJmtQvX5yklHQ+3xbFZ7+dstln3csPUn/85hue59DUtjmWj1q5n2/2xtKqlg8fPBdgUbfxwDle/SIC10KOzHymLo0qerDt+AVGrthPWsb19SUequ7FUw3LMGRZ+H+5TbnD8mNZ6kceeYSZM2fyxhtv8NJLL3Hy5EnmzZvHq6++Svv27Zk9ezZTpkzhmWeeYfny5aSkpNChw/XB6T169KBXr14EBARQt25dpkyZQosWLShbtmwuV807BQz3uAyTiXVzpxJ/9pS57GrSJT6fOoKajR+mY/AIjkfs5IvprzPw7U9ynFzp5blfWbze+X+rObT9F6o1ePCm1wH4c882zhyOoN+kD/h5xadsXb+cNs+9BMDva5fS7MnnFCzIXWMwwEd96nPgbBJdQ7dT3rMwM5+py7nLqXwbcX20eEl3Zz7sUx+XQjdfXfWhqb9YvO7dtDwd6pXkp0PxAFTxKcJPh88zYe0h8z5/BwVdG5bGo4iR7h9uZ/pTdXmqYRmWbTsDwKBHKjNx3SGkYMuPX1tFixZl0aJFTJkyhW7duuHh4cFLL73E008/jcFgYP78+YwfP56vvvqK6tWrs2DBAgoXvr5Ee2BgIJMmTWLOnDkkJSXRtGlTJk+ebNf2KWC4h8WfPc26uVMBy1k39//+I65uxejQ/2UcHBzx8ivHiX1h7Nm0npbPPG9znn9PuJR4PpZdG9fSffhkXAq73fQ6ABdioyhdpSZepctT2b8Rf4Zt/euYU1w6F2sRdIjcaZ5uRiJjrzDx68NcM2Vy+sI1th+/SP3yJfg2Io5WNb2Z8GRt4q+k5Xquv2drBChdwpXnHizH4CV7Sf5rXYhK3kU4di7ZYr+/VfIuws4TFzmVcI0dxy9Q0bsIAM2reRF/JY3DsVfsdMdyp+TXnzlVqlRh4cKFOdbVq1ePtWvX3vDYLl260KVLlzvVNI1huJediYygQi1/+k6YY1F+6XwspSpWNS8UBeBbrhJnj+X+V82WVYupUDuQinUa5HodAHdPH+Kjz5CRbiLu1FGKeV4fgPP72mU07fyssgtyVyVcMfHa8n3mtRsCyxUnqEIJdp28Pj3zQzW8+WDTMaZtiLyl8w5pXZntxy+y7fg/0zxX9nHjVMK1HPePTUylesmiODkYqFGqGLGJ11d5femRSny4+fjt3JpIvlOG4R7WoPUTOZYXKVaC86dPWJRdvnCelCs3n3EuKeEcB7f+RJ8J7+fpOgA1GjUnbNN6ZvTrSDEvH54dM4OE6NNcjIumelDTPN6JiP1tGtEcvxKu/Hw4nh8OXF/7Yfxf3QcNK5bI83lKubvwmH8pnp1nOVldBe8iNK3qycAWFXEwGNh44Byhm46RnpnNyl1n6RhQir2TWnM07gpf7TxL06qeXEw2Kbtwr9DfOjYUMNyHajRqzu/rlrH3p2/xf7g9Jw/u4c892yhawvOmx4X/8j2lKlUzLyqVF45Oheg97l2Sky5RpKg7BgcH1oVOoWnnZ4k5dphvP3mHrKws2vYeTKW6DXI/oYidvPxFBF5uRsZ1qsWox6ozdcOR2zpP16DSHIy+zL6zSeYyv+IuFDY6YsrIYtiX+yhdwpUxHWvgUsiBaRuOkHgtnU7vb8XTzciFv7osXnqkElPWR/JITW+Gd6jGtbRM3lxzUAFEAZUfgx4LOnVJ3Id8ylbkseeHsenzeUzv04FfVnxGg9aPY3QtctPjInf+Sp2mrW7rmm7uJTA4OJAQc4aE2CiqBzVl/YJZPPxUPzoNGsU3H04jw44zjonk5mD0ZbYcSeDt747QvVFZCjne3gdA2zq+rA+PsSiLSUzlgck/MXb1QSJjr7D50HmmfxvJUw3L4PCvy/wdLDxQxYPEa+mcOH+Vt7rW5rUv9zHvlxNMe6oOIvcKZRjuU/4Pt6du8zZcTUqkaAlPNn+xgOI3WX768oXzJESf/s+DFP9Y9znNOj9L6tUrXIg5Q6V6QeYltC/ERuFbXlOKy53j6WYkoKw7mw/Hm8uOn0/G6ORAEWcnEq+l39L5Sro7U8XXzfxkxL8lpWRYvD4RfxWXQo64Fy7EpauW1xn0SGWmboikkk8RMrKyORx7hRPxV5nzbABuzk7mgZRScGj4lS1lGO5Dpw6Gs/aDt3BwcKRoCU+ys7M5HrHTPHdCTqKPRVLM0zvHxy7z6kLsWeKjT1M9qBkGw/Vvreys64+ZZWVlmiccEblTSpdw5f1nA/Ap5mwuq+VXjAvJplsOFgDqlS1ObGIKsUmpFuVNq3qy9Y0WuBT651dojVJFuXTVZBMsNKnsQVJKOodjrpCdDQ5/fRI5Ovy96uAtN0vuAjtO9HjfUMBwH/IoVYaje7cTtukbLp2P4ftFc0i9mky9h65PRJOZkU5y4kWysjLNx8SfPYlX6fL/6bp/rFtGs049MRgMuBRxo4RvacJ//o5D238BwLNUmf90fpHcHDibxMGYy7zVpTaVfYrwUDUvRnSoxvxfTuR6bCFHA15uRosuhaq+bhw7f9Vm372nE0lNz2JSl9pU8CpM82peDG9fjU9/PWWz77+fjDidcA0nBwNtavvQvVEZjp9P5kqqsgtyb1DAcB8q5uHFk0PeYNfGdXw8aiAXY8/Sc8wMjC7XFyE5++dB3h/cncsX/kmzXk26hEuRord9zYtx0Zw/e4rqDZubyx57/lV2blzLpmXz6DhwBIWcXW5yBpH/LisbQpaGk5KeyRcvNmJSl1os23qGZVvP5HpsQLni/DqmBSXd//k+9XQzcjnFNjNxzZTJwIVheBQ2snJwEyZ3qcXKXWdtZpRsVKkEyamZHIq5PrAxJT2TCesO8eYTNeneqAxjVx/8bzcsd45SDDYM2QUkT7xkd1R+N0GkwJm+5nB+N0GkQLrR1N32si8q2W7nqlfWzW7nyk/KMIiIiEiu9JSEiIiIFQ1GtaWAQURExIriBVsKGERERKwpYrChMQwiIiKSK2UYRERErGgtCVsKGERERKxo0KMtdUmIiIhIrpRhEBERsaIEgy0FDCIiItYUMdhQl4SIiIjkShkGERERK3pKwpYCBhERESt6SsKWuiREREQkV8owiIiIWFGCwZYCBhEREWuKGGwoYBAREbGiQY+2NIZBREREcqUMg4iIiBU9JWFLAYOIiIgVxQu21CUhIiJSQJhMJiZOnEjDhg158MEHeeedd8jOzgbg0KFDPPXUU/j7+9O1a1cOHDhgceyGDRto3bo1/v7+DB48mIsXL9q1bQoYRERErBnsuN2Ct956i61bt/Lpp58ye/ZsvvrqK1asWMG1a9cYOHAgQUFBrFmzhsDAQIKDg7l27RoA+/btY+zYsYSEhLBixQouX77M6NGj//Pb8G/qkhAREbGSH09JJCYmsnr1ahYuXEi9evUA6N+/PxERETg5OeHs7MzIkSMxGAyMHTuWX3/9le+//54uXbqwbNkyOnToQOfOnQGYMWMGLVu2JCoqirJly9qlfcowiIiIFABhYWG4ubnRqFEjc9nAgQOZNm0aERERNGjQAMNfozENBgP169cnPDwcgIiICIKCgszHlSpVCj8/PyIiIuzWPgUMIiIiVgwG+215FRUVRenSpVm3bh3t27enVatWzJ07l6ysLOLj4/Hx8bHY39PTk7i4OADOnz9/03p7UJeEiIiIFXt2SJhMJkwmk0WZ0WjEaDRalF27do3Tp0+zfPlypk2bRnx8POPGjcPV1ZWUlBSb/Y1Go/m8qampN623BwUMIiIid9D8+fMJDQ21KAsJCWHIkCEWZU5OTiQnJzN79mxKly4NQExMDF9++SXly5e3+fA3mUy4uLgA4OzsnGO9q6ur3e5DAYOIiIg1O6YYgoOD6devn0WZdTYAwNvbG2dnZ3OwAFCxYkViY2Np1KgRCQkJFvsnJCSYuyF8fX1zrPf29rbXbWgMg4iIiDWDHf8zGo24ublZbDkFDP7+/qSlpXHy5Elz2YkTJyhdujT+/v7s3bvXPCdDdnY2e/bswd/f33xsWFiY+bjY2FhiY2PN9faggEFERMRKfgx6rFSpEi1atGD06NFERkby22+/sWDBAnr06EH79u25fPkyU6ZM4dixY0yZMoWUlBQ6dOgAQI8ePfj6669ZuXIlkZGRjBw5khYtWtjtkUpQwCAiIlJgzJo1i3LlytGjRw9ef/11nn32WXr16oWbmxvz588nLCyMLl26EBERwYIFCyhcuDAAgYGBTJo0iblz59KjRw/c3d2ZNm2aXdtmyP47v5HPluyOyu8miBQ409cczu8miBRIh6a2vaPnj7qYZrdzlfVwttu58pMGPYqIiFjRapW21CUhIiIiuVKGQURExIZSDNYUMIiIiFhRl4QtdUmIiIhIrpRhEBERsaIEgy0FDCIiIlbUJWFLXRIiIiKSK2UYRERErBjUKWFDAYOIiIg1xQs2FDCIiIhYUbxgS2MYREREJFfKMIiIiFjRUxK2FDCIiIhY0aBHW+qSEBERkVwpwyAiImJNCQYbChhERESsKF6wpS4JERERyZUyDCIiIlb0lIQtBQwiIiJW9JSELXVJiIiISK6UYRAREbGiLglbyjCIiIhIrpRhEBERsaIMgy1lGERERCRXyjCIiIhY0VMSthQwiIiIWFGXhC11SYiIiEiulGEQERGxogSDLQUMIiIi1hQx2FCXhIiISAE0cOBARo0aZX596NAhnnrqKfz9/enatSsHDhyw2H/Dhg20bt0af39/Bg8ezMWLF+3aHgUMIiIiVgx2/O92fPvtt2zZssX8+tq1awwcOJCgoCDWrFlDYGAgwcHBXLt2DYB9+/YxduxYQkJCWLFiBZcvX2b06NF2eS/+poBBRETEisFgv+1WJSYmMmPGDOrWrWsu++6773B2dmbkyJFUrlyZsWPHUqRIEb7//nsAli1bRocOHejcuTM1atRgxowZbNmyhaioKHu9JQoYRERECpK3336bTp06UaVKFXNZREQEDRo0wPBXBGIwGKhfvz7h4eHm+qCgIPP+pUqVws/Pj4iICLu1SwGDiIiIFYMdN5PJRHJyssVmMplyvO62bdvYvXs3gwYNsiiPj4/Hx8fHoszT05O4uDgAzp8/f9N6e1DAICIiYs2OEcP8+fNp0KCBxTZ//nybS6alpTF+/HjGjRuHi4uLRV1KSgpGo9GizGg0mgOP1NTUm9bbgx6rFBERsWLPqaGDg4Pp16+fRZn1hztAaGgoderUoXnz5jZ1zs7ONh/+JpPJHFjcqN7V1fW/Nt9MAYOIiMgdZDQacwwQrH377bckJCQQGBgIYA4ANm7cSMeOHUlISLDYPyEhwdwN4evrm2O9t7e3PW4BUMAgIiJiIz/Wkli6dCkZGRnm17NmzQJg+PDh7Nq1i48//pjs7GwMBgPZ2dns2bOHF198EQB/f3/CwsLo0qULALGxscTGxuLv72+39hWYgKF3UNn8boJIgaOfC5H84ZIPn46lS5e2eF2kSBEAypcvj6enJ7Nnz2bKlCk888wzLF++nJSUFDp06ABAjx496NWrFwEBAdStW5cpU6bQokULypa13+8QDXoUEREp4Nzc3Jg/f745ixAREcGCBQsoXLgwAIGBgUyaNIm5c+fSo0cP3N3dmTZtml3bYMjOzs626xlFRETkvqMMg4iIiORKAYOIiIjkSgGDiIiI5EoBg4iIiORKAYOIiIjkSgGDiIiI5EoBg4iIiORKAYOYpaWlMWbMGIKCgmjWrBmfffZZfjdJpMAwmUx07NiRHTt25HdTRPJFgZkaWvLfjBkzOHDgAIsXLyYmJobXX38dPz8/2rdvn99NE8lXaWlpvPbaaxw9ejS/myKSbxQwCADXrl1j5cqVfPzxx9SuXZvatWtz9OhRPv/8cwUM8j/t2LFjvPbaa2hSXPlfpy4JASAyMpKMjAzzsqoADRo0ICIigqysrHxsmUj+2rlzJ40bN2bFihX53RSRfKUMgwAQHx9PiRIlLNZs9/LyIi0tjcTERDw8PPKxdSL5p2fPnvndBJECQRkGASAlJcUiWADMr00mU340SUREChAFDAKAs7OzTWDw92sXF5f8aJKIiBQgChgEAF9fXy5dukRGRoa5LD4+HhcXF4oVK5aPLRMRkYJAAYMAULNmTZycnAgPDzeXhYWFUbduXRwc9G0iIvK/Tp8EAoCrqyudO3dmwoQJ7Nu3j02bNvHZZ5/Ru3fv/G6aiIgUAHpKQsxGjx7NhAkT6NOnD25ubgwZMoS2bdvmd7NERKQAMGRrNhIRERHJhbokREREJFcKGERERCRXChhEREQkVwoYREREJFcKGERERCRXChhEREQkVwoYREREJFcKGERERCRXChhEREQkVwoYREREJFcKGERERCRXChhEREQkV/8P1oPCiLhgiVIAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:44:17.518438Z",
     "start_time": "2024-08-24T05:44:17.028714Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier_lr = LogisticRegression(max_iter=1000)\n",
    "print(\"Logistic regression model for imbalanced dataset\")\n",
    "classifier=model(classifier_lr,x_train,y_train)\n",
    "model_evaluation(classifier,x_test,y_test)"
   ],
   "id": "4475f16f34c553bd",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model for imbalanced dataset\n",
      "Accuracy :  80.79%\n",
      "ROC_AUC Score :  57.05%\n",
      "#########  Confusion Matrix  ########\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.98      0.89      2609\n",
      "           1       0.67      0.16      0.26       691\n",
      "\n",
      "    accuracy                           0.81      3300\n",
      "   macro avg       0.74      0.57      0.58      3300\n",
      "weighted avg       0.78      0.81      0.76      3300\n",
      "\n",
      "    ytest  ypred\n",
      "0       0      0\n",
      "1       0      0\n",
      "2       0      0\n",
      "3       0      0\n",
      "4       0      0\n",
      "5       0      0\n",
      "6       0      0\n",
      "7       0      0\n",
      "8       0      1\n",
      "9       0      0\n",
      "10      0      0\n",
      "11      1      0\n",
      "12      0      0\n",
      "13      0      0\n",
      "14      0      0\n",
      "15      0      0\n",
      "16      0      0\n",
      "17      0      0\n",
      "18      0      0\n",
      "19      0      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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y5GHnju18uW4NVapWY+jwUaxZ9xUXL1zg7FldyvxfYTab6dOnD5GRkaxcuZLp06ezbds2ZsyYgdlspmfPnnh5ebFu3Tpef/11evXqxZUrVwC4cuUKPXv2pGnTpqxdu5bMmTPTo0cPy8/9jz/+SGBgIKNHj2bZsmUcOXKEyZMnP9f8FDCkQgdPXKREgWw0rVmKl3JmYc6w1sTFxVv6Z6/YysiejahfpRj5cnozb0RrypfMy5nz1xJ0/NvhDxg66ytyZ/sz3br511NcunqbxWPbU/QlPyqWysecYS15GBVDfLyZxet3UbZ4bvp2qMlLObPQr1NtKr38Ek9Ihoj8bc5OTri6urL5p02Ehl7ml10/WxYq2i56zJgxI1s2/8Tkj8YTcukSBw/s58zp0xQqVIS8+fJRsVJlBg34kOPHjnLq5AmGDRlE5MOHuLu7/6255cmbj0qVqzBk0ACOHzvKsaNHGTZ4AKUDypA/fwHi4+OZOmUSWzb/RGjoZb7asB4XV1dy58r9Tz8W+TtMibglUHBwMIcPH2bChAnkz5+fgIAA+vTpw8aNG9mzZw8hISGMHj2afPny0a1bN0qWLMm6dY+zYGvWrKFYsWJ06tSJ/PnzM2HCBEJDQ9m3bx8Ay5cvp3379lSvXp0SJUowatQo1q1bR2RkZILnp4AhFdq29wyzVmwlcFgrti39gBPnrrD/+J/rB6Yv38LSL3cTOLQVe1cNJKdvZl7rMYe79xP+g7Nsw6/8ejjI8jo+3kyz9xaQJo2Jncs/5LMpb/PDrpP0nbQWgEtX79C63yLaN67AgTWDKO+fl2+2HSHmUWzinbj856V1cmL8xMls3vQjTV9rwJRJH9Gl2zt4e3tz+tQpw9iZgXM5c+Y0zZq+Rr8P3qPJG81o2qw5AOM+mkS2bNnp2rkDXTt3JHfuPEycMu0fzW/shIlkz56Drp078E63zuR7KT8zZs0BoFr1V+nRqw9TJk6gccN6/Pj9d8ycPRf3jBn/0XvK35McV0l4e3vzySef4PWXtS/wOHN25MgRihQpQrp0f677Kl26NIcPHwbgyJEjBAQEWPpcXV0pWrQohw8fJi4ujmPHjln1lyxZkkePHnH69OmEfybmJxW8k4FrqV7JPQVJQkXy+ZLW0YEjZy5b2tbP6s7BE5cYt+C7ZJxZynZnf2ByT0EkRXJJ4hV42d75MtGOFTqvyd/aLz4+ntatW5MpUyb8/Py4ffs206dPt/R/9tlnrFy5km+//ZZGjRrRunVrWrVqZel/7733yJQpE3369KF8+fJ899135MuXz9JfoUIFhg0bRr169RI0H2UY5IXIm8OLb+f35tVyhcjpm4kOTV6hetmCfLX1cHJPTUTEIDEzDDExMURERFhtCVkLM3nyZE6ePMn7779PZGQkTk7WC2ednJwsx3lW/x83HHvW/gmhqyTkhdi4/Riz8m1l/ojWeGfOwO8Xr9N24GKOn72S3FMTETFIzKskFixYQGCgdbawV69e9O7d+6n7TJ48mWXLljF9+nQKFCiAs7Mzd+/etRoTExODi4sL8PhhZ7Zf/jExMbi7u+Ps7Gx5bdv/PFdJKGCQF2bSoh+ZtOjH5J6GiIh9iXhVZbdu3ejYsaNVm+1v+381ZswYPv/8cyZPnkydOo/vluvj48O5/9+F9A9hYWFkyZLF0h8WFmboL1y4MB4eHjg7OxMWFmYpScTGxnL37l28vb0TfB4qSYiIiCQhJycn3NzcrLanBQyBgYGsWrWKadOm0aBBA0u7v78/J06csHqeycGDB/H397f0Hzx40NIXGRnJyZMn8ff3J02aNBQvXtyq//Dhwzg6OlKoUMLvuKsMQyrm552RKf2bUbVMAaKiH7F2028Mn/010TGxTOn3Bj1bV7ca//5HXzB/9U4Aru6chEcG67swelX4gAeR1imt9bO6E3Yngq4jViTtyYi8IFs2/8QH71ovwq5Zqw5TZ8zi3V7vsH2b9ZMnZ82ZT9Vq1n+X5N8vOW7cFBQUxNy5c+natSulS5fm5s2blr6yZcvi6+vLoEGD6NGjB9u2bePo0aNMmDABgDfeeINFixaxcOFCqlevzpw5c8iePTvlyj1+6GDr1q0ZPnw4BQoUIEuWLIwcOZIWLVqoJCGPfTblbe7ce0jNTtPJnDE980e+RVxcPINnbKBQXl+GzfqKT7/eYxl/78HjyNXPOyMeGdJRuOEIIqP+DBBsg4XmdUpTr3Ixq2OI/NsFB52jarXqDB/557MqnP5fAw4OCmL8xMmUK/eKpU+XPaZOyREwbNmyhbi4OObNm8e8efOs+s6cOcPcuXMZMmQITZs2JVeuXMyZMwc/Pz8AsmfPzuzZsxk/fjxz5syhVKlSzJkzx3IeDRo0IDQ0lOHDhxMTE0Pt2rXp16/fc81PAUMqVSC3D+VK5CFXjUHcuH0fePzApwnvN3kcMOTxYfqyzVy/dd+wb8G8Wbl6M5wLobeeevxM7ukY/15jDhy/kFSnIJIsgoODeCl/AbxsarsxMTGEhl6maLHihj6RxNC1a1e6du361P5cuXKxYsXTs7lVq1alatWqf/v49ihgSKWuh92jUY85lmDhD+5urmRI70I2n0ycvXjjifsWzpv1qX1/mPB+Ez77dh++3vrtSlKX4KAgypevYGi/cD4Yk8lE9uw5nrCXpDZ6+JTR3170eOfOHa5fv869e/cScz6SSMIjItn86593tjOZTHR/swrb9p2hUB4f4uPjGfB2Hc79MIa9qwfyVqNylrEF82QlnUtafvz4XYI3jePL2e/wUs4/n2RZtUwBKr38EhM+/uGFnpNIUjObzVy4cJ7dv+yiUf06NKhbkxnTpvAoJobg4GDc3NwYMrA/NapWovWbzdj1847knrIkkeS402NK91wZhk2bNrFixQqOHj1KdHS0pd3FxYVixYrRvn17atasmeiTlH9u/HuNKVkoB5XaTKZUkZyYzfD7hevMW7WDyqXzM2doS+5HRPL1tqMUzO1DJvf0DA9cxf2IKPp2rMX3C3pT6o2xPIqNI3BoS9776AurJ2SKpAZXr14h6v83wJk8dQahoZeZOGEs0dFRZMzoQVRUFBUqVqLT213ZuuUn+vR8h08/W03RYsWTe+oiSS7BAcOSJUsIDAzk7bffplevXnh6elruEhUWFsaBAwcYOHAg7777Lm3btk3KOctzGtvndXq1rkbbgUs4GXSVk0FX+W7HMe7ce/w46+Nnr5A/Vxa6NK/M19uO8lrPuaR1TGNZ5Nhh8FLO/jCGBlWKUzS/H7+dvGSVvRBJLfz8srHzl724Z8yIyWSiUOHCmOPjGTywH7/uP0Trt9paFjkWLFSIkydOsG7NFwoYUqPUkxhINAkOGBYvXszEiROfmEHIly8f5cqVo2DBgowZM0YBQwoybUBzujSrRKehy9mw5bCl/Y9g4Q+ng69RtUwBAGIexRLzl+RBdEwsF0Nv4ZclI83rvIyPpzs3f5kKgHPaxz9CTWqWwrti36Q9GZEXIKOHh9XrPHnzER0dTXh4OJkzZ7bqy5s3L0FB1jfTkdQhNZUSEkuC1zBERUWRPXv2Z47x8fHh/n3jqntJHoO71uPtNyrRbtAS1vz45w07hr3TgG/nW19nXqJgdn6/cB2AE1+PoM1f1jSkc3EiX84snLlwnTpdZhLQYjzlWk6gXMsJfLvzGN/uPEa5lhNezEmJJKFfdv1MlQrlrB75e+b0KTw8PJg+ZRLDhw6yGn/6zGly58n7oqcpkiwSHDDUqlWLgQMHcuDAAWJjrR9JHB8fz2+//cbgwYMtt7GU5FUwjw+DutRlytJN7D4UhI9nBsv23Y5jVH45P++1rUGe7F50aV6JtxqWZcbyLQD8sOsEw95pQOXS+SmcNyuLx7Yj9Ppdfth1gktX7xAcEmbZ7j+I4v6DKIJDwuzMSCTlK1mqFM4uzowaPpQL54PZ9fMOpk2dRIdOb1O1+qt8+803fPPVBi5dvMj8uYEc/u0grd5qk9zTliSgRY9GCS5JjBw5kokTJ9K5c2fi4uLw8PCwrGG4e/cujo6OvP766wwaNMj+wSTJNapWAkdHBwZ1qcegLtaPLnUt1YvW/T9h2DsNGN6jARev3KbD4KXsPXoegMEzNvAoNo5lEzrg7ubC9n2/06T3XOLjU8ST0EWSTPr0bsxbuIjJH42nVYs3SJ8+Pc1atKRDp7cxmUwMGTaChQvmce3qFfK9lJ+5Cz4hW7ZnZ17l3ykVfc8nGpPZbH6ub4HIyEhOnz7NzZs3iYyMxNnZGR8fHwoXLmx5atbf4Vqql/1BIv8xd/YH2h8k8h/kksR3EcrfL/EuGz87uW6iHSs5PfdH7urqSqlSpZJiLiIiIpJC6U6PIiIiNlSSMFLAICIiYiM1LVZMLH/71tAiIiLy36EMg4iIiA0lGIyUYfiXatOoHJGHAg3bg4Oz+PHjd5/YN3/EW088lncmN1ZO6sS1nZM5/9N4xvZ5HQeHP3808mT3YuO8XtzYNYX9XwymbqWilr50Lk6smdGN6z9PZtWUt3FxTmvpq1upKF9M65J0H4LIc4iJiaHp6w3Zv2/vU8ec/f0M7du0ouzLJXijcSP27d0DQGjoZfyLFnzidvDAfgBmzZhGpfIBtGzelAsXzluOeevWLRrWrUVUVFTSnqAkqjRpTIm2pRbKMPxLrd30Gz/tPml5ndbRge8X9uH7nceZ8PEPOKV1sPSVKZ6bFRM7sXDNz0881pLxHTCbzVTrMBXPjOlZMq494RGRTF68CWcnR76d14sTQVep2m4KJQvn4NOJnajXdRYHTlykY5MKeGdyo1KbySwa045OTSsw9/PHT/Ab3LUevcetStoPQiQBoqOjGdi/L0Hnzj51zP379+n2dieqVX+VMeM+YuM3X/HBu7346tsfyZrVly3bd1mNnzLpIy5dukgJ/5KcOX2a1Z+v5JOln/Ll2jXMnD6V6TMfXxK7fMkiWrZ+6x9ddi6SEihg+JeKin5k9bTIDzvVxoSJobO+JubRn3fiTJPGxKherzFt2WZ+O3nJcByntI7cuHWPsQu+s9yt8csth6lQKh8A9asUw9MjPZ2HLuNeRBSngq9R3j8vvdtUp/2gpRTI48OO/b9z9uINtu//nQK5fQCoXbEI18LCOXLmclJ+DCJ2BZ07x6D+fbF3y5mvv/qSdOnSMWT4SBwcHOjRqw+7du7g5InjVK5SFS9vb8vYw4d+Y/NPP7Jm/VekTZuW8+eDyfdSfgoXLsLt6q8yZeLjW6XfuXObrVs2s+bLr5P0HCXxqSRhpJJEKpDJPR19O9Rk2OyvrIIFgLavlSeTezqmLvnpifvGPIql09DllmChcN6sNKhSnJ8PPP5NLE82L36/cJ17EX+mU4+fDaVciTwAhFy7Q/EC2XB0TEOJgtm4fO0O8Di7MG7B94l+riLP6+CBfZQpW47ln61+5rgD+/dR7dUaODj8mZ377It1VK5S1TB25vSpNG3Wgjx5HwfWvr6+XA4J4f79+5w6eYKsvr4ALFuymBYtWyu78C+kW0MbKcOQCnRpXpmrN8P5cvNhQ1/fDrWY89k2y6Oqn2XTJ+9SuXR+Dp68xPzVOwG4fvs+Wb0yWo3L7pMJTw83AJas/4WW9QK4u2cGx89d4ZO1u6j5SmFu3L6v7IKkCC1atk7QuNCQEIoVK8HoEcPYvm0rftmy0bffAEq9XNpq3KHfDnL0yGE+mjzN0uZfshQBZctSpUJZMmbMSOC8hdy9e4ctP21SdkFSDWUYUoGOTV5h3qodhvYqAfnJlsWDxet3J+g4fSetpfbbM3FO68iyjzoCsGnXCdzdXBjavT5pHR14uUhO2jd+xbJG4tbdBwQ0H0/e2kMo3/Ij7t6PZHDXuoxf8B0NqxXn6Ibh7P5sAP4Fdb99SdkePnzIkkUL8fL2Zs6CjykdUIbuXTtz7epVq3Hr1nzBqzVr4ePjY9U+acp0tu78hc3bd1GseAmWL11Ci5atuX79Gm+1bE7DurX45usNL/CM5J8wmRJvSy0UMPzLlS6Sk2xZMrHmh4OGviY1S7Fp90nu3HuYoGMd+z2Unw+epdvIFTSsWpycvpm5eSeCdoOW0KV5Ze7smc7yCR2Zt2qHVYkC4Mbtx481f7VcIW6FP+T0+evMH9GGtgMWM/GTH/hkTLt/frIiScjB0YGChQrTo1cfChcuwvt9+5ErV242fvOVZUxsbCzbt22hQcPXnniMTJky4+joSPjdu/y06Ueav9mSiePHUq9+AxYt/ZRJE8Zz48b1F3VK8g+oJGGkgOFfrlbFIuz67Rx370ca+mpXKMzX2448c/8M6V1oVvtlqx/qU8GPf6PyyvS47PDjrpPkqjGIl+oOpXjj0dwJf8jFq7efeLzBXesybv63FMrjQ2xcHEfOXObHX05SLL8f7m6q40rK5eXlTZ68ea3acuXOzbVrf2YYjh45zKPYWF6pUPGZx/p02RKat2iJi4sLhw/9RoWKlfHJmpWcuXJx4vixJJm/JC4FDEYKGP7lyhTLza9Hgg3tnh7pyZvDm18PG/v+Kp1LWj6d2ImyxXNb2l4unJPY2DjOXrxBwTw+fDe/NyaTiWth9zCbzdStXJSd+383HKta2QLcuR/J4dOXiY83W64/dvz/PR1MpJ6/OJL6lPAvye9nzli1XQgOxs8vm+X1saNHKFKkKM7Ozk89zr3wcH788QdatGwFQJo0aYg3xwMQFxfH8z0fWCTlUMDwL1f0JV9OB18ztBfJ50dkVAwXQm8Z+tK7OlmyB9dv3WfDlsNMG9Ac/4LZqVgqH3OHt2beqh3cfxDFxSu3KZQ3K8PeqU8uP08GdqlLhZL5nrhmYnDXeoxf8B0A5y7dJK2jA41rlKTzGxU5HXyN8AhjFkQkOYXdvGm5oVLzN1vy+5kzzJszm0sXLzJn9kwuXw6hQaPXLePPnT1L3nz5nnnMT5cvpVmLNy1XRhQtVpyvvlzPwQP7OR8cRJEiRZ+5v6QMWsNgpIDhXy5L5gxPXKPg45nhiWUKgPfa1WTXin6W191GruDY76FsnNeLVVO78MOuEwyZ+bhuGxX9iJZ9P6Z2xaIcXDuY+pWL8XqvuYT8//LJP1QJyM+9iCgOnQoB4GFUDL3HrmL6wBZ0fqMS3UauSKxTFkk0NapV4sfvHwe5fn7ZmLfwE3Zs38YbjRuyY/s2AucttFrceOtWGO7uGZ92OO7du8cP339HizdbWdo+HDCIX3b9TN/3evPhgEGWSy4lZVNJwshktnc3kxfEtVSv5J6CSIpzZ39gck9BJEVySeKbApQatTXRjnVoxKuJdqzkpPswiIiI2EhFiYFEo4BBRETERmoqJSQWrWEQERERu5RhEBERsaEEg5ECBhERERsqSRipJCEiIiJ2KcMgIiJiQwkGIwUMIiIiNlSSMFLAICIiYkPxgpHWMIiIiIhdyjCIiIjYUEnCSAGDiIiIDcULRipJiIiIiF3KMIiIiNhQScJIAYOIiIgNxQtGKkmIiIiIXcowiIiI2FBJwkgBg4iIiA0FDEYqSYiIiIhdyjCIiIjYUILBSAGDiIiIDZUkjBQwiIiI2FC8YKQ1DCIiImKXMgwiIiI2VJIwUsAgIiJiQ/GCkUoSIiIiYpcyDCIiIjbSKMVgoIBBRETEhuIFI5UkRERExC5lGERERGzoKgkjBQwiIiI20iheMFDAICIiYkMZBiOtYRARERG7lGEQERGxoQSDkQIGERERGyYUMdhSSUJERETsUsAgIiJiI40p8ba/IyYmhoYNG7J3715L29ixYylYsKDVtmLFCkv/xo0bqVmzJv7+/vTs2ZPbt29b+sxmM1OmTKF8+fKULVuWSZMmER8f/1xzUklCRETERnJeJREdHU3fvn05e/asVXtQUBB9+/alSZMmljY3NzcAjh49ypAhQxg1ahSFChVi3LhxDBo0iAULFgCwZMkSNm7cSGBgILGxsfTr1w9PT086d+6c4HkpwyAiIpJCnDt3jhYtWnDp0iVDX1BQEEWKFMHb29uyubq6ArBixQrq1atH48aNKVSoEJMmTWLHjh2EhIQAsHz5cvr06UNAQADly5fnww8/ZOXKlc81NwUMIiIiNkymxNuex759+yhXrhyrV6+2ao+IiOD69evkzp37ifsdOXKEgIAAy2tfX1/8/Pw4cuQI169f5+rVq5QpU8bSX7p0aUJDQ7lx40aC56aShIiIiI3kelpl69atn9geFBSEyWRi/vz57Ny5Ew8PDzp27GgpT9y4cYMsWbJY7ePp6cm1a9e4efMmgFW/l5cXANeuXTPs9zQKGERERJJQTEwMMTExVm1OTk44OTkl+BjBwcGYTCby5s1LmzZt2L9/P8OGDcPNzY1atWoRFRVlOJ6TkxMxMTFERUVZXv+174+5JZQCBhERERuJmWBYsGABgYGBVm29evWid+/eCT5G48aNqV69Oh4eHgAUKlSICxcu8Pnnn1OrVi2cnZ0NX/4xMTG4urpaBQfOzs6WPwOWNRAJoYBBRETERmJeJdGtWzc6duxo1fY82YU/5vNHsPCHvHnzsmfPHgB8fHwICwuz6g8LC8Pb2xsfHx8Abt68Sfbs2S1/BvD29k7wHLToUURExEZiLnp0cnLCzc3NanvegGHmzJl06NDBqu306dPkzZsXAH9/fw4ePGjpu3r1KlevXsXf3x8fHx/8/Pys+g8ePIifn1+C1y+AMgwiIiIpXvXq1Vm4cCGLFi2iVq1a7Nq1iw0bNrB8+XIAWrVqRdu2bSlZsiTFixdn3LhxVKtWjRw5clj6p0yZQtasWQGYOnUqnTp1eq45KGAQERGxkVxXSTxNiRIlmDlzJrNmzWLmzJlky5aNqVOnUqpUKQBKlSrF6NGjmTVrFuHh4VSsWJExY8ZY9u/cuTO3bt2iV69eODg40KxZM0PGwh6T2Ww2J+ZJ/V2upXol9xREUpw7+wPtDxL5D3JJ4l93Wy47lGjHWtW+VKIdKzlpDYOIiIjYpZKEiIiIjeR8lkRKpYBBRETExt99ymRqppKEiIiI2KUMg4iIiA2VJIwUMIiIiNhQvGCkkoSIiIjYpQyDiIiIDZUkjBQwiIiI2NBVEkYKGERERGwow2CkNQwiIiJilzIMIiIiNpRfMFLAICIiYiOlPa0yJVBJQkREROxShkFERMSGEgxGChhERERs6CoJI5UkRERExC5lGERERGwowWCkgEFERMSGrpIwUklCRERE7FKGQURExIYSDEYKGERERGzoKgmjFBMw7N4wIbmnIJLixMaZk3sKIimTY9J+oateb6TPREREROxKMRkGERGRlEIlCSMFDCIiIjbSKF4wUElCRERE7FKGQURExIYyDEYKGERERGxoDYORShIiIiJilzIMIiIiNlSSMFLAICIiYkMVCSOVJERERMQuZRhERERs6PHWRgoYREREbCj9bqSAQURExIYSDEYKokRERMQuZRhERERsaA2DkQIGERERG4oXjFSSEBEREbuUYRAREbGhOz0aKWAQERGxoTUMRipJiIiIiF3KMIiIiNhQgsFIAYOIiIgNrWEwUklCRERE7FKGQURExIYJpRhsKWAQERGxoZKEkQIGERERGwoYjLSGQUREROxShkFERMSGSddVGihgEBERsaGShJFKEiIiImKXMgwiIiI2VJEwUsAgIiJiQw+fMlJJQkREROxShkFERMSGFj0aKWAQERGxoYqEkUoSIiIiYpcyDCIiIjbS6OFTBgoYREREbKgkYaSAQURExIYWPRppDYOIiEgKExMTQ8OGDdm7d6+lLSQkhA4dOlCyZEnq16/Prl27rPbZvXs3DRs2xN/fn3bt2hESEmLVv3TpUipXrkypUqUYPHgwkZGRzzUnBQwiIiI20phMibY9r+joaD744APOnj1raTObzfTs2RMvLy/WrVvH66+/Tq9evbhy5QoAV65coWfPnjRt2pS1a9eSOXNmevTogdlsBuDHH38kMDCQ0aNHs2zZMo4cOcLkyZOf7zN57jMRERFJ5UymxNuex7lz52jRogWXLl2yat+zZw8hISGMHj2afPny0a1bN0qWLMm6desAWLNmDcWKFaNTp07kz5+fCRMmEBoayr59+wBYvnw57du3p3r16pQoUYJRo0axbt2658oyKGAQERFJIfbt20e5cuVYvXq1VfuRI0coUqQI6dKls7SVLl2aw4cPW/oDAgIsfa6urhQtWpTDhw8TFxfHsWPHrPpLlizJo0ePOH36dILnpkWPIiIiNpLrWRKtW7d+YvvNmzfJkiWLVZunpyfXrl2z23/v3j2io6Ot+h0dHfHw8LDsnxAKGERERGwkZrwQExNDTEyMVZuTkxNOTk4JPkZkZKRhvJOTk+W4z+qPioqyvH7a/gmhkoSIiEgSWrBgAaVLl7baFixY8FzHcHZ2Nny5x8TE4OLi8sx+V1dXnJ2dLa+f1J9QyjCIiIjYSMzfprt160bHjh2t2p4nuwDg4+PDuXPnrNrCwsIsZQYfHx/CwsIM/YULF8bDwwNnZ2fCwsLIly8fALGxsdy9exdvb+8Ez0EZBhERERsmkynRNicnJ9zc3Ky25w0Y/P39OXHihKW8AHDw4EH8/f0t/QcPHrT0RUZGcvLkSfz9/UmTJg3Fixe36j98+DCOjo4UKlQowXNQwCAiIpLClS1bFl9fXwYNGsTZs2dZuHAhR48epVmzZgC88cYb/PbbbyxcuJCzZ88yaNAgsmfPTrly5YDHiykXLVrE5s2bOXr0KCNHjqRFixbPVZJQwCAiImLDlIhbYnBwcGDu3LncvHmTpk2b8vXXXzNnzhz8/PwAyJ49O7Nnz2bdunU0a9aMu3fvMmfOHEz/X73ZoEEDunXrxvDhw+nUqRMlSpSgX79+zzUHk/mP20Als0MX7yf3FERSnPxZ3ZJ7CiIpkptz0l72uOLg5UQ7VpvS2RPtWMlJix5FRERs6NlTRipJiIiIiF3KMIiIiNhIphs9pmgKGERERGyYFDEYqCQhIiIidinDICIiYkO/TRspYBAREbGhkoSRgigRERGxSxkGERERG8ovGClgEBERsaGShJFKEiIiImKXMgwiIiI29Nu0kQIGERERGypJGClgEBERsaFwwUhZFxEREbFLGQYREREbqkgYKWAQERGxkUZFCQOVJERERMQuZRhSqF5tGxF2/aqhvWBRf0ZNX2R3/5a1Axg2eT5F/QMSbU4njhxgTL/uvP3uYGo2aGrVN3fySAB69BuZaO8n8iwjhg5k49cbntq/YNEyAsqUS9I5NKz7KlevXLG8dnB0JHv2HDRr3pLWbdsn6XtL0lJJwkgBQwrW7p2+VKhay6rN0TFtMs3mT6sWz6FspVdxz+iR3FOR/7APBwyh93t9Adj0w/esWLaY5Z+vsfRnzJjxhcyjb//B1K5bD4DY2Fj2793DmJFDcc+YkYavNX4hc5DEZ1JJwkAliRQsXXo3PDJ7WW1u7i/mH8FncU2Xns8+mZnc05D/uAwZMuDl5Y2XlzduGdxI45DG8trLy5u0aZ1eyDzcMrhZ3jNrVl8avd6EsuVeYeuWn17I+4u8KAoY/qUePohg/tRRdG1ei7fql+eDTm+w/5ftTxx7/NB+BnRvTdsGFejT7nU2b1xn6XsQcZ/AicPo2Lgq77Ssy5I5k4iJjnrme7fv0ZcdmzZy5sThp445dewQg3u2pW3DivTr+iZ7f95i1f/tupW806oeHRtXZemcyYzu143tm75J8PmL2HMl9DKlSxTi4wVzqVaxLBPHj2bB3Nl07dTWalzDuq/y9VfrATCbzXy8YC51alSmasUyvNerO1evXnnS4Z/JwdGBtGkfZwPj4+NZvmQRr9WrSYUy/nTt3I6zv5+xjN30w3c0bVSXVwJK0KxxA7Zt3fwPzloSi8mUeFtqoYDhX2rZvKlcCbnI4I8CmfLxFxQqXoqF08cQ++iR1bj4uDhmjB1I+So1mLpoLS3ad2dR4EQuXwwGYMG00UQ+iGDU9EX0HTmFoDMnWRw46ZnvHfBKVV4uX5lFsz4iLi7W0H/3dhiThr1HldoNmbxgFa+1aM+8KaM4dewQALu2fM/aTxfS/p2+jJ6xmJvXr3Dq6G+J9MmIWDty6Dc+XbWWVm+1szt29ecr+P7bbxj30RSWrlhNZk8venbrzCObv1dP8+jRI7Zu3sSe3bupWr0GAB/Pn8OnyxbTt/9gVq5ej6+vH717dCHy4UNu37rFsMED6PB2V9Z//T2vNW7KkAF9CQ+/+09OWRJBGkyJtqUWWsOQgi2aOYElNl/eC1ZvwsXVlcIlXqbhG2+RI89LADRs1oat32/g7p1beGXJahn/8EEEEffDyZjJkyxZ/ciS1Y9Mnt54ZPbi2pXL7N+9g0XrtpIuvRsAXd8fyoB3WtOu+weWtifp0KMfH77dnO/Wf06j5ta/sf349RqKv1yWuq+/CUDWbDm4EHSG77/8jMLFS/HjN2uo16QV5avUBOCdfqPo2br+P//ARJ6gdZv25MiRM0Fjly9ZxMAhwy2LJYcMH0WdVyvz6y8/U6Xaq0/cZ8KYkUwaPxaA6OgoXFxceKtte+o3aITZbGb15yvo9e4HVK3+eP+hI8bweoPafPft1xQtVoLY2Ef4+GTF1y8bbdt3In+Bgjg5OSfCmYskLgUMKVjz9t0oW9H6HylnFxcAqtRswIHd29ny3ZeEhlzg/NnTwOP051+5uWekVsNmLJw+lvUrP+Hl8pWpXud13DK4c+bEEczx8bzTqp7VPub4eK6FhpC3QOGnzs3bx5emb73NuhUfU6Fabau+0EvnObjnZ9q/VtnSFhcXi2+2x/9oXwo+S+M3O/w5xwzu+ObIlcBPReT5+GbLlqBxDx8+4Pr1awzs/wFpTH8mX6Ojo7h48cJT9+veow+v1ny8ONnJyRkvb28cHBwAuH37FuHh4RQr7m8ZnzZtWooULcb54GCaNnuTSlWq0qNrJ3LlzkO16jVo3LQZrq6uf+NMJTGlplJCYlHAkIK5e2Qma7YcT+ybO2kEv588SuWa9anVsBmZPL0Y9m7HJ47t3GcgtV9rzoHd29m/ewdbvl3Ph6OmER8XR7r0bowP/NSwT2Yvb7vza9CsDTs3f8uyeVNwcU1vaY+Pi6NyjXo0btnJaryD4+MfNwcHB8yYrQ9mtnktkkicnf5c/PikBwrFxcZZ/X/ilBnkzp3Haoz7M664yOSZmRw5nxzwPi1TEB8XR3x8HCaTiZmBCzh+7Cg7t29l65afWLP6Mz5ZupKChZ4esEvSU8BgpDUM/0IPH0Twy7YfeHfIeJq360bZStWJuB/+uNPmi/fu7TAWz55IVr8cNGndmfGByylWqiwH9+zEL0cuHj6IAJOJrNlykDVbDmJioln58cwE1WwdHR3p1HsA+3Zts1qD4JsjF1dDQyzHzJotBwd+3cGurd8DkD1XXktG5I/zuXblciJ8MiLP5pg2LQ8ePLC8fvjwAbdv3wYgg7s7mTN7cissjBw5c5EjZy6y+voyc/oULl44/7feL0OGDHh6enHs6GFL26NHjzh18gS5cufh/Plgpk+ZSLHiJejR+z3WfLkRn6y+/Lp71z86T/nnTIn4X2qhgOFfyMnJGWcXV/bu2sqNa1c4cuBXlgROBjB80btlyMi+X7ayfP40rl25zKmjv3Eh6Hdy5ytItpx58A+oQOBHQwk6c4LzZ08zb/JIoiIfkt4tQ4LmUtQ/gEo16nHz+p8ryWs3ak7w76dYvWQuV0MvsWvrD6xaMgfvLL4A1H39Tb7/8nP27dpK6KXzLJg2hqjIh6nqL5akTEWLFefs72f4adMPXLxwnrGjhuPg8Oc/g2+168Dc2TPYuX0rly5eYMyIYRw59Bu58+T92+/5VtsOzJ8zm53bt3I+OIixo4YRHRNN7Tr1yZAhA2u/WMUnC+cRevkyP+/czpUrocouSIqkksS/kGPatPQaMJpPF87ghw2ryJI1G01ad+KLpfO4EHSabDlzW43tN2oaS+dNZUC3lrikS0/1uq/xar3GAPQcMJqlcyYxdkAP0jg44B/wCh179nuu+bTp+h6H9v75G5G3jy/9R0/js09m883aT8nsmYU2Xd+jUo3HayUqVK/DtSshfDJzAo8exfBq/SZ4+fjimFY/jpK0ypZ7hbfadmDcqOGkcUhDm7YdCLt5w9Lftn0nHj54wNjRI3gQEUGRokUJnP8J7v/g/idt2nfkwYMIxo4aTsSDCPz9S7Fw0XIyZc4MwOTps5g9YyqLP55Ppsye9OrzAa9UqPSPz1X+mTT6/cXAZDanjOLxoYv3k3sK8oKcPHqQLFmzWa7miIuLpUuzmvQdOSVRb2WdGuTP+vQrVUT+y9yck/YbfevpW4l2rFcLeSbasZKTfqWTF27/L9v5/eRR3n53EK6u6fl+w+e4pktP/sLFk3tqIiLyFAoY5IVr0b47iwMnMn5gT2KioylQpASDxs/WtecikmLoKgkjBQzywrmmS0/P/qOTexoiIk+lRdhGukpCRERE7FKG4T9k365tTBttfQVE2Uqvcv/e3Sc+y6FanUZ07zuC2NhYVi+dy67N3xEbF0uVWg1p3bkXDg768ZHUJSYmhjZvvkH/wUMtt4cGCLl0kTffeI3d+49Yjf96wzqWLv6EG9evkzffS3zQbyAlS738oqctSUBXSRjpX/z/kNBLwZQuX5ku7w2xtKV1csYcH09s7J/3bzh3+jgzxg2iVqPmAKxZNp+dP31L977D8cjkyYJpo/l0/nQ6POfllyIpWXR0NEMGfkhQ0Fmr9mvXrvJur+5ER0dbte/e9TMfjR/D0BFjKF68BN98vYE+PbuybsO3eGfxeZFTlySgkoSRShL/IaGXLpA990t4ZPaybOndMuDmntHy2j1jJlYtmUuj5u3IV6AIZrOZTd+soVWnnpQqW5E8+QvRuc9gfvp2HVGRD5P7lEQSRXDQOTq0eZPLIZes2rdt3UybN9/A6S+3l/7DN19/ScNGjanfoBE5cuaiR6938fL04uedO17UtEVeKAUM/yGXLwXjm/3ZT+3bvukbIu6H8/qb7QG4d/cOkQ8f8FKhYpYxufK+RFxsLEG/n0zS+Yq8KAcP7CegTDmWfLrKqn3Xzh2806sPHw4YbNinXYfOtGnXwdAeEaF7yqQGJlPibamFShL/EWazmashFzl64Fc2fL6E+Pg4ylepSYt23XFMm9Yy5usvllOvSStcXNMB4ObujoOjI7fDbpA91+Pb44bdvA7A/fC7yXIuIomt+Zutntg+bOQYAA7s32voK1ykqNXr3bt+5uLFC5QpWz7xJygvXCr6nk80Chj+I8JuXCM6OgrHtE68N3QCN65eYencKcRER9Ohx4cAnDxykNth16lRr4llPwcHR8pWrM6qJXPJljMPrunSs3LhDBwcHIiNjU2u0xFJUUJCLjFy2CDqNWhkCCTk3ylNakoNJBIFDP8R3j6+fLJ2C+kzuGMymcidryBmczyBE4fTrtv7pHFwYO/PWyhZpgJuNvfN79CjH7PGD6bnWw1wdnGlaevOnDt9Atd06Z/ybiL/HRcvnOedrp3IniMHQ0eMSe7piCSZ5woY9u/fn+CxZcqUee7JSNKyDQSy5czDo5hoIu7fw90jE4cP7KZZ266G/TJmysywyfOJuBf++KoKs5nPFweSxcf3RU1dJEUKOneWd7p0JFv27Mya+zEuLi7JPSVJJMovGD1XwDB69GjOnTsHPK53P43JZOLUqVP/bGaSqI4c+JXZE4YyZ+W3OP//H7ULQb+TwT0j7h6ZuBd+lxtXQylY1N+wb+DEYVSu0QD/gMe12T07N5PRIzPZcv39R/6K/NvdvHmDnt07kyNnLmbPW0g6ZdxSF0UMBs8VMKxbt44PPviAy5cvs3r1apydde//f4sCRUrg5OzMguljaNamC9evhrLy45k0atEOgJAL50jr5EyWrNkM+2Zw92D10rlk9vTi3r27LAmcxOstO5AmjS6ykf+uGVMnER8Xz/BRY3n48CEPHz6+zDhdunQKHiRVeq5/8Z2cnJg2bRoAM2bMSIr5SBJxTZeeQeNnc//uHQb3asfCaWOoUb8JjZo/DhjC79wmvZsbpics9Hmzwztky5mbER+8zZyJw6nftDX1m7Z+0acgkmKYzWa2bd3MrVthNH2tHnVerWzZPl26OLmnJ4nAlIj/pRYm87NqC08RFBTEvn37aNXqyZci/R2HLuraZRFb+bO6JfcURFIkN+ek/SLeFxyeaMcqmzej/UH/An/rKol8+fKRL1++xJ6LiIiIpFC6rFJERMRG6ikkJB4FDCIiIrYUMRhombuIiIjYpYAhFXgUE8OHXVpw4sgBAOZOHknL2gGGbUy/7k/cP+L+PcPYLs1qWPq/XbfS0P/pwhkA3L0dxogP3qZj46osnD7O6v4cP379BcvmTU26ExdJgJBLF+nZvTOVyr1M/drVWb5k0VPHfvftNzRpVIcKZfzp2LYlx48dtfSZzWaWL11Eo7o1qFqxDCOHDeLhwweW/q2bN1H71crUr1WNndu3Wh23XesWnD6lh7X9m+gqCSOVJP7lYmKimT1hKJcvBlvaOvT4kNade1le37x+ldEfdqNu45ZPPEbopWAyuGdk8sLVljaTKc1f+s9Tu1Fzmr7V2dLm7OIKwFerl+GeMRNjZi5h4tD3OLhnJwGvVCX20SO+/3IVI6YsTLRzFXle8fHxvNuzG0WKFeezL9Zz6dJFBg/oi3eWLNRr0Mhq7KGDBxgzYgjDRo6lRMlSrFn1GX16dGHjj1tJly4969euZuG8OQwdMZr8BQoydfIEhgz4kOmz5xEXF8fY0cP5cMAQzPHxjBw+mC07fsVkMrHr5x14eXtTqHCRZPoU5O/QoySMlGH4F7t8MZhhfTpy/eplq/Z06d3wyOxl2dYsX0C5KjUoU7HaE48TeukCWbPnstonY6bMf+k/T868+a36/3iOxJWQi/gHvEL2XHnJX7gYV0IuALD1h68oVbYimTy9kuTcRRLi1q0wChQqzKChI8iZKzeVKlelbLlXOHzoN8PYsFthvN21B/Ubvkb27Dno0r0n4eHhBAcFAbD6s5W0adeBuvUbku+l/Iwa+xE/79zOhfPB3L17h/C7d6lVuw616tQj/O5d7ty+DcDH8+fStXvPF3re8s+ZEnFLLRQw/IudPPobRfxLM2bGkqeOOXZoH6eOHaJlx6f/g3X5YjC+2XI+tT/00gV8sz+53yuLDxeCzhATE83li8F4eWf9f3bhM15r0T7hJyOSBLy9s/DR5OmkT++G2Wzm8KHf+O3gfkqXKWsYW6t2XTp3fVy2i4qK4rNPl5I5syd5/38J+eXQEIqV8Lc6dqZMmTl69DAeHplwcXHl9MmTnDp5HFfXdGT08GD3rp/x9PRUdkFSBZUk/sVqN2pmd8zXq5ZStXZDvLJkfeqY0EsXiIuLZUjvdtwOu0mhYiVp170vmTy9uHvnFhH3w9mxaSPzpozCycmZ6nVfp2GzNphMJho2b8uYfu+w5bsvKeofQNnKNdj+w1f4B1RQdkFSlIZ1a3Dt6hUqV6lGjZq1nzpu355f6dm9M2azmbETJltu8+yZ2ZMb169bxkU+fEj4vXDu3rmDg4MDfd7vS+eObUhjMtF/0FAcHBz4eMEcBgwenuTnJkkgNaUGEokChlTs+tXLHD9ygPY9PnzmuCshF3D38KBd9w8wm2HV4jlMGvYe42Yvs5QYMmbKTP/R07lw7jRL504hTZo0NHjjLXyz5STw06+5fy+cjJkyExsby3frP2PY5AVs3riOr79YTmavLPToP4osWf1ewFmLPNnkaTMJCwvjo7GjmDp5Av0HDn3iuHz587Ni1Tp+3rmdkcMGkS1bdor7l6RW3fosXbSQUi+Xxi9bdqZN+QiA2EePAHizVRsavtYYk8lEunTp+XX3LjJlykyuXLkZ8OF7HDt6hJq1avP+hwOfeAt2SVlS02LFxKKAIRXb9/NWcuctQHY7T5Wc8vEXmEzg5Pz4KZbvD5tI91Z1OXv6OEVKlObjtZvJ4O4BQM48L3Ev/C4/bVxHgzfeAiCNg4NlzcOOTd9QonR5zOZ4Pls0mykff8Gv2zexdM5k+o+ZnnQnK2JHkaLFAYiJjmbooH6837c/adM6GcZ5enrh6elFwUKFOXb0MGvXrKK4f0m6dH2H0MshNG/SEEdHR5o2f5OCBQuR3u3P23enT//nnz9eMJd+A4awetVK4uLiWP/193Tp2IatmzdRo1adpD9hkUSmNQyp2OEDvxJQoZrdcc4uLpZgAR5nEzJkyMidWzcBLMHCH7LlzM2dsBuG48TGxvLtupW81rID504fxzd7LjJ7ZaFk2YqcOXH4n5yKyN9y61YY27ZutmrLm+8lHj16RETEA6v2E8ePcerkCeuxeV/i7t07ALimS8fEKTPYtmsfm3f8Sv+BQ7l65Qq+fsYnvO7ds5uM7hkpXKQoRw79RrnyFXBxcaFM2fJPXHApKY/JlHhbaqGAIZUym80E/36SgkX9nznu4YMIOjetzonDByxtt8NucP/eXfxy5Gbr9xt4v1NTq/srXAj6Hb8cuQ3H2vnTRkq8XI7Mnt6YTGks+8TFxfE3nnEm8o9duXyZfu/3tlp7cOrkCTJlykymTJmsxn715VoCZ02zajt18gR58jxe9Dhz2mS++epLMmTIgJubGyeOHyMi4j7+JUsZ3vfj+XPp+s7jhcamNGkwm+MBiI2L1d+FfwldJWGkgCGVunn9KpEPH5A9Vx5DX0x0FHdvhwGPL8EsVKwky+dPI+jMCc6fPc3M8YPxD3iFnHleovjL5bh7+xYrFs7gWmgIu7f9yNerl/Ham9ZXQMTFPV678FrLDgDkfqkgly8GcerYIbb98BX5C5dI8nMWsVWkWHEKFynKqBGDCQ46x66fdzBz2mQ6dXl8NURY2E2ioqIAaPJGC/bv28tnK5Zz6eIF5s+ZxYnjR2nd5vHPurd3Fj6eP+f/mYjjDBvUj2YtWpExo4fVe+7buwc3NzcKFykGQNGixdm86QeCzp3l5x3bKOFf8oWdv0hiUsCQSoXfeXwNeHo3d0Pf7h0/0b1lXcvrd/qNIk/+gnw09F1G9+uGt48vvQaOBcDbx5cBY2fy+8mj9O/eks8Xz6H12715pWotq2P+vPk7ipYMILOnt2W/lh17MnXkh5w+doj2Pfom1amKPJWDgwPTZs7B1TUdHdq2ZMzIobRs3YZWb7UFoM6rldn043cAFC5SlCnTZ/PVl2tp2ex1ftm1k8D5n5DFxweAN1u3oUq1V+ndowu93+lKparVeK9vf8N7frJgjiW78Md+Lq7p6NiuFaUDylKzdl3DPpICKcVgYDKnkPzYoYv3k3sKIilO/qxu9geJ/Ae5OSftN/HRkIhEO1aJHKnj77EyDCIiIinETz/9RMGCBa22Pn36AHDy5EmaN2+Ov78/b7zxBsePH7fad+PGjdSsWRN/f3969uzJ7f/fbTSxKGAQERGxkVxXSZw7d47q1auza9cuyzZ27FgePnxI165dCQgIYP369ZQqVYpu3brx8OFDAI4ePcqQIUPo1asXq1ev5t69ewwaNChRPxMFDCIiIjaSawlDUFAQBQoUwNvb27K5u7vz3Xff4ezsTP/+/cmXLx9Dhgwhffr0/PDDDwCsWLGCevXq0bhxYwoVKsSkSZPYsWMHISEh//SjsFDAICIiYiuZIoagoCBy585taD9y5AilS5e23CXUZDLx8ssvc/jwYUt/QECAZbyvry9+fn4cOXLk+SbwDAoYREREklBMTAwRERFWW0xMjGGc2Wzm/Pnz7Nq1izp16lCzZk2mTJlCTEwMN2/eJEuWLFbjPT09uXbtGgA3btx4Zn9i0K2hRUREbCTmsyQWLFhAYGCgVVuvXr3o3bu3VduVK1eIjIzEycmJGTNmcPnyZcaOHUtUVJSl/a+cnJwsgUdUVNQz+xODAgYREREbiXlL527dutGxY0erNtsvd4Bs2bKxd+9eMmbMiMlkonDhwsTHx9OvXz/Kli1r+PKPiYnBxeXxbf2dnZ2f2O/q6ppo56GAQUREJAk5OTk9MUB4Eg8PD6vX+fLlIzo6Gm9vb8LCwqz6wsLCLGUIHx+fJ/Z7e3v//Ynb0BoGERERG8mx5vHnn3+mXLlyREZGWtpOnTqFh4cHpUuX5tChQ5ZnkZjNZn777Tf8/R8/L8jf35+DBw9a9rt69SpXr1619CcGBQwiIiK2kiFiKFWqFM7OzgwdOpTg4GB27NjBpEmTePvtt6lbty737t1j3LhxnDt3jnHjxhEZGUm9evUAaNWqFV999RVr1qzh9OnT9O/fn2rVqpEjR45E+ThAt4YWSdF0a2iRJ0vqW0OfuvrA/qAEKuybPsFjz549y/jx4zl8+DDp06enZcuW9OzZE5PJxNGjRxkxYgRBQUEULFiQUaNGUaRIEcu+69evZ9asWYSHh1OxYkXGjBljeCrrP6GAQSQFU8Ag8mRJHTCcvvow0Y5VyDddoh0rOWnRo4iIiI3EvEoitdAaBhEREbFLGQYREREbSjAYKWAQERGxpYjBQAGDiIiIjcS8NXRqoTUMIiIiYpcyDCIiIjZ0lYSRAgYREREbiheMVJIQERERu5RhEBERsaUUg4ECBhERERu6SsJIJQkRERGxSxkGERERG7pKwkgBg4iIiA3FC0YqSYiIiIhdyjCIiIjYUorBQAGDiIiIDV0lYaSAQURExIYWPRppDYOIiIjYpQyDiIiIDSUYjBQwiIiI2FBJwkglCREREbFLGQYREREDpRhsKWAQERGxoZKEkUoSIiIiYpcyDCIiIjaUYDBSwCAiImJDJQkjlSRERETELmUYREREbOhZEkYKGERERGwpXjBQwCAiImJD8YKR1jCIiIiIXcowiIiI2NBVEkYKGERERGxo0aORShIiIiJilzIMIiIitpRgMFDAICIiYkPxgpFKEiIiImKXMgwiIiI2dJWEkQIGERERG7pKwkglCREREbFLGQYREREbKkkYKcMgIiIidinDICIiYkMZBiNlGERERMQuZRhERERs6CoJIwUMIiIiNlSSMFJJQkREROxShkFERMSGEgxGChhERERsKWIwUElCRERE7FKGQURExIaukjBSwCAiImJDV0kYqSQhIiIidinDICIiYkMJBiMFDCIiIrYUMRgoYBAREbGhRY9GWsMgIiIidinDICIiYkNXSRiZzGazObknISIiIimbShIiIiJilwIGERERsUsBg4iIiNilgEFERETsUsAgIiIidilgEBEREbsUMIiIiIhdChhERETELgUMIiIiYpcCBrGIjo5m8ODBBAQEUKlSJRYvXpzcUxJJMWJiYmjYsCF79+5N7qmIJAs9S0IsJk2axPHjx1m2bBlXrlxhwIAB+Pn5Ubdu3eSemkiyio6Opm/fvpw9eza5pyKSbBQwCAAPHz5kzZo1fPzxxxQtWpSiRYty9uxZVq5cqYBB/tPOnTtH37590WN35L9OJQkB4PTp08TGxlKqVClLW+nSpTly5Ajx8fHJODOR5LVv3z7KlSvH6tWrk3sqIslKGQYB4ObNm2TKlAknJydLm5eXF9HR0dy9e5fMmTMn4+xEkk/r1q2TewoiKYIyDAJAZGSkVbAAWF7HxMQkx5RERCQFUcAgADg7OxsCgz9eu7i4JMeUREQkBVHAIAD4+Phw584dYmNjLW03b97ExcUFd3f3ZJyZiIikBAoYBIDChQvj6OjI4cOHLW0HDx6kePHipEmjHxMRkf86fRMIAK6urjRu3JiRI0dy9OhRNm/ezOLFi2nXrl1yT01ERFIAXSUhFoMGDWLkyJG0b98eNzc3evfuTe3atZN7WiIikgKYzLobiYiIiNihkoSIiIjYpYBBRERE7FLAICIiInYpYBARERG7FDCIiIiIXQoYRERExC4FDCIiImKXAgYRERGxSwGDiIiI2KWAQUREROxSwCAiIiJ2KWAQERERu/4HQ/Q/dcciFjUAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:44:39.863302Z",
     "start_time": "2024-08-24T05:44:38.410564Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"logistic regression model for balanced dataset\")\n",
    "classifier=model(classifier_lr,x_train_re,y_train_re)\n",
    "model_evaluation(classifier,x_test_re,y_test_re)"
   ],
   "id": "7daf9817268e7ff7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logistic regression model for balanced dataset\n",
      "Accuracy :  70.00%\n",
      "ROC_AUC Score :  70.02%\n",
      "#########  Confusion Matrix  ########\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.71      0.70      2551\n",
      "           1       0.72      0.69      0.70      2705\n",
      "\n",
      "    accuracy                           0.70      5256\n",
      "   macro avg       0.70      0.70      0.70      5256\n",
      "weighted avg       0.70      0.70      0.70      5256\n",
      "\n",
      "    ytest  ypred\n",
      "0       0      1\n",
      "1       1      1\n",
      "2       1      1\n",
      "3       1      0\n",
      "4       1      1\n",
      "5       0      0\n",
      "6       0      1\n",
      "7       0      0\n",
      "8       1      0\n",
      "9       1      0\n",
      "10      1      1\n",
      "11      1      1\n",
      "12      1      0\n",
      "13      0      0\n",
      "14      0      0\n",
      "15      0      0\n",
      "16      0      0\n",
      "17      1      0\n",
      "18      1      1\n",
      "19      0      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:45:02.256380Z",
     "start_time": "2024-08-24T05:45:00.582841Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.ensemble import  GradientBoostingClassifier\n",
    "gb=GradientBoostingClassifier(n_estimators=100)\n",
    "print(\"GradientBoostingClassifier model for imbalanced dataset\")\n",
    "classifier=model(gb,x_train,y_train)\n",
    "model_evaluation(classifier,x_test,y_test)"
   ],
   "id": "ccce7973ffdf3f4c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingClassifier model for imbalanced dataset\n",
      "Accuracy :  86.00%\n",
      "ROC_AUC Score :  70.93%\n",
      "#########  Confusion Matrix  ########\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.97      0.92      2609\n",
      "           1       0.79      0.45      0.57       691\n",
      "\n",
      "    accuracy                           0.86      3300\n",
      "   macro avg       0.83      0.71      0.75      3300\n",
      "weighted avg       0.85      0.86      0.84      3300\n",
      "\n",
      "    ytest  ypred\n",
      "0       0      0\n",
      "1       0      0\n",
      "2       0      0\n",
      "3       0      0\n",
      "4       0      0\n",
      "5       0      0\n",
      "6       0      0\n",
      "7       0      0\n",
      "8       0      0\n",
      "9       0      0\n",
      "10      0      0\n",
      "11      1      0\n",
      "12      0      0\n",
      "13      0      0\n",
      "14      0      0\n",
      "15      0      0\n",
      "16      0      0\n",
      "17      0      0\n",
      "18      0      0\n",
      "19      0      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 42
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:45:19.627194Z",
     "start_time": "2024-08-24T05:45:16.884802Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"GradientBoostingClassifier model for balanced dataset\")\n",
    "classifier=model(gb,x_train_re,y_train_re)\n",
    "model_evaluation(classifier,x_test_re,y_test_re)"
   ],
   "id": "be0ce75e06b4f739",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingClassifier model for balanced dataset\n",
      "Accuracy :  84.17%\n",
      "ROC_AUC Score :  84.23%\n",
      "#########  Confusion Matrix  ########\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.86      0.84      2551\n",
      "           1       0.86      0.82      0.84      2705\n",
      "\n",
      "    accuracy                           0.84      5256\n",
      "   macro avg       0.84      0.84      0.84      5256\n",
      "weighted avg       0.84      0.84      0.84      5256\n",
      "\n",
      "    ytest  ypred\n",
      "0       0      0\n",
      "1       1      1\n",
      "2       1      1\n",
      "3       1      0\n",
      "4       1      1\n",
      "5       0      0\n",
      "6       0      1\n",
      "7       0      0\n",
      "8       1      0\n",
      "9       1      1\n",
      "10      1      0\n",
      "11      1      1\n",
      "12      1      0\n",
      "13      0      0\n",
      "14      0      0\n",
      "15      0      0\n",
      "16      0      0\n",
      "17      1      1\n",
      "18      1      1\n",
      "19      0      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 43
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:45:49.851511Z",
     "start_time": "2024-08-24T05:45:46.401786Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=100,random_state = 42)\n",
    "print(\"RandomForestClassifier model for imbalanced dataset\")\n",
    "classifier=model(rf,x_train,y_train)\n",
    "model_evaluation(classifier,x_test,y_test)"
   ],
   "id": "9e4b214e5031b3e5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier model for imbalanced dataset\n",
      "Accuracy :  85.36%\n",
      "ROC_AUC Score :  70.26%\n",
      "#########  Confusion Matrix  ########\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.96      0.91      2609\n",
      "           1       0.76      0.44      0.56       691\n",
      "\n",
      "    accuracy                           0.85      3300\n",
      "   macro avg       0.81      0.70      0.74      3300\n",
      "weighted avg       0.84      0.85      0.84      3300\n",
      "\n",
      "    ytest  ypred\n",
      "0       0      0\n",
      "1       0      0\n",
      "2       0      0\n",
      "3       0      0\n",
      "4       0      0\n",
      "5       0      0\n",
      "6       0      0\n",
      "7       0      0\n",
      "8       0      0\n",
      "9       0      0\n",
      "10      0      0\n",
      "11      1      0\n",
      "12      0      0\n",
      "13      0      0\n",
      "14      0      0\n",
      "15      0      0\n",
      "16      0      0\n",
      "17      0      0\n",
      "18      0      0\n",
      "19      0      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 44
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:46:33.148083Z",
     "start_time": "2024-08-24T05:46:30.825117Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"Random Forest Classifier model for balanced dataset\")\n",
    "classifier=model(rf,x_train_re,y_train_re)\n",
    "model_evaluation(classifier,x_test_re,y_test_re)"
   ],
   "id": "a97b5a760695f8a3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier model for balanced dataset\n",
      "Accuracy :  87.46%\n",
      "ROC_AUC Score :  87.49%\n",
      "#########  Confusion Matrix  ########\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.89      0.87      2551\n",
      "           1       0.89      0.86      0.88      2705\n",
      "\n",
      "    accuracy                           0.87      5256\n",
      "   macro avg       0.87      0.87      0.87      5256\n",
      "weighted avg       0.88      0.87      0.87      5256\n",
      "\n",
      "    ytest  ypred\n",
      "0       0      0\n",
      "1       1      1\n",
      "2       1      1\n",
      "3       1      0\n",
      "4       1      1\n",
      "5       0      0\n",
      "6       0      1\n",
      "7       0      0\n",
      "8       1      1\n",
      "9       1      1\n",
      "10      1      1\n",
      "11      1      1\n",
      "12      1      1\n",
      "13      0      0\n",
      "14      0      0\n",
      "15      0      0\n",
      "16      0      0\n",
      "17      1      1\n",
      "18      1      1\n",
      "19      0      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 45
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:46:42.178477Z",
     "start_time": "2024-08-24T05:46:42.166452Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.ensemble import BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score"
   ],
   "id": "c6d30d632a58a99",
   "outputs": [],
   "execution_count": 46
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:47:00.442477Z",
     "start_time": "2024-08-24T05:46:57.446375Z"
    }
   },
   "cell_type": "code",
   "source": [
    "classifier_bg=ExtraTreesClassifier(n_estimators=300)\n",
    "print(\"ExtraTreesClassifier model for imbalanced dataset\")\n",
    "classifier=model(classifier_bg,x_train,y_train)\n",
    "model_evaluation(classifier,x_test,y_test)"
   ],
   "id": "4e16d6b517b300a7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesClassifier model for imbalanced dataset\n",
      "Accuracy :  85.58%\n",
      "ROC_AUC Score :  69.12%\n",
      "#########  Confusion Matrix  ########\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.97      0.91      2609\n",
      "           1       0.81      0.41      0.54       691\n",
      "\n",
      "    accuracy                           0.86      3300\n",
      "   macro avg       0.83      0.69      0.73      3300\n",
      "weighted avg       0.85      0.86      0.84      3300\n",
      "\n",
      "    ytest  ypred\n",
      "0       0      0\n",
      "1       0      0\n",
      "2       0      0\n",
      "3       0      0\n",
      "4       0      0\n",
      "5       0      0\n",
      "6       0      0\n",
      "7       0      0\n",
      "8       0      0\n",
      "9       0      0\n",
      "10      0      0\n",
      "11      1      0\n",
      "12      0      0\n",
      "13      0      0\n",
      "14      0      0\n",
      "15      0      0\n",
      "16      0      0\n",
      "17      0      0\n",
      "18      0      0\n",
      "19      0      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 47
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:47:14.057767Z",
     "start_time": "2024-08-24T05:47:10.922067Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"ExtraTreesClassifier model for balanced dataset\")\n",
    "classifier=model(classifier_bg,x_train_re,y_train_re)\n",
    "model_evaluation(classifier,x_test_re,y_test_re)"
   ],
   "id": "654ddbfdd156eca7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTreesClassifier model for balanced dataset\n",
      "Accuracy :  89.61%\n",
      "ROC_AUC Score :  89.59%\n",
      "#########  Confusion Matrix  ########\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.89      0.89      2551\n",
      "           1       0.90      0.90      0.90      2705\n",
      "\n",
      "    accuracy                           0.90      5256\n",
      "   macro avg       0.90      0.90      0.90      5256\n",
      "weighted avg       0.90      0.90      0.90      5256\n",
      "\n",
      "    ytest  ypred\n",
      "0       0      0\n",
      "1       1      1\n",
      "2       1      1\n",
      "3       1      0\n",
      "4       1      1\n",
      "5       0      0\n",
      "6       0      1\n",
      "7       0      0\n",
      "8       1      1\n",
      "9       1      1\n",
      "10      1      1\n",
      "11      1      1\n",
      "12      1      1\n",
      "13      0      0\n",
      "14      0      0\n",
      "15      0      0\n",
      "16      0      0\n",
      "17      1      1\n",
      "18      1      1\n",
      "19      0      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 48
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:47:35.844092Z",
     "start_time": "2024-08-24T05:47:35.785529Z"
    }
   },
   "cell_type": "code",
   "source": "type(x_train)",
   "id": "52bb0aa3ceaf72e1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:47:43.710072Z",
     "start_time": "2024-08-24T05:47:43.667021Z"
    }
   },
   "cell_type": "code",
   "source": "df.columns",
   "id": "b4611e035fb196d9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
       "       'IsActiveMember', 'EstimatedSalary', 'Exited'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 50
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:48:16.983196Z",
     "start_time": "2024-08-24T05:48:12.391462Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from yellowbrick.model_selection import FeatureImportances\n",
    "fig = plt.figure(figsize = (12,8))\n",
    "viz = FeatureImportances(RandomForestClassifier(n_estimators=300), labels=df.columns, relative=False)\n",
    "# Fit and show the feature importances\n",
    "viz.fit(x_train,y_train)\n",
    "viz.show()"
   ],
   "id": "b4580c8838ae6b43",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Feature Importances of 8 Features using RandomForestClassifier'}, xlabel='feature importance'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 52
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T05:48:40.423781Z",
     "start_time": "2024-08-24T05:48:38.400731Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig = plt.figure(figsize = (12,8))\n",
    "viz = FeatureImportances(GradientBoostingClassifier(n_estimators=100), labels=df.columns, relative=False)\n",
    "# Fit and show the feature importances\n",
    "viz.fit(x_train,y_train)\n",
    "viz.show()"
   ],
   "id": "ecdb2852169d668b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Feature Importances of 8 Features using GradientBoostingClassifier'}, xlabel='feature importance'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 53
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "f11ede17e201a598"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}