loan-prediction-auc-0-94.ipynb

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    {
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     "text": [
      "/kaggle/input/loan-prediction-based-on-customer-behavior/Test Data.csv\n",
      "/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv\n",
      "/kaggle/input/loan-prediction-based-on-customer-behavior/Sample Prediction Dataset.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd# data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt  \n",
    "from collections import Counter\n",
    "\n",
    "#Importing Models\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cluster import KMeans\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "#Importing preprocessors\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV , GridSearchCV, RepeatedStratifiedKFold, StratifiedShuffleSplit,cross_val_score\n",
    "from sklearn.metrics import confusion_matrix , classification_report, make_scorer\n",
    "from sklearn.metrics import precision_score,recall_score,f1_score,accuracy_score\n",
    "from sklearn.metrics import plot_roc_curve ,roc_curve, roc_auc_score\n",
    "from sklearn.metrics import fbeta_score\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "\n",
    "from imblearn.pipeline import Pipeline\n",
    "from imblearn.combine import SMOTETomek,SMOTEENN\n",
    "from imblearn.under_sampling import RandomUnderSampler, TomekLinks, EditedNearestNeighbours\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
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       "      <th>Income</th>\n",
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       "      <td>Mechanical_engineer</td>\n",
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       "      <td>3</td>\n",
       "      <td>13</td>\n",
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       "      <th>2</th>\n",
       "      <td>7574516</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>single</td>\n",
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       "      <td>no</td>\n",
       "      <td>Software_Developer</td>\n",
       "      <td>Parbhani</td>\n",
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       "      <td>9</td>\n",
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       "      <td>Software_Developer</td>\n",
       "      <td>Bhubaneswar</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
       "      <td>5768871</td>\n",
       "      <td>47</td>\n",
       "      <td>11</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Civil_servant</td>\n",
       "      <td>Tiruchirappalli[10]</td>\n",
       "      <td>Tamil_Nadu</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>251996</th>\n",
       "      <td>8154883</td>\n",
       "      <td>43</td>\n",
       "      <td>13</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Surgeon</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>West_Bengal</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
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       "      <th>251997</th>\n",
       "      <td>2843572</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Army_officer</td>\n",
       "      <td>Rewa</td>\n",
       "      <td>Madhya_Pradesh</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
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       "      <th>251998</th>\n",
       "      <td>4522448</td>\n",
       "      <td>46</td>\n",
       "      <td>7</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Design_Engineer</td>\n",
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       "      <td>6507128</td>\n",
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       "      <td>single</td>\n",
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       "      <td>11</td>\n",
       "      <td>0</td>\n",
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       "         Income  Age  Experience Married/Single House_Ownership Car_Ownership  \\\n",
       "Id                                                                              \n",
       "1       1303834   23           3         single          rented            no   \n",
       "2       7574516   40          10         single          rented            no   \n",
       "3       3991815   66           4        married          rented            no   \n",
       "4       6256451   41           2         single          rented           yes   \n",
       "5       5768871   47          11         single          rented            no   \n",
       "...         ...  ...         ...            ...             ...           ...   \n",
       "251996  8154883   43          13         single          rented            no   \n",
       "251997  2843572   26          10         single          rented            no   \n",
       "251998  4522448   46           7         single          rented            no   \n",
       "251999  6507128   45           0         single          rented            no   \n",
       "252000  9070230   70          17         single          rented            no   \n",
       "\n",
       "                 Profession                 CITY           STATE  \\\n",
       "Id                                                                 \n",
       "1       Mechanical_engineer                 Rewa  Madhya_Pradesh   \n",
       "2        Software_Developer             Parbhani     Maharashtra   \n",
       "3          Technical_writer            Alappuzha          Kerala   \n",
       "4        Software_Developer          Bhubaneswar          Odisha   \n",
       "5             Civil_servant  Tiruchirappalli[10]      Tamil_Nadu   \n",
       "...                     ...                  ...             ...   \n",
       "251996              Surgeon              Kolkata     West_Bengal   \n",
       "251997         Army_officer                 Rewa  Madhya_Pradesh   \n",
       "251998      Design_Engineer      Kalyan-Dombivli     Maharashtra   \n",
       "251999     Graphic_Designer          Pondicherry      Puducherry   \n",
       "252000         Statistician                Avadi      Tamil_Nadu   \n",
       "\n",
       "        CURRENT_JOB_YRS  CURRENT_HOUSE_YRS  Risk_Flag  \n",
       "Id                                                     \n",
       "1                     3                 13          0  \n",
       "2                     9                 13          0  \n",
       "3                     4                 10          0  \n",
       "4                     2                 12          1  \n",
       "5                     3                 14          1  \n",
       "...                 ...                ...        ...  \n",
       "251996                6                 11          0  \n",
       "251997                6                 11          0  \n",
       "251998                7                 12          0  \n",
       "251999                0                 10          0  \n",
       "252000                7                 11          0  \n",
       "\n",
       "[252000 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "df = pd.read_csv(\"/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv\", index_col='Id')\n",
    "sss=StratifiedShuffleSplit(n_splits=1,test_size=0.25,random_state=42)\n",
    "\n",
    "for train_index, test_index in sss.split(df,df[\"Risk_Flag\"], ):\n",
    "    sss_train = df.iloc[train_index]\n",
    "    sss_test = df.iloc[test_index]\n",
    "    \n",
    "x_train= sss_train.drop(['Risk_Flag','Profession','STATE','CITY'], axis=1)\n",
    "x_test= sss_test.drop(['Risk_Flag','Profession','STATE','CITY'], axis=1)\n",
    "y_train = sss_train.Risk_Flag\n",
    "y_test = sss_test.Risk_Flag\n",
    "count = Counter(y_train)\n",
    "df\n"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "**Check for null elements**"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 252000 entries, 1 to 252000\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count   Dtype \n",
      "---  ------             --------------   ----- \n",
      " 0   Income             252000 non-null  int64 \n",
      " 1   Age                252000 non-null  int64 \n",
      " 2   Experience         252000 non-null  int64 \n",
      " 3   Married/Single     252000 non-null  object\n",
      " 4   House_Ownership    252000 non-null  object\n",
      " 5   Car_Ownership      252000 non-null  object\n",
      " 6   Profession         252000 non-null  object\n",
      " 7   CITY               252000 non-null  object\n",
      " 8   STATE              252000 non-null  object\n",
      " 9   CURRENT_JOB_YRS    252000 non-null  int64 \n",
      " 10  CURRENT_HOUSE_YRS  252000 non-null  int64 \n",
      " 11  Risk_Flag          252000 non-null  int64 \n",
      "dtypes: int64(6), object(6)\n",
      "memory usage: 25.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c4ef843",
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     "start_time": "2021-10-09T18:07:04.388060",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Basic stats of data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0cc379e",
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   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>CURRENT_JOB_YRS</th>\n",
       "      <th>CURRENT_HOUSE_YRS</th>\n",
       "      <th>Risk_Flag</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>2.520000e+05</td>\n",
       "      <td>252000.000000</td>\n",
       "      <td>252000.000000</td>\n",
       "      <td>252000.000000</td>\n",
       "      <td>252000.000000</td>\n",
       "      <td>252000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.997117e+06</td>\n",
       "      <td>49.954071</td>\n",
       "      <td>10.084437</td>\n",
       "      <td>6.333877</td>\n",
       "      <td>11.997794</td>\n",
       "      <td>0.123000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.878311e+06</td>\n",
       "      <td>17.063855</td>\n",
       "      <td>6.002590</td>\n",
       "      <td>3.647053</td>\n",
       "      <td>1.399037</td>\n",
       "      <td>0.328438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.031000e+04</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.503015e+06</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000694e+06</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.477502e+06</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999938e+06</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Income            Age     Experience  CURRENT_JOB_YRS  \\\n",
       "count  2.520000e+05  252000.000000  252000.000000    252000.000000   \n",
       "mean   4.997117e+06      49.954071      10.084437         6.333877   \n",
       "std    2.878311e+06      17.063855       6.002590         3.647053   \n",
       "min    1.031000e+04      21.000000       0.000000         0.000000   \n",
       "25%    2.503015e+06      35.000000       5.000000         3.000000   \n",
       "50%    5.000694e+06      50.000000      10.000000         6.000000   \n",
       "75%    7.477502e+06      65.000000      15.000000         9.000000   \n",
       "max    9.999938e+06      79.000000      20.000000        14.000000   \n",
       "\n",
       "       CURRENT_HOUSE_YRS      Risk_Flag  \n",
       "count      252000.000000  252000.000000  \n",
       "mean           11.997794       0.123000  \n",
       "std             1.399037       0.328438  \n",
       "min            10.000000       0.000000  \n",
       "25%            11.000000       0.000000  \n",
       "50%            12.000000       0.000000  \n",
       "75%            13.000000       0.000000  \n",
       "max            14.000000       1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f748a6f",
   "metadata": {
    "papermill": {
     "duration": 0.024123,
     "end_time": "2021-10-09T18:07:04.589644",
     "exception": false,
     "start_time": "2021-10-09T18:07:04.565521",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "694b33f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:04.644706Z",
     "iopub.status.busy": "2021-10-09T18:07:04.644024Z",
     "iopub.status.idle": "2021-10-09T18:07:04.923096Z",
     "shell.execute_reply": "2021-10-09T18:07:04.923567Z",
     "shell.execute_reply.started": "2021-10-09T16:20:20.306168Z"
    },
    "papermill": {
     "duration": 0.30998,
     "end_time": "2021-10-09T18:07:04.923763",
     "exception": false,
     "start_time": "2021-10-09T18:07:04.613783",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = df.groupby('Risk_Flag')['Risk_Flag'].count()\n",
    "plt.pie(r, explode=[0.05, 0.1], labels=['Non-Defaulter', 'Defaulter'], radius=1.5, autopct='%1.1f%%',  shadow=True)\n",
    ";\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe1e6a61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:04.978737Z",
     "iopub.status.busy": "2021-10-09T18:07:04.978093Z",
     "iopub.status.idle": "2021-10-09T18:07:06.244715Z",
     "shell.execute_reply": "2021-10-09T18:07:06.245183Z",
     "shell.execute_reply.started": "2021-10-09T16:20:52.779794Z"
    },
    "papermill": {
     "duration": 1.296448,
     "end_time": "2021-10-09T18:07:06.245350",
     "exception": false,
     "start_time": "2021-10-09T18:07:04.948902",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df.hist(figsize = (20, 15))\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a05d7f9e",
   "metadata": {
    "papermill": {
     "duration": 0.026449,
     "end_time": "2021-10-09T18:07:06.298841",
     "exception": false,
     "start_time": "2021-10-09T18:07:06.272392",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "All numerical features are uniformly distributed and there's a heavy imbalance in the target class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "897820f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:06.357304Z",
     "iopub.status.busy": "2021-10-09T18:07:06.356608Z",
     "iopub.status.idle": "2021-10-09T18:07:07.420716Z",
     "shell.execute_reply": "2021-10-09T18:07:07.421235Z",
     "shell.execute_reply.started": "2021-10-09T16:21:47.4647Z"
    },
    "papermill": {
     "duration": 1.094826,
     "end_time": "2021-10-09T18:07:07.421415",
     "exception": false,
     "start_time": "2021-10-09T18:07:06.326589",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1296 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,18))\n",
    "df['Profession'].value_counts().plot(kind='barh')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd99c7e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:07.487529Z",
     "iopub.status.busy": "2021-10-09T18:07:07.486855Z",
     "iopub.status.idle": "2021-10-09T18:07:07.781097Z",
     "shell.execute_reply": "2021-10-09T18:07:07.781575Z",
     "shell.execute_reply.started": "2021-10-09T16:21:53.846533Z"
    },
    "papermill": {
     "duration": 0.329615,
     "end_time": "2021-10-09T18:07:07.781765",
     "exception": false,
     "start_time": "2021-10-09T18:07:07.452150",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Risk_Flag'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Risk_Flag,df.Car_Ownership).plot(kind= \"bar\",figsize = (10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d917fb4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:07.846789Z",
     "iopub.status.busy": "2021-10-09T18:07:07.846143Z",
     "iopub.status.idle": "2021-10-09T18:07:08.238241Z",
     "shell.execute_reply": "2021-10-09T18:07:08.238678Z",
     "shell.execute_reply.started": "2021-10-09T16:22:25.226295Z"
    },
    "papermill": {
     "duration": 0.426217,
     "end_time": "2021-10-09T18:07:08.238863",
     "exception": false,
     "start_time": "2021-10-09T18:07:07.812646",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Risk_Flag'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Risk_Flag,df.CURRENT_JOB_YRS).plot(kind= \"bar\",figsize = (10,6) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e54777e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:08.309211Z",
     "iopub.status.busy": "2021-10-09T18:07:08.307448Z",
     "iopub.status.idle": "2021-10-09T18:07:08.597357Z",
     "shell.execute_reply": "2021-10-09T18:07:08.596666Z",
     "shell.execute_reply.started": "2021-10-09T16:22:28.770734Z"
    },
    "papermill": {
     "duration": 0.327542,
     "end_time": "2021-10-09T18:07:08.597504",
     "exception": false,
     "start_time": "2021-10-09T18:07:08.269962",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Risk_Flag'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Risk_Flag,df.House_Ownership).plot(kind= \"bar\",figsize = (10,6) , color = [\"salmon\",\"lightblue\",\"blue\"] )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c86734",
   "metadata": {
    "papermill": {
     "duration": 0.033401,
     "end_time": "2021-10-09T18:07:08.664042",
     "exception": false,
     "start_time": "2021-10-09T18:07:08.630641",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The number of defaulters is higher among those who have rented homes as can be expected in real life scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3555791b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:08.758425Z",
     "iopub.status.busy": "2021-10-09T18:07:08.757775Z",
     "iopub.status.idle": "2021-10-09T18:07:09.196903Z",
     "shell.execute_reply": "2021-10-09T18:07:09.197388Z",
     "shell.execute_reply.started": "2021-10-09T16:22:36.437152Z"
    },
    "papermill": {
     "duration": 0.500911,
     "end_time": "2021-10-09T18:07:09.197560",
     "exception": false,
     "start_time": "2021-10-09T18:07:08.696649",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots( figsize = (12,8) )\n",
    "\n",
    "corr_matrix = df.corr()\n",
    "corr_heatmap = sns.heatmap( corr_matrix, cmap = \"YlGnBu\", annot=True, ax=ax, annot_kws={\"size\": 14})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "730ac2c3",
   "metadata": {
    "papermill": {
     "duration": 0.033984,
     "end_time": "2021-10-09T18:07:09.265526",
     "exception": false,
     "start_time": "2021-10-09T18:07:09.231542",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There's some collinearity between Current Job years and Experience, one of them can be removed from the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1a52e0",
   "metadata": {
    "papermill": {
     "duration": 0.033767,
     "end_time": "2021-10-09T18:07:09.333526",
     "exception": false,
     "start_time": "2021-10-09T18:07:09.299759",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> **Seperating Numerical and Categorical columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9abff73c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:09.406300Z",
     "iopub.status.busy": "2021-10-09T18:07:09.405578Z",
     "iopub.status.idle": "2021-10-09T18:07:09.416735Z",
     "shell.execute_reply": "2021-10-09T18:07:09.417396Z",
     "shell.execute_reply.started": "2021-10-09T17:12:22.219573Z"
    },
    "papermill": {
     "duration": 0.049843,
     "end_time": "2021-10-09T18:07:09.417639",
     "exception": false,
     "start_time": "2021-10-09T18:07:09.367796",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Income', 'Age', 'Experience', 'CURRENT_JOB_YRS', 'CURRENT_HOUSE_YRS']\n",
      "['Married/Single', 'House_Ownership', 'Car_Ownership']\n"
     ]
    }
   ],
   "source": [
    "cat_var = ['Married/Single','House_Ownership','Car_Ownership']\n",
    "num_var = list(x_train.select_dtypes(include =['int64','float64']).columns)\n",
    "\n",
    "y_train,y_test = np.array(y_train), np.array(y_test)\n",
    "print(num_var)\n",
    "print(cat_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2cd36ce",
   "metadata": {
    "papermill": {
     "duration": 0.034326,
     "end_time": "2021-10-09T18:07:09.487092",
     "exception": false,
     "start_time": "2021-10-09T18:07:09.452766",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Preprocessing Data**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaa2c75a",
   "metadata": {
    "papermill": {
     "duration": 0.034099,
     "end_time": "2021-10-09T18:07:09.556434",
     "exception": false,
     "start_time": "2021-10-09T18:07:09.522335",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Numerical columns:** Scaled using MinMaxScaler\n",
    "\n",
    "\n",
    "**Categorical columns:**\n",
    "(i) Profession, State and City Columns were dropped because of their high cardinality\n",
    "(i) Remaining columns were encoded using One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a7f09e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:09.628212Z",
     "iopub.status.busy": "2021-10-09T18:07:09.627524Z",
     "iopub.status.idle": "2021-10-09T18:07:09.895099Z",
     "shell.execute_reply": "2021-10-09T18:07:09.894411Z",
     "shell.execute_reply.started": "2021-10-09T17:12:24.59194Z"
    },
    "papermill": {
     "duration": 0.304941,
     "end_time": "2021-10-09T18:07:09.895268",
     "exception": false,
     "start_time": "2021-10-09T18:07:09.590327",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "preprocessor= ColumnTransformer(transformers=[('o',OneHotEncoder(sparse=False,handle_unknown='ignore'),cat_var),('s',MinMaxScaler(), num_var)], remainder='passthrough')\n",
    "x_arr = preprocessor.fit_transform(x_train)\n",
    "x_arr_test = preprocessor.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad3d40f",
   "metadata": {
    "papermill": {
     "duration": 0.034288,
     "end_time": "2021-10-09T18:07:09.964786",
     "exception": false,
     "start_time": "2021-10-09T18:07:09.930498",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Visualizing preprocessed data in DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52593f1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:10.059973Z",
     "iopub.status.busy": "2021-10-09T18:07:10.059084Z",
     "iopub.status.idle": "2021-10-09T18:07:10.063012Z",
     "shell.execute_reply": "2021-10-09T18:07:10.062438Z",
     "shell.execute_reply.started": "2021-10-09T16:27:52.935572Z"
    },
    "papermill": {
     "duration": 0.06388,
     "end_time": "2021-10-09T18:07:10.063157",
     "exception": false,
     "start_time": "2021-10-09T18:07:09.999277",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<p>189000 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "        x0_married  x0_single  x1_norent_noown  x1_owned  x1_rented  x2_no  \\\n",
       "0              0.0        1.0              0.0       0.0        1.0    0.0   \n",
       "1              0.0        1.0              0.0       0.0        1.0    0.0   \n",
       "2              0.0        1.0              0.0       0.0        1.0    1.0   \n",
       "3              0.0        1.0              0.0       0.0        1.0    1.0   \n",
       "4              0.0        1.0              0.0       0.0        1.0    1.0   \n",
       "...            ...        ...              ...       ...        ...    ...   \n",
       "188995         0.0        1.0              0.0       0.0        1.0    1.0   \n",
       "188996         0.0        1.0              0.0       0.0        1.0    1.0   \n",
       "188997         0.0        1.0              0.0       0.0        1.0    1.0   \n",
       "188998         0.0        1.0              0.0       0.0        1.0    1.0   \n",
       "188999         1.0        0.0              0.0       0.0        1.0    0.0   \n",
       "\n",
       "        x2_yes         7         8     9        10    11  \n",
       "0          1.0  0.143242  0.586207  0.30  0.428571  0.00  \n",
       "1          1.0  0.811570  0.293103  0.05  0.071429  1.00  \n",
       "2          0.0  0.342000  0.793103  0.05  0.071429  0.50  \n",
       "3          0.0  0.102532  0.103448  0.85  0.571429  0.50  \n",
       "4          0.0  0.378861  0.517241  1.00  0.428571  0.50  \n",
       "...        ...       ...       ...   ...       ...   ...  \n",
       "188995     0.0  0.666301  1.000000  0.20  0.285714  0.50  \n",
       "188996     0.0  0.513951  0.586207  0.20  0.285714  0.75  \n",
       "188997     0.0  0.903086  0.482759  0.25  0.285714  0.00  \n",
       "188998     0.0  0.157654  0.741379  0.45  0.642857  0.00  \n",
       "188999     1.0  0.477911  0.672414  0.95  0.642857  1.00  \n",
       "\n",
       "[189000 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_names1 = preprocessor.transformers_[0][1].get_feature_names()\n",
    "new_x = pd.DataFrame(x_arr)\n",
    "new_x.rename(columns= {i:new_names1[i] for i in range(7)}, inplace=True)\n",
    "new_x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa499476",
   "metadata": {
    "papermill": {
     "duration": 0.035963,
     "end_time": "2021-10-09T18:07:10.134631",
     "exception": false,
     "start_time": "2021-10-09T18:07:10.098668",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Evaluating different baseline models**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20699e6",
   "metadata": {
    "papermill": {
     "duration": 0.034691,
     "end_time": "2021-10-09T18:07:10.205254",
     "exception": false,
     "start_time": "2021-10-09T18:07:10.170563",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Models are trained with cost-sensitive methods to account for class imbalance.\n",
    "\n",
    "Cross validation proved to be computationally expensive for this size of dataset so a shuffled stratified split was done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8dac8b96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:07:10.279417Z",
     "iopub.status.busy": "2021-10-09T18:07:10.278464Z",
     "iopub.status.idle": "2021-10-09T18:09:23.417499Z",
     "shell.execute_reply": "2021-10-09T18:09:23.418161Z",
     "shell.execute_reply.started": "2021-10-09T16:30:52.911632Z"
    },
    "papermill": {
     "duration": 133.178212,
     "end_time": "2021-10-09T18:09:23.418428",
     "exception": false,
     "start_time": "2021-10-09T18:07:10.240216",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> RandomForestClassifier : f2_score: 0.713 , Accuracy score: 0.894 , Roc_Auc_Score: 0.841\n",
      "> LogisticRegression : f2_score: 0.348 , Accuracy score: 0.508 , Roc_Auc_Score: 0.533\n",
      "> KNeighborsClassifier : f2_score: 0.503 , Accuracy score: 0.888 , Roc_Auc_Score: 0.718\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/xgboost/sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> XGBClassifier : f2_score: 0.654 , Accuracy score: 0.820 , Roc_Auc_Score: 0.807\n"
     ]
    }
   ],
   "source": [
    "# def evaluate_model(X, y, model):\n",
    "#     # define evaluation procedure\n",
    "#     cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=3, random_state=42)\n",
    "#     # define the model evaluation metric\n",
    "#     metric = make_scorer(f2_measure)\n",
    "#     # evaluate model\n",
    "#     scores = cross_val_score(model, X, y, scoring=metric, cv=cv, n_jobs=-1)\n",
    "#     return scores\n",
    "def f2_measure(y_true, y_pred): #Modified version of F1 measure for more weight on false negatives\n",
    "    return fbeta_score(y_true, y_pred, beta=2)\n",
    "scale_pos = count[0] / count[1] \n",
    "\n",
    "models = [RandomForestClassifier(class_weight='balanced', random_state=42), LogisticRegression(solver='lbfgs', class_weight='balanced', random_state=42),\n",
    "KNeighborsClassifier(n_jobs=-1,weights='distance'), XGBClassifier(scale_pos_weight = scale_pos, verbosity=0, silent=True) ]\n",
    "#intial cost modified base models to evaluate\n",
    "\n",
    "model_names = ['RandomForestClassifier','LogisticRegression','KNeighborsClassifier', 'XGBClassifier' ]\n",
    "\n",
    "for x in range(len(models)) :\n",
    "    model = models[x] \n",
    "    model.fit(x_arr,y_train)\n",
    "    y_pred = model.predict(x_arr_test)\n",
    "    print('> %s : f2_score: %.3f , Accuracy score: %.3f , Roc_Auc_Score: %.3f' \n",
    "        %(model_names[x],f2_measure(y_test,y_pred),accuracy_score(y_test,y_pred),roc_auc_score(y_test,y_pred)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c39b933",
   "metadata": {
    "papermill": {
     "duration": 0.035587,
     "end_time": "2021-10-09T18:09:23.490431",
     "exception": false,
     "start_time": "2021-10-09T18:09:23.454844",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As seen from the results, Baseline RandomForestClassifier model does a better job than other models with higher f2_score and roc_auc_score of 0.713 and 0.841 respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb3ddf7",
   "metadata": {
    "papermill": {
     "duration": 0.03529,
     "end_time": "2021-10-09T18:09:23.561432",
     "exception": false,
     "start_time": "2021-10-09T18:09:23.526142",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Resampling Data for target class balancing**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4664f0",
   "metadata": {
    "papermill": {
     "duration": 0.035737,
     "end_time": "2021-10-09T18:09:23.633043",
     "exception": false,
     "start_time": "2021-10-09T18:09:23.597306",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To Undersample majority class (i.e class 0) : **RandomUnderSampler**\n",
    "\n",
    "To Oversample majority class (i.e class 0) : **SMOTE (Synthetic Minority Over-sampling technique)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f7c7009f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:09:23.710409Z",
     "iopub.status.busy": "2021-10-09T18:09:23.709740Z",
     "iopub.status.idle": "2021-10-09T18:09:23.944079Z",
     "shell.execute_reply": "2021-10-09T18:09:23.943573Z",
     "shell.execute_reply.started": "2021-10-09T17:12:49.395367Z"
    },
    "papermill": {
     "duration": 0.274818,
     "end_time": "2021-10-09T18:09:23.944231",
     "exception": false,
     "start_time": "2021-10-09T18:09:23.669413",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of samples after Under-Sampling: (69741,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "under_sampler = RandomUnderSampler(sampling_strategy=0.5, random_state=42)\n",
    "x_arr,y_train=under_sampler.fit_resample(x_arr,y_train)\n",
    "\n",
    "print(\"No. of samples after Under-Sampling:\",y_train.shape)\n",
    "sns.countplot(x = y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e89aa02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:09:24.024337Z",
     "iopub.status.busy": "2021-10-09T18:09:24.023351Z",
     "iopub.status.idle": "2021-10-09T18:09:29.878003Z",
     "shell.execute_reply": "2021-10-09T18:09:29.877449Z",
     "shell.execute_reply.started": "2021-10-09T17:12:57.953558Z"
    },
    "papermill": {
     "duration": 5.897074,
     "end_time": "2021-10-09T18:09:29.878147",
     "exception": false,
     "start_time": "2021-10-09T18:09:23.981073",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of samples after Over-Sampling: (92988,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "over_sampler = SMOTE(random_state=42, n_jobs=-1, sampling_strategy=1)\n",
    "x_arr,y_train = over_sampler.fit_resample(x_arr,y_train)\n",
    "print(\"No. of samples after Over-Sampling:\",y_train.shape)\n",
    "sns.countplot(x = y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4bd595",
   "metadata": {
    "papermill": {
     "duration": 0.038066,
     "end_time": "2021-10-09T18:09:29.954589",
     "exception": false,
     "start_time": "2021-10-09T18:09:29.916523",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> **Evaluation after resampling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "305f977c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:09:30.038667Z",
     "iopub.status.busy": "2021-10-09T18:09:30.037578Z",
     "iopub.status.idle": "2021-10-09T18:09:34.758060Z",
     "shell.execute_reply": "2021-10-09T18:09:34.758568Z",
     "shell.execute_reply.started": "2021-10-09T16:35:36.497441Z"
    },
    "papermill": {
     "duration": 4.765978,
     "end_time": "2021-10-09T18:09:34.758753",
     "exception": false,
     "start_time": "2021-10-09T18:09:29.992775",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting done\n",
      "> Resampled_result : f2_score: 0.726 , Accuracy score: 0.875 , Roc_Auc_Score: 0.852 \n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_jobs=-1, random_state=42,class_weight='balanced')\n",
    "model.fit(x_arr,y_train)\n",
    "print(\"Fitting done\")\n",
    "y_pred = model.predict(x_arr_test)\n",
    "print('> %s : f2_score: %.3f , Accuracy score: %.3f , Roc_Auc_Score: %.3f ' %('Resampled_result',f2_measure(y_test,y_pred),accuracy_score(y_test,y_pred),roc_auc_score(y_test,y_pred)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67cbf48c",
   "metadata": {
    "papermill": {
     "duration": 0.037981,
     "end_time": "2021-10-09T18:09:34.836206",
     "exception": false,
     "start_time": "2021-10-09T18:09:34.798225",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Hyperparameter tuning using 3 fold RandomSearchCV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5bf0b5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:09:34.917659Z",
     "iopub.status.busy": "2021-10-09T18:09:34.916705Z",
     "iopub.status.idle": "2021-10-09T18:09:34.925498Z",
     "shell.execute_reply": "2021-10-09T18:09:34.924824Z",
     "shell.execute_reply.started": "2021-10-09T16:36:26.786594Z"
    },
    "papermill": {
     "duration": 0.050599,
     "end_time": "2021-10-09T18:09:34.925686",
     "exception": false,
     "start_time": "2021-10-09T18:09:34.875087",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]}\n"
     ]
    }
   ],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "02544225",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:09:35.012232Z",
     "iopub.status.busy": "2021-10-09T18:09:35.011547Z",
     "iopub.status.idle": "2021-10-09T18:28:39.672832Z",
     "shell.execute_reply": "2021-10-09T18:28:39.673501Z",
     "shell.execute_reply.started": "2021-10-09T16:36:33.433699Z"
    },
    "papermill": {
     "duration": 1144.708016,
     "end_time": "2021-10-09T18:28:39.673862",
     "exception": false,
     "start_time": "2021-10-09T18:09:34.965846",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "/opt/conda/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py:691: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed: 18.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting done\n",
      "{'n_estimators': 400, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 60, 'bootstrap': False}\n",
      "> Random_search_result , f2_score: 0.726 , Accuracy score: 0.874 , Roc_Auc_Score: 0.852 \n"
     ]
    }
   ],
   "source": [
    "rf_random = RandomizedSearchCV(estimator = model, param_distributions = random_grid, cv = 3, verbose=True, random_state=42, n_jobs = -1)\n",
    "rf_random.fit(x_arr,y_train)\n",
    "best_random = rf_random.best_estimator_\n",
    "best_random.fit(x_arr,y_train)\n",
    "print(\"Fitting done\")\n",
    "print(rf_random.best_params_)\n",
    "y_pred = best_random.predict(x_arr_test)\n",
    "print('> %s , f2_score: %.3f , Accuracy score: %.3f , Roc_Auc_Score: %.3f ' %('Random_search_result',f2_measure(y_test,y_pred),accuracy_score(y_test,y_pred),roc_auc_score(y_test,y_pred)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc81ed3",
   "metadata": {
    "papermill": {
     "duration": 0.040878,
     "end_time": "2021-10-09T18:28:39.755404",
     "exception": false,
     "start_time": "2021-10-09T18:28:39.714526",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Random_search_result , f2_score: 0.724 , Accuracy score: 0.873 , Roc_Auc_Score: 0.851  \n",
    "\n",
    "There is hardly any improvement in the results after random search, one of the reasons could be the loss of data because of resampling \n",
    "but if the data samples were higher in number than it would be computationally very expensive to find optimal hyperparameters using RandomSearchCV."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b316e0c7",
   "metadata": {
    "papermill": {
     "duration": 0.040372,
     "end_time": "2021-10-09T18:28:39.836216",
     "exception": false,
     "start_time": "2021-10-09T18:28:39.795844",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Configuring Final Pipeline for analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "169f5723",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:28:39.933905Z",
     "iopub.status.busy": "2021-10-09T18:28:39.933214Z",
     "iopub.status.idle": "2021-10-09T18:29:12.107773Z",
     "shell.execute_reply": "2021-10-09T18:29:12.108285Z",
     "shell.execute_reply.started": "2021-10-09T17:14:11.583079Z"
    },
    "papermill": {
     "duration": 32.231137,
     "end_time": "2021-10-09T18:29:12.108455",
     "exception": false,
     "start_time": "2021-10-09T18:28:39.877318",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> Final_evaluation_score : f2_score: 0.726 , Accuracy score: 0.874 , Roc_Auc_Score: 0.852 \n"
     ]
    }
   ],
   "source": [
    "def load_data(file_path):\n",
    "    data=pd.read_csv(file_path,index_col='Id')\n",
    "    for train_index, test_index in sss.split(df,df[\"Risk_Flag\"]):\n",
    "        sss_train = df.iloc[train_index]\n",
    "        sss_test = df.iloc[test_index]\n",
    "    X_train= sss_train.drop(['Risk_Flag','Profession','STATE','CITY'], axis=1)\n",
    "    X_val= sss_test.drop(['Risk_Flag','Profession','STATE','CITY'], axis=1)\n",
    "    Y_train = sss_train.Risk_Flag\n",
    "    Y_val = sss_test.Risk_Flag\n",
    "    return X_train,X_val,Y_train,Y_val\n",
    "\n",
    "X_train,X_val,Y_train,Y_val  = load_data('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')\n",
    "Y_train,Y_val = np.array(Y_train), np.array(Y_val)\n",
    "\n",
    "final_model = RandomForestClassifier(n_estimators= 400, min_samples_split= 10, min_samples_leaf= 1, max_features='sqrt',\n",
    "                                     class_weight='balanced',n_jobs=-1,random_state=42,max_depth= 60, bootstrap=False)\n",
    "config =[('pre',preprocessor),('u',under_sampler),('o',over_sampler),('m',final_model)]\n",
    "pipeline = Pipeline(steps=config)\n",
    "pipeline.fit(X_train,Y_train)\n",
    "Y_pred = pipeline.predict(X_val)\n",
    "\n",
    "print('> %s : f2_score: %.3f , Accuracy score: %.3f , Roc_Auc_Score: %.3f ' \n",
    "      %('Final_evaluation_score',f2_measure(Y_val,Y_pred),accuracy_score(Y_val,Y_pred),roc_auc_score(Y_val,Y_pred)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4de764d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:29:12.196018Z",
     "iopub.status.busy": "2021-10-09T18:29:12.195385Z",
     "iopub.status.idle": "2021-10-09T18:29:17.384645Z",
     "shell.execute_reply": "2021-10-09T18:29:17.385099Z",
     "shell.execute_reply.started": "2021-10-09T17:03:45.044906Z"
    },
    "papermill": {
     "duration": 5.235944,
     "end_time": "2021-10-09T18:29:17.385273",
     "exception": false,
     "start_time": "2021-10-09T18:29:12.149329",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'RandomForestClassifier Classification Report'}>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = [\"0\", \"1\"]\n",
    "visualizer = ClassificationReport(pipeline, classes=classes, support=True)\n",
    "visualizer.fit(X_train, Y_train)        # Fit the visualizer and the model\n",
    "visualizer.score(X_val, Y_val)        # Evaluate the model on the test data\n",
    "visualizer.show()                       # Finalize and show the figure\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bb154b8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:29:17.475226Z",
     "iopub.status.busy": "2021-10-09T18:29:17.474591Z",
     "iopub.status.idle": "2021-10-09T18:29:17.628103Z",
     "shell.execute_reply": "2021-10-09T18:29:17.628908Z",
     "shell.execute_reply.started": "2021-10-09T17:19:10.777676Z"
    },
    "papermill": {
     "duration": 0.202315,
     "end_time": "2021-10-09T18:29:17.629177",
     "exception": false,
     "start_time": "2021-10-09T18:29:17.426862",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion  Matrix')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DnJ2d0y3v06ePOnbsSKzzWVO/2qpYvoRO7Jwv6daZraOjg2pWK6dnO4xNt+3V6wnq+8aH9tvvvPmC9v3r1itXz0de0Y7dR9Wx15Q7HmfWgnWateDWq1fv9gKzoyfOy6d2Re07dGu/PrUq6uiJ85KkFo0eV+OGtdSmxdOSpGJFPfTU45X0VO2KGjb+k3t/EIB8ULiIu0p5FdVtzyyl+/ftqlYvq8P/OnPHdV6li6pMuWJaHf73O65v08FXbTr4Srr7C8y8q5TWyRORatW2jiTp5PE/VKx4IftZ9X+zpqbpj3PRmd095KE8uZ5hsVjsl79vd/ny5XTPfyJ/LF0Woceb/FUN241Ww3ajteSLzdq05aACe0/NsG3liqVUrKiHHBwseq75U+rXs6WmzftakvRNxAFVq1xaPbo0lpOTo5ycHOX7pLdqVC2bYT93s2zNDgW/0l5lvTxVxstTbwzooM9XbZckvTpiker4j7TPeuDwb5ry/mq9PXPl/T8QQD7o2Lm+/rF8h2KuxCn2eqK+/HybGjV9PMN27Tr66ufDZ7Vn53FZrWla8fk2FS1aUJW9vVTbp6IKFiygz5ZuVlJSiqzWNJ0+eUG//vx7jmZpH1hP69bs0m+nLyouNlEfh3yvjp3qS5LO/haln3b8qqSkFKXetGrj+n06uP+06tTlqcz8kCdn1mPHjlWfPn1UsWJFlSlTRpIUGRmp33//XePGjcuLQyIHbiSl6MZtL2iJT0xSUtJNRcfEqVH9Glr76WiVrNVXkvSMj7dmvv2SihR218nfLqhv8Hz7WW98QpI6vjhV08f11vRxveXgYNGRX3/XqHezftHa7ZZ8sVmVK5TS3u9nSJI+WfGD/W1b12MTdf22bVNupio2/oZib3uOG3iY9B/YRtevJahbwGS5uDirVZun1XdAa128cFXdO03VirAxKl3GUxUre+mdqS9q+rtfKSYmTjVrldesea/I2fnW/7bfm/+qPpi1Vn9p+65upqSqQuVSGvRa+xzN4te4lnr39deQfvOVnHxTLVo9pVeHtpMk2WTTkoWbdGZklBwcLXqsQklNnvWyatbmBWb5wWLLo1ftpKWl6fDhw4qKipIkeXl5ycfHR46Ojtn6ebcKd/5gDAB568Kpl/J7BOCRVdSl3R2X59mrwR0cHPT000/n1e4BAHhk8Bp8AAAMR6wBADAcsQYAwHDEGgAAwxFrAAAMR6wBADAcsQYAwHDEGgAAwxFrAAAMR6wBADAcsQYAwHDEGgAAwxFrAAAMR6wBADAcsQYAwHDEGgAAwxFrAAAMR6wBADAcsQYAwHDEGgAAwxFrAAAMR6wBADAcsQYAwHDEGgAAwxFrAAAMR6wBADAcsQYAwHDEGgAAwxFrAAAMR6wBADAcsQYAwHDEGgAAwxFrAAAMR6wBADAcsQYAwHDEGgAAwxFrAAAMR6wBADAcsQYAwHDEGgAAwxFrAAAMR6wBADAcsQYAwHDEGgAAwxFrAAAM55TZiqCgIFkslkx/cNWqVXkyEAAASC/TWI8aNepBzgEAADKRaazr16+f7nZMTIyKFSuW5wMBAID0snzO+tChQ2rRooX+8pe/SJKOHDmicePG5flgAADglixjPXXqVC1evFienp6SJB8fHx04cCDPBwMAALdkGeubN2+qatWq6ZY5Ozvn2UAAACC9LGPt4uKihIQE+yvDT506JVdX1zwfDAAA3JLpC8z+NGjQIPXv31+XLl3S6NGjtWPHDs2cOfNBzAYAAJSNWDdr1kze3t7asWOHbDabBg8erIoVKz6I2QAAgLIRa0kqXbq06tatK0kqV65cng4EAADSyzLW+/bt04gRI1SgQAFJUnJysmbPnq1nnnkmz4cDAADZiPXEiRM1c+ZM+4ek7Nu3TxMmTNC6devyfDgAAJDNL/K4/dPM/rwcDgAAHowsY92oUaN0Z9Hr169X48aN83QoAADwHxabzWa704qGDRvKYrHIZrPp2rVrcnFxkSSlpKTI09NTO3fuzNPB3Cr0yNP9A7izC6deyu8RgEdWUZd2d1ye6XPWq1evzrNhAABA9mUaa96iBQCAGbJ8NfiFCxc0c+ZMHTt2TMnJyfblEREReToYAAC4JcsXmI0dO1Z+fn6y2WyaNWuWfH197V+XCQAA8l6Wsb569aq6desmJycn1alTR9OmTdO2bdsexGwAAEDZiPWfX4fp7u6uyMhIpaamKiYmJs8HAwAAt2T5nHXdunV17do19ejRQ126dJGLi4vatm37IGYDAAC6y/us7yQyMlLx8fGqXr16Xs4kifdZA/mF91kD+SfH77O+ceNGhmWenp7y9PTUjRs35ObmlnvTAQCATGV6Zl2zZk37J5jZN/7/2xaLRUePHs3j0U7k8f4B3Mn1lN/yewTgkVXE5c5PM2d6Zn3s2LE8GwYAAGRftr51CwAA5B9iDQCA4Yg1AACGI9YAABguy1hfuXJFI0eOVK9evSTdeuHZl19+meeDAQCAW7KM9d///nf5+voqNjZWkuTt7a3ly5fn+WAAAOCWLGMdFRWlHj16yNHRUZLk4uIiBweungMA8KBkWV0np/RvxY6NjVUOPqEUAADcpyy/yKN169YaP368EhIStGbNGi1fvlxBQUEPYjYAAKBsfpHHunXrtGXLFtlsNvn7+6tTp04PYDQ+bhTID3zcKJB/Mvu40Rx969aDRayB/ECsgfyT488G/1NwcLAsFkuG5R988MH9TwUAALKUZaxbtGhh/3dycrK+/fZbValSJU+HAgAA/5Hjy+ApKSnq37+/Pv/887ya6f9xGRzID1wGB/JPZpfBc/yGaYvFoqioqPseCAAAZE+OnrO22Ww6fvy4nn322TwfDAAA3JKj56wdHR3Vv39/PfXUU3k6FAAA+I+7xtpqtWr//v2aNGnSg5oHAAD8l7s+Z+3o6Kjjx48/qFkAAMAdZHkZvGHDhpo4caI6d+4sd3d3+/KqVavm6WAAAOCWTN+6NXbsWE2ZMkX+/v4Zf8hiUURERB6Pxlu3gPzAW7eA/JPjTzA7evSoJGnLli15MxEAAMgWvpgaAADDZXpmfeLECfn5+WVYbrPZZLFYtHPnzjwdDAAA3JJprCtVqqSQkJAHOQsAALiDTGPt4uKicuXKPchZAADAHWT6nLWzs/ODnAMAAGQix9+69eDw1i0gP/DWLSD/5Nq3bgEAgAeLWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGc8rvAfDgffHFBq1ZE6ETJ86qY8emmjZtWJY/8/LLb2nXrsP65Ze1cnJyzLVZbDabZs36VKtWfSdJ6tr1OY0c+bIsFotiYq5ryJDJOnPmvKzWNFWpUl5vvtlPvr61c+34QH74buMBLVm4SRcvXlXx4oU0flIv1fGtkun2Q16Zr327T+qng7Nz/fdv/pz1CluzU5LUqYufXhsWIIvFomtX4zUyeInOnolSWppNlSp76Y2RnfRUHe9cOz6yj1g/gkqVKqYhQ57Xjh0HlZycnOX269ZtVWqq9Z6Pt3v3Ec2fv1yffz41w7qVKzdp8+ZdCgubK4vFor59x6l8eS/16NFOBQu6acqUYFWqVFYWi0UREbs0ePC7+umnL3L1f1jAg7T7p2OaP2edJs/so8d9Kij6cuxdt9+0YZ9Sb6bd8/H27z2pxQs2aVHo6xnWff3VT9r2wxEtWzVKFov0+oAFKlu+mIKebyw3d1eNm9hDj1UsKYvFom1bjmjEa4u1adskfv/yAZfBH0HPPfesWrXyU9GihbLcNi4uQR9++KX+9rc+GdadPn1OffuOU/36PdSmzSB9882OHM+ydu0W9evXWaVLl5CXV3H17dtZX38dIUlydXWRt3d5OTg4yGazycHBUdevx+v69bgcHwcwRciCjeo/qI18nqokBwcHlfIqqlJeRe+4bXzcDS1etEmvDw/MsO7sb1F67dUP1arRGHUNmKzvNx3M8Szh6/ao10st5FX61gw9X26h8LA9kiRXV2dVrOxl//1zdHRQbGyiYq8n5vg4uH+cWeOuZs/+TD16tFOJEp7plicmJqlfv/EKDu6lxYsn6MSJs+rbd5yqV6+oqlUrZHv/J0/+rpo1K9tv16xZWSdP/p5um4CA13XmzHndvJmqbt2eU/HiRe/rPgH5xWpN09Ffzqlp8yfUpf27Skm+qWb+T+r1EYEqUMAlw/YLPtigoOcbqXiJ9H9Y30hM1msDFmjg0HZ6f+EgnT55Qa8NWKAq1crIu0rpbM/z2+mLqlajrP12tRrl9Nupi+m26dllms6euaTUVKs6BfmpWPGs/8hH7uPMGpk6cuSkDhw4qhdfDMiwbuvWvSpXrpSCglrJyclRtWtXUZs2z2rTph9zdIzExCR5eLjbbxcqVFCJiTdks9nsy9avn6f9+1fqvfdG8nw1HmoxV+KUmmpVxPeHFPJpsL5Y9aaOHzuvj0O+y7Dtr7/8rkP/OqPnezbNsO6f239RmXLFFPCXhnJyclSNWuXl3+opRXyXs7PrG4nJ8vBws9/28CigxMTkdL9/y9eM1g+7puvd6S/xfHU+euBn1qtXr1ZQUNCDPixyKC0tTe+8s1BvvfXqHZ+f+uOPSzp8+ITq1u1uX2a1WhUY2EKSFBLylUJCVtuXJyenpNt2374VkiR39wJKSLhhXx4fnyh3dzdZLJZ0x3N1dVHHjs3Urt1g1arlne5sHHhYuLo6S5Ke79lUJUoWkST1fKm5Pg75TkOCO9q3S0tL04xJX2nEqC53/P27EHlVvxz+t/yfHW1fZk21ql1APUnSp0u+16cfR9iXp6Skptt2y0/TJElu7q5KSEiyL0+IT5a7u+sdfv+c1aa9r54PnKLqNcupeo1y9/U4IOceeKznzZtHrB8C8fGJ+vnnUxo2bIakW5fvJKlZsz764IPRKlOmhOrVe0Khoe/e8ecHDOimAQO6Sbr7C8yqVaugY8fO6Mknq0uSjh07o2rVMr+Mnppq1blzF4k1HkqFi7irlFdR3d7C/w6jJCXEJ+noL+c09m+fSLoVb0nq2OptTX2vr7xKF9Uzdato/uKhdzzOy6+01suvtJZ09xeYeVcprZPH/9DjPhUlSSdP/CHvqplfRk9NteqP89HEOh/kSawDAjJeNv1TdHR0XhwSOZCaapXValVaWpqs1jQlJ6fI0dEx3V/whQoV1I4dn9pvX7hwWd26jdCaNe/L07OwatasrPfe+1Rr125Rhw63LtMdPfqbChZ0U5Uqj2V7lk6d/BUaulbNmtWVJIWGfm2/7P6vfx1TaqpVTz5ZXWlpafrss/WKjr5mDzvwMAro3ED/WL5dfo1qycnJUcs/36rGTR9Pt41HITeFb5lov33p4lX16TFbn60cKc9iHkpJKacP31+vb9bv1XNtn5EknTh+Xm7urqrsnf3nrNsH1tPyz7bq2Sa1ZbFYtOzTH/R8zyaSpCOHzspqtepxn4qyWtO0ctl2xVyJ0xM+le77MUDO5Umsr1y5oqVLl6pw4cLplttsNnXv3j2Tn8KDsnDhSs2f/6X99rp1W/Xaaz0UFNRKHToMVXj4hypbtpRKlvzPi8qSk1MkScWLF5WTk6NcXJy1dOlETZu2VNOmLZXNZlONGpU1Zkz/HM3SvXtbnTt3UQEBr0m69T7r7t3bSpJSUm5q0qQQnTsXJWdnR1WvXlEhIePl5VX8fh8CIN/0H9hG167Fq2vAZLm4OKlVmzrqO+A5XbwQoxc6TdXKsDEqXaaYSpT4z/8/U5JvSpKKFS8kJydHOTs7ad5Hg/X+zLV6f+bXSkuzqVqNchr2t845mqVLt0b64/wV9ewyXZIUGNRQXbo1kiTdTEnVrGmrFXn+ipycHFWlWhnN/nCASpYqkjsPBHLEYrv9lQS5ZOzYserSpYvq1q2bYd2IESP03nvvZWMvJ3J7LADZcD3lt/weAXhkFXFpe8fleRLr3EGsgfxArIH8k1mseesWAACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGs9hsNlt+DwEAADLHmTUAAIYj1gAAGI5YAwBgOGINAIDhiDUAAIYj1gAAGI5YI1dt375dbdq0UevWrRUSEpLf4wCPjDFjxsjPz08dO3bM71GQB4g1co3VatXEiRO1ZMkShYeHa8OGDTp16lR+jwU8Erp06aIlS5bk9xjII8Qauebw4cOqWLGiHnvsMbm4uKhDhw6KiIjI77GAR0K9evVUpEiR/B4DeYRYI9dERUWpdOnS9tteXl6KiorKx4kA4H8DsQYAwHDEGrnGy8tLFy9etN+OioqSl5dXPk4EAP8biDVyjY+Pj86ePatz584pJSVF4eHh8vf3z++xAOChx7duIVdt27ZNU6ZMkdVqVVBQkAYPHpzfIwGPhOHDh2vPnj26evWqihcvrtdff13dunXL77GQS4g1AACG4zI4AACGI9YAABiOWAMAYDhiDQCA4Yg1AACGI9bAA+bv76+2bdsqMDBQHTt2VHh4+H3vc82aNQoODpYkRUREaPr06XfdPjY2VosXL77n4/n7++vEiRN3neNu5s2bl+WMdzJ69Gh98cUXOf454GHnlN8DAI+iuXPnqnr16vr111/VvXt3+fn5qVixYvb1qampcnK6t1/Pli1bqmXLlnfdJjY2VkuWLNGrr756T8cA8GARayAf1a5dWwULFtT58+c1Y8YMOTo66syZM0pISFBYWJi+/vprLV++XFarVR4eHpowYYK8vb2VkpKiSZMmadeuXfL09FStWrXs+1yzZo22bt2quXPnSpJWrVqlzz77TJLk7Oysjz76SBMnTlRcXJw6deokNzc3rVixQpcuXdKkSZMUGRmp5ORkdejQQYMGDZIk7du3T++8846kW9/ulJ2PZ7h8+bKGDx+uhIQEJScnq1mzZnrzzTft6yMjI/XSSy/p0qVLqlatmqZMmaJChQopJSVFc+bM0d69e5WSkqIaNWpowoQJKliwYK497sDDhlgD+WjXrl1KTk5WpUqVJElHjx7VF198IXd3d+3bt08bN27UsmXL5OLiom3btmns2LFasWKFVq5cqfPnzys8PFypqanq1auXypcvn2H/u3fv1kcffaTly5erZMmSSkhIkJOTk8aPH6+goCCFhYXZtx01apSGDBmievXqKSUlRX369JGPj4/q1aunYcOGadasWWrQoIG++eYbLVu2LMv7VrhwYS1atEgFCxbUzZs31b9/f23fvl1NmzaVJO3fv19r165ViRIlNGbMGC1YsECjRo3SkiVLVKhQIa1atUqSNHPmTIWEhGjYsGG58IgDDydiDeSD4OBgubq6ysPDQ/PmzVPhwoUlSW3btpW7u7skacuWLTp27Jj9IyNtNptiY2Ml3Ypw586d5ezsLGdnZwUGBurAgQMZjrN161Z16tRJJUuWlKRMz04TExO1Z88excTE2JclJCTo9OnTKl68uNzc3NSgQQNJUvv27TV+/Pgs76PVatWMGTN08OBB2Ww2RUdH69ixY/ZYN2/eXCVKlJAkde3aVZMmTbLf7/j4eH377beSpJSUFNWsWTPL4wH/y4g1kA/+fM76v/0ZaulWnIOCgvTGG2/k+TxpaWmyWCxatWqVnJ2d0607duxYhu0tFkuW+wwNDVVsbKy++uorubq6aty4cUpOTs7y52w2m95++235+fll/w4A/+N4NThgKH9/f4WFhdm/dtRqternn3+WJDVs2FBhYWFKTU1VUlKSNmzYcMd9NG/eXGFhYYqOjpYk+/PHHh4eSkpKUmpqqiTJw8NDvr6+CgkJsf/shQsXdPnyZXl7eyspKUn79u2TJG3atMl+hn83cXFxKlmypFxdXRUVFaWIiIh067du3Wo/k1+zZo0aNmxov9+ffPKJkpKSJEnx8fE6ffp09h404H8UZ9aAoerVq6e//vWvGjx4sKxWq27evKm2bdvqiSee0PPPP6/jx4+rffv28vT0lI+Pj65cuZJhHw0aNNCAAQPUt29fWSwWubi4aNGiRSpRooQCAgIUEBCgIkWKaMWKFZo1a5amTp2qgIAASbcumU+ePFklS5bU7Nmz073ArGzZslnO37t3b73xxhvq2LGjvLy8Mpwp161bV8OGDVNUVJSqVq2q0aNHS5IGDBig+fPnq2vXrrJYLLJYLHrttddUpUqV+31IgYcW37oFAIDhuAwOAIDhiDUAAIYj1gAAGI5YAwBgOGINAIDhiDUAAIYj1gAAGI5YAwBguP8D3iy+R8ozh8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing confusion matrix using seaborn\n",
    "sns.heatmap(confusion_matrix(Y_val,Y_pred),annot=True,cbar=False,cmap= 'YlGnBu')\n",
    "plt.xlabel(\"Predicted label\") # predictions go on the x-axis\n",
    "plt.ylabel(\"True label\") # true labels go on the y-axis \n",
    "plt.title(\"Confusion  Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fa822020",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:29:17.744538Z",
     "iopub.status.busy": "2021-10-09T18:29:17.743891Z",
     "iopub.status.idle": "2021-10-09T18:29:20.335453Z",
     "shell.execute_reply": "2021-10-09T18:29:20.335902Z",
     "shell.execute_reply.started": "2021-10-09T17:03:55.893726Z"
    },
    "papermill": {
     "duration": 2.645338,
     "end_time": "2021-10-09T18:29:20.336095",
     "exception": false,
     "start_time": "2021-10-09T18:29:17.690757",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f2754f56f10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(pipeline,X_val,Y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f556dcc3",
   "metadata": {
    "papermill": {
     "duration": 0.043621,
     "end_time": "2021-10-09T18:29:20.423484",
     "exception": false,
     "start_time": "2021-10-09T18:29:20.379863",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Submission of Test Results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "421ea5f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-09T18:29:20.516578Z",
     "iopub.status.busy": "2021-10-09T18:29:20.515929Z",
     "iopub.status.idle": "2021-10-09T18:29:21.761478Z",
     "shell.execute_reply": "2021-10-09T18:29:21.760947Z",
     "shell.execute_reply.started": "2021-10-09T17:06:41.691045Z"
    },
    "papermill": {
     "duration": 1.29286,
     "end_time": "2021-10-09T18:29:21.761633",
     "exception": false,
     "start_time": "2021-10-09T18:29:20.468773",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_data=pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Test Data.csv', index_col='ID')\n",
    "test_data = test_data.drop(['Profession','STATE','CITY'], axis=1)\n",
    "y_final = pipeline.predict(test_data)\n",
    "my_submission = pd.DataFrame({'id': test_data.index, 'risk_flag': y_final})\n",
    "my_submission.to_csv('submission.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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