{"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport random","metadata":{"id":"jedEZTw1GXiW","execution":{"iopub.status.busy":"2023-07-29T17:10:01.240801Z","iopub.execute_input":"2023-07-29T17:10:01.242121Z","iopub.status.idle":"2023-07-29T17:10:02.197397Z","shell.execute_reply.started":"2023-07-29T17:10:01.242061Z","shell.execute_reply":"2023-07-29T17:10:02.195847Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import LabelEncoder\nfrom joblib import dump, load\nfrom sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neural_network import MLPClassifier\nfrom xgboost import XGBClassifier\nfrom lightgbm import LGBMClassifier\nfrom sklearn.metrics import (confusion_matrix, precision_recall_curve, auc, roc_curve, recall_score, precision_score,\n                             classification_report, f1_score, average_precision_score, precision_recall_fscore_support)","metadata":{"id":"ytEkLFA1h6uV","execution":{"iopub.status.busy":"2023-07-29T17:11:54.589352Z","iopub.execute_input":"2023-07-29T17:11:54.589816Z","iopub.status.idle":"2023-07-29T17:11:54.598785Z","shell.execute_reply.started":"2023-07-29T17:11:54.589782Z","shell.execute_reply":"2023-07-29T17:11:54.597428Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv(r\"/kaggle/input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv\")","metadata":{"id":"07LUuFKiGrSr","execution":{"iopub.status.busy":"2023-07-29T17:10:10.486366Z","iopub.execute_input":"2023-07-29T17:10:10.486786Z","iopub.status.idle":"2023-07-29T17:10:10.552127Z","shell.execute_reply.started":"2023-07-29T17:10:10.486753Z","shell.execute_reply":"2023-07-29T17:10:10.550522Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"pd.set_option('display.max_rows', None)\npd.set_option('display.max_columns', None)","metadata":{"id":"wblzqycaH35S","execution":{"iopub.status.busy":"2023-07-29T17:10:13.100591Z","iopub.execute_input":"2023-07-29T17:10:13.101059Z","iopub.status.idle":"2023-07-29T17:10:13.106719Z","shell.execute_reply.started":"2023-07-29T17:10:13.101026Z","shell.execute_reply":"2023-07-29T17:10:13.105259Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"data.set_index(keys = 'customer_id', inplace = True)\ndata.head()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":336},"id":"xR65u319H8hO","outputId":"425f64da-8402-443b-c32e-3eee9c33401c","execution":{"iopub.status.busy":"2023-07-29T17:10:14.689609Z","iopub.execute_input":"2023-07-29T17:10:14.690938Z","iopub.status.idle":"2023-07-29T17:10:14.727904Z","shell.execute_reply.started":"2023-07-29T17:10:14.690884Z","shell.execute_reply":"2023-07-29T17:10:14.726562Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"             credit_score country  gender  age  tenure    balance  \\\ncustomer_id                                                         \n15634602              619  France  Female   42       2       0.00   \n15647311              608   Spain  Female   41       1   83807.86   \n15619304              502  France  Female   42       8  159660.80   \n15701354              699  France  Female   39       1       0.00   \n15737888              850   Spain  Female   43       2  125510.82   \n\n             products_number  credit_card  active_member  estimated_salary  \\\ncustomer_id                                                                  \n15634602                   1            1              1         101348.88   \n15647311                   1            0              1         112542.58   \n15619304                   3            1              0         113931.57   \n15701354                   2            0              0          93826.63   \n15737888                   1            1              1          79084.10   \n\n             churn  \ncustomer_id         \n15634602         1  \n15647311         0  \n15619304         1  \n15701354         0  \n15737888         0  ","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>credit_score</th>\n      <th>country</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>tenure</th>\n      <th>balance</th>\n      <th>products_number</th>\n      <th>credit_card</th>\n      <th>active_member</th>\n      <th>estimated_salary</th>\n      <th>churn</th>\n    </tr>\n    <tr>\n      <th>customer_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>15634602</th>\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      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15647311</th>\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      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15619304</th>\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    </tr>\n    <tr>\n      <th>15701354</th>\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      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15737888</th>\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    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"missed_count = data.isnull().sum().sort_values(ascending=False).head(50)\nmissingPercnt = (missed_count/len(data))*100\n\npd.DataFrame({'No. missing values': missed_count, '% of missing data':\n              missingPercnt.values})","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k6IAgH21H-th","outputId":"6094f123-cad7-4f12-d5be-6bbbce0f4212","execution":{"iopub.status.busy":"2023-07-29T17:10:16.233375Z","iopub.execute_input":"2023-07-29T17:10:16.233953Z","iopub.status.idle":"2023-07-29T17:10:16.262696Z","shell.execute_reply.started":"2023-07-29T17:10:16.233909Z","shell.execute_reply":"2023-07-29T17:10:16.261411Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"                  No. missing values  % of missing data\ncredit_score                       0                0.0\ncountry                            0                0.0\ngender                             0                0.0\nage                                0                0.0\ntenure                             0                0.0\nbalance                            0                0.0\nproducts_number                    0                0.0\ncredit_card                        0                0.0\nactive_member                      0                0.0\nestimated_salary                   0                0.0\nchurn                              0                0.0","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>No. missing values</th>\n      <th>% of missing data</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>credit_score</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>country</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>gender</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>tenure</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>balance</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>products_number</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>credit_card</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>active_member</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>estimated_salary</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>churn</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Numeric Data\ndata.select_dtypes(np.number).info()","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fiSTNxzuZkiW","outputId":"7a29274d-49b1-40cb-9a74-31229050158d","execution":{"iopub.status.busy":"2023-07-29T17:10:17.950241Z","iopub.execute_input":"2023-07-29T17:10:17.950714Z","iopub.status.idle":"2023-07-29T17:10:18.207222Z","shell.execute_reply.started":"2023-07-29T17:10:17.950655Z","shell.execute_reply":"2023-07-29T17:10:18.205784Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 10000 entries, 15634602 to 15628319\nData columns (total 9 columns):\n #   Column            Non-Null Count  Dtype  \n---  ------            --------------  -----  \n 0   credit_score      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   products_number   10000 non-null  int64  \n 5   credit_card       10000 non-null  int64  \n 6   active_member     10000 non-null  int64  \n 7   estimated_salary  10000 non-null  float64\n 8   churn             10000 non-null  int64  \ndtypes: float64(2), int64(7)\nmemory usage: 781.2 KB\n","output_type":"stream"}]},{"cell_type":"code","source":"numeric_data = data.select_dtypes(np.number).columns\n\nplt.figure(figsize=(10, 10))\nfor i, column in enumerate(numeric_data):\n    plt.subplot(3,3, i+1)\n    sns.boxenplot(x = data[column])\nplt.show()","metadata":{"id":"4Ew0eD2FaZG1","execution":{"iopub.status.busy":"2023-07-29T17:10:19.549725Z","iopub.execute_input":"2023-07-29T17:10:19.551143Z","iopub.status.idle":"2023-07-29T17:10:20.719788Z","shell.execute_reply.started":"2023-07-29T17:10:19.551093Z","shell.execute_reply":"2023-07-29T17:10:20.718411Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x1000 with 9 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Catagorical Data\ndata.select_dtypes('O').info()","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AvHF6N4fIFh2","outputId":"29626813-49f2-48e7-84d2-1b31aed916fa","execution":{"iopub.status.busy":"2023-07-29T17:10:22.369846Z","iopub.execute_input":"2023-07-29T17:10:22.370292Z","iopub.status.idle":"2023-07-29T17:10:22.391258Z","shell.execute_reply.started":"2023-07-29T17:10:22.370258Z","shell.execute_reply":"2023-07-29T17:10:22.390065Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 10000 entries, 15634602 to 15628319\nData columns (total 2 columns):\n #   Column   Non-Null Count  Dtype \n---  ------   --------------  ----- \n 0   country  10000 non-null  object\n 1   gender   10000 non-null  object\ndtypes: object(2)\nmemory usage: 234.4+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"categorical_data = ['country', 'gender']","metadata":{"id":"1NP-KPdvKfty","execution":{"iopub.status.busy":"2023-07-29T17:10:23.549503Z","iopub.execute_input":"2023-07-29T17:10:23.550351Z","iopub.status.idle":"2023-07-29T17:10:23.556194Z","shell.execute_reply.started":"2023-07-29T17:10:23.550305Z","shell.execute_reply":"2023-07-29T17:10:23.554806Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(15, 15))\nfor i, col in enumerate(categorical_data):\n    plt.subplot(4,4, i+1)\n    sns.countplot(x=col, hue=\"churn\", data=data)\n    plt.legend(['No', 'Yes'])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:10:26.233817Z","iopub.execute_input":"2023-07-29T17:10:26.234249Z","iopub.status.idle":"2023-07-29T17:10:26.738059Z","shell.execute_reply.started":"2023-07-29T17:10:26.234216Z","shell.execute_reply":"2023-07-29T17:10:26.737180Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1500 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"churn = data[data['churn'] == 1].shape[0]\nstay = data[data['churn'] == 0].shape[0]\nexplode = (0, 0.1)\n\nplt.title(\"Customer Churn Distribution\")\nplt.pie([churn, stay], labels=['Yes', 'No'], autopct='%1.1f%%', shadow=True, explode = explode)\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:10:29.069547Z","iopub.execute_input":"2023-07-29T17:10:29.069992Z","iopub.status.idle":"2023-07-29T17:10:29.331051Z","shell.execute_reply.started":"2023-07-29T17:10:29.069959Z","shell.execute_reply":"2023-07-29T17:10:29.329051Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAGZCAYAAABmNy2oAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABYyUlEQVR4nO3dd3xb9b3/8dfRtuS97Qw7wxlkJ+xAAiQQEqBlFCillDA6obe34xbor0DXZZbeUiirLZTSAS2bskIzgJBB9o5jO97x3kv7+/tDiYIjOWRYPpL8eT4efiQ5R5Y+khW9fb5TU0ophBBCCMCgdwFCCCGih4SCEEKIIAkFIYQQQRIKQgghgiQUhBBCBEkoCCGECJJQEEIIESShIIQQIkhCQQghRJCEwgnavn07N910E2PGjMFms5GYmMjs2bN56KGHaG1tjchjvvPOO/zsZz+LyH1Hi4aGBu68806mTZtGYmIiNpuNoqIivve971FSUhK83dKlS0lMTNSx0mNXUVGBpmnBL7PZTEZGBqeddhrf//732bVrV8j3rFq1Ck3TWLVq1XE91hNPPMGf//zn4/qecI8Vidd3zZo1/OxnP6O9vT3k3Hnnncd55503qI8nTpASx+2ZZ55RJpNJTZkyRf3+979XK1euVMuWLVP33XefGjNmjLr88ssj8ri33Xabiucf2fr161VWVpbKzMxUP/vZz9T777+vVq5cqZ566il1zjnnqNTU1OBtb7zxRuVwOHSs9tiVl5crQH33u99Va9euVZ988ol6++231a9+9Ss1duxYZTQa1UMPPdTvezo6OtTatWtVR0fHcT3WlClT1Pz584/re8I9ViRe34cfflgBqry8POTcrl271K5duwb18cSJid9PmAhZs2aNMhqN6uKLL1ZOpzPkvMvlUm+88UZEHjvWQ6G3t1f5/f6w5zo6OlRubq4aNWqUqq6uDnubf/3rX8G/D1Uo9PT0nPR9HAqFhx9+OORcb2+vuvjiixWg3nnnnZN+rOMJBbfbrTweT9hzQx0KInrE7ieMTi699FJlMplUVVXVMd0eUPfee2/I8YKCAnXjjTcG/93T06N++MMfqsLCQmW1WlVaWpqaM2eO+vvf/66UCvwnBUK+Dv0H6+vrU3feeacqLCxUZrNZ5efnq+985zuqra0t5HEvueQS9dZbb6mZM2cqm82mJk2apN566y2llFLPPfecmjRpkrLb7eq0005TGzZsCKl9w4YN6rLLLlNpaWnKarWqmTNnqpdeeqnfbZ577jkFqPfff1/ddNNNKjMzUwGqr68v7Ov061//WgHqH//4xzG9roc+tEpKStTixYuVw+FQI0eOVD/4wQ/6hfXKlSsVoFauXNnv+w99UD/33HMh97l9+3Z14YUXqsTERHXmmWcqpQI/x9tuu0395S9/UZMmTVIJCQlq+vTpwdftaI4WCkopVVtbq8xmszr//POPWndZWZm69tprVV5enrJYLCo7O1tdcMEFasuWLUqpwM/2yPdHQUFBv/v7y1/+on7wgx+o/Px8pWma2rNnT9jHOvRa7Ny5U11wwQXKbrerzMxMddttt/ULynCv4yGffe/fe++9Yd+/hx5z/vz5IWHW0tKivv3tb6v8/HxlNpvVmDFj1E9+8pOQX8ZO5mcjQpki3DoVV3w+HytWrGDOnDmMGjVqUO/7Bz/4AS+88AK/+tWvmDVrFj09PezcuZOWlhYA7r77bnp6enj55ZdZu3Zt8Pvy8vJQSnH55ZezfPly7rrrLs4991y2b9/Ovffey9q1a1m7di1WqzX4Pdu2beOuu+7i//2//0dKSgo///nPufLKK7nrrrtYvnw59913H5qmcccdd3DppZdSXl5OQkICACtXruTiiy/mjDPO4KmnniIlJYUXX3yRa6+9lt7eXpYuXdrved18881ccsklvPDCC/T09GA2m8M+/2XLlmE0GrnsssuO+TXzeDx84Qtf4JZbbuGHP/whH330Eb/85S9JSUnhnnvuOeb7+Sy3280XvvAFvvnNb3LnnXfi9XqD595++202bNjAL37xCxITE3nooYe44oorKC4uZuzYsSf0eAD5+fnMmTOHNWvW4PV6MZnC/7dcsmQJPp+Phx56iNGjR9Pc3MyaNWuCbfSvvfYaX/rSl0hJSeGJJ54A6PdzB7jrrrs466yzeOqppzAYDGRnZ1NfXx/28TweD0uWLAm+FmvWrOFXv/oVlZWVvPXWW8f1HG+99VZaW1t57LHHePXVV8nLywPglFNOCXt7p9PJ+eefT1lZGT//+c+ZPn06H3/8Mffffz9bt27l7bff7nf7SP1shiW9UymW1NfXK0B9+ctfPubv4RivFKZOnfq5fREDNR+99957Cghpl37ppZcUoJ555pl+j5uQkKBqamqCx7Zu3aoAlZeX1++3wNdff10B6s033wwemzRpkpo1a1ZIs8Oll16q8vLylM/nU0odvlL42te+dtTn9Nn7zc3NPabbKnX4yumf//xnv+NLlixREydODP77eK8UAPXss8+GPB6gcnJyVGdnZ/BYfX29MhgM6v777z9qrZ93paCUUtdee60CVENDQ9i6m5ubFaB++9vfHvWxBmo+OnR/8+bNG/DckVcKgHr00Uf73fZ///d/FaBWr17d77l93pWCUkdvPjrySuGpp54K+/N98MEHFaCWLVvW73FO9GcjQsnooyhx+umn8+6773LnnXeyatUq+vr6jvl7V6xYARDyW/rVV1+Nw+Fg+fLl/Y7PnDmTESNGBP89efJkIDACxG63hxyvrKwEoLS0lL1793L99dcD4PV6g19Lliyhrq6O4uLifo911VVXHfPzOF6apoVcWUyfPj1Y74kaqObzzz+fpKSk4L9zcnLIzs4+6ccDUJ+zrUl6ejrjxo3j4Ycf5je/+Q1btmzB7/cf9+Mc78/j0M/6kK985StA4IoxklasWIHD4eBLX/pSv+OH3uNHvqcj+bMZbiQUjkNmZiZ2u53y8vJBv+/f/e533HHHHbz++uucf/75pKenc/nll/cbhjmQlpYWTCYTWVlZ/Y5rmkZubm6wCeqQ9PT0fv+2WCxHPe50OoHAcFGAH/3oR5jN5n5f3/nOdwBobm7udx+Hmgk+z+jRo2lqaqKnp+eYbg9gt9ux2Wz9jlmt1mC9J8Jut5OcnBz2XEZGRsgxq9V6XAE+kMrKSqxWa8jP4BBN01i+fDmLFi3ioYceYvbs2WRlZfFf//VfdHV1HfPjHOvPA8BkMoU859zcXICQ99Rga2lpITc3F03T+h3Pzs7GZDKFPH4kfzbDjYTCcTAajSxYsIBNmzZRU1NzTN9jtVpxuVwhx498UzscDn7+85+zd+9e6uvrefLJJ1m3bt0xtbFnZGTg9Xppamrqd1wpRX19PZmZmcdU6+c5dD933XUXGzZsCPs1c+bMft9z5H/qgSxatAifz3fcbdWf51BoHPkzODK8DjnWegdTbW0tmzZt4pxzzhmwPwGgoKCAP/3pT9TX11NcXMz3v/99nnjiCf7nf/7nmB/reJ6f1+sNeZ8e6n849CE80Ot7sqGRkZFBQ0NDyBVUY2MjXq930N7TIpSEwnG66667UErx9a9/HbfbHXLe4/H0+2ArLCxk+/bt/W6zYsUKuru7B3yMnJwcli5dynXXXUdxcTG9vb3A4U7DI3/7WbBgAQB//etf+x1/5ZVX6OnpCZ4/WRMnTqSoqIht27Zx6qmnhv367CX88bjlllvIzc3lxz/+MbW1tWFv8+qrrx73/RYWFgKE/AzefPPN476vSOjr6+PWW2/F6/Xy4x//+Ji/b8KECfz0pz9l2rRpbN68OXh8sH87/tvf/tbv33//+98BghPNcnJysNlsIa/vG2+8EXJfA71/w1mwYAHd3d28/vrr/Y7/5S9/CZ4XkSGjj47TWWedxZNPPsl3vvMd5syZw7e//W2mTJmCx+Nhy5YtPPPMM0ydOjX4G/4NN9zA3XffzT333MP8+fPZvXs3jz/+OCkpKf3u94wzzuDSSy9l+vTppKWlsWfPHl544QXOOuusYDv/tGnTAHjwwQdZvHgxRqOR6dOnc+GFF7Jo0SLuuOMOOjs7mTt3bnD00axZs7jhhhsG7fk//fTTLF68mEWLFrF06VJGjBhBa2sre/bsYfPmzfzrX/86oftNSUnhjTfe4NJLL2XWrFncfvvtnHXWWVgsFkpKSvjrX//Ktm3buPLKK4/rfnNzc1m4cCH3338/aWlpFBQUsHz58hMKmJNVVVXFunXr8Pv9dHR0sGXLFp599lkqKyt55JFHuOiiiwb83u3bt3P77bdz9dVXU1RUhMViYcWKFWzfvp0777wzeLtp06bx4osv8tJLLzF27FhsNlvwfXO8LBYLjzzyCN3d3Zx22mnB0UeLFy/mnHPOAQJXHl/96ld59tlnGTduHDNmzODTTz8NhsdnHarj0Ucf5cYbb8RsNjNx4sSwv0h87Wtf4/e//z033ngjFRUVTJs2jdWrV3PfffexZMkSFi5ceELPSRwDffu5Y9fWrVvVjTfeqEaPHq0sFotyOBxq1qxZ6p577lGNjY3B27lcLvXjH/9YjRo1SiUkJKj58+errVu3how+uvPOO9Wpp54aHPs/duxY9f3vf181Nzf3u69bb71VZWVlKU3TQuYp3HHHHaqgoECZzWaVl5envv3tbw84T+FIHBzr/VkDjZrZtm2buuaaa1R2drYym80qNzdXXXDBBeqpp54K3ubQ6KNw8xyOpr6+Xt1xxx1qypQpym63K6vVqsaPH6+++c1vqh07dgRvN9DkqkPj4T+rrq5OfelLX1Lp6ekqJSVFffWrX1UbN24ccJ5COOFeH6VCR5GFc+h1PPRlNBqD81D++7//O+xM3iNHBDU0NKilS5eqSZMmKYfDoRITE9X06dPV//3f/ymv1xv8voqKCnXRRReppKSksPMUPjsBcKDH+uxrsX37dnXeeeephIQElZ6err797W+r7u7uft/f0dGhbr31VpWTk6McDoe67LLLVEVFRdiRd3fddZfKz89XBoPhmOYpfOtb31J5eXnKZDKpgoICdddddw04T+FIx/KzEaE0pT5n2IMQQohhQ/oUhBBCBEkoCCGECJJQEEIIESShIIQQIkhCQQghRJCEghBCiCAJBSGEEEESCkIIIYIkFIQQQgRJKAghhAiSUBBCCBEkoSCEECJIls4WQsQcv98fdj+T4cxsNmM0Gk/6fiQUhBAxxe12U15efkJ7VMe71NTUsNuYHg8JBSFEzFBKUVdXh9FoZNSoURgM0gIOgdelt7eXxsZG4Pj24j6ShIIQImZ4vV56e3vJz88P7kgoAhISEoDAPtbZ2dkn3JQkMSuEiBk+nw8IbBUqQh0KSo/Hc8L3IaEghIg5J9NmHs8G43WRUBBCCBEkoSCEECJIOpqFEDGv8M63h/TxKh64ZEgfbyjJlYIQQkSQUoqFCxeyaNGikHNPPPEEKSkpVFVV6VBZeBIKQggRQZqm8dxzz7F+/Xqefvrp4PHy8nLuuOMOHn30UUaPHq1jhf1JKAghRISNGjWKRx99lB/96EeUl5ejlOKWW25hwYIFnH766SxZsoTExERycnK44YYbaG5uDn7vyy+/zLRp00hISCAjI4OFCxfS09MTsVolFIQQYgjceOONLFiwgJtuuonHH3+cnTt38uijjzJ//nxmzpzJxo0bee+992hoaOCaa64BoK6ujuuuu46bb76ZPXv2sGrVKq688kqUUhGrU1ORvHchhBhETqeT8vJyxowZg81mCx6PlY7mxsZGpk6dSktLCy+//DJbtmxh/fr1vP/++8Hb1NTUMGrUKIqLi+nu7mbOnDlUVFRQUFDwufc/0OtzPORKQQghhkh2djbf+MY3mDx5MldccQWbNm1i5cqVJCYmBr8mTZoEQFlZGTNmzGDBggVMmzaNq6++mj/84Q+0tbVFtEYJBSGEGEImkwmTKTAbwO/3c9lll7F169Z+XyUlJcybNw+j0cgHH3zAu+++yymnnMJjjz3GxIkTKS8vj1h9EgpCCKGT2bNns2vXLgoLCxk/fny/L4fDAQRGL82dO5ef//znbNmyBYvFwmuvvRaxmiQUhBBCJ7fddhutra1cd911fPrpp+zfv59ly5Zx88034/P5WL9+Pffddx8bN26kqqqKV199laamJiZPnhyxmmRGsxAi5sXqDOP8/Hw++eQT7rjjDhYtWoTL5aKgoICLL74Yg8FAcnIyH330Eb/97W/p7OykoKCARx55hMWLF0esJhl9JISIGYMxuiaeyegjIYQQg0pCQQghRJCEghBCiCAJBSGEEEESCkIIIYIkFIQQQgRJKAghhAiSUBBCCBEkoSCEECJIlrkQQsS+n6UM8eN1DO3jDSG5UhBCiAhbunQpmqbxwAMP9Dv++uuvo2maTlWFJ6EghBBDwGaz8eCDD0Z8k5yTJaEghBBDYOHCheTm5nL//fcPeJtXXnmFKVOmYLVaKSws5JFHHhnCCgMkFIQQYggYjUbuu+8+HnvsMWpqakLOb9q0iWuuuYYvf/nL7Nixg5/97Gfcfffd/PnPfx7SOiUUhBBiiFxxxRXMnDmTe++9N+Tcb37zGxYsWMDdd9/NhAkTWLp0KbfffjsPP/zwkNYooSCEEEPowQcf5Pnnn2f37t39ju/Zs4e5c+f2OzZ37lxKSkrw+XxDVp+EghBCDKF58+axaNEifvKTn/Q7rpQKGYmkxx5oMk9BiBjzcUkTs0ankWiV/76x6oEHHmDmzJlMmDAheOyUU05h9erV/W63Zs0aJkyYgNFoHLLa5F0lRAxp6XZx47OfYjIaOHtcBoum5HLhKTlkJlr1Lk0ch2nTpnH99dfz2GOPBY/98Ic/5LTTTuOXv/wl1157LWvXruXxxx/niSeeGNLaZI9mIWLIvzZW8z8vb+93zKDB7NFpXD5rBF+cmU+SzaxTdZEXq3s0L126lPb2dl5//fXgscrKSiZOnIjL5Qo2E73yyivcc889lJSUkJeXx3e/+11+9KMfHfPjDMbrI6EgopvHCV110N0Q+LOrPvDl7ACvEzx9n/nTBX4PKD/4faBU4O8AFgfYksGaBNbP/HnkMVsyJOZAUj4Yoq/L7VsvbOK9XfUDnk8wG1k8LZcvnzaa08ekD2FlQyNWQ2GoDMbrI81HQl+9rdCwE5qKoaMmzId/uz51Ga2QOhrSx0DaGEgr7P9389B8IG3YtotlH61h+uQJjCss4OPSpqPevs/j49XNtby6uZaxWQ6uPXUUV80ZKc1L4pjJlYIYGj4PNO+Dhl2BEGjYFfjqqtO7shOgQVJuICAyxkH+LBh5KuRMBcPgdQgqpXjoyT+zfusOLCYzfQnZbDNPOu77MRs1LpiUzZdPG828CVkYDdG11s7xkCuFo5MrBRGdfF6o3QQ1n0L9wQBoLgafW+/KBok6eDVTB1VrYMsLgcNmO+TNhJFzYMSpgaBIGXnCj9LY0kp5dS2FI/NJT03hwxYHuI7/fjw+xfu7Gnh/VwN5KTaunjOSr55ZQHayfKiKUBIK4uQpBY27Yf+HUP4hVHwC7i69qxp6nt5ASFStOXwsKQ9GzAkExMjTAl+mY2vKKS2voqOrm/ycLADqvI6TLrGuw8nvVpTy9Ef7uebUUXzrvHGMSE046fsV8UNCQZyYtorDIVD+EfQcva172Oqqg73/DnwBmB0wZh4ULYTxF0JawYDfurtkPwZNw2Aw0Oox0e0bvP+uLq+fF9ZV8uKGKq6YNYLvnDeewsyTD52hIq3e4fn9/pO+D+lTEMfG44TS/0DJ+4EwaK/Uu6L4kFEE174A2ZP7He7p7eMnD/4On99PblYmW7sS2diVHLEyjAaNS6fncfv54ynKSYrY45wsn89HSUkJdrudrKysqNuLQC9KKdxuN01NTfh8PoqKijCc4Og5uVIQA/M4ofQD2PU67Ht/eDYJRVpHdWCU0xFKK6ppbetgzOhAn0SVM7Lt/z6/4o2tB3hz2wEWnZLL7ReMZ+qIId7N7BgYjUZGjhxJTU0NFRUVepcTdex2O6NHjz7hQAAJBXEknxfKVsCOf0LxexIEkVZ4bmAOxRH27a/A6/NjsZjp8xlo8gzNhDSl4L1d9by3q54LJmXzXwuKmDkqdUge+1glJiZSVFSEx+PRu5SoYjQaMZlMJ331JKEgAmo3w/Z/ws6XpX9gKE28OOSQ1+tly869JCXaAah2WVEMfTPJir2NrNjbyOUz87lryWRyomi0ktFoHNL1gIYTCYXhzNkZGE656fnAkFEx9CaEhkJVbT31TS1kZwZmJFdHuOno87y+9QAf7G7guwuKuHnuGCym6JvpLQaPhMJw1Lof1j8NW/4mzUN6ypkWdh5DSUUVvU4nDnsCfgU1Lv1nI/e4fTzw7l7+uaGauy87hfMnZutdkogQCYXhpPwjWPck7Hvv8JpAQj8TFoUcUkqxbfc+bBYLmqZxwGXBo6LnN/P9zT3c9NwGFkzK5p7LTqEgI3aGsYpjI6EQ77wu2PEvWPcUNOzQuxrxWRMXhxxqam2jvKqG9LTAyJ9Ijzo6Ucv3NvJxaTO3njOG2y8Yj90iHyXxQn6S8aq7ETb8ETY+Kx3H0ciRHZjpfIRDs5jzDs5ijtZQAHB7/TyxqoxXN9fyk0sm84UZ+XqXJAaBhEK86W2F1b+BT/8QWFJaRKeiiyDM0MHdJfvRDs5ibvOY6BrEWcyRUt/p5L/+sYV3ttdx/5XTSHNY9C5JnITof8eJY+PuhfVPwupHwdWhdzXi84QZitrb52RncSlpKYGZy9F8lRDOe7vq2VzVxkNfms550hEds6KnB0ucGJ8XNvwJfjcLlv9CAiEWGK0w7oKQw6UVVTS3tpOeerA/IQpGHR2vxi4XS5/bwD1v7MTp8eldjjgBcqUQq5SCXa+iVvwvWmuZ3tWI41F4zgCzmCvx+XxYLGacfo1Gd+w2w/xlbSVrylr47bUzo3K5DDEwuVKIRWUr4Jnz4OWbJRBiUZhRRz6fjy279pKYGAiLaqdNl1nMg6m0sZsrnviE368sxe+XdTdjhYRCLGnaB3/5IrxwBdRt1bsacaIGmsXc2ExGanQPRT1eHp/i4feLufaZtVS39updjjgGEgqxwOeBDx9CPXUO7F+ldzXiZGRPgdRRIYdLKqro6YuuWcyDaUNFG4sf/Zg3ttbqXYr4HNKnEO2qN+B/43YMzXtjvDFBAGFHHSml2LqrGGuUzmIeLN0uL997cSt76rr48aKJGGJ4r+h4Fn/vvHjh6sb/7x+h/nQRhua9elcjBsuE0P6E5tZ2yqtqyIjyWcyD5akPy/j6XzbS7fLqXYoIQ0IhGu1bhvd3czBs/AMaskZR3HBkhZ/FXBGYxZyaHNjxTO9VUYfC8r2NXPnEJ9LPEIUkFKJJTzO+l26Ev1+Nqade72rEYCu6CMLsiLVrX1m/WcydMTCLeTDsa+jmC4+vZm1Zi96liM+QUIgSatuL+B6djXHP63qXIiIlzKqoR85iro6zDubP09br4WvPrufv66v0LkUcJKGgN1c3npeWor32TYxumY0ct4yWsLOYyyqraWnrIC01Npe2GAwen+Inr+3g3jd24vVJc6nehsd1apTy127F+/evYOmRYXpxr/AcsCaFHC4uq8Dr9WK1WHD6NRpieBbzyXp+bSVlTT38/iuzSbEPzZ7UIpRcKeikd/lDqD9eIIEwXISZsObz+di6qzg4i7kmDmYxn6zVpc186ak1NHbJCr96kVAYYsrZQedTi7F//L8YlSwYNmyECYXqAw3UxeEs5pNV0tjNtU+vo66jT+9ShiUJhSHkrNhI729OJbl+jd6liKGUfQqkFYQc3ldeSU9fX9zOYj4Z5c09XPv0OmraZMjqUJNQGCJtK36H6c+LcLgb9S5FDLUB9mLevmdfcBZzvduCOw5nMZ+MqtZern16HZUtPXqXMqzIuzDC/F4PTX/6Mmkf3Y0JmcE5LIWZxdzS1kFZZc3hvROk6Sis2vY+rnl6LWVN3XqXMmxIKERQX1sjzb89l6zqd/UuRejFngkjTws5XFJeSUdnV3AWs4TCwBo6XVz79DqK67v0LmVYkFCIkOayLfT+/lyyu/foXYrQU9GFYWcx7zm4F7PRaKDdaxw2s5hPVHO3iy8/s5adtTKXJ9IkFAaZUoqy1a9gfuEyMryyVMWwF2bUUZ/TyY7iUlJjdC9mvbT1erj+j+vZVt2udylxTUJhEPl8Pna88gh5H3yLFORSd9gzWmD8gpDDpRXVB/dillA4Xh19Hr76x/XsOhC5KwalFAsXLmTRotABAk888QQpKSlUVcXvshwSCoPE43ax/fk7mLTjPuyaW+9yRDQoODvsLOaS8qrgLGbXMJ/FfCK6XF5uem4Dte2RmcegaRrPPfcc69ev5+mnnw4eLy8v54477uDRRx9l9OjREXnsaCChMAh6u7vY/fStzKj8IxZNJqSJg8KMOvL5fGzZuZdEhx2Ij72Y9dDY5eKm5z6lo88TkfsfNWoUjz76KD/60Y8oLy9HKcUtt9zCggULOP3001myZAmJiYnk5ORwww030NzcHPzel19+mWnTppGQkEBGRgYLFy6kpyd2htVKKJykjtYmKp68hhktb2LQZHNy8RlhdlmrPtDAgYam4FDU4bYq6mDa19DNN1/YiNsbmUX0brzxRhYsWMBNN93E448/zs6dO3n00UeZP38+M2fOZOPGjbz33ns0NDRwzTXXAFBXV8d1113HzTffzJ49e1i1ahVXXnklSsXOZ4OmYqnaKNNYU0H7819hgmeX3qWIaJM1CW5bH3L4g4/X8acXX2PKhHEoNP5anyuT1k7SF2fm89trZ6Jpg3/F1djYyNSpU2lpaeHll19my5YtrF+/nvfffz94m5qaGkaNGkVxcTHd3d3MmTOHiooKCgpCZ7HHAnk3nqCq0t00PXudBIIIL8yoI+DgLGazzGIeRG9sPcBD7xdH5L6zs7P5xje+weTJk7niiivYtGkTK1euJDExMfg1adIkAMrKypgxYwYLFixg2rRpXH311fzhD3+gra0tIrVFirwjT8DeretofuFmpvh3612KiFZhQqG5tZ3SiurDTUcy6mjQPLmqjL+tr4zIfZtMJkymwDwSv9/PZZddxtatW/t9lZSUMG/ePIxGIx988AHvvvsup5xyCo899hgTJ06kvLw8IrVFgoTCcVBKsWX1B7T/67+ZrcmkNDGAhHQYdXrI4dKKqoOzmGUoaiTc88Yulu9piOhjzJ49m127dlFYWMj48eP7fTkcgSXQNU1j7ty5/PznP2fLli1YLBZee+21iNY1mCQUjpFSio2r3qHr7bs50yyBII6i6CIwGEMO7ynZDwRmMXd4jXTILOZB5fMrvvuPLWyvaY/YY9x22220trZy3XXX8emnn7J//36WLVvGzTffjM/nY/369dx3331s3LiRqqoqXn31VZqampg8eXLEahpsEgrHQCnF1k/+Q9f79zHPKoEgPkeYVVH7nE627y0hLUXWOoqkXrePW57fSGNnZDbpyc/P55NPPsHn87Fo0SKmTp3K9773PVJSUjAYDCQnJ/PRRx+xZMkSJkyYwE9/+lMeeeQRFi8OHZ4crWT00THYvm4VDW/cy4XWrXqXIqKdwQw/3g+25H6HdxaX8uATz1E4Kh+rxcLbzRnUuWU4aqScPiadf3z9TIwGmQNyvORK4XPs2riaA2/+koWWrXqXImJBwdkhgQCBVVE/O4u5XmYxR9Sn5a089P5evcuISRIKR7F36zoqXvsVF5k3EYEh0CIeDbAX8+Yde3EcnMVc47LKLOYh8MxH+/lgd2Q7nuORhMIASnZsZOvLv+Yi06cyU1kcuzCzmGvqGqhrbJK9mIeYUvDDf26lulW29DweEgph7N+zlY9feoxLDB9jlrWMxLHKnAjpY0MOl5RX0d3TR6LDHtiLWUJhyHQ6vdz+jy14fJFZCiMeSSgcoXLfTpa9+BQXa6tIMkRmBIOIU2FGHQFs27MPy8FZzA1uCy6ZxTyktlW385sP9uldRsyQd+dn1FWV8d6Lz3Cu5yPyje16lyNizcQB9mKuqCYjTZqO9PT0h2V8Utr8+TcUEgqHdLY2s+xfz1LUvY4pllq9yxGxJiENRp0Rcri0oor2rs/sxSyrourCr+D7L22ltUf2Ovk8EgqAy9nHf179M7aa1Zxnl8tMcQLGXzjALOZyNDSMRmNgFrPXrENxAgJ7MPz45W16lxH1hn0o+P1+Pn77nzTvWM4VKbsxICONxAkIM+rI6XKxfc8+UpJlFnO0+M+eRt7YKi0BRzPsQ2HL6g/Yvfotrk3bgQW5tBQnwGCC8QtDDpdV1tDc1iarokaZX/57Nx29kdmxLR4M61Ao272Fj995icuTtpNCl97liFg1+iywpYQcLtlfidvtxWa14JZZzFGjudvN/e/KGmYDGbah0HSgiuWvPM8cbRejDTLrUZyEMKOO/H4/mz+7F7PLil9mMUeNlzZWs6GiVe8yotKwDIWerg6W/etZLK27mWuVjmVxksIsbVFTF9iL+dBQVGk6ii5KwU9e3RGx/Z1j2bALBY/bzYrX/sKBfVu4KnUPBuRNIU5CRhFkjAs5XFJeRU9vb3AWs4RC9Clp7ObpD8v0LiPqDKtQUEqxfsVb7NzwMVfk1pKkOvUuScS6MKOOALbvKcFsDsxibpRZzFHr8ZWllDf36F1GVBlW79SK4u1sWPk2c9J7GO8r0bscEQ/CNB21tndQUlEVHHUkE9ail8vr56ev79C7jKgybEKhq72VVW/+Haung/mGjXqXI+KBLRVGnRlyuLQ8sBez7LIWGz4pbeHVzTV6lxE1hkUo+Hw+Pnr7Jeoqy/hCeilWJQvdiUFQdCEYQ/dZ3l1SjlIKo9FIp9dIu8xijnr/+/Ye2ntlnhIMk1DY+emH7Pz0I87NczLSW6F3OSJehGk6crpcbN+7j9SUwO5rcpUQG1p63Dy5SjqdYRiEQtOBKj557xUy7XCmb73e5Yh4McAs5v1VtTS3th/uT5BQiBnPr62gsVNaEeI6FDxuNx/++0XaWxq5OHEPZiWXh2KQjD4LElJDDu8rq8Dt8cgs5hjk9Ph5fGWp3mXoLq5DYesnH1C6cyOnjTAxwlOudzkinoTZUMfv97N5114S7Yf3YpZZzLHlxU+rqWkb3tt3xm0o1FWVsW75myQlJ3OmZ43e5Yh4MyF0aYva+kbqGppIlw11Ypbb5+fR/wzv4epxGQpul5OP/v0SPR3tzE2qI8nfrndJIp6kj4PM8SGH9+2vpKu7lySHHaUCVwoi9ry6pZaypm69y9BNXIbCjvUfsn/vNsaOymFKn3Qui0EWZgE8gB17S/rtxez0h266I6Kfz6/4v2G8p3PchUJ7SyMbV72N3ZHE6d51mJF108UgG2gWc3kV6akHh6LKVUJMe3tHHbsPDM9lcOIqFJRSbFj5Ni2NB5icAYXuYr1LEvHGlhIYeXSE0vIq2jo6STs4P0EWwIttSsFvPhienx9xFQrVZXvY+elHZGbncVrfKr3LEfFo/MKws5j3llUAYDQa6fIaaZNZzDHvP3sa2VLVpncZQy5uQsHr8bDug9dxOfuYbashzdekd0kiHoUZdeRyudm+Zx+pB/dirpSrhLjx6PLhNxIpbkJh96ZP2L93O6NH5jKtT4agigjQjFAUZi/mqhoaW9qCQ1GrpT8hbny4r4mKYba0dlyEQndHG5+ueAuLNYFZ/u2y4J2IjNFnQkJayOGS/ZV4PB5sVituv0adhELcUAr+srZS7zKGVFyEwsaP3qOxtpKRuVmMd27XuxwRr8KMOvL7/WzZtRe7PQGAWpnFHHde3lRNr9urdxlDJuZDoa6qjG1rlpOalcsUzxYZgioiJ0wo1NY3UlvfSIYsgBe3Op1eXttSq3cZQyZ0GEUM8fl8rP3gDXq7Oxmfk8WEjq16lyTiVfpYyJoQcrikvIrunl5Gj8hDqejoT+hY+096963F01qDZrJgHTGZtPlLMWeMDN5GKUXHJ3+ne9v7+J3dWPImkH7ht7FkFRzTY/Ts/pDmtx4moehMsq/8afB4966VtH/4PMrjJHH6RaSdf3PwnLejgYaX7ibvxt9isNoH7wkPgRfWVnL9Gcf22sS6mL5SqNy3g7Jdm8keUcgk1xZZBVVETphRRxCYxWwymQJ7MXvMUTGL2Vm9k6TZl5D71V+Tc+0vwe+j4Z9343cf7mvrXP8KnRteJ33ht8j92m8wOtJo/Ofd+F2fvxict6ORtpXPYh05pd9xX28Hre89Rtr5N5N9zS/o3rmc3rINwfMt7z9B2vylMRcIAHvru1i3v0XvMoZEzIaC3+9ny+r/4PN5SUowM9G5We+SRDwLsypqW0cn+/ZXRt0CeDnX/ILEaQuxZBVgyR5LxpL/xtfZhLshsCy0UoqujW+Qcta12CeejSWrkMxLfoDf46Jnz4dHvW/l99H81q9JOed6TKm5/c552+vRrHYck+dhzZuAbfR0PM1VAPTsXoVmNGGfeHZknvQQeH5Nhd4lDImYDYXKfTsp37uNrNxRTHRuwaJcepck4pU1BQpCP8xKyqto7+wKzmKOllA4kt8VGFJpsCUCgWYcX08bCWNmBW+jmczYRk3FVbvnqPfV8cmLGOzJJM24KOScKX0EyuPC3VCGr68Ld90+LFmF+Pq6aP/4b6Rf+K1BfFZD74PdDdR19OldRsTFZCgopdi6Zjlej4ckh5WJzk16lyTi2fgLwBg6Q7m4rAKlwBTFs5iVUrSt+CPWkadgySoEwNcdmKVrsKf2u63RkRo8F46zZjfd25eRcfF3w5432hLJvOT7NP/7N9T/5Qc4pl5Awtg5tK38E0lzLsXb0cCB5/6LA3/6Dj17Vw/K8xtKXr/ib+uq9C4j4mKyo7m6dDf7d28hM28kRc5t2GRegoikAWYxb9tdTEpy4LfvaF0Ar/WDp3A3VpB7/UOhJ7Ujhs4qFXrsIL+rl+Z/P0LGxd/FaE8Z8PHsE87GPuHwVZWzajuepkrSL/wWB575BpmX/Q9GRxp1f/kBtlFTMTpST+Rp6ebFDVX814IiLKaY/H36mMRcKCil2PrJcjwuF0mOBCa1bdS7JBHPNCMUXRhyeH9VDU0tbYzMD7SrR2PTUesHT9FXup6crzyAKTkzeNyYGJiA5+9pg8T04HFfb8eAH9Le9np8HQ00vvKLwweVAqDyoS+Q//WnMafl9fse5fXQuuxJMi79Id62OpTfh230NADM6SNw1RVjH3/GYDzVIdPc7Wb5ngYWT8v7/BvHqJgLhdr9xZTu2kxG7gjGuHaRoIb31nkiwkadDvb0kMMl5VW4PB4SbFY8fo36KLpSUErR9p+n6N23lpzr7sd8RIewKSUHoyONvootWHLGBb7H58FZvZO085aGvU9zxkjybn6837H2j/+KcveStuAb/UIneH7Ni9jGzsGaOx53Qxn4fYdr9HvB7z/JZ6qPt3fUSShEi0N9CS5nL7nJYylql9nLIsIGmMW8eeceHAmBWcw1Liu+KJrF3PrBk/Ts/pDsK3+KwWIP9hNoVjsGsxVN00g69Yt0rP0X5rR8TGn5dKz9FwazFcfk+cH7af73IxiTMkibvxTNZAn2SRxisDrwQ8hxAHdTJb17PyJv6WMAmNJHgmaga9syjIlpeFpqsOQVReoliKgVextxenzYzPoPP46EmAqFuspSSnZsJCNnBBmeOlkJVURemF3WDjQ0BWYxR9lQ1EO6t7wDQMM/7up3PGPJf5M4LbCgX/IZV6G8LlqXPYnP2Y01fyLZ1/yi3xwCb2cTaMffdq6UovX9x0m74OsYLIHXxmC2krHkv2n94EmUz0P6hd/ClBR6dRELet0+VhU3cvHU+Lxa0JQ62DAY5ZRSvPfiM2xds5zRRVM4o+cDxrt26F2WiGdpY+B7W0MOr1yzgWf+9jKnTBgHaPytIScqJq2JoXPZjHweu27W598wBsVMF3pzXTX7tm8gPTsPMx4KXHv1LknEuzBNR3BwFrM5MIu5KUpmMYuhtWJPA06P7/NvGINiJhRKdm6iu7OdxJR0Clx7ZeE7EXkTQ0OhvbOL4v0VpB9cAE821Bmeetw+VhXHZ/N1TISC2+Vkz6Y1OJJT0TSN8U5pNhIRZk2Ggrkhh0vKq2jv6JK9mAXv7qzTu4SIiIlQqCjeTnN9DWmZOaR6G8nw1etdkoh348LPYt5bWo5SCpPRSLfXSGsUzmIWQ2P5nsa4bEKK+lBQSrF38zoUCrPFKpvoiKERZtRRYBbzPlIO7sUcrbOYxdDodnn5aF/8NSFFfSi0Nh6gvHg7aZk5GJWHQrd0MIsI0wxQFLrgW3l1LU2tbcH+hGgbiiqG3js74q8JKepDoXTnZnq6Ah3Mo137ZDVUEXkjB5jFXFGF2+0OzmKWvZjF8j2N+PwxMar/mEV1KHjcLnZtXE2CIxlN0xjj3q13SWI4CLN3glKKzTv2Yk8IXB3URtksZqGPLpeXXQc69C5jUEV1KFTu20lzXRVpWblY/H1ke2r0LkkMB2H6E2rrG6mtayA9LRWQ/gRx2KflrXqXMKiiOhSKt67H71dYrDZGusswEJsLaIkYkloA2ZNDDpdWVNPZ3UNyoiOwF7P0J4iD1u2XUBgSbU31lO3ZSmpmNgCj3CU6VySGhYFmMe/Zh/kzs5j7ZBazOGhDRSsxslrQMYnaUKjYt5PujjYSU9IxKTe5nkq9SxLDwYCzmCtl1JEIq6PPw976Lr3LGDRRGQpKKUp3bsJssWIwGMh378dI/E0SEVHGkgQF54QcLq2opq2jk7SUg/MTJBTEEdbvb9G7hEETlaHQ3txAXWUpyWmBpXVHuPfrXJEYFsadDyZLyOG9peX4/QqTyUS3zyCzmEWI9XHU2RyVoVBdtpeezg4cyamgFHmecr1LEsNBmFFHbreHbbuLSZWrBHEU8TQCKSpDYf/urRjNZgwGA5neA9iUU++SRLw72izmljbSUwML4EkoiHBaetyUNsZHv0LUhUJnWws1+/eQlBqYUZovVwliKIw4FRyhO4HtK6/E5XaTYLPhlVnM4ijiZWhq1IXCgYp9wX0TQPoTxBAJM+pIKcXWXcUyi1kck3hpQoq6UKguKwbAaDRi9ffKPsxiaEwIvxdz9YH6wxvqyFWCOIrddZ16lzAooioUPG4X5Xu3BTqYgUzvAX0LEsND6mjIOSXkcEl5FV3dPSQnJcosZvG5Klt68Ppif9WFqAqF+ur9dLY2kXSw6SjTE3/L0oooNMAs5p3FpZhMgVnMzTKLWXwOj09R0dKjdxknLapCoba8BI/HhcWWAMiVghgiYUKho6ubvaXlpMmoI3EcShu79S7hpEVVKFQU78BitQOgKT8ZXtl2U0SYJREKzw05XFJeRVtHJ+kH92KWVVHFsZBQGES93V201NdgTwr8J0z1NWHCq3NVIu4NMIu5uOzgXswmEz0+Ay2e0NsIcaQSCYXB01xfTW93J/bEQChkeaTpSAyBME1HgVnM+0hOSgSk6UgcO7lSGEQt9bV4vR7MlsBluvQniIjTDFAUustaeXUtjc2tsiqqOG5lTd34Y3x7zqgJhbqq/RiMh0d3SCiIiBsxBxKzQg6XVFThcruxJwRmMR+Q/gRxjJweP7XtfXqXcVKiIhR8Xi+15cXYHYGmI5u/h0R/fEwEEVFsgL2Yt+zcS8KhWcxui8xiFselJMbXQIqKUGhrrqe7s52ExMBKlJnSnyCGwjHMYpamI3G8Yr1fISpCobmuGldvNzZ7oGMvwydDUUWEpYyC3Kkhh0srArOYU2QWszhB5c2xPYEtOkKhvgYFGAyBcpJ8bfoWJOJfmKYjgF3FZcFZzC0eM70yi1kcp+Zut94lnJSoCIXqsr1YbfbgvyUURMSFaTrq7Opmd8l+0g5OWKuUqwRxAlp7JBROSk9XBy0NtcFJayhFkq9d15pEnLMkwpgws5grqmnv6Dy8tIWMOhInQELhJLU2HqCvu4sER6CTOcHfLTOZRWSNPQ9MoR/4xWXl+JTCHJzFLHsxi+MnoXCSOtta8Ho9mMyBZQSS/O36FiTiX5hZzB6Pl627ikk5OIs50MEsQ1HF8et0emJ6CW3dQ6GrPbBbkaYF/gNKf4KILC1sJ3N5dS2NLYdnMUt/gjhRSkFrb+xeLegeCh0tjRgMh0d4SH+CiKgRsyExO+RwSXkVLufBWcwKDrhlATxx4tp6PHqXcMJ0D4Xmhtrg/gkAidJ8JCIpzKgjpRRbdh2exXzAZcWndP+vIWJYS49L7xJOmK7vfI/bTWdrExbb4Ut1aT4SERWm6aiusZnq2jrS02QWsxgcsdzZrGsodHe04nL2HZ6jIMNRRSQlj4S86SGHSyuq6OzpJTnRAUgoiJPXJqFwYro6WnG7+rBYA81HCapHhqOKyBlgFvPOvaUYDQYMBgPNbpnFLE5ei4TCielqb8Xn9WI0mYDA6qhCREyYoaidXd3sKS0/vACeTFgTg6CzL3Z/udU9FODwcFSLcupZjohnZjuMmRdyuLSymrbPzmKWpiMxCHx+madwQo4cjmrxx26PvYhyY88Hc+gHfnFpBX6/H7PJRK/PQLPMYhaDwKdid/c1XUPhyOGocqUgIiZMf8KhWcz992KWWczi5PlieEtO3UJBKUV3Rxtm8+FJQmYlVwoiErSw/QkVNQf6zWKW/gQxWCQUToDH7cLn9QQ7mQEsEgoiEvJnQVJOyOF9+ytxulwk2KyBWcwSCmKQeGM4FEyff5PIcLuc+LweLAmH91Gw+KX5SERAmKsEpRRbd+0lwWYLDHRQcGlGiw7FiVjX3ddHd3cP37rhajJSUwFItcdu35RuoeBxOfH5fBiNcqUgImxiaCjUN7VQ9Zm9mE0aZFpid70aoR+r24VB9TIpJ5HcrBS9yzlpujUfBa4UvP1CQfoUxKBLHgF5M0IOl5ZX0dHVTXKSQ4eiRDzRNA2Fwh/Dw1A/S+crBS9G0+HLLBl9JAZd0UVhD+/cV4rL7aGxuXWICxLxxuV2o5SEwklzufrw+7wYjJ+ZpyBXCmKwTQxdFRUgOTGRwhF5Q1yMiEdmk4msjDRSk5P0LmVQ6HqloJTCYDjcgmVS0qYrBpHZDmPmhz11/RVLuP6KJUNckBDRT8c+BVfINCGN2B3GJaLQmPlhZzELIQam3zwFl1MiQERWmFFHQoij0+9Kwe0MLoR3iISEGDwaFIVfKlsIMTD9+hTcLlQMLxolop2CR0M31BHihIw8HW56W+8qhoRuoSCBICLOF7sbnYgoo+JjuOmxiKrdyVV0lSOEEAGG4bMbn66fwkdeLfgZPi+8ECKGaMPnF1bdnqmmaSEdzT5NQkEIEYUMurW0D7moij+/fl0cQggxMGk+irwjrxJArhSEEFHKGh9LWBwL3UIhsDdz/2DwSygIIaKRPUPvCoaMbqFgNJlClrlwa7LzlRAiCtkz9a5gyOh7pXBEKjg1e/gbCyGEnuzpelcwZPS7UjCGdio7DbLhiRAiCknz0RA8sDG0/6BPQkEIEY0kFCLPbLGijlgCT64UhBBRSUIh8mx2B6j+s5qlT0EIEZUkFCLPZk/EZDLj9RzebU2aj4QQUUlCIfISHImYzBa8nsMrWUrzkRAi6liTwWTRu4oho++VgqV/KPg1Iy5Ntk8UQkSRYTQcFfQMhQRHyJUCgNMg/QpCiCiSmKN3BUNK1xnNdkdSSCj0adKEJISIIplFelcwpHRdJTUxJS00FAyJOlUjhBBhZE7Qu4IhpXMopPcbfQTQYRw+vfxCiBggoTB0EpNT8fv7733aYZJQEEJEEQmFoWOzJ3LktgrtxuGzGqEQIsoZrZBWqHcVQ0rXUEhwJKKU6jeruceQggezjlUJIcRB6WOH1a5roHMoJKdmhA5L1TRpQhJCRIes4dV0BDqHQmpmDrYEO87enn7HO6QJSQgRDYZZfwLoHAqO5FTsSam4+vqHgvQrCCGigoTC0NI0jdyRhTj7jrxSkOYjIUQUkFAYehm5I/F5vf2OtZvkSkEIoTdt2M1mhigIhdSMLOCIfRUMiThlYTwhhJ6yTwHL8Ft2R/dQSMnIxmKx4XY5+x2XzmYhhK4KztK7Al3oHwrp2Vjt9pDO5mZzvk4VCSEEUHC23hXoQvdQSHAkkpSaETIstcE0SqeKhBACGC2hoIvACKQxIVcKTeYR+BheMwmFEFEirRCS8/SuQhe6hwJAek5+yMJ4Ps1MiylXp4qEEMPaML1KgGgJhew8DAYjXm//ZbQbzNKEJITQwTDtZIYoCYWckWNwJKXQ29XR77iEghBCF3KloK/E5FSyRxTQ3dne73izKV/6FYQQQ8uRDZnj9a5CN1ERCgCji6bgOWKugl8z0WySoalCiCE0+ky9K9BV1IRCzshCDEZjyJ7N0oQkhBhSBXP1rkBXURUKiUmp9BzRhCShIIQYUuMu0LsCXUVNKNgTk8kdNZaeIzqbW0x5eDHpVJUQYljJGD8sN9b5rKgJBYBR4yeH6VcwUm8erVNFQohhZeISvSvQXVSFQs7IQoxmMx63q9/xasvwTm4hxBCZdIneFeguqkIhe0Qhicmh/Qo1lnH4oqtUIUS8cWTByNP1rkJ3UfVJm+BIJG/0+JB+BY/BRoM0IQkhImnCxWCIqo9EXUTdKzB6/Cl43a5+m+4AVFuG3w5IQoghNOUKvSuIClEXCiPHTcJmT6Svp6vf8RrLePzRV64QIh7YM2DMfL2riApRN9YzK3802SMKqK8px56YHDzuMtipN48m31OhX3EnqfC3XVR2qJDj3znVzO8vSaCh288d/3GxrMxLu1Mxr8DIY4ttFGUcfamPdqfi/y138upeL219ijFpBh65yMqSIjMAf9vu4c7lTnrciltmWXj4osNbnVa0+7nohV42fsNBslUb3CcsRKyYfBkYo+7jUBdR9yoYDAYmTD+NqpJdKKXQtMMfVBXWyTEdChu+7sD3mUzY2ejnwhd6uXqKGaUUl7/Uh9kAb3zZTrIVfrPWzcIXetn9nUQclvAf2G6f4sIXesh2GHj56gRGJhuo7vSTdPD2zb1+bn2rjz9/MYGxaQYu+Xsv5xUauWRCIDC+/XYfDyy0SiCI4W3KlXpXEDWiLhQg0IRkTXDg7O0mwZEUPF5jGY8XEya8OlZ34rIc/Zu/HljtZlyaxvwCIyWtftbV+Nj5bQdTsgNXBk9cYiP71938Y6eHW2dbwt7ns1s8tPYp1tycgNkY+GAvSD38OPvbFClWjWunBkLg/DFGdjf5uWQC/H2HB4tR48rJ5kg8XSFigyMbCs/Ru4qoEZWN9NkjCsnMG0lHa3O/417NQq1lnE5VDS63T/HX7R5unmVB0zRcB3POZjr8G7vRoGExwuoq34D382axl7NGmrjtHSc5v+5i6hPd3PexC58/cElSlG6g16PYUuejtU+xodbH9BwjrX2Ke1Y6eXyxbcD7FmJYmH4NGGQ15kOiMhSMRiOTZp6Js7crZBRShXWyTlUNrtf3BvoNls4M/JY+KdNAQYrGXcudtPUp3D7FA6td1Hcr6rr9A97P/jY/L+/24PPDO1+x89N5Vh5Z6+Z/Pw4sLJiWoPH85Ql87fU+Tv9DN1+bYWbReBM/Wubku6dbKG/3M+vpbqY+0c3Luz0DPo4Q8UmDU2/Wu4ioEpXNRwAFE6aS4Eimt7sTR1JK8PgB8xh6DYnY/d06Vnfy/rTFzeIiE/lJgVw2GzVeucbOLW/2kf5QF0YNFo41snj80X9EfgXZDo1nLrNhNGjMyTdyoMvPw2vc3DPfCsAVk81c8ZkmolUVXnY0+nh8iY3xv+vmH1clkJuocfofe5hXYCTbEZW/Kwgx+MadDxnx0fowWKI2FLJHFJA3eiw1+4v7hYLSDJRYZzCj7xMdqzs5le1+/rPfx6vXJPQ7PiffyNZvJdLhDFwpZDkMnPHHbk7NG/jSNi9Jw2zQMBoONztNzjRQ3x24D4uxfweyy6v4zttO/nplAqWtfrx+mF8YeBtMyDCwvsbHZRMlFMQwcdqtelcQdaL2f7+maUyceSYelxO/v3/zSaltekyvnPrcVjfZDo1LJoR/Dik2jSyHgZIWHxsP+PnipIE7gueOMlLa6sf/mWa2fS1+8hK1kEAA+OVHLhaPNzE7z4jPD17/4e/z+Og3OkqIuJY8MjCLWfQTtaEAUDhhKo7kVLo72voddxnsVFon6lTVyfErxXNbPdw4w4zJ0P9D+1+7PKyq8LK/zc8bez1c+EIvl08ycdG4w+Hxtdf6uOs/h1eS/fapFlr6FN9718m+Fh9v7/Nw32o3t50WOlppV6OPl3Z5+cX5gWalSZkGDJrGnza7eXufh73Nfk7Llw43MUzMWSodzGFE9a/bqZk5FE6czp7Na0hOy+h3bp9tFuNcu3Sq7MT9Z7+Pqg7FzbNCf/uv6/bzg2VuGroVeUkaX5tu5u6D/QKHVHX4MWiHs3xUioFlX7Xz/fddTH+yhxHJGt87w8Idc/uHglKKb/zbyf8tsgbnPCSYNf58uY3b3nHi8sLjS2yMSI7q3xOEGBwGM8z+mt5VRCVNHTm8J8qU7drMK398hOz80Vhs/dvgF3a8SLa3VqfKhBAxa8oVcPWf9a4iKkX9r4UFE6aRO2osLQ0HQs4V22bpUJEQIuadeoveFUStqA8Fk9nM9DPPw+3sw+frP4mrxlJEjyFRp8qEEDEpaxKMOVfvKqJW1IcCQNG0U0nNyqG9uaHf8cDw1Jn6FCWEiE1ylXBUMREKjqQUpsw5h8625pAZzmW2aTE9PFUIMYQSc6WD+XPERCgATJp1Jo6kFLo7Q4enxsvSF0KICDvn+2CW9b6OJmZCISt/NGMnz6S9qSHk3K6EM/Ah442FEEeRlA+n3qR3FVEvZkJB0zSmnHYOmsGAy9nX71yPMYUy6zSdKhNCxIRzfwAm6+ffbpiLmVAAKCiaSt7ocbSGGZ66036m9C0IIcJLGQWzb9S7ipgQU6FgMpuZdsZ83K4+fN7+G+04DQ6KbbN1qkwIEdXO/QGYwm9UJfqLqVAAmDjjDLLzC2iqqw45tzvhNNyaXB4KIT4jdTTMukHvKmJGzIVCgiOROfMvxtXXg8ft7nfOY7Cx23aaTpUJIaLSvP8Bo2w5e6xiLhQAJs8+m/zCIprqqkLOFSfMpk+z61CVECLqpI2BGV/Ru4qYEpOhYLUlcOr8xXg9btwuZ79zPs3MroQzdKpMCBFV5v0PGGUAyvGIyVAAmDD9dEaNm0zTgdCrhVLbDLoNyTpUJYSIGnkzYMZ1elcRc2I2FMwWC6fOvxjl9+Ps6+l3zq8Z2ZFwlk6VCSH0pjQDXPJ/YIjZjzjdxPQrNn7KHMZMmk5TbejVQrl1Ck2mPB2qEkLoTZt9I4yco3cZMSmmQ8FoMjFn/sUYjUb6urv6n9Q0PnVchC+2n6IQ4jj5EzJgwT16lxGzYv4Ts3DidMZNnRN23kKHKZO9CafqUJUQQi+Gi34B9nS9y4hZMR8KBoOBOfMWYbHaQlZQBdiRcBZdhtShL0wIMeT8I8+AmdfrXUZMi/lQABg1bjKT55xN04Fq/H5/v3N+zcSnjoU6VSaEGCpKM2K47P9A0/QuJabFRShomsZZF15OZk4+zWGakRosBZRbZM8FIeLaGd+CnCl6VxHz4iIUAFIzsjnzwstx9vXg6usNOb/ZcR5OTTbXECIe+R05aOf/RO8y4kLchALAlFPPpWjqHOqr94ds2+ky2Nlqn69TZUKISDIseRCsiXqXERfiKhRMZjNzL74KR3Iq7c31Ief326bSYBqlQ2VCiEjxT74cplyhdxlxI65CASB31FhOnbeYjtZmvB53yPlPExfKZjxCxAmvPRvDF36rdxlxJe5CAWD2uRcxetxkGqrKQ851GdPZ7JBmJCFinULD+KU/QEKa3qXElbgMBZvdwdkXX4nBZKKrozXkfKltJtWW8TpUJoQYLJ5ZN6ONPU/vMuJOXIYCwJhJM5h2xnya62rw+3wh59c7LqLHIB1TQsQiZ9IYLJfcr3cZcSluQ0HTNM5c+EVyRhTQUFMRct5tSGBN4iX4kYkuQsQSr2bBcsNLYJKtdyMhbkMBIDktg7kXX4Xy++hqD21GajKPZFfCmTpUJoQ4Ud6LHsCQPVHvMuJWXIcCwKRZZzHrnItoqa8JOxppR8JZ1JkLdKhMCHG8ugovxnbWLXqXEdfiPhQ0TePsRVdSMGEqBypKQia1oWmsSVwi/QtCRLleWy6J1/1J7zLiXtyHAkCCI5HzL/8qjuRUWuprQ867DHY+SbxU9l4QIkq5NQum619Ek1nLERdVn4JLly5F0zQeeOCBfsdff/11tJNc+TC/YDxzF11FX08nvd2dIeebzSPYap93Uo8hhBh8fjR6Fz+GZdQsvUsZFqIqFABsNhsPPvggbW2heyOcrBlnL2DaGefRUFOO1+MJOV+cMIdS69RBf1whxIlrmPotUk//st5lDBtRFwoLFy4kNzeX++8feAzyK6+8wpQpU7BarRQWFvLII48c030bjUbmX3YdBeOnhO9fADY4LuSAufBEyxdCDKLqjPnkXiXzEYZS1IWC0Wjkvvvu47HHHqOmpibk/KZNm7jmmmv48pe/zI4dO/jZz37G3XffzZ///Odjun9HUgoXXPk1klLTwm7hqTQDq5Muo82YdbJPRQhxEmrNY8m66a8n3XQsjk/UhQLAFVdcwcyZM7n33ntDzv3mN79hwYIF3H333UyYMIGlS5dy++238/DDDx/z/ecXjOfcJdfidvaFXQbDq1lYlXwlPYakk3oeQogT06KlY7vxn9gSk/UuZdiJylAAePDBB3n++efZvXt3v+N79uxh7ty5/Y7NnTuXkpISfGGWsxjI1NPnMWfexbTU1+Ls7Qk532dIZFXSFbg1y4k9ASHECelRNlxXPEfGyCK9SxmWojYU5s2bx6JFi/jJT/rvpqSUCrmcDNc38HkMBgPzLrmGaafPo76qDI/bFXKbDlMWHyd+QYaqCjFEPMpIw7n3kz/9PL1LGbai+tPugQce4K233mLNmjXBY6eccgqrV6/ud7s1a9YwYcIEjEbjcd2/2WJlwZVLGXvKLGr378Pn84bcpsFSwKeOi07sCQghjplSUD75O4xZcJPepQxrUR0K06ZN4/rrr+exxx4LHvvhD3/I8uXL+eUvf8m+fft4/vnnefzxx/nRj350Qo9hT0xi0bW3kj9mPLX7i/H7/SG3KbdNYXvCWSf8PIQQR6cU7Mm7inFX/1w6lnUW1aEA8Mtf/rJf89Ds2bP55z//yYsvvsjUqVO55557+MUvfsHSpUtP+DHSMnO4+Nqvk5aZQ11ladjmqJ32s2UOgxARsi1tMeOW/v64r/bF4NPUiTTIx6n9e7by7xd+j18psvNHh95AKU7vWcZ4186hL06IOPWpbT6TvvkcyWkZepciiIErhaE0dvJMLrj8BnxuN+3NDaE30DQ+dVxEiXXG0BcnRBz6RDuDgq/+TgIhikgoHGHKaecyd/FVdLW30N0ZZqkNTWND4kKKbbIOixAn42PfLPKueYickYV6lyI+w6R3AdFG0zROO/9Sujva2bDybQwGI/YwE2g2OS7Aj4HJzk06VClEbPvYN4u8ax9h7OSZepcijiBXCmEE1kj6MjPPWUhjbSU9XR1hb7fFcR67Ek4f4uqEiG2rfTPIvebXjJ86R+9SRBhypTAAs8XKwitvxKAZ2Lz6fVCjcCSnhtxum/1c/BiY1rdu6IsUIsas9s0g5+pHKJp2qt6liAFIKByF2WLlgiu+hsFoZOOH76KAxDDBsMM+Fz9GZvR9MuQ1ChEL/Ao+9s8i7+qHKJp+mt7liKOQUPgcZouF87/4VTTNwMYP30EpP0kp6SG322U/E79mZGbvR8jUGyEOcysj73vPZMI19zJhujS3RjuZp3CMfF4vH/77H3y64t+kZeWSlBp+CN0oVzFndb+HidAlM4QYbrr9Nt7yncOsq/+HSTPP1LsccQwkFI6Dz+vl43f+ybrlb5KakU1yWmbY26V765nX+QZ21T3EFQoRPZr8ybzpOZe513yXSbNkmZhYIaFwnHw+H6vf/RfrPnid5PQsUtLDb8aT4O9mXufrZPjCTIITIs6Ve7N4X53L/Ku+zuTZZ+tdjjgOEgonwOfzsXbZa6x5/1UcySmkZeWFvZ1ReTir+z1Gu/cNcYVC6GeLcyQb7QtYeM0tjJkks/9jjYTCCfL7/WxY+TZr3n8Vv/KTO2ps+NUdlWJ63xqmypBVEecUsKK7iNq8RSy69usyUzlGSSicBKUUxdvWs+L1v9LZ2syIsRMwGsMP6Cpw7eGM7mXSAS3ikgcTb3Wcgm/SpSy6+haS08P3t4noJ6EwCA5UlLDs5Wc5UF5C/pgJWKy2sLfL8NQxr+sNElTo9p9CxKpWUni1Yyr5p32BCy6/AZvdoXdJ4iRIKAySjtYmlv3rWUq2byBrRAGOpJSwt0vwd3N21zvkeKuHuEIhBt9eXwHv9ZzCnIVXcdaFl2Mym/UuSZwkCYVB5OrrZdWbf2fLmv+QlJpOWmZu+BsqxdS+dUztW4sBeflF7PFiZLlzKnu1Ccy/7DpmnHWB7JgWJyQUBpnP52PDyn+z5v1XQdPIGTlmwP8sWZ4azu5+B4e/a4irFOLEtRnSebVtMp7UcSy8aqksbBdnJBQiQCnF3i1rWfHGX+lqb2XEmIE7oM1+J2f0LGO0u2SIqxTi+O0ynMK/G3IZUTSdBVd+jbzR4/QuSQwyCYUIqi3fxwevPEdteQk5IwvD7stwSKFrN6f2rMCiXENYoRDHpk+z84F7Nnu6k5lx9gXMu+Tao76fReySUIiwrvZWPnr7JXZ++hEJjkQyckcO2Jxk93VyZvd75EontIgi1aYxvN48Fi0xi7mLr2b6medjNBr1LktEiITCEPD5fGxft5JP3n2Z7s428gqKMFss4W+sFBOdm5nRu1rmNAhd9RoSWa2dzsZ6IyPHTWbBFTcwYswEvcsSESahMIQOVJay8o2/UVm8g/Sc/AEX1ANw+DqY3bOSUZ6yIaxQCPCjUWydyX/aRuD0asw46wLOWfwlaS4aJiQUhlhfTzfrPnidLZ98gM/nI3f02AE7oQHy3OWc2rOCJH/70BUphq0WYy6rzeewu6aT9Jx8zl1yDZNmnYXBIDv3DhcSCjpQSrF/9xY+/PdL1FWVkX2UyW4ABuVlct9GpvStlyYlERFuzcJW21zWdWTS293JhOmnM/+yr5CRk693aWKISSjoqLujjdXvvcyOdR9iMBrIHll41KsGu6+TOb0rGeUuHcIqRbyrtEzkYzWHqgPNpGZlc8b5lzH9rAsG7vcScU1CQWd+v5/iretYs+x1Gqr3k5qZQ0pG9lFnh+a5y5nTs5Jkf9sQViriTachjU9t89ja4EcpmDz7bM6+6ArSs8MvBS+GBwmFKNHT1cGmD99jyycf0NvdSc7IMUddWMygvEzq28QU56eYlXsIKxWxrtuQzI6EM9nanUlbcxP5Y4o4+6IrGD/1VOk7EBIK0aauqoy1H7xO6Y5NGE1mskcWHLVJyeLvY5JzExOdWyQcxFH1GhLZmXAGu/3jqKupxJ6YzJx5FzP73EXYE5P0Lk9ECQmFKOTz+Sjespa1/3nzmJuUAuGwmQnOzVgkHMRn9Gl2diecTrFlGg11B3D19TBuymzmXnwV+QXj9S5PRBkJhSjW09XBpo/eZ+snH9DT1UHOyEJs9sSjfo/Z7zx45SDhMNy5NBt7Ek5lr3UWra1ttDc3kJk7kjMWfpEpp54jy1yLsCQUYkB99X7WLHuN0h2b0AwGsvJHD7iRzyGHw2GLrKc0zHg0C3tts9ljnUNLRxdtTfUkpaQx7fT5zDr3IlLSs/QuUUQxCYUY4fP52LdtPZs+ep/a8mKMJhOZeccaDpsPXjlIOMSzTkMaJbYZlFmm0NbVQ0vDARxJKUw59Vxmzl1AZu5IvUsUMUBCIcZ43G5Kdmxg88fLjiscjMpDgauY8a7tZHrrhqhaEWl+NOrMY9hnm0WduYDurnZa6mtJcCQxadaZzJp7ITkjC/UuU8QQCYUY5XG7Kd25kc0fL6NmfzEGo5GsvFFYbAmf+72p3kaKnNspdO+REUsxyqXZKLNOpcQ2kx5jCj1dHTTX1WBLsDNhxunMPvcickeNld3QxHGTUIhxJxMOJuWmwLWXIuc20n2NQ1CtOFmtxmz22WZSaZ2EFxM9Xe20NtRhtlgpmjqH2fMWMWLMBAkDccIkFOKE1+MJhMPqD6gu3YNmMJCenUeCI+mYPiDSvfWMd26jwFWMGc8QVCyOlVuzUm0posw6jWZzPj6fl/bmRrraW7A7kiicNJ3Z51zIqPGnSBiIkyahEGcOhcP2dauoKS/G2dNDUloGqZnZR50Ed4jJ72KUu4TR7n3keqow4huCqsWRPJiptYyj0jqROvMY/JoRV18vLQ21eNwu0jJzOGXOOUyceQbZIwokDMSgkVCIU36/n/rq/ezbvoE9m9fQ3tKIyWQmPSuPhGOcvWr2OxnpKWOUax95nkoJiAjzaBYOmAuptkyg1jIWn2ZGKUVXewvtTQ0YzWbyC8Yz9fT5FE2bI/sbiIiQUBgGers7Kdu1hV0bP6a2vAS3s4+ktAxSMrKPeVtFk3KT565ghKeMfHc5NtUX4aqHB6eWQI1lHDWWIurNo/Frgas5r8dNW1M9PZ3tJKakUTTtVCbNOovR40/BaPr8Kz4hTpSEwjDi9/s5UFHCvu2fsnfrejpaGjFbrKRm5hxz3wMASpHpPcAIdxl5nkpSfU0YkLfRsXBrFppMI2g0j6TRNIpWUw5KCyxC5/N56WxrpqutFU2DjJwRTD19HkXTTpN9DcSQkVAYpro72ynbtYXdGz+mvrqcvt4urDYHKRlZxxcQBK4iMrx1ZHlqyfIeINNbJ0NdD3JptmAINJhH0W7MCoYAgN/no6ujlc7WZpTyk5Sawbgpsxg7eQYFRVOxJth1rF4MRxIKw5zf76extpLqsj3s276BxpoK+nq7sNjsJKdlYE9MOe7llDXlJ8XXTJa3lizPAbK8tTj8XRF6BtGlT7PTbM6n0TSKBvNI2o1ZcETAer0eutpb6G5rReEnMTmNggnTGD91NqOLphx1Fz4hIk1CQQT5/X6a66qpLt1Dyc6NNNRU0NvVicFkJCklncTUdEymE1tEze7rJMNbT5KvjWR/G8m+VpJ8bViVc5CfxdDo0xx0GDPoMGUE/jz45TaEzg9RSuHq66Gns4OeznbQNJLT0imcOJ3CCVMZOW4yyWkZQ/8khAhDQkGEpZSirame2vJ9VO7bSWXJLro72vD7fFhsCdiTkrEnppz0lo1Wf28gKHxtBwMjEBZJvnZdRzv50XBrNpwGO32GxOCHfqcxnXZjJh7DwMuKKKVw9vbQ09VOb1cnSvmx2uwkpaRROGkGhROmMGLsRBk9JKKShII4Jr3dnRyoKKGhpoLqsr0011fT292Jz+vBaDJjT0zBnpSM1WYfnDHzSmFWLqzKiUU5sfr7Dv4Z+He/Y8qJSbnRDnZ2awff0hoKhYZfM+DHgMIY/PuhD3yXwY5TSwj8abDj1AJ/urSEkGafgUtVOHu6AyHQ3YXy+7Ha7SSlpDN6/CnkFYwne8RoMnNHyXLVIupJKIgT0tvdSdOBKprqqjlQUcqByhJ6Ojtwu/rQNA2bIwl7YhJWmx2T2RI3k6t8Pi+uvt7DX85eAGwJDpLTMhhdNIW80ePIyh9NZu5IGT4qYo6EghgUHreLloZamg5UU19dTs3+PXS1t+Jy9uH1uEHTMGgaFpsdqy0Bq82OJSHhmGZZ68Hn9eJy9uLq68HZ24vbFZiXoWkGbHYH1gQ76Vl5ZOWPJntEATkjCkjPzpcQEDFPQkFEhN/vp7u9lc72FrraW+hqb6WtqZ6mumo625pxO/twOfvw+wP9BiazGaPJgslkxmgyHfw6+Hdj4M+T3VReKYXP68Hr8eD1uvF6PPg++3evh0ALlEIzGLAmOLDZHWRk55M1ooDUjGxS0jNJTssiOS3zpPtThIhGEgpiyLldTrraWwMTtdpb6WpvobWxjp6uDnq7O3E7e/F6Pfi83oNfHnw+L4feqoeaoj771tWCPQrq8Hngs41WSilMJjMmswWj2YzJZMZqS8CelIIjOZWklHQSHIkkOJJITsskNSObpNQM6QcQw4qEgog6Pp8Pt7MPt6sveEXR7+/OPjyeg5PjDr59lVIoFQgEg9GIphkwGo0YDEY0gwGLLQFbgh1bggOr3YHt4FVAPPV3CDEYJBSEEEIEnVwjrRBCiLgioSCEECJIQkEIIUSQhIIQQoggCQUhhBBBEgpCCCGCJBSEEEIESSgIIYQIklAQQggRJKEghBAiSEJBCCFEkISCEEKIIAkFIYQQQRIKQgghgiQUhBBCBEkoCCGECJJQEEIIESShIIQQIkhCQQghRJCEghBCiCAJBSGEEEESCkIIIYIkFIQQQgRJKAghhAiSUBBCCBEkoSCEECJIQkEIIUSQhIIQQoggCQUhhBBBEgpCCCGCJBSEEEIESSgIIYQI+v+e+5MRrKyQzgAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"code","source":"for column in categorical_data:\n    unique_vals = data[column].unique()\n    print(f'Column: {column}')\n    print(unique_vals)\n    print(len(unique_vals))\n    print('----------------------')","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d52IfuYedh8C","outputId":"820b6be1-d91b-4d7f-ee13-f031a3ce1ab0","execution":{"iopub.status.busy":"2023-07-29T17:10:31.649521Z","iopub.execute_input":"2023-07-29T17:10:31.649932Z","iopub.status.idle":"2023-07-29T17:10:31.660285Z","shell.execute_reply.started":"2023-07-29T17:10:31.649899Z","shell.execute_reply":"2023-07-29T17:10:31.658814Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Column: country\n['France' 'Spain' 'Germany']\n3\n----------------------\nColumn: gender\n['Female' 'Male']\n2\n----------------------\n","output_type":"stream"}]},{"cell_type":"code","source":"for col in categorical_data:\n    sns.countplot(x=col, data=data)\n    plt.xticks()\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:10:33.913759Z","iopub.execute_input":"2023-07-29T17:10:33.914208Z","iopub.status.idle":"2023-07-29T17:10:34.339386Z","shell.execute_reply.started":"2023-07-29T17:10:33.914175Z","shell.execute_reply":"2023-07-29T17:10:34.338076Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}]},{"cell_type":"markdown","source":"**Label Encoding**","metadata":{"id":"d0ksDPFKZXng"}},{"cell_type":"code","source":"label_encode_cols = categorical_data\nencoded_data = data.copy()\n\nencoders = {}\n\nfor column in label_encode_cols:\n    encoder = LabelEncoder()\n    encoded_data[column] = encoder.fit_transform(encoded_data[column])\n    encoders[column] = encoder\n\ndump(encoders, '/kaggle/working/encoders.joblib')","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zQ1spZW4YZvP","outputId":"03638fd9-c9ce-48a7-adde-3e25be15a92b","execution":{"iopub.status.busy":"2023-07-29T17:10:39.675833Z","iopub.execute_input":"2023-07-29T17:10:39.676317Z","iopub.status.idle":"2023-07-29T17:10:39.698308Z","shell.execute_reply.started":"2023-07-29T17:10:39.676273Z","shell.execute_reply":"2023-07-29T17:10:39.696791Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"['/kaggle/working/encoders.joblib']"},"metadata":{}}]},{"cell_type":"code","source":"encoded_data.head()","metadata":{"id":"k3Qdu4rwrcDX","colab":{"base_uri":"https://localhost:8080/","height":301},"outputId":"d373cb5b-c1fc-448a-fb54-47c0986cc312","execution":{"iopub.status.busy":"2023-07-29T17:10:44.153472Z","iopub.execute_input":"2023-07-29T17:10:44.153901Z","iopub.status.idle":"2023-07-29T17:10:44.173958Z","shell.execute_reply.started":"2023-07-29T17:10:44.153867Z","shell.execute_reply":"2023-07-29T17:10:44.172694Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"             credit_score  country  gender  age  tenure    balance  \\\ncustomer_id                                                          \n15634602              619        0       0   42       2       0.00   \n15647311              608        2       0   41       1   83807.86   \n15619304              502        0       0   42       8  159660.80   \n15701354              699        0       0   39       1       0.00   \n15737888              850        2       0   43       2  125510.82   \n\n             products_number  credit_card  active_member  estimated_salary  \\\ncustomer_id                                                                  \n15634602                   1            1              1         101348.88   \n15647311                   1            0              1         112542.58   \n15619304                   3            1              0         113931.57   \n15701354                   2            0              0          93826.63   \n15737888                   1            1              1          79084.10   \n\n             churn  \ncustomer_id         \n15634602         1  \n15647311         0  \n15619304         1  \n15701354         0  \n15737888         0  ","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>credit_score</th>\n      <th>country</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>tenure</th>\n      <th>balance</th>\n      <th>products_number</th>\n      <th>credit_card</th>\n      <th>active_member</th>\n      <th>estimated_salary</th>\n      <th>churn</th>\n    </tr>\n    <tr>\n      <th>customer_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>15634602</th>\n      <td>619</td>\n      <td>0</td>\n      <td>0</td>\n      <td>42</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15647311</th>\n      <td>608</td>\n      <td>2</td>\n      <td>0</td>\n      <td>41</td>\n      <td>1</td>\n      <td>83807.86</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15619304</th>\n      <td>502</td>\n      <td>0</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>15701354</th>\n      <td>699</td>\n      <td>0</td>\n      <td>0</td>\n      <td>39</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15737888</th>\n      <td>850</td>\n      <td>2</td>\n      <td>0</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    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#Heatmap\n\nplt.figure(figsize=(10, 8))\ncorr_matrix = encoded_data.corr()\n\nsns.heatmap(corr_matrix, cmap='coolwarm', linewidths=0.1)\n\nplt.plot()","metadata":{"id":"eI9g7LarIsun","colab":{"base_uri":"https://localhost:8080/","height":966},"outputId":"99b2fbb4-a8cf-43e4-b3d8-e04ae1316259","execution":{"iopub.status.busy":"2023-07-29T17:10:46.349463Z","iopub.execute_input":"2023-07-29T17:10:46.349952Z","iopub.status.idle":"2023-07-29T17:10:46.855082Z","shell.execute_reply.started":"2023-07-29T17:10:46.349912Z","shell.execute_reply":"2023-07-29T17:10:46.853887Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"[]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x800 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"encoded_data.describe()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:10:49.709430Z","iopub.execute_input":"2023-07-29T17:10:49.709898Z","iopub.status.idle":"2023-07-29T17:10:49.767764Z","shell.execute_reply.started":"2023-07-29T17:10:49.709864Z","shell.execute_reply":"2023-07-29T17:10:49.766331Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"       credit_score       country        gender           age        tenure  \\\ncount  10000.000000  10000.000000  10000.000000  10000.000000  10000.000000   \nmean     650.528800      0.746300      0.545700     38.921800      5.012800   \nstd       96.653299      0.827529      0.497932     10.487806      2.892174   \nmin      350.000000      0.000000      0.000000     18.000000      0.000000   \n25%      584.000000      0.000000      0.000000     32.000000      3.000000   \n50%      652.000000      0.000000      1.000000     37.000000      5.000000   \n75%      718.000000      1.000000      1.000000     44.000000      7.000000   \nmax      850.000000      2.000000      1.000000     92.000000     10.000000   \n\n             balance  products_number  credit_card  active_member  \\\ncount   10000.000000     10000.000000  10000.00000   10000.000000   \nmean    76485.889288         1.530200      0.70550       0.515100   \nstd     62397.405202         0.581654      0.45584       0.499797   \nmin         0.000000         1.000000      0.00000       0.000000   \n25%         0.000000         1.000000      0.00000       0.000000   \n50%     97198.540000         1.000000      1.00000       1.000000   \n75%    127644.240000         2.000000      1.00000       1.000000   \nmax    250898.090000         4.000000      1.00000       1.000000   \n\n       estimated_salary         churn  \ncount      10000.000000  10000.000000  \nmean      100090.239881      0.203700  \nstd        57510.492818      0.402769  \nmin           11.580000      0.000000  \n25%        51002.110000      0.000000  \n50%       100193.915000      0.000000  \n75%       149388.247500      0.000000  \nmax       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>credit_score</th>\n      <th>country</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>tenure</th>\n      <th>balance</th>\n      <th>products_number</th>\n      <th>credit_card</th>\n      <th>active_member</th>\n      <th>estimated_salary</th>\n      <th>churn</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>10000.000000</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>0.746300</td>\n      <td>0.545700</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>0.827529</td>\n      <td>0.497932</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>0.000000</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</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>0.000000</td>\n      <td>1.000000</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>1.000000</td>\n      <td>1.000000</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>2.000000</td>\n      <td>1.000000</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>"},"metadata":{}}]},{"cell_type":"markdown","source":"**Splitting Data**","metadata":{"id":"UOsT4MPf9UJF"}},{"cell_type":"code","source":"X = encoded_data.drop(['churn'], axis = 1)\ny = encoded_data['churn']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=14)\n\nprint('Training Data: ', X_train.shape, ', ' , y_train.shape)\nprint('Testing Data: ', X_test.shape, ', ' , y_test.shape)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y0DOmkfl9Swn","outputId":"d6dbb030-a503-45a9-b417-4843f46c7788","execution":{"iopub.status.busy":"2023-07-29T17:10:54.130264Z","iopub.execute_input":"2023-07-29T17:10:54.131105Z","iopub.status.idle":"2023-07-29T17:10:54.145889Z","shell.execute_reply.started":"2023-07-29T17:10:54.131063Z","shell.execute_reply":"2023-07-29T17:10:54.144484Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Training Data:  (7000, 10) ,  (7000,)\nTesting Data:  (3000, 10) ,  (3000,)\n","output_type":"stream"}]},{"cell_type":"code","source":"scaler = StandardScaler()\nscaler.fit(X_train)\nX_train = scaler.transform(X_train)\nX_test = scaler.transform(X_test)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:12:01.470119Z","iopub.execute_input":"2023-07-29T17:12:01.470974Z","iopub.status.idle":"2023-07-29T17:12:01.486645Z","shell.execute_reply.started":"2023-07-29T17:12:01.470925Z","shell.execute_reply":"2023-07-29T17:12:01.484961Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"evaluation_dict = {}","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:12:03.569978Z","iopub.execute_input":"2023-07-29T17:12:03.570499Z","iopub.status.idle":"2023-07-29T17:12:03.576522Z","shell.execute_reply.started":"2023-07-29T17:12:03.570461Z","shell.execute_reply":"2023-07-29T17:12:03.574815Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"# Logistic Regression\nlr_model = LogisticRegression()\nlr_model.fit(X_train, y_train)\nlr_pred = lr_model.predict(X_test)\nlr_accuracy = accuracy_score(y_test, lr_pred)\nlr_precision = precision_score(y_test, lr_pred,average='macro')\nlr_recall = recall_score(y_test, lr_pred,average='macro')\nlr_f1_score = f1_score(y_test, lr_pred,average='macro')\nevaluation_dict['Logistic Regression'] = [lr_precision, lr_recall, lr_f1_score, lr_accuracy]","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:15:08.393913Z","iopub.execute_input":"2023-07-29T17:15:08.394316Z","iopub.status.idle":"2023-07-29T17:15:08.439380Z","shell.execute_reply.started":"2023-07-29T17:15:08.394288Z","shell.execute_reply":"2023-07-29T17:15:08.437745Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"# Support Vector Classifier (SVC)\nsvc_model = SVC()\nsvc_model.fit(X_train, y_train)\nsvc_pred = svc_model.predict(X_test)\nsvc_accuracy = accuracy_score(y_test, svc_pred)\nsvc_precision = precision_score(y_test, svc_pred,average='macro')\nsvc_recall = recall_score(y_test, svc_pred,average='macro')\nsvc_f1_score = f1_score(y_test, svc_pred,average='macro')\nevaluation_dict['SVC'] = [svc_precision, svc_recall, svc_f1_score, svc_accuracy]","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:12:07.870247Z","iopub.execute_input":"2023-07-29T17:12:07.870706Z","iopub.status.idle":"2023-07-29T17:12:09.494130Z","shell.execute_reply.started":"2023-07-29T17:12:07.870656Z","shell.execute_reply":"2023-07-29T17:12:09.493195Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"# Random Forest\nrf_model = RandomForestClassifier()\nrf_model.fit(X_train, y_train)\nrf_pred = rf_model.predict(X_test)\nrf_accuracy = accuracy_score(y_test, rf_pred)\nrf_precision = precision_score(y_test, rf_pred,average='macro')\nrf_recall = recall_score(y_test, rf_pred,average='macro')\nrf_f1_score = f1_score(y_test, rf_pred,average='macro')\nevaluation_dict['Random Forest'] = [rf_precision, rf_recall, rf_f1_score, rf_accuracy]\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:12:10.269869Z","iopub.execute_input":"2023-07-29T17:12:10.270319Z","iopub.status.idle":"2023-07-29T17:12:11.620799Z","shell.execute_reply.started":"2023-07-29T17:12:10.270284Z","shell.execute_reply":"2023-07-29T17:12:11.619796Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"# Gradient Boosting\ngb_model = GradientBoostingClassifier()\ngb_model.fit(X_train, y_train)\ngb_pred = gb_model.predict(X_test)\ngb_accuracy = accuracy_score(y_test, gb_pred)\ngb_precision = precision_score(y_test, gb_pred,average='macro')\ngb_recall = recall_score(y_test, gb_pred,average='macro')\ngb_f1_score = f1_score(y_test, gb_pred,average='macro')\nevaluation_dict['Gradient Boosting'] = [gb_precision, gb_recall, gb_f1_score, gb_accuracy]\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:12:11.622891Z","iopub.execute_input":"2023-07-29T17:12:11.623335Z","iopub.status.idle":"2023-07-29T17:12:12.771499Z","shell.execute_reply.started":"2023-07-29T17:12:11.623303Z","shell.execute_reply":"2023-07-29T17:12:12.770653Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"# Multilayer Perceptron (Neural Network)\nmlp_model = MLPClassifier()\nmlp_model.fit(X_train, y_train)\nmlp_pred = mlp_model.predict(X_test)\nmlp_accuracy = accuracy_score(y_test, mlp_pred)\nmlp_precision = precision_score(y_test, mlp_pred,average='macro')\nmlp_recall = recall_score(y_test, mlp_pred,average='macro')\nmlp_f1_score = f1_score(y_test, mlp_pred,average='macro')\nevaluation_dict['Multilayer Perceptron'] = [mlp_precision, mlp_recall, mlp_f1_score, mlp_accuracy]\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:20:39.197177Z","iopub.execute_input":"2023-07-29T17:20:39.197740Z","iopub.status.idle":"2023-07-29T17:20:55.039560Z","shell.execute_reply.started":"2023-07-29T17:20:39.197696Z","shell.execute_reply":"2023-07-29T17:20:55.037966Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n  warnings.warn(\n","output_type":"stream"}]},{"cell_type":"code","source":"# K-Nearest Neighbors (KNN)\nknn_model = KNeighborsClassifier()\nknn_model.fit(X_train, y_train)\nknn_pred = knn_model.predict(X_test)\nknn_accuracy = accuracy_score(y_test, knn_pred)\nknn_precision = precision_score(y_test, knn_pred,average='macro')\nknn_recall = recall_score(y_test, knn_pred,average='macro')\nknn_f1_score = f1_score(y_test, knn_pred,average='macro')\nevaluation_dict['KNN'] = [knn_precision, knn_recall, knn_f1_score, knn_accuracy]","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:20:55.042967Z","iopub.execute_input":"2023-07-29T17:20:55.045058Z","iopub.status.idle":"2023-07-29T17:20:55.421783Z","shell.execute_reply.started":"2023-07-29T17:20:55.044990Z","shell.execute_reply":"2023-07-29T17:20:55.420377Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"# XGBoost\nxgb_model = XGBClassifier()\nxgb_model.fit(X_train, y_train)\nxgb_pred = xgb_model.predict(X_test)\nxgb_accuracy = accuracy_score(y_test, xgb_pred)\nxgb_precision = precision_score(y_test, xgb_pred,average='macro')\nxgb_recall = recall_score(y_test, xgb_pred,average='macro')\nxgb_f1_score = f1_score(y_test, xgb_pred,average='macro')\nevaluation_dict['XGBoost'] = [xgb_precision, xgb_recall, xgb_f1_score, xgb_accuracy]","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:20:55.423638Z","iopub.execute_input":"2023-07-29T17:20:55.424210Z","iopub.status.idle":"2023-07-29T17:20:56.007171Z","shell.execute_reply.started":"2023-07-29T17:20:55.424163Z","shell.execute_reply":"2023-07-29T17:20:56.006230Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"# LightGBM\nlgbm_model = LGBMClassifier()\nlgbm_model.fit(X_train, y_train)\nlgbm_pred = lgbm_model.predict(X_test)\nlgbm_accuracy = accuracy_score(y_test, lgbm_pred)\nlgbm_precision = precision_score(y_test, lgbm_pred,average='macro')\nlgbm_recall = recall_score(y_test, lgbm_pred,average='macro')\nlgbm_f1_score = f1_score(y_test, lgbm_pred,average='macro')\nevaluation_dict['LightGBM'] = [lgbm_precision, lgbm_recall, lgbm_f1_score, lgbm_accuracy]","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:20:56.009321Z","iopub.execute_input":"2023-07-29T17:20:56.010095Z","iopub.status.idle":"2023-07-29T17:20:56.711854Z","shell.execute_reply.started":"2023-07-29T17:20:56.010059Z","shell.execute_reply":"2023-07-29T17:20:56.710596Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"# AdaBoost\nadaboost_model = AdaBoostClassifier()\nadaboost_model.fit(X_train, y_train)\nadaboost_pred = adaboost_model.predict(X_test)\nadaboost_accuracy = accuracy_score(y_test, adaboost_pred)\nadaboost_precision = precision_score(y_test, adaboost_pred,average='macro')\nadaboost_recall = recall_score(y_test, adaboost_pred,average='macro')\nadaboost_f1_score = f1_score(y_test, adaboost_pred,average='macro')\nevaluation_dict['AdaBoost'] = [adaboost_precision, adaboost_recall, adaboost_f1_score, adaboost_accuracy]","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:20:56.713063Z","iopub.execute_input":"2023-07-29T17:20:56.713368Z","iopub.status.idle":"2023-07-29T17:20:57.156195Z","shell.execute_reply.started":"2023-07-29T17:20:56.713341Z","shell.execute_reply":"2023-07-29T17:20:57.155194Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"df = pd.DataFrame.from_dict(evaluation_dict, orient='index', columns=['Precision', 'Recall', 'F1 Score','Accuracy'])\ndf","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:21:02.367642Z","iopub.execute_input":"2023-07-29T17:21:02.368762Z","iopub.status.idle":"2023-07-29T17:21:02.385782Z","shell.execute_reply.started":"2023-07-29T17:21:02.368710Z","shell.execute_reply":"2023-07-29T17:21:02.384285Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"                       Precision    Recall  F1 Score  Accuracy\nLogistic Regression     0.713552  0.567472  0.572109  0.806000\nSVC                     0.836072  0.655813  0.690961  0.847333\nRandom Forest           0.823764  0.695842  0.731569  0.856667\nGradient Boosting       0.842471  0.694556  0.733175  0.860333\nMultilayer Perceptron   0.805899  0.703150  0.735075  0.854000\nKNN                     0.769690  0.658411  0.686799  0.834333\nXGBoost                 0.798302  0.708512  0.738115  0.853000\nLightGBM                0.823503  0.718793  0.752404  0.862667\nAdaBoost                0.816701  0.701024  0.735212  0.856333","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>Precision</th>\n      <th>Recall</th>\n      <th>F1 Score</th>\n      <th>Accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Logistic Regression</th>\n      <td>0.713552</td>\n      <td>0.567472</td>\n      <td>0.572109</td>\n      <td>0.806000</td>\n    </tr>\n    <tr>\n      <th>SVC</th>\n      <td>0.836072</td>\n      <td>0.655813</td>\n      <td>0.690961</td>\n      <td>0.847333</td>\n    </tr>\n    <tr>\n      <th>Random Forest</th>\n      <td>0.823764</td>\n      <td>0.695842</td>\n      <td>0.731569</td>\n      <td>0.856667</td>\n    </tr>\n    <tr>\n      <th>Gradient Boosting</th>\n      <td>0.842471</td>\n      <td>0.694556</td>\n      <td>0.733175</td>\n      <td>0.860333</td>\n    </tr>\n    <tr>\n      <th>Multilayer Perceptron</th>\n      <td>0.805899</td>\n      <td>0.703150</td>\n      <td>0.735075</td>\n      <td>0.854000</td>\n    </tr>\n    <tr>\n      <th>KNN</th>\n      <td>0.769690</td>\n      <td>0.658411</td>\n      <td>0.686799</td>\n      <td>0.834333</td>\n    </tr>\n    <tr>\n      <th>XGBoost</th>\n      <td>0.798302</td>\n      <td>0.708512</td>\n      <td>0.738115</td>\n      <td>0.853000</td>\n    </tr>\n    <tr>\n      <th>LightGBM</th>\n      <td>0.823503</td>\n      <td>0.718793</td>\n      <td>0.752404</td>\n      <td>0.862667</td>\n    </tr>\n    <tr>\n      <th>AdaBoost</th>\n      <td>0.816701</td>\n      <td>0.701024</td>\n      <td>0.735212</td>\n      <td>0.856333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"colors = ['blue', 'green', 'red', 'orange']\nfor i, matric in enumerate(df.columns):\n    plt.plot(df[matric], marker='o', color=colors[i], label=matric)\nplt.legend()\nplt.xticks(rotation=45)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:21:06.797743Z","iopub.execute_input":"2023-07-29T17:21:06.798222Z","iopub.status.idle":"2023-07-29T17:21:07.167124Z","shell.execute_reply.started":"2023-07-29T17:21:06.798188Z","shell.execute_reply":"2023-07-29T17:21:07.165446Z"},"trusted":true},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Random Forest\nprint('Random Forest')\nprint(\"accuracy: \", rf_accuracy)\nprint(\"precison: \", rf_precision)\nprint(\"recall: \", rf_recall)\nprint(\"f1_score: \", rf_f1_score)\n\nlabels = ('churn', 'notChurn')\ncm = confusion_matrix(y_test, rf_pred)\n\nsns.heatmap(cm, xticklabels=labels, yticklabels=labels, annot = True, cmap='Blues', fmt='g')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:49:51.695493Z","iopub.execute_input":"2023-07-29T17:49:51.695998Z","iopub.status.idle":"2023-07-29T17:49:52.024478Z","shell.execute_reply.started":"2023-07-29T17:49:51.695962Z","shell.execute_reply":"2023-07-29T17:49:52.023092Z"},"trusted":true},"execution_count":64,"outputs":[{"name":"stdout","text":"Random Forest\naccuracy:  0.8566666666666667\nprecison:  0.8237643243146116\nrecall:  0.6958417907771066\nf1_score:  0.731568538667448\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"encoded_data.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:14:37.330158Z","iopub.execute_input":"2023-07-29T17:14:37.330633Z","iopub.status.idle":"2023-07-29T17:14:37.351660Z","shell.execute_reply.started":"2023-07-29T17:14:37.330599Z","shell.execute_reply":"2023-07-29T17:14:37.350275Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"             credit_score  country  gender  age  tenure    balance  \\\ncustomer_id                                                          \n15634602              619        0       0   42       2       0.00   \n15647311              608        2       0   41       1   83807.86   \n15619304              502        0       0   42       8  159660.80   \n15701354              699        0       0   39       1       0.00   \n15737888              850        2       0   43       2  125510.82   \n\n             products_number  credit_card  active_member  estimated_salary  \\\ncustomer_id                                                                  \n15634602                   1            1              1         101348.88   \n15647311                   1            0              1         112542.58   \n15619304                   3            1              0         113931.57   \n15701354                   2            0              0          93826.63   \n15737888                   1            1              1          79084.10   \n\n             churn  \ncustomer_id         \n15634602         1  \n15647311         0  \n15619304         1  \n15701354         0  \n15737888         0  ","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>credit_score</th>\n      <th>country</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>tenure</th>\n      <th>balance</th>\n      <th>products_number</th>\n      <th>credit_card</th>\n      <th>active_member</th>\n      <th>estimated_salary</th>\n      <th>churn</th>\n    </tr>\n    <tr>\n      <th>customer_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>15634602</th>\n      <td>619</td>\n      <td>0</td>\n      <td>0</td>\n      <td>42</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15647311</th>\n      <td>608</td>\n      <td>2</td>\n      <td>0</td>\n      <td>41</td>\n      <td>1</td>\n      <td>83807.86</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15619304</th>\n      <td>502</td>\n      <td>0</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>15701354</th>\n      <td>699</td>\n      <td>0</td>\n      <td>0</td>\n      <td>39</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15737888</th>\n      <td>850</td>\n      <td>2</td>\n      <td>0</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    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]}]}