Merc-Benz_project.ipynb

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   "source": [
    "# Project - Mercedes-Benz Greener Manufacturing \n",
    "### Description - Reduce the time a Mercedes-Benz spends on the test bench.\n",
    "\n",
    "##### Name- Arka Prava Panda\n",
    "##### Programme - Data Scientist Masters Programme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "----------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have imported the necessary modules/libraries in order to process the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('Datasets/Project Dataset/train.csv')\n",
    "data_test = pd.read_csv('Datasets/Project Dataset/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "i) We have read the train and test datasets and saved it as a pandas dataframe in data and data_test respectively\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ii) We have saved the y column which is the dependent variable as target and the rest of data which are the independent variables as data_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data.drop('y',axis=1)\n",
    "target = data['y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the first five rows of the training and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
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   "source": [
    "data_train.head()"
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  {
   "cell_type": "code",
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   "source": [
    "Observing the statistical insights from the dataframe.describe() function"
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   "cell_type": "code",
   "execution_count": 62,
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   "outputs": [
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2095.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4220.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6314.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8417.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 369 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID          X10     X11          X12          X13  \\\n",
       "count  4209.000000  4209.000000  4209.0  4209.000000  4209.000000   \n",
       "mean   4205.960798     0.013305     0.0     0.075077     0.057971   \n",
       "std    2437.608688     0.114590     0.0     0.263547     0.233716   \n",
       "min       0.000000     0.000000     0.0     0.000000     0.000000   \n",
       "25%    2095.000000     0.000000     0.0     0.000000     0.000000   \n",
       "50%    4220.000000     0.000000     0.0     0.000000     0.000000   \n",
       "75%    6314.000000     0.000000     0.0     0.000000     0.000000   \n",
       "max    8417.000000     1.000000     0.0     1.000000     1.000000   \n",
       "\n",
       "               X14          X15          X16          X17          X18  ...  \\\n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000  ...   \n",
       "mean      0.428130     0.000475     0.002613     0.007603     0.007840  ...   \n",
       "std       0.494867     0.021796     0.051061     0.086872     0.088208  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       1.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  ...   \n",
       "\n",
       "              X375         X376         X377         X378         X379  \\\n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000   \n",
       "mean      0.318841     0.057258     0.314802     0.020670     0.009503   \n",
       "std       0.466082     0.232363     0.464492     0.142294     0.097033   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       1.000000     0.000000     1.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "              X380         X382         X383         X384         X385  \n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000  \n",
       "mean      0.008078     0.007603     0.001663     0.000475     0.001426  \n",
       "std       0.089524     0.086872     0.040752     0.021796     0.037734  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 369 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\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>X10</th>\n",
       "      <th>X11</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>...</th>\n",
       "      <th>X375</th>\n",
       "      <th>X376</th>\n",
       "      <th>X377</th>\n",
       "      <th>X378</th>\n",
       "      <th>X379</th>\n",
       "      <th>X380</th>\n",
       "      <th>X382</th>\n",
       "      <th>X383</th>\n",
       "      <th>X384</th>\n",
       "      <th>X385</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4211.039202</td>\n",
       "      <td>0.019007</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.074364</td>\n",
       "      <td>0.061060</td>\n",
       "      <td>0.427893</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>0.002613</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.010216</td>\n",
       "      <td>...</td>\n",
       "      <td>0.325968</td>\n",
       "      <td>0.049656</td>\n",
       "      <td>0.311951</td>\n",
       "      <td>0.019244</td>\n",
       "      <td>0.011879</td>\n",
       "      <td>0.008078</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.000475</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>0.001663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2423.078926</td>\n",
       "      <td>0.136565</td>\n",
       "      <td>0.015414</td>\n",
       "      <td>0.262394</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.494832</td>\n",
       "      <td>0.026691</td>\n",
       "      <td>0.051061</td>\n",
       "      <td>0.093357</td>\n",
       "      <td>0.100570</td>\n",
       "      <td>...</td>\n",
       "      <td>0.468791</td>\n",
       "      <td>0.217258</td>\n",
       "      <td>0.463345</td>\n",
       "      <td>0.137399</td>\n",
       "      <td>0.108356</td>\n",
       "      <td>0.089524</td>\n",
       "      <td>0.093357</td>\n",
       "      <td>0.021796</td>\n",
       "      <td>0.026691</td>\n",
       "      <td>0.040752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2115.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4202.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6310.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8416.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 369 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID          X10          X11          X12          X13  \\\n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000   \n",
       "mean   4211.039202     0.019007     0.000238     0.074364     0.061060   \n",
       "std    2423.078926     0.136565     0.015414     0.262394     0.239468   \n",
       "min       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%    2115.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%    4202.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%    6310.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max    8416.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "               X14          X15          X16          X17          X18  ...  \\\n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000  ...   \n",
       "mean      0.427893     0.000713     0.002613     0.008791     0.010216  ...   \n",
       "std       0.494832     0.026691     0.051061     0.093357     0.100570  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       1.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  ...   \n",
       "\n",
       "              X375         X376         X377         X378         X379  \\\n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000   \n",
       "mean      0.325968     0.049656     0.311951     0.019244     0.011879   \n",
       "std       0.468791     0.217258     0.463345     0.137399     0.108356   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       1.000000     0.000000     1.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "              X380         X382         X383         X384         X385  \n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000  \n",
       "mean      0.008078     0.008791     0.000475     0.000713     0.001663  \n",
       "std       0.089524     0.093357     0.021796     0.026691     0.040752  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 369 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.describe()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since it has been clearly mentioned that the columns with variance of 0 to be dropped we will look for the columns with variance 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do this we can get help from the describe function but before that we need to check the columns that have categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8'}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#As the describe doesnt capture the categorical variables \n",
    "#we need to check the number of categorical variables in the dataset\n",
    "\n",
    "set(data_train.columns) - set(data_train._get_numeric_data().columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8'], dtype='<U2')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hence we can see that the above are the categorical variables\n",
    "#Those variables were ignored in the describe function\n",
    "\n",
    "categorical_features = np.array(['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8'])\n",
    "categorical_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the T method we can transpose a dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have transposed the dataset in order to get the features as rows, thus we can easily eliminate the rows which have $0$ variance as we all know that $\\sqrt(variance) = stdev$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "desc_train = data_train.describe().T\n",
    "desc_test = data_test.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4220.0</td>\n",
       "      <td>6314.0</td>\n",
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       "      <td>4209.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>X12</th>\n",
       "      <td>4209.0</td>\n",
       "      <td>0.075077</td>\n",
       "      <td>0.263547</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>X13</th>\n",
       "      <td>4209.0</td>\n",
       "      <td>0.057971</td>\n",
       "      <td>0.233716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count         mean          std  min     25%     50%     75%     max\n",
       "ID   4209.0  4205.960798  2437.608688  0.0  2095.0  4220.0  6314.0  8417.0\n",
       "X10  4209.0     0.013305     0.114590  0.0     0.0     0.0     0.0     1.0\n",
       "X11  4209.0     0.000000     0.000000  0.0     0.0     0.0     0.0     0.0\n",
       "X12  4209.0     0.075077     0.263547  0.0     0.0     0.0     0.0     1.0\n",
       "X13  4209.0     0.057971     0.233716  0.0     0.0     0.0     0.0     1.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4209.0</td>\n",
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       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "      count         mean          std  min     25%     50%     75%     max\n",
       "ID   4209.0  4211.039202  2423.078926  1.0  2115.0  4202.0  6310.0  8416.0\n",
       "X10  4209.0     0.019007     0.136565  0.0     0.0     0.0     0.0     1.0\n",
       "X11  4209.0     0.000238     0.015414  0.0     0.0     0.0     0.0     1.0\n",
       "X12  4209.0     0.074364     0.262394  0.0     0.0     0.0     0.0     1.0\n",
       "X13  4209.0     0.061060     0.239468  0.0     0.0     0.0     0.0     1.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will only take the column-names where the Standard deviation is not $0$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will perform the same operation for the training and the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4209.0</td>\n",
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       "      <td>0.089524</td>\n",
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       "    <tr>\n",
       "      <th>X382</th>\n",
       "      <td>4209.0</td>\n",
       "      <td>0.007603</td>\n",
       "      <td>0.086872</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>X383</th>\n",
       "      <td>4209.0</td>\n",
       "      <td>0.001663</td>\n",
       "      <td>0.040752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>X384</th>\n",
       "      <td>4209.0</td>\n",
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       "      <td>4209.0</td>\n",
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       "<p>357 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "       count         mean          std  min     25%     50%     75%     max\n",
       "ID    4209.0  4205.960798  2437.608688  0.0  2095.0  4220.0  6314.0  8417.0\n",
       "X10   4209.0     0.013305     0.114590  0.0     0.0     0.0     0.0     1.0\n",
       "X12   4209.0     0.075077     0.263547  0.0     0.0     0.0     0.0     1.0\n",
       "X13   4209.0     0.057971     0.233716  0.0     0.0     0.0     0.0     1.0\n",
       "X14   4209.0     0.428130     0.494867  0.0     0.0     0.0     1.0     1.0\n",
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       "X382  4209.0     0.007603     0.086872  0.0     0.0     0.0     0.0     1.0\n",
       "X383  4209.0     0.001663     0.040752  0.0     0.0     0.0     0.0     1.0\n",
       "X384  4209.0     0.000475     0.021796  0.0     0.0     0.0     0.0     1.0\n",
       "X385  4209.0     0.001426     0.037734  0.0     0.0     0.0     0.0     1.0\n",
       "\n",
       "[357 rows x 8 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc_train[desc_train['std']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "    </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>X380</th>\n",
       "      <td>4209.0</td>\n",
       "      <td>0.008078</td>\n",
       "      <td>0.089524</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X382</th>\n",
       "      <td>4209.0</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.093357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X383</th>\n",
       "      <td>4209.0</td>\n",
       "      <td>0.000475</td>\n",
       "      <td>0.021796</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X384</th>\n",
       "      <td>4209.0</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>0.026691</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X385</th>\n",
       "      <td>4209.0</td>\n",
       "      <td>0.001663</td>\n",
       "      <td>0.040752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       count         mean          std  min     25%     50%     75%     max\n",
       "ID    4209.0  4211.039202  2423.078926  1.0  2115.0  4202.0  6310.0  8416.0\n",
       "X10   4209.0     0.019007     0.136565  0.0     0.0     0.0     0.0     1.0\n",
       "X11   4209.0     0.000238     0.015414  0.0     0.0     0.0     0.0     1.0\n",
       "X12   4209.0     0.074364     0.262394  0.0     0.0     0.0     0.0     1.0\n",
       "X13   4209.0     0.061060     0.239468  0.0     0.0     0.0     0.0     1.0\n",
       "...      ...          ...          ...  ...     ...     ...     ...     ...\n",
       "X380  4209.0     0.008078     0.089524  0.0     0.0     0.0     0.0     1.0\n",
       "X382  4209.0     0.008791     0.093357  0.0     0.0     0.0     0.0     1.0\n",
       "X383  4209.0     0.000475     0.021796  0.0     0.0     0.0     0.0     1.0\n",
       "X384  4209.0     0.000713     0.026691  0.0     0.0     0.0     0.0     1.0\n",
       "X385  4209.0     0.001663     0.040752  0.0     0.0     0.0     0.0     1.0\n",
       "\n",
       "[364 rows x 8 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "desc_test[desc_test['std']!=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will save the features whose variance is not zero in num_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ID', 'X10', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18',\n",
       "       'X19', 'X20', 'X21', 'X22', 'X23', 'X24', 'X26', 'X27', 'X28',\n",
       "       'X29', 'X30', 'X31', 'X32', 'X33', 'X34', 'X35', 'X36', 'X37',\n",
       "       'X38', 'X39', 'X40', 'X41', 'X42', 'X43', 'X44', 'X45', 'X46',\n",
       "       'X47', 'X48', 'X49', 'X50', 'X51', 'X52', 'X53', 'X54', 'X55',\n",
       "       'X56', 'X57', 'X58', 'X59', 'X60', 'X61', 'X62', 'X63', 'X64',\n",
       "       'X65', 'X66', 'X67', 'X68', 'X69', 'X70', 'X71', 'X73', 'X74',\n",
       "       'X75', 'X76', 'X77', 'X78', 'X79', 'X80', 'X81', 'X82', 'X83',\n",
       "       'X84', 'X85', 'X86', 'X87', 'X88', 'X89', 'X90', 'X91', 'X92',\n",
       "       'X94', 'X95', 'X96', 'X97', 'X98', 'X99', 'X100', 'X101', 'X102',\n",
       "       'X103', 'X104', 'X105', 'X106', 'X108', 'X109', 'X110', 'X111',\n",
       "       'X112', 'X113', 'X114', 'X115', 'X116', 'X117', 'X118', 'X119',\n",
       "       'X120', 'X122', 'X123', 'X124', 'X125', 'X126', 'X127', 'X128',\n",
       "       'X129', 'X130', 'X131', 'X132', 'X133', 'X134', 'X135', 'X136',\n",
       "       'X137', 'X138', 'X139', 'X140', 'X141', 'X142', 'X143', 'X144',\n",
       "       'X145', 'X146', 'X147', 'X148', 'X150', 'X151', 'X152', 'X153',\n",
       "       'X154', 'X155', 'X156', 'X157', 'X158', 'X159', 'X160', 'X161',\n",
       "       'X162', 'X163', 'X164', 'X165', 'X166', 'X167', 'X168', 'X169',\n",
       "       'X170', 'X171', 'X172', 'X173', 'X174', 'X175', 'X176', 'X177',\n",
       "       'X178', 'X179', 'X180', 'X181', 'X182', 'X183', 'X184', 'X185',\n",
       "       'X186', 'X187', 'X189', 'X190', 'X191', 'X192', 'X194', 'X195',\n",
       "       'X196', 'X197', 'X198', 'X199', 'X200', 'X201', 'X202', 'X203',\n",
       "       'X204', 'X205', 'X206', 'X207', 'X208', 'X209', 'X210', 'X211',\n",
       "       'X212', 'X213', 'X214', 'X215', 'X216', 'X217', 'X218', 'X219',\n",
       "       'X220', 'X221', 'X222', 'X223', 'X224', 'X225', 'X226', 'X227',\n",
       "       'X228', 'X229', 'X230', 'X231', 'X232', 'X234', 'X236', 'X237',\n",
       "       'X238', 'X239', 'X240', 'X241', 'X242', 'X243', 'X244', 'X245',\n",
       "       'X246', 'X247', 'X248', 'X249', 'X250', 'X251', 'X252', 'X253',\n",
       "       'X254', 'X255', 'X256', 'X257', 'X258', 'X259', 'X260', 'X261',\n",
       "       'X262', 'X263', 'X264', 'X265', 'X266', 'X267', 'X269', 'X270',\n",
       "       'X271', 'X272', 'X273', 'X274', 'X275', 'X276', 'X277', 'X278',\n",
       "       'X279', 'X280', 'X281', 'X282', 'X283', 'X284', 'X285', 'X286',\n",
       "       'X287', 'X288', 'X291', 'X292', 'X294', 'X295', 'X296', 'X298',\n",
       "       'X299', 'X300', 'X301', 'X302', 'X304', 'X305', 'X306', 'X307',\n",
       "       'X308', 'X309', 'X310', 'X311', 'X312', 'X313', 'X314', 'X315',\n",
       "       'X316', 'X317', 'X318', 'X319', 'X320', 'X321', 'X322', 'X323',\n",
       "       'X324', 'X325', 'X326', 'X327', 'X328', 'X329', 'X331', 'X332',\n",
       "       'X333', 'X334', 'X335', 'X336', 'X337', 'X338', 'X339', 'X340',\n",
       "       'X341', 'X342', 'X343', 'X344', 'X345', 'X346', 'X348', 'X349',\n",
       "       'X350', 'X351', 'X352', 'X353', 'X354', 'X355', 'X356', 'X357',\n",
       "       'X358', 'X359', 'X360', 'X361', 'X362', 'X363', 'X364', 'X365',\n",
       "       'X366', 'X367', 'X368', 'X369', 'X370', 'X371', 'X372', 'X373',\n",
       "       'X374', 'X375', 'X376', 'X377', 'X378', 'X379', 'X380', 'X382',\n",
       "       'X383', 'X384', 'X385'], dtype=object)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "num_features = desc_train[desc_train['std']!=0].index.values\n",
    "num_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have concatenated the categorical feature names and numerical feature names in features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8', 'ID', 'X10', 'X12',\n",
       "       'X13', 'X14', 'X15', 'X16', 'X17', 'X18', 'X19', 'X20', 'X21',\n",
       "       'X22', 'X23', 'X24', 'X26', 'X27', 'X28', 'X29', 'X30', 'X31',\n",
       "       'X32', 'X33', 'X34', 'X35', 'X36', 'X37', 'X38', 'X39', 'X40',\n",
       "       'X41', 'X42', 'X43', 'X44', 'X45', 'X46', 'X47', 'X48', 'X49',\n",
       "       'X50', 'X51', 'X52', 'X53', 'X54', 'X55', 'X56', 'X57', 'X58',\n",
       "       'X59', 'X60', 'X61', 'X62', 'X63', 'X64', 'X65', 'X66', 'X67',\n",
       "       'X68', 'X69', 'X70', 'X71', 'X73', 'X74', 'X75', 'X76', 'X77',\n",
       "       'X78', 'X79', 'X80', 'X81', 'X82', 'X83', 'X84', 'X85', 'X86',\n",
       "       'X87', 'X88', 'X89', 'X90', 'X91', 'X92', 'X94', 'X95', 'X96',\n",
       "       'X97', 'X98', 'X99', 'X100', 'X101', 'X102', 'X103', 'X104',\n",
       "       'X105', 'X106', 'X108', 'X109', 'X110', 'X111', 'X112', 'X113',\n",
       "       'X114', 'X115', 'X116', 'X117', 'X118', 'X119', 'X120', 'X122',\n",
       "       'X123', 'X124', 'X125', 'X126', 'X127', 'X128', 'X129', 'X130',\n",
       "       'X131', 'X132', 'X133', 'X134', 'X135', 'X136', 'X137', 'X138',\n",
       "       'X139', 'X140', 'X141', 'X142', 'X143', 'X144', 'X145', 'X146',\n",
       "       'X147', 'X148', 'X150', 'X151', 'X152', 'X153', 'X154', 'X155',\n",
       "       'X156', 'X157', 'X158', 'X159', 'X160', 'X161', 'X162', 'X163',\n",
       "       'X164', 'X165', 'X166', 'X167', 'X168', 'X169', 'X170', 'X171',\n",
       "       'X172', 'X173', 'X174', 'X175', 'X176', 'X177', 'X178', 'X179',\n",
       "       'X180', 'X181', 'X182', 'X183', 'X184', 'X185', 'X186', 'X187',\n",
       "       'X189', 'X190', 'X191', 'X192', 'X194', 'X195', 'X196', 'X197',\n",
       "       'X198', 'X199', 'X200', 'X201', 'X202', 'X203', 'X204', 'X205',\n",
       "       'X206', 'X207', 'X208', 'X209', 'X210', 'X211', 'X212', 'X213',\n",
       "       'X214', 'X215', 'X216', 'X217', 'X218', 'X219', 'X220', 'X221',\n",
       "       'X222', 'X223', 'X224', 'X225', 'X226', 'X227', 'X228', 'X229',\n",
       "       'X230', 'X231', 'X232', 'X234', 'X236', 'X237', 'X238', 'X239',\n",
       "       'X240', 'X241', 'X242', 'X243', 'X244', 'X245', 'X246', 'X247',\n",
       "       'X248', 'X249', 'X250', 'X251', 'X252', 'X253', 'X254', 'X255',\n",
       "       'X256', 'X257', 'X258', 'X259', 'X260', 'X261', 'X262', 'X263',\n",
       "       'X264', 'X265', 'X266', 'X267', 'X269', 'X270', 'X271', 'X272',\n",
       "       'X273', 'X274', 'X275', 'X276', 'X277', 'X278', 'X279', 'X280',\n",
       "       'X281', 'X282', 'X283', 'X284', 'X285', 'X286', 'X287', 'X288',\n",
       "       'X291', 'X292', 'X294', 'X295', 'X296', 'X298', 'X299', 'X300',\n",
       "       'X301', 'X302', 'X304', 'X305', 'X306', 'X307', 'X308', 'X309',\n",
       "       'X310', 'X311', 'X312', 'X313', 'X314', 'X315', 'X316', 'X317',\n",
       "       'X318', 'X319', 'X320', 'X321', 'X322', 'X323', 'X324', 'X325',\n",
       "       'X326', 'X327', 'X328', 'X329', 'X331', 'X332', 'X333', 'X334',\n",
       "       'X335', 'X336', 'X337', 'X338', 'X339', 'X340', 'X341', 'X342',\n",
       "       'X343', 'X344', 'X345', 'X346', 'X348', 'X349', 'X350', 'X351',\n",
       "       'X352', 'X353', 'X354', 'X355', 'X356', 'X357', 'X358', 'X359',\n",
       "       'X360', 'X361', 'X362', 'X363', 'X364', 'X365', 'X366', 'X367',\n",
       "       'X368', 'X369', 'X370', 'X371', 'X372', 'X373', 'X374', 'X375',\n",
       "       'X376', 'X377', 'X378', 'X379', 'X380', 'X382', 'X383', 'X384',\n",
       "       'X385'], dtype=object)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = np.append(categorical_features, num_features)\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering both the training and test dataset which contains only the above feature names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filtering out the dataset with certain features\n",
    "\n",
    "data_train_1 = data_train[features]\n",
    "data_test_1 = data_test[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>5 rows × 365 columns</p>\n",
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      ],
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       "   X0 X1  X2 X3 X4 X5 X6 X8  ID  X10  ...  X375  X376  X377  X378  X379  X380  \\\n",
       "0   k  v  at  a  d  u  j  o   0    0  ...     0     0     1     0     0     0   \n",
       "1   k  t  av  e  d  y  l  o   6    0  ...     1     0     0     0     0     0   \n",
       "2  az  w   n  c  d  x  j  x   7    0  ...     0     0     0     0     0     0   \n",
       "3  az  t   n  f  d  x  l  e   9    0  ...     0     0     0     0     0     0   \n",
       "4  az  v   n  f  d  h  d  n  13    0  ...     0     0     0     0     0     0   \n",
       "\n",
       "   X382  X383  X384  X385  \n",
       "0     0     0     0     0  \n",
       "1     0     0     0     0  \n",
       "2     1     0     0     0  \n",
       "3     0     0     0     0  \n",
       "4     0     0     0     0  \n",
       "\n",
       "[5 rows x 365 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the dataset if everything is in order \n",
    "data_train_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X8</th>\n",
       "      <th>ID</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X375</th>\n",
       "      <th>X376</th>\n",
       "      <th>X377</th>\n",
       "      <th>X378</th>\n",
       "      <th>X379</th>\n",
       "      <th>X380</th>\n",
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       "      <th>X383</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>az</td>\n",
       "      <td>v</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>t</td>\n",
       "      <td>b</td>\n",
       "      <td>ai</td>\n",
       "      <td>a</td>\n",
       "      <td>d</td>\n",
       "      <td>b</td>\n",
       "      <td>g</td>\n",
       "      <td>y</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>az</td>\n",
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       "      <td>as</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>a</td>\n",
       "      <td>j</td>\n",
       "      <td>j</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>az</td>\n",
       "      <td>l</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>z</td>\n",
       "      <td>l</td>\n",
       "      <td>n</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>w</td>\n",
       "      <td>s</td>\n",
       "      <td>as</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>y</td>\n",
       "      <td>i</td>\n",
       "      <td>m</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 365 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   X0 X1  X2 X3 X4 X5 X6 X8  ID  X10  ...  X375  X376  X377  X378  X379  X380  \\\n",
       "0  az  v   n  f  d  t  a  w   1    0  ...     0     0     0     1     0     0   \n",
       "1   t  b  ai  a  d  b  g  y   2    0  ...     0     0     1     0     0     0   \n",
       "2  az  v  as  f  d  a  j  j   3    0  ...     0     0     0     1     0     0   \n",
       "3  az  l   n  f  d  z  l  n   4    0  ...     0     0     0     1     0     0   \n",
       "4   w  s  as  c  d  y  i  m   5    0  ...     1     0     0     0     0     0   \n",
       "\n",
       "   X382  X383  X384  X385  \n",
       "0     0     0     0     0  \n",
       "1     0     0     0     0  \n",
       "2     0     0     0     0  \n",
       "3     0     0     0     0  \n",
       "4     0     0     0     0  \n",
       "\n",
       "[5 rows x 365 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset(training) has no null/missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X0      0\n",
       "X1      0\n",
       "X2      0\n",
       "X3      0\n",
       "X4      0\n",
       "       ..\n",
       "X380    0\n",
       "X382    0\n",
       "X383    0\n",
       "X384    0\n",
       "X385    0\n",
       "Length: 365, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null values\n",
    "data_train_1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X0      0\n",
       "X1      0\n",
       "X2      0\n",
       "X3      0\n",
       "X4      0\n",
       "       ..\n",
       "X380    0\n",
       "X382    0\n",
       "X383    0\n",
       "X384    0\n",
       "X385    0\n",
       "Length: 365, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test_1.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting a boxplot to check if the features have any significant outliers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set()\n",
    "plt.boxplot(data_train_1[num_features])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing some EDA to check the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "z     360\n",
       "ak    349\n",
       "y     324\n",
       "ay    313\n",
       "t     306\n",
       "x     300\n",
       "o     269\n",
       "f     227\n",
       "n     195\n",
       "w     182\n",
       "j     181\n",
       "az    175\n",
       "aj    151\n",
       "s     106\n",
       "ap    103\n",
       "h      75\n",
       "d      73\n",
       "al     67\n",
       "v      36\n",
       "af     35\n",
       "m      34\n",
       "ai     34\n",
       "e      32\n",
       "ba     27\n",
       "at     25\n",
       "a      21\n",
       "ax     19\n",
       "aq     18\n",
       "am     18\n",
       "i      18\n",
       "u      17\n",
       "aw     16\n",
       "l      16\n",
       "ad     14\n",
       "au     11\n",
       "k      11\n",
       "b      11\n",
       "r      10\n",
       "as     10\n",
       "bc      6\n",
       "ao      4\n",
       "c       3\n",
       "aa      2\n",
       "q       2\n",
       "ac      1\n",
       "g       1\n",
       "ab      1\n",
       "Name: X0, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We will further check the categorical values\n",
    "\n",
    "data_train_1[categorical_features].iloc[:,0].value_counts()\n",
    "#From the first column we can see that there are several unique values for the categorical columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since the instruction clearly says to apply label encoder, we will do so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we will label encode the categorical columns \n",
    "#Since in label encoder we have to pass a 1-d array, it will take time to encode the whole columns\n",
    "#But instead we can use the Ordinal Encoder which works the same \n",
    "#yet processess all the categorical features to return an encoded array\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "encode = OrdinalEncoder()\n",
    "\n",
    "data_train_1_encoded = encode.fit_transform(data_train_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[32., 23., 17., ...,  0.,  0.,  0.],\n",
       "       [32., 21., 19., ...,  0.,  0.,  0.],\n",
       "       [20., 24., 34., ...,  0.,  0.,  0.],\n",
       "       ...,\n",
       "       [ 8., 23., 38., ...,  0.,  0.,  0.],\n",
       "       [ 9., 19., 25., ...,  0.,  0.,  0.],\n",
       "       [46., 19.,  3., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_1_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.99659608e-01, 1.38083753e-04, 7.69770342e-05, 4.40107808e-05,\n",
       "       3.31849481e-05, 2.66009200e-05, 5.73061934e-06, 2.67845282e-06,\n",
       "       1.56203752e-06, 1.05802559e-06, 8.69673184e-07, 8.51175070e-07,\n",
       "       7.32194816e-07, 6.01103751e-07, 5.33242231e-07, 4.63034402e-07,\n",
       "       3.72740073e-07, 3.44879436e-07, 3.21997035e-07, 2.83068830e-07,\n",
       "       2.55585324e-07, 2.31969262e-07, 2.24009901e-07, 2.10430764e-07,\n",
       "       1.84605062e-07, 1.73950883e-07, 1.55694846e-07, 1.49111645e-07,\n",
       "       1.35987698e-07, 1.32567075e-07])"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can use the principal component analysis to sort out the most important features\n",
    "#We have selected 30 principal components in order to ensure the model doesnt overfit the training dataset\n",
    "\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=30)\n",
    "data_train_1_encoded_reduced  = pca.fit_transform(data_train_1_encoded,target)\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3156, 30), (1053, 30))"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating the training and validation data out of the train dataset\n",
    "#Although we have a test dataset separately, Still we will chalk out a validation dataset...\n",
    "#...out of training dataset in order to check for overfitting and underfitting\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(data_train_1_encoded_reduced,target)\n",
    "\n",
    "X_train.shape, X_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project instruction asks to use XGBoost, Hence we will do so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "               colsample_bylevel=1, colsample_bytree=1,\n",
       "               early_stopping_rounds=None, enable_categorical=False,\n",
       "               eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "               grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "               interaction_constraints=&#x27;&#x27;, max_bin=256, max_cat_threshold=64,\n",
       "               max_cat_to_onehot=4, max_delta_step=0, max_depth=10,\n",
       "               max_leaves=0, min_child_weight=1, missing=nan,\n",
       "               monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=-1,\n",
       "               num_parallel_tree=100, objective=&#x27;reg:squarederror&#x27;,\n",
       "               predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRFRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRFRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "               colsample_bylevel=1, colsample_bytree=1,\n",
       "               early_stopping_rounds=None, enable_categorical=False,\n",
       "               eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "               grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "               interaction_constraints=&#x27;&#x27;, max_bin=256, max_cat_threshold=64,\n",
       "               max_cat_to_onehot=4, max_delta_step=0, max_depth=10,\n",
       "               max_leaves=0, min_child_weight=1, missing=nan,\n",
       "               monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=-1,\n",
       "               num_parallel_tree=100, objective=&#x27;reg:squarederror&#x27;,\n",
       "               predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRFRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "               colsample_bylevel=1, colsample_bytree=1,\n",
       "               early_stopping_rounds=None, enable_categorical=False,\n",
       "               eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "               grow_policy='depthwise', importance_type=None,\n",
       "               interaction_constraints='', max_bin=256, max_cat_threshold=64,\n",
       "               max_cat_to_onehot=4, max_delta_step=0, max_depth=10,\n",
       "               max_leaves=0, min_child_weight=1, missing=nan,\n",
       "               monotone_constraints='()', n_estimators=100, n_jobs=-1,\n",
       "               num_parallel_tree=100, objective='reg:squarederror',\n",
       "               predictor='auto', random_state=0, reg_alpha=0, ...)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We have imported the XGBRFRegressor\n",
    "#We imported the XGB Random Forest Regressor as we have to predict values and not probability\n",
    "\n",
    "from xgboost import XGBRFRegressor\n",
    "\n",
    "xgb = XGBRFRegressor(n_jobs=-1,max_depth=10) \n",
    "\n",
    "xgb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7867312762329959"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The xgb.score shows the mean accuracy of self.predict(X) wrt. y\n",
    "\n",
    "xgb.score(X_train,y_train) #training xgb.score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting using the validation dataset\n",
    "\n",
    "y_pred = xgb.predict(X_val)  #test xgb.score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5055256466285112"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating the score using the validation dataset\n",
    "#Although the score in the validation set is lower than that of the score of the training set...\n",
    "#...still the training set is not overfitting the model\n",
    "\n",
    "xgb.score(X_val,y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RMSE shows that on average the predicted values differ from the original values by $8.716$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.71620625839888"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we will evaluate the model\n",
    "\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(list(y_val),y_pred))\n",
    "\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will plot the predicted values over the real values of y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0bd30cc890>]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the predicted vs real gives a visual idea or the differences between the real and predicted\n",
    "\n",
    "\n",
    "plt.plot(list(y_val),'b.')\n",
    "plt.plot(y_pred,'r.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "#As we can see that our prediction was able to capture the main trend of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can use the pipeline to process the data\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline = Pipeline([('encode',OrdinalEncoder()),('pca', PCA(n_components=30))])\n",
    "\n",
    "data_test_1_encoded_reduced = pipeline.fit_transform(data_test_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb.predict(data_test_1_encoded_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 80.51625 , 101.39458 ,  79.300545, ..., 109.5979  , 110.05873 ,\n",
       "        98.30962 ], dtype=float32)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We have performed/followed all the instructions prescribed in the project description\n",
    "\n",
    "#which are--\n",
    "        #If for any column(s), the variance is equal to zero, then you need to remove those variable(s).\n",
    "        #Check for null and unique values for test and train sets.\n",
    "        #Apply label encoder.\n",
    "        #Perform dimensionality reduction.\n",
    "        #Predict your test_df values using XGBoost.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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