exp-2.ipynb

{
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "#preprocesing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from xgboost.sklearn import XGBRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Removing warnings\n",
    "\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Storing plots\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: catboost in /opt/conda/lib/python3.7/site-packages (1.1)\r\n",
      "Requirement already satisfied: plotly in /opt/conda/lib/python3.7/site-packages (from catboost) (5.10.0)\r\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from catboost) (1.15.0)\r\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from catboost) (1.7.3)\r\n",
      "Requirement already satisfied: pandas>=0.24.0 in /opt/conda/lib/python3.7/site-packages (from catboost) (1.3.5)\r\n",
      "Requirement already satisfied: numpy>=1.16.0 in /opt/conda/lib/python3.7/site-packages (from catboost) (1.21.6)\r\n",
      "Requirement already satisfied: graphviz in /opt/conda/lib/python3.7/site-packages (from catboost) (0.8.4)\r\n",
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from catboost) (3.5.3)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.24.0->catboost) (2.8.2)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.24.0->catboost) (2022.1)\r\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->catboost) (1.4.3)\r\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->catboost) (3.0.9)\r\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->catboost) (4.33.3)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->catboost) (21.3)\r\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->catboost) (9.1.1)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->catboost) (0.11.0)\r\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from plotly->catboost) (8.0.1)\r\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (4.4.0)\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install catboost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae76a5d9",
   "metadata": {
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     "start_time": "2023-01-16T15:17:37.182517",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Load train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53702a3a",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "train_dataset_ = pd.read_feather('/kaggle/input/amexfeather/train_data.ftr')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae2d4b6",
   "metadata": {
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     "start_time": "2023-01-16T15:17:56.576908",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Keep the latest statement features for each customer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a60c7f0",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# # Keep the latest statement features for each customer\n",
    "train_dataset = train_dataset_.groupby('customer_ID').tail(1).set_index('customer_ID', drop=True).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12300eee",
   "metadata": {
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     "duration": 0.009739,
     "end_time": "2023-01-16T15:17:59.491279",
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     "start_time": "2023-01-16T15:17:59.481540",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Remove Features with min 75% null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "743de883",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "min_null_count =  int(((100-25)/100)*train_dataset.shape[0] + 1)\n",
    "train_dataset = train_dataset.dropna( axis=1, \n",
    "                thresh=min_null_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5653755f",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset.drop([\"S_2\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5fc36e5",
   "metadata": {
    "papermill": {
     "duration": 0.009659,
     "end_time": "2023-01-16T15:18:00.564789",
     "exception": false,
     "start_time": "2023-01-16T15:18:00.555130",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Drop unuseful coulums"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b9fb06f",
   "metadata": {
    "papermill": {
     "duration": 0.009436,
     "end_time": "2023-01-16T15:18:00.584259",
     "exception": false,
     "start_time": "2023-01-16T15:18:00.574823",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Handle Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f7634532",
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['D_63',\n",
       " 'D_64',\n",
       " 'D_68',\n",
       " 'B_30',\n",
       " 'B_38',\n",
       " 'D_114',\n",
       " 'D_116',\n",
       " 'D_117',\n",
       " 'D_120',\n",
       " 'D_126']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories=[]\n",
    "for categorical_column in train_dataset.select_dtypes(include=['category','object']).columns:\n",
    "    categories.append(categorical_column)\n",
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91369be6",
   "metadata": {
    "execution": {
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     "start_time": "2023-01-16T15:18:00.635193",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "enc = LabelEncoder()\n",
    "for categorical_column in train_dataset.select_dtypes(include=['category','object']).columns:\n",
    "    train_dataset[categorical_column]=enc.fit_transform(train_dataset[categorical_column])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b27a9121",
   "metadata": {
    "papermill": {
     "duration": 0.009957,
     "end_time": "2023-01-16T15:18:01.141631",
     "exception": false,
     "start_time": "2023-01-16T15:18:01.131674",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Handle Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb489ebf",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for category in categories:\n",
    "    train_dataset[category]=train_dataset[category].fillna(train_dataset[category].mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1ec4146a",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for column in train_dataset.columns:\n",
    "    if(column not in categories):\n",
    "#         print(column)\n",
    "        train_dataset[column]=train_dataset[column].fillna(train_dataset[column].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767cb47b",
   "metadata": {
    "papermill": {
     "duration": 0.00968,
     "end_time": "2023-01-16T15:18:04.282070",
     "exception": false,
     "start_time": "2023-01-16T15:18:04.272390",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Split fatrues and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b50a7ea",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-01-16T15:18:04.303815Z",
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     "end_time": "2023-01-16T15:18:04.591031",
     "exception": false,
     "start_time": "2023-01-16T15:18:04.292202",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = train_dataset.iloc[:, :-1]\n",
    "y = train_dataset.iloc[:, -1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8fcce6c",
   "metadata": {
    "papermill": {
     "duration": 0.010075,
     "end_time": "2023-01-16T15:18:04.612092",
     "exception": false,
     "start_time": "2023-01-16T15:18:04.602017",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Drop Correlated Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91f06f2b",
   "metadata": {
    "execution": {
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     "duration": 30.609985,
     "end_time": "2023-01-16T15:18:35.232125",
     "exception": false,
     "start_time": "2023-01-16T15:18:04.622140",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'B_11',\n",
       " 'B_13',\n",
       " 'B_15',\n",
       " 'B_23',\n",
       " 'B_33',\n",
       " 'B_37',\n",
       " 'D_104',\n",
       " 'D_119',\n",
       " 'D_141',\n",
       " 'D_143',\n",
       " 'D_74',\n",
       " 'D_75',\n",
       " 'S_24',\n",
       " 'S_7'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_matrix = X.corr()\n",
    "col_core = set()\n",
    "\n",
    "for i in range(len(cor_matrix.columns)):\n",
    "    for j in range(i):\n",
    "        if(cor_matrix.iloc[i, j] > 0.9):\n",
    "            col_name = cor_matrix.columns[i]\n",
    "            col_core.add(col_name)\n",
    "col_core"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ab396cf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:18:35.255394Z",
     "iopub.status.busy": "2023-01-16T15:18:35.254385Z",
     "iopub.status.idle": "2023-01-16T15:18:35.527632Z",
     "shell.execute_reply": "2023-01-16T15:18:35.526207Z"
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     "end_time": "2023-01-16T15:18:35.530888",
     "exception": false,
     "start_time": "2023-01-16T15:18:35.242157",
     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "X = X.drop(col_core, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0235d0b9",
   "metadata": {
    "papermill": {
     "duration": 0.009974,
     "end_time": "2023-01-16T15:18:35.551209",
     "exception": false,
     "start_time": "2023-01-16T15:18:35.541235",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5325f6c2",
   "metadata": {
    "execution": {
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     "start_time": "2023-01-16T15:18:35.561311",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32eac46b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:18:36.570401Z",
     "iopub.status.busy": "2023-01-16T15:18:36.569969Z",
     "iopub.status.idle": "2023-01-16T15:18:36.829771Z",
     "shell.execute_reply": "2023-01-16T15:18:36.828481Z"
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     "end_time": "2023-01-16T15:18:36.832458",
     "exception": false,
     "start_time": "2023-01-16T15:18:36.558493",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "                                                         P_2      D_39  \\\n",
       "customer_ID                                                              \n",
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       "\n",
       "                                                         B_1       B_2  \\\n",
       "customer_ID                                                              \n",
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       "                                                         R_1       S_3  \\\n",
       "customer_ID                                                              \n",
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       "customer_ID                                                              \n",
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       "9bd44ca1f2f84150e5bd4b763c09cd138eeba13750cf883...  0.377441  0.631836  ...   \n",
       "30321b4a8b80d3429ce16322d5e46bf3de1d9b3ccecdd40...  0.128662  0.030167  ...   \n",
       "ccb34362035900506354729bb17636558cadb1dfed6a617...  0.006618  0.015053  ...   \n",
       "972243d1b0a2ea4dcda2427ebd31db9b8ccddb5b78ee673...  0.132812  0.645996  ...   \n",
       "44fb840a4e521cc42f809c571bab9d438b7c863d6dc043d...  0.754883  0.711914  ...   \n",
       "...                                                      ...       ...  ...   \n",
       "bcced58957ffcc297512f4ee7d51dcf9f9f80e2f34b8d91...  0.882812  0.804199  ...   \n",
       "7c3847b6382892f5c198a908fc4a1342f471ba7e2562f35...  0.627930  0.312012  ...   \n",
       "9722ce1374e54306b3a0e4d0c27453eb788072c49f9a63b...  0.000676  0.019135  ...   \n",
       "46f25716efd9c47422d0b7d1b87369a1f94cc0bcefce0a7...  0.005688  0.111450  ...   \n",
       "cdc2d27957f60a8f44d9e802dc9a2b96aa430618284a931...  0.004807  0.154663  ...   \n",
       "\n",
       "                                                       D_129      B_41  \\\n",
       "customer_ID                                                              \n",
       "9bd44ca1f2f84150e5bd4b763c09cd138eeba13750cf883...  1.001953  0.009758   \n",
       "30321b4a8b80d3429ce16322d5e46bf3de1d9b3ccecdd40...  1.000000  0.007587   \n",
       "ccb34362035900506354729bb17636558cadb1dfed6a617...  0.005711  0.008308   \n",
       "972243d1b0a2ea4dcda2427ebd31db9b8ccddb5b78ee673...  0.009903  0.009026   \n",
       "44fb840a4e521cc42f809c571bab9d438b7c863d6dc043d...  1.005859  0.007210   \n",
       "...                                                      ...       ...   \n",
       "bcced58957ffcc297512f4ee7d51dcf9f9f80e2f34b8d91...  1.006836  0.009735   \n",
       "7c3847b6382892f5c198a908fc4a1342f471ba7e2562f35...  1.000977  0.003057   \n",
       "9722ce1374e54306b3a0e4d0c27453eb788072c49f9a63b...  0.009964  0.001906   \n",
       "46f25716efd9c47422d0b7d1b87369a1f94cc0bcefce0a7...  0.009285  0.009361   \n",
       "cdc2d27957f60a8f44d9e802dc9a2b96aa430618284a931...  0.006233  0.004845   \n",
       "\n",
       "                                                       D_130     D_131  \\\n",
       "customer_ID                                                              \n",
       "9bd44ca1f2f84150e5bd4b763c09cd138eeba13750cf883...  0.006367  0.000141   \n",
       "30321b4a8b80d3429ce16322d5e46bf3de1d9b3ccecdd40...  0.005093  0.000281   \n",
       "ccb34362035900506354729bb17636558cadb1dfed6a617...  1.006836  0.008163   \n",
       "972243d1b0a2ea4dcda2427ebd31db9b8ccddb5b78ee673...  1.008789  0.998535   \n",
       "44fb840a4e521cc42f809c571bab9d438b7c863d6dc043d...  1.009766  0.007805   \n",
       "...                                                      ...       ...   \n",
       "bcced58957ffcc297512f4ee7d51dcf9f9f80e2f34b8d91...  1.000977  0.007809   \n",
       "7c3847b6382892f5c198a908fc4a1342f471ba7e2562f35...  0.008034  0.001480   \n",
       "9722ce1374e54306b3a0e4d0c27453eb788072c49f9a63b...  0.009804  0.005119   \n",
       "46f25716efd9c47422d0b7d1b87369a1f94cc0bcefce0a7...  0.004078  0.003099   \n",
       "cdc2d27957f60a8f44d9e802dc9a2b96aa430618284a931...  0.009682  0.007729   \n",
       "\n",
       "                                                       D_133      R_28  \\\n",
       "customer_ID                                                              \n",
       "9bd44ca1f2f84150e5bd4b763c09cd138eeba13750cf883...  0.004658  0.005013   \n",
       "30321b4a8b80d3429ce16322d5e46bf3de1d9b3ccecdd40...  0.008339  0.006248   \n",
       "ccb34362035900506354729bb17636558cadb1dfed6a617...  0.006931  0.000503   \n",
       "972243d1b0a2ea4dcda2427ebd31db9b8ccddb5b78ee673...  0.393311  0.000484   \n",
       "44fb840a4e521cc42f809c571bab9d438b7c863d6dc043d...  0.001237  0.000468   \n",
       "...                                                      ...       ...   \n",
       "bcced58957ffcc297512f4ee7d51dcf9f9f80e2f34b8d91...  0.001313  0.001293   \n",
       "7c3847b6382892f5c198a908fc4a1342f471ba7e2562f35...  0.006527  0.006973   \n",
       "9722ce1374e54306b3a0e4d0c27453eb788072c49f9a63b...  0.002993  0.001152   \n",
       "46f25716efd9c47422d0b7d1b87369a1f94cc0bcefce0a7...  0.000169  0.001531   \n",
       "cdc2d27957f60a8f44d9e802dc9a2b96aa430618284a931...  0.001159  0.001510   \n",
       "\n",
       "                                                       D_139     D_140  \\\n",
       "customer_ID                                                              \n",
       "9bd44ca1f2f84150e5bd4b763c09cd138eeba13750cf883...  0.006992  0.004738   \n",
       "30321b4a8b80d3429ce16322d5e46bf3de1d9b3ccecdd40...  0.000399  0.002218   \n",
       "ccb34362035900506354729bb17636558cadb1dfed6a617...  0.007233  0.003401   \n",
       "972243d1b0a2ea4dcda2427ebd31db9b8ccddb5b78ee673...  0.006371  0.002411   \n",
       "44fb840a4e521cc42f809c571bab9d438b7c863d6dc043d...  0.007271  0.009758   \n",
       "...                                                      ...       ...   \n",
       "bcced58957ffcc297512f4ee7d51dcf9f9f80e2f34b8d91...  0.008934  0.007343   \n",
       "7c3847b6382892f5c198a908fc4a1342f471ba7e2562f35...  0.006828  0.008919   \n",
       "9722ce1374e54306b3a0e4d0c27453eb788072c49f9a63b...  0.008934  0.000588   \n",
       "46f25716efd9c47422d0b7d1b87369a1f94cc0bcefce0a7...  0.007263  0.003910   \n",
       "cdc2d27957f60a8f44d9e802dc9a2b96aa430618284a931...  0.008034  0.004261   \n",
       "\n",
       "                                                       D_144     D_145  \n",
       "customer_ID                                                             \n",
       "9bd44ca1f2f84150e5bd4b763c09cd138eeba13750cf883...  0.003790  0.006172  \n",
       "30321b4a8b80d3429ce16322d5e46bf3de1d9b3ccecdd40...  0.005753  0.008446  \n",
       "ccb34362035900506354729bb17636558cadb1dfed6a617...  0.009064  0.007889  \n",
       "972243d1b0a2ea4dcda2427ebd31db9b8ccddb5b78ee673...  0.009789  0.003139  \n",
       "44fb840a4e521cc42f809c571bab9d438b7c863d6dc043d...  0.005482  0.005642  \n",
       "...                                                      ...       ...  \n",
       "bcced58957ffcc297512f4ee7d51dcf9f9f80e2f34b8d91...  0.004494  0.009529  \n",
       "7c3847b6382892f5c198a908fc4a1342f471ba7e2562f35...  0.002354  0.004658  \n",
       "9722ce1374e54306b3a0e4d0c27453eb788072c49f9a63b...  0.002995  0.004997  \n",
       "46f25716efd9c47422d0b7d1b87369a1f94cc0bcefce0a7...  0.007538  0.001724  \n",
       "cdc2d27957f60a8f44d9e802dc9a2b96aa430618284a931...  0.007626  0.004860  \n",
       "\n",
       "[321239 rows x 141 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f945eebb",
   "metadata": {
    "papermill": {
     "duration": 0.010685,
     "end_time": "2023-01-16T15:18:36.854182",
     "exception": false,
     "start_time": "2023-01-16T15:18:36.843497",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2b87c266",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:18:36.878390Z",
     "iopub.status.busy": "2023-01-16T15:18:36.877936Z",
     "iopub.status.idle": "2023-01-16T15:18:36.883265Z",
     "shell.execute_reply": "2023-01-16T15:18:36.881915Z"
    },
    "papermill": {
     "duration": 0.020117,
     "end_time": "2023-01-16T15:18:36.885689",
     "exception": false,
     "start_time": "2023-01-16T15:18:36.865572",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import lightgbm as lgb\n",
    "\n",
    "# d_train = lgb.Dataset(x_train, label=y_train, categorical_feature = categories)\n",
    "\n",
    "# params = {'objective': 'binary','n_estimators': 1200,'metric': 'binary_logloss','boosting': 'gbdt','num_leaves': 90,'reg_lambda' : 50,'colsample_bytree': 0.19,'learning_rate': 0.03,'min_child_samples': 2400,'max_bins': 511,'seed': 42,'verbose': -1}\n",
    "\n",
    "# # trained model with 100 iterations\n",
    "# model = lgb.train(params, d_train, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a21eb9",
   "metadata": {
    "papermill": {
     "duration": 0.010618,
     "end_time": "2023-01-16T15:18:36.907543",
     "exception": false,
     "start_time": "2023-01-16T15:18:36.896925",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ff1db49",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:18:36.931904Z",
     "iopub.status.busy": "2023-01-16T15:18:36.930817Z",
     "iopub.status.idle": "2023-01-16T15:18:36.935878Z",
     "shell.execute_reply": "2023-01-16T15:18:36.934676Z"
    },
    "papermill": {
     "duration": 0.019792,
     "end_time": "2023-01-16T15:18:36.938310",
     "exception": false,
     "start_time": "2023-01-16T15:18:36.918518",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from sklearn import svm\n",
    "# model = svm.SVC(kernel='linear').fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2afa035b",
   "metadata": {
    "papermill": {
     "duration": 0.010847,
     "end_time": "2023-01-16T15:18:36.960085",
     "exception": false,
     "start_time": "2023-01-16T15:18:36.949238",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3c69a1ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:18:36.984553Z",
     "iopub.status.busy": "2023-01-16T15:18:36.983595Z",
     "iopub.status.idle": "2023-01-16T15:18:36.989057Z",
     "shell.execute_reply": "2023-01-16T15:18:36.987903Z"
    },
    "papermill": {
     "duration": 0.020389,
     "end_time": "2023-01-16T15:18:36.991480",
     "exception": false,
     "start_time": "2023-01-16T15:18:36.971091",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# #Import knearest neighbors Classifier model\n",
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# #Create KNN Classifier\n",
    "# model = KNeighborsClassifier(n_neighbors=11).fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b362e4f7",
   "metadata": {
    "papermill": {
     "duration": 0.010772,
     "end_time": "2023-01-16T15:18:37.013299",
     "exception": false,
     "start_time": "2023-01-16T15:18:37.002527",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Load Test Data and apply same methods done for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c3257b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:18:37.037168Z",
     "iopub.status.busy": "2023-01-16T15:18:37.036733Z",
     "iopub.status.idle": "2023-01-16T15:19:15.653655Z",
     "shell.execute_reply": "2023-01-16T15:19:15.652343Z"
    },
    "papermill": {
     "duration": 38.632568,
     "end_time": "2023-01-16T15:19:15.656885",
     "exception": false,
     "start_time": "2023-01-16T15:18:37.024317",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_dataset_ = pd.read_feather('/kaggle/input/amexfeather/test_data.ftr')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "058be6a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:19:15.680627Z",
     "iopub.status.busy": "2023-01-16T15:19:15.680195Z",
     "iopub.status.idle": "2023-01-16T15:19:22.670665Z",
     "shell.execute_reply": "2023-01-16T15:19:22.667322Z"
    },
    "papermill": {
     "duration": 7.010446,
     "end_time": "2023-01-16T15:19:22.678514",
     "exception": false,
     "start_time": "2023-01-16T15:19:15.668068",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_dataset = test_dataset_.groupby('customer_ID').tail(1).set_index('customer_ID', drop=True).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dfdfdb40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:19:22.707785Z",
     "iopub.status.busy": "2023-01-16T15:19:22.707071Z",
     "iopub.status.idle": "2023-01-16T15:19:22.712525Z",
     "shell.execute_reply": "2023-01-16T15:19:22.711542Z"
    },
    "papermill": {
     "duration": 0.020274,
     "end_time": "2023-01-16T15:19:22.714779",
     "exception": false,
     "start_time": "2023-01-16T15:19:22.694505",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "num_columns = [col for col in X.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1096eab2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:19:22.739875Z",
     "iopub.status.busy": "2023-01-16T15:19:22.739088Z",
     "iopub.status.idle": "2023-01-16T15:19:23.570064Z",
     "shell.execute_reply": "2023-01-16T15:19:23.568727Z"
    },
    "papermill": {
     "duration": 0.84664,
     "end_time": "2023-01-16T15:19:23.573354",
     "exception": false,
     "start_time": "2023-01-16T15:19:22.726714",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_dataset=test_dataset[num_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b001c442",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:19:23.598313Z",
     "iopub.status.busy": "2023-01-16T15:19:23.597824Z",
     "iopub.status.idle": "2023-01-16T15:19:24.743287Z",
     "shell.execute_reply": "2023-01-16T15:19:24.742093Z"
    },
    "papermill": {
     "duration": 1.161334,
     "end_time": "2023-01-16T15:19:24.745993",
     "exception": false,
     "start_time": "2023-01-16T15:19:23.584659",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "enc = LabelEncoder()\n",
    "for categorical_column in test_dataset.select_dtypes(include=['category','object']).columns:\n",
    "    test_dataset[categorical_column]=enc.fit_transform(test_dataset[categorical_column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2cbca38",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:19:24.770414Z",
     "iopub.status.busy": "2023-01-16T15:19:24.769482Z",
     "iopub.status.idle": "2023-01-16T15:19:25.337596Z",
     "shell.execute_reply": "2023-01-16T15:19:25.336051Z"
    },
    "papermill": {
     "duration": 0.583546,
     "end_time": "2023-01-16T15:19:25.340664",
     "exception": false,
     "start_time": "2023-01-16T15:19:24.757118",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for category in categories:\n",
    "    test_dataset[category]=test_dataset[category].fillna(test_dataset[category].mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b237490e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:19:25.364921Z",
     "iopub.status.busy": "2023-01-16T15:19:25.364391Z",
     "iopub.status.idle": "2023-01-16T15:19:31.103260Z",
     "shell.execute_reply": "2023-01-16T15:19:31.101869Z"
    },
    "papermill": {
     "duration": 5.754623,
     "end_time": "2023-01-16T15:19:31.106494",
     "exception": false,
     "start_time": "2023-01-16T15:19:25.351871",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for column in test_dataset.columns:\n",
    "    if(column not in categories):\n",
    "#         print(column)\n",
    "        test_dataset[column]=test_dataset[column].fillna(test_dataset[column].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bbb3729b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:19:31.135206Z",
     "iopub.status.busy": "2023-01-16T15:19:31.134671Z",
     "iopub.status.idle": "2023-01-16T15:19:31.140731Z",
     "shell.execute_reply": "2023-01-16T15:19:31.139193Z"
    },
    "papermill": {
     "duration": 0.024064,
     "end_time": "2023-01-16T15:19:31.143484",
     "exception": false,
     "start_time": "2023-01-16T15:19:31.119420",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# y_pred = model.predict(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d4f83b4",
   "metadata": {
    "papermill": {
     "duration": 0.011611,
     "end_time": "2023-01-16T15:19:31.167370",
     "exception": false,
     "start_time": "2023-01-16T15:19:31.155759",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# CatBoost with bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dac4a7c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:19:31.192457Z",
     "iopub.status.busy": "2023-01-16T15:19:31.191930Z",
     "iopub.status.idle": "2023-01-16T15:19:31.543120Z",
     "shell.execute_reply": "2023-01-16T15:19:31.541896Z"
    },
    "papermill": {
     "duration": 0.367434,
     "end_time": "2023-01-16T15:19:31.546092",
     "exception": false,
     "start_time": "2023-01-16T15:19:31.178658",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "22df6563",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T15:19:31.571081Z",
     "iopub.status.busy": "2023-01-16T15:19:31.570575Z",
     "iopub.status.idle": "2023-01-16T17:10:52.977160Z",
     "shell.execute_reply": "2023-01-16T17:10:52.973731Z"
    },
    "papermill": {
     "duration": 6681.422976,
     "end_time": "2023-01-16T17:10:52.980889",
     "exception": false,
     "start_time": "2023-01-16T15:19:31.557913",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.121157\n",
      "0:\tlearn: 0.5574551\ttotal: 866ms\tremaining: 14m 25s\n",
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      "Learning rate set to 0.121157\n",
      "0:\tlearn: 0.5611061\ttotal: 764ms\tremaining: 12m 42s\n",
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      "999:\tlearn: 0.1848759\ttotal: 10m 19s\tremaining: 0us\n",
      "Learning rate set to 0.121157\n",
      "0:\tlearn: 0.5657015\ttotal: 744ms\tremaining: 12m 23s\n",
      "1:\tlearn: 0.4781434\ttotal: 1.37s\tremaining: 11m 25s\n",
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      "Learning rate set to 0.121157\n",
      "0:\tlearn: 0.5609322\ttotal: 786ms\tremaining: 13m 4s\n",
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      "999:\tlearn: 0.1857748\ttotal: 10m 8s\tremaining: 0us\n",
      "Learning rate set to 0.121157\n",
      "0:\tlearn: 0.5594058\ttotal: 703ms\tremaining: 11m 42s\n",
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      "999:\tlearn: 0.1857092\ttotal: 10m 5s\tremaining: 0us\n",
      "Learning rate set to 0.121157\n",
      "0:\tlearn: 0.5588438\ttotal: 792ms\tremaining: 13m 11s\n",
      "1:\tlearn: 0.4746580\ttotal: 1.43s\tremaining: 11m 54s\n",
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      "Learning rate set to 0.121157\n",
      "0:\tlearn: 0.5657386\ttotal: 832ms\tremaining: 13m 50s\n",
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      "Learning rate set to 0.121157\n",
      "0:\tlearn: 0.5623810\ttotal: 854ms\tremaining: 14m 13s\n",
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      "999:\tlearn: 0.1852693\ttotal: 10m 12s\tremaining: 0us\n",
      "Learning rate set to 0.121157\n",
      "0:\tlearn: 0.5589191\ttotal: 744ms\tremaining: 12m 23s\n",
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     ]
    }
   ],
   "source": [
    "predictions = []\n",
    "\n",
    "for i in range(10):\n",
    "    classifier = CatBoostClassifier(random_seed=i)\n",
    "    classifier.fit(x_train, y_train, cat_features=categories)\n",
    "    predictions.append(classifier.predict_proba(test_dataset)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6e38f884",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T17:10:54.190966Z",
     "iopub.status.busy": "2023-01-16T17:10:54.190281Z",
     "iopub.status.idle": "2023-01-16T17:10:54.251148Z",
     "shell.execute_reply": "2023-01-16T17:10:54.250070Z"
    },
    "papermill": {
     "duration": 0.67149,
     "end_time": "2023-01-16T17:10:54.254047",
     "exception": false,
     "start_time": "2023-01-16T17:10:53.582557",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = np.mean(predictions, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab96c5e",
   "metadata": {
    "papermill": {
     "duration": 0.609028,
     "end_time": "2023-01-16T17:10:55.548980",
     "exception": false,
     "start_time": "2023-01-16T17:10:54.939952",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "27877024",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-16T17:10:56.844901Z",
     "iopub.status.busy": "2023-01-16T17:10:56.844121Z",
     "iopub.status.idle": "2023-01-16T17:10:57.225460Z",
     "shell.execute_reply": "2023-01-16T17:10:57.223995Z"
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    "output = pd.DataFrame({'customer_ID': test_dataset.index, 'prediction': y_pred})"
   ]
  },
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   "execution_count": 31,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/working\n"
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    }
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   "source": [
    "cd /kaggle/working/"
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   "cell_type": "code",
   "execution_count": 32,
   "id": "37ac6849",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "output.to_csv('submission9.csv', index=False)"
   ]
  }
 ],
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