hitters-salary-prediction-with-lgbm-rmse-180-91.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab6d7cd7",
   "metadata": {
    "papermill": {
     "duration": 0.031648,
     "end_time": "2022-02-05T19:19:21.286453",
     "exception": false,
     "start_time": "2022-02-05T19:19:21.254805",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Dataset\n",
    "\n",
    "#### *Dataset: Hitters*\n",
    "#### *Sport Art: Baseball*\n",
    "#### *Description: We have some baseball players with their season '86 and also career statistics. And the most important data for the dataset, Salary is from Sports Illustrated.<br> Steps are gonna be like this: Firstly, we check the dataset if there is missing values or some outliers. And then we create some features.<br> Modelling part is gonna be with Linear Regression and we get the Test, Train scores.<br> At the end with our Model we try to predict salaries from baseball players, that they have no information about their salaries.*<br>\n",
    "\n",
    "##### *Columns of Dataset:*\n",
    "\n",
    "* ***AtBat:*** Number of times at bat in 1986\n",
    "* ***Hits:*** Number of hits in 1986\n",
    "* ***HmRun:*** Number of home runs in 1986\n",
    "* ***Runs:*** Number of runs in 1986\n",
    "* ***RBI:*** Number of runs batted in in 1986\n",
    "* ***Walks:*** Number of walks in 1986\n",
    "* ***Years:*** Number of years in the major leagues\n",
    "* ***CAtBat:*** Number of times at bat during his career\n",
    "* ***CHits:*** Number of hits during his career\n",
    "* ***CHmRun:*** Number of home runs during his career\n",
    "* ***CRuns:*** Number of runs during his career\n",
    "* ***CRBI:*** Number of runs batted in during his career\n",
    "* ***CWalks:*** Number of walks during his career\n",
    "* ***League:*** A factor with levels A and N indicating player’s league at the end of 1986\n",
    "* ***Division:*** A factor with levels E and W indicating player’s division at the end of 1986\n",
    "* ***PutOuts:*** Number of put outs in 1986\n",
    "* ***Assists:*** Number of assists in 1986\n",
    "* ***Errors:*** Number of errors in 1986\n",
    "* ***Salary:*** 1987 annual salary on opening day in thousands of dollars\n",
    "* ***NewLeague:*** A factor with levels A and N indicating player’s league at the beginning of 1987"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff58b572",
   "metadata": {
    "papermill": {
     "duration": 0.029502,
     "end_time": "2022-02-05T19:19:21.346417",
     "exception": false,
     "start_time": "2022-02-05T19:19:21.316915",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<hr style=\"height:2px;border-width:0;color:white;background-color:green\">\n",
    "\n",
    "# Import Dataset & Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b617bc9a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:21.411035Z",
     "iopub.status.busy": "2022-02-05T19:19:21.409881Z",
     "iopub.status.idle": "2022-02-05T19:19:23.619338Z",
     "shell.execute_reply": "2022-02-05T19:19:23.618736Z",
     "shell.execute_reply.started": "2022-02-05T18:50:09.016190Z"
    },
    "papermill": {
     "duration": 2.242919,
     "end_time": "2022-02-05T19:19:23.619457",
     "exception": false,
     "start_time": "2022-02-05T19:19:21.376538",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type='text/css'>\n",
       ".datatable table.frame { margin-bottom: 0; }\n",
       ".datatable table.frame thead { border-bottom: none; }\n",
       ".datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n",
       ".datatable .bool    { background: #DDDD99; }\n",
       ".datatable .object  { background: #565656; }\n",
       ".datatable .int     { background: #5D9E5D; }\n",
       ".datatable .float   { background: #4040CC; }\n",
       ".datatable .str     { background: #CC4040; }\n",
       ".datatable .time    { background: #40CC40; }\n",
       ".datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n",
       ".datatable .frame tbody td { text-align: left; }\n",
       ".datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n",
       ".datatable th:nth-child(2) { padding-left: 12px; }\n",
       ".datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n",
       ".datatable .vellipsis {  background: var(--jp-layout-color0);  color: var(--jp-cell-editor-border-color);}\n",
       ".datatable .na {  color: var(--jp-cell-editor-border-color);  font-size: 80%;}\n",
       ".datatable .sp {  opacity: 0.25;}\n",
       ".datatable .footer { font-size: 9px; }\n",
       ".datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn import preprocessing\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.width', 1500)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bdcc250a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:23.665592Z",
     "iopub.status.busy": "2022-02-05T19:19:23.664819Z",
     "iopub.status.idle": "2022-02-05T19:19:23.674045Z",
     "shell.execute_reply": "2022-02-05T19:19:23.674668Z",
     "shell.execute_reply.started": "2022-02-05T18:15:57.159086Z"
    },
    "papermill": {
     "duration": 0.035131,
     "end_time": "2022-02-05T19:19:23.674886",
     "exception": false,
     "start_time": "2022-02-05T19:19:23.639755",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/hitters/Hitters.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8bcb5d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:23.743683Z",
     "iopub.status.busy": "2022-02-05T19:19:23.741036Z",
     "iopub.status.idle": "2022-02-05T19:19:23.761965Z",
     "shell.execute_reply": "2022-02-05T19:19:23.762541Z",
     "shell.execute_reply.started": "2022-02-05T18:15:58.095577Z"
    },
    "papermill": {
     "duration": 0.0563,
     "end_time": "2022-02-05T19:19:23.762722",
     "exception": false,
     "start_time": "2022-02-05T19:19:23.706422",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_ = pd.read_csv(\"/kaggle/input/hitters/Hitters.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c93701a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:23.828825Z",
     "iopub.status.busy": "2022-02-05T19:19:23.828151Z",
     "iopub.status.idle": "2022-02-05T19:19:23.830961Z",
     "shell.execute_reply": "2022-02-05T19:19:23.831642Z",
     "shell.execute_reply.started": "2022-02-05T18:15:59.008332Z"
    },
    "papermill": {
     "duration": 0.038111,
     "end_time": "2022-02-05T19:19:23.831822",
     "exception": false,
     "start_time": "2022-02-05T19:19:23.793711",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df_.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "050b437b",
   "metadata": {
    "papermill": {
     "duration": 0.025536,
     "end_time": "2022-02-05T19:19:23.888650",
     "exception": false,
     "start_time": "2022-02-05T19:19:23.863114",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<hr style=\"height:2px;border-width:0;color:white;background-color:green\">\n",
    "\n",
    "# Dataset Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6e2eae7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:23.935311Z",
     "iopub.status.busy": "2022-02-05T19:19:23.934745Z",
     "iopub.status.idle": "2022-02-05T19:19:23.999897Z",
     "shell.execute_reply": "2022-02-05T19:19:23.999241Z",
     "shell.execute_reply.started": "2022-02-05T18:16:04.383196Z"
    },
    "papermill": {
     "duration": 0.090743,
     "end_time": "2022-02-05T19:19:24.000205",
     "exception": false,
     "start_time": "2022-02-05T19:19:23.909462",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "######################## Shape ########################\n",
      "\n",
      "(322, 20)\n",
      "######################## Types ########################\n",
      "\n",
      "AtBat          int64\n",
      "Hits           int64\n",
      "HmRun          int64\n",
      "Runs           int64\n",
      "RBI            int64\n",
      "Walks          int64\n",
      "Years          int64\n",
      "CAtBat         int64\n",
      "CHits          int64\n",
      "CHmRun         int64\n",
      "CRuns          int64\n",
      "CRBI           int64\n",
      "CWalks         int64\n",
      "League        object\n",
      "Division      object\n",
      "PutOuts        int64\n",
      "Assists        int64\n",
      "Errors         int64\n",
      "Salary       float64\n",
      "NewLeague     object\n",
      "dtype: object\n",
      "######################## Head ########################\n",
      "\n",
      "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary NewLeague\n",
      "0    293    66      1    30   29     14      1     293     66       1     30    29      14      A        E      446       33      20     NaN         A\n",
      "1    315    81      7    24   38     39     14    3449    835      69    321   414     375      N        W      632       43      10 475.000         N\n",
      "2    479   130     18    66   72     76      3    1624    457      63    224   266     263      A        W      880       82      14 480.000         A\n",
      "3    496   141     20    65   78     37     11    5628   1575     225    828   838     354      N        E      200       11       3 500.000         N\n",
      "4    321    87     10    39   42     30      2     396    101      12     48    46      33      N        E      805       40       4  91.500         N\n",
      "######################## Tail ########################\n",
      "\n",
      "     AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  CRBI  CWalks League Division  PutOuts  Assists  Errors   Salary NewLeague\n",
      "319    475   126      3    61   43     52      6    1700    433       7    217    93     146      A        W       37      113       7  385.000         A\n",
      "320    573   144      9    85   60     78      8    3198    857      97    470   420     332      A        E     1314      131      12  960.000         A\n",
      "321    631   170      9    77   44     31     11    4908   1457      30    775   357     249      A        W      408        4       3 1000.000         A\n",
      "######################## NA ########################\n",
      "\n",
      "AtBat         0\n",
      "Hits          0\n",
      "HmRun         0\n",
      "Runs          0\n",
      "RBI           0\n",
      "Walks         0\n",
      "Years         0\n",
      "CAtBat        0\n",
      "CHits         0\n",
      "CHmRun        0\n",
      "CRuns         0\n",
      "CRBI          0\n",
      "CWalks        0\n",
      "League        0\n",
      "Division      0\n",
      "PutOuts       0\n",
      "Assists       0\n",
      "Errors        0\n",
      "Salary       59\n",
      "NewLeague     0\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "######################## Quantiles ########################\n",
      "\n",
      "          count     mean      std    min      5%      50%      75%      95%      99%       max\n",
      "AtBat   322.000  380.929  153.405 16.000 161.000  379.500  512.000  609.900  658.590   687.000\n",
      "Hits    322.000  101.025   46.455  1.000  39.000   96.000  137.000  174.000  210.790   238.000\n",
      "HmRun   322.000   10.770    8.709  0.000   0.000    8.000   16.000   28.950   33.000    40.000\n",
      "Runs    322.000   50.910   26.024  0.000  17.000   48.000   69.000   94.000  107.790   130.000\n",
      "RBI     322.000   48.028   26.167  0.000  14.000   44.000   64.750   96.000  112.370   121.000\n",
      "Walks   322.000   38.742   21.639  0.000  11.050   35.000   53.000   78.000   93.580   105.000\n",
      "Years   322.000    7.444    4.926  1.000   1.000    6.000   11.000   17.000   19.790    24.000\n",
      "CAtBat  322.000 2648.683 2324.206 19.000 216.500 1928.000 3924.250 7125.600 8749.970 14053.000\n",
      "CHits   322.000  717.571  654.473  4.000  54.000  508.000 1059.250 2017.150 2500.340  4256.000\n",
      "CHmRun  322.000   69.491   86.266  0.000   2.000   37.500   90.000  265.650  373.120   548.000\n",
      "CRuns   322.000  358.795  334.106  1.000  27.000  247.000  526.250 1032.300 1174.370  2165.000\n",
      "CRBI    322.000  330.118  333.220  0.000  22.050  220.500  426.250 1071.750 1310.850  1659.000\n",
      "CWalks  322.000  260.239  267.058  0.000  15.050  170.500  339.250  817.600 1139.140  1566.000\n",
      "PutOuts 322.000  288.938  280.705  0.000  33.200  212.000  325.000  910.650 1301.190  1378.000\n",
      "Assists 322.000  106.913  136.855  0.000   0.000   39.500  166.000  431.450  478.160   492.000\n",
      "Errors  322.000    8.040    6.368  0.000   0.000    6.000   11.000   20.000   25.790    32.000\n",
      "Salary  263.000  535.926  451.119 67.500  86.600  425.000  750.000 1346.000 2032.887  2460.000\n"
     ]
    }
   ],
   "source": [
    "def check_df(dataframe, head=5, tail=3):\n",
    "    print(\"######################## Shape ########################\\n\")\n",
    "    print(dataframe.shape)\n",
    "    print(\"######################## Types ########################\\n\")\n",
    "    print(dataframe.dtypes)\n",
    "    print(\"######################## Head ########################\\n\")\n",
    "    print(dataframe.head(head))\n",
    "    print(\"######################## Tail ########################\\n\")\n",
    "    print(dataframe.tail(tail))\n",
    "    print(\"######################## NA ########################\\n\")\n",
    "    print(dataframe.isnull().sum())\n",
    "    print(\"\\n\\n######################## Quantiles ########################\\n\")\n",
    "    print(dataframe.describe([0.05, 0.50, 0.75, 0.95, 0.99]).T)\n",
    "\n",
    "check_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9d610a02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:24.075015Z",
     "iopub.status.busy": "2022-02-05T19:19:24.074330Z",
     "iopub.status.idle": "2022-02-05T19:19:24.082768Z",
     "shell.execute_reply": "2022-02-05T19:19:24.082147Z",
     "shell.execute_reply.started": "2022-02-05T18:16:08.318691Z"
    },
    "papermill": {
     "duration": 0.049264,
     "end_time": "2022-02-05T19:19:24.082907",
     "exception": false,
     "start_time": "2022-02-05T19:19:24.033643",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# As we see, Salary feature has some missing values.\n",
    "# Firstly we drop all missing values and convert the categoric columns to binary.\n",
    "\n",
    "df1 = df.dropna()\n",
    "df1 = pd.get_dummies(df1, columns = [\"League\",\"Division\",\"NewLeague\"], drop_first = True)\n",
    "\n",
    "df2 = df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c935584",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:24.161999Z",
     "iopub.status.busy": "2022-02-05T19:19:24.155642Z",
     "iopub.status.idle": "2022-02-05T19:19:24.174525Z",
     "shell.execute_reply": "2022-02-05T19:19:24.173982Z",
     "shell.execute_reply.started": "2022-02-05T18:16:12.920044Z"
    },
    "papermill": {
     "duration": 0.059766,
     "end_time": "2022-02-05T19:19:24.174642",
     "exception": false,
     "start_time": "2022-02-05T19:19:24.114876",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 263\n",
      "Variables: 20\n",
      "cat_cols: 3\n",
      "num_cols: 17\n",
      "cat_but_car: 0\n",
      "num_but_cat: 3\n"
     ]
    }
   ],
   "source": [
    "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
    "    \"\"\"\n",
    "\n",
    "    Parameters\n",
    "    ------\n",
    "        dataframe: dataframe\n",
    "\n",
    "        cat_th: int, optional\n",
    "                The limit value of numerical but categorical columns\n",
    "                \n",
    "        car_th: int, optional\n",
    "                The limit value of categorical but cardinal columns\n",
    "\n",
    "    Returns\n",
    "    ------\n",
    "        cat_cols: list\n",
    "                Categrical columns\n",
    "        num_cols: list\n",
    "                Numerical columns\n",
    "        cat_but_car: list\n",
    "                Categorical but cardinal columns\n",
    "\n",
    "    Examples\n",
    "    ------\n",
    "        import seaborn as sns\n",
    "        df = sns.load_dataset(\"iris\")\n",
    "        print(grab_col_names(df))\n",
    "\n",
    "\n",
    "    Notes\n",
    "    ------\n",
    "        cat_cols + num_cols + cat_but_car = Total columns\n",
    "        num_but_cat is in cat_cols. It is just for info.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # cat_cols, cat_but_car\n",
    "    cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == \"O\"]\n",
    "\n",
    "    num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and\n",
    "                   dataframe[col].dtypes != \"O\"]\n",
    "\n",
    "    cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and\n",
    "                   dataframe[col].dtypes == \"O\"]\n",
    "\n",
    "    cat_cols = cat_cols + num_but_cat\n",
    "\n",
    "    cat_cols = [col for col in cat_cols if col not in cat_but_car]\n",
    "\n",
    "    # num_cols\n",
    "    num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != \"O\"]\n",
    "\n",
    "    num_cols = [col for col in num_cols if col not in num_but_cat]\n",
    "\n",
    "    print(f\"Observations: {dataframe.shape[0]}\")\n",
    "    print(f\"Variables: {dataframe.shape[1]}\")\n",
    "    print(f'cat_cols: {len(cat_cols)}')\n",
    "    print(f'num_cols: {len(num_cols)}')\n",
    "    print(f'cat_but_car: {len(cat_but_car)}')\n",
    "    print(f'num_but_cat: {len(num_but_cat)}')\n",
    "\n",
    "    return cat_cols, num_cols, cat_but_car\n",
    "\n",
    "cat_cols, num_cols, cat_but_car = grab_col_names(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29005bc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:24.219351Z",
     "iopub.status.busy": "2022-02-05T19:19:24.218791Z",
     "iopub.status.idle": "2022-02-05T19:19:24.230372Z",
     "shell.execute_reply": "2022-02-05T19:19:24.229880Z",
     "shell.execute_reply.started": "2022-02-05T18:16:15.023436Z"
    },
    "papermill": {
     "duration": 0.034814,
     "end_time": "2022-02-05T19:19:24.230486",
     "exception": false,
     "start_time": "2022-02-05T19:19:24.195672",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Checking which columns has skewness...\n",
    "\n",
    "df2.skew(axis = 0, skipna = True)\n",
    "skew_cols = df2.skew(axis = 0, skipna = True)[(df2.skew(axis = 0, skipna = True) > 1) |\n",
    "                                             (df2.skew(axis = 0, skipna = True) < -1)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62a013c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:24.277717Z",
     "iopub.status.busy": "2022-02-05T19:19:24.277161Z",
     "iopub.status.idle": "2022-02-05T19:19:24.280734Z",
     "shell.execute_reply": "2022-02-05T19:19:24.280252Z",
     "shell.execute_reply.started": "2022-02-05T18:16:15.868868Z"
    },
    "papermill": {
     "duration": 0.029057,
     "end_time": "2022-02-05T19:19:24.280852",
     "exception": false,
     "start_time": "2022-02-05T19:19:24.251795",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'PutOuts', 'Assists', 'Salary'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "13291c24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:24.330582Z",
     "iopub.status.busy": "2022-02-05T19:19:24.326493Z",
     "iopub.status.idle": "2022-02-05T19:19:24.661369Z",
     "shell.execute_reply": "2022-02-05T19:19:24.660810Z",
     "shell.execute_reply.started": "2022-02-05T18:16:17.232964Z"
    },
    "papermill": {
     "duration": 0.359618,
     "end_time": "2022-02-05T19:19:24.661490",
     "exception": false,
     "start_time": "2022-02-05T19:19:24.301872",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take a one column with skewness and check the histogram graphic. After that we convert that columns to logaritmic.\n",
    "\n",
    "sns.distplot(df1[\"CAtBat\"], hist=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a844a61d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:24.721625Z",
     "iopub.status.busy": "2022-02-05T19:19:24.720487Z",
     "iopub.status.idle": "2022-02-05T19:19:24.732524Z",
     "shell.execute_reply": "2022-02-05T19:19:24.731897Z",
     "shell.execute_reply.started": "2022-02-05T18:16:19.741258Z"
    },
    "papermill": {
     "duration": 0.048737,
     "end_time": "2022-02-05T19:19:24.732663",
     "exception": false,
     "start_time": "2022-02-05T19:19:24.683926",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>8.146</td>\n",
       "      <td>6.727</td>\n",
       "      <td>4.234</td>\n",
       "      <td>5.771</td>\n",
       "      <td>6.026</td>\n",
       "      <td>5.927</td>\n",
       "      <td>6.449</td>\n",
       "      <td>3.761</td>\n",
       "      <td>10</td>\n",
       "      <td>6.163</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>7.393</td>\n",
       "      <td>6.125</td>\n",
       "      <td>4.143</td>\n",
       "      <td>5.412</td>\n",
       "      <td>5.583</td>\n",
       "      <td>5.572</td>\n",
       "      <td>6.780</td>\n",
       "      <td>4.407</td>\n",
       "      <td>14</td>\n",
       "      <td>6.174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>8.636</td>\n",
       "      <td>7.362</td>\n",
       "      <td>5.416</td>\n",
       "      <td>6.719</td>\n",
       "      <td>6.731</td>\n",
       "      <td>5.869</td>\n",
       "      <td>5.298</td>\n",
       "      <td>2.398</td>\n",
       "      <td>3</td>\n",
       "      <td>6.215</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>5.981</td>\n",
       "      <td>4.615</td>\n",
       "      <td>2.485</td>\n",
       "      <td>3.871</td>\n",
       "      <td>3.829</td>\n",
       "      <td>3.497</td>\n",
       "      <td>6.691</td>\n",
       "      <td>3.689</td>\n",
       "      <td>4</td>\n",
       "      <td>4.516</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>8.391</td>\n",
       "      <td>7.033</td>\n",
       "      <td>2.944</td>\n",
       "      <td>6.217</td>\n",
       "      <td>5.817</td>\n",
       "      <td>5.268</td>\n",
       "      <td>5.642</td>\n",
       "      <td>6.043</td>\n",
       "      <td>25</td>\n",
       "      <td>6.620</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>185</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>5.366</td>\n",
       "      <td>3.738</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.401</td>\n",
       "      <td>2.197</td>\n",
       "      <td>3.178</td>\n",
       "      <td>4.331</td>\n",
       "      <td>4.844</td>\n",
       "      <td>7</td>\n",
       "      <td>4.248</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>298</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6.232</td>\n",
       "      <td>4.682</td>\n",
       "      <td>-inf</td>\n",
       "      <td>3.714</td>\n",
       "      <td>3.611</td>\n",
       "      <td>2.485</td>\n",
       "      <td>4.796</td>\n",
       "      <td>5.645</td>\n",
       "      <td>9</td>\n",
       "      <td>4.605</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>323</td>\n",
       "      <td>81</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5.832</td>\n",
       "      <td>4.454</td>\n",
       "      <td>1.792</td>\n",
       "      <td>3.466</td>\n",
       "      <td>3.526</td>\n",
       "      <td>2.079</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5.670</td>\n",
       "      <td>19</td>\n",
       "      <td>4.317</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>401</td>\n",
       "      <td>92</td>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>8.558</td>\n",
       "      <td>7.194</td>\n",
       "      <td>5.533</td>\n",
       "      <td>6.664</td>\n",
       "      <td>6.791</td>\n",
       "      <td>6.764</td>\n",
       "      <td>-inf</td>\n",
       "      <td>-inf</td>\n",
       "      <td>0</td>\n",
       "      <td>7.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>574</td>\n",
       "      <td>159</td>\n",
       "      <td>21</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "      <td>59</td>\n",
       "      <td>10</td>\n",
       "      <td>8.441</td>\n",
       "      <td>7.170</td>\n",
       "      <td>4.500</td>\n",
       "      <td>6.554</td>\n",
       "      <td>6.223</td>\n",
       "      <td>6.190</td>\n",
       "      <td>5.472</td>\n",
       "      <td>6.098</td>\n",
       "      <td>22</td>\n",
       "      <td>6.248</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  CRBI  CWalks  PutOuts  Assists  Errors  Salary  League_N  Division_W  NewLeague_N\n",
       "1     315    81      7    24   38     39     14   8.146  6.727   4.234  5.771 6.026   5.927    6.449    3.761      10   6.163         1           1            1\n",
       "2     479   130     18    66   72     76      3   7.393  6.125   4.143  5.412 5.583   5.572    6.780    4.407      14   6.174         0           1            0\n",
       "3     496   141     20    65   78     37     11   8.636  7.362   5.416  6.719 6.731   5.869    5.298    2.398       3   6.215         1           0            1\n",
       "4     321    87     10    39   42     30      2   5.981  4.615   2.485  3.871 3.829   3.497    6.691    3.689       4   4.516         1           0            1\n",
       "5     594   169      4    74   51     35     11   8.391  7.033   2.944  6.217 5.817   5.268    5.642    6.043      25   6.620         0           1            0\n",
       "6     185    37      1    23    8     21      2   5.366  3.738   0.000  3.401 2.197   3.178    4.331    4.844       7   4.248         1           0            0\n",
       "7     298    73      0    24   24      7      3   6.232  4.682    -inf  3.714 3.611   2.485    4.796    5.645       9   4.605         0           1            0\n",
       "8     323    81      6    26   32      8      2   5.832  4.454   1.792  3.466 3.526   2.079    4.963    5.670      19   4.317         1           1            1\n",
       "9     401    92     17    49   66     65     13   8.558  7.194   5.533  6.664 6.791   6.764     -inf     -inf       0   7.003         0           0            0\n",
       "10    574   159     21   107   75     59     10   8.441  7.170   4.500  6.554 6.223   6.190    5.472    6.098      22   6.248         0           0            0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in skew_cols:\n",
    "    df2[col] = np.log(df2[col])\n",
    "\n",
    "# We get some NaN und -inf values, because log0 is undefined.\n",
    "# We can put for theese values again 0.\n",
    "\n",
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c96e849",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:24.803481Z",
     "iopub.status.busy": "2022-02-05T19:19:24.802840Z",
     "iopub.status.idle": "2022-02-05T19:19:24.809201Z",
     "shell.execute_reply": "2022-02-05T19:19:24.809658Z",
     "shell.execute_reply.started": "2022-02-05T18:16:21.707208Z"
    },
    "papermill": {
     "duration": 0.043279,
     "end_time": "2022-02-05T19:19:24.809792",
     "exception": false,
     "start_time": "2022-02-05T19:19:24.766513",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "num_cols = [col for col in num_cols if col not in \"Salary\"]\n",
    "\n",
    "# Seperate as dataset the numeric, categoric columns and the \"Salary\" column.\n",
    "numeric_df = df2.loc[:, num_cols]\n",
    "cat_df = df2.loc[:, cat_cols]\n",
    "y_df = df1.loc[:, \"Salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b14de87a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:24.858637Z",
     "iopub.status.busy": "2022-02-05T19:19:24.857927Z",
     "iopub.status.idle": "2022-02-05T19:19:24.897099Z",
     "shell.execute_reply": "2022-02-05T19:19:24.897568Z",
     "shell.execute_reply.started": "2022-02-05T18:16:22.413013Z"
    },
    "papermill": {
     "duration": 0.065446,
     "end_time": "2022-02-05T19:19:24.897717",
     "exception": false,
     "start_time": "2022-02-05T19:19:24.832271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AtBat :  Int64Index([], dtype='int64')\n",
      "Hits :  Int64Index([], dtype='int64')\n",
      "HmRun :  Int64Index([], dtype='int64')\n",
      "Runs :  Int64Index([], dtype='int64')\n",
      "RBI :  Int64Index([], dtype='int64')\n",
      "Walks :  Int64Index([], dtype='int64')\n",
      "Years :  Int64Index([], dtype='int64')\n",
      "CAtBat :  Int64Index([], dtype='int64')\n",
      "CHits :  Int64Index([], dtype='int64')\n",
      "CHmRun :  Int64Index([7, 188, 239], dtype='int64')\n",
      "CRuns :  Int64Index([], dtype='int64')\n",
      "CRBI :  Int64Index([], dtype='int64')\n",
      "CWalks :  Int64Index([], dtype='int64')\n",
      "PutOuts :  Int64Index([9, 65, 132, 149, 186, 196, 198, 207, 249, 251, 267], dtype='int64')\n",
      "Assists :  Int64Index([9, 65, 132, 149, 176, 186, 196, 198, 207, 249, 251, 255, 267, 304], dtype='int64')\n",
      "Errors :  Int64Index([], dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "# Checking which column and which index has undefined value.\n",
    "\n",
    "for col in num_cols:\n",
    "    print(col, \": \", numeric_df[numeric_df[col].astype(\"str\").str.get(1)==\"i\"].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e475b623",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:24.948986Z",
     "iopub.status.busy": "2022-02-05T19:19:24.948516Z",
     "iopub.status.idle": "2022-02-05T19:19:24.959360Z",
     "shell.execute_reply": "2022-02-05T19:19:24.959733Z",
     "shell.execute_reply.started": "2022-02-05T18:16:24.657714Z"
    },
    "papermill": {
     "duration": 0.038213,
     "end_time": "2022-02-05T19:19:24.959880",
     "exception": false,
     "start_time": "2022-02-05T19:19:24.921667",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "index_CHmRun = numeric_df[numeric_df[\"CHmRun\"].astype(\"str\").str.get(1)==\"i\"].index\n",
    "numeric_df.loc[index_CHmRun,\"CHmRun\"] = 0\n",
    "\n",
    "index_PutOuts = numeric_df[numeric_df[\"PutOuts\"].astype(\"str\").str.get(1)==\"i\"].index.tolist()\n",
    "numeric_df.loc[index_PutOuts,\"PutOuts\"] = 0\n",
    "\n",
    "index_Assists = numeric_df[numeric_df[\"Assists\"].astype(\"str\").str.get(1)==\"i\"].index.tolist()\n",
    "numeric_df.loc[index_Assists,\"Assists\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3af31edc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:25.009073Z",
     "iopub.status.busy": "2022-02-05T19:19:25.008559Z",
     "iopub.status.idle": "2022-02-05T19:19:25.020935Z",
     "shell.execute_reply": "2022-02-05T19:19:25.021343Z",
     "shell.execute_reply.started": "2022-02-05T18:16:25.658114Z"
    },
    "papermill": {
     "duration": 0.038484,
     "end_time": "2022-02-05T19:19:25.021481",
     "exception": false,
     "start_time": "2022-02-05T19:19:24.982997",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>4408</td>\n",
       "      <td>1133</td>\n",
       "      <td>19</td>\n",
       "      <td>501</td>\n",
       "      <td>336</td>\n",
       "      <td>194</td>\n",
       "      <td>282</td>\n",
       "      <td>421</td>\n",
       "      <td>25</td>\n",
       "      <td>750.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>185</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>214</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>76</td>\n",
       "      <td>127</td>\n",
       "      <td>7</td>\n",
       "      <td>70.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>298</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>509</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>121</td>\n",
       "      <td>283</td>\n",
       "      <td>9</td>\n",
       "      <td>100.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>323</td>\n",
       "      <td>81</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>341</td>\n",
       "      <td>86</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>143</td>\n",
       "      <td>290</td>\n",
       "      <td>19</td>\n",
       "      <td>75.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>401</td>\n",
       "      <td>92</td>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>5206</td>\n",
       "      <td>1332</td>\n",
       "      <td>253</td>\n",
       "      <td>784</td>\n",
       "      <td>890</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1100.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>574</td>\n",
       "      <td>159</td>\n",
       "      <td>21</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "      <td>59</td>\n",
       "      <td>10</td>\n",
       "      <td>4631</td>\n",
       "      <td>1300</td>\n",
       "      <td>90</td>\n",
       "      <td>702</td>\n",
       "      <td>504</td>\n",
       "      <td>488</td>\n",
       "      <td>238</td>\n",
       "      <td>445</td>\n",
       "      <td>22</td>\n",
       "      <td>517.143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  CRBI  CWalks  PutOuts  Assists  Errors   Salary  League_N  Division_W  NewLeague_N\n",
       "1     315    81      7    24   38     39     14    3449    835      69    321   414     375      632       43      10  475.000         1           1            1\n",
       "2     479   130     18    66   72     76      3    1624    457      63    224   266     263      880       82      14  480.000         0           1            0\n",
       "3     496   141     20    65   78     37     11    5628   1575     225    828   838     354      200       11       3  500.000         1           0            1\n",
       "4     321    87     10    39   42     30      2     396    101      12     48    46      33      805       40       4   91.500         1           0            1\n",
       "5     594   169      4    74   51     35     11    4408   1133      19    501   336     194      282      421      25  750.000         0           1            0\n",
       "6     185    37      1    23    8     21      2     214     42       1     30     9      24       76      127       7   70.000         1           0            0\n",
       "7     298    73      0    24   24      7      3     509    108       0     41    37      12      121      283       9  100.000         0           1            0\n",
       "8     323    81      6    26   32      8      2     341     86       6     32    34       8      143      290      19   75.000         1           1            1\n",
       "9     401    92     17    49   66     65     13    5206   1332     253    784   890     866        0        0       0 1100.000         0           0            0\n",
       "10    574   159     21   107   75     59     10    4631   1300      90    702   504     488      238      445      22  517.143         0           0            0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8d018a1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:25.071458Z",
     "iopub.status.busy": "2022-02-05T19:19:25.070888Z",
     "iopub.status.idle": "2022-02-05T19:19:25.082498Z",
     "shell.execute_reply": "2022-02-05T19:19:25.082894Z",
     "shell.execute_reply.started": "2022-02-05T18:16:27.784231Z"
    },
    "papermill": {
     "duration": 0.038202,
     "end_time": "2022-02-05T19:19:25.083033",
     "exception": false,
     "start_time": "2022-02-05T19:19:25.044831",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>8.146</td>\n",
       "      <td>6.727</td>\n",
       "      <td>4.234</td>\n",
       "      <td>5.771</td>\n",
       "      <td>6.026</td>\n",
       "      <td>5.927</td>\n",
       "      <td>6.449</td>\n",
       "      <td>3.761</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>7.393</td>\n",
       "      <td>6.125</td>\n",
       "      <td>4.143</td>\n",
       "      <td>5.412</td>\n",
       "      <td>5.583</td>\n",
       "      <td>5.572</td>\n",
       "      <td>6.780</td>\n",
       "      <td>4.407</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>8.636</td>\n",
       "      <td>7.362</td>\n",
       "      <td>5.416</td>\n",
       "      <td>6.719</td>\n",
       "      <td>6.731</td>\n",
       "      <td>5.869</td>\n",
       "      <td>5.298</td>\n",
       "      <td>2.398</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>5.981</td>\n",
       "      <td>4.615</td>\n",
       "      <td>2.485</td>\n",
       "      <td>3.871</td>\n",
       "      <td>3.829</td>\n",
       "      <td>3.497</td>\n",
       "      <td>6.691</td>\n",
       "      <td>3.689</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>8.391</td>\n",
       "      <td>7.033</td>\n",
       "      <td>2.944</td>\n",
       "      <td>6.217</td>\n",
       "      <td>5.817</td>\n",
       "      <td>5.268</td>\n",
       "      <td>5.642</td>\n",
       "      <td>6.043</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>185</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>5.366</td>\n",
       "      <td>3.738</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.401</td>\n",
       "      <td>2.197</td>\n",
       "      <td>3.178</td>\n",
       "      <td>4.331</td>\n",
       "      <td>4.844</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>298</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6.232</td>\n",
       "      <td>4.682</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.714</td>\n",
       "      <td>3.611</td>\n",
       "      <td>2.485</td>\n",
       "      <td>4.796</td>\n",
       "      <td>5.645</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>323</td>\n",
       "      <td>81</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5.832</td>\n",
       "      <td>4.454</td>\n",
       "      <td>1.792</td>\n",
       "      <td>3.466</td>\n",
       "      <td>3.526</td>\n",
       "      <td>2.079</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5.670</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>401</td>\n",
       "      <td>92</td>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>8.558</td>\n",
       "      <td>7.194</td>\n",
       "      <td>5.533</td>\n",
       "      <td>6.664</td>\n",
       "      <td>6.791</td>\n",
       "      <td>6.764</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>574</td>\n",
       "      <td>159</td>\n",
       "      <td>21</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "      <td>59</td>\n",
       "      <td>10</td>\n",
       "      <td>8.441</td>\n",
       "      <td>7.170</td>\n",
       "      <td>4.500</td>\n",
       "      <td>6.554</td>\n",
       "      <td>6.223</td>\n",
       "      <td>6.190</td>\n",
       "      <td>5.472</td>\n",
       "      <td>6.098</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  CRBI  CWalks  PutOuts  Assists  Errors\n",
       "1     315    81      7    24   38     39     14   8.146  6.727   4.234  5.771 6.026   5.927    6.449    3.761      10\n",
       "2     479   130     18    66   72     76      3   7.393  6.125   4.143  5.412 5.583   5.572    6.780    4.407      14\n",
       "3     496   141     20    65   78     37     11   8.636  7.362   5.416  6.719 6.731   5.869    5.298    2.398       3\n",
       "4     321    87     10    39   42     30      2   5.981  4.615   2.485  3.871 3.829   3.497    6.691    3.689       4\n",
       "5     594   169      4    74   51     35     11   8.391  7.033   2.944  6.217 5.817   5.268    5.642    6.043      25\n",
       "6     185    37      1    23    8     21      2   5.366  3.738   0.000  3.401 2.197   3.178    4.331    4.844       7\n",
       "7     298    73      0    24   24      7      3   6.232  4.682   0.000  3.714 3.611   2.485    4.796    5.645       9\n",
       "8     323    81      6    26   32      8      2   5.832  4.454   1.792  3.466 3.526   2.079    4.963    5.670      19\n",
       "9     401    92     17    49   66     65     13   8.558  7.194   5.533  6.664 6.791   6.764    0.000    0.000       0\n",
       "10    574   159     21   107   75     59     10   8.441  7.170   4.500  6.554 6.223   6.190    5.472    6.098      22"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With just a little compare, as we see at the first dataset (df1)\n",
    "# the values are 0 or totally less than others, which were in numeric_df dataset as undefined.\n",
    "# That's why we filled all with 0 like as before.\n",
    "\n",
    "numeric_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "773c2e44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:25.142086Z",
     "iopub.status.busy": "2022-02-05T19:19:25.136166Z",
     "iopub.status.idle": "2022-02-05T19:19:25.151778Z",
     "shell.execute_reply": "2022-02-05T19:19:25.152189Z",
     "shell.execute_reply.started": "2022-02-05T18:16:30.576713Z"
    },
    "papermill": {
     "duration": 0.04406,
     "end_time": "2022-02-05T19:19:25.152372",
     "exception": false,
     "start_time": "2022-02-05T19:19:25.108312",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# LOF (Local Outlier Factor) Outlier Detection\n",
    "\n",
    "clf = LocalOutlierFactor(n_neighbors = 20, contamination = 0.1)\n",
    "clf.fit_predict(numeric_df)\n",
    "numeric_df_scores = clf.negative_outlier_factor_\n",
    "\n",
    "np.sort(numeric_df_scores)[0:10]\n",
    "outlier_indexes = numeric_df.loc[numeric_df_scores < -1.73878565]\n",
    "index_drop = outlier_indexes.index.tolist()\n",
    "\n",
    "numeric_df = numeric_df.drop(index = index_drop)\n",
    "cat_df = cat_df.drop(index = index_drop)\n",
    "cat_df = cat_df.reset_index(drop=True)\n",
    "y_df = y_df.drop(index = index_drop)\n",
    "y_df = y_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3346ef1",
   "metadata": {
    "papermill": {
     "duration": 0.024041,
     "end_time": "2022-02-05T19:19:25.201083",
     "exception": false,
     "start_time": "2022-02-05T19:19:25.177042",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<hr style=\"height:2px;border-width:0;color:white;background-color:green\">\n",
    "\n",
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4a70860b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:25.273455Z",
     "iopub.status.busy": "2022-02-05T19:19:25.272897Z",
     "iopub.status.idle": "2022-02-05T19:19:25.295553Z",
     "shell.execute_reply": "2022-02-05T19:19:25.295972Z",
     "shell.execute_reply.started": "2022-02-05T18:16:36.541952Z"
    },
    "papermill": {
     "duration": 0.070399,
     "end_time": "2022-02-05T19:19:25.296107",
     "exception": false,
     "start_time": "2022-02-05T19:19:25.225708",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Percentages by Hits\n",
    "numeric_df[\"New_Hits/AtBat\"] = numeric_df[\"Hits\"] / numeric_df[\"AtBat\"]\n",
    "numeric_df[\"New_HmRun/Hits\"] = numeric_df[\"HmRun\"] / numeric_df[\"Hits\"]\n",
    "numeric_df[\"New_Runs/Hits\"] = numeric_df[\"Runs\"] / numeric_df[\"Hits\"]\n",
    "numeric_df[\"New_RBI/Hits\"] = numeric_df[\"RBI\"] / numeric_df[\"Hits\"]\n",
    "\n",
    "numeric_df[\"New_CHits/CAtBat\"] = numeric_df[\"CHits\"] / numeric_df[\"CAtBat\"]\n",
    "numeric_df[\"New_CHmRun/CHits\"] = numeric_df[\"CHmRun\"] / numeric_df[\"CHits\"]\n",
    "numeric_df[\"New_CRuns/CHits\"] = numeric_df[\"CRuns\"] / numeric_df[\"CHits\"]\n",
    "numeric_df[\"New_CRBI/CHits\"] = numeric_df[\"CRBI\"] / numeric_df[\"CHits\"]\n",
    "\n",
    "# Career average statistics\n",
    "numeric_df[\"New_CAtBat_mean\"] = numeric_df[\"CAtBat\"]/numeric_df[\"Years\"]\n",
    "numeric_df[\"New_CHits_mean\"] = numeric_df[\"CHits\"]/numeric_df[\"Years\"]\n",
    "numeric_df[\"New_CHmRun_mean\"] = numeric_df[\"CHmRun\"]/numeric_df[\"Years\"]\n",
    "numeric_df[\"New_CRBI_mean\"] = numeric_df[\"CRBI\"]/numeric_df[\"Years\"]\n",
    "numeric_df[\"New_CWalks_mean\"] = numeric_df[\"CWalks\"]/numeric_df[\"Years\"]\n",
    "\n",
    "# Benefits ans Loss\n",
    "numeric_df[\"New_Errors/Walks\"] = numeric_df[\"Errors\"] / numeric_df[\"Walks\"]\n",
    "numeric_df[\"New_Benefit_Loss\"] = numeric_df[\"Assists\"] + numeric_df[\"PutOuts\"] + numeric_df[\"Walks\"] - numeric_df[\"Errors\"]\n",
    "\n",
    "# Advanced statistics\n",
    "numeric_df[\"New_OBP\"] = (numeric_df[\"Hits\"] + numeric_df[\"Walks\"] + (numeric_df[\"Hits\"]/numeric_df[\"AtBat\"])) / (numeric_df[\"AtBat\"]+numeric_df[\"Walks\"]+(numeric_df[\"Hits\"]/numeric_df[\"AtBat\"])+(numeric_df[\"RBI\"]-numeric_df[\"Runs\"]))\n",
    "numeric_df[\"New_COBP\"] = (numeric_df[\"CHits\"] + numeric_df[\"CWalks\"] + (numeric_df[\"CHits\"]/numeric_df[\"CAtBat\"])) / (numeric_df[\"CAtBat\"]+numeric_df[\"CWalks\"]+(numeric_df[\"CHits\"]/numeric_df[\"CAtBat\"])+(numeric_df[\"CRBI\"]-numeric_df[\"CRuns\"]))\n",
    "numeric_df[\"New_SLG\"] = ((4*numeric_df[\"HmRun\"]) + numeric_df[\"Runs\"])/numeric_df[\"AtBat\"]\n",
    "numeric_df[\"New_CSLG\"] = ((4*numeric_df[\"CHmRun\"]) + numeric_df[\"CRuns\"])/numeric_df[\"CAtBat\"]\n",
    "numeric_df[\"New_COPS\"] = numeric_df[\"New_COBP\"] + numeric_df[\"New_CSLG\"]\n",
    "numeric_df[\"BABIP\"] = (numeric_df[\"Hits\"] - numeric_df[\"HmRun\"]) / (numeric_df[\"AtBat\"] - numeric_df[\"HmRun\"] - numeric_df[\"Errors\"] + (numeric_df[\"RBI\"]-numeric_df[\"Runs\"]))\n",
    "numeric_df[\"New_ERA\"] = numeric_df[\"Runs\"] / ((numeric_df[\"Walks\"]/numeric_df[\"Hits\"]) * 9)\n",
    "numeric_df[\"New_ERA\"].fillna(0, inplace=True)\n",
    "numeric_df[\"New_CERA\"] = (numeric_df[\"CRuns\"] / (numeric_df[\"CWalks\"]/numeric_df[\"CHits\"])) * 9\n",
    "numeric_df[\"New_WHIP\"] = (numeric_df[\"Walks\"] + numeric_df[\"Hits\"]) / (numeric_df[\"Walks\"] / numeric_df[\"Hits\"])\n",
    "numeric_df[\"New_CWHIP\"] = (numeric_df[\"CWalks\"] + numeric_df[\"CHits\"]) / (numeric_df[\"CWalks\"] / numeric_df[\"CHits\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b1448df3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:25.349329Z",
     "iopub.status.busy": "2022-02-05T19:19:25.347708Z",
     "iopub.status.idle": "2022-02-05T19:19:25.385182Z",
     "shell.execute_reply": "2022-02-05T19:19:25.384718Z",
     "shell.execute_reply.started": "2022-02-05T18:16:40.019494Z"
    },
    "papermill": {
     "duration": 0.064625,
     "end_time": "2022-02-05T19:19:25.385313",
     "exception": false,
     "start_time": "2022-02-05T19:19:25.320688",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>New_Hits/AtBat</th>\n",
       "      <th>New_HmRun/Hits</th>\n",
       "      <th>New_Runs/Hits</th>\n",
       "      <th>New_RBI/Hits</th>\n",
       "      <th>New_CHits/CAtBat</th>\n",
       "      <th>New_CHmRun/CHits</th>\n",
       "      <th>New_CRuns/CHits</th>\n",
       "      <th>New_CRBI/CHits</th>\n",
       "      <th>New_CAtBat_mean</th>\n",
       "      <th>New_CHits_mean</th>\n",
       "      <th>New_CHmRun_mean</th>\n",
       "      <th>New_CRBI_mean</th>\n",
       "      <th>New_CWalks_mean</th>\n",
       "      <th>New_Errors/Walks</th>\n",
       "      <th>New_Benefit_Loss</th>\n",
       "      <th>New_OBP</th>\n",
       "      <th>New_COBP</th>\n",
       "      <th>New_SLG</th>\n",
       "      <th>New_CSLG</th>\n",
       "      <th>New_COPS</th>\n",
       "      <th>BABIP</th>\n",
       "      <th>New_ERA</th>\n",
       "      <th>New_CERA</th>\n",
       "      <th>New_WHIP</th>\n",
       "      <th>New_CWHIP</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.637</td>\n",
       "      <td>-0.624</td>\n",
       "      <td>-0.540</td>\n",
       "      <td>-1.239</td>\n",
       "      <td>-0.543</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>1.391</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.211</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>-0.322</td>\n",
       "      <td>-2.076</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-0.617</td>\n",
       "      <td>0.635</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-0.825</td>\n",
       "      <td>-0.715</td>\n",
       "      <td>-0.823</td>\n",
       "      <td>-0.809</td>\n",
       "      <td>-0.064</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>-0.972</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.336</td>\n",
       "      <td>-0.343</td>\n",
       "      <td>-0.994</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>-0.709</td>\n",
       "      <td>0.334</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>475.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.504</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.788</td>\n",
       "      <td>1.611</td>\n",
       "      <td>-0.912</td>\n",
       "      <td>-0.091</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.451</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.469</td>\n",
       "      <td>1.212</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.768</td>\n",
       "      <td>1.443</td>\n",
       "      <td>0.976</td>\n",
       "      <td>1.093</td>\n",
       "      <td>-0.294</td>\n",
       "      <td>1.447</td>\n",
       "      <td>0.861</td>\n",
       "      <td>-0.349</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.642</td>\n",
       "      <td>-0.074</td>\n",
       "      <td>-0.474</td>\n",
       "      <td>-0.598</td>\n",
       "      <td>-0.359</td>\n",
       "      <td>-0.377</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>480.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.622</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.391</td>\n",
       "      <td>1.022</td>\n",
       "      <td>-0.206</td>\n",
       "      <td>0.763</td>\n",
       "      <td>1.173</td>\n",
       "      <td>1.184</td>\n",
       "      <td>1.384</td>\n",
       "      <td>1.224</td>\n",
       "      <td>1.301</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.102</td>\n",
       "      <td>-0.620</td>\n",
       "      <td>-0.847</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-0.439</td>\n",
       "      <td>0.512</td>\n",
       "      <td>1.042</td>\n",
       "      <td>1.031</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.943</td>\n",
       "      <td>-0.642</td>\n",
       "      <td>-0.632</td>\n",
       "      <td>-0.335</td>\n",
       "      <td>-0.595</td>\n",
       "      <td>-0.663</td>\n",
       "      <td>-0.623</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.555</td>\n",
       "      <td>1.162</td>\n",
       "      <td>1.169</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.636</td>\n",
       "      <td>1.555</td>\n",
       "      <td>0.747</td>\n",
       "      <td>1.388</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>500.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.596</td>\n",
       "      <td>-0.489</td>\n",
       "      <td>-0.196</td>\n",
       "      <td>-0.642</td>\n",
       "      <td>-0.386</td>\n",
       "      <td>-0.532</td>\n",
       "      <td>-1.122</td>\n",
       "      <td>-1.527</td>\n",
       "      <td>-1.484</td>\n",
       "      <td>-0.764</td>\n",
       "      <td>-1.499</td>\n",
       "      <td>-1.340</td>\n",
       "      <td>-1.457</td>\n",
       "      <td>1.145</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.696</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.101</td>\n",
       "      <td>-0.565</td>\n",
       "      <td>0.019</td>\n",
       "      <td>-1.321</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-1.115</td>\n",
       "      <td>-0.462</td>\n",
       "      <td>1.071</td>\n",
       "      <td>1.039</td>\n",
       "      <td>1.166</td>\n",
       "      <td>1.043</td>\n",
       "      <td>0.948</td>\n",
       "      <td>-0.457</td>\n",
       "      <td>-0.236</td>\n",
       "      <td>-0.084</td>\n",
       "      <td>-1.265</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.397</td>\n",
       "      <td>-0.453</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>-0.473</td>\n",
       "      <td>-1.326</td>\n",
       "      <td>-0.403</td>\n",
       "      <td>-1.391</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>91.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.304</td>\n",
       "      <td>1.364</td>\n",
       "      <td>-0.884</td>\n",
       "      <td>0.749</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.299</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.864</td>\n",
       "      <td>-0.428</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.359</td>\n",
       "      <td>1.327</td>\n",
       "      <td>2.478</td>\n",
       "      <td>0.720</td>\n",
       "      <td>-1.252</td>\n",
       "      <td>-0.669</td>\n",
       "      <td>-1.249</td>\n",
       "      <td>0.621</td>\n",
       "      <td>-0.810</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.502</td>\n",
       "      <td>-0.660</td>\n",
       "      <td>-0.663</td>\n",
       "      <td>-0.784</td>\n",
       "      <td>-0.699</td>\n",
       "      <td>-0.735</td>\n",
       "      <td>1.394</td>\n",
       "      <td>-0.908</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.859</td>\n",
       "      <td>-1.134</td>\n",
       "      <td>-0.660</td>\n",
       "      <td>-0.616</td>\n",
       "      <td>1.454</td>\n",
       "      <td>1.539</td>\n",
       "      <td>1.444</td>\n",
       "      <td>1.787</td>\n",
       "      <td>1.305</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>750.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat   Hits  HmRun   Runs    RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns   CRBI  CWalks  PutOuts  Assists  Errors  New_Hits/AtBat  New_HmRun/Hits  New_Runs/Hits  New_RBI/Hits  New_CHits/CAtBat  New_CHmRun/CHits  New_CRuns/CHits  New_CRBI/CHits  New_CAtBat_mean  New_CHits_mean  New_CHmRun_mean  New_CRBI_mean  New_CWalks_mean  New_Errors/Walks  New_Benefit_Loss  New_OBP  New_COBP  New_SLG  New_CSLG  New_COPS  BABIP  New_ERA  New_CERA  New_WHIP  New_CWHIP  League_N  Division_W  NewLeague_N  Salary\n",
       "0 -0.637 -0.624 -0.540 -1.239 -0.543 -0.113  1.391   0.675  0.567   0.517  0.318  0.659   0.798    0.964    0.108   0.211          -0.220          -0.322         -2.076        -0.077             0.264             0.414           -0.617           0.635           -0.800          -0.825           -0.715         -0.823           -0.809            -0.064            -0.107   -0.200    -0.600   -0.972     0.366     0.336 -0.343   -0.994    -0.075    -0.709      0.334         1           1            1 475.000\n",
       "1  0.504  0.483  0.722  0.431  0.788  1.611 -0.912  -0.091 -0.018   0.451 -0.026  0.257   0.469    1.212    0.453   0.815           0.270           0.450          0.026         0.516             0.340             0.687            0.054           0.899            0.662           0.768            1.443          0.976            1.093            -0.294             1.447    0.861    -0.349    0.504     0.663     0.642 -0.074   -0.474    -0.598    -0.359     -0.377         0           1            0 480.000\n",
       "2  0.622  0.731  0.951  0.391  1.022 -0.206  0.763   1.173  1.184   1.384  1.224  1.301   0.745    0.102   -0.620  -0.847           0.711           0.500         -0.439         0.512             1.042             1.031            0.814           0.943           -0.642          -0.632           -0.335         -0.595           -0.663            -0.623             0.007   -0.205     0.321    0.555     1.162     1.169 -0.028    0.636     1.555     0.747      1.388         1           0            1 500.000\n",
       "3 -0.596 -0.489 -0.196 -0.642 -0.386 -0.532 -1.122  -1.527 -1.484  -0.764 -1.499 -1.340  -1.457    1.145    0.070  -0.696           0.257           0.101         -0.565         0.019            -1.321            -0.115           -1.115          -0.462            1.071           1.039            1.166          1.043            0.948            -0.457            -0.236   -0.084    -1.265    0.000    -0.397    -0.453 -0.043   -0.473    -1.326    -0.403     -1.391         1           0            1  91.500\n",
       "4  1.304  1.364 -0.884  0.749 -0.034 -0.299  0.763   0.925  0.864  -0.428  0.743  0.469   0.186    0.359    1.327   2.478           0.720          -1.252         -0.669        -1.249             0.621            -0.810            0.064          -0.502           -0.660          -0.663           -0.784         -0.699           -0.735             1.394            -0.908    0.069     0.859   -1.134    -0.660    -0.616  1.454    1.539     1.444     1.787      1.305         0           1            0 750.000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaler\n",
    "\n",
    "numeric_df_columns = numeric_df.columns\n",
    "standardized_numeric_df = preprocessing.scale(numeric_df)\n",
    "standardized_numeric_df = pd.DataFrame(standardized_numeric_df, columns = numeric_df_columns)\n",
    "standardized_numeric_df.head()\n",
    "\n",
    "# New Dataset with categorical columns and \"Salary\"\n",
    "df3 = pd.concat([standardized_numeric_df, cat_df, y_df], axis=1)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "467c264c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:25.442850Z",
     "iopub.status.busy": "2022-02-05T19:19:25.440437Z",
     "iopub.status.idle": "2022-02-05T19:19:25.450671Z",
     "shell.execute_reply": "2022-02-05T19:19:25.450176Z",
     "shell.execute_reply.started": "2022-02-05T18:16:42.303413Z"
    },
    "papermill": {
     "duration": 0.041343,
     "end_time": "2022-02-05T19:19:25.450786",
     "exception": false,
     "start_time": "2022-02-05T19:19:25.409443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 261\n",
      "Variables: 45\n",
      "cat_cols: 3\n",
      "num_cols: 42\n",
      "cat_but_car: 0\n",
      "num_but_cat: 3\n"
     ]
    }
   ],
   "source": [
    "cat_cols, num_cols, cat_but_car = grab_col_names(df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ce99f3",
   "metadata": {
    "papermill": {
     "duration": 0.024329,
     "end_time": "2022-02-05T19:19:25.499676",
     "exception": false,
     "start_time": "2022-02-05T19:19:25.475347",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<hr style=\"height:2px;border-width:0;color:white;background-color:green\">\n",
    "\n",
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54a8d5ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:25.558374Z",
     "iopub.status.busy": "2022-02-05T19:19:25.557610Z",
     "iopub.status.idle": "2022-02-05T19:19:26.100547Z",
     "shell.execute_reply": "2022-02-05T19:19:26.101190Z",
     "shell.execute_reply.started": "2022-02-05T18:16:45.017738Z"
    },
    "papermill": {
     "duration": 0.576567,
     "end_time": "2022-02-05T19:19:26.101396",
     "exception": false,
     "start_time": "2022-02-05T19:19:25.524829",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression 240.27284962222905\n",
      "Ridge 233.96786308647773\n",
      "Lasso 232.8269800556364\n",
      "ElasticNet 241.92173036672048\n",
      "KNN 207.9206807790785\n",
      "CART 301.75434867370274\n",
      "RF 197.72175919070787\n",
      "SVR 329.3716865694426\n",
      "LightGBM 183.39981563143175\n"
     ]
    }
   ],
   "source": [
    "y = df3[\"Salary\"]\n",
    "X = df3.drop([\"Salary\"], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.20,\n",
    "                                                    random_state=46)\n",
    "\n",
    "models = []\n",
    "\n",
    "models.append(('Regression', LinearRegression()))\n",
    "models.append(('Ridge', Ridge()))\n",
    "models.append(('Lasso', Lasso()))\n",
    "models.append(('ElasticNet', ElasticNet()))\n",
    "models.append(('KNN', KNeighborsRegressor()))\n",
    "models.append(('CART', DecisionTreeRegressor()))\n",
    "models.append(('RF', RandomForestRegressor()))\n",
    "models.append(('SVR', SVR()))\n",
    "models.append((\"LightGBM\", LGBMRegressor()))\n",
    "\n",
    "for name, model in models:\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred=model.predict(X_test)\n",
    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "    print(name,rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4c184eef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:26.188097Z",
     "iopub.status.busy": "2022-02-05T19:19:26.187417Z",
     "iopub.status.idle": "2022-02-05T19:19:26.234637Z",
     "shell.execute_reply": "2022-02-05T19:19:26.235502Z",
     "shell.execute_reply.started": "2022-02-05T18:17:12.840628Z"
    },
    "papermill": {
     "duration": 0.094523,
     "end_time": "2022-02-05T19:19:26.235681",
     "exception": false,
     "start_time": "2022-02-05T19:19:26.141158",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "183.39981563143175"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The best score is from Light GBM and we're gonna go on with LGBM.\n",
    "\n",
    "lgbm_model = LGBMRegressor().fit(X_train, y_train)\n",
    "y_pred = lgbm_model.predict(X_test)\n",
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939f2d24",
   "metadata": {
    "papermill": {
     "duration": 0.038804,
     "end_time": "2022-02-05T19:19:26.313966",
     "exception": false,
     "start_time": "2022-02-05T19:19:26.275162",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<hr style=\"height:2px;border-width:0;color:white;background-color:green\">\n",
    "\n",
    "# Model Optimization with LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9ed2e794",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:26.398572Z",
     "iopub.status.busy": "2022-02-05T19:19:26.394889Z",
     "iopub.status.idle": "2022-02-05T19:19:26.400264Z",
     "shell.execute_reply": "2022-02-05T19:19:26.400668Z",
     "shell.execute_reply.started": "2022-02-05T18:55:13.428428Z"
    },
    "papermill": {
     "duration": 0.047279,
     "end_time": "2022-02-05T19:19:26.400826",
     "exception": false,
     "start_time": "2022-02-05T19:19:26.353547",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Model Optimization\n",
    "\n",
    "# I tried this parameters:\n",
    "# lgbm_params = {\"learning_rate\": [0.001, 0.005, 0.01, 0.1],\n",
    "               # \"n_estimators\": [200, 300, 400, 500, 800],\n",
    "               # \"max_depth\": [2, 3, 5, 8, 10],\n",
    "               # \"num_leaves\": [4, 6, 8, 10, 15],\n",
    "               # \"colsample_bytree\": [0.4, 0.5, 0.8, 1]}\n",
    "\n",
    "# Best paramaters:\n",
    "lgbm_params = {\n",
    "\"learning_rate\": [0.01],\n",
    "\"n_estimators\": [800],\n",
    "\"max_depth\": [2],\n",
    "\"num_leaves\": [4],\n",
    "\"colsample_bytree\": [1]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "118a3924",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:26.456903Z",
     "iopub.status.busy": "2022-02-05T19:19:26.455360Z",
     "iopub.status.idle": "2022-02-05T19:19:30.309768Z",
     "shell.execute_reply": "2022-02-05T19:19:30.309189Z",
     "shell.execute_reply.started": "2022-02-05T18:56:08.085830Z"
    },
    "papermill": {
     "duration": 3.883293,
     "end_time": "2022-02-05T19:19:30.309908",
     "exception": false,
     "start_time": "2022-02-05T19:19:26.426615",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lgbm_cv_model = GridSearchCV(lgbm_model,\n",
    "                             lgbm_params,\n",
    "                             cv = 10,\n",
    "                             n_jobs = -1,\n",
    "                             verbose = False).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4e68c375",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:30.390707Z",
     "iopub.status.busy": "2022-02-05T19:19:30.390069Z",
     "iopub.status.idle": "2022-02-05T19:19:30.508642Z",
     "shell.execute_reply": "2022-02-05T19:19:30.509137Z",
     "shell.execute_reply.started": "2022-02-05T18:56:26.508819Z"
    },
    "papermill": {
     "duration": 0.155894,
     "end_time": "2022-02-05T19:19:30.509305",
     "exception": false,
     "start_time": "2022-02-05T19:19:30.353411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180.90872377938538"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_lgbm = LGBMRegressor(**lgbm_cv_model.best_params_).fit(X_train, y_train)\n",
    "y_pred = tuned_lgbm.predict(X_test)\n",
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "db80b3e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-05T19:19:30.570783Z",
     "iopub.status.busy": "2022-02-05T19:19:30.570290Z",
     "iopub.status.idle": "2022-02-05T19:19:31.197262Z",
     "shell.execute_reply": "2022-02-05T19:19:31.196714Z",
     "shell.execute_reply.started": "2022-02-05T19:07:37.736692Z"
    },
    "papermill": {
     "duration": 0.661032,
     "end_time": "2022-02-05T19:19:31.197379",
     "exception": false,
     "start_time": "2022-02-05T19:19:30.536347",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Variable Importance\n",
    "\n",
    "def plot_importance(model, features, save=False):\n",
    "    feature_imp = pd.DataFrame({'Value': model.feature_importances_, 'Feature': features.columns})\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    sns.set(font_scale=1)\n",
    "    sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False))\n",
    "    plt.title('Features')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    if save:\n",
    "        plt.savefig('importances.png')\n",
    "\n",
    "plot_importance(tuned_lgbm, X)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 20.15573,
   "end_time": "2022-02-05T19:19:32.444980",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-02-05T19:19:12.289250",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}