project_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import of basic packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import operator\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Import of chart packages\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import altair as alt\n",
    "\n",
    "# Import of machine learning metric packages\n",
    "from sklearn.metrics import f1_score, classification_report, confusion_matrix, mean_squared_error, r2_score, accuracy_score, recall_score, precision_score, roc_auc_score, roc_curve, fbeta_score\n",
    "from sklearn import metrics\n",
    "\n",
    "# Import of preprossesor packages\n",
    "from sklearn import preprocessing\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, OrdinalEncoder, LabelBinarizer, PolynomialFeatures\n",
    "\n",
    "# Import of machine learning packages\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_predict, RandomizedSearchCV\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, LogisticRegression, SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor, plot_tree\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, VotingClassifier, StackingRegressor, StackingClassifier, AdaBoostClassifier\n",
    "#from xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Set random seed \n",
    "RSEED = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>DATOP</th>\n",
       "      <th>FLTID</th>\n",
       "      <th>DEPSTN</th>\n",
       "      <th>ARRSTN</th>\n",
       "      <th>STD</th>\n",
       "      <th>STA</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>AC</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016-01-03 12.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>TU 0757</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-13 15:05:00</td>\n",
       "      <td>2016-01-13 16.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31BIMO</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>TU 0214</td>\n",
       "      <td>TUN</td>\n",
       "      <td>IST</td>\n",
       "      <td>2016-01-16 04:10:00</td>\n",
       "      <td>2016-01-16 06.45.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID       DATOP     FLTID DEPSTN ARRSTN                  STD   \n",
       "0  train_id_0  2016-01-03  TU 0712     CMN    TUN  2016-01-03 10:30:00  \\\n",
       "1  train_id_1  2016-01-13  TU 0757     MXP    TUN  2016-01-13 15:05:00   \n",
       "2  train_id_2  2016-01-16  TU 0214     TUN    IST  2016-01-16 04:10:00   \n",
       "\n",
       "                   STA STATUS         AC  target  \n",
       "0  2016-01-03 12.55.00    ATA  TU 32AIMN   260.0  \n",
       "1  2016-01-13 16.55.00    ATA  TU 31BIMO    20.0  \n",
       "2  2016-01-16 06.45.00    ATA  TU 32AIMN     0.0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load csv\n",
    "df = pd.read_csv('data/train.csv')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>statistic</th>\n",
       "      <th>ID</th>\n",
       "      <th>DATOP</th>\n",
       "      <th>FLTID</th>\n",
       "      <th>DEPSTN</th>\n",
       "      <th>ARRSTN</th>\n",
       "      <th>STD</th>\n",
       "      <th>STA</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>AC</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dtype</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>std</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3451.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  statistic      ID   DATOP   FLTID  DEPSTN  ARRSTN     STD     STA  STATUS   \n",
       "0     dtype  object  object  object  object  object  object  object  object  \\\n",
       "1      mean     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2       std     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3       min     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4       25%     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "5       50%     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "6       75%     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "7       max     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "       AC   target  \n",
       "0  object  float64  \n",
       "1     NaN     48.7  \n",
       "2     NaN    117.1  \n",
       "3     NaN      0.0  \n",
       "4     NaN      0.0  \n",
       "5     NaN     14.0  \n",
       "6     NaN     43.0  \n",
       "7     NaN   3451.0  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDA info & describe\n",
    "info = pd.concat([\n",
    "df.dtypes.to_frame().T,\n",
    "df.mean(numeric_only=True).to_frame().T,\n",
    "df.std(numeric_only=True).to_frame().T,\n",
    "df.min(numeric_only=True).to_frame().T,\n",
    "df.quantile(0.25, numeric_only=True).to_frame().T,\n",
    "df.quantile(0.5, numeric_only=True).to_frame().T, \n",
    "df.quantile(0.75, numeric_only=True).to_frame().T,\n",
    "df.max(numeric_only=True).to_frame().T,], ignore_index=True).applymap(lambda x: round(x, 1) if isinstance(x, (int, float)) else x)\n",
    "\n",
    "info.insert(0, 'statistic', ['dtype', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'])\n",
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>DATOP</th>\n",
       "      <th>FLTID</th>\n",
       "      <th>DEPSTN</th>\n",
       "      <th>ARRSTN</th>\n",
       "      <th>STD</th>\n",
       "      <th>STA</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>AC</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID, DATOP, FLTID, DEPSTN, ARRSTN, STD, STA, STATUS, AC, target]\n",
       "Index: []"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDA duplicates\n",
    "duplicates = df.duplicated().sum()\n",
    "duplicate_percentage = round((duplicates / df.shape[0]) * 100, 1)\n",
    "df[df.duplicated(keep=False)].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "Empty DataFrame\n",
       "Columns: [Amount, Percentage]\n",
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   "source": [
    "# EDA NaNs\n",
    "missing = pd.DataFrame(df.isnull().sum(), columns=['Amount'])\n",
    "missing['Percentage'] = round((missing['Amount']/df.shape[0]) * 100, 1)\n",
    "missing[missing['Amount'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows and columns:  (107833, 10)\n",
      "--------------------------------------------------\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>DATOP</th>\n",
       "      <th>FLTID</th>\n",
       "      <th>DEPSTN</th>\n",
       "      <th>ARRSTN</th>\n",
       "      <th>STD</th>\n",
       "      <th>STA</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>AC</th>\n",
       "      <th>target</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016-01-03 12.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>TU 0757</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-13 15:05:00</td>\n",
       "      <td>2016-01-13 16.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31BIMO</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>TU 0214</td>\n",
       "      <td>TUN</td>\n",
       "      <td>IST</td>\n",
       "      <td>2016-01-16 04:10:00</td>\n",
       "      <td>2016-01-16 06.45.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_id_107830</td>\n",
       "      <td>2018-11-07</td>\n",
       "      <td>SGT 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2018-11-07 05:00:00</td>\n",
       "      <td>2018-11-07 12.50.00</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 736IOK</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_id_107831</td>\n",
       "      <td>2018-01-23</td>\n",
       "      <td>UG 0010</td>\n",
       "      <td>TUN</td>\n",
       "      <td>DJE</td>\n",
       "      <td>2018-01-23 18:00:00</td>\n",
       "      <td>2018-01-23 18.45.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU CR9ISA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>train_id_107832</td>\n",
       "      <td>2018-11-13</td>\n",
       "      <td>UG 0002</td>\n",
       "      <td>TUN</td>\n",
       "      <td>DJE</td>\n",
       "      <td>2018-11-13 06:15:00</td>\n",
       "      <td>2018-11-13 07.05.00</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU CR9ISA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID       DATOP      FLTID DEPSTN ARRSTN                  STD   \n",
       "0       train_id_0  2016-01-03   TU 0712     CMN    TUN  2016-01-03 10:30:00  \\\n",
       "1       train_id_1  2016-01-13   TU 0757     MXP    TUN  2016-01-13 15:05:00   \n",
       "2       train_id_2  2016-01-16   TU 0214     TUN    IST  2016-01-16 04:10:00   \n",
       "3  train_id_107830  2018-11-07  SGT 0000     TUN    TUN  2018-11-07 05:00:00   \n",
       "4  train_id_107831  2018-01-23   UG 0010     TUN    DJE  2018-01-23 18:00:00   \n",
       "5  train_id_107832  2018-11-13   UG 0002     TUN    DJE  2018-11-13 06:15:00   \n",
       "\n",
       "                   STA STATUS         AC  target  \n",
       "0  2016-01-03 12.55.00    ATA  TU 32AIMN   260.0  \n",
       "1  2016-01-13 16.55.00    ATA  TU 31BIMO    20.0  \n",
       "2  2016-01-16 06.45.00    ATA  TU 32AIMN     0.0  \n",
       "3  2018-11-07 12.50.00    SCH  TU 736IOK     0.0  \n",
       "4  2018-01-23 18.45.00    ATA  TU CR9ISA     0.0  \n",
       "5  2018-11-13 07.05.00    SCH  TU CR9ISA     0.0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDA shape\n",
    "print('Number of rows and columns: ',df.shape)\n",
    "print('-'*50)\n",
    "pd.concat([df.head(3), df.tail(3)]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "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>ID</th>\n",
       "      <th>STA</th>\n",
       "      <th>STD</th>\n",
       "      <th>FLTID</th>\n",
       "      <th>DATOP</th>\n",
       "      <th>target</th>\n",
       "      <th>DEPSTN</th>\n",
       "      <th>ARRSTN</th>\n",
       "      <th>AC</th>\n",
       "      <th>STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>107833</td>\n",
       "      <td>85136</td>\n",
       "      <td>81697</td>\n",
       "      <td>1861</td>\n",
       "      <td>1011</td>\n",
       "      <td>968</td>\n",
       "      <td>132</td>\n",
       "      <td>128</td>\n",
       "      <td>68</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID    STA    STD  FLTID  DATOP  target  DEPSTN  ARRSTN  AC  STATUS\n",
       "Amount  107833  85136  81697   1861   1011     968     132     128  68       5"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDA Uniques\n",
    "unique_counts = pd.DataFrame(df.nunique(), columns=['Amount']).sort_values('Amount', ascending=False).T\n",
    "unique_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "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>ID</th>\n",
       "      <th>DATOP</th>\n",
       "      <th>FLTID</th>\n",
       "      <th>DEPSTN</th>\n",
       "      <th>ARRSTN</th>\n",
       "      <th>STD</th>\n",
       "      <th>STA</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>AC</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016-01-03 12.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>TU 0757</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-13 15:05:00</td>\n",
       "      <td>2016-01-13 16.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31BIMO</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>TU 0214</td>\n",
       "      <td>TUN</td>\n",
       "      <td>IST</td>\n",
       "      <td>2016-01-16 04:10:00</td>\n",
       "      <td>2016-01-16 06.45.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_id_3</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0480</td>\n",
       "      <td>DJE</td>\n",
       "      <td>NTE</td>\n",
       "      <td>2016-01-17 14:10:00</td>\n",
       "      <td>2016-01-17 17.00.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 736IOK</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_id_4</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0338</td>\n",
       "      <td>TUN</td>\n",
       "      <td>ALG</td>\n",
       "      <td>2016-01-17 14:30:00</td>\n",
       "      <td>2016-01-17 15.50.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 320IMU</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>train_id_5</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0283</td>\n",
       "      <td>TLS</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-17 16:20:00</td>\n",
       "      <td>2016-01-17 18.15.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 736IOP</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>train_id_6</td>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>TU 0514</td>\n",
       "      <td>TUN</td>\n",
       "      <td>BCN</td>\n",
       "      <td>2016-01-18 07:15:00</td>\n",
       "      <td>2016-01-18 09.00.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMH</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>train_id_7</td>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>TU 0716</td>\n",
       "      <td>TUN</td>\n",
       "      <td>ORY</td>\n",
       "      <td>2016-01-18 07:35:00</td>\n",
       "      <td>2016-01-18 09.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMI</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>train_id_8</td>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>TU 0752</td>\n",
       "      <td>TUN</td>\n",
       "      <td>FCO</td>\n",
       "      <td>2016-01-18 07:40:00</td>\n",
       "      <td>2016-01-18 09.00.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMC</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>train_id_9</td>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>TU 0996</td>\n",
       "      <td>TUN</td>\n",
       "      <td>NCE</td>\n",
       "      <td>2016-01-18 07:45:00</td>\n",
       "      <td>2016-01-18 09.15.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31AIMK</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID       DATOP     FLTID DEPSTN ARRSTN                  STD   \n",
       "0  train_id_0  2016-01-03  TU 0712     CMN    TUN  2016-01-03 10:30:00  \\\n",
       "1  train_id_1  2016-01-13  TU 0757     MXP    TUN  2016-01-13 15:05:00   \n",
       "2  train_id_2  2016-01-16  TU 0214     TUN    IST  2016-01-16 04:10:00   \n",
       "3  train_id_3  2016-01-17  TU 0480     DJE    NTE  2016-01-17 14:10:00   \n",
       "4  train_id_4  2016-01-17  TU 0338     TUN    ALG  2016-01-17 14:30:00   \n",
       "5  train_id_5  2016-01-17  TU 0283     TLS    TUN  2016-01-17 16:20:00   \n",
       "6  train_id_6  2016-01-18  TU 0514     TUN    BCN  2016-01-18 07:15:00   \n",
       "7  train_id_7  2016-01-18  TU 0716     TUN    ORY  2016-01-18 07:35:00   \n",
       "8  train_id_8  2016-01-18  TU 0752     TUN    FCO  2016-01-18 07:40:00   \n",
       "9  train_id_9  2016-01-18  TU 0996     TUN    NCE  2016-01-18 07:45:00   \n",
       "\n",
       "                   STA STATUS         AC  target  \n",
       "0  2016-01-03 12.55.00    ATA  TU 32AIMN   260.0  \n",
       "1  2016-01-13 16.55.00    ATA  TU 31BIMO    20.0  \n",
       "2  2016-01-16 06.45.00    ATA  TU 32AIMN     0.0  \n",
       "3  2016-01-17 17.00.00    ATA  TU 736IOK     0.0  \n",
       "4  2016-01-17 15.50.00    ATA  TU 320IMU    22.0  \n",
       "5  2016-01-17 18.15.00    ATA  TU 736IOP    53.0  \n",
       "6  2016-01-18 09.00.00    ATA  TU 32AIMH    10.0  \n",
       "7  2016-01-18 09.55.00    ATA  TU 32AIMI    15.0  \n",
       "8  2016-01-18 09.00.00    ATA  TU 32AIMC    16.0  \n",
       "9  2016-01-18 09.15.00    ATA  TU 31AIMK    21.0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CMN', 'MXP', 'TUN', 'DJE', 'TLS', 'IST', 'ORY', 'MIR', 'BRU',\n",
       "       'ABJ', 'VCE', 'AMS', 'FRA', 'BCN', 'JED', 'ALG', 'LIS', 'SXB',\n",
       "       'LYS', 'OUA', 'LGW', 'BEY', 'NCE', 'OPO', 'MRS', 'DUS', 'SFA',\n",
       "       'FCO', 'CDG', 'NKC', 'NTE', 'ZRH', 'GVA', 'OUD', 'MUC', 'SXF',\n",
       "       'HAM', 'NDR', 'NBE', 'CAI', 'BEG', 'VIE', 'ORN', 'MAD', 'TOE',\n",
       "       'BKO', 'DKR', 'KGL', 'BLQ', 'MLA', 'AHU', 'LHR', 'BOD', 'PRG',\n",
       "       'LJU', 'SVO', 'MED', 'BUD', 'ARN', 'CPH', 'CRL', 'OST', 'TNG',\n",
       "       'GAF', 'NAP', 'BRQ', 'OSR', 'YUL', 'NIM', 'TMR', 'JIB', 'CGN',\n",
       "       'EBL', 'GAE', 'BJA', 'AYT', 'RAK', 'LFW', 'LIL', 'PMO', 'FBM',\n",
       "       'TBJ', 'PSA', 'KRT', 'GNB', 'CZL', 'MVB', 'VNO', 'ESB', 'LBV',\n",
       "       'CKY', 'LED', 'KSC', 'BTS', 'AMM', 'OTP', 'TRN', 'IEV', 'HBE',\n",
       "       'CAG', 'KBP', 'ATH', 'SKG', 'ADB', 'DSS', 'DOH', 'COO', 'LUX',\n",
       "       'FIH', 'BYJ', 'KEF', 'EBM', 'BDS', 'AAL', 'VKO', 'AAE', 'BRI',\n",
       "       'VRN', 'SKX', 'HAJ', 'BLL', 'TLL', 'VOG', 'LAD', 'GHA', 'KTW',\n",
       "       'SJJ', 'KRR', 'RTM', 'STR', 'TPS', 'CTA'], dtype=object)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get unique departure airports\n",
    "df[\"DEPSTN\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TUN', 'IST', 'NTE', 'ALG', 'BCN', 'ORY', 'FCO', 'NCE', 'MRS',\n",
       "       'MED', 'FRA', 'BRU', 'DJE', 'LYS', 'CMN', 'BEG', 'OUA', 'GVA',\n",
       "       'MXP', 'BEY', 'MAD', 'JED', 'ABJ', 'VIE', 'MLA', 'BLQ', 'SFA',\n",
       "       'LIS', 'LHR', 'CDG', 'MIR', 'CAI', 'DUS', 'HAM', 'NBE', 'ZRH',\n",
       "       'AMS', 'NDR', 'TLS', 'VCE', 'SXB', 'MUC', 'LGW', 'CRL', 'ORN',\n",
       "       'DKR', 'BOD', 'SXF', 'LJU', 'OST', 'NKC', 'BKO', 'TOE', 'AHU',\n",
       "       'YUL', 'PRG', 'CPH', 'ARN', 'OUD', 'BRQ', 'GAF', 'JIB', 'BUD',\n",
       "       'OPO', 'KGL', 'NIM', 'SVO', 'LIL', 'OSR', 'EBL', 'TNG', 'PSA',\n",
       "       'CGN', 'AYT', 'GAE', 'NAP', 'BJA', 'KRT', 'LFW', 'TBJ', 'PMO',\n",
       "       'TMR', 'FBM', 'RAK', 'GNB', 'ESB', 'CZL', 'LBV', 'KSC', 'CKY',\n",
       "       'AMM', 'LED', 'BTS', 'MVB', 'HBE', 'OTP', 'CAG', 'VNO', 'TRN',\n",
       "       'ATH', 'ADB', 'SKG', 'BYJ', 'DSS', 'COO', 'IEV', 'LUX', 'KBP',\n",
       "       'DOH', 'FIH', 'EBM', 'BDS', 'VKO', 'AAE', 'BLL', 'HAJ', 'BRI',\n",
       "       'CTA', 'VRN', 'SKX', 'VOG', 'BGY', 'LAD', 'KRR', 'SJJ', 'GHA',\n",
       "       'RTM', 'TPS'], dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get unique arrival airports\n",
    "df[\"ARRSTN\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 69665 entries, 0 to 107675\n",
      "Data columns (total 10 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   ID      69665 non-null  object \n",
      " 1   DATOP   69665 non-null  object \n",
      " 2   FLTID   69665 non-null  object \n",
      " 3   DEPSTN  69665 non-null  object \n",
      " 4   ARRSTN  69665 non-null  object \n",
      " 5   STD     69665 non-null  object \n",
      " 6   STA     69665 non-null  object \n",
      " 7   STATUS  69665 non-null  object \n",
      " 8   AC      69665 non-null  object \n",
      " 9   target  69665 non-null  float64\n",
      "dtypes: float64(1), object(9)\n",
      "memory usage: 5.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Get dataframe for delayed flights\n",
    "df_delayed = df.copy()\n",
    "df_delayed = df_delayed[df_delayed[\"target\"] > 0]\n",
    "df_delayed.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "General statistics of target:\n",
      " count    107833.000000\n",
      "mean         48.733013\n",
      "std         117.135562\n",
      "min           0.000000\n",
      "25%           0.000000\n",
      "50%          14.000000\n",
      "75%          43.000000\n",
      "max        3451.000000\n",
      "Name: target, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overview of target distribution\n",
    "print(\"General statistics of target:\\n\", (df['target']).describe())\n",
    "\n",
    "# Creating a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    " \n",
    "# Assigning a graph to each ax\n",
    "sns.boxplot(df[\"target\"], orient=\"h\", ax=ax_box, color='lightblue')\n",
    "sns.histplot(data=df, x=\"target\", ax=ax_hist, color='lightblue')\n",
    "\n",
    "# Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "ax_box.set_xlim([-100, 500])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "General statistics of target (of delayed flights):\n",
      " count    69665.000000\n",
      "mean        75.432814\n",
      "std        138.650946\n",
      "min          1.000000\n",
      "25%         15.000000\n",
      "50%         30.000000\n",
      "75%         74.000000\n",
      "max       3451.000000\n",
      "Name: target, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overview of target distribution (of delayed flights)\n",
    "print(\"General statistics of target (of delayed flights):\\n\", (df_delayed['target']).describe())\n",
    "\n",
    "# Creating a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    " \n",
    "# Assigning a graph to each ax\n",
    "sns.boxplot(df_delayed[\"target\"], orient=\"h\", ax=ax_box, color='lightblue')\n",
    "sns.histplot(data=df_delayed, x=\"target\", ax=ax_hist, color='lightblue')\n",
    "\n",
    "# Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "ax_box.set_xlim([-20, 500])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Clean column names\n",
    "df.columns = df.columns.str.replace(' ','_')\n",
    "df.columns = df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature 'ac' holds information about the model of the airplane. Extracting and converting the information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "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>id</th>\n",
       "      <th>datop</th>\n",
       "      <th>fltid</th>\n",
       "      <th>depstn</th>\n",
       "      <th>arrstn</th>\n",
       "      <th>std</th>\n",
       "      <th>sta</th>\n",
       "      <th>status</th>\n",
       "      <th>ac</th>\n",
       "      <th>target</th>\n",
       "      <th>airplane_model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016-01-03 12.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>260.0</td>\n",
       "      <td>32A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>TU 0757</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-13 15:05:00</td>\n",
       "      <td>2016-01-13 16.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31BIMO</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>TU 0214</td>\n",
       "      <td>TUN</td>\n",
       "      <td>IST</td>\n",
       "      <td>2016-01-16 04:10:00</td>\n",
       "      <td>2016-01-16 06.45.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_id_3</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0480</td>\n",
       "      <td>DJE</td>\n",
       "      <td>NTE</td>\n",
       "      <td>2016-01-17 14:10:00</td>\n",
       "      <td>2016-01-17 17.00.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 736IOK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_id_4</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0338</td>\n",
       "      <td>TUN</td>\n",
       "      <td>ALG</td>\n",
       "      <td>2016-01-17 14:30:00</td>\n",
       "      <td>2016-01-17 15.50.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 320IMU</td>\n",
       "      <td>22.0</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       datop     fltid depstn arrstn                  std   \n",
       "0  train_id_0  2016-01-03  TU 0712     CMN    TUN  2016-01-03 10:30:00  \\\n",
       "1  train_id_1  2016-01-13  TU 0757     MXP    TUN  2016-01-13 15:05:00   \n",
       "2  train_id_2  2016-01-16  TU 0214     TUN    IST  2016-01-16 04:10:00   \n",
       "3  train_id_3  2016-01-17  TU 0480     DJE    NTE  2016-01-17 14:10:00   \n",
       "4  train_id_4  2016-01-17  TU 0338     TUN    ALG  2016-01-17 14:30:00   \n",
       "\n",
       "                   sta status         ac  target airplane_model  \n",
       "0  2016-01-03 12.55.00    ATA  TU 32AIMN   260.0            32A  \n",
       "1  2016-01-13 16.55.00    ATA  TU 31BIMO    20.0            31B  \n",
       "2  2016-01-16 06.45.00    ATA  TU 32AIMN     0.0            32A  \n",
       "3  2016-01-17 17.00.00    ATA  TU 736IOK     0.0            736  \n",
       "4  2016-01-17 15.50.00    ATA  TU 320IMU    22.0            320  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['airplane_model'] = df['ac'].str[3:6]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sta</th>\n",
       "      <th>std</th>\n",
       "      <th>fltid</th>\n",
       "      <th>datop</th>\n",
       "      <th>target</th>\n",
       "      <th>depstn</th>\n",
       "      <th>arrstn</th>\n",
       "      <th>ac</th>\n",
       "      <th>airplane_model</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>107833</td>\n",
       "      <td>85136</td>\n",
       "      <td>81697</td>\n",
       "      <td>1861</td>\n",
       "      <td>1011</td>\n",
       "      <td>968</td>\n",
       "      <td>132</td>\n",
       "      <td>128</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            id    sta    std  fltid  datop  target  depstn  arrstn  ac   \n",
       "Amount  107833  85136  81697   1861   1011     968     132     128  68  \\\n",
       "\n",
       "        airplane_model  status  \n",
       "Amount              16       5  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDA Uniques\n",
    "unique_counts = pd.DataFrame(df.nunique(), columns=['Amount']).sort_values('Amount', ascending=False).T\n",
    "unique_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'319',\n",
       " '31A',\n",
       " '31B',\n",
       " '320',\n",
       " '321',\n",
       " '32A',\n",
       " '332',\n",
       " '343',\n",
       " '345',\n",
       " '733',\n",
       " '734',\n",
       " '736',\n",
       " '738',\n",
       " 'AT7',\n",
       " 'CR9',\n",
       " 'M87'}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df['airplane_model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "replacement_dict = {\n",
    "    '31A': 'Airbus',\n",
    "    '31B': 'Airbus',\n",
    "    '320': 'Airbus',\n",
    "    '321': 'Airbus',\n",
    "    '32A': 'Airbus',\n",
    "    '332': 'Airbus',\n",
    "    '343': 'Airbus',\n",
    "    '345': 'Airbus',\n",
    "    '733': 'Boeing',\n",
    "    '734': 'Boeing',\n",
    "    '736': 'Boeing',\n",
    "    'AT7': 'ATR',\n",
    "    'CR9': 'Bombardier'\n",
    "}\n",
    "\n",
    "df['producer'] = df['airplane_model']\n",
    "\n",
    "# Replace values in the 'purpose' column\n",
    "df['producer'] = df['producer'].replace(replacement_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>producer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016-01-03 12.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>260.0</td>\n",
       "      <td>32A</td>\n",
       "      <td>Airbus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>TU 0757</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-13 15:05:00</td>\n",
       "      <td>2016-01-13 16.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31BIMO</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31B</td>\n",
       "      <td>Airbus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>TU 0214</td>\n",
       "      <td>TUN</td>\n",
       "      <td>IST</td>\n",
       "      <td>2016-01-16 04:10:00</td>\n",
       "      <td>2016-01-16 06.45.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32A</td>\n",
       "      <td>Airbus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_id_3</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0480</td>\n",
       "      <td>DJE</td>\n",
       "      <td>NTE</td>\n",
       "      <td>2016-01-17 14:10:00</td>\n",
       "      <td>2016-01-17 17.00.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 736IOK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>736</td>\n",
       "      <td>Boeing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_id_4</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0338</td>\n",
       "      <td>TUN</td>\n",
       "      <td>ALG</td>\n",
       "      <td>2016-01-17 14:30:00</td>\n",
       "      <td>2016-01-17 15.50.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 320IMU</td>\n",
       "      <td>22.0</td>\n",
       "      <td>320</td>\n",
       "      <td>Airbus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       datop     fltid depstn arrstn                  std   \n",
       "0  train_id_0  2016-01-03  TU 0712     CMN    TUN  2016-01-03 10:30:00  \\\n",
       "1  train_id_1  2016-01-13  TU 0757     MXP    TUN  2016-01-13 15:05:00   \n",
       "2  train_id_2  2016-01-16  TU 0214     TUN    IST  2016-01-16 04:10:00   \n",
       "3  train_id_3  2016-01-17  TU 0480     DJE    NTE  2016-01-17 14:10:00   \n",
       "4  train_id_4  2016-01-17  TU 0338     TUN    ALG  2016-01-17 14:30:00   \n",
       "\n",
       "                   sta status         ac  target airplane_model producer  \n",
       "0  2016-01-03 12.55.00    ATA  TU 32AIMN   260.0            32A   Airbus  \n",
       "1  2016-01-13 16.55.00    ATA  TU 31BIMO    20.0            31B   Airbus  \n",
       "2  2016-01-16 06.45.00    ATA  TU 32AIMN     0.0            32A   Airbus  \n",
       "3  2016-01-17 17.00.00    ATA  TU 736IOK     0.0            736   Boeing  \n",
       "4  2016-01-17 15.50.00    ATA  TU 320IMU    22.0            320   Airbus  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>airline_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016-01-03 12.55.00</td>\n",
       "      <td>ATA</td>\n",
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       "      <td>260.0</td>\n",
       "      <td>32A</td>\n",
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       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>TU 0757</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-13 15:05:00</td>\n",
       "      <td>2016-01-13 16.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31BIMO</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31B</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>TU 0214</td>\n",
       "      <td>TUN</td>\n",
       "      <td>IST</td>\n",
       "      <td>2016-01-16 04:10:00</td>\n",
       "      <td>2016-01-16 06.45.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32A</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_id_3</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0480</td>\n",
       "      <td>DJE</td>\n",
       "      <td>NTE</td>\n",
       "      <td>2016-01-17 14:10:00</td>\n",
       "      <td>2016-01-17 17.00.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 736IOK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>736</td>\n",
       "      <td>Boeing</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_id_4</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0338</td>\n",
       "      <td>TUN</td>\n",
       "      <td>ALG</td>\n",
       "      <td>2016-01-17 14:30:00</td>\n",
       "      <td>2016-01-17 15.50.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 320IMU</td>\n",
       "      <td>22.0</td>\n",
       "      <td>320</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       datop     fltid depstn arrstn                  std   \n",
       "0  train_id_0  2016-01-03  TU 0712     CMN    TUN  2016-01-03 10:30:00  \\\n",
       "1  train_id_1  2016-01-13  TU 0757     MXP    TUN  2016-01-13 15:05:00   \n",
       "2  train_id_2  2016-01-16  TU 0214     TUN    IST  2016-01-16 04:10:00   \n",
       "3  train_id_3  2016-01-17  TU 0480     DJE    NTE  2016-01-17 14:10:00   \n",
       "4  train_id_4  2016-01-17  TU 0338     TUN    ALG  2016-01-17 14:30:00   \n",
       "\n",
       "                   sta status         ac  target airplane_model producer   \n",
       "0  2016-01-03 12.55.00    ATA  TU 32AIMN   260.0            32A   Airbus  \\\n",
       "1  2016-01-13 16.55.00    ATA  TU 31BIMO    20.0            31B   Airbus   \n",
       "2  2016-01-16 06.45.00    ATA  TU 32AIMN     0.0            32A   Airbus   \n",
       "3  2016-01-17 17.00.00    ATA  TU 736IOK     0.0            736   Boeing   \n",
       "4  2016-01-17 15.50.00    ATA  TU 320IMU    22.0            320   Airbus   \n",
       "\n",
       "  airline_1  \n",
       "0        TU  \n",
       "1        TU  \n",
       "2        TU  \n",
       "3        TU  \n",
       "4        TU  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['airline_1'] = df['fltid'].str[0:2]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'12',\n",
       " '20',\n",
       " '6Y',\n",
       " 'A ',\n",
       " 'AO',\n",
       " 'AT',\n",
       " 'AU',\n",
       " 'C ',\n",
       " 'D4',\n",
       " 'DA',\n",
       " 'GJ',\n",
       " 'IN',\n",
       " 'PR',\n",
       " 'SG',\n",
       " 'TU',\n",
       " 'UG',\n",
       " 'UH',\n",
       " 'WK',\n",
       " 'X9'}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df['airline_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>datop</th>\n",
       "      <th>fltid</th>\n",
       "      <th>depstn</th>\n",
       "      <th>arrstn</th>\n",
       "      <th>std</th>\n",
       "      <th>sta</th>\n",
       "      <th>status</th>\n",
       "      <th>ac</th>\n",
       "      <th>target</th>\n",
       "      <th>airplane_model</th>\n",
       "      <th>producer</th>\n",
       "      <th>airline_1</th>\n",
       "      <th>airline_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016-01-03 12.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>260.0</td>\n",
       "      <td>32A</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>TU 0757</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-13 15:05:00</td>\n",
       "      <td>2016-01-13 16.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31BIMO</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31B</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>TU 0214</td>\n",
       "      <td>TUN</td>\n",
       "      <td>IST</td>\n",
       "      <td>2016-01-16 04:10:00</td>\n",
       "      <td>2016-01-16 06.45.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32A</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_id_3</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0480</td>\n",
       "      <td>DJE</td>\n",
       "      <td>NTE</td>\n",
       "      <td>2016-01-17 14:10:00</td>\n",
       "      <td>2016-01-17 17.00.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 736IOK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>736</td>\n",
       "      <td>Boeing</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_id_4</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0338</td>\n",
       "      <td>TUN</td>\n",
       "      <td>ALG</td>\n",
       "      <td>2016-01-17 14:30:00</td>\n",
       "      <td>2016-01-17 15.50.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 320IMU</td>\n",
       "      <td>22.0</td>\n",
       "      <td>320</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       datop     fltid depstn arrstn                  std   \n",
       "0  train_id_0  2016-01-03  TU 0712     CMN    TUN  2016-01-03 10:30:00  \\\n",
       "1  train_id_1  2016-01-13  TU 0757     MXP    TUN  2016-01-13 15:05:00   \n",
       "2  train_id_2  2016-01-16  TU 0214     TUN    IST  2016-01-16 04:10:00   \n",
       "3  train_id_3  2016-01-17  TU 0480     DJE    NTE  2016-01-17 14:10:00   \n",
       "4  train_id_4  2016-01-17  TU 0338     TUN    ALG  2016-01-17 14:30:00   \n",
       "\n",
       "                   sta status         ac  target airplane_model producer   \n",
       "0  2016-01-03 12.55.00    ATA  TU 32AIMN   260.0            32A   Airbus  \\\n",
       "1  2016-01-13 16.55.00    ATA  TU 31BIMO    20.0            31B   Airbus   \n",
       "2  2016-01-16 06.45.00    ATA  TU 32AIMN     0.0            32A   Airbus   \n",
       "3  2016-01-17 17.00.00    ATA  TU 736IOK     0.0            736   Boeing   \n",
       "4  2016-01-17 15.50.00    ATA  TU 320IMU    22.0            320   Airbus   \n",
       "\n",
       "  airline_1 airline_2  \n",
       "0        TU        TU  \n",
       "1        TU        TU  \n",
       "2        TU        TU  \n",
       "3        TU        TU  \n",
       "4        TU        TU  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['airline_2'] = df['ac'].str[0:2]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'5K',\n",
       " '5M',\n",
       " '6P',\n",
       " 'BJ',\n",
       " 'D4',\n",
       " 'GJ',\n",
       " 'GW',\n",
       " 'OL',\n",
       " 'PS',\n",
       " 'QS',\n",
       " 'TU',\n",
       " 'UG',\n",
       " 'UJ',\n",
       " 'X9'}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df['airline_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>datop</th>\n",
       "      <th>fltid</th>\n",
       "      <th>depstn</th>\n",
       "      <th>arrstn</th>\n",
       "      <th>std</th>\n",
       "      <th>sta</th>\n",
       "      <th>status</th>\n",
       "      <th>ac</th>\n",
       "      <th>target</th>\n",
       "      <th>airplane_model</th>\n",
       "      <th>producer</th>\n",
       "      <th>airline_1</th>\n",
       "      <th>airline_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016-01-03 12.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>260.0</td>\n",
       "      <td>32A</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>TU 0757</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-13 15:05:00</td>\n",
       "      <td>2016-01-13 16.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31BIMO</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31B</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>TU 0214</td>\n",
       "      <td>TUN</td>\n",
       "      <td>IST</td>\n",
       "      <td>2016-01-16 04:10:00</td>\n",
       "      <td>2016-01-16 06.45.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32A</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_id_3</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0480</td>\n",
       "      <td>DJE</td>\n",
       "      <td>NTE</td>\n",
       "      <td>2016-01-17 14:10:00</td>\n",
       "      <td>2016-01-17 17.00.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 736IOK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>736</td>\n",
       "      <td>Boeing</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_id_4</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>TU 0338</td>\n",
       "      <td>TUN</td>\n",
       "      <td>ALG</td>\n",
       "      <td>2016-01-17 14:30:00</td>\n",
       "      <td>2016-01-17 15.50.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 320IMU</td>\n",
       "      <td>22.0</td>\n",
       "      <td>320</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       datop     fltid depstn arrstn                  std   \n",
       "0  train_id_0  2016-01-03  TU 0712     CMN    TUN  2016-01-03 10:30:00  \\\n",
       "1  train_id_1  2016-01-13  TU 0757     MXP    TUN  2016-01-13 15:05:00   \n",
       "2  train_id_2  2016-01-16  TU 0214     TUN    IST  2016-01-16 04:10:00   \n",
       "3  train_id_3  2016-01-17  TU 0480     DJE    NTE  2016-01-17 14:10:00   \n",
       "4  train_id_4  2016-01-17  TU 0338     TUN    ALG  2016-01-17 14:30:00   \n",
       "\n",
       "                   sta status         ac  target airplane_model producer   \n",
       "0  2016-01-03 12.55.00    ATA  TU 32AIMN   260.0            32A   Airbus  \\\n",
       "1  2016-01-13 16.55.00    ATA  TU 31BIMO    20.0            31B   Airbus   \n",
       "2  2016-01-16 06.45.00    ATA  TU 32AIMN     0.0            32A   Airbus   \n",
       "3  2016-01-17 17.00.00    ATA  TU 736IOK     0.0            736   Boeing   \n",
       "4  2016-01-17 15.50.00    ATA  TU 320IMU    22.0            320   Airbus   \n",
       "\n",
       "  airline_1 airline_2  \n",
       "0        TU        TU  \n",
       "1        TU        TU  \n",
       "2        TU        TU  \n",
       "3        TU        TU  \n",
       "4        TU        TU  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load airpots data set and clean. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge data sets based on airport short handle. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add columns with weekdays, yyyy, mm, dd, hh:mm:ss\n",
    "\n",
    "y = '_year'\n",
    "m = '_month'\n",
    "wd = '_wd'\n",
    "M = '_min'\n",
    "\n",
    "### std ###\n",
    "\n",
    "date = 'std'\n",
    "\n",
    "idx = df.columns.get_loc(date)\n",
    "\n",
    "df[date] = pd.to_datetime(df[date], format='%Y-%m-%d %H:%M:%S')\n",
    "df.insert(loc=idx+1, column=date+y, value=df[date].dt.strftime('%Y')) # year yyyy\n",
    "df.insert(loc=idx+2, column=date+m, value=df[date].dt.strftime('%#m')) # month m\n",
    "df.insert(loc=idx+3, column=date+wd, value=df[date].dt.strftime('%w')) # weekday wd\n",
    "h = df[date].dt.strftime('%#H').astype(int) # hours\n",
    "minutes = df[date].dt.strftime('%#M').astype(int) # minutes\n",
    "# calcualte time in just minutes\n",
    "t = 60*h + minutes\n",
    "df.insert(loc=idx+4, column=date+M, value=t) # minutes\n",
    "\n",
    "### sta ###\n",
    "\n",
    "date = 'sta'\n",
    "\n",
    "idx = df.columns.get_loc(date)\n",
    "\n",
    "df[date] = pd.to_datetime(df[date], format='%Y-%m-%d %H.%M.%S')\n",
    "df.insert(loc=idx+1, column=date+y, value=df[date].dt.strftime('%Y')) # year yyyy\n",
    "df.insert(loc=idx+2, column=date+m, value=df[date].dt.strftime('%#m')) # month m\n",
    "df.insert(loc=idx+3, column=date+wd, value=df[date].dt.strftime('%w')) # weekday wd\n",
    "h = df[date].dt.strftime('%#H').astype(int) # hours\n",
    "minutes = df[date].dt.strftime('%#M').astype(int)\n",
    "# calcualte time in just minutes\n",
    "t = 60*h + minutes\n",
    "df.insert(loc=idx+4, column=date+M, value=t) # minutes\n",
    "\n",
    "### datop ###\n",
    "\n",
    "date = 'datop'\n",
    "\n",
    "idx = df.columns.get_loc(date)\n",
    "\n",
    "df[date] = pd.to_datetime(df[date], format='%Y-%m-%d')\n",
    "df.insert(loc=idx+1, column=date+y, value=df[date].dt.strftime('%Y')) # year yyyy\n",
    "df.insert(loc=idx+2, column=date+m, value=df[date].dt.strftime('%#m')) # month m\n",
    "df.insert(loc=idx+3, column=date+wd, value=df[date].dt.strftime('%w')) # weekday wd\n",
    "\n",
    "# convert new columns as integers\n",
    "list = ['std_year', 'std_month', 'std_wd', 'sta_year', 'sta_month', 'sta_wd', 'datop_year', 'datop_month', 'datop_wd', 'target']\n",
    "\n",
    "for date in list:\n",
    "    df[date] = df[date].astype(int)\n",
    "\n",
    "# change weekday numbers to EU where day 1 = Monday\n",
    "list = ['std_wd', 'sta_wd', 'datop_wd']\n",
    "\n",
    "for date in list:\n",
    "    df[date][df[date] == 0] = 7 # Sunday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geo-encoding of airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "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>city</th>\n",
       "      <th>country</th>\n",
       "      <th>short</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pitt Meadows</td>\n",
       "      <td>Canada</td>\n",
       "      <td>\\N</td>\n",
       "      <td>49.216099</td>\n",
       "      <td>-122.709999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Blida</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>\\N</td>\n",
       "      <td>36.503601</td>\n",
       "      <td>2.814170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bou Saada</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>\\N</td>\n",
       "      <td>35.332500</td>\n",
       "      <td>4.206390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           city  country short   latitude   longitude\n",
       "0  Pitt Meadows   Canada    \\N  49.216099 -122.709999\n",
       "1         Blida  Algeria    \\N  36.503601    2.814170\n",
       "2     Bou Saada  Algeria    \\N  35.332500    4.206390"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load csv\n",
    "df_airports = pd.read_csv('data/airports.csv')\n",
    "df_airports.columns = ['id', 'name', 'city', 'country', 'short', 'rubbish_6', 'latitude', 'longitude', 'rubbish_1', 'rubbish_2', 'rubbish_3', 'rubbish_4', 'type', 'rubbish_5']\n",
    "df_airports = df_airports.drop(['id', 'name', 'rubbish_1', 'rubbish_2', 'rubbish_3', 'rubbish_4', 'rubbish_5', 'rubbish_6', 'type'], axis=1)\n",
    "df_airports = df_airports.dropna(subset=['short'])\n",
    "df_airports.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>datop</th>\n",
       "      <th>datop_year</th>\n",
       "      <th>datop_month</th>\n",
       "      <th>datop_wd</th>\n",
       "      <th>fltid</th>\n",
       "      <th>depstn</th>\n",
       "      <th>arrstn</th>\n",
       "      <th>std</th>\n",
       "      <th>std_year</th>\n",
       "      <th>std_month</th>\n",
       "      <th>std_wd</th>\n",
       "      <th>std_min</th>\n",
       "      <th>sta</th>\n",
       "      <th>sta_year</th>\n",
       "      <th>sta_month</th>\n",
       "      <th>sta_wd</th>\n",
       "      <th>sta_min</th>\n",
       "      <th>status</th>\n",
       "      <th>ac</th>\n",
       "      <th>target</th>\n",
       "      <th>airplane_model</th>\n",
       "      <th>producer</th>\n",
       "      <th>airline_1</th>\n",
       "      <th>airline_2</th>\n",
       "      <th>city_dep</th>\n",
       "      <th>country_dep</th>\n",
       "      <th>short</th>\n",
       "      <th>latitude_dep</th>\n",
       "      <th>longitude_dep</th>\n",
       "      <th>city_arr</th>\n",
       "      <th>country_arr</th>\n",
       "      <th>short_arr</th>\n",
       "      <th>latitude_arr</th>\n",
       "      <th>longitude_arr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>630</td>\n",
       "      <td>2016-01-03 12:55:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>775</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>260</td>\n",
       "      <td>32A</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "      <td>Casablanca</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>CMN</td>\n",
       "      <td>33.3675</td>\n",
       "      <td>-7.58997</td>\n",
       "      <td>Tunis</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>TUN</td>\n",
       "      <td>36.851002</td>\n",
       "      <td>10.227200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>630</td>\n",
       "      <td>2016-01-03 12:55:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>775</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>260</td>\n",
       "      <td>32A</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "      <td>Casablanca</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>CMN</td>\n",
       "      <td>33.3675</td>\n",
       "      <td>-7.58997</td>\n",
       "      <td>Tunis</td>\n",
       "      <td>Tunesia</td>\n",
       "      <td>TUN</td>\n",
       "      <td>36.847685</td>\n",
       "      <td>10.217603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>TU 0757</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-13 15:05:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>905</td>\n",
       "      <td>2016-01-13 16:55:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1015</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31BIMO</td>\n",
       "      <td>20</td>\n",
       "      <td>31B</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>TU</td>\n",
       "      <td>TU</td>\n",
       "      <td>Milano</td>\n",
       "      <td>Italy</td>\n",
       "      <td>MXP</td>\n",
       "      <td>45.6306</td>\n",
       "      <td>8.72811</td>\n",
       "      <td>Tunis</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>TUN</td>\n",
       "      <td>36.851002</td>\n",
       "      <td>10.227200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id      datop  datop_year  datop_month  datop_wd     fltid depstn   \n",
       "0  train_id_0 2016-01-03        2016            1         7  TU 0712     CMN  \\\n",
       "1  train_id_0 2016-01-03        2016            1         7  TU 0712     CMN   \n",
       "2  train_id_1 2016-01-13        2016            1         3  TU 0757     MXP   \n",
       "\n",
       "  arrstn                 std  std_year  std_month  std_wd  std_min   \n",
       "0    TUN 2016-01-03 10:30:00      2016          1       7      630  \\\n",
       "1    TUN 2016-01-03 10:30:00      2016          1       7      630   \n",
       "2    TUN 2016-01-13 15:05:00      2016          1       3      905   \n",
       "\n",
       "                  sta  sta_year  sta_month  sta_wd  sta_min status         ac   \n",
       "0 2016-01-03 12:55:00      2016          1       7      775    ATA  TU 32AIMN  \\\n",
       "1 2016-01-03 12:55:00      2016          1       7      775    ATA  TU 32AIMN   \n",
       "2 2016-01-13 16:55:00      2016          1       3     1015    ATA  TU 31BIMO   \n",
       "\n",
       "   target airplane_model producer airline_1 airline_2    city_dep country_dep   \n",
       "0     260            32A   Airbus        TU        TU  Casablanca     Morocco  \\\n",
       "1     260            32A   Airbus        TU        TU  Casablanca     Morocco   \n",
       "2      20            31B   Airbus        TU        TU      Milano       Italy   \n",
       "\n",
       "  short  latitude_dep  longitude_dep city_arr country_arr short_arr   \n",
       "0   CMN       33.3675       -7.58997    Tunis     Tunisia       TUN  \\\n",
       "1   CMN       33.3675       -7.58997    Tunis    Tunesia        TUN   \n",
       "2   MXP       45.6306        8.72811    Tunis     Tunisia       TUN   \n",
       "\n",
       "   latitude_arr  longitude_arr  \n",
       "0     36.851002      10.227200  \n",
       "1     36.847685      10.217603  \n",
       "2     36.851002      10.227200  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.merge(df_airports, left_on='depstn', right_on='short', how='left', suffixes=('', '_dep'))\n",
    "\n",
    "# Merge based on arrival station\n",
    "df = df.merge(df_airports, left_on='arrstn', right_on='short', how='left', suffixes=('', '_arr'))\n",
    "\n",
    "# Rename columns for clarity\n",
    "df = df.rename(columns={\n",
    "    'city': 'city_dep',\n",
    "    'country': 'country_dep',\n",
    "    'latitude': 'latitude_dep',\n",
    "    'longitude': 'longitude_dep'\n",
    "})\n",
    "\n",
    "df.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197247, 35)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if datum datop matches with datum std\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197247, 35)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[(df[\"datop_year\"]==df[\"std_year\"])&(df[\"datop_month\"]==df[\"std_month\"])&(df[\"datop_wd\"]==df[\"std_wd\"])]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "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>statistic</th>\n",
       "      <th>id</th>\n",
       "      <th>datop</th>\n",
       "      <th>datop_year</th>\n",
       "      <th>datop_month</th>\n",
       "      <th>datop_wd</th>\n",
       "      <th>fltid</th>\n",
       "      <th>depstn</th>\n",
       "      <th>arrstn</th>\n",
       "      <th>std</th>\n",
       "      <th>std_year</th>\n",
       "      <th>std_month</th>\n",
       "      <th>std_wd</th>\n",
       "      <th>std_min</th>\n",
       "      <th>sta</th>\n",
       "      <th>sta_year</th>\n",
       "      <th>sta_month</th>\n",
       "      <th>sta_wd</th>\n",
       "      <th>sta_min</th>\n",
       "      <th>status</th>\n",
       "      <th>ac</th>\n",
       "      <th>target</th>\n",
       "      <th>airplane_model</th>\n",
       "      <th>producer</th>\n",
       "      <th>airline_1</th>\n",
       "      <th>airline_2</th>\n",
       "      <th>city_dep</th>\n",
       "      <th>country_dep</th>\n",
       "      <th>short</th>\n",
       "      <th>latitude_dep</th>\n",
       "      <th>longitude_dep</th>\n",
       "      <th>city_arr</th>\n",
       "      <th>country_arr</th>\n",
       "      <th>short_arr</th>\n",
       "      <th>latitude_arr</th>\n",
       "      <th>longitude_arr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dtype</td>\n",
       "      <td>object</td>\n",
       "      <td>datetime64[ns]</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>datetime64[ns]</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>datetime64[ns]</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int32</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>759.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>793.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.4</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>std</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>315.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>334.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.6</td>\n",
       "      <td>-73.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.6</td>\n",
       "      <td>-73.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.8</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.9</td>\n",
       "      <td>10.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.9</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>985.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.4</td>\n",
       "      <td>10.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.4</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1439.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1439.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3451.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>51.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.8</td>\n",
       "      <td>51.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  statistic      id           datop datop_year datop_month datop_wd   fltid   \n",
       "0     dtype  object  datetime64[ns]      int32       int32    int32  object  \\\n",
       "1      mean     NaN             NaN     2017.0         6.6      4.1     NaN   \n",
       "2       std     NaN             NaN        0.8         3.3      2.0     NaN   \n",
       "3       min     NaN             NaN     2016.0         1.0      1.0     NaN   \n",
       "4       25%     NaN             NaN     2016.0         4.0      2.0     NaN   \n",
       "5       50%     NaN             NaN     2017.0         7.0      4.0     NaN   \n",
       "6       75%     NaN             NaN     2018.0         9.0      6.0     NaN   \n",
       "7       max     NaN             NaN     2018.0        12.0      7.0     NaN   \n",
       "\n",
       "   depstn  arrstn             std std_year std_month std_wd std_min   \n",
       "0  object  object  datetime64[ns]    int32     int32  int32   int32  \\\n",
       "1     NaN     NaN             NaN   2017.0       6.6    4.1   759.7   \n",
       "2     NaN     NaN             NaN      0.8       3.3    2.0   315.7   \n",
       "3     NaN     NaN             NaN   2016.0       1.0    1.0     0.0   \n",
       "4     NaN     NaN             NaN   2016.0       4.0    2.0   500.0   \n",
       "5     NaN     NaN             NaN   2017.0       7.0    4.0   760.0   \n",
       "6     NaN     NaN             NaN   2018.0       9.0    6.0   985.0   \n",
       "7     NaN     NaN             NaN   2018.0      12.0    7.0  1439.0   \n",
       "\n",
       "              sta sta_year sta_month sta_wd sta_min  status      ac  target   \n",
       "0  datetime64[ns]    int32     int32  int32   int32  object  object   int32  \\\n",
       "1             NaN   2017.0       6.6    4.1   793.3     NaN     NaN    47.2   \n",
       "2             NaN      0.8       3.3    2.0   334.4     NaN     NaN   112.0   \n",
       "3             NaN   2016.0       1.0    1.0     0.0     NaN     NaN     0.0   \n",
       "4             NaN   2016.0       4.0    2.0   580.0     NaN     NaN     0.0   \n",
       "5             NaN   2017.0       7.0    4.0   795.0     NaN     NaN    13.0   \n",
       "6             NaN   2018.0       9.0    6.0  1050.0     NaN     NaN    42.0   \n",
       "7             NaN   2019.0      12.0    7.0  1439.0     NaN     NaN  3451.0   \n",
       "\n",
       "  airplane_model producer airline_1 airline_2 city_dep country_dep   short   \n",
       "0         object   object    object    object   object      object  object  \\\n",
       "1            NaN      NaN       NaN       NaN      NaN         NaN     NaN   \n",
       "2            NaN      NaN       NaN       NaN      NaN         NaN     NaN   \n",
       "3            NaN      NaN       NaN       NaN      NaN         NaN     NaN   \n",
       "4            NaN      NaN       NaN       NaN      NaN         NaN     NaN   \n",
       "5            NaN      NaN       NaN       NaN      NaN         NaN     NaN   \n",
       "6            NaN      NaN       NaN       NaN      NaN         NaN     NaN   \n",
       "7            NaN      NaN       NaN       NaN      NaN         NaN     NaN   \n",
       "\n",
       "  latitude_dep longitude_dep city_arr country_arr short_arr latitude_arr   \n",
       "0      float64       float64   object      object    object      float64  \\\n",
       "1         38.4           8.9      NaN         NaN       NaN         38.4   \n",
       "2          7.2           8.4      NaN         NaN       NaN          7.1   \n",
       "3        -11.6         -73.7      NaN         NaN       NaN        -11.6   \n",
       "4         36.8           6.8      NaN         NaN       NaN         36.8   \n",
       "5         36.9          10.2      NaN         NaN       NaN         36.9   \n",
       "6         43.4          10.2      NaN         NaN       NaN         43.4   \n",
       "7         64.0          51.6      NaN         NaN       NaN         59.8   \n",
       "\n",
       "  longitude_arr  \n",
       "0       float64  \n",
       "1           8.9  \n",
       "2           8.4  \n",
       "3         -73.7  \n",
       "4           6.8  \n",
       "5          10.2  \n",
       "6          10.2  \n",
       "7          51.6  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDA info & describe\n",
    "info = pd.concat([\n",
    "df.dtypes.to_frame().T,\n",
    "df.mean(numeric_only=True).to_frame().T,\n",
    "df.std(numeric_only=True).to_frame().T,\n",
    "df.min(numeric_only=True).to_frame().T,\n",
    "df.quantile(0.25, numeric_only=True).to_frame().T,\n",
    "df.quantile(0.5, numeric_only=True).to_frame().T, \n",
    "df.quantile(0.75, numeric_only=True).to_frame().T,\n",
    "df.max(numeric_only=True).to_frame().T,], ignore_index=True).applymap(lambda x: round(x, 1) if isinstance(x, (int, float)) else x)\n",
    "\n",
    "info.insert(0, 'statistic', ['dtype', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'])\n",
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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>id</th>\n",
       "      <th>datop_year</th>\n",
       "      <th>datop_month</th>\n",
       "      <th>datop_wd</th>\n",
       "      <th>depstn</th>\n",
       "      <th>arrstn</th>\n",
       "      <th>std_year</th>\n",
       "      <th>std_month</th>\n",
       "      <th>std_wd</th>\n",
       "      <th>std_min</th>\n",
       "      <th>sta_year</th>\n",
       "      <th>sta_month</th>\n",
       "      <th>sta_wd</th>\n",
       "      <th>sta_min</th>\n",
       "      <th>status</th>\n",
       "      <th>target</th>\n",
       "      <th>airline_1</th>\n",
       "      <th>latitude_dep</th>\n",
       "      <th>longitude_dep</th>\n",
       "      <th>latitude_arr</th>\n",
       "      <th>longitude_arr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>630</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>775</td>\n",
       "      <td>ATA</td>\n",
       "      <td>260</td>\n",
       "      <td>TU</td>\n",
       "      <td>33.3675</td>\n",
       "      <td>-7.58997</td>\n",
       "      <td>36.851002</td>\n",
       "      <td>10.227200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>630</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>775</td>\n",
       "      <td>ATA</td>\n",
       "      <td>260</td>\n",
       "      <td>TU</td>\n",
       "      <td>33.3675</td>\n",
       "      <td>-7.58997</td>\n",
       "      <td>36.847685</td>\n",
       "      <td>10.217603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>905</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1015</td>\n",
       "      <td>ATA</td>\n",
       "      <td>20</td>\n",
       "      <td>TU</td>\n",
       "      <td>45.6306</td>\n",
       "      <td>8.72811</td>\n",
       "      <td>36.851002</td>\n",
       "      <td>10.227200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  datop_year  datop_month  datop_wd depstn arrstn  std_year   \n",
       "0  train_id_0        2016            1         7    CMN    TUN      2016  \\\n",
       "1  train_id_0        2016            1         7    CMN    TUN      2016   \n",
       "2  train_id_1        2016            1         3    MXP    TUN      2016   \n",
       "\n",
       "   std_month  std_wd  std_min  sta_year  sta_month  sta_wd  sta_min status   \n",
       "0          1       7      630      2016          1       7      775    ATA  \\\n",
       "1          1       7      630      2016          1       7      775    ATA   \n",
       "2          1       3      905      2016          1       3     1015    ATA   \n",
       "\n",
       "   target airline_1  latitude_dep  longitude_dep  latitude_arr  longitude_arr  \n",
       "0     260        TU       33.3675       -7.58997     36.851002      10.227200  \n",
       "1     260        TU       33.3675       -7.58997     36.847685      10.217603  \n",
       "2      20        TU       45.6306        8.72811     36.851002      10.227200  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['datop', 'fltid', 'std', 'sta', 'ac', 'short', 'short_arr', 'city_dep', 'country_dep', 'city_arr', 'country_arr'], axis=1)\n",
    "\n",
    "# aggressive feature drop\n",
    "df = df.drop(['airplane_model', 'producer', 'airline_2'], axis=1)\n",
    "\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of columns to encode\n",
    "columns_to_encode = ['depstn', 'status', 'arrstn', 'airline_1'] # reduced by aggressive feature drop\n",
    "\n",
    "# Create a copy of the original dataframe\n",
    "df_encoded = df.copy()\n",
    "\n",
    "# Encode each column separately\n",
    "for column in columns_to_encode:\n",
    "    lb = LabelBinarizer()\n",
    "    encoded = lb.fit_transform(df[column])\n",
    "    \n",
    "    # If binary classification, create a single column\n",
    "    if len(lb.classes_) == 2:\n",
    "        df_encoded[f'{column}_encoded'] = encoded\n",
    "    else:\n",
    "        # For multiclass, create multiple columns\n",
    "        encoded_df = pd.DataFrame(encoded, columns=[f'{column}_{cls}' for cls in lb.classes_], index=df.index)\n",
    "        df_encoded = pd.concat([df_encoded, encoded_df], axis=1)\n",
    "\n",
    "df_encoded = df_encoded.drop(column, axis=1)\n",
    "\n",
    "# Now, combine the non-encoded columns from df with the encoded columns from df_encoded\n",
    "df = pd.concat([df, df_encoded], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "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>id</th>\n",
       "      <th>datop_year</th>\n",
       "      <th>datop_month</th>\n",
       "      <th>datop_wd</th>\n",
       "      <th>std_year</th>\n",
       "      <th>std_month</th>\n",
       "      <th>std_wd</th>\n",
       "      <th>std_min</th>\n",
       "      <th>sta_year</th>\n",
       "      <th>sta_month</th>\n",
       "      <th>sta_wd</th>\n",
       "      <th>sta_min</th>\n",
       "      <th>target</th>\n",
       "      <th>latitude_dep</th>\n",
       "      <th>longitude_dep</th>\n",
       "      <th>latitude_arr</th>\n",
       "      <th>longitude_arr</th>\n",
       "      <th>depstn_AAE</th>\n",
       "      <th>depstn_AAL</th>\n",
       "      <th>depstn_ABJ</th>\n",
       "      <th>depstn_ADB</th>\n",
       "      <th>depstn_AHU</th>\n",
       "      <th>depstn_ALG</th>\n",
       "      <th>depstn_AMM</th>\n",
       "      <th>depstn_AMS</th>\n",
       "      <th>depstn_ARN</th>\n",
       "      <th>depstn_ATH</th>\n",
       "      <th>depstn_AYT</th>\n",
       "      <th>depstn_BCN</th>\n",
       "      <th>depstn_BDS</th>\n",
       "      <th>depstn_BEG</th>\n",
       "      <th>depstn_BEY</th>\n",
       "      <th>depstn_BJA</th>\n",
       "      <th>depstn_BKO</th>\n",
       "      <th>depstn_BLL</th>\n",
       "      <th>depstn_BLQ</th>\n",
       "      <th>depstn_BOD</th>\n",
       "      <th>depstn_BRI</th>\n",
       "      <th>depstn_BRQ</th>\n",
       "      <th>depstn_BRU</th>\n",
       "      <th>depstn_BTS</th>\n",
       "      <th>depstn_BUD</th>\n",
       "      <th>depstn_BYJ</th>\n",
       "      <th>depstn_CAG</th>\n",
       "      <th>depstn_CAI</th>\n",
       "      <th>depstn_CDG</th>\n",
       "      <th>depstn_CGN</th>\n",
       "      <th>depstn_CKY</th>\n",
       "      <th>depstn_CMN</th>\n",
       "      <th>depstn_COO</th>\n",
       "      <th>depstn_CPH</th>\n",
       "      <th>depstn_CRL</th>\n",
       "      <th>depstn_CTA</th>\n",
       "      <th>depstn_CZL</th>\n",
       "      <th>depstn_DJE</th>\n",
       "      <th>depstn_DKR</th>\n",
       "      <th>depstn_DOH</th>\n",
       "      <th>depstn_DSS</th>\n",
       "      <th>depstn_DUS</th>\n",
       "      <th>depstn_EBL</th>\n",
       "      <th>depstn_EBM</th>\n",
       "      <th>depstn_ESB</th>\n",
       "      <th>depstn_FBM</th>\n",
       "      <th>depstn_FCO</th>\n",
       "      <th>depstn_FIH</th>\n",
       "      <th>depstn_FRA</th>\n",
       "      <th>depstn_GAE</th>\n",
       "      <th>depstn_GAF</th>\n",
       "      <th>depstn_GHA</th>\n",
       "      <th>depstn_GNB</th>\n",
       "      <th>depstn_GVA</th>\n",
       "      <th>depstn_HAJ</th>\n",
       "      <th>depstn_HAM</th>\n",
       "      <th>depstn_HBE</th>\n",
       "      <th>depstn_IEV</th>\n",
       "      <th>depstn_IST</th>\n",
       "      <th>depstn_JED</th>\n",
       "      <th>depstn_JIB</th>\n",
       "      <th>depstn_KBP</th>\n",
       "      <th>depstn_KEF</th>\n",
       "      <th>depstn_KGL</th>\n",
       "      <th>depstn_KRR</th>\n",
       "      <th>depstn_KRT</th>\n",
       "      <th>depstn_KSC</th>\n",
       "      <th>depstn_KTW</th>\n",
       "      <th>depstn_LAD</th>\n",
       "      <th>depstn_LBV</th>\n",
       "      <th>depstn_LED</th>\n",
       "      <th>depstn_LFW</th>\n",
       "      <th>depstn_LGW</th>\n",
       "      <th>depstn_LHR</th>\n",
       "      <th>depstn_LIL</th>\n",
       "      <th>depstn_LIS</th>\n",
       "      <th>depstn_LJU</th>\n",
       "      <th>depstn_LUX</th>\n",
       "      <th>depstn_LYS</th>\n",
       "      <th>depstn_MAD</th>\n",
       "      <th>depstn_MED</th>\n",
       "      <th>depstn_MIR</th>\n",
       "      <th>depstn_MLA</th>\n",
       "      <th>depstn_MRS</th>\n",
       "      <th>depstn_MUC</th>\n",
       "      <th>depstn_MVB</th>\n",
       "      <th>depstn_MXP</th>\n",
       "      <th>depstn_NAP</th>\n",
       "      <th>depstn_NBE</th>\n",
       "      <th>depstn_NCE</th>\n",
       "      <th>depstn_NDR</th>\n",
       "      <th>depstn_NIM</th>\n",
       "      <th>depstn_NKC</th>\n",
       "      <th>depstn_NTE</th>\n",
       "      <th>depstn_OPO</th>\n",
       "      <th>depstn_ORN</th>\n",
       "      <th>depstn_ORY</th>\n",
       "      <th>depstn_OSR</th>\n",
       "      <th>depstn_OST</th>\n",
       "      <th>depstn_OTP</th>\n",
       "      <th>depstn_OUA</th>\n",
       "      <th>depstn_OUD</th>\n",
       "      <th>depstn_PMO</th>\n",
       "      <th>depstn_PRG</th>\n",
       "      <th>depstn_PSA</th>\n",
       "      <th>depstn_RAK</th>\n",
       "      <th>depstn_RTM</th>\n",
       "      <th>depstn_SFA</th>\n",
       "      <th>depstn_SJJ</th>\n",
       "      <th>depstn_SKG</th>\n",
       "      <th>depstn_SKX</th>\n",
       "      <th>depstn_STR</th>\n",
       "      <th>depstn_SVO</th>\n",
       "      <th>depstn_SXB</th>\n",
       "      <th>depstn_SXF</th>\n",
       "      <th>depstn_TBJ</th>\n",
       "      <th>depstn_TLL</th>\n",
       "      <th>depstn_TLS</th>\n",
       "      <th>depstn_TMR</th>\n",
       "      <th>depstn_TNG</th>\n",
       "      <th>depstn_TOE</th>\n",
       "      <th>depstn_TPS</th>\n",
       "      <th>depstn_TRN</th>\n",
       "      <th>depstn_TUN</th>\n",
       "      <th>depstn_VCE</th>\n",
       "      <th>depstn_VIE</th>\n",
       "      <th>depstn_VKO</th>\n",
       "      <th>depstn_VNO</th>\n",
       "      <th>depstn_VOG</th>\n",
       "      <th>depstn_VRN</th>\n",
       "      <th>depstn_YUL</th>\n",
       "      <th>depstn_ZRH</th>\n",
       "      <th>status_ATA</th>\n",
       "      <th>status_DEL</th>\n",
       "      <th>status_DEP</th>\n",
       "      <th>status_RTR</th>\n",
       "      <th>status_SCH</th>\n",
       "      <th>arrstn_AAE</th>\n",
       "      <th>arrstn_ABJ</th>\n",
       "      <th>arrstn_ADB</th>\n",
       "      <th>arrstn_AHU</th>\n",
       "      <th>arrstn_ALG</th>\n",
       "      <th>arrstn_AMM</th>\n",
       "      <th>arrstn_AMS</th>\n",
       "      <th>arrstn_ARN</th>\n",
       "      <th>arrstn_ATH</th>\n",
       "      <th>arrstn_AYT</th>\n",
       "      <th>arrstn_BCN</th>\n",
       "      <th>arrstn_BDS</th>\n",
       "      <th>arrstn_BEG</th>\n",
       "      <th>arrstn_BEY</th>\n",
       "      <th>arrstn_BGY</th>\n",
       "      <th>arrstn_BJA</th>\n",
       "      <th>arrstn_BKO</th>\n",
       "      <th>arrstn_BLL</th>\n",
       "      <th>arrstn_BLQ</th>\n",
       "      <th>arrstn_BOD</th>\n",
       "      <th>arrstn_BRI</th>\n",
       "      <th>arrstn_BRQ</th>\n",
       "      <th>arrstn_BRU</th>\n",
       "      <th>arrstn_BTS</th>\n",
       "      <th>arrstn_BUD</th>\n",
       "      <th>arrstn_BYJ</th>\n",
       "      <th>arrstn_CAG</th>\n",
       "      <th>arrstn_CAI</th>\n",
       "      <th>arrstn_CDG</th>\n",
       "      <th>arrstn_CGN</th>\n",
       "      <th>arrstn_CKY</th>\n",
       "      <th>arrstn_CMN</th>\n",
       "      <th>arrstn_COO</th>\n",
       "      <th>arrstn_CPH</th>\n",
       "      <th>arrstn_CRL</th>\n",
       "      <th>arrstn_CTA</th>\n",
       "      <th>arrstn_CZL</th>\n",
       "      <th>arrstn_DJE</th>\n",
       "      <th>arrstn_DKR</th>\n",
       "      <th>arrstn_DOH</th>\n",
       "      <th>arrstn_DSS</th>\n",
       "      <th>arrstn_DUS</th>\n",
       "      <th>arrstn_EBL</th>\n",
       "      <th>arrstn_EBM</th>\n",
       "      <th>arrstn_ESB</th>\n",
       "      <th>arrstn_FBM</th>\n",
       "      <th>arrstn_FCO</th>\n",
       "      <th>arrstn_FIH</th>\n",
       "      <th>arrstn_FRA</th>\n",
       "      <th>arrstn_GAE</th>\n",
       "      <th>arrstn_GAF</th>\n",
       "      <th>arrstn_GHA</th>\n",
       "      <th>arrstn_GNB</th>\n",
       "      <th>arrstn_GVA</th>\n",
       "      <th>arrstn_HAJ</th>\n",
       "      <th>arrstn_HAM</th>\n",
       "      <th>arrstn_HBE</th>\n",
       "      <th>arrstn_IEV</th>\n",
       "      <th>arrstn_IST</th>\n",
       "      <th>arrstn_JED</th>\n",
       "      <th>arrstn_JIB</th>\n",
       "      <th>arrstn_KBP</th>\n",
       "      <th>arrstn_KGL</th>\n",
       "      <th>arrstn_KRR</th>\n",
       "      <th>arrstn_KRT</th>\n",
       "      <th>arrstn_KSC</th>\n",
       "      <th>arrstn_LAD</th>\n",
       "      <th>arrstn_LBV</th>\n",
       "      <th>arrstn_LED</th>\n",
       "      <th>arrstn_LFW</th>\n",
       "      <th>arrstn_LGW</th>\n",
       "      <th>arrstn_LHR</th>\n",
       "      <th>arrstn_LIL</th>\n",
       "      <th>arrstn_LIS</th>\n",
       "      <th>arrstn_LJU</th>\n",
       "      <th>arrstn_LUX</th>\n",
       "      <th>arrstn_LYS</th>\n",
       "      <th>arrstn_MAD</th>\n",
       "      <th>arrstn_MED</th>\n",
       "      <th>arrstn_MIR</th>\n",
       "      <th>arrstn_MLA</th>\n",
       "      <th>arrstn_MRS</th>\n",
       "      <th>arrstn_MUC</th>\n",
       "      <th>arrstn_MVB</th>\n",
       "      <th>arrstn_MXP</th>\n",
       "      <th>arrstn_NAP</th>\n",
       "      <th>arrstn_NBE</th>\n",
       "      <th>arrstn_NCE</th>\n",
       "      <th>arrstn_NDR</th>\n",
       "      <th>arrstn_NIM</th>\n",
       "      <th>arrstn_NKC</th>\n",
       "      <th>arrstn_NTE</th>\n",
       "      <th>arrstn_OPO</th>\n",
       "      <th>arrstn_ORN</th>\n",
       "      <th>arrstn_ORY</th>\n",
       "      <th>arrstn_OSR</th>\n",
       "      <th>arrstn_OST</th>\n",
       "      <th>arrstn_OTP</th>\n",
       "      <th>arrstn_OUA</th>\n",
       "      <th>arrstn_OUD</th>\n",
       "      <th>arrstn_PMO</th>\n",
       "      <th>arrstn_PRG</th>\n",
       "      <th>arrstn_PSA</th>\n",
       "      <th>arrstn_RAK</th>\n",
       "      <th>arrstn_RTM</th>\n",
       "      <th>arrstn_SFA</th>\n",
       "      <th>arrstn_SJJ</th>\n",
       "      <th>arrstn_SKG</th>\n",
       "      <th>arrstn_SKX</th>\n",
       "      <th>arrstn_SVO</th>\n",
       "      <th>arrstn_SXB</th>\n",
       "      <th>arrstn_SXF</th>\n",
       "      <th>arrstn_TBJ</th>\n",
       "      <th>arrstn_TLS</th>\n",
       "      <th>arrstn_TMR</th>\n",
       "      <th>arrstn_TNG</th>\n",
       "      <th>arrstn_TOE</th>\n",
       "      <th>arrstn_TPS</th>\n",
       "      <th>arrstn_TRN</th>\n",
       "      <th>arrstn_TUN</th>\n",
       "      <th>arrstn_VCE</th>\n",
       "      <th>arrstn_VIE</th>\n",
       "      <th>arrstn_VKO</th>\n",
       "      <th>arrstn_VNO</th>\n",
       "      <th>arrstn_VOG</th>\n",
       "      <th>arrstn_VRN</th>\n",
       "      <th>arrstn_YUL</th>\n",
       "      <th>arrstn_ZRH</th>\n",
       "      <th>airline_1_12</th>\n",
       "      <th>airline_1_20</th>\n",
       "      <th>airline_1_6Y</th>\n",
       "      <th>airline_1_A</th>\n",
       "      <th>airline_1_AO</th>\n",
       "      <th>airline_1_AT</th>\n",
       "      <th>airline_1_AU</th>\n",
       "      <th>airline_1_C</th>\n",
       "      <th>airline_1_D4</th>\n",
       "      <th>airline_1_DA</th>\n",
       "      <th>airline_1_GJ</th>\n",
       "      <th>airline_1_IN</th>\n",
       "      <th>airline_1_PR</th>\n",
       "      <th>airline_1_SG</th>\n",
       "      <th>airline_1_TU</th>\n",
       "      <th>airline_1_UG</th>\n",
       "      <th>airline_1_UH</th>\n",
       "      <th>airline_1_WK</th>\n",
       "      <th>airline_1_X9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>630</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>775</td>\n",
       "      <td>260</td>\n",
       "      <td>33.3675</td>\n",
       "      <td>-7.58997</td>\n",
       "      <td>36.851002</td>\n",
       "      <td>10.227200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>630</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>775</td>\n",
       "      <td>260</td>\n",
       "      <td>33.3675</td>\n",
       "      <td>-7.58997</td>\n",
       "      <td>36.847685</td>\n",
       "      <td>10.217603</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>905</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1015</td>\n",
       "      <td>20</td>\n",
       "      <td>45.6306</td>\n",
       "      <td>8.72811</td>\n",
       "      <td>36.851002</td>\n",
       "      <td>10.227200</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  datop_year  datop_month  datop_wd  std_year  std_month  std_wd   \n",
       "0  train_id_0        2016            1         7      2016          1       7  \\\n",
       "1  train_id_0        2016            1         7      2016          1       7   \n",
       "2  train_id_1        2016            1         3      2016          1       3   \n",
       "\n",
       "   std_min  sta_year  sta_month  sta_wd  sta_min  target  latitude_dep   \n",
       "0      630      2016          1       7      775     260       33.3675  \\\n",
       "1      630      2016          1       7      775     260       33.3675   \n",
       "2      905      2016          1       3     1015      20       45.6306   \n",
       "\n",
       "   longitude_dep  latitude_arr  longitude_arr  depstn_AAE  depstn_AAL   \n",
       "0       -7.58997     36.851002      10.227200           0           0  \\\n",
       "1       -7.58997     36.847685      10.217603           0           0   \n",
       "2        8.72811     36.851002      10.227200           0           0   \n",
       "\n",
       "   depstn_ABJ  depstn_ADB  depstn_AHU  depstn_ALG  depstn_AMM  depstn_AMS   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_ARN  depstn_ATH  depstn_AYT  depstn_BCN  depstn_BDS  depstn_BEG   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_BEY  depstn_BJA  depstn_BKO  depstn_BLL  depstn_BLQ  depstn_BOD   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_BRI  depstn_BRQ  depstn_BRU  depstn_BTS  depstn_BUD  depstn_BYJ   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_CAG  depstn_CAI  depstn_CDG  depstn_CGN  depstn_CKY  depstn_CMN   \n",
       "0           0           0           0           0           0           1  \\\n",
       "1           0           0           0           0           0           1   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_COO  depstn_CPH  depstn_CRL  depstn_CTA  depstn_CZL  depstn_DJE   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_DKR  depstn_DOH  depstn_DSS  depstn_DUS  depstn_EBL  depstn_EBM   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_ESB  depstn_FBM  depstn_FCO  depstn_FIH  depstn_FRA  depstn_GAE   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_GAF  depstn_GHA  depstn_GNB  depstn_GVA  depstn_HAJ  depstn_HAM   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_HBE  depstn_IEV  depstn_IST  depstn_JED  depstn_JIB  depstn_KBP   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_KEF  depstn_KGL  depstn_KRR  depstn_KRT  depstn_KSC  depstn_KTW   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_LAD  depstn_LBV  depstn_LED  depstn_LFW  depstn_LGW  depstn_LHR   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_LIL  depstn_LIS  depstn_LJU  depstn_LUX  depstn_LYS  depstn_MAD   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_MED  depstn_MIR  depstn_MLA  depstn_MRS  depstn_MUC  depstn_MVB   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_MXP  depstn_NAP  depstn_NBE  depstn_NCE  depstn_NDR  depstn_NIM   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           1           0           0           0           0           0   \n",
       "\n",
       "   depstn_NKC  depstn_NTE  depstn_OPO  depstn_ORN  depstn_ORY  depstn_OSR   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_OST  depstn_OTP  depstn_OUA  depstn_OUD  depstn_PMO  depstn_PRG   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_PSA  depstn_RAK  depstn_RTM  depstn_SFA  depstn_SJJ  depstn_SKG   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_SKX  depstn_STR  depstn_SVO  depstn_SXB  depstn_SXF  depstn_TBJ   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_TLL  depstn_TLS  depstn_TMR  depstn_TNG  depstn_TOE  depstn_TPS   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_TRN  depstn_TUN  depstn_VCE  depstn_VIE  depstn_VKO  depstn_VNO   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   depstn_VOG  depstn_VRN  depstn_YUL  depstn_ZRH  status_ATA  status_DEL   \n",
       "0           0           0           0           0           1           0  \\\n",
       "1           0           0           0           0           1           0   \n",
       "2           0           0           0           0           1           0   \n",
       "\n",
       "   status_DEP  status_RTR  status_SCH  arrstn_AAE  arrstn_ABJ  arrstn_ADB   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_AHU  arrstn_ALG  arrstn_AMM  arrstn_AMS  arrstn_ARN  arrstn_ATH   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_AYT  arrstn_BCN  arrstn_BDS  arrstn_BEG  arrstn_BEY  arrstn_BGY   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_BJA  arrstn_BKO  arrstn_BLL  arrstn_BLQ  arrstn_BOD  arrstn_BRI   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_BRQ  arrstn_BRU  arrstn_BTS  arrstn_BUD  arrstn_BYJ  arrstn_CAG   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_CAI  arrstn_CDG  arrstn_CGN  arrstn_CKY  arrstn_CMN  arrstn_COO   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_CPH  arrstn_CRL  arrstn_CTA  arrstn_CZL  arrstn_DJE  arrstn_DKR   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_DOH  arrstn_DSS  arrstn_DUS  arrstn_EBL  arrstn_EBM  arrstn_ESB   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_FBM  arrstn_FCO  arrstn_FIH  arrstn_FRA  arrstn_GAE  arrstn_GAF   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_GHA  arrstn_GNB  arrstn_GVA  arrstn_HAJ  arrstn_HAM  arrstn_HBE   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_IEV  arrstn_IST  arrstn_JED  arrstn_JIB  arrstn_KBP  arrstn_KGL   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_KRR  arrstn_KRT  arrstn_KSC  arrstn_LAD  arrstn_LBV  arrstn_LED   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_LFW  arrstn_LGW  arrstn_LHR  arrstn_LIL  arrstn_LIS  arrstn_LJU   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_LUX  arrstn_LYS  arrstn_MAD  arrstn_MED  arrstn_MIR  arrstn_MLA   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_MRS  arrstn_MUC  arrstn_MVB  arrstn_MXP  arrstn_NAP  arrstn_NBE   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_NCE  arrstn_NDR  arrstn_NIM  arrstn_NKC  arrstn_NTE  arrstn_OPO   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_ORN  arrstn_ORY  arrstn_OSR  arrstn_OST  arrstn_OTP  arrstn_OUA   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_OUD  arrstn_PMO  arrstn_PRG  arrstn_PSA  arrstn_RAK  arrstn_RTM   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_SFA  arrstn_SJJ  arrstn_SKG  arrstn_SKX  arrstn_SVO  arrstn_SXB   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_SXF  arrstn_TBJ  arrstn_TLS  arrstn_TMR  arrstn_TNG  arrstn_TOE   \n",
       "0           0           0           0           0           0           0  \\\n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "\n",
       "   arrstn_TPS  arrstn_TRN  arrstn_TUN  arrstn_VCE  arrstn_VIE  arrstn_VKO   \n",
       "0           0           0           1           0           0           0  \\\n",
       "1           0           0           1           0           0           0   \n",
       "2           0           0           1           0           0           0   \n",
       "\n",
       "   arrstn_VNO  arrstn_VOG  arrstn_VRN  arrstn_YUL  arrstn_ZRH  airline_1_12   \n",
       "0           0           0           0           0           0             0  \\\n",
       "1           0           0           0           0           0             0   \n",
       "2           0           0           0           0           0             0   \n",
       "\n",
       "   airline_1_20  airline_1_6Y  airline_1_A   airline_1_AO  airline_1_AT   \n",
       "0             0             0             0             0             0  \\\n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "\n",
       "   airline_1_AU  airline_1_C   airline_1_D4  airline_1_DA  airline_1_GJ   \n",
       "0             0             0             0             0             0  \\\n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "\n",
       "   airline_1_IN  airline_1_PR  airline_1_SG  airline_1_TU  airline_1_UG   \n",
       "0             0             0             0             1             0  \\\n",
       "1             0             0             0             1             0   \n",
       "2             0             0             0             1             0   \n",
       "\n",
       "   airline_1_UH  airline_1_WK  airline_1_X9  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['depstn', 'arrstn', 'status', 'airline_1'], axis=1) # reduced by aggressive feature drop\n",
    "duplicate_columns = df.columns[df.columns.duplicated()]\n",
    "df = df.loc[:, ~df.columns.duplicated()]\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting data for testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define features and target variable (target)\n",
    "X = df.drop(['target', 'id'], axis=1)\n",
    "y = df['target']\n",
    "\n",
    "# Split into train and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RSEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trainining the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline model: linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate and train linear regression model\n",
    "model_0 = LinearRegression()\n",
    "model_0.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict\n",
    "y_pred = model_0.predict(X_test)\n",
    "\n",
    "y_pred_train = model_0.predict(X_train)\n",
    "y_pred_test = model_0.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared (train):  0.078\n",
      "R-squared (test):  -406677.45\n",
      "R-squared adjusted (train):  0.076\n",
      "R-squared adjusted (test):  -407449.509\n",
      "RMSE (train):  108.156\n",
      "RMSE (test):  69656.206\n"
     ]
    }
   ],
   "source": [
    "# Calculate r-squared\n",
    "r_squared_train = r2_score(y_train, y_pred_train)\n",
    "r_squared_test = r2_score(y_test, y_pred_test)\n",
    "print('R-squared (train): ', round(r_squared_train, 3))\n",
    "print('R-squared (test): ', round(r_squared_test, 3))\n",
    "\n",
    "# Calculate adjusted r-squared\n",
    "r_squared_adjusted_train = 1 - ((1 - r_squared_train) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "r_squared_adjusted_test = 1 - ((1 - r_squared_test) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "print('R-squared adjusted (train): ', round(r_squared_adjusted_train, 3))\n",
    "print('R-squared adjusted (test): ', round(r_squared_adjusted_test, 3))\n",
    "\n",
    "# Calculate RMSE\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
    "print('RMSE (train): ', round(rmse_train, 3))\n",
    "print('RMSE (test): ', round(rmse_test, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 1: Polynominal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate and train degree degree polynomial model\n",
    "#model_1 = PolynomialFeatures(degree=8)\n",
    "#model_1.fit_transform(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_pred = model_0.predict(X_test)\n",
    "\n",
    "#y_pred_train = model_0.predict(X_train)\n",
    "#y_pred_test = model_0.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 2: Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=1, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=1, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=1, random_state=0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate and train Ridge regression model\n",
    "model_2 = Ridge(alpha=1, random_state=RSEED)\n",
    "model_2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model_2.predict(X_test)\n",
    "\n",
    "y_pred_train = model_2.predict(X_train)\n",
    "y_pred_test = model_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared (train):  0.078\n",
      "R-squared (test):  0.08\n",
      "R-squared adjusted (train):  0.076\n",
      "R-squared adjusted (test):  0.079\n",
      "RMSE (train):  108.161\n",
      "RMSE (test):  104.742\n"
     ]
    }
   ],
   "source": [
    "# Calculate r-squared\n",
    "r_squared_train = r2_score(y_train, y_pred_train)\n",
    "r_squared_test = r2_score(y_test, y_pred_test)\n",
    "print('R-squared (train): ', round(r_squared_train, 3))\n",
    "print('R-squared (test): ', round(r_squared_test, 3))\n",
    "\n",
    "# Calculate adjusted r-squared\n",
    "r_squared_adjusted_train = 1 - ((1 - r_squared_train) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "r_squared_adjusted_test = 1 - ((1 - r_squared_test) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "print('R-squared adjusted (train): ', round(r_squared_adjusted_train, 3))\n",
    "print('R-squared adjusted (test): ', round(r_squared_adjusted_test, 3))\n",
    "\n",
    "# Calculate RMSE\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
    "print('RMSE (train): ', round(rmse_train, 3))\n",
    "print('RMSE (test): ', round(rmse_test, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 3: Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.5, max_iter=1000000, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.5, max_iter=1000000, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=0.5, max_iter=1000000, random_state=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate and train Lasso regression model\n",
    "model_3 = Lasso(alpha=0.5, max_iter=int(10e5), random_state=RSEED)\n",
    "model_3.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model_3.predict(X_test)\n",
    "\n",
    "y_pred_train = model_3.predict(X_train)\n",
    "y_pred_test = model_3.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared (train):  0.058\n",
      "R-squared (test):  0.06\n",
      "R-squared adjusted (train):  0.056\n",
      "R-squared adjusted (test):  0.058\n",
      "RMSE (train):  109.336\n",
      "RMSE (test):  105.905\n"
     ]
    }
   ],
   "source": [
    "# Calculate r-squared\n",
    "r_squared_train = r2_score(y_train, y_pred_train)\n",
    "r_squared_test = r2_score(y_test, y_pred_test)\n",
    "print('R-squared (train): ', round(r_squared_train, 3))\n",
    "print('R-squared (test): ', round(r_squared_test, 3))\n",
    "\n",
    "# Calculate adjusted r-squared\n",
    "r_squared_adjusted_train = 1 - ((1 - r_squared_train) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "r_squared_adjusted_test = 1 - ((1 - r_squared_test) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "print('R-squared adjusted (train): ', round(r_squared_adjusted_train, 3))\n",
    "print('R-squared adjusted (test): ', round(r_squared_adjusted_test, 3))\n",
    "\n",
    "# Calculate RMSE\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
    "print('RMSE (train): ', round(rmse_train, 3))\n",
    "print('RMSE (test): ', round(rmse_test, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 4: KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate and train KNN Regression model\n",
    "model_4 = KNeighborsRegressor()\n",
    "model_4.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model_4.predict(X_test)\n",
    "\n",
    "y_pred_train = model_4.predict(X_train)\n",
    "y_pred_test = model_4.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared (train):  0.384\n",
      "R-squared (test):  0.068\n",
      "R-squared adjusted (train):  0.382\n",
      "R-squared adjusted (test):  0.066\n",
      "RMSE (train):  88.423\n",
      "RMSE (test):  105.441\n"
     ]
    }
   ],
   "source": [
    "# Calculate r-squared\n",
    "r_squared_train = r2_score(y_train, y_pred_train)\n",
    "r_squared_test = r2_score(y_test, y_pred_test)\n",
    "print('R-squared (train): ', round(r_squared_train, 3))\n",
    "print('R-squared (test): ', round(r_squared_test, 3))\n",
    "\n",
    "# Calculate adjusted r-squared\n",
    "r_squared_adjusted_train = 1 - ((1 - r_squared_train) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "r_squared_adjusted_test = 1 - ((1 - r_squared_test) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "print('R-squared adjusted (train): ', round(r_squared_adjusted_train, 3))\n",
    "print('R-squared adjusted (test): ', round(r_squared_adjusted_test, 3))\n",
    "\n",
    "# Calculate RMSE\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
    "print('RMSE (train): ', round(rmse_train, 3))\n",
    "print('RMSE (test): ', round(rmse_test, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 5: Random Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:   17.7s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=20, min_samples_leaf=5, n_jobs=1,\n",
       "                       random_state=0, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=20, min_samples_leaf=5, n_jobs=1,\n",
       "                       random_state=0, verbose=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=20, min_samples_leaf=5, n_jobs=1,\n",
       "                       random_state=0, verbose=1)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate and train Random Forest Classifier model\n",
    "model_5 = RandomForestClassifier(n_estimators=100, max_depth=20, min_samples_leaf=5, random_state=RSEED, max_features = 'sqrt', n_jobs=1, verbose = 1)\n",
    "model_5.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    9.8s\n",
      "[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:   39.6s\n",
      "[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    9.9s\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_5.predict(X_test)\n",
    "\n",
    "y_pred_train = model_5.predict(X_train)\n",
    "y_pred_test = model_5.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared (train):  -0.173\n",
      "R-squared (test):  -0.183\n",
      "R-squared adjusted (train):  -0.175\n",
      "R-squared adjusted (test):  -0.185\n",
      "RMSE (train):  121.987\n",
      "RMSE (test):  118.787\n"
     ]
    }
   ],
   "source": [
    "# Calculate r-squared\n",
    "r_squared_train = r2_score(y_train, y_pred_train)\n",
    "r_squared_test = r2_score(y_test, y_pred_test)\n",
    "print('R-squared (train): ', round(r_squared_train, 3))\n",
    "print('R-squared (test): ', round(r_squared_test, 3))\n",
    "\n",
    "# Calculate adjusted r-squared\n",
    "r_squared_adjusted_train = 1 - ((1 - r_squared_train) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "r_squared_adjusted_test = 1 - ((1 - r_squared_test) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "print('R-squared adjusted (train): ', round(r_squared_adjusted_train, 3))\n",
    "print('R-squared adjusted (test): ', round(r_squared_adjusted_test, 3))\n",
    "\n",
    "# Calculate RMSE\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
    "print('RMSE (train): ', round(rmse_train, 3))\n",
    "print('RMSE (test): ', round(rmse_test, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 6: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    2.5s\n",
      "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:   14.3s\n",
      "[Parallel(n_jobs=-1)]: Done 400 out of 400 | elapsed:   31.9s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=30, max_features=&#x27;sqrt&#x27;, min_samples_leaf=5,\n",
       "                      n_estimators=400, n_jobs=-1, random_state=0, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=30, max_features=&#x27;sqrt&#x27;, min_samples_leaf=5,\n",
       "                      n_estimators=400, n_jobs=-1, random_state=0, verbose=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=30, max_features='sqrt', min_samples_leaf=5,\n",
       "                      n_estimators=400, n_jobs=-1, random_state=0, verbose=1)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate and train Random Forest Classifier model\n",
    "# Instantiate and train Random Forest Regressor model\n",
    "model_6 = RandomForestRegressor(n_estimators=300, max_depth=30, min_samples_leaf=5, random_state=RSEED, max_features='sqrt', n_jobs=-1, verbose=1)\n",
    "model_6.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=16)]: Done 400 out of 400 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=16)]: Done 400 out of 400 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=16)]: Done 400 out of 400 | elapsed:    0.2s finished\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_6.predict(X_test)\n",
    "\n",
    "y_pred_train = model_6.predict(X_train)\n",
    "y_pred_test = model_6.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared (train):  0.227\n",
      "R-squared (test):  0.159\n",
      "R-squared adjusted (train):  0.225\n",
      "R-squared adjusted (test):  0.157\n",
      "RMSE (train):  99.051\n",
      "RMSE (test):  100.176\n"
     ]
    }
   ],
   "source": [
    "# Calculate r-squared\n",
    "r_squared_train = r2_score(y_train, y_pred_train)\n",
    "r_squared_test = r2_score(y_test, y_pred_test)\n",
    "print('R-squared (train): ', round(r_squared_train, 3))\n",
    "print('R-squared (test): ', round(r_squared_test, 3))\n",
    "\n",
    "# Calculate adjusted r-squared\n",
    "r_squared_adjusted_train = 1 - ((1 - r_squared_train) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "r_squared_adjusted_test = 1 - ((1 - r_squared_test) * (X_train.shape[0] - 1) / (X_train.shape[0] - X_train.shape[1] - 1))\n",
    "print('R-squared adjusted (train): ', round(r_squared_adjusted_train, 3))\n",
    "print('R-squared adjusted (test): ', round(r_squared_adjusted_test, 3))\n",
    "\n",
    "# Calculate RMSE\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
    "print('RMSE (train): ', round(rmse_train, 3))\n",
    "print('RMSE (test): ', round(rmse_test, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packaging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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