{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b3e2ef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c81ee67a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Stream</th>\n",
       "      <th>Internships</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Hostel</th>\n",
       "      <th>HistoryOfBacklogs</th>\n",
       "      <th>PlacedOrNot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Electronics And Communication</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mechanical</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender                         Stream  Internships  CGPA  Hostel  \\\n",
       "0   22    Male  Electronics And Communication            1     8       1   \n",
       "1   21  Female               Computer Science            0     7       1   \n",
       "2   22  Female         Information Technology            1     6       0   \n",
       "3   21    Male         Information Technology            0     8       0   \n",
       "4   22    Male                     Mechanical            0     8       1   \n",
       "\n",
       "   HistoryOfBacklogs  PlacedOrNot  \n",
       "0                  1            1  \n",
       "1                  1            1  \n",
       "2                  0            1  \n",
       "3                  1            1  \n",
       "4                  0            1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"collegePlace.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c170ef6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2966, 8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Stream', 'Internships', 'CGPA', 'Hostel',\n",
       "       'HistoryOfBacklogs', 'PlacedOrNot'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f3c005c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Internships</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Hostel</th>\n",
       "      <th>HistoryOfBacklogs</th>\n",
       "      <th>PlacedOrNot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2966.000000</td>\n",
       "      <td>2966.000000</td>\n",
       "      <td>2966.000000</td>\n",
       "      <td>2966.000000</td>\n",
       "      <td>2966.000000</td>\n",
       "      <td>2966.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.485840</td>\n",
       "      <td>0.703641</td>\n",
       "      <td>7.073837</td>\n",
       "      <td>0.269049</td>\n",
       "      <td>0.192178</td>\n",
       "      <td>0.552596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.324933</td>\n",
       "      <td>0.740197</td>\n",
       "      <td>0.967748</td>\n",
       "      <td>0.443540</td>\n",
       "      <td>0.394079</td>\n",
       "      <td>0.497310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</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>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</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>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age  Internships         CGPA       Hostel  HistoryOfBacklogs  \\\n",
       "count  2966.000000  2966.000000  2966.000000  2966.000000        2966.000000   \n",
       "mean     21.485840     0.703641     7.073837     0.269049           0.192178   \n",
       "std       1.324933     0.740197     0.967748     0.443540           0.394079   \n",
       "min      19.000000     0.000000     5.000000     0.000000           0.000000   \n",
       "25%      21.000000     0.000000     6.000000     0.000000           0.000000   \n",
       "50%      21.000000     1.000000     7.000000     0.000000           0.000000   \n",
       "75%      22.000000     1.000000     8.000000     1.000000           0.000000   \n",
       "max      30.000000     3.000000     9.000000     1.000000           1.000000   \n",
       "\n",
       "       PlacedOrNot  \n",
       "count  2966.000000  \n",
       "mean      0.552596  \n",
       "std       0.497310  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       1.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b325b11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2966 entries, 0 to 2965\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   Age                2966 non-null   int64 \n",
      " 1   Gender             2966 non-null   object\n",
      " 2   Stream             2966 non-null   object\n",
      " 3   Internships        2966 non-null   int64 \n",
      " 4   CGPA               2966 non-null   int64 \n",
      " 5   Hostel             2966 non-null   int64 \n",
      " 6   HistoryOfBacklogs  2966 non-null   int64 \n",
      " 7   PlacedOrNot        2966 non-null   int64 \n",
      "dtypes: int64(6), object(2)\n",
      "memory usage: 185.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "675860d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Electronics And Communication', 'Computer Science',\n",
       "       'Information Technology', 'Mechanical', 'Electrical', 'Civil'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Stream\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e2314e74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                  0\n",
       "Gender               0\n",
       "Stream               0\n",
       "Internships          0\n",
       "CGPA                 0\n",
       "Hostel               0\n",
       "HistoryOfBacklogs    0\n",
       "PlacedOrNot          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b97ff5fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(1829)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4dbcf217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Stream</th>\n",
       "      <th>Internships</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Hostel</th>\n",
       "      <th>HistoryOfBacklogs</th>\n",
       "      <th>PlacedOrNot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Electronics And Communication</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mechanical</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender                         Stream  Internships  CGPA  Hostel  \\\n",
       "0   22    Male  Electronics And Communication            1     8       1   \n",
       "1   21  Female               Computer Science            0     7       1   \n",
       "2   22  Female         Information Technology            1     6       0   \n",
       "3   21    Male         Information Technology            0     8       0   \n",
       "4   22    Male                     Mechanical            0     8       1   \n",
       "\n",
       "   HistoryOfBacklogs  PlacedOrNot  \n",
       "0                  1            1  \n",
       "1                  1            1  \n",
       "2                  0            1  \n",
       "3                  1            1  \n",
       "4                  0            1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92d238e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df,x=\"PlacedOrNot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb21dbc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.select_dtypes(\"number\").corr(),annot=True,fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8565ab2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                  0.817990\n",
       "Internships          0.697572\n",
       "CGPA                 0.122766\n",
       "Hostel               0.699651\n",
       "HistoryOfBacklogs    0.892883\n",
       "PlacedOrNot         -0.322920\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(\"number\").skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b4036c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Stream</th>\n",
       "      <th>Internships</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Hostel</th>\n",
       "      <th>HistoryOfBacklogs</th>\n",
       "      <th>PlacedOrNot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Electronics And Communication</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mechanical</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender                         Stream  Internships  CGPA  Hostel  \\\n",
       "0   22    Male  Electronics And Communication            1     8       1   \n",
       "1   21  Female               Computer Science            0     7       1   \n",
       "2   22  Female         Information Technology            1     6       0   \n",
       "3   21    Male         Information Technology            0     8       0   \n",
       "4   22    Male                     Mechanical            0     8       1   \n",
       "\n",
       "   HistoryOfBacklogs  PlacedOrNot  \n",
       "0                  1            1  \n",
       "1                  1            1  \n",
       "2                  0            1  \n",
       "3                  1            1  \n",
       "4                  0            1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "170415d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Stream</th>\n",
       "      <th>Internships</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Hostel</th>\n",
       "      <th>HistoryOfBacklogs</th>\n",
       "      <th>PlacedOrNot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Electronics And Communication</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mechanical</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender                         Stream  Internships  CGPA Hostel  \\\n",
       "0   22    Male  Electronics And Communication            1     8    yes   \n",
       "1   21  Female               Computer Science            0     7    yes   \n",
       "2   22  Female         Information Technology            1     6     no   \n",
       "3   21    Male         Information Technology            0     8     no   \n",
       "4   22    Male                     Mechanical            0     8    yes   \n",
       "\n",
       "  HistoryOfBacklogs  PlacedOrNot  \n",
       "0               yes            1  \n",
       "1               yes            1  \n",
       "2                no            1  \n",
       "3               yes            1  \n",
       "4                no            1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def int_to_str(num):\n",
    "    if num == 1:\n",
    "        return \"yes\"\n",
    "    else:\n",
    "        return \"no\"\n",
    "\n",
    "df[\"Hostel\"] = df[\"Hostel\"].apply(int_to_str)\n",
    "df[\"HistoryOfBacklogs\"] = df[\"HistoryOfBacklogs\"].apply(int_to_str)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fb830e50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Age', 'Internships', 'CGPA']\n",
      "['Gender', 'Stream', 'Hostel', 'HistoryOfBacklogs']\n"
     ]
    }
   ],
   "source": [
    "num_cols = df.select_dtypes(\"number\").columns.tolist()\n",
    "cat_cols = df.drop(num_cols,axis=1).columns.tolist()\n",
    "\n",
    "num_cols.pop(-1)\n",
    "\n",
    "print(num_cols)\n",
    "print(cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "51baf2a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in num_cols:\n",
    "    sns.histplot(df,x=i,bins=25,kde=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e77ee0d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cuW3IkCE2YcKErCwGAABA/HXc3rZtm504cSKriwEAAJA1p9t0xChSKBSy3bt323vvvWd9+/bNrGIAAADEV0j68ssvTzvVVqZMGXv66afPeOUbAABAtg1JH3/8cWZ9FAAAQOKEpMCPP/5omzdvdv9fdNFF7mgSAABAju24ffjwYXdarUKFCtamTRv3qFixovXv399++eWXzCoGAABAfIUkddzWD9vOnTvX3UBSjzlz5rhhf/7znzOrGAAAAPF1uu2tt96yN99809q2bRse1qVLFytUqJD9/ve/tylTpmRWUQAAAOLnSJJOqZUrV+604WXLluV0GwAAyLkhqWXLljZq1Cg7cuRIeNivv/5qjzzyiHsNAAAgR55umzhxonXq1MkqVapkjRs3dsPWrVtnBQoUsAULFmRWMQAAAOIrJDVs2NC2bNliM2fOtG+++cYNu+GGG6x3796uXxIAAECODEljx451fZIGDBgQNXzatGnu3knDhg3LrKIAAADET5+kF154werUqXPa8Pr169vUqVMzqxgAAADxFZKSkpLcjSRj6Y7b+qFbAACAHBmSKleubP/6179OG65huvM2AABAjuyTpL5IgwYNsuPHj1u7du3csEWLFtn999/PHbcBAEDODUlDhw61vXv32p133mnHjh1zwwoWLOg6bA8fPjyzigEAABBfISlXrlw2btw4GzFihG3atMld9l+rVi13nyQAAIAcG5ICRYsWtebNm2f2xwIAAMRnx20AAIBEQkgCAADwICQBAAB4EJIAAAA8CEkAAAAehCQAAAAPQhIAAIAHIQkAAMCDkAQAAOBBSAIAAPAgJAEAAHgQkgAAADwISQAAAB6EJAAAgOwSkpYtW2bdunWzihUrWq5cueydd96Jej0UCtnIkSOtQoUKVqhQIevQoYNt2bIlapyffvrJevfubcWKFbMSJUpY//797dChQ5k8JwAAIF4lZEg6fPiwNW7c2CZPnux9ffz48fbss8/a1KlT7bPPPrMiRYpYx44d7ciRI+FxFJA2bNhgCxcutHnz5rngdfvtt2fiXAAAgHiW1xJQ586d3cNHR5EmTpxoDz30kF177bVu2KuvvmrlypVzR5x69eplmzZtsvnz59vq1autWbNmbpxJkyZZly5d7KmnnnJHqAAAQM6WkEeSUrN9+3ZLSkpyp9gCxYsXtxYtWtiKFSvcc/3VKbYgIInGz507tzvy5HP06FE7cOBA1AMAAGRf2S4kKSCJjhxF0vPgNf0tW7Zs1Ot58+a1kiVLhseJNXbsWBe2gkflypUzbB4AAEDWy3YhKaMMHz7c9u/fH37s3Lkzq4sEAAAyULYLSeXLl3d/9+zZEzVcz4PX9Dc5OTnq9RMnTrgr3oJxYhUoUMBdCRf5AAAA2Ve2C0nVqlVzQWfRokXhYeo/pL5GLVu2dM/1d9++fbZmzZrwOIsXL7ZTp065vksAAAAJeXWb7me0devWqM7aa9eudX2KqlSpYoMGDbIxY8ZYrVq1XGgaMWKEu2Kte/fubvy6detap06dbMCAAe42AcePH7e7777bXfnGlW0AACBhQ9Lnn39uV1xxRfj5kCFD3N++ffvajBkz7P7773f3UtJ9j3TEqHXr1u6S/4IFC4bfM3PmTBeM2rdv765q69mzp7u3EgAAQMKGpLZt27r7IaVEd+EePXq0e6RER51mzZqVQSUEAACJLtv1SQIAADgXCEkAAAAehCQAAAAPQhIAAIAHIQkAAMCDkAQAAOBBSAIAAPAgJAEAAHgQkgAAADwISQAAAB6EJAAAAA9CEgAAgAchCQAAwIOQBAAA4EFIAgAA8CAkAQAAeBCSAAAAPAhJAAAAHoQkAAAAD0ISAACAByEJAADAg5AEAADgQUgCAADwICQBAAB4EJIAAAA8CEkAAAAehCQAAAAPQhIAAIAHIQkAAMCDkAQAAOBBSAIAAPAgJAEAAHgQkgAAADwISQAAAB6EJAAAAA9CEgAAgAchCQAAwIOQBAAA4EFIAgAA8CAkAQAAeBCSAAAAPAhJAAAAHoQkAAAAD0ISAACAByEJAADAg5AEAADgQUgCAADwICQBAAB4EJIAAAA8CEkAAAAehCQAAAAPQhIAAIAHIQkAAMCDkAQAAOBBSAIAAPAgJAEAAHgQkgAAADwISQAAAB6EJAAAAA9CEgAAgAchCQAAwIOQBAAAkFNC0sMPP2y5cuWKetSpUyf8+pEjR+yuu+6yUqVKWdGiRa1nz562Z8+eLC0zAACIL9kyJEn9+vVt9+7d4cenn34afm3w4ME2d+5ce+ONN2zp0qW2a9cu69GjR5aWFwAAxJe8lk3lzZvXypcvf9rw/fv328svv2yzZs2ydu3auWHTp0+3unXr2sqVK+3SSy/1Tu/o0aPuEThw4EAGlh4AAGS1bHskacuWLVaxYkWrXr269e7d23bs2OGGr1mzxo4fP24dOnQIj6tTcVWqVLEVK1akOL2xY8da8eLFw4/KlStnynwAAICskS1DUosWLWzGjBk2f/58mzJlim3fvt0uv/xyO3jwoCUlJVn+/PmtRIkSUe8pV66cey0lw4cPd0ehgsfOnTszYU4AAEBWyZan2zp37hz+v1GjRi40Va1a1f75z39aoUKFzmqaBQoUcA8AAJAzZMsjSbF01Kh27dq2detW10/p2LFjtm/fvqhxdHWbrw8TAADImXJESDp06JBt27bNKlSoYE2bNrV8+fLZokWLwq9v3rzZ9Vlq2bJllpYTAADEj2x5uu2+++6zbt26uVNsurx/1KhRlidPHrvhhhtcp+v+/fvbkCFDrGTJklasWDG75557XEBK6co2AACQ82TLkPT999+7QLR3714rU6aMtW7d2l3er/9lwoQJljt3bncTSV3W37FjR3v++eezutgAACCOZMuQ9Prrr6f6esGCBW3y5MnuAQAAkGP7JAEAAKQXIQkAAMCDkAQAAOBBSAIAAPAgJAEAAHgQkgAAADwISQAAAB6EJAAAAA9CEgAAgAchCQAAwIOQBAAA4EFIAgAA8CAkAQAAeBCSAAAAPAhJAAAAHoQkAAAAD0ISAACAByEJAADAg5AEAADgQUgCAADwICQBAAB4EJIAAAA8CEkAAAAehCQAAAAPQhIAAIAHIQkAAMCDkAQAAOBBSAIAAPAgJAEAAHgQkgAAADwISQAAAB6EJAAAAA9CEgAAgAchCQAAwIOQBAAA4EFIAgAA8CAkAQAAeBCSAAAAPAhJAAAAHoQkAAAAD0ISAACAByEJAADAg5AEAADgQUgCAADwICQBAAB4EJIAAAA8CEkAAAAehCQAAAAPQhIAAIAHIQkAAMCDkAQAAOBBSAIAAPAgJAEAAHgQkgAAADwISQAAAB6EJAAAAA9CEgAAgAchCQAAwIOQBAAA4EFIAgAA8CAkAQAAeOT4kDR58mS78MILrWDBgtaiRQtbtWqVbzkBAIAcJkeHpH/84x82ZMgQGzVqlH3xxRfWuHFj69ixoyUnJ2d10QAAQBbL0SHpmWeesQEDBtgtt9xi9erVs6lTp1rhwoVt2rRpWV00AACQxfJaDnXs2DFbs2aNDR8+PDwsd+7c1qFDB1uxYsVp4x89etQ9Avv373d/Dxw4kOJnnDz66zkvd6JKbTmlBcvy3CxHluX/oU6eOyzL+FiOQlt55mUZvBYKheyMQjnUDz/8oKUTWr58edTwoUOHhn7zm9+cNv6oUaPc+DxYBtQB6gB1gDpAHbCEXwY7d+48Y1bIsUeS0ktHnNR/KXDq1Cn76aefrFSpUpYrVy6LV0rMlStXtp07d1qxYsWyujgJi+XIsoxH1EuWY7w5kAD7HB1BOnjwoFWsWPGM4+bYkFS6dGnLkyeP7dmzJ2q4npcvX/608QsUKOAekUqUKGGJQpU1XitsImE5sizjEfWS5RhvisX5Pqd48eJpGi/HdtzOnz+/NW3a1BYtWhR1dEjPW7ZsmaVlAwAAWS/HHkkSnT7r27evNWvWzH7zm9/YxIkT7fDhw+5qNwAAkLPl6JD0hz/8wX788UcbOXKkJSUlWZMmTWz+/PlWrlw5yy50ilD3gYo9VQiWY1ahTrIs4w11kmWZklzqvZ3iqwAAADlUju2TBAAAkBpCEgAAgAchCQAAIDNCkm6s+M4771iiatu2rQ0aNMgSzcMPP+w6niPrls2FF17orpBMiS4OuPLKK61IkSJpusfWmaaXWWbMmJFQ9wSL1a9fP+vevXtCfU5mlflcbxuZUVeych9zrvcPmbWeM1qudK6T2LYtnnND7vSuUM1M7KNTp04ZVsDMXnhvv/22Pfrooxn6GfptON3IsmvXrpbZ3nrrLbeh60ZaRYsWtUaNGtno0aPd3cPTQzv8e+65x6pXr+6uDNEdVrt16xZ136l4cd99951Wrv+mMdd7dZ+t2MZt9erVdvvtt4efz5492y699FK3rM877zxr0KCBffnll7Z27Vr797//fcbPiZ1eZvAFM10Fmpbynq1vv/3W265EPrTMs1rQ/t1xxx2nvXbXXXfZK6+8Yl988YUlEt+2kdUyez+Tnn1MZuwf4lHSGdr73bt3W+fOndM8vaxo2zLtFgCqqNOnT48altWXl+vHarXTOhdKlixpGe3ll192FU5/d+3alaZbo58LDz74oI0bN84GDx5sjz/+uPvcLVu22NSpU+1vf/ub/elPf0rzTq1Vq1YuZDz55JPWsGFDO378uH344YduZ/HNN99YPFEY1CMjnDx50jWy+nHkMmXKhIer8VC4eOyxx+yaa65x46jxVxitVatWmqYdOb2MqOtpVahQIffIKGpw1cgGnnrqKXcrjo8++ijdd8fNaCrr66+/bhMmTAgvkyNHjtisWbPcEcJEk5Hbxn8j3vYzwXaXGfuHePNtGtp7369UpLdti1vp+VHYvn37hq699tpUx9EkZ8+eHX6+Y8eO0PXXXx8qXrx46Pzzzw9dc801oe3bt0e95+WXXw7Vq1cvlD9//lD58uVDd911lxtetWrVqB+j0/Pgx2YbN24ceumll0IXXnhhKFcudyeD0HfffeemX6RIkdB5553nPjcpKSnqR2r1vldffdVNq1ixYqE//OEPoQMHDoTH+e1vfxv605/+FH5+5MiR0P333x+qVKmSK1+NGjVCf/3rX91rP/30U+jGG28MlS5dOlSwYMFQzZo1Q9OmTUt1+Rw8eDBUtGjR0DfffOM++7HHHot6/eOPP3bz+tFHH4WaNm0aKlSoUKhly5Zu/Ehjx44NlS1b1k3r1ltvDQ0bNszNW0o+++wzN92JEyd6X//555/D/z///POh6tWrh/LlyxeqXbu2W16RNJ0SJUqEOnbs6MpXp04d90PBW7ZsCbVq1SpUuHBhV+Zly5aF14eWnerAM888E6pcubIb1qxZM1fm6667LpQ7d243Xf248IkTJ0Ljxo1zy1XDgvoQlFPDBg0aFOrUqZObrp6PHDkyvLxUlzQsmCetd82HhqnuBcs48tG8efPw+lYdyJs3rxuuZaB50HoTLefY96q+6D2qc1dccYWr51oGF1xwQXj+VUcj36NtSeVSvdK4wXCVf+vWre6zVEe1jIO6XqVKFTdOsFw0T5dffnl4Hage6K8e+rw8efKEunbtGq7fmm63bt1cndHrepQpUyY0ZsyYcN2PnTeV/8knn3TrLlJa6ojK3L17d1c+bRtz5swJpUWwnQZOnjwZevzxx922ru2sUaNGoTfeeCPqPevXr3fzqnWg+WvdunV4OQbtluZD7UvJkiVDd955Z+jYsWPh92tZa1u85ZZb3PtVR1944YWoz9Cy0LLXctM6HTBggKsXM2fOdGXSe/QIyjx69GjXxmhZ6D0XXXRRaNWqVVFl1jYQ1A0t4/Hjx7v/b7jhhtDVV18dKlCggNvWIsus9lV1RuVTnVd5y5Ur596zZ8+edLUlscs6tfZYnn766VCDBg3c56vuDhw4MLxtyPTp00+rK+l1pv1MVu5j0rN/UDumbTKot9pOYtvftOxTs1rnzp1dW3bo0KHTXgva2Mh1ojqmZRIpOTnZtalLly51z7WcJ0yYkOI6jScZGpK0QdetW9dVlK+++iq0ceNGFyrUWBw9ejTc2KoCqfJs3rzZNSLBwtOC1fS04e3evds9DyqwGintJL/44ovQunXrXKPUpEkT1zh+/vnnoZUrV7qGQZU6oPepQenRo0fo66+/djtxbTAPPPBAeJzYjeD3v/+9a/jefvvt0LZt21yD8/rrr7vXtKHpM1evXu02yoULF4befffdVJePNlY1jDJ37ly3UZ06deq0hq1FixahJUuWhDZs2OB2hJdddll4nH/84x+u8dTGqAbvwQcfdDuH1ELSvffe6+Y9csfgo/nUjm/y5MlufahR1M528eLF7vW9e/e68qnxVzk0jnaEagjatWsXmj9/vlvPKr/KFKyP/v37uyCknYzmSctJzzVt7UDnzZvnyqhpX3rppaF77rnHLeug8dL6jAxJ+nw1YNo5B+O88sorbtoNGzZMNSQ9++yzboNVvfv0009D77//fuiJJ55w4952221uXlQftfz/+Mc/ugZSOyDRZ6rMGqZ5/OSTT9yOSfVGwxRq/v3vf7vgp8/T+tX8f/DBB67MCiWqy/v27Qv16tUrHIy0nLUetUwUhiJDUlDXFSD1Oapvep/Wpx4KwFoH2pmqbAqqCiT169d3DXdQv9euXRu66qqrXDk0v6q/ej2Yd32h0HK55JJL3PpWg6btVdtI5I7vTHVEVD7tOGbNmuXCc1D/VH/OJHbHrRCn4Ke6pW1Q7YHqv9aPfP/99y5EaLvWslGZ9GUlCANqtzTPd9xxR2jTpk1uu9NO/sUXXwx/hpa1pqF5Unn1JUTrIpiGdhBaTxUqVAgNHTrULaNq1aq5abdv3961WZEhSWXWDlvT1Dav55pesAxUZq0v1Zk+ffq4+qG6pi8+QUhSmdV+tmnTJqrMCgE333yzm67qrpbJihUr3M5JO7T0tCWxyzq19lj0v9aztqNFixa5tlxBKatCUmbuY9K7f1DZ9OVNdfI///lP6LXXXnPrUO1mWuc1q+3du9fVUX1JSU3kOnnuuedcGxa5X5s0aVLUsGwdktQYqvJEPiKPhkTO7N/+9jdXWSMXliquGpsPP/zQPa9YsaLbOaRYQM/CUwVWIx1UaFmwYIErm75VBNQo6P3Btze9T5U08siRGjw1IoHIjUAblN6v8OOjb+X65pkeaqCCbxPHjx93oUGNme/bX+C9995zw3799Vf3XI2hvlVG0jykFpLUeOrbblrKp2/IkfQtrUuXLlFHpBQCAmqgNUyNdmD48OFuWLA+tOzVUEWuDwUIbYAKDAGtH+1cFHrVEGt8hRbttCJDknYUkctLO9Ggsda4GqZGzxeSVOf0LT22MdeRSNWhH374IWq4QpfqrKgx1XQUJiIbN9Ub7biDDV8hJfLbqY4aqoyR8x8cEVLDHlB9CsYJQpLqukKldpr65ioa56GHHnLLUEcUgnWg0BPU77///e+unEH91nCVUUEvoCClBizYXjW+jvZFbq/6fG3naa0jkeULKGRomMLAmUTuuDW/qhM6UhlJoTsIrqprCiwpfQFQu6VlqW/2keXVOgnodYWVgNosBZYpU6a45wonWraqN2p3tBxVF1R/Va9//PHHcEgKyqy6pKNMketW9UhHi1Rm1T8t/0hqC4OQpDJpvjWdXbt2uTKrzmkdBQExUhCegyM7aWlLYkPSmdrjWDqiV6pUqXMeklLbz2TVPia9+wcffTHp2bNnwoSkz/63vVcITE3kMgyOGukgRED7LB2FDyRSSEr31W1XXHGF63ga+fB1ZJR169bZ1q1bXafV4Ny3zunqHP62bdssOTnZ9clp3759uk8TVq1aNeq85qZNm1x/AT0C9erVc+dR9Vpkx1SVJ1ChQgVXDh/NmzpY//a3v/W+PnDgQNc/QVeH3H///bZ8+fJUy7x582ZbtWqV3XDDDe553rx5Xb8V9U2KpQ7VkWWUoJyanxYtWkSNf6Yf5U3rjdU1bZ1/jqTnwTIMpqPlGAh+xkXnqgMHDx48rS+JOv1Frg/14dAjchzVEY2jPj4B1ZnYdaR1G0nrKJhuqVKl3F/95Eys//mf/3F1Lvb98vXXX7s+RjVq1HDrRmVQXyIN//XXX+2XX35x4+k1DY+VL1++qPP46rNy3XXX2UMPPeTmS9tCUK9E/ZNUv+rWrRt+n5ahPjeyzqqu79ixww4dOuTmLehDov4B27dvd9tSsA4uuOCCcP3WsBMnTrhOl7JmzRo7evSoPfHEE24Zazr6HE072F41/l/+8peo7VX9D/Tjz2mtI746rPWsXwRPaVtLiZaZlruuCgzKpMerr77q5jtYnpdffnnU8o9Vv359t6xT2+4jy6v1q34Wkdvc+eef76ahdkcXXWh71vagbbF06dKnlVl1acCAAeEyv/baa1awYEE3LZVZy0O/GRkp8rnKrO1af9UpXGXesGGDqw9t2rRx61OdZ6tUqeLWedBOBeszLW1JpLS0x+onpteDenbTTTfZ3r17w9vGuZLW/Uxm7mPSu3+QyZMnux9S13RUthdffPG09RPPQmfxgxya16uuuspmzpzpnquN0sVKvXv3tkSU7o7bauxq1qyZpnHVqKuCBAsrdkFG7gjPphxnI7YhVWMYuQOIdKbOqurN/91339n7779vCxcudBuiOrKp46mPwpB2QpEdtVUJ1SHxueeeiwoLkeUMdsgplTMtateubZ9++qnb4aW2MzmToNNxsOONLJ9vupFl9i372DoQXMkiwWuR60jl94mcTvB/5GcHG7t2UqnVVzV62rmp8e/SpYsLE59//rk98MADrvNmMB9aj7F8wUn19LbbbnMPNY6qJ5999pnbeeqzYtdpStNQ2bSDW7JkSXg9qPOwwoPKqNeDssVOK/iM559/PnxFk0JNEOKC+dI01DlVr0f+yLOuiFRH/4zc1lISzNd7773ndsy+jrxp6VSelrKkp7y33nqr3Xnnne5/1RNfmUVtQ2S51T6kt8yqO9rZqjOzdviqiwolHTt2dA+1r2pPVb/0PFif6W1LzlQmBf+rr77afTnUBQkKI2pT+vfv7z6zcOHCdq6kdT+TlfuYMy0vfYHWtvT000+7sKsgpy822v4TRa1atVydSe/FOApE9957r02aNMld1KAvf5FfohNJht5M8pJLLnFXT5UtW9ZV+MhHcFm0jkikdgmqNnDttM5E38Z37tzpHoGNGzfavn37vEcN0kIrVY3J0qVLUxxHG2Lfvn3dt0RdOq1vCj7aqerbrzaYyG9H+iak0PT3v/89zeXSvMZuaCtXrkz1PTfeeKNrUIIdZSwtp2Da//rXv6Je0/NgGQZXd3zwwQd2+PDhFKejb2Hy/fffh1/Tt7v0rI/gW1xkox8chdG6jaQgExyR0Tf+2G/LOhIk+janOqejALH16uKLL3bDtM4VaK+//noXQmLHU6Nxpm9YKos+M3IZBdMJlqH+alhknVX41OfHLiNtS3pNR7GCnYdCk/6PPIqRGi0zvV8P1W0dKdGOL/IzVE+1s4vcVnVEKjK8namOnEuapsKQAkBsGxIcNdaRkk8++STFAH0uaJ5//vnn8DpUYAnWbZ8+fbxlVgj+4YcfwuXVNqEjQXpdZT5w4IC7FDpS7PNg+voyprqvcKT2RjstHcHRUUEdRatTp066j9LFOlN7rCNXqptqw3RrC33xUmjLSpm5j0nv/kHbxGWXXebCtNoWlSk4+pkoSpYs6YK3Qnpq7X2sa6+91rX3ukpVISlRjyKdVUjS4Xo11pEPncLw0YJRA64FpkZMh930TVgJM9h56mZm2uieffZZV9l1nxGlz0BQwfU5aqRS0qFDB1dp9Zmahk5r3Xzzze5QaLNmzdI7m+HPVoOkb426j0ZQ/n/+85/u9ZEjR9qcOXPc4V41fvPmzYs6dRJJr6n8+tal++VEPnr27Ok95ZYSXao/bdo0d4ms7l8zatQo9/mp0SkBnRL885//7P7q8KcaXi1bhQEdzpehQ4e6e9JMmTLFrY9nnnnG3RtE34giqXHQqQEdZdByEW0MwWk/fbsT3QtD60M7C+3o0rM+9E1NjbHep1NTaox06kr0v5ZBEDDUgN99993u/2DnqdCqedAyijz1pjqnkKfQqPnWMlAdVKOvowJqMPXtXZ+hcfXtL5K+nSpMaJqq+75TDfqGraMyOj0xd+5cd4sFbQOiOiBqfKR169ZuOWr56XYM2sHGLiPVby1b3ZtpwYIFbph2lLqtgwJiWmj+tD0OGTLExowZ47ZLBYug7mjb0ZE2HaHSOtcyVX3Xt/TIUJjWOnIuaCen6eq2FVpX2skEbURQZ7XeFTh69erlloXKpOWtIHyuaNko9GiZrF+/3pYtW+bKplPnsbfwCMqsna+OHKmevvnmm249B22Ayqx6pGWvdkrrVMNeeuml0z5bob9Hjx6u/uj/SpUquVNsql9aDv/5z3/s3XffPSf370mtPdZOXvUl+EwtY9XXjJDW/Uxm7mPSu3/QURjVR10qrzZoxIgR3hAc7yZPnuzaxKC91zLUKWMtz5S6eegonNoqzbPGDbqYJKT0dGBSJ7PYS4T1UMe5lDpgqfOsrsRQJ1V1dtRlw+r0uX///vA4U6dOddNQRzldPaIrmwLqsKqrn9QRLPbyzFhpvQVAJHUeC6bru3pBHRwHDx7syqWOm5GX+T/66KPuygp1EtRVLOqAp6sYfNThM7Jjq69znK6gCDpbRl6S/+WXX4Y7HQfUiVHLVFfLaL3oksvUOm4HdGWFrpbR8tFyUmduXaqc3lsAqJO2OiFq2QWX4WvZBZ3Qg/lQh/HIWwBErg+VV52RI+lSZn12QJ2aNY46cupKQnXQDy67v/LKK10Z9Tzy1gvB8lKdUqdaXVURXOobLEPVOZUnqMO6ckzU+VdXgOnzNFx1VldJBusk6Jiq5RJ8tupVcAuAoDOirgBSx99gOurgq2npCsCA3qdLayMvE9e0dVVY7C0Ago7X2jbUEVXjq8Ns7969Xef4oJO7yhUI1kFwxZXG0S0KVG6VS1dbqUyRVzvpKkN1nA+Wi9aF5u1sbgEQ2xFT09DyO5PY7VSdcnWxQ9BG6ApBLZfgcmLRtqP1pg7TWg+6iktXG6XUOTa4bUNKHUlFZVBZYm8BoDql7T24BUAg8uo2lVm3HNAVbMGy1P+qd5FlVp2OvAWAtkX9r07kkWXWlWQarsvYA7pyUBc1aB2qY6zaSo2j+i9paUt8bWJq7bE69WuY2jytg+Dq0uAzzlXH7dT2M1m5j0nP/kEd+Pv16+eWh9a9Liz5y1/+EjXdeO+4HdCFA5HtfXB7k6C9923vuvJSw7W/iZVIHbfdzR+yOqgB6aFTP7qbdXa4nT8QSX19dHQm8hSs6KiNjqbp9FZm30wUyMnS3XEbAHBuqI9g8+bN3VWL6sOiU7vBaWPRqVzdjVx9j/74xz8SkIDs1HEbAJAy9e9Qfxp15lafIvUZVB+awPjx412nbHWyHz58OIsSyGScbgMAAPDgSBIAAIAHIQkAAMCDkAQAAOBBSAIAAPAgJAEAAHgQkgAAADwISQASln4/T79Kr98y02/e6X5C+k284Ad4dXd2/a4WAJwN7rgNIGHpx6GPHTvmfuy2evXqtmfPHvdjpXv37k3zNPR+fuoDgA9HkgAkpH379rlffh83bpxdccUVVrVqVfdL5boz9TXXXON+pV1+97vfuSNKwXPd0bpJkyb217/+1apVq2YFCxYMT++2226zMmXKWLFixaxdu3a2bt268Odt27bN3R27XLlyVrRoUfdzIh999FFUmfQZY8aMsZtvvtmNozK9++677oiX3qthjRo1cr8ODyD+EZIAJCQFDj10Ou3o0aOnvb569Wr3d/r06e73z4LnsnXrVnvrrbfs7bfftrVr17ph119/vSUnJ9sHH3xga9assUsuucTat29vP/30k3v90KFD1qVLF3ek6ssvv7ROnTpZt27dbMeOHVGfO2HCBGvVqpUbp2vXrnbTTTe50NSnTx/74osvrEaNGu45vy0OJIAQACSoN998M3T++eeHChYsGLrssstCw4cPD61bty78upq42bNnR71n1KhRoXz58oWSk5PDwz755JNQsWLFQkeOHIkat0aNGqEXXnghxc+vX79+aNKkSeHnVatWDfXp0yf8fPfu3a4MI0aMCA9bsWKFG6bXAMQ3jiQBSOg+Sbt27XKntHRkZ8mSJe4I0IwZM1J9n06D6bRaQKfVdKSoVKlS4SNUemzfvt2dZhO9ft9991ndunWtRIkS7vVNmzaddiRJp9MCOjUnDRs2PG2YjloBiG903AaQ0NSn6Morr3SPESNGuH5Fo0aNsn79+qX4niJFikQ9VwCqUKGCC1mxFIhEAWnhwoX21FNPWc2aNa1QoUJ23XXXuY7fkfLlyxf+X32hUhp26tSps55nAJmDkAQgW6lXr174sn+Fk5MnT57xPTr6lJSUZHnz5g138I6l2wooeKkjeBCsvv3223NcegDxhNNtABKSLvPXFWivvfaaffXVV+7U2BtvvGHjx493V5KJAo86WisA/fzzzylOq0OHDtayZUvr3r27LViwwIWf5cuX24MPPhi+Eq1WrVrhjt46PXfjjTdyNAjI5ghJABKS+gS1aNHCXU3Wpk0ba9CggTvdNmDAAHvuuefcOE8//bQ7RVa5cmW7+OKLU5yWToG9//77bjq33HKL1a5d23r16mXfffdduA/RM888Y+eff75ddtll7qo23bRSR6AAZF+51Hs7qwsBAAAQbziSBAAA4EFIAgAA8CAkAQAAeBCSAAAAPAhJAAAAHoQkAAAAD0ISAACAByEJAADAg5AEAADgQUgCAADwICQBAADY6f4fJKVw13KiXTYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in cat_cols:\n",
    "    sns.countplot(df,x=i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6871730e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in num_cols:\n",
    "    sns.boxplot(df,x=i,y=\"PlacedOrNot\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6a5fb6d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder,PowerTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import train_test_split,RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c49e6eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline(steps=[\n",
    "    (\"skew\",PowerTransformer(method=\"yeo-johnson\")),\n",
    "    (\"scaler\",StandardScaler())\n",
    "])\n",
    "\n",
    "cat_pipeline = Pipeline(steps=[\n",
    "    (\"encode\",OneHotEncoder(handle_unknown=\"ignore\"))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "016424ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;skew&#x27;, PowerTransformer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;Internships&#x27;, &#x27;CGPA&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;encode&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Gender&#x27;, &#x27;Stream&#x27;, &#x27;Hostel&#x27;,\n",
       "                                  &#x27;HistoryOfBacklogs&#x27;])])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformers',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples\">\n",
       "            transformers\n",
       "            <span class=\"param-doc-description\">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;num&#x27;, ...), (&#x27;cat&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('remainder',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27\">\n",
       "            remainder\n",
       "            <span class=\"param-doc-description\">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;drop&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3\">\n",
       "            sparse_threshold\n",
       "            <span class=\"param-doc-description\">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformer_weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone\">\n",
       "            transformer_weights\n",
       "            <span class=\"param-doc-description\">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue\">\n",
       "            verbose_feature_names_out\n",
       "            <span class=\"param-doc-description\">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out=\"{transformer_name}__{feature_name}\"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``\"{feature_name}__{transformer_name}\"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('force_int_remainder_cols',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse\">\n",
       "            force_int_remainder_cols\n",
       "            <span class=\"param-doc-description\">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"num__\"><pre>[&#x27;Age&#x27;, &#x27;Internships&#x27;, &#x27;CGPA&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>PowerTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html\">?<span>Documentation for PowerTransformer</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"num__skew__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html#:~:text=method,-%7B%27yeo-johnson%27%2C%20%27box-cox%27%7D%2C%20default%3D%27yeo-johnson%27\">\n",
       "            method\n",
       "            <span class=\"param-doc-description\">method: {'yeo-johnson', 'box-cox'}, default='yeo-johnson'<br><br>The power transform method. Available methods are:<br><br>- 'yeo-johnson' [1]_, works with positive and negative values<br>- 'box-cox' [2]_, only works with strictly positive values</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;yeo-johnson&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('standardize',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html#:~:text=standardize,-bool%2C%20default%3DTrue\">\n",
       "            standardize\n",
       "            <span class=\"param-doc-description\">standardize: bool, default=True<br><br>Set to True to apply zero-mean, unit-variance normalization to the<br>transformed output.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.PowerTransformer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>Set to False to perform inplace computation during transformation.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"num__scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"cat__\"><pre>[&#x27;Gender&#x27;, &#x27;Stream&#x27;, &#x27;Hostel&#x27;, &#x27;HistoryOfBacklogs&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"cat__encode__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('categories',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27\">\n",
       "            categories\n",
       "            <span class=\"param-doc-description\">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values within a single feature, and should be sorted in case of<br>  numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('drop',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop,-%7B%27first%27%2C%20%27if_binary%27%7D%20or%20an%20array-like%20of%20shape%20%28n_features%2C%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
       "            drop\n",
       "            <span class=\"param-doc-description\">drop: {'first', 'if_binary'} or an array-like of shape (n_features,),             default=None<br><br>Specifies a methodology to use to drop one of the categories per<br>feature. This is useful in situations where perfectly collinear<br>features cause problems, such as when feeding the resulting data<br>into an unregularized linear regression model.<br><br>However, dropping one category breaks the symmetry of the original<br>representation and can therefore induce a bias in downstream models,<br>for instance for penalized linear classification or regression models.<br><br>- None : retain all features (the default).<br>- 'first' : drop the first category in each feature. If only one<br>  category is present, the feature will be dropped entirely.<br>- 'if_binary' : drop the first category in each feature with two<br>  categories. Features with 1 or more than 2 categories are<br>  left intact.<br>- array : ``drop[i]`` is the category in feature ``X[:, i]`` that<br>  should be dropped.<br><br>When `max_categories` or `min_frequency` is configured to group<br>infrequent categories, the dropping behavior is handled after the<br>grouping.<br><br>.. versionadded:: 0.21<br>   The parameter `drop` was added in 0.21.<br><br>.. versionchanged:: 0.23<br>   The option `drop='if_binary'` was added in 0.23.<br><br>.. versionchanged:: 1.1<br>    Support for dropping infrequent categories.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_output',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=sparse_output,-bool%2C%20default%3DTrue\">\n",
       "            sparse_output\n",
       "            <span class=\"param-doc-description\">sparse_output: bool, default=True<br><br>When ``True``, it returns a :class:`scipy.sparse.csr_matrix`,<br>i.e. a sparse matrix in \"Compressed Sparse Row\" (CSR) format.<br><br>.. versionadded:: 1.2<br>   `sparse` was renamed to `sparse_output`</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dtype',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64\">\n",
       "            dtype\n",
       "            <span class=\"param-doc-description\">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&lt;class &#x27;numpy.float64&#x27;&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('handle_unknown',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27ignore%27%2C%20%27infrequent_if_exist%27%2C%20%27warn%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27error%27\">\n",
       "            handle_unknown\n",
       "            <span class=\"param-doc-description\">handle_unknown: {'error', 'ignore', 'infrequent_if_exist', 'warn'},                      default='error'<br><br>Specifies the way unknown categories are handled during :meth:`transform`.<br><br>- 'error' : Raise an error if an unknown category is present during transform.<br>- 'ignore' : When an unknown category is encountered during<br>  transform, the resulting one-hot encoded columns for this feature<br>  will be all zeros. In the inverse transform, an unknown category<br>  will be denoted as None.<br>- 'infrequent_if_exist' : When an unknown category is encountered<br>  during transform, the resulting one-hot encoded columns for this<br>  feature will map to the infrequent category if it exists. The<br>  infrequent category will be mapped to the last position in the<br>  encoding. During inverse transform, an unknown category will be<br>  mapped to the category denoted `'infrequent'` if it exists. If the<br>  `'infrequent'` category does not exist, then :meth:`transform` and<br>  :meth:`inverse_transform` will handle an unknown category as with<br>  `handle_unknown='ignore'`. Infrequent categories exist based on<br>  `min_frequency` and `max_categories`. Read more in the<br>  :ref:`User Guide <encoder_infrequent_categories>`.<br>- 'warn' : When an unknown category is encountered during transform<br>  a warning is issued, and the encoding then proceeds as described for<br>  `handle_unknown=\"infrequent_if_exist\"`.<br><br>.. versionchanged:: 1.1<br>    `'infrequent_if_exist'` was added to automatically handle unknown<br>    categories and infrequent categories.<br><br>.. versionadded:: 1.6<br>   The option `\"warn\"` was added in 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;ignore&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_frequency',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone\">\n",
       "            min_frequency\n",
       "            <span class=\"param-doc-description\">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_categories',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone\">\n",
       "            max_categories\n",
       "            <span class=\"param-doc-description\">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output features for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
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       "                 onclick=\"copyToClipboard('feature_name_combiner',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=feature_name_combiner,-%22concat%22%20or%20callable%2C%20default%3D%22concat%22\">\n",
       "            feature_name_combiner\n",
       "            <span class=\"param-doc-description\">feature_name_combiner: \"concat\" or callable, default=\"concat\"<br><br>Callable with signature `def callable(input_feature, category)` that returns a<br>string. This is used to create feature names to be returned by<br>:meth:`get_feature_names_out`.<br><br>`\"concat\"` concatenates encoded feature name and category with<br>`feature + \"_\" + str(category)`.E.g. feature X with values 1, 6, 7 create<br>feature names `X_1, X_6, X_7`.<br><br>.. versionadded:: 1.3</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;concat&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
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      "text/plain": [
       "ColumnTransformer(transformers=[('num',\n",
       "                                 Pipeline(steps=[('skew', PowerTransformer()),\n",
       "                                                 ('scaler', StandardScaler())]),\n",
       "                                 ['Age', 'Internships', 'CGPA']),\n",
       "                                ('cat',\n",
       "                                 Pipeline(steps=[('encode',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                 ['Gender', 'Stream', 'Hostel',\n",
       "                                  'HistoryOfBacklogs'])])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing = ColumnTransformer(transformers=[\n",
    "    (\"num\",num_pipeline,num_cols),\n",
    "    (\"cat\",cat_pipeline,cat_cols)\n",
    "])\n",
    "\n",
    "preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94db17d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"PlacedOrNot\",axis=1)\n",
    "y = df[\"PlacedOrNot\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6f7444a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pre = preprocessing.fit_transform(X_train)\n",
    "X_test_pre = preprocessing.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "add1a110",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Amir sohail\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\export\\tf2onnx_lib.py:8: FutureWarning: In the future `np.object` will be defined as the corresponding NumPy scalar.\n",
      "  if not hasattr(np, \"object\"):\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, Input\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "08dbea68",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ann = Sequential([\n",
    "    Input(shape=(X_train_pre.shape[1],)),\n",
    "    Dense(128,activation=\"relu\"),\n",
    "    Dense(64,activation=\"relu\"),\n",
    "    Dense(128,activation=\"relu\"),\n",
    "    Dropout(0.2),\n",
    "    BatchNormalization(),\n",
    "    Dense(64,activation=\"relu\"),\n",
    "    Dense(32,activation=\"relu\"),\n",
    "    Dense(16,activation=\"relu\"),\n",
    "    Dropout(0.2),\n",
    "    BatchNormalization(),\n",
    "    Dense(8,activation=\"relu\"),\n",
    "    Dense(1,activation=\"sigmoid\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "73a1b6a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ann.compile(loss=\"binary_crossentropy\",optimizer=\"adam\",metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d948869a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 29ms/step - accuracy: 0.6176 - loss: 0.6452 - val_accuracy: 0.6702 - val_loss: 0.6816\n",
      "Epoch 2/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7004 - loss: 0.5493 - val_accuracy: 0.6968 - val_loss: 0.6702\n",
      "Epoch 3/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7372 - loss: 0.5006 - val_accuracy: 0.7340 - val_loss: 0.6536\n",
      "Epoch 4/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7543 - loss: 0.4710 - val_accuracy: 0.7447 - val_loss: 0.6308\n",
      "Epoch 5/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7766 - loss: 0.4513 - val_accuracy: 0.7553 - val_loss: 0.6131\n",
      "Epoch 6/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8055 - loss: 0.4258 - val_accuracy: 0.7500 - val_loss: 0.5943\n",
      "Epoch 7/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7989 - loss: 0.4044 - val_accuracy: 0.7713 - val_loss: 0.5752\n",
      "Epoch 8/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7884 - loss: 0.4079 - val_accuracy: 0.7819 - val_loss: 0.5434\n",
      "Epoch 9/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7950 - loss: 0.4060 - val_accuracy: 0.7766 - val_loss: 0.5022\n",
      "Epoch 10/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8095 - loss: 0.3837 - val_accuracy: 0.7846 - val_loss: 0.4835\n",
      "Epoch 11/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.8055 - loss: 0.3832 - val_accuracy: 0.7899 - val_loss: 0.4496\n",
      "Epoch 12/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8187 - loss: 0.3736 - val_accuracy: 0.7872 - val_loss: 0.4387\n",
      "Epoch 13/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8371 - loss: 0.3487 - val_accuracy: 0.7979 - val_loss: 0.4210\n",
      "Epoch 14/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8187 - loss: 0.3507 - val_accuracy: 0.7846 - val_loss: 0.4274\n",
      "Epoch 15/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8318 - loss: 0.3491 - val_accuracy: 0.7952 - val_loss: 0.4201\n",
      "Epoch 16/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8279 - loss: 0.3329 - val_accuracy: 0.8059 - val_loss: 0.4040\n",
      "Epoch 17/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8594 - loss: 0.3193 - val_accuracy: 0.7979 - val_loss: 0.3962\n",
      "Epoch 18/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8476 - loss: 0.3318 - val_accuracy: 0.7739 - val_loss: 0.4024\n",
      "Epoch 19/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8463 - loss: 0.3172 - val_accuracy: 0.7766 - val_loss: 0.4340\n",
      "Epoch 20/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8265 - loss: 0.3245 - val_accuracy: 0.7553 - val_loss: 0.4203\n",
      "Epoch 21/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8371 - loss: 0.3081 - val_accuracy: 0.7686 - val_loss: 0.4181\n",
      "Epoch 22/50\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8581 - loss: 0.2964 - val_accuracy: 0.7633 - val_loss: 0.4253\n"
     ]
    }
   ],
   "source": [
    "history = model_ann.fit(\n",
    "    X_train_pre,\n",
    "    y_train,\n",
    "    batch_size=32,\n",
    "    epochs=50,\n",
    "    validation_data=(X_test_pre, y_test),\n",
    "    callbacks=[\n",
    "        tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=5)\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e0275381",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier ,AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "01399abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "models = {\n",
    "    \"logistic_regression\": LogisticRegression(class_weight=\"balanced\"),\n",
    "    \"Decision_tree\": DecisionTreeClassifier(class_weight=\"balanced\"),\n",
    "    \"knn\": KNeighborsClassifier(),\n",
    "    \"AdaBoostClassifier\": AdaBoostClassifier(),\n",
    "    \"XGBClassifier\": XGBClassifier(),\n",
    "    \"GradientBoostingClassifier\": GradientBoostingClassifier(),\n",
    "    \"RandomForestClassifier\": RandomForestClassifier(class_weight=\"balanced\")\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8ca22307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>accuracy_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>logistic_regression</td>\n",
       "      <td>0.747340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision_tree</td>\n",
       "      <td>0.765957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>knn</td>\n",
       "      <td>0.704787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.803191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.760638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.835106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.744681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name  accuracy_score\n",
       "0         logistic_regression        0.747340\n",
       "1               Decision_tree        0.765957\n",
       "2                         knn        0.704787\n",
       "3          AdaBoostClassifier        0.803191\n",
       "4               XGBClassifier        0.760638\n",
       "5  GradientBoostingClassifier        0.835106\n",
       "6      RandomForestClassifier        0.744681"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for name,model in models.items():\n",
    "    model.fit(X_train_pre,y_train)\n",
    "    y_pred = model.predict(X_test_pre)\n",
    "    acc = accuracy_score(y_test,y_pred)\n",
    "    results.append({\n",
    "        \"Name\":name,\n",
    "        \"accuracy_score\":acc,\n",
    "    })\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "12b81906",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7659574468085106\n",
      "0.8351063829787234\n"
     ]
    }
   ],
   "source": [
    "model_dt = models[\"Decision_tree\"]\n",
    "model_gb = models[\"GradientBoostingClassifier\"]\n",
    "\n",
    "y_pred = model_dt.predict(X_test_pre)\n",
    "acc = accuracy_score(y_test,y_pred)\n",
    "\n",
    "print(acc)\n",
    "\n",
    "y_pred = model_gb.predict(X_test_pre)\n",
    "acc = accuracy_score(y_test,y_pred)\n",
    "\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7e55d1e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['columns.pkl']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "\n",
    "dump(model_gb,\"model_gb_b.pkl\")\n",
    "dump(model_dt,\"model_dt_nb.pkl\")\n",
    "\n",
    "dump(preprocessing,\"preprocessing.pkl\")\n",
    "dump(X.columns.tolist(),\"columns.pkl\")"
   ]
  }
 ],
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