Project 1 - Mercedes-Benz Greener Manufacturing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ef6c4e0a",
   "metadata": {},
   "source": [
    "# Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f8046c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import xgboost,time\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32dfe1b9",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "193ebed9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7dd247",
   "metadata": {},
   "source": [
    "# Check Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14ae11b4",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3f6364a",
   "metadata": {},
   "outputs": [
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       "   ID  X0 X1  X2 X3 X4 X5 X6 X8  X10  ...  X375  X376  X377  X378  X379  X380  \\\n",
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       "[5 rows x 377 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab23aedf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID        int64\n",
       "y       float64\n",
       "X0       object\n",
       "X1       object\n",
       "X2       object\n",
       "         ...   \n",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb496ed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID       int64\n",
       "X0      object\n",
       "X1      object\n",
       "X2      object\n",
       "X3      object\n",
       "         ...  \n",
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     "execution_count": 6,
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    "test.dtypes"
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  },
  {
   "cell_type": "markdown",
   "id": "b083b129",
   "metadata": {},
   "source": [
    "# Dropping irrelevant column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7359eca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop('ID',axis =1)\n",
    "test= test.drop('ID',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc1a4be3",
   "metadata": {},
   "outputs": [
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       "      <td>76.26</td>\n",
       "      <td>az</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>x</td>\n",
       "      <td>j</td>\n",
       "      <td>x</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.62</td>\n",
       "      <td>az</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>x</td>\n",
       "      <td>l</td>\n",
       "      <td>e</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78.02</td>\n",
       "      <td>az</td>\n",
       "      <td>v</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>h</td>\n",
       "      <td>d</td>\n",
       "      <td>n</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 377 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        y  X0 X1  X2 X3 X4 X5 X6 X8  X10  ...  X375  X376  X377  X378  X379  \\\n",
       "0  130.81   k  v  at  a  d  u  j  o    0  ...     0     0     1     0     0   \n",
       "1   88.53   k  t  av  e  d  y  l  o    0  ...     1     0     0     0     0   \n",
       "2   76.26  az  w   n  c  d  x  j  x    0  ...     0     0     0     0     0   \n",
       "3   80.62  az  t   n  f  d  x  l  e    0  ...     0     0     0     0     0   \n",
       "4   78.02  az  v   n  f  d  h  d  n    0  ...     0     0     0     0     0   \n",
       "\n",
       "   X380  X382  X383  X384  X385  \n",
       "0     0     0     0     0     0  \n",
       "1     0     0     0     0     0  \n",
       "2     0     1     0     0     0  \n",
       "3     0     0     0     0     0  \n",
       "4     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 377 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "433d0fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 376 columns</p>\n",
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       "   X0 X1  X2 X3 X4 X5 X6 X8  X10  X11  ...  X375  X376  X377  X378  X379  \\\n",
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       "3  az  l   n  f  d  z  l  n    0    0  ...     0     0     0     1     0   \n",
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       "[5 rows x 376 columns]"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e25257",
   "metadata": {},
   "source": [
    "# Treat Target Column Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "235a6482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['y'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "87df8df0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63.534999999999975\n",
      "136.29500000000002\n"
     ]
    }
   ],
   "source": [
    "Q1 = train['y'].quantile(0.25)\n",
    "Q3 = train['y'].quantile(0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "lower_range = Q1 - 1.5 * IQR\n",
    "upper_range = Q3 + 1.5 * IQR\n",
    "\n",
    "print(lower_range)\n",
    "print(upper_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "29b18402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  43,  203,  216,  253,  342,  420,  429,  681,  846,  883,  889,\n",
       "             900,  995,  998, 1033, 1036, 1060, 1141, 1203, 1205, 1269, 1279,\n",
       "            1349, 1459, 1730, 2240, 2263, 2348, 2357, 2376, 2414, 2470, 2496,\n",
       "            2735, 2736, 2852, 2887, 2888, 2905, 2983, 3028, 3090, 3133, 3177,\n",
       "            3215, 3442, 3744, 3773, 3980, 4176],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_index = train[(train.y > upper_range)].index\n",
    "outlier_index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e79aea4",
   "metadata": {},
   "source": [
    "# Drop Outliers from Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ac496ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(outlier_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "54bc139d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4159, 377)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a57fcb9",
   "metadata": {},
   "source": [
    "# Seprating categorical and numerical data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bf3cae61",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_num_train = train.select_dtypes(exclude = np.object)\n",
    "df_cat_train = train.select_dtypes(include = np.object)\n",
    "\n",
    "df_num_test = test.select_dtypes(exclude = np.object)\n",
    "df_cat_test = test.select_dtypes(include = np.object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "633c9a4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Cat. Test Data: (4209, 8)\n",
      "Shape of Num. Test Data: (4209, 368)\n",
      "Shape of Cat. Train Data: (4159, 8)\n",
      "Shape of Num. Train Data: (4159, 369)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Cat. Test Data:',df_cat_test.shape)\n",
    "print('Shape of Num. Test Data:',df_num_test.shape)\n",
    "print('Shape of Cat. Train Data:',df_cat_train.shape)\n",
    "print('Shape of Num. Train Data:',df_num_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c40e0070",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 368 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   X10  X11  X12  X13  X14  X15  X16  X17  X18  X19  ...  X375  X376  X377  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0  ...     0     0     0   \n",
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       "\n",
       "   X378  X379  X380  X382  X383  X384  X385  \n",
       "0     1     0     0     0     0     0     0  \n",
       "1     0     0     0     0     0     0     0  \n",
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       "3     1     0     0     0     0     0     0  \n",
       "4     0     0     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 368 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "82690170",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   X0 X1  X2 X3 X4 X5 X6 X8\n",
       "0   k  v  at  a  d  u  j  o\n",
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d3d0b9",
   "metadata": {},
   "source": [
    "# If for any column(s),Variance = 0, then remove those variable(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "97744a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for train data\n",
    "for i in df_num_train.columns :\n",
    "    if df_num_train.var()[i] == 0:\n",
    "        df_num_train.drop(i,axis=1,inplace=True)\n",
    "        \n",
    "# for test data\n",
    "for i in df_num_test.columns :\n",
    "    if df_num_test.var()[i] == 0:\n",
    "        df_num_test.drop(i,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4d3ccb0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Num. Test Data: (4209, 363)\n",
      "Shape of Num. Train Data: (4159, 356)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Num. Test Data:',df_num_test.shape)\n",
    "print('Shape of Num. Train Data:',df_num_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eba2de3",
   "metadata": {},
   "source": [
    "# Check for null and unique values for test and train sets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "040bdda0",
   "metadata": {},
   "source": [
    "Concat. df_cat & df_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a88269d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain = pd.concat([df_num_train,df_cat_train],axis=1)\n",
    "newtest = pd.concat([df_num_test,df_cat_test],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "83425cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of New Test Data: (4209, 371)\n",
      "Shape of New Train Data: (4159, 364)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of New Test Data:',newtest.shape)\n",
    "print('Shape of New Train Data:',newtrain.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571d02e1",
   "metadata": {},
   "source": [
    "**`Check for null values`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3bd3b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in newtrain.columns :\n",
    "    if newtrain[i].isna().value_counts() is True:\n",
    "        print(newtrain[i])\n",
    "        \n",
    "for i in newtest.columns :\n",
    "    if newtest[i].isna().value_counts() is True:\n",
    "        print(newtest[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8984633",
   "metadata": {},
   "source": [
    "So no null values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d4d8cc1",
   "metadata": {},
   "source": [
    "**`Check for unique values`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0dff3ad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "X0 :['k' 'az' 't' 'al' 'o' 'w' 'j' 'h' 's' 'n' 'ay' 'f' 'x' 'y' 'aj' 'ak' 'am'\n",
      " 'z' 'q' 'at' 'ap' 'v' 'af' 'a' 'e' 'ai' 'd' 'aq' 'c' 'aa' 'ba' 'as' 'i'\n",
      " 'r' 'b' 'ax' 'bc' 'u' 'ad' 'au' 'm' 'l' 'aw' 'ao' 'ac' 'g' 'ab']\n",
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      " 'i' 'y' 'd' 'f' 'm' 'k' 'g' 'q' 'ab']\n",
      "X2 :['at' 'av' 'n' 'e' 'as' 'aq' 'r' 'ai' 'ak' 'm' 'a' 'k' 'ae' 's' 'f' 'd'\n",
      " 'ag' 'ay' 'ac' 'ap' 'g' 'i' 'aw' 'y' 'b' 'ao' 'al' 'x' 'au' 't' 'an' 'z'\n",
      " 'ah' 'p' 'am' 'h' 'j' 'q' 'af' 'l' 'c' 'o' 'ar']\n",
      "X3 :['a' 'e' 'c' 'f' 'd' 'b' 'g']\n",
      "X4 :['d' 'b' 'c' 'a']\n",
      "X5 :['u' 'y' 'x' 'h' 'g' 'f' 'j' 'i' 'd' 'c' 'af' 'ag' 'ab' 'ac' 'ad' 'ae'\n",
      " 'ah' 'l' 'k' 'n' 'm' 'p' 'q' 's' 'r' 'v' 'w' 'o' 'aa']\n",
      "X6 :['j' 'l' 'd' 'h' 'i' 'a' 'g' 'c' 'k' 'e' 'f' 'b']\n",
      "X8 :['o' 'x' 'e' 'n' 's' 'a' 'h' 'p' 'm' 'k' 'd' 'i' 'v' 'j' 'b' 'q' 'w' 'g'\n",
      " 'y' 'l' 'f' 'u' 'r' 't' 'c']\n"
     ]
    }
   ],
   "source": [
    "for i in newtrain.columns :\n",
    "    print(i,':',end=\"\")\n",
    "    print(newtrain[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "22d59cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "X364 :[0 1]\n",
      "X365 :[0 1]\n",
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      "X368 :[0 1]\n",
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      "X378 :[1 0]\n",
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      "X380 :[0 1]\n",
      "X382 :[0 1]\n",
      "X383 :[0 1]\n",
      "X384 :[0 1]\n",
      "X385 :[0 1]\n",
      "X0 :['az' 't' 'w' 'y' 'x' 'f' 'ap' 'o' 'ay' 'al' 'h' 'z' 'aj' 'd' 'v' 'ak'\n",
      " 'ba' 'n' 'j' 's' 'af' 'ax' 'at' 'aq' 'av' 'm' 'k' 'a' 'e' 'ai' 'i' 'ag'\n",
      " 'b' 'am' 'aw' 'as' 'r' 'ao' 'u' 'l' 'c' 'ad' 'au' 'bc' 'g' 'an' 'ae' 'p'\n",
      " 'bb']\n",
      "X1 :['v' 'b' 'l' 's' 'aa' 'r' 'a' 'i' 'p' 'c' 'o' 'm' 'z' 'e' 'h' 'w' 'g' 'k'\n",
      " 'y' 't' 'u' 'd' 'j' 'q' 'n' 'f' 'ab']\n",
      "X2 :['n' 'ai' 'as' 'ae' 's' 'b' 'e' 'ak' 'm' 'a' 'aq' 'ag' 'r' 'k' 'aj' 'ay'\n",
      " 'ao' 'an' 'ac' 'af' 'ax' 'h' 'i' 'f' 'ap' 'p' 'au' 't' 'z' 'y' 'aw' 'd'\n",
      " 'at' 'g' 'am' 'j' 'x' 'ab' 'w' 'q' 'ah' 'ad' 'al' 'av' 'u']\n",
      "X3 :['f' 'a' 'c' 'e' 'd' 'g' 'b']\n",
      "X4 :['d' 'b' 'a' 'c']\n",
      "X5 :['t' 'b' 'a' 'z' 'y' 'x' 'h' 'g' 'f' 'j' 'i' 'd' 'c' 'af' 'ag' 'ab' 'ac'\n",
      " 'ad' 'ae' 'ah' 'l' 'k' 'n' 'm' 'p' 'q' 's' 'r' 'v' 'w' 'o' 'aa']\n",
      "X6 :['a' 'g' 'j' 'l' 'i' 'd' 'f' 'h' 'c' 'k' 'e' 'b']\n",
      "X8 :['w' 'y' 'j' 'n' 'm' 's' 'a' 'v' 'r' 'o' 't' 'h' 'c' 'k' 'p' 'u' 'd' 'g'\n",
      " 'b' 'q' 'e' 'l' 'f' 'i' 'x']\n"
     ]
    }
   ],
   "source": [
    "for i in newtest.columns :\n",
    "    print(i,':',end=\"\")\n",
    "    print(newtest[i].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86abaafa",
   "metadata": {},
   "source": [
    "# Apply label encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2dbc72d7",
   "metadata": {},
   "outputs": [
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       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4206</th>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4207</th>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4208</th>\n",
       "      <td>46</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4159 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      X0  X1  X2  X3  X4  X5  X6  X8\n",
       "0     32  23  16   0   3  24   9  14\n",
       "1     32  21  18   4   3  28  11  14\n",
       "2     20  24  33   2   3  27   9  23\n",
       "3     20  21  33   5   3  27  11   4\n",
       "4     20  23  33   5   3  12   3  13\n",
       "...   ..  ..  ..  ..  ..  ..  ..  ..\n",
       "4204   8  20  15   2   3   0   3  16\n",
       "4205  31  16  39   3   3   0   7   7\n",
       "4206   8  23  37   0   3   0   6   4\n",
       "4207   9  19  24   5   3   0  11  20\n",
       "4208  46  19   2   2   3   0   6  22\n",
       "\n",
       "[4159 rows x 8 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for train data\n",
    "for i in df_cat_train.columns:\n",
    "    df_cat_train[i] = labelencoder.fit_transform(df_cat_train[i])\n",
    "df_cat_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d52ca4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4204</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4205</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4206</th>\n",
       "      <td>47</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4207</th>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4208</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4209 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      X0  X1  X2  X3  X4  X5  X6  X8\n",
       "0     21  23  34   5   3  26   0  22\n",
       "1     42   3   8   0   3   9   6  24\n",
       "2     21  23  17   5   3   0   9   9\n",
       "3     21  13  34   5   3  31  11  13\n",
       "4     45  20  17   2   3  30   8  12\n",
       "...   ..  ..  ..  ..  ..  ..  ..  ..\n",
       "4204   6   9  17   5   3   1   9   4\n",
       "4205  42   1   8   3   3   1   9  24\n",
       "4206  47  23  17   5   3   1   3  22\n",
       "4207   7  23  17   0   3   1   2  16\n",
       "4208  42   1   8   2   3   1   6  17\n",
       "\n",
       "[4209 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # for test data\n",
    "for i in df_cat_test.columns:\n",
    "    df_cat_test[i] = labelencoder.fit_transform(df_cat_test[i])\n",
    "df_cat_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9c1da47b",
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain1 = pd.concat([df_num_train,df_cat_train],axis=1)\n",
    "newtest1 = pd.concat([df_num_test,df_cat_test],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10dcff9b",
   "metadata": {},
   "source": [
    "# Divide Data into features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b9f50d84",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = newtrain1.drop('y',axis=1)\n",
    "target = newtrain1['y']\n",
    "# X_test = newtest1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae82ee80",
   "metadata": {},
   "source": [
    "# Train & Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "59e27f59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (2911, 363)\n",
      "Shape of y_train: (2911,)\n",
      "Shape of X_test: (1248, 363)\n",
      "Shape of y_test: (1248,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(features, target, train_size = 0.70, random_state = 3)\n",
    "print('Shape of X_train:',X_train.shape)\n",
    "print('Shape of y_train:',y_train.shape)\n",
    "print('Shape of X_test:',X_test.shape)\n",
    "print('Shape of y_test:',y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c444a3f",
   "metadata": {},
   "source": [
    "# Perform Dimensionality Reduction (PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d0948c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "66435245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=0.95)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c4c4e7d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.38545701, 0.21176206, 0.13354339, 0.11817431, 0.09057302,\n",
       "       0.01645431])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9402a81e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9559640947644873"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "331fe53c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_transformed = pca.transform(X_train)\n",
    "X_test_transformed = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9b0934eb",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.561022</td>\n",
       "      <td>15.422601</td>\n",
       "      <td>-9.550391</td>\n",
       "      <td>-3.074573</td>\n",
       "      <td>3.436354</td>\n",
       "      <td>4.176733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.825024</td>\n",
       "      <td>0.776333</td>\n",
       "      <td>1.042777</td>\n",
       "      <td>-12.679915</td>\n",
       "      <td>-0.189836</td>\n",
       "      <td>3.678164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-14.442352</td>\n",
       "      <td>-5.225660</td>\n",
       "      <td>12.982459</td>\n",
       "      <td>-10.424020</td>\n",
       "      <td>9.888473</td>\n",
       "      <td>6.303518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.813386</td>\n",
       "      <td>3.263954</td>\n",
       "      <td>-0.921325</td>\n",
       "      <td>11.784038</td>\n",
       "      <td>7.090589</td>\n",
       "      <td>-3.781918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.144741</td>\n",
       "      <td>-9.597645</td>\n",
       "      <td>12.576378</td>\n",
       "      <td>-5.245294</td>\n",
       "      <td>-8.473391</td>\n",
       "      <td>0.034307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2906</th>\n",
       "      <td>19.399721</td>\n",
       "      <td>-7.716407</td>\n",
       "      <td>-10.862942</td>\n",
       "      <td>-0.190089</td>\n",
       "      <td>1.057341</td>\n",
       "      <td>-1.199447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2907</th>\n",
       "      <td>5.954690</td>\n",
       "      <td>14.740205</td>\n",
       "      <td>-10.709911</td>\n",
       "      <td>2.746565</td>\n",
       "      <td>-6.159345</td>\n",
       "      <td>1.491938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>20.931689</td>\n",
       "      <td>-8.400373</td>\n",
       "      <td>-9.206365</td>\n",
       "      <td>-3.825675</td>\n",
       "      <td>9.945546</td>\n",
       "      <td>-2.348915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>-14.709168</td>\n",
       "      <td>-11.149776</td>\n",
       "      <td>3.800036</td>\n",
       "      <td>9.644795</td>\n",
       "      <td>-4.540294</td>\n",
       "      <td>5.558305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>21.838512</td>\n",
       "      <td>-7.953137</td>\n",
       "      <td>-4.700524</td>\n",
       "      <td>-9.153851</td>\n",
       "      <td>5.653786</td>\n",
       "      <td>0.459855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2911 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0          1          2          3         4         5\n",
       "0     23.561022  15.422601  -9.550391  -3.074573  3.436354  4.176733\n",
       "1     -3.825024   0.776333   1.042777 -12.679915 -0.189836  3.678164\n",
       "2    -14.442352  -5.225660  12.982459 -10.424020  9.888473  6.303518\n",
       "3     -2.813386   3.263954  -0.921325  11.784038  7.090589 -3.781918\n",
       "4      3.144741  -9.597645  12.576378  -5.245294 -8.473391  0.034307\n",
       "...         ...        ...        ...        ...       ...       ...\n",
       "2906  19.399721  -7.716407 -10.862942  -0.190089  1.057341 -1.199447\n",
       "2907   5.954690  14.740205 -10.709911   2.746565 -6.159345  1.491938\n",
       "2908  20.931689  -8.400373  -9.206365  -3.825675  9.945546 -2.348915\n",
       "2909 -14.709168 -11.149776   3.800036   9.644795 -4.540294  5.558305\n",
       "2910  21.838512  -7.953137  -4.700524  -9.153851  5.653786  0.459855\n",
       "\n",
       "[2911 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_train_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcda115b",
   "metadata": {},
   "source": [
    "# xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "73e13fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_clf = xgboost.XGBRegressor(colsample_bytree= 0.7,\n",
    "                               learning_rate= 0.03,\n",
    "                               max_depth= 5,\n",
    "                               min_child_weight= 4,\n",
    "                               n_estimators= 200,\n",
    "                               nthread= 4,\n",
    "                               subsample= 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "86eb227f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 3.322930097579956\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "xgb_clf.fit(X_train,y_train)\n",
    "\n",
    "end = time.time()\n",
    "time_elapsed = end - start\n",
    "print('Time taken:',time_elapsed)\n",
    "\n",
    "y_pred = xgb_clf.predict(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2ca29f84",
   "metadata": {},
   "source": [
    "# Check R Squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d6638da7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6334253267224981"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  }
 ],
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