job-a-thon-march-2022.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","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":"markdown","source":"# Importing Libraries","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport seaborn as sns\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import roc_curve","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-03-12T11:54:41.898894Z","iopub.execute_input":"2022-03-12T11:54:41.899415Z","iopub.status.idle":"2022-03-12T11:54:41.907707Z","shell.execute_reply.started":"2022-03-12T11:54:41.899383Z","shell.execute_reply":"2022-03-12T11:54:41.907046Z"},"trusted":true},"execution_count":125,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(r'../input/jobathon-march-2022/train_PDjVQMB.csv')\ntest = pd.read_csv(r'../input/jobathon-march-2022/test_lTY72QC.csv')\nsub = pd.read_csv(r'../input/jobathon-march-2022/sample_OoSmYo5.csv')","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:41.909752Z","iopub.execute_input":"2022-03-12T11:54:41.910114Z","iopub.status.idle":"2022-03-12T11:54:41.944762Z","shell.execute_reply.started":"2022-03-12T11:54:41.910079Z","shell.execute_reply":"2022-03-12T11:54:41.944122Z"},"trusted":true},"execution_count":126,"outputs":[]},{"cell_type":"code","source":"train.shape, test.shape, sub.shape","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:41.945900Z","iopub.execute_input":"2022-03-12T11:54:41.946146Z","iopub.status.idle":"2022-03-12T11:54:41.952010Z","shell.execute_reply.started":"2022-03-12T11:54:41.946112Z","shell.execute_reply":"2022-03-12T11:54:41.951380Z"},"trusted":true},"execution_count":127,"outputs":[{"execution_count":127,"output_type":"execute_result","data":{"text/plain":"((6650, 11), (2851, 10), (2851, 2))"},"metadata":{}}]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:41.953393Z","iopub.execute_input":"2022-03-12T11:54:41.953948Z","iopub.status.idle":"2022-03-12T11:54:41.972218Z","shell.execute_reply.started":"2022-03-12T11:54:41.953914Z","shell.execute_reply":"2022-03-12T11:54:41.971468Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":"         ID  Age  Gender         Income     Balance  Vintage  \\\n0  84e2fcc9   36  Female       5L - 10L   563266.44        4   \n1  57fea15e   53  Female   Less than 5L   875572.11        2   \n2  8df34ef3   35  Female  More than 15L   701607.06        2   \n3  c5c0788b   43  Female  More than 15L  1393922.16        0   \n4  951d69c4   39  Female  More than 15L   893146.23        1   \n\n   Transaction_Status Product_Holdings  Credit_Card Credit_Category  Is_Churn  \n0                   0                1            0         Average         1  \n1                   1                1            1            Poor         0  \n2                   1                2            0            Poor         0  \n3                   1                2            1            Poor         1  \n4                   1                1            1            Good         1  ","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>ID</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Income</th>\n      <th>Balance</th>\n      <th>Vintage</th>\n      <th>Transaction_Status</th>\n      <th>Product_Holdings</th>\n      <th>Credit_Card</th>\n      <th>Credit_Category</th>\n      <th>Is_Churn</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>84e2fcc9</td>\n      <td>36</td>\n      <td>Female</td>\n      <td>5L - 10L</td>\n      <td>563266.44</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Average</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>57fea15e</td>\n      <td>53</td>\n      <td>Female</td>\n      <td>Less than 5L</td>\n      <td>875572.11</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Poor</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8df34ef3</td>\n      <td>35</td>\n      <td>Female</td>\n      <td>More than 15L</td>\n      <td>701607.06</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>Poor</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>c5c0788b</td>\n      <td>43</td>\n      <td>Female</td>\n      <td>More than 15L</td>\n      <td>1393922.16</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>Poor</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>951d69c4</td>\n      <td>39</td>\n      <td>Female</td>\n      <td>More than 15L</td>\n      <td>893146.23</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Good</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"train.drop('ID',axis=1,inplace=True)\ntest.drop('ID',axis=1,inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:41.974556Z","iopub.execute_input":"2022-03-12T11:54:41.974817Z","iopub.status.idle":"2022-03-12T11:54:41.982471Z","shell.execute_reply.started":"2022-03-12T11:54:41.974784Z","shell.execute_reply":"2022-03-12T11:54:41.981629Z"},"trusted":true},"execution_count":129,"outputs":[]},{"cell_type":"code","source":"train.nunique()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:41.984072Z","iopub.execute_input":"2022-03-12T11:54:41.986435Z","iopub.status.idle":"2022-03-12T11:54:41.999218Z","shell.execute_reply.started":"2022-03-12T11:54:41.986395Z","shell.execute_reply":"2022-03-12T11:54:41.998481Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"Age                     52\nGender                   2\nIncome                   4\nBalance               6643\nVintage                  6\nTransaction_Status       2\nProduct_Holdings         3\nCredit_Card              2\nCredit_Category          3\nIs_Churn                 2\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"train.dtypes","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:42.000551Z","iopub.execute_input":"2022-03-12T11:54:42.001099Z","iopub.status.idle":"2022-03-12T11:54:42.008219Z","shell.execute_reply.started":"2022-03-12T11:54:42.001064Z","shell.execute_reply":"2022-03-12T11:54:42.007348Z"},"trusted":true},"execution_count":131,"outputs":[{"execution_count":131,"output_type":"execute_result","data":{"text/plain":"Age                     int64\nGender                 object\nIncome                 object\nBalance               float64\nVintage                 int64\nTransaction_Status      int64\nProduct_Holdings       object\nCredit_Card             int64\nCredit_Category        object\nIs_Churn                int64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"train.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:42.009597Z","iopub.execute_input":"2022-03-12T11:54:42.010164Z","iopub.status.idle":"2022-03-12T11:54:42.025573Z","shell.execute_reply.started":"2022-03-12T11:54:42.010110Z","shell.execute_reply":"2022-03-12T11:54:42.024907Z"},"trusted":true},"execution_count":132,"outputs":[{"execution_count":132,"output_type":"execute_result","data":{"text/plain":"Age                   0\nGender                0\nIncome                0\nBalance               0\nVintage               0\nTransaction_Status    0\nProduct_Holdings      0\nCredit_Card           0\nCredit_Category       0\nIs_Churn              0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:42.026566Z","iopub.execute_input":"2022-03-12T11:54:42.027232Z","iopub.status.idle":"2022-03-12T11:54:42.043441Z","shell.execute_reply.started":"2022-03-12T11:54:42.027198Z","shell.execute_reply":"2022-03-12T11:54:42.042819Z"},"trusted":true},"execution_count":133,"outputs":[{"execution_count":133,"output_type":"execute_result","data":{"text/plain":"   Age  Gender         Income     Balance  Vintage  Transaction_Status  \\\n0   36  Female       5L - 10L   563266.44        4                   0   \n1   53  Female   Less than 5L   875572.11        2                   1   \n2   35  Female  More than 15L   701607.06        2                   1   \n3   43  Female  More than 15L  1393922.16        0                   1   \n4   39  Female  More than 15L   893146.23        1                   1   \n\n  Product_Holdings  Credit_Card Credit_Category  Is_Churn  \n0                1            0         Average         1  \n1                1            1            Poor         0  \n2                2            0            Poor         0  \n3                2            1            Poor         1  \n4                1            1            Good         1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Income</th>\n      <th>Balance</th>\n      <th>Vintage</th>\n      <th>Transaction_Status</th>\n      <th>Product_Holdings</th>\n      <th>Credit_Card</th>\n      <th>Credit_Category</th>\n      <th>Is_Churn</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>36</td>\n      <td>Female</td>\n      <td>5L - 10L</td>\n      <td>563266.44</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Average</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>53</td>\n      <td>Female</td>\n      <td>Less than 5L</td>\n      <td>875572.11</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Poor</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>35</td>\n      <td>Female</td>\n      <td>More than 15L</td>\n      <td>701607.06</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>Poor</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>43</td>\n      <td>Female</td>\n      <td>More than 15L</td>\n      <td>1393922.16</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>Poor</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>39</td>\n      <td>Female</td>\n      <td>More than 15L</td>\n      <td>893146.23</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Good</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# EDA","metadata":{}},{"cell_type":"code","source":"labels = 'Exited', 'Retained'\nsizes = [train.Is_Churn[train['Is_Churn']==1].count(), train.Is_Churn[train['Is_Churn']==0].count()]\nexplode = (0, 0.1)\nfig1, ax1 = plt.subplots(figsize=(10, 8))\nax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n        shadow=True, startangle=90)\nax1.axis('equal')\nplt.title(\"Proportion of customer churned and retained\", size = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:42.044641Z","iopub.execute_input":"2022-03-12T11:54:42.045342Z","iopub.status.idle":"2022-03-12T11:54:42.192338Z","shell.execute_reply.started":"2022-03-12T11:54:42.045308Z","shell.execute_reply":"2022-03-12T11:54:42.191536Z"},"trusted":true},"execution_count":134,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"# Relations based on the categorical variables\n#['Product_Holdings','Income','Gender','Credit_Category']\nfig, axarr = plt.subplots(3, 2, figsize=(20, 12))\nsns.countplot(x='Product_Holdings', hue = 'Is_Churn',data = train, ax=axarr[0][0])\nsns.countplot(x='Income', hue = 'Is_Churn',data = train, ax=axarr[0][1])\nsns.countplot(x='Gender', hue = 'Is_Churn',data = train, ax=axarr[1][0])\nsns.countplot(x='Credit_Category', hue = \"Is_Churn\",data = train, ax=axarr[1][1])\nsns.countplot(x='Vintage', hue = 'Is_Churn',data = train, ax=axarr[2][0])\nsns.countplot(x='Credit_Card', hue = \"Is_Churn\",data = train, ax=axarr[2][1])","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:42.195307Z","iopub.execute_input":"2022-03-12T11:54:42.196132Z","iopub.status.idle":"2022-03-12T11:54:43.260082Z","shell.execute_reply.started":"2022-03-12T11:54:42.196094Z","shell.execute_reply":"2022-03-12T11:54:43.259324Z"},"trusted":true},"execution_count":135,"outputs":[{"execution_count":135,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='Credit_Card', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x864 with 6 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"def Age_encoding(age):\n    if (age<30):\n        return 0\n    elif age<40 and age>30:\n        return 1\n    elif age<50 and age>40:\n        return 2\n    elif age<60 and age>50:\n        return 3\n    else:\n        return 4","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:43.261387Z","iopub.execute_input":"2022-03-12T11:54:43.261739Z","iopub.status.idle":"2022-03-12T11:54:43.269083Z","shell.execute_reply.started":"2022-03-12T11:54:43.261701Z","shell.execute_reply":"2022-03-12T11:54:43.268351Z"},"trusted":true},"execution_count":136,"outputs":[]},{"cell_type":"code","source":"def balance_encoding(age):\n    if (age<100000):\n        return 0\n    elif age<500000 and age>100000:\n        return 1\n    elif age<1000000 and age>500000:\n        return 2\n    elif age<1500000 and age>1000000:\n        return 3\n    else:\n        return 4","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:43.270587Z","iopub.execute_input":"2022-03-12T11:54:43.270894Z","iopub.status.idle":"2022-03-12T11:54:43.278206Z","shell.execute_reply.started":"2022-03-12T11:54:43.270857Z","shell.execute_reply":"2022-03-12T11:54:43.277563Z"},"trusted":true},"execution_count":137,"outputs":[]},{"cell_type":"code","source":"train['balance_encoding']=train['Balance'].apply(balance_encoding)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:43.279623Z","iopub.execute_input":"2022-03-12T11:54:43.279983Z","iopub.status.idle":"2022-03-12T11:54:43.293737Z","shell.execute_reply.started":"2022-03-12T11:54:43.279949Z","shell.execute_reply":"2022-03-12T11:54:43.293072Z"},"trusted":true},"execution_count":138,"outputs":[]},{"cell_type":"code","source":"train['Age_encoding']=train['Age'].apply(Age_encoding)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:43.295214Z","iopub.execute_input":"2022-03-12T11:54:43.295410Z","iopub.status.idle":"2022-03-12T11:54:43.311158Z","shell.execute_reply.started":"2022-03-12T11:54:43.295385Z","shell.execute_reply":"2022-03-12T11:54:43.310498Z"},"trusted":true},"execution_count":139,"outputs":[]},{"cell_type":"code","source":"sns.countplot(x='Age_encoding', hue = 'Is_Churn',data = train)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:43.312397Z","iopub.execute_input":"2022-03-12T11:54:43.312652Z","iopub.status.idle":"2022-03-12T11:54:43.551829Z","shell.execute_reply.started":"2022-03-12T11:54:43.312614Z","shell.execute_reply":"2022-03-12T11:54:43.551113Z"},"trusted":true},"execution_count":140,"outputs":[{"execution_count":140,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='Age_encoding', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"sns.countplot(x='balance_encoding', hue = 'Is_Churn',data = train)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:43.553173Z","iopub.execute_input":"2022-03-12T11:54:43.553413Z","iopub.status.idle":"2022-03-12T11:54:43.775529Z","shell.execute_reply.started":"2022-03-12T11:54:43.553380Z","shell.execute_reply":"2022-03-12T11:54:43.774840Z"},"trusted":true},"execution_count":141,"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='balance_encoding', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"def decode_income(income):\n    if (income==\"10L - 15L\"):\n        return 1250000\n    elif(income==\"5L - 10L\"):\n        return 750000\n    elif(income==\"Less than 5L\"):\n        return 250000\n    else:\n        return 2000000\n    ","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:43.776615Z","iopub.execute_input":"2022-03-12T11:54:43.777037Z","iopub.status.idle":"2022-03-12T11:54:43.782225Z","shell.execute_reply.started":"2022-03-12T11:54:43.776996Z","shell.execute_reply":"2022-03-12T11:54:43.781071Z"},"trusted":true},"execution_count":142,"outputs":[]},{"cell_type":"code","source":"train['income_decoding']=train['Income'].apply(decode_income)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:43.784369Z","iopub.execute_input":"2022-03-12T11:54:43.785255Z","iopub.status.idle":"2022-03-12T11:54:43.797100Z","shell.execute_reply.started":"2022-03-12T11:54:43.785217Z","shell.execute_reply":"2022-03-12T11:54:43.796248Z"},"trusted":true},"execution_count":143,"outputs":[]},{"cell_type":"code","source":"train['BalanceSalaryRatio'] = train.Balance/train.income_decoding\nsns.boxplot(y='BalanceSalaryRatio',x = 'Is_Churn', hue = 'Is_Churn',data = train)\nplt.ylim(-1, 5)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:43.798258Z","iopub.execute_input":"2022-03-12T11:54:43.798578Z","iopub.status.idle":"2022-03-12T11:54:44.061767Z","shell.execute_reply.started":"2022-03-12T11:54:43.798544Z","shell.execute_reply":"2022-03-12T11:54:44.061069Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":"(-1.0, 5.0)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAYAAAAELCAYAAADQsFGkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAYGklEQVR4nO3de5RV5Z3m8eehCim8D3JJUoVWaTEawAZDEdNJhkYdL9GEJCZ4mZl2MYhkZilhZjqdRGfWpG3TdvfY3StGszIhToZcOkq8JGbUaGwTxExHTEFwBC+hDBCrgtxMFCIgFL/54xy06ljU2XDOPrvq7O9nLVad95x99n6KVad+td/9vu92RAgAkD8jsg4AAMgGBQAAcooCAAA5RQEAgJyiAABATlEAACCnGrM8uO2NknZK6pW0PyI6sswDAHmSaQEoOicitmcdAgDyhi4gAMgpZzkT2PYGSb+TFJK+FhFLBthmoaSFknTMMcfMOOOMM2obsopeeOGFtz13+umnZ5AEwGDq7bO6atWq7RExrvT5rAtAc0T02B4v6VFJiyJixaG27+joiM7OztoFrLLZs2e/7bnly5fXPAeAwdXbZ9X2qoGusWbaBRQRPcWvWyV9X9J7s8wDAHmSWQGwfYzt4w4+lnSBpLVZ5QGAvMlyFNAESd+3fTDHdyPi4QzzAECuZFYAIuLXkqZldXwAOJRRo0Zp7969/dr1iGGgAFCi7y//gdr1ggIAADlFAQCAEsVrk4ds1wsKAACUKJ0fVa+3zqUAAECJ0aNHD9quFxQAACixe/fuQdv1ggIAADlFAQCAnKIAAEBOUQAAoMSIESMGbdeL+vyuAKAC73rXuwZt1wsKAACU2LJlS7/21q1bM0qSLgoAAJTYv39/v/a+ffsySpIuCgAAlGAmMACgrlEAACCnKAAAkFMUAADIKQoAAOQUBQAASjATGABy6sCBA4O26wUFAAByigIAACXoAgKAnGpsbOzXHjlyZEZJ0kUBAIASY8eO7dc+6aSTMkqSLgoAAJT47W9/O2i7XlAAACCnMi8Athts/9L2A1lnAYA8ybwASFos6bmsQwBA3mRaAGy3SLpE0h1Z5gCAvhgGWhtfkvRZSfU5zQ7AsDRhwoR+7Xe84x0ZJUlXZgXA9oclbY2IVWW2W2i703bntm3bapQOQJ69/PLL/dqbN2/OKEm6sjwD+ICkObY3SrpL0rm2v1O6UUQsiYiOiOgYN25crTMCyCFuCZmyiLg+IloiolXSFZJ+EhH/Lqs8AJA3WV8DAABkpLH8JumLiOWSlmccAwAkSbb7dfvYzjBNejgDAIAS559/fr/2BRdckFGSdFEAAKDEZZdd1q89d+7cjJKkiwIAACXuvvvuQdv1ggIAACUee+yxQdv1ggIAACWYBwAAOTV9+vRB2/WCAgAAJdatWzdou15QAACgxJ49ewZt1wsKAADkFAUAAHKKAgAAOUUBAIASpWv/sBYQAOQE8wAAAHWNAgAAOUUBAICcogAAQE5RAAAgpygAAJBTFAAAyCkKAADkVGPSDW1PkDSz2HwqIramE2lou+2229TV1VW1/S1evPiI3tfe3q5FixZVLQdQTyr9nNruN/nL9hF9Vof65zTRGYDtyyQ9JWmupMskrbT9yTSDAUBWWltb+7Xb2tqyCZIyJ5nibPtpSecf/Kvf9jhJ/xQR01LO109HR0d0dnbW8pBV9eUvf1n33Xffm+25c+fq2muvzTARgEM555xzFBE6+uij9dBDD2UdpyK2V0VER+nzSa8BjCjp8tlxGO9F0ac//el+bX75A0PXwbOAm266KdsgKUp6DeBh249IurPYvlzS8C6JGRkzZoxeeeUVzZ07N+soAAZx/PHHa9q0aZoxY0bWUVKTqABExJ/b/oSkDxSfWhIR308vVv2aOHGiJk6cyF//ADKXeBRQRNwr6d4UswAAamjQfnzbPyt+3Wn7tT7/dtp+rZID226y/ZTtp22vs31jJfsDAByeQc8AIuKDxa/HpXDsvZLOjYhdtkdK+pntH0XEkykcCwBQIuk8gG8nee5wRMGuYnNk8V993nYHAIagpEM5p/Rt2G6UVPGlcdsNttdI2irp0YhYOcA2C2132u7ctm1bpYcEABSVuwZwve2dkv6ob/+/pC2S7q/04BHRGxHTJbVIeq/tqQNssyQiOiKiY9y4cZUeEgBQNGgBiIi/Lvb/3xIRxxf/HRcRJ0XE9dUKERG/l/RTSRdVa58AgMElnQdwve1/IWmSpKY+z6840gMXl5PYFxG/tz1a0vmS/vZI9wcAODyJCoDtBZIWq9BVs0bS+yT9XNK5FRz7nZK+abtBhTOR70XEAxXsDwBwGJJOBFuswlLQT0bEObbPkHRzJQeOiP8n6axK9gEAOHJJRwHtiYg9kmR7VEQ8L+n09GIBANKW9Ayg2/aJkn4g6VHbv5O0Ka1QAID0Jb0I/PHiw7+w/VNJJ0j6UWqpAACpO+w1/SPicUl7xHLQADCslZsIdq7tX9neZfs7ts+03SnpryV9tTYRAQBpKHcG8PeSFko6SdI9Kgz9XBoRMyLivkHfCQAY0spdA4iIWF58/APbPRFxe8qZAAA1UK4AnGj70r7b921zFgAAw1e5AvC4pI/0aa/o0w5JFAAAGKbK3RDm30uFZZsjorc2kQAAtZB0GOh627fYnpxqGgBAzSQtANMk/UrSHbafLN6k5fgUcwEAUpaoAETEzoj4ekS8X9LnJH1B0mbb37TdnmpCAEAqkt4TuMH2HNvfl/QlFeYHnCrp/4gZwQAwLCVdDG69CnfsuiUi/rnP8/fYnlX9WACAtJUtAMUbtiyNiL8c6PWI+HTVUwEAUle2C6g4/PPDNcgCAKihpF1A/9f27ZKWSfrDwScjYnUqqQAAqUtaAKYXv/btBgpVdk9gAECGkt4Q5py0gwAAaivpGYBsXyJpiqSmg88d6sIwAGDoSzoP4H9KulzSIkmWNFfSKSnmAgCkLOlSEO+PiKsk/S4ibpT0x5L+ZXqxAABpS1oAdhe/vm77XZL2SXpnOpEAALWQ9BrAA7ZPlHSLpNUqjAC6I61QAID0JR0FdFPx4b22H5DUFBGvphcLAJC2QQtAye0gS1/jlpAAMIyVOwP4yCCvVXRLSNsTJX1L0oTivpZExK1Huj8AwOFJdEvIlOyX9GcRsdr2cZJW2X40Ip5N8ZgAgKLMJoJFxGZJm4uPd9p+TlKzJAoAANTAkJgIZrtV0lmSVg7w2kLbnbY7t23bVq1DAkDuZT4RzPaxku6V9J8i4rXS1yNiSUR0RETHuHHjqnFIAIAynghme6QKv/z/kRFFAFBblUwE+3olB7ZtSf9L0nMR8Q+V7AsAcPiynAj2AUl/KukZ22uKz90QEdxkHgBqoNxEsJmSXoqIl4vtqyR9QtIm238REa8c6YEj4mcqXFAGAGSg3DWAr0l6Q5Jsz5L0NypM3npV0pJ0owEA0lSuC6ihz1/5l6swW/deFbqC1qSaDACQqnJnAA22DxaJ8yT9pM9riSeRAQCGnnK/xO+U9Ljt7SoMBX1Ckmy3q9ANBAAYpsqtBfRXth9TYcz/jyMiii+NUGFWMABgmCrbjRMRT0qS7Q/anhQR/1vS71SYDAYAGKaSrgX0BUmfk3R98amRkr6TVigAQPqSLgXxcUlzJP1BkiLit5KOSysUACB9SQvAG8X+/5Ak28ekFwkAUAtJh3J+z/bXJJ1o+xpJ81XhWkAAcCi33Xaburq6Ms1w8PiLFy/OLEN7e7sWLUpvvE3StYD+zvb5kl6TdLqk/x4Rj6aWKiX8UL0l7R8soBJdXV1av+6XOvnY3swyHLWv0EGyd1NnJsf/za6G1I+RqADYbpP0xMFf+rZH226NiI1phqu2rq4urVn7nHqPHpNZhhFvFEbSrvr1lswyNLx+xEs4ATVz8rG9uuE9b7tFSG7cvPr41I+RtAvobknv79PuLT43s+qJUtZ79BjtPuPirGNkavTzLLgKIPlF4MaIeONgo/j4qHQiAQBqIWkB2GZ7zsGG7Y9K2p5OJABALSTtAvoPkv7R9u0qrOH/kqSrUksFAEhd0lFAL0p6X/EG7oqIXammAgCkLukooFEq3AmsVVJj4Xa+UkT8ZWrJAACpStoFdL8Kyz+vkrQ3vTgAgFpJWgBaIuKiVJMAAGoq6Sigf7Z9ZqpJAAA1lfQM4IOS5tneoEIXkCVFRPxRaskAAKlKWgA+lGoKAEDNJR0GukmSbI+X1JRqIgBATSS9I9gc2+slbZD0uKSNkn6UYi4AQMqSXgS+SdL7JP0qItoknSfpydRSAQBSl7QA7IuIHZJG2B4RET+V1JFiLgBAypJeBP59cRmIFSqsCbRVxfsDV8L2NyR9WNLWiJha6f4AAMklPQP4qKTdkv6zpIclvSjpI1U4/lJJTDADgAwkHQXU96/9b1br4BGxwnZrtfYHAEhu0AJge6ekGOglFSaCpX7PMtsLJS2UpJNPPjntwwFAbgxaACLiuFoFGSTDEklLJKmjo2OgYgQAOAJJLwJLevtEsIj4TdUTAQBqgolgAJBTmU4Es32npJ9LOt12t+2rK90nACCZpF1A+yJih+03J4LZ/lKlB4+IKyvdB4D609PToz/sbNDNq1MfZzJkbdrZoGN6elI9RqYTwQAA2UlaAPpOBPu3kk6QxP2AAaSiublZe/dv1g3veS3rKJm5efXxGtXcnOoxDnci2AHbD0raERHDbkhmT0+PGl5/VaOffyjrKJlqeH2Henr2Zx0DQMYGvQhs+322l9u+z/ZZttdKWitpi22WcACAYazcGcDtkm5QocvnJ5I+FBFP2j5D0p0qrAs0bDQ3N+vlvY3afcbFWUfJ1OjnH1Jz84SsYwDIWLlhoI0R8eOIuFvSyxHxpCRFxPPpRwMApKlcATjQ5/HukteG3TUAAMBbynUBTbP9mgqLv40uPlaxzb2BAWAYK7cYXEOtggAAaivpUhAAgDpDAQCAnKIAAEBOUQAAIKcO64YwwHC0b98+dXd3a8+ePVlHqUhTU5NaWlo0cuTIrKOgTlAAUPe6u7t13HHHqbW1VbazjnNEIkI7duxQd3e32traso5TE7/Zle1y0FteL3SQTDj6QJkt0/GbXQ2alPIxKACoe3v27BnWv/wlybZOOukkbdu2LesoNdHe3p51BL3R1SVJGnVKNlkmKf3/h9wVgIbXX8l0NdARewpz6Q40ZfeXTcPrr0jK11pAw/mX/0H18D0ktWjRoqwjaPHixZKkW2+9NeMk6clVARgKf1V0de2UJLWfmuUv4AlD4v8CQLZyVQD4qwIA3sIwUOTSsccee9jv2bVrlz71qU/ptNNO04wZMzR79mytXLlSGzdu1NSpU1NICaQrV2cAQCUWLFigtrY2rV+/XiNGjNCGDRv07LPPasKEyrrz9u/fr8ZGPoqoPc4AkGubN2/WrFmzNH36dE2dOlVPPPHEgNu9+OKLWrlypb74xS9qxIjCx6atrU2XXHKJJKm3t1fXXHONpkyZogsuuEC7dxdWT589e7Y6OzslSdu3b1dra6skaenSpZozZ47OPfdcnXfeeVq6dKkuvfRSXXTRRZo0aZI++9nPpvydAxQA5Nx3v/tdXXjhhVqzZo2efvppTZ8+fcDt1q1bp+nTp6uhYeAFctevX69rr71W69at04knnqh777237LFXr16te+65R48//rgkac2aNVq2bJmeeeYZLVu2TC+99NIRf19AEpx3Itdmzpyp+fPna9++ffrYxz52yAJQTltb25vvnTFjhjZu3Fj2Peeff77GjBnzZvu8887TCSecIEmaPHmyNm3apIkTJx5RHiAJzgCQa7NmzdKKFSvU3NysefPm6Vvf+taA202ZMkVPP/20ent7B3x91KhRbz5uaGjQ/v37JUmNjY06cKAwk7R0KYpjjjkm0T6AtFAAkGubNm3ShAkTdM0112jBggVavXr1gNuddtpp6ujo0Be+8AVFFO6GunHjRj344IOD7r+1tVWrVq2SJN1zzz3VDQ9UiAKAXFu+fLmmTZums846S8uWLXtznsZA7rjjDm3ZskXt7e2aOnWq5s2bp/Hjxw+6/8985jP66le/qrPOOkvbt2+vdnygIj7410wmB7cvknSrpAZJd0TE3wy2fUdHRxwcUTFcMRGs9p577jm9+93vzjpGVdTT9zLU1dNn1faqiOgofT6zMwDbDZK+IulDkiZLutL25KzyAEDeZDkK6L2SuiLi15Jk+y5JH5X0bIaZAJ199tnau3dvv+e+/e1v68wzz8woEZCOLAtAs6S+A527JZ2dUZbEbrvtNnUVl4k9EgffO1hfcxLt7e1DYm2jerRy5cqsI6BClX5Opep8Vof653TIzwOwvVDSQkk6+eSTM05TudGjR2cdAUACefisZlkAeiT1neXSUnyun4hYImmJVLgIXJtohzaUqzmAAj6nyWQ5DPQXkibZbrN9lKQrJP0wwzwAkCuZnQFExH7b10l6RIVhoN+IiHVZ5UF+Xfdf/lxbt79Stf2NHztGt//DLWW3e/jhh7V48WL19vZqwYIF+vznP1+1DEASmV4DiIiHJGV3f0ZA0tbtr+jFCX9SvR1uebzsJr29vbr22mv16KOPqqWlRTNnztScOXM0eTIjoVE7zAQGMvDUU0+pvb1dp556qo466ihdccUVuv/++7OOhZyhAAAZ6Onp6bfSZ0tLi3p63jYGAkgVBQAAcooCAGSgubm53w1furu71dzcnGEi5BEFAMjAzJkztX79em3YsEFvvPGG7rrrLs2ZMyfrWMiZIT8TGEjb+LFjEo3cOaz9ldHY2Kjbb79dF154oXp7ezV//nxNmTKlahmAJCgAyL0kY/bTcPHFF+viiy/O5NiARBcQAOQWBQAAcooCAAA5RQEAgJyiAABATlEAACCnGAaK3Lvhz67Tq9u3VG1/J4ydoJv//vZBt5k/f74eeOABjR8/XmvXrq3asYHDQQFA7r26fYs+d9rzVdvf375Yfpt58+bpuuuu01VXXVW14wKHiy4gIAOzZs3SmDHlZwwDaaIAAEBOUQAAIKcoAACQUxQAAMgpRgEh904YOyHRyJ3D2V85V155pZYvX67t27erpaVFN954o66++urqhQASoAAg98qN2U/DnXfeWfNjAqXoAgKAnKIAAEBOUQCQCxGRdYSK1cP3gKGFAoC619TUpB07dgzrX6ARoR07dqipqSnrKKgjXARG3WtpaVF3d7e2bduWdZSKNDU1qaWlJesYqCMUANS9kSNHqq2tLesYwJCTSReQ7bm219k+YLsjiwwAkHdZXQNYK+lSSSsyOj4A5F4mXUAR8Zwk2c7i8AAADYNrALYXSlpYbO6y/UKWeerMWEnbsw4BDICfzeo6ZaAnUysAtv9J0jsGeOm/RsT9SfcTEUskLalaMLzJdmdEcA0GQw4/m7WRWgGIiH+d1r4BAJVjIhgA5FRWw0A/brtb0h9LetD2I1nkAF1rGLL42awBD+fp8QCAI0cXEADkFAUAAHKKApBDti+y/YLtLtufzzoPcJDtb9jeantt1lnygAKQM7YbJH1F0ockTZZ0pe3J2aYC3rRU0kVZh8gLCkD+vFdSV0T8OiLekHSXpI9mnAmQJEXECkmvZJ0jLygA+dMs6aU+7e7icwByhgIAADlFAcifHkkT+7Rbis8ByBkKQP78QtIk2222j5J0haQfZpwJQAYoADkTEfslXSfpEUnPSfpeRKzLNhVQYPtOST+XdLrtbttXZ52pnrEUBADkFGcAAJBTFAAAyCkKAADkFAUAAHKKAgAAOUUBAICcogAgl2zvOoL3HGv7a7ZftL3K9nLbZ9tuZfliDEeNWQcAhpE7JG2QNCkiDthuU2FJ7S2V7NR2Y3GCHlBTnAEg12y/0/YK22tsr7X9rw6x3WmSzpb03yLigCRFxIaIeLC4SYPtr9teZ/vHtkcX37fcdkfx8VjbG4uP59n+oe2fSHqs2L7P9sO219v+Hyl/6wAFALn3byQ9EhHTJU2TtOYQ202RtCYieg/x+iRJX4mIKZJ+L+kTCY79HkmfjIg/KbanS7pc0pmSLrc98VBvBKqBLiDk3S8kfcP2SEk/iIg1R7ifDX3eu0pSa4L3PBoRfW9+8lhEvCpJtp+VdIr637sBqCrOAJBrxTtQzVJhSeyltq86xKbrJE0r3lJzIHv7PO7VW39c7ddbn7Omkvf8IeE+gFRQAJBrtk+RtCUivq7CRd73DLRdRLwoqVPSjbZdfG+r7UvKHGKjpBnFx5+sSmigSigAyLvZkp62/UsV+t9vHWTbBZImSOoqDvtcKmlrmf3/naT/WNz/2IrTAlXEctAAkFOcAQBATnGRCShhe6WkUSVP/2lEPJNFHiAtdAEBQE7RBQQAOUUBAICcogAAQE5RAAAgp/4/3DTaOwEkWUAAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.062970Z","iopub.execute_input":"2022-03-12T11:54:44.063203Z","iopub.status.idle":"2022-03-12T11:54:44.077880Z","shell.execute_reply.started":"2022-03-12T11:54:44.063169Z","shell.execute_reply":"2022-03-12T11:54:44.077249Z"},"trusted":true},"execution_count":145,"outputs":[{"execution_count":145,"output_type":"execute_result","data":{"text/plain":"   Age  Gender         Income     Balance  Vintage  Transaction_Status  \\\n0   36  Female       5L - 10L   563266.44        4                   0   \n1   53  Female   Less than 5L   875572.11        2                   1   \n2   35  Female  More than 15L   701607.06        2                   1   \n3   43  Female  More than 15L  1393922.16        0                   1   \n4   39  Female  More than 15L   893146.23        1                   1   \n\n  Product_Holdings  Credit_Card Credit_Category  Is_Churn  balance_encoding  \\\n0                1            0         Average         1                 2   \n1                1            1            Poor         0                 2   \n2                2            0            Poor         0                 2   \n3                2            1            Poor         1                 3   \n4                1            1            Good         1                 2   \n\n   Age_encoding  income_decoding  BalanceSalaryRatio  \n0             1           750000            0.751022  \n1             3           250000            3.502288  \n2             1          2000000            0.350804  \n3             2          2000000            0.696961  \n4             1          2000000            0.446573  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Income</th>\n      <th>Balance</th>\n      <th>Vintage</th>\n      <th>Transaction_Status</th>\n      <th>Product_Holdings</th>\n      <th>Credit_Card</th>\n      <th>Credit_Category</th>\n      <th>Is_Churn</th>\n      <th>balance_encoding</th>\n      <th>Age_encoding</th>\n      <th>income_decoding</th>\n      <th>BalanceSalaryRatio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>36</td>\n      <td>Female</td>\n      <td>5L - 10L</td>\n      <td>563266.44</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Average</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>750000</td>\n      <td>0.751022</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>53</td>\n      <td>Female</td>\n      <td>Less than 5L</td>\n      <td>875572.11</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Poor</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>250000</td>\n      <td>3.502288</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>35</td>\n      <td>Female</td>\n      <td>More than 15L</td>\n      <td>701607.06</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>Poor</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2000000</td>\n      <td>0.350804</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>43</td>\n      <td>Female</td>\n      <td>More than 15L</td>\n      <td>1393922.16</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>Poor</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>2000000</td>\n      <td>0.696961</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>39</td>\n      <td>Female</td>\n      <td>More than 15L</td>\n      <td>893146.23</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Good</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2000000</td>\n      <td>0.446573</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test['balance_encoding']=test['Balance'].apply(balance_encoding)\ntest['Age_encoding']=test['Age'].apply(Age_encoding)\ntest['income_decoding']=test['Income'].apply(decode_income)\ntest['BalanceSalaryRatio'] = test.Balance/test.income_decoding","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.079208Z","iopub.execute_input":"2022-03-12T11:54:44.079647Z","iopub.status.idle":"2022-03-12T11:54:44.096347Z","shell.execute_reply.started":"2022-03-12T11:54:44.079606Z","shell.execute_reply":"2022-03-12T11:54:44.095601Z"},"trusted":true},"execution_count":146,"outputs":[]},{"cell_type":"markdown","source":"# Insights\n1. Customers who have deposited less products are more likely to churn.\n2. Customers who are having poor credit score are more likely to churn.\n3. No relation between males and females genders and their churning .\n4. Customers are more likely to churn after 1 st year of getting banks service.\n5. Customers who chosen credit card are more likely to churn.\n6. customers whose age greater than 30 and less than 50 are more likely to churn \n7.Customers whose balance is in range of 5-10 lakhs are most likely to churn","metadata":{}},{"cell_type":"markdown","source":"# Feature Engineering","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\nvar = ['Product_Holdings','Income','Gender','Credit_Category']\nfor i in var:\n    train[i] = le.fit_transform(train[i])\n    test[i] = le.transform(test[i])","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.097569Z","iopub.execute_input":"2022-03-12T11:54:44.098250Z","iopub.status.idle":"2022-03-12T11:54:44.120549Z","shell.execute_reply.started":"2022-03-12T11:54:44.098213Z","shell.execute_reply":"2022-03-12T11:54:44.119912Z"},"trusted":true},"execution_count":147,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.122317Z","iopub.execute_input":"2022-03-12T11:54:44.122869Z","iopub.status.idle":"2022-03-12T11:54:44.139909Z","shell.execute_reply.started":"2022-03-12T11:54:44.122832Z","shell.execute_reply":"2022-03-12T11:54:44.139260Z"},"trusted":true},"execution_count":148,"outputs":[{"execution_count":148,"output_type":"execute_result","data":{"text/plain":"   Age  Gender  Income     Balance  Vintage  Transaction_Status  \\\n0   36       0       1   563266.44        4                   0   \n1   53       0       2   875572.11        2                   1   \n2   35       0       3   701607.06        2                   1   \n3   43       0       3  1393922.16        0                   1   \n4   39       0       3   893146.23        1                   1   \n\n   Product_Holdings  Credit_Card  Credit_Category  Is_Churn  balance_encoding  \\\n0                 0            0                0         1                 2   \n1                 0            1                2         0                 2   \n2                 1            0                2         0                 2   \n3                 1            1                2         1                 3   \n4                 0            1                1         1                 2   \n\n   Age_encoding  income_decoding  BalanceSalaryRatio  \n0             1           750000            0.751022  \n1             3           250000            3.502288  \n2             1          2000000            0.350804  \n3             2          2000000            0.696961  \n4             1          2000000            0.446573  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Income</th>\n      <th>Balance</th>\n      <th>Vintage</th>\n      <th>Transaction_Status</th>\n      <th>Product_Holdings</th>\n      <th>Credit_Card</th>\n      <th>Credit_Category</th>\n      <th>Is_Churn</th>\n      <th>balance_encoding</th>\n      <th>Age_encoding</th>\n      <th>income_decoding</th>\n      <th>BalanceSalaryRatio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>36</td>\n      <td>0</td>\n      <td>1</td>\n      <td>563266.44</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>750000</td>\n      <td>0.751022</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>53</td>\n      <td>0</td>\n      <td>2</td>\n      <td>875572.11</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>250000</td>\n      <td>3.502288</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>35</td>\n      <td>0</td>\n      <td>3</td>\n      <td>701607.06</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2000000</td>\n      <td>0.350804</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>43</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1393922.16</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>2000000</td>\n      <td>0.696961</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>39</td>\n      <td>0</td>\n      <td>3</td>\n      <td>893146.23</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2000000</td>\n      <td>0.446573</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"train.describe().T","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.145730Z","iopub.execute_input":"2022-03-12T11:54:44.146416Z","iopub.status.idle":"2022-03-12T11:54:44.192461Z","shell.execute_reply.started":"2022-03-12T11:54:44.146376Z","shell.execute_reply":"2022-03-12T11:54:44.191837Z"},"trusted":true},"execution_count":149,"outputs":[{"execution_count":149,"output_type":"execute_result","data":{"text/plain":"                     count          mean            std           min  \\\nAge                 6650.0  4.113023e+01       9.685747      21.00000   \nGender              6650.0  5.431579e-01       0.498171       0.00000   \nIncome              6650.0  1.357594e+00       1.096052       0.00000   \nBalance             6650.0  8.045954e+05  515754.867315      63.00000   \nVintage             6650.0  2.250226e+00       1.458795       0.00000   \nTransaction_Status  6650.0  5.157895e-01       0.499788       0.00000   \nProduct_Holdings    6650.0  5.590977e-01       0.571977       0.00000   \nCredit_Card         6650.0  6.643609e-01       0.472249       0.00000   \nCredit_Category     6650.0  1.155338e+00       0.863572       0.00000   \nIs_Churn            6650.0  2.311278e-01       0.421586       0.00000   \nbalance_encoding    6650.0  2.056241e+00       1.086773       0.00000   \nAge_encoding        6650.0  1.852932e+00       1.199098       0.00000   \nincome_decoding     6650.0  1.026278e+06  608071.045480  250000.00000   \nBalanceSalaryRatio  6650.0  1.302805e+00       1.504659       0.00005   \n\n                              25%            50%           75%           max  \nAge                     34.000000      40.000000  4.700000e+01  7.200000e+01  \nGender                   0.000000       1.000000  1.000000e+00  1.000000e+00  \nIncome                   0.000000       1.000000  2.000000e+00  3.000000e+00  \nBalance             392264.212500  764938.575000  1.147124e+06  2.436616e+06  \nVintage                  1.000000       2.000000  3.000000e+00  5.000000e+00  \nTransaction_Status       0.000000       1.000000  1.000000e+00  1.000000e+00  \nProduct_Holdings         0.000000       1.000000  1.000000e+00  2.000000e+00  \nCredit_Card              0.000000       1.000000  1.000000e+00  1.000000e+00  \nCredit_Category          0.000000       1.000000  2.000000e+00  2.000000e+00  \nIs_Churn                 0.000000       0.000000  0.000000e+00  1.000000e+00  \nbalance_encoding         1.000000       2.000000  3.000000e+00  4.000000e+00  \nAge_encoding             1.000000       2.000000  3.000000e+00  4.000000e+00  \nincome_decoding     750000.000000  750000.000000  1.250000e+06  2.000000e+06  \nBalanceSalaryRatio       0.377261       0.776229  1.531872e+00  9.687639e+00  ","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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Age</th>\n      <td>6650.0</td>\n      <td>4.113023e+01</td>\n      <td>9.685747</td>\n      <td>21.00000</td>\n      <td>34.000000</td>\n      <td>40.000000</td>\n      <td>4.700000e+01</td>\n      <td>7.200000e+01</td>\n    </tr>\n    <tr>\n      <th>Gender</th>\n      <td>6650.0</td>\n      <td>5.431579e-01</td>\n      <td>0.498171</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000e+00</td>\n      <td>1.000000e+00</td>\n    </tr>\n    <tr>\n      <th>Income</th>\n      <td>6650.0</td>\n      <td>1.357594e+00</td>\n      <td>1.096052</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>2.000000e+00</td>\n      <td>3.000000e+00</td>\n    </tr>\n    <tr>\n      <th>Balance</th>\n      <td>6650.0</td>\n      <td>8.045954e+05</td>\n      <td>515754.867315</td>\n      <td>63.00000</td>\n      <td>392264.212500</td>\n      <td>764938.575000</td>\n      <td>1.147124e+06</td>\n      <td>2.436616e+06</td>\n    </tr>\n    <tr>\n      <th>Vintage</th>\n      <td>6650.0</td>\n      <td>2.250226e+00</td>\n      <td>1.458795</td>\n      <td>0.00000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>3.000000e+00</td>\n      <td>5.000000e+00</td>\n    </tr>\n    <tr>\n      <th>Transaction_Status</th>\n      <td>6650.0</td>\n      <td>5.157895e-01</td>\n      <td>0.499788</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000e+00</td>\n      <td>1.000000e+00</td>\n    </tr>\n    <tr>\n      <th>Product_Holdings</th>\n      <td>6650.0</td>\n      <td>5.590977e-01</td>\n      <td>0.571977</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000e+00</td>\n      <td>2.000000e+00</td>\n    </tr>\n    <tr>\n      <th>Credit_Card</th>\n      <td>6650.0</td>\n      <td>6.643609e-01</td>\n      <td>0.472249</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000e+00</td>\n      <td>1.000000e+00</td>\n    </tr>\n    <tr>\n      <th>Credit_Category</th>\n      <td>6650.0</td>\n      <td>1.155338e+00</td>\n      <td>0.863572</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>2.000000e+00</td>\n      <td>2.000000e+00</td>\n    </tr>\n    <tr>\n      <th>Is_Churn</th>\n      <td>6650.0</td>\n      <td>2.311278e-01</td>\n      <td>0.421586</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>1.000000e+00</td>\n    </tr>\n    <tr>\n      <th>balance_encoding</th>\n      <td>6650.0</td>\n      <td>2.056241e+00</td>\n      <td>1.086773</td>\n      <td>0.00000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>3.000000e+00</td>\n      <td>4.000000e+00</td>\n    </tr>\n    <tr>\n      <th>Age_encoding</th>\n      <td>6650.0</td>\n      <td>1.852932e+00</td>\n      <td>1.199098</td>\n      <td>0.00000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>3.000000e+00</td>\n      <td>4.000000e+00</td>\n    </tr>\n    <tr>\n      <th>income_decoding</th>\n      <td>6650.0</td>\n      <td>1.026278e+06</td>\n      <td>608071.045480</td>\n      <td>250000.00000</td>\n      <td>750000.000000</td>\n      <td>750000.000000</td>\n      <td>1.250000e+06</td>\n      <td>2.000000e+06</td>\n    </tr>\n    <tr>\n      <th>BalanceSalaryRatio</th>\n      <td>6650.0</td>\n      <td>1.302805e+00</td>\n      <td>1.504659</td>\n      <td>0.00005</td>\n      <td>0.377261</td>\n      <td>0.776229</td>\n      <td>1.531872e+00</td>\n      <td>9.687639e+00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(12,6))\nsns.heatmap(train.corr())\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.193855Z","iopub.execute_input":"2022-03-12T11:54:44.194094Z","iopub.status.idle":"2022-03-12T11:54:44.561705Z","shell.execute_reply.started":"2022-03-12T11:54:44.194064Z","shell.execute_reply":"2022-03-12T11:54:44.561020Z"},"trusted":true},"execution_count":150,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x432 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"train=train.drop(['Balance','income_decoding','Age'], axis=1) ","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.564389Z","iopub.execute_input":"2022-03-12T11:54:44.565062Z","iopub.status.idle":"2022-03-12T11:54:44.570363Z","shell.execute_reply.started":"2022-03-12T11:54:44.565022Z","shell.execute_reply":"2022-03-12T11:54:44.569378Z"},"trusted":true},"execution_count":151,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.571957Z","iopub.execute_input":"2022-03-12T11:54:44.572254Z","iopub.status.idle":"2022-03-12T11:54:44.588959Z","shell.execute_reply.started":"2022-03-12T11:54:44.572207Z","shell.execute_reply":"2022-03-12T11:54:44.588248Z"},"trusted":true},"execution_count":152,"outputs":[{"execution_count":152,"output_type":"execute_result","data":{"text/plain":"   Gender  Income  Vintage  Transaction_Status  Product_Holdings  Credit_Card  \\\n0       0       1        4                   0                 0            0   \n1       0       2        2                   1                 0            1   \n2       0       3        2                   1                 1            0   \n3       0       3        0                   1                 1            1   \n4       0       3        1                   1                 0            1   \n\n   Credit_Category  Is_Churn  balance_encoding  Age_encoding  \\\n0                0         1                 2             1   \n1                2         0                 2             3   \n2                2         0                 2             1   \n3                2         1                 3             2   \n4                1         1                 2             1   \n\n   BalanceSalaryRatio  \n0            0.751022  \n1            3.502288  \n2            0.350804  \n3            0.696961  \n4            0.446573  ","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>Gender</th>\n      <th>Income</th>\n      <th>Vintage</th>\n      <th>Transaction_Status</th>\n      <th>Product_Holdings</th>\n      <th>Credit_Card</th>\n      <th>Credit_Category</th>\n      <th>Is_Churn</th>\n      <th>balance_encoding</th>\n      <th>Age_encoding</th>\n      <th>BalanceSalaryRatio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.751022</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>3.502288</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.350804</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>0.696961</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.446573</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Training Model","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX = train.drop(['Is_Churn'], axis=1) #features (independent variables)\ny = train['Is_Churn'] #target (dependent variable)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.591652Z","iopub.execute_input":"2022-03-12T11:54:44.592045Z","iopub.status.idle":"2022-03-12T11:54:44.600792Z","shell.execute_reply.started":"2022-03-12T11:54:44.592009Z","shell.execute_reply":"2022-03-12T11:54:44.600062Z"},"trusted":true},"execution_count":153,"outputs":[]},{"cell_type":"markdown","source":"# Ridge Classifier","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import RidgeClassifier\nfrom sklearn.metrics import accuracy_score\nclf_ridge = RidgeClassifier() #create a ridge classifier object\nclf_ridge.fit(X_train, y_train) #train the model","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.602122Z","iopub.execute_input":"2022-03-12T11:54:44.602510Z","iopub.status.idle":"2022-03-12T11:54:44.617217Z","shell.execute_reply.started":"2022-03-12T11:54:44.602476Z","shell.execute_reply":"2022-03-12T11:54:44.616479Z"},"trusted":true},"execution_count":154,"outputs":[{"execution_count":154,"output_type":"execute_result","data":{"text/plain":"RidgeClassifier()"},"metadata":{}}]},{"cell_type":"code","source":"pred_ridge = clf_ridge.predict(X_train)\naccuracy_score(y_train, pred_ridge)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.618437Z","iopub.execute_input":"2022-03-12T11:54:44.619030Z","iopub.status.idle":"2022-03-12T11:54:44.629025Z","shell.execute_reply.started":"2022-03-12T11:54:44.618989Z","shell.execute_reply":"2022-03-12T11:54:44.628287Z"},"trusted":true},"execution_count":155,"outputs":[{"execution_count":155,"output_type":"execute_result","data":{"text/plain":"0.7704887218045112"},"metadata":{}}]},{"cell_type":"code","source":"pred_test_ridge = clf_ridge.predict(X_test)\naccuracy_score(y_test, pred_test_ridge)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.630335Z","iopub.execute_input":"2022-03-12T11:54:44.630738Z","iopub.status.idle":"2022-03-12T11:54:44.641274Z","shell.execute_reply.started":"2022-03-12T11:54:44.630695Z","shell.execute_reply":"2022-03-12T11:54:44.640626Z"},"trusted":true},"execution_count":156,"outputs":[{"execution_count":156,"output_type":"execute_result","data":{"text/plain":"0.762406015037594"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import f1_score\nf1_score(y_test,pred_test_ridge, average='macro')","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.642606Z","iopub.execute_input":"2022-03-12T11:54:44.643388Z","iopub.status.idle":"2022-03-12T11:54:44.652411Z","shell.execute_reply.started":"2022-03-12T11:54:44.643355Z","shell.execute_reply":"2022-03-12T11:54:44.651696Z"},"trusted":true},"execution_count":157,"outputs":[{"execution_count":157,"output_type":"execute_result","data":{"text/plain":"0.4325938566552901"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_test,pred_test_ridge))","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.653738Z","iopub.execute_input":"2022-03-12T11:54:44.654685Z","iopub.status.idle":"2022-03-12T11:54:44.668906Z","shell.execute_reply.started":"2022-03-12T11:54:44.654613Z","shell.execute_reply":"2022-03-12T11:54:44.668236Z"},"trusted":true},"execution_count":158,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.76      1.00      0.87      1014\n           1       0.00      0.00      0.00       316\n\n    accuracy                           0.76      1330\n   macro avg       0.38      0.50      0.43      1330\nweighted avg       0.58      0.76      0.66      1330\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Random Forest","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nclf_forest = RandomForestClassifier(n_estimators=100, max_depth=10)\nclf_forest.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:44.670001Z","iopub.execute_input":"2022-03-12T11:54:44.670698Z","iopub.status.idle":"2022-03-12T11:54:45.128596Z","shell.execute_reply.started":"2022-03-12T11:54:44.670664Z","shell.execute_reply":"2022-03-12T11:54:45.127916Z"},"trusted":true},"execution_count":159,"outputs":[{"execution_count":159,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(max_depth=10)"},"metadata":{}}]},{"cell_type":"code","source":"pred = clf_forest.predict(X_train)\naccuracy_score(y_train, pred)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:45.129766Z","iopub.execute_input":"2022-03-12T11:54:45.130001Z","iopub.status.idle":"2022-03-12T11:54:45.211849Z","shell.execute_reply.started":"2022-03-12T11:54:45.129967Z","shell.execute_reply":"2022-03-12T11:54:45.211051Z"},"trusted":true},"execution_count":160,"outputs":[{"execution_count":160,"output_type":"execute_result","data":{"text/plain":"0.8109022556390978"},"metadata":{}}]},{"cell_type":"code","source":"pred_test_rf = clf_forest.predict(X_test)\naccuracy_score(y_test, pred_test_rf)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:45.213302Z","iopub.execute_input":"2022-03-12T11:54:45.213568Z","iopub.status.idle":"2022-03-12T11:54:45.250911Z","shell.execute_reply.started":"2022-03-12T11:54:45.213534Z","shell.execute_reply":"2022-03-12T11:54:45.250241Z"},"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":"0.7616541353383459"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import f1_score\nf1_score(y_test,pred_test_rf, average='macro')","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:45.252263Z","iopub.execute_input":"2022-03-12T11:54:45.252736Z","iopub.status.idle":"2022-03-12T11:54:45.260262Z","shell.execute_reply.started":"2022-03-12T11:54:45.252701Z","shell.execute_reply":"2022-03-12T11:54:45.259493Z"},"trusted":true},"execution_count":162,"outputs":[{"execution_count":162,"output_type":"execute_result","data":{"text/plain":"0.4641588472062826"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_test,pred_test_rf))","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:45.261647Z","iopub.execute_input":"2022-03-12T11:54:45.262034Z","iopub.status.idle":"2022-03-12T11:54:45.273884Z","shell.execute_reply.started":"2022-03-12T11:54:45.261998Z","shell.execute_reply":"2022-03-12T11:54:45.272590Z"},"trusted":true},"execution_count":163,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.77      0.99      0.86      1014\n           1       0.48      0.03      0.06       316\n\n    accuracy                           0.76      1330\n   macro avg       0.62      0.51      0.46      1330\nweighted avg       0.70      0.76      0.67      1330\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Gridsearch RF","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import GridSearchCV\nparameters = {'n_estimators':[150,200,250,300], 'max_depth':[15,20,25]}\nforest = RandomForestClassifier()\nclf = GridSearchCV(estimator=forest, param_grid=parameters, n_jobs=-1, cv=5)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:45.275066Z","iopub.execute_input":"2022-03-12T11:54:45.275308Z","iopub.status.idle":"2022-03-12T11:54:45.280758Z","shell.execute_reply.started":"2022-03-12T11:54:45.275277Z","shell.execute_reply":"2022-03-12T11:54:45.279599Z"},"trusted":true},"execution_count":164,"outputs":[]},{"cell_type":"code","source":"clf.fit(X, y)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:54:45.282157Z","iopub.execute_input":"2022-03-12T11:54:45.282654Z","iopub.status.idle":"2022-03-12T11:55:45.278158Z","shell.execute_reply.started":"2022-03-12T11:54:45.282613Z","shell.execute_reply":"2022-03-12T11:55:45.277335Z"},"trusted":true},"execution_count":165,"outputs":[{"execution_count":165,"output_type":"execute_result","data":{"text/plain":"GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n             param_grid={'max_depth': [15, 20, 25],\n                         'n_estimators': [150, 200, 250, 300]})"},"metadata":{}}]},{"cell_type":"code","source":"print(\"Best params\",clf.best_params_)\nprint(\"Best params\",clf.best_score_)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:45.280421Z","iopub.execute_input":"2022-03-12T11:55:45.280923Z","iopub.status.idle":"2022-03-12T11:55:45.286467Z","shell.execute_reply.started":"2022-03-12T11:55:45.280883Z","shell.execute_reply":"2022-03-12T11:55:45.285758Z"},"trusted":true},"execution_count":166,"outputs":[{"name":"stdout","text":"Best params {'max_depth': 15, 'n_estimators': 300}\nBest params 0.7514285714285714\n","output_type":"stream"}]},{"cell_type":"code","source":"pred = clf.predict(X_train)\naccuracy_score(y_train, pred)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:45.287834Z","iopub.execute_input":"2022-03-12T11:55:45.288516Z","iopub.status.idle":"2022-03-12T11:55:45.585871Z","shell.execute_reply.started":"2022-03-12T11:55:45.288480Z","shell.execute_reply":"2022-03-12T11:55:45.585048Z"},"trusted":true},"execution_count":167,"outputs":[{"execution_count":167,"output_type":"execute_result","data":{"text/plain":"0.9738721804511278"},"metadata":{}}]},{"cell_type":"code","source":"pred_test_grf = clf.predict(X_test)\naccuracy_score(y_test, pred_test_grf)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:45.587196Z","iopub.execute_input":"2022-03-12T11:55:45.587518Z","iopub.status.idle":"2022-03-12T11:55:45.704739Z","shell.execute_reply.started":"2022-03-12T11:55:45.587484Z","shell.execute_reply":"2022-03-12T11:55:45.703947Z"},"trusted":true},"execution_count":168,"outputs":[{"execution_count":168,"output_type":"execute_result","data":{"text/plain":"0.9842105263157894"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import f1_score\nf1_score(y_test,pred_test_grf, average='macro')","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:45.706226Z","iopub.execute_input":"2022-03-12T11:55:45.706491Z","iopub.status.idle":"2022-03-12T11:55:45.714634Z","shell.execute_reply.started":"2022-03-12T11:55:45.706456Z","shell.execute_reply":"2022-03-12T11:55:45.713773Z"},"trusted":true},"execution_count":169,"outputs":[{"execution_count":169,"output_type":"execute_result","data":{"text/plain":"0.9776906063314585"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_test,pred_test_grf))","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:45.716170Z","iopub.execute_input":"2022-03-12T11:55:45.716418Z","iopub.status.idle":"2022-03-12T11:55:45.727941Z","shell.execute_reply.started":"2022-03-12T11:55:45.716384Z","shell.execute_reply":"2022-03-12T11:55:45.727187Z"},"trusted":true},"execution_count":170,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.98      1.00      0.99      1014\n           1       1.00      0.93      0.97       316\n\n    accuracy                           0.98      1330\n   macro avg       0.99      0.97      0.98      1330\nweighted avg       0.98      0.98      0.98      1330\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# XGB","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom xgboost import XGBClassifier","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:45.729262Z","iopub.execute_input":"2022-03-12T11:55:45.729522Z","iopub.status.idle":"2022-03-12T11:55:45.733567Z","shell.execute_reply.started":"2022-03-12T11:55:45.729487Z","shell.execute_reply":"2022-03-12T11:55:45.732681Z"},"trusted":true},"execution_count":171,"outputs":[]},{"cell_type":"code","source":"# Fit Extreme Gradient Boost Classifier\nXGB = XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,colsample_bytree=1, gamma=0.01, learning_rate=0.1, max_delta_step=0,max_depth=7,\n                    min_child_weight=5, missing=None, n_estimators=20,n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,reg_alpha=0, \n                    reg_lambda=1, scale_pos_weight=1, seed=None, silent=True, subsample=1)\n","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:45.735013Z","iopub.execute_input":"2022-03-12T11:55:45.735581Z","iopub.status.idle":"2022-03-12T11:55:45.743147Z","shell.execute_reply.started":"2022-03-12T11:55:45.735549Z","shell.execute_reply":"2022-03-12T11:55:45.742480Z"},"trusted":true},"execution_count":172,"outputs":[]},{"cell_type":"code","source":"XGB.fit(X, y)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:45.744481Z","iopub.execute_input":"2022-03-12T11:55:45.744950Z","iopub.status.idle":"2022-03-12T11:55:45.900726Z","shell.execute_reply.started":"2022-03-12T11:55:45.744914Z","shell.execute_reply":"2022-03-12T11:55:45.900004Z"},"trusted":true},"execution_count":173,"outputs":[{"name":"stdout","text":"[11:55:45] WARNING: ../src/learner.cc:576: \nParameters: { \"silent\" } might not be used.\n\n  This could be a false alarm, with some parameters getting used by language bindings but\n  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n  but getting flagged wrongly here. Please open an issue if you find any such cases.\n\n\n[11:55:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n","output_type":"stream"},{"execution_count":173,"output_type":"execute_result","data":{"text/plain":"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n              gamma=0.01, gpu_id=-1, importance_type=None,\n              interaction_constraints='', learning_rate=0.1, max_delta_step=0,\n              max_depth=7, min_child_weight=5, missing=None,\n              monotone_constraints='()', n_estimators=20, n_jobs=1, nthread=1,\n              num_parallel_tree=1, predictor='auto', random_state=0,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=0,\n              silent=True, subsample=1, tree_method='exact',\n              validate_parameters=1, ...)"},"metadata":{}}]},{"cell_type":"code","source":"pred_xgb = clf.predict(X_train)\naccuracy_score(y_train, pred_xgb)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:45.902262Z","iopub.execute_input":"2022-03-12T11:55:45.902750Z","iopub.status.idle":"2022-03-12T11:55:46.194988Z","shell.execute_reply.started":"2022-03-12T11:55:45.902714Z","shell.execute_reply":"2022-03-12T11:55:46.194318Z"},"trusted":true},"execution_count":174,"outputs":[{"execution_count":174,"output_type":"execute_result","data":{"text/plain":"0.9738721804511278"},"metadata":{}}]},{"cell_type":"code","source":"pred_test_xgb = clf.predict(X_test)\naccuracy_score(y_test, pred_test_xgb)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:46.196131Z","iopub.execute_input":"2022-03-12T11:55:46.196372Z","iopub.status.idle":"2022-03-12T11:55:46.315757Z","shell.execute_reply.started":"2022-03-12T11:55:46.196338Z","shell.execute_reply":"2022-03-12T11:55:46.314968Z"},"trusted":true},"execution_count":175,"outputs":[{"execution_count":175,"output_type":"execute_result","data":{"text/plain":"0.9842105263157894"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import f1_score\nf1_score(y_test,pred_test_xgb, average='macro')","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:46.317259Z","iopub.execute_input":"2022-03-12T11:55:46.317541Z","iopub.status.idle":"2022-03-12T11:55:46.327927Z","shell.execute_reply.started":"2022-03-12T11:55:46.317502Z","shell.execute_reply":"2022-03-12T11:55:46.327267Z"},"trusted":true},"execution_count":176,"outputs":[{"execution_count":176,"output_type":"execute_result","data":{"text/plain":"0.9776906063314585"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_test,pred_test_xgb))","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:46.331024Z","iopub.execute_input":"2022-03-12T11:55:46.331203Z","iopub.status.idle":"2022-03-12T11:55:46.343593Z","shell.execute_reply.started":"2022-03-12T11:55:46.331181Z","shell.execute_reply":"2022-03-12T11:55:46.342894Z"},"trusted":true},"execution_count":177,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.98      1.00      0.99      1014\n           1       1.00      0.93      0.97       316\n\n    accuracy                           0.98      1330\n   macro avg       0.99      0.97      0.98      1330\nweighted avg       0.98      0.98      0.98      1330\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Logistic Regression","metadata":{}},{"cell_type":"code","source":"param_grid = {'C': [0.1,0.5,1,10,50,100], 'max_iter': [250], 'fit_intercept':[True],'intercept_scaling':[1],\n              'penalty':['l2'], 'tol':[0.00001,0.0001,0.000001]}\nlog_primal_Grid = GridSearchCV(LogisticRegression(solver='lbfgs'),param_grid, cv=10, refit=True, verbose=0)\nlog_primal_Grid.fit(X, y)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:46.344630Z","iopub.execute_input":"2022-03-12T11:55:46.345379Z","iopub.status.idle":"2022-03-12T11:55:53.963044Z","shell.execute_reply.started":"2022-03-12T11:55:46.345343Z","shell.execute_reply":"2022-03-12T11:55:53.962322Z"},"trusted":true},"execution_count":178,"outputs":[{"execution_count":178,"output_type":"execute_result","data":{"text/plain":"GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={'C': [0.1, 0.5, 1, 10, 50, 100],\n                         'fit_intercept': [True], 'intercept_scaling': [1],\n                         'max_iter': [250], 'penalty': ['l2'],\n                         'tol': [1e-05, 0.0001, 1e-06]})"},"metadata":{}}]},{"cell_type":"code","source":"pred_lr = log_primal_Grid.predict(X_train)\naccuracy_score(y_train, pred_xgb)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:53.964582Z","iopub.execute_input":"2022-03-12T11:55:53.965045Z","iopub.status.idle":"2022-03-12T11:55:53.977404Z","shell.execute_reply.started":"2022-03-12T11:55:53.965008Z","shell.execute_reply":"2022-03-12T11:55:53.976573Z"},"trusted":true},"execution_count":179,"outputs":[{"execution_count":179,"output_type":"execute_result","data":{"text/plain":"0.9738721804511278"},"metadata":{}}]},{"cell_type":"code","source":"pred_test_lr = log_primal_Grid.predict(X_test)\naccuracy_score(y_test, pred_test_lr)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:53.981475Z","iopub.execute_input":"2022-03-12T11:55:53.981751Z","iopub.status.idle":"2022-03-12T11:55:53.996624Z","shell.execute_reply.started":"2022-03-12T11:55:53.981717Z","shell.execute_reply":"2022-03-12T11:55:53.995307Z"},"trusted":true},"execution_count":180,"outputs":[{"execution_count":180,"output_type":"execute_result","data":{"text/plain":"0.7609022556390977"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import f1_score\nf1_score(y_test,pred_test_lr, average='macro')","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:53.997837Z","iopub.execute_input":"2022-03-12T11:55:53.998171Z","iopub.status.idle":"2022-03-12T11:55:54.008826Z","shell.execute_reply.started":"2022-03-12T11:55:53.998138Z","shell.execute_reply":"2022-03-12T11:55:54.007889Z"},"trusted":true},"execution_count":181,"outputs":[{"execution_count":181,"output_type":"execute_result","data":{"text/plain":"0.43517628205128206"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_test,pred_test_lr))","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:54.010058Z","iopub.execute_input":"2022-03-12T11:55:54.010354Z","iopub.status.idle":"2022-03-12T11:55:54.028563Z","shell.execute_reply.started":"2022-03-12T11:55:54.010320Z","shell.execute_reply":"2022-03-12T11:55:54.026275Z"},"trusted":true},"execution_count":182,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.76      1.00      0.86      1014\n           1       0.25      0.00      0.01       316\n\n    accuracy                           0.76      1330\n   macro avg       0.51      0.50      0.44      1330\nweighted avg       0.64      0.76      0.66      1330\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Choosing Best Model","metadata":{}},{"cell_type":"markdown","source":"# Checking on test data","metadata":{}},{"cell_type":"code","source":"test.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:56:27.618971Z","iopub.execute_input":"2022-03-12T11:56:27.619229Z","iopub.status.idle":"2022-03-12T11:56:27.635968Z","shell.execute_reply.started":"2022-03-12T11:56:27.619201Z","shell.execute_reply":"2022-03-12T11:56:27.635319Z"},"trusted":true},"execution_count":184,"outputs":[{"execution_count":184,"output_type":"execute_result","data":{"text/plain":"   Age  Gender  Income     Balance  Vintage  Transaction_Status  \\\n0   50       0       3  1008636.39        2                   1   \n1   36       1       1   341460.72        2                   0   \n2   25       0       0   439460.10        0                   0   \n3   41       1       2    28581.93        0                   1   \n4   48       1       3  1104540.03        2                   1   \n\n   Product_Holdings  Credit_Card  Credit_Category  balance_encoding  \\\n0                 1            1                0                 3   \n1                 1            1                0                 1   \n2                 1            1                1                 1   \n3                 1            1                2                 0   \n4                 2            0                1                 3   \n\n   Age_encoding  income_decoding  BalanceSalaryRatio  \n0             4          2000000            0.504318  \n1             1           750000            0.455281  \n2             0          1250000            0.351568  \n3             2           250000            0.114328  \n4             2          2000000            0.552270  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Income</th>\n      <th>Balance</th>\n      <th>Vintage</th>\n      <th>Transaction_Status</th>\n      <th>Product_Holdings</th>\n      <th>Credit_Card</th>\n      <th>Credit_Category</th>\n      <th>balance_encoding</th>\n      <th>Age_encoding</th>\n      <th>income_decoding</th>\n      <th>BalanceSalaryRatio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>50</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1008636.39</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>4</td>\n      <td>2000000</td>\n      <td>0.504318</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>36</td>\n      <td>1</td>\n      <td>1</td>\n      <td>341460.72</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>750000</td>\n      <td>0.455281</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>25</td>\n      <td>0</td>\n      <td>0</td>\n      <td>439460.10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1250000</td>\n      <td>0.351568</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>41</td>\n      <td>1</td>\n      <td>2</td>\n      <td>28581.93</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>250000</td>\n      <td>0.114328</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>48</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1104540.03</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>2000000</td>\n      <td>0.552270</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test=test.drop(['Balance','income_decoding','Age'], axis=1) ","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:56:43.675932Z","iopub.execute_input":"2022-03-12T11:56:43.676190Z","iopub.status.idle":"2022-03-12T11:56:43.682646Z","shell.execute_reply.started":"2022-03-12T11:56:43.676160Z","shell.execute_reply":"2022-03-12T11:56:43.681908Z"},"trusted":true},"execution_count":185,"outputs":[]},{"cell_type":"code","source":"test.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:56:46.022306Z","iopub.execute_input":"2022-03-12T11:56:46.022874Z","iopub.status.idle":"2022-03-12T11:56:46.034506Z","shell.execute_reply.started":"2022-03-12T11:56:46.022834Z","shell.execute_reply":"2022-03-12T11:56:46.033838Z"},"trusted":true},"execution_count":186,"outputs":[{"execution_count":186,"output_type":"execute_result","data":{"text/plain":"   Gender  Income  Vintage  Transaction_Status  Product_Holdings  Credit_Card  \\\n0       0       3        2                   1                 1            1   \n1       1       1        2                   0                 1            1   \n2       0       0        0                   0                 1            1   \n3       1       2        0                   1                 1            1   \n4       1       3        2                   1                 2            0   \n\n   Credit_Category  balance_encoding  Age_encoding  BalanceSalaryRatio  \n0                0                 3             4            0.504318  \n1                0                 1             1            0.455281  \n2                1                 1             0            0.351568  \n3                2                 0             2            0.114328  \n4                1                 3             2            0.552270  ","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>Gender</th>\n      <th>Income</th>\n      <th>Vintage</th>\n      <th>Transaction_Status</th>\n      <th>Product_Holdings</th>\n      <th>Credit_Card</th>\n      <th>Credit_Category</th>\n      <th>balance_encoding</th>\n      <th>Age_encoding</th>\n      <th>BalanceSalaryRatio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>4</td>\n      <td>0.504318</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.455281</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.351568</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0.114328</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>0.552270</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# test['Balance'] = test['Balance'].round(decimals = 2)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:55:54.089115Z","iopub.status.idle":"2022-03-12T11:55:54.090619Z","shell.execute_reply.started":"2022-03-12T11:55:54.090369Z","shell.execute_reply":"2022-03-12T11:55:54.090394Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"churn_xgb = clf.predict(test)\n# churn_rf_pred =clf_forest.predict(test)\n# churn_grf_pred = clf.predict(test)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:57:53.842807Z","iopub.execute_input":"2022-03-12T11:57:53.843160Z","iopub.status.idle":"2022-03-12T11:57:54.123609Z","shell.execute_reply.started":"2022-03-12T11:57:53.843120Z","shell.execute_reply":"2022-03-12T11:57:54.122859Z"},"trusted":true},"execution_count":187,"outputs":[]},{"cell_type":"code","source":"test['churn_xgb'] = churn_xgb\n# test['churn_rf'] = churn_rf_pred\n# test['churn_grf'] = churn_grf_pred","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:57:57.173828Z","iopub.execute_input":"2022-03-12T11:57:57.174076Z","iopub.status.idle":"2022-03-12T11:57:57.178443Z","shell.execute_reply.started":"2022-03-12T11:57:57.174050Z","shell.execute_reply":"2022-03-12T11:57:57.177715Z"},"trusted":true},"execution_count":188,"outputs":[]},{"cell_type":"code","source":"test_data=pd.read_csv(r'../input/jobathon-march-2022/test_lTY72QC.csv')","metadata":{"execution":{"iopub.status.busy":"2022-03-12T11:58:18.443110Z","iopub.execute_input":"2022-03-12T11:58:18.443380Z","iopub.status.idle":"2022-03-12T11:58:18.457503Z","shell.execute_reply.started":"2022-03-12T11:58:18.443351Z","shell.execute_reply":"2022-03-12T11:58:18.456816Z"},"trusted":true},"execution_count":189,"outputs":[]},{"cell_type":"code","source":"sub = pd.DataFrame()\nsub['ID']=test_data['ID']\nsub['Is_Churn']=test['churn_xgb']","metadata":{"execution":{"iopub.status.busy":"2022-03-12T12:00:19.190387Z","iopub.execute_input":"2022-03-12T12:00:19.191100Z","iopub.status.idle":"2022-03-12T12:00:19.200797Z","shell.execute_reply.started":"2022-03-12T12:00:19.191056Z","shell.execute_reply":"2022-03-12T12:00:19.200079Z"},"trusted":true},"execution_count":190,"outputs":[]},{"cell_type":"code","source":"sub","metadata":{"execution":{"iopub.status.busy":"2022-03-12T12:00:31.111291Z","iopub.execute_input":"2022-03-12T12:00:31.112127Z","iopub.status.idle":"2022-03-12T12:00:31.122710Z","shell.execute_reply.started":"2022-03-12T12:00:31.112087Z","shell.execute_reply":"2022-03-12T12:00:31.122048Z"},"trusted":true},"execution_count":191,"outputs":[{"execution_count":191,"output_type":"execute_result","data":{"text/plain":"            ID  Is_Churn\n0     55480787         0\n1     9aededf2         0\n2     a5034a09         0\n3     b3256702         0\n4     dc28adb5         0\n...        ...       ...\n2846  19e40adf         1\n2847  52d5bc8d         0\n2848  f708121b         0\n2849  f008715d         0\n2850  36b81f59         0\n\n[2851 rows x 2 columns]","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>ID</th>\n      <th>Is_Churn</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>55480787</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>9aededf2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>a5034a09</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>b3256702</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>dc28adb5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2846</th>\n      <td>19e40adf</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2847</th>\n      <td>52d5bc8d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2848</th>\n      <td>f708121b</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2849</th>\n      <td>f008715d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2850</th>\n      <td>36b81f59</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2851 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"sub.to_csv(\"xgb.csv\",index=False)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T12:00:43.881158Z","iopub.execute_input":"2022-03-12T12:00:43.881834Z","iopub.status.idle":"2022-03-12T12:00:43.894365Z","shell.execute_reply.started":"2022-03-12T12:00:43.881795Z","shell.execute_reply":"2022-03-12T12:00:43.893398Z"},"trusted":true},"execution_count":192,"outputs":[]},{"cell_type":"code","source":"set(sub['ID']) - set(test_data['ID'])","metadata":{"execution":{"iopub.status.busy":"2022-03-12T12:01:08.221824Z","iopub.execute_input":"2022-03-12T12:01:08.222073Z","iopub.status.idle":"2022-03-12T12:01:08.229113Z","shell.execute_reply.started":"2022-03-12T12:01:08.222046Z","shell.execute_reply":"2022-03-12T12:01:08.228357Z"},"trusted":true},"execution_count":194,"outputs":[{"execution_count":194,"output_type":"execute_result","data":{"text/plain":"set()"},"metadata":{}}]}]}