Mercedes_Benz_Greener_Manufacturing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c2b3188",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7dd976ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"train.csv\")\n",
    "df_test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e1aeb54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for null values in training as well as testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "46bec351",
   "metadata": {},
   "outputs": [
    {
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       "       ..\n",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca4aea8d",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36527b11",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   "source": [
    "df_train.head()"
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  },
  {
   "cell_type": "code",
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   "id": "0f6e0f93",
   "metadata": {},
   "outputs": [
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    "df_train.dtypes.value_counts()"
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  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "61d46f28",
   "metadata": {},
   "outputs": [
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  {
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   "id": "5b859bd8",
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   "outputs": [],
   "source": [
    "# Drop the ID column from the tet data and ID and y from the training data\n",
    "# No null values are found in the data\n",
    "# Check for the columns with zero variance and remove those columns\n",
    "# Before that seperate the dependent and independent variable\n",
    "y_train = df_train[\"y\"]\n",
    "df_id = df_train[\"ID\"]\n",
    "df_train = df_train.drop([\"ID\", \"y\"], axis = 1)\n",
    "df_test = df_test.drop(\"ID\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a8b4d2b",
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  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "59296abf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    130.81\n",
       "1     88.53\n",
       "2     76.26\n",
       "3     80.62\n",
       "4     78.02\n",
       "Name: y, dtype: float64"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c5aba999",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-7c8aa6cfa58a>:3: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  df_num_train = df_train.select_dtypes(exclude = np.object)\n",
      "<ipython-input-15-7c8aa6cfa58a>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  df_cat_train = df_train.select_dtypes(include = np.object)\n",
      "<ipython-input-15-7c8aa6cfa58a>:5: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  df_num_test = df_test.select_dtypes(exclude = np.object)\n",
      "<ipython-input-15-7c8aa6cfa58a>:6: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  df_cat_test = df_test.select_dtypes(include = np.object)\n"
     ]
    }
   ],
   "source": [
    "# Seperate the categorical and numerical data for the training and the testing data \n",
    "# So that we can eaisly preprocess the data\n",
    "df_num_train = df_train.select_dtypes(exclude = np.object) \n",
    "df_cat_train = df_train.select_dtypes(include = np.object)\n",
    "df_num_test = df_test.select_dtypes(exclude = np.object)\n",
    "df_cat_test = df_test.select_dtypes(include = np.object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ff1464f7",
   "metadata": {},
   "outputs": [
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       "[5 rows x 368 columns]"
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     "execution_count": 16,
     "metadata": {},
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    "df_num_train.head()"
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  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8a232344",
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a9580791",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now search for the columns which has zero variance and remove them\n",
    "# Because those columns are of no use for us to build a model\n",
    "v = np.array(df_num_train.var())\n",
    "col_names = list(df_num_train.columns)\n",
    "for i in range(368):\n",
    "    if v[i] ==0:\n",
    "        df_num_train = df_num_train.drop(col_names[i],  axis =1)\n",
    "        df_num_test = df_num_test.drop(col_names[i], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
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    "## All the columns with zero variance is reoved from the taining and testing numerical data set\n",
    "df_num_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7cf6897e",
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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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 356 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   X10  X12  X13  X14  X15  X16  X17  X18  X19  X20  ...  X375  X376  X377  \\\n",
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       "4    0    0    0    1    0    0    0    0    0    0  ...     1     0     0   \n",
       "\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",
       "2     1     0     0     0     0     0     0  \n",
       "3     1     0     0     0     0     0     0  \n",
       "4     0     0     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 356 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "24fe83bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now apply the label encoder on the categorical columns in the training and testing data as well\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8c15c16e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-57cc79ed0218>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_cat_train[i] = le.fit_transform(df_cat_train[i])\n",
      "<ipython-input-22-57cc79ed0218>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_cat_test[i] = le.fit_transform(df_cat_test[i])\n"
     ]
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "col_names = list(df_cat_train.columns)\n",
    "for i in col_names:\n",
    "    df_cat_train[i] = le.fit_transform(df_cat_train[i])\n",
    "    df_cat_test[i] = le.fit_transform(df_cat_test[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dea6d56d",
   "metadata": {},
   "outputs": [
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       "1  32  21  19   4   3  28  11  14\n",
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_cat_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "14640fe7",
   "metadata": {},
   "outputs": [
    {
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       "3  21  13  34   5   3  31  11  13\n",
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "068c2dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int32    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat_train.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2c88acfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int32    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat_test.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f95a31ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now apply the MinMaxScaler technique on the categorical data sets\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "df7e58eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "mn = MinMaxScaler()\n",
    "df_cat_train_1 = mn.fit_transform(df_cat_train) \n",
    "df_cat_test_1 = mn.fit_transform(df_cat_test)\n",
    "df_cat_train_sc = pd.DataFrame(df_cat_train_1, index = df_cat_train.index, columns = df_cat_train.columns)\n",
    "df_cat_test_sc = pd.DataFrame(df_cat_test_1, index = df_cat_test.index, columns = df_cat_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a763b429",
   "metadata": {},
   "outputs": [],
   "source": [
    "## All the categorical data is chnaged into the numerical data\n",
    "# Now concat the numerical and categorical data set of the training and testing data respectively\n",
    "df_final_train = pd.concat([df_num_train, df_cat_train_sc], axis = 1)\n",
    "df_final_test = pd.concat([df_num_test, df_cat_test_sc], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8ac18015",
   "metadata": {},
   "outputs": [
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       "\n",
       "[5 rows x 364 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ca116968",
   "metadata": {},
   "outputs": [
    {
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       "        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>X10</th>\n",
       "      <th>X12</th>\n",
       "      <th>X13</th>\n",
       "      <th>X14</th>\n",
       "      <th>X15</th>\n",
       "      <th>X16</th>\n",
       "      <th>X17</th>\n",
       "      <th>X18</th>\n",
       "      <th>X19</th>\n",
       "      <th>X20</th>\n",
       "      <th>...</th>\n",
       "      <th>X384</th>\n",
       "      <th>X385</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.884615</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.884615</td>\n",
       "      <td>0.386364</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.386364</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 364 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   X10  X12  X13  X14  X15  X16  X17  X18  X19  X20  ...  X384  X385      X0  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0  ...     0     0  0.4375   \n",
       "1    0    0    0    0    0    0    0    0    1    0  ...     0     0  0.8750   \n",
       "2    0    0    0    1    0    0    0    0    0    0  ...     0     0  0.4375   \n",
       "3    0    0    0    0    0    0    0    0    0    0  ...     0     0  0.4375   \n",
       "4    0    0    0    1    0    0    0    0    0    0  ...     0     0  0.9375   \n",
       "\n",
       "         X1        X2        X3   X4        X5        X6        X8  \n",
       "0  0.884615  0.772727  0.833333  1.0  0.838710  0.000000  0.916667  \n",
       "1  0.115385  0.181818  0.000000  1.0  0.290323  0.545455  1.000000  \n",
       "2  0.884615  0.386364  0.833333  1.0  0.000000  0.818182  0.375000  \n",
       "3  0.500000  0.772727  0.833333  1.0  1.000000  1.000000  0.541667  \n",
       "4  0.769231  0.386364  0.333333  1.0  0.967742  0.727273  0.500000  \n",
       "\n",
       "[5 rows x 364 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "edf338e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now apply the Dimensionality Reduction via using PCA\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "631c2be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_comp = 12\n",
    "pca = PCA(n_components = n_comp, random_state = 420)\n",
    "df_train_pca = pca.fit_transform(df_final_train)\n",
    "df_test_pca = pca.fit_transform(df_final_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9c264b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Number of columns are reduced to 12 for both training as well as the testing data\n",
    "# But the data is in nd array format so covert that into data frame\n",
    "df_train_pca2 = pd.DataFrame(df_train_pca, columns = [\"PC1\", \"PC2\", \"PC3\", \"PC4\", \"PC5\", \"PC6\", \"PC7\", \"PC8\", \"PC9\", \"PC10\", \"PC11\", \"PC12\"])\n",
    "df_test_pca2 = pd.DataFrame(df_test_pca, columns = [\"PC1\", \"PC2\", \"PC3\", \"PC4\", \"PC5\", \"PC6\", \"PC7\", \"PC8\", \"PC9\", \"PC10\", \"PC11\", \"PC12\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e7f02116",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>PC11</th>\n",
       "      <th>PC12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.682272</td>\n",
       "      <td>2.217390</td>\n",
       "      <td>1.233625</td>\n",
       "      <td>0.885738</td>\n",
       "      <td>1.401422</td>\n",
       "      <td>0.054223</td>\n",
       "      <td>0.654832</td>\n",
       "      <td>-0.937322</td>\n",
       "      <td>0.192252</td>\n",
       "      <td>-0.679032</td>\n",
       "      <td>-1.755969</td>\n",
       "      <td>-0.550592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.279051</td>\n",
       "      <td>1.164201</td>\n",
       "      <td>-0.764263</td>\n",
       "      <td>-0.660639</td>\n",
       "      <td>0.237863</td>\n",
       "      <td>0.066804</td>\n",
       "      <td>1.237285</td>\n",
       "      <td>-0.530337</td>\n",
       "      <td>-0.108870</td>\n",
       "      <td>0.499733</td>\n",
       "      <td>0.104093</td>\n",
       "      <td>1.037764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.018083</td>\n",
       "      <td>2.979512</td>\n",
       "      <td>0.558557</td>\n",
       "      <td>2.540751</td>\n",
       "      <td>-0.926714</td>\n",
       "      <td>3.282631</td>\n",
       "      <td>-0.940264</td>\n",
       "      <td>0.557082</td>\n",
       "      <td>-0.925952</td>\n",
       "      <td>-0.108909</td>\n",
       "      <td>0.442002</td>\n",
       "      <td>-0.503769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.658559</td>\n",
       "      <td>2.545045</td>\n",
       "      <td>-0.425408</td>\n",
       "      <td>2.997377</td>\n",
       "      <td>-1.681632</td>\n",
       "      <td>3.134975</td>\n",
       "      <td>0.074145</td>\n",
       "      <td>0.084039</td>\n",
       "      <td>-1.072854</td>\n",
       "      <td>0.243470</td>\n",
       "      <td>0.255022</td>\n",
       "      <td>0.015665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.652313</td>\n",
       "      <td>2.370739</td>\n",
       "      <td>-0.583703</td>\n",
       "      <td>3.194208</td>\n",
       "      <td>-1.999394</td>\n",
       "      <td>3.167652</td>\n",
       "      <td>-0.143351</td>\n",
       "      <td>0.229232</td>\n",
       "      <td>-1.754659</td>\n",
       "      <td>-0.355691</td>\n",
       "      <td>0.175018</td>\n",
       "      <td>0.297524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
       "0  0.682272  2.217390  1.233625  0.885738  1.401422  0.054223  0.654832   \n",
       "1 -0.279051  1.164201 -0.764263 -0.660639  0.237863  0.066804  1.237285   \n",
       "2 -1.018083  2.979512  0.558557  2.540751 -0.926714  3.282631 -0.940264   \n",
       "3 -0.658559  2.545045 -0.425408  2.997377 -1.681632  3.134975  0.074145   \n",
       "4 -0.652313  2.370739 -0.583703  3.194208 -1.999394  3.167652 -0.143351   \n",
       "\n",
       "        PC8       PC9      PC10      PC11      PC12  \n",
       "0 -0.937322  0.192252 -0.679032 -1.755969 -0.550592  \n",
       "1 -0.530337 -0.108870  0.499733  0.104093  1.037764  \n",
       "2  0.557082 -0.925952 -0.108909  0.442002 -0.503769  \n",
       "3  0.084039 -1.072854  0.243470  0.255022  0.015665  \n",
       "4  0.229232 -1.754659 -0.355691  0.175018  0.297524  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_pca2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5ac97a6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>PC11</th>\n",
       "      <th>PC12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.425629</td>\n",
       "      <td>0.308747</td>\n",
       "      <td>-2.524555</td>\n",
       "      <td>2.641045</td>\n",
       "      <td>-1.995091</td>\n",
       "      <td>3.513185</td>\n",
       "      <td>-0.302433</td>\n",
       "      <td>0.095607</td>\n",
       "      <td>-1.952755</td>\n",
       "      <td>-0.717907</td>\n",
       "      <td>0.015888</td>\n",
       "      <td>0.340202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.714128</td>\n",
       "      <td>1.677736</td>\n",
       "      <td>-0.003393</td>\n",
       "      <td>-0.604436</td>\n",
       "      <td>1.448695</td>\n",
       "      <td>-0.106079</td>\n",
       "      <td>-1.490445</td>\n",
       "      <td>-1.305825</td>\n",
       "      <td>-1.211747</td>\n",
       "      <td>0.289297</td>\n",
       "      <td>-0.150786</td>\n",
       "      <td>0.545249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.204703</td>\n",
       "      <td>0.513145</td>\n",
       "      <td>-0.911797</td>\n",
       "      <td>1.237162</td>\n",
       "      <td>-0.392674</td>\n",
       "      <td>3.213377</td>\n",
       "      <td>-0.737050</td>\n",
       "      <td>0.718334</td>\n",
       "      <td>-1.504446</td>\n",
       "      <td>-0.034382</td>\n",
       "      <td>0.889985</td>\n",
       "      <td>-0.607807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.367696</td>\n",
       "      <td>0.181923</td>\n",
       "      <td>-2.419289</td>\n",
       "      <td>2.552553</td>\n",
       "      <td>-2.111593</td>\n",
       "      <td>3.544317</td>\n",
       "      <td>-0.253927</td>\n",
       "      <td>0.027803</td>\n",
       "      <td>-1.974823</td>\n",
       "      <td>-0.768651</td>\n",
       "      <td>0.091519</td>\n",
       "      <td>0.396899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.860633</td>\n",
       "      <td>0.470359</td>\n",
       "      <td>0.775156</td>\n",
       "      <td>-1.612700</td>\n",
       "      <td>0.796252</td>\n",
       "      <td>0.178710</td>\n",
       "      <td>-0.133292</td>\n",
       "      <td>0.098793</td>\n",
       "      <td>-0.079083</td>\n",
       "      <td>-0.894342</td>\n",
       "      <td>0.573811</td>\n",
       "      <td>0.482867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
       "0 -0.425629  0.308747 -2.524555  2.641045 -1.995091  3.513185 -0.302433   \n",
       "1  3.714128  1.677736 -0.003393 -0.604436  1.448695 -0.106079 -1.490445   \n",
       "2 -1.204703  0.513145 -0.911797  1.237162 -0.392674  3.213377 -0.737050   \n",
       "3 -0.367696  0.181923 -2.419289  2.552553 -2.111593  3.544317 -0.253927   \n",
       "4 -2.860633  0.470359  0.775156 -1.612700  0.796252  0.178710 -0.133292   \n",
       "\n",
       "        PC8       PC9      PC10      PC11      PC12  \n",
       "0  0.095607 -1.952755 -0.717907  0.015888  0.340202  \n",
       "1 -1.305825 -1.211747  0.289297 -0.150786  0.545249  \n",
       "2  0.718334 -1.504446 -0.034382  0.889985 -0.607807  \n",
       "3  0.027803 -1.974823 -0.768651  0.091519  0.396899  \n",
       "4  0.098793 -0.079083 -0.894342  0.573811  0.482867  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_pca2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "63393c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now all preprocessing steps are done \n",
    "# Now apply the train_test_split to seperate the training and validation data\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "156549b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now define the dependent and independent variables\n",
    "x = df_train_pca2\n",
    "y = y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "16bf4d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, Y_train, Y_valid = train_test_split(x, y, test_size = 0.2, random_state = 4242)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a7a0539c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Training and validation data is created \n",
    "# Now train the model using XGBoost algorithm\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dbb0d0fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_train = xgb.DMatrix(X_train, label = Y_train)\n",
    "d_valid = xgb.DMatrix(X_valid, label = Y_valid)\n",
    "d_test = xgb.DMatrix(df_test_pca2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c106cc66",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {}\n",
    "params[\"objective\"] = \"reg:linear\"\n",
    "params[\"eta\"] = 0.02\n",
    "params[\"max_depth\"] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1cddb0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = [(d_train, \"train\"), (d_valid, \"valid\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "65bc0087",
   "metadata": {},
   "outputs": [],
   "source": [
    "## import the r2_score metric for the evaluation of the model\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "46eeca23",
   "metadata": {},
   "outputs": [],
   "source": [
    "## create a function to \n",
    "#generate the r2_score\n",
    "def score(preds, dtrain):\n",
    "    labels = dtrain.get_label()\n",
    "    return\"r2\", r2_score(labels, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "beb67aca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:05:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/objective/regression_obj.cu:171: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[0]\ttrain-rmse:99.14659\ttrain-r2:-58.35085\tvalid-rmse:98.26758\tvalid-r2:-67.64398\n",
      "[10]\ttrain-rmse:81.25641\ttrain-r2:-38.86453\tvalid-rmse:80.39062\tvalid-r2:-44.94020\n",
      "[20]\ttrain-rmse:66.67461\ttrain-r2:-25.84061\tvalid-rmse:65.79161\tvalid-r2:-29.76968\n",
      "[30]\ttrain-rmse:54.79623\ttrain-r2:-17.12896\tvalid-rmse:53.86914\tvalid-r2:-19.62824\n",
      "[40]\ttrain-rmse:45.13720\ttrain-r2:-11.30101\tvalid-rmse:44.16470\tvalid-r2:-12.86541\n",
      "[50]\ttrain-rmse:37.29795\ttrain-r2:-7.39927\tvalid-rmse:36.29005\tvalid-r2:-8.36175\n",
      "[60]\ttrain-rmse:30.94680\ttrain-r2:-4.78233\tvalid-rmse:29.90724\tvalid-r2:-5.35821\n",
      "[70]\ttrain-rmse:25.83114\ttrain-r2:-3.02864\tvalid-rmse:24.75159\tvalid-r2:-3.35500\n",
      "[80]\ttrain-rmse:21.73255\ttrain-r2:-1.85163\tvalid-rmse:20.61720\tvalid-r2:-2.02163\n",
      "[90]\ttrain-rmse:18.47799\ttrain-r2:-1.06149\tvalid-rmse:17.33865\tvalid-r2:-1.13704\n",
      "[100]\ttrain-rmse:15.91879\ttrain-r2:-0.53000\tvalid-rmse:14.75732\tvalid-r2:-0.54809\n",
      "[110]\ttrain-rmse:13.93206\ttrain-r2:-0.17193\tvalid-rmse:12.76408\tvalid-r2:-0.15814\n",
      "[120]\ttrain-rmse:12.40632\ttrain-r2:0.07070\tvalid-rmse:11.25407\tvalid-r2:0.09967\n",
      "[130]\ttrain-rmse:11.25278\ttrain-r2:0.23548\tvalid-rmse:10.13224\tvalid-r2:0.27022\n",
      "[140]\ttrain-rmse:10.39089\ttrain-r2:0.34811\tvalid-rmse:9.30835\tvalid-r2:0.38407\n",
      "[150]\ttrain-rmse:9.75497\ttrain-r2:0.42546\tvalid-rmse:8.72695\tvalid-r2:0.45861\n",
      "[160]\ttrain-rmse:9.29393\ttrain-r2:0.47848\tvalid-rmse:8.32602\tvalid-r2:0.50721\n",
      "[170]\ttrain-rmse:8.97829\ttrain-r2:0.51330\tvalid-rmse:8.06254\tvalid-r2:0.53791\n",
      "[180]\ttrain-rmse:8.75464\ttrain-r2:0.53725\tvalid-rmse:7.89498\tvalid-r2:0.55692\n",
      "[190]\ttrain-rmse:8.58798\ttrain-r2:0.55470\tvalid-rmse:7.78532\tvalid-r2:0.56914\n",
      "[200]\ttrain-rmse:8.46478\ttrain-r2:0.56738\tvalid-rmse:7.72132\tvalid-r2:0.57620\n",
      "[210]\ttrain-rmse:8.36830\ttrain-r2:0.57719\tvalid-rmse:7.68090\tvalid-r2:0.58062\n",
      "[220]\ttrain-rmse:8.29351\ttrain-r2:0.58471\tvalid-rmse:7.66025\tvalid-r2:0.58287\n",
      "[230]\ttrain-rmse:8.22629\ttrain-r2:0.59142\tvalid-rmse:7.64971\tvalid-r2:0.58402\n",
      "[240]\ttrain-rmse:8.17027\ttrain-r2:0.59696\tvalid-rmse:7.64299\tvalid-r2:0.58475\n",
      "[250]\ttrain-rmse:8.12457\ttrain-r2:0.60146\tvalid-rmse:7.64451\tvalid-r2:0.58459\n",
      "[260]\ttrain-rmse:8.08198\ttrain-r2:0.60563\tvalid-rmse:7.65097\tvalid-r2:0.58388\n",
      "[270]\ttrain-rmse:8.05113\ttrain-r2:0.60863\tvalid-rmse:7.65869\tvalid-r2:0.58304\n",
      "[280]\ttrain-rmse:8.00920\ttrain-r2:0.61270\tvalid-rmse:7.66112\tvalid-r2:0.58278\n",
      "[290]\ttrain-rmse:7.96914\ttrain-r2:0.61656\tvalid-rmse:7.66797\tvalid-r2:0.58203\n",
      "[292]\ttrain-rmse:7.96561\ttrain-r2:0.61690\tvalid-rmse:7.66941\tvalid-r2:0.58188\n"
     ]
    }
   ],
   "source": [
    "## Now lets train the model\n",
    "trained = xgb.train(params, d_train, 1000, w, early_stopping_rounds = 50, feval = score, maximize = True, verbose_eval = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f3f0a7ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now predict the test values using xgboost\n",
    "predict = trained.predict(d_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "432428cf",
   "metadata": {},
   "outputs": [
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     "execution_count": 67,
     "metadata": {},
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   ],
   "source": [
    "## Now create a seperate data frame \n",
    "# so that we can clearly see the results\n",
    "res = pd.DataFrame()\n",
    "res[\"ID\"] = df_id\n",
    "res[\"y\"] = predict\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cf082878",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now save the result in a csv file\n",
    "res.to_csv(\"Final_Output.csv\", index = False)"
   ]
  }
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