Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb70b42f",
   "metadata": {},
   "source": [
    "### **WEB MINING PROJECT**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447dd7c1",
   "metadata": {},
   "source": [
    "### Aston Glen Noronha, Abhimanyu Agarwala, Shrutik Pawale"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed96c32d",
   "metadata": {},
   "source": [
    "#### Company: Oracle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2479e7",
   "metadata": {},
   "source": [
    "### **Data Cleaning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7052a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "with open('1-256-data.json') as f:\n",
    "    data = json.load(f)\n",
    "df1 = pd.DataFrame(data)\n",
    "with open('257_512.json') as f:\n",
    "    data = json.load(f)\n",
    "df2 = pd.DataFrame(data)\n",
    "with open('513-772-data.json') as f:\n",
    "    data = json.load(f)\n",
    "df3 = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3811147e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.drop('date', axis=1)\n",
    "df2 = df2.drop('date', axis=1)\n",
    "df3 = df3.drop('date', axis=1)\n",
    "df2 = df2.rename(columns={'Text': 'text'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6934da28",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>Offer</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Interview Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I interviewed at Oracle (Bangalore),It started...</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I interviewed at Oracle (Prague),Three rounds ...</td>\n",
       "      <td>Accepted</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I interviewed at Oracle,3 Stages, the first in...</td>\n",
       "      <td>No</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I interviewed at Oracle,Entrevistas en ingles,...</td>\n",
       "      <td>Declined</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I interviewed at Oracle (Redwood City, CA),One...</td>\n",
       "      <td>Declined</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
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       "      <th>...</th>\n",
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       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6953</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Accepted</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6954</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Accepted</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6955</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
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       "      <td>Neutral</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6956</th>\n",
       "      <td>The process took 1 day. I interviewed at Oracl...</td>\n",
       "      <td>Accepted</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6957 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text     Offer Experience   \n",
       "0     I interviewed at Oracle (Bangalore),It started...        No   Positive  \\\n",
       "1     I interviewed at Oracle (Prague),Three rounds ...  Accepted   Positive   \n",
       "2     I interviewed at Oracle,3 Stages, the first in...        No    Neutral   \n",
       "3     I interviewed at Oracle,Entrevistas en ingles,...  Declined   Negative   \n",
       "4     I interviewed at Oracle (Redwood City, CA),One...  Declined   Positive   \n",
       "...                                                 ...       ...        ...   \n",
       "6952  The process took 4 days. I interviewed at Orac...  Accepted   Positive   \n",
       "6953  I applied through college or university. The p...  Accepted   Positive   \n",
       "6954  I applied through college or university. The p...  Accepted   Positive   \n",
       "6955  I applied through college or university. The p...  Declined    Neutral   \n",
       "6956  The process took 1 day. I interviewed at Oracl...  Accepted    Neutral   \n",
       "\n",
       "     Interview Level  \n",
       "0            Average  \n",
       "1            Average  \n",
       "2          Difficult  \n",
       "3          Difficult  \n",
       "4               Easy  \n",
       "...              ...  \n",
       "6952         Average  \n",
       "6953            Easy  \n",
       "6954            Easy  \n",
       "6955         Average  \n",
       "6956         Average  \n",
       "\n",
       "[6957 rows x 4 columns]"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.concat([df1, df2, df3])\n",
    "df = df.reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4eb7e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Offer'] = df['Offer'].replace({'Accepted': 'Yes', 'Declined': 'Yes'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1b4c0e5",
   "metadata": {
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       "      <td>I interviewed at Oracle (Redwood City, CA),One...</td>\n",
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       "      <th>6953</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
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       "    <tr>\n",
       "      <th>6954</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
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      "text/plain": [
       "                                                   text Offer Experience   \n",
       "0     I interviewed at Oracle (Bangalore),It started...    No   Positive  \\\n",
       "1     I interviewed at Oracle (Prague),Three rounds ...   Yes   Positive   \n",
       "2     I interviewed at Oracle,3 Stages, the first in...    No    Neutral   \n",
       "3     I interviewed at Oracle,Entrevistas en ingles,...   Yes   Negative   \n",
       "4     I interviewed at Oracle (Redwood City, CA),One...   Yes   Positive   \n",
       "...                                                 ...   ...        ...   \n",
       "6952  The process took 4 days. I interviewed at Orac...   Yes   Positive   \n",
       "6953  I applied through college or university. The p...   Yes   Positive   \n",
       "6954  I applied through college or university. The p...   Yes   Positive   \n",
       "6955  I applied through college or university. The p...   Yes    Neutral   \n",
       "6956  The process took 1 day. I interviewed at Oracl...   Yes    Neutral   \n",
       "\n",
       "     Interview Level  \n",
       "0            Average  \n",
       "1            Average  \n",
       "2          Difficult  \n",
       "3          Difficult  \n",
       "4               Easy  \n",
       "...              ...  \n",
       "6952         Average  \n",
       "6953            Easy  \n",
       "6954            Easy  \n",
       "6955         Average  \n",
       "6956         Average  \n",
       "\n",
       "[6957 rows x 4 columns]"
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   "source": [
    "df"
   ]
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  {
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       "      <th>Interview Level</th>\n",
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       "      <td>Positive</td>\n",
       "      <td>Average</td>\n",
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       "      <td>I interviewed at Oracle,3 Stages, the first in...</td>\n",
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       "      <td>Difficult</td>\n",
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       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
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       "      <th>6955</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Average</td>\n",
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       "    <tr>\n",
       "      <th>6956</th>\n",
       "      <td>The process took 1 day. I interviewed at Oracl...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>6910 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "                                                   text Offer Experience   \n",
       "0     I interviewed at Oracle (Bangalore),It started...    No   Positive  \\\n",
       "1     I interviewed at Oracle (Prague),Three rounds ...   Yes   Positive   \n",
       "2     I interviewed at Oracle,3 Stages, the first in...    No    Neutral   \n",
       "3     I interviewed at Oracle,Entrevistas en ingles,...   Yes   Negative   \n",
       "4     I interviewed at Oracle (Redwood City, CA),One...   Yes   Positive   \n",
       "...                                                 ...   ...        ...   \n",
       "6952  The process took 4 days. I interviewed at Orac...   Yes   Positive   \n",
       "6953  I applied through college or university. The p...   Yes   Positive   \n",
       "6954  I applied through college or university. The p...   Yes   Positive   \n",
       "6955  I applied through college or university. The p...   Yes    Neutral   \n",
       "6956  The process took 1 day. I interviewed at Oracl...   Yes    Neutral   \n",
       "\n",
       "     Interview Level  \n",
       "0            Average  \n",
       "1            Average  \n",
       "2          Difficult  \n",
       "3          Difficult  \n",
       "4               Easy  \n",
       "...              ...  \n",
       "6952         Average  \n",
       "6953            Easy  \n",
       "6954            Easy  \n",
       "6955         Average  \n",
       "6956         Average  \n",
       "\n",
       "[6910 rows x 4 columns]"
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     "execution_count": 8,
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   ],
   "source": [
    "df=df.dropna()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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       "      <th>6953</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6954</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6955</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6956</th>\n",
       "      <td>The process took 1 day. I interviewed at Oracl...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6658 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text Offer Experience   \n",
       "0     I interviewed at Oracle (Bangalore),It started...    No   Positive  \\\n",
       "1     I interviewed at Oracle (Prague),Three rounds ...   Yes   Positive   \n",
       "2     I interviewed at Oracle,3 Stages, the first in...    No    Neutral   \n",
       "3     I interviewed at Oracle,Entrevistas en ingles,...   Yes   Negative   \n",
       "4     I interviewed at Oracle (Redwood City, CA),One...   Yes   Positive   \n",
       "...                                                 ...   ...        ...   \n",
       "6952  The process took 4 days. I interviewed at Orac...   Yes   Positive   \n",
       "6953  I applied through college or university. The p...   Yes   Positive   \n",
       "6954  I applied through college or university. The p...   Yes   Positive   \n",
       "6955  I applied through college or university. The p...   Yes    Neutral   \n",
       "6956  The process took 1 day. I interviewed at Oracl...   Yes    Neutral   \n",
       "\n",
       "     Interview Level  \n",
       "0            Average  \n",
       "1            Average  \n",
       "2          Difficult  \n",
       "3          Difficult  \n",
       "4               Easy  \n",
       "...              ...  \n",
       "6952         Average  \n",
       "6953            Easy  \n",
       "6954            Easy  \n",
       "6955         Average  \n",
       "6956         Average  \n",
       "\n",
       "[6658 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['text'] != 'NA']\n",
    "df = df[df['Offer'] != 'NA']\n",
    "df = df[df['Experience'] != 'NA']\n",
    "df = df[df['Interview Level'] != 'NA']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb5b03f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_json('Oracle_data.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb4bf28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a1af8501",
   "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>text</th>\n",
       "      <th>Offer</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Interview Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I interviewed at Oracle (Bangalore),It started...</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I interviewed at Oracle (Prague),Three rounds ...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I interviewed at Oracle,3 Stages, the first in...</td>\n",
       "      <td>No</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I interviewed at Oracle,Entrevistas en ingles,...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I interviewed at Oracle (Redwood City, CA),One...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6653</th>\n",
       "      <td>The process took 4 days. I interviewed at Orac...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6654</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6655</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6656</th>\n",
       "      <td>I applied through college or university. The p...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6657</th>\n",
       "      <td>The process took 1 day. I interviewed at Oracl...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6658 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text Offer Experience   \n",
       "0     I interviewed at Oracle (Bangalore),It started...    No   Positive  \\\n",
       "1     I interviewed at Oracle (Prague),Three rounds ...   Yes   Positive   \n",
       "2     I interviewed at Oracle,3 Stages, the first in...    No    Neutral   \n",
       "3     I interviewed at Oracle,Entrevistas en ingles,...   Yes   Negative   \n",
       "4     I interviewed at Oracle (Redwood City, CA),One...   Yes   Positive   \n",
       "...                                                 ...   ...        ...   \n",
       "6653  The process took 4 days. I interviewed at Orac...   Yes   Positive   \n",
       "6654  I applied through college or university. The p...   Yes   Positive   \n",
       "6655  I applied through college or university. The p...   Yes   Positive   \n",
       "6656  I applied through college or university. The p...   Yes    Neutral   \n",
       "6657  The process took 1 day. I interviewed at Oracl...   Yes    Neutral   \n",
       "\n",
       "     Interview Level  \n",
       "0            Average  \n",
       "1            Average  \n",
       "2          Difficult  \n",
       "3          Difficult  \n",
       "4               Easy  \n",
       "...              ...  \n",
       "6653         Average  \n",
       "6654            Easy  \n",
       "6655            Easy  \n",
       "6656         Average  \n",
       "6657         Average  \n",
       "\n",
       "[6658 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "with open('Oracle_data.json') as f:\n",
    "    data = json.load(f)\n",
    "df = pd.DataFrame(data)\n",
    "df = df.reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a0888501",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Aston\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "import string\n",
    "import re\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5b85e654",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(text):\n",
    "    text = text.replace(',', ' ').replace('.', ' ')\n",
    "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
    "    text = text.lower()\n",
    "    text = re.sub(r\"(?<=,)\", \" \", text)\n",
    "    text = re.sub(r'\\b\\w{1,2}\\b', '', text)\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    text = ' '.join(word for word in text.split() if word not in stop_words)\n",
    "    return text\n",
    "\n",
    "df['text'] = df['text'].apply(preprocess_text)\n",
    "df['Offer'] = df['Offer'].replace({'Yes': 1, 'No': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d0ef41a1",
   "metadata": {
    "scrolled": false
   },
   "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>text</th>\n",
       "      <th>Offer</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Interview Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>interviewed oracle bangalore started introduct...</td>\n",
       "      <td>0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>interviewed oracle prague three rounds differe...</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>interviewed oracle stages first interview good...</td>\n",
       "      <td>0</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>interviewed oracle entrevistas ingles code cha...</td>\n",
       "      <td>1</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>interviewed oracle redwood city one two phone ...</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6653</th>\n",
       "      <td>process took days interviewed oracle redwood c...</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6654</th>\n",
       "      <td>applied college university process took days i...</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6655</th>\n",
       "      <td>applied college university process took months...</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6656</th>\n",
       "      <td>applied college university process took day in...</td>\n",
       "      <td>1</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6657</th>\n",
       "      <td>process took day interviewed oracle minneapoli...</td>\n",
       "      <td>1</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6658 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  Offer Experience   \n",
       "0     interviewed oracle bangalore started introduct...      0   Positive  \\\n",
       "1     interviewed oracle prague three rounds differe...      1   Positive   \n",
       "2     interviewed oracle stages first interview good...      0    Neutral   \n",
       "3     interviewed oracle entrevistas ingles code cha...      1   Negative   \n",
       "4     interviewed oracle redwood city one two phone ...      1   Positive   \n",
       "...                                                 ...    ...        ...   \n",
       "6653  process took days interviewed oracle redwood c...      1   Positive   \n",
       "6654  applied college university process took days i...      1   Positive   \n",
       "6655  applied college university process took months...      1   Positive   \n",
       "6656  applied college university process took day in...      1    Neutral   \n",
       "6657  process took day interviewed oracle minneapoli...      1    Neutral   \n",
       "\n",
       "     Interview Level  \n",
       "0            Average  \n",
       "1            Average  \n",
       "2          Difficult  \n",
       "3          Difficult  \n",
       "4               Easy  \n",
       "...              ...  \n",
       "6653         Average  \n",
       "6654            Easy  \n",
       "6655            Easy  \n",
       "6656         Average  \n",
       "6657         Average  \n",
       "\n",
       "[6658 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "caccb0e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df[['text', 'Experience', 'Interview Level']], df['Offer'], test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4b2e6edb",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(stop_words='english')\n",
    "X_train_text = vectorizer.fit_transform(X_train['text'])\n",
    "X_test_text = vectorizer.transform(X_test['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e610a625",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = OneHotEncoder()\n",
    "X_train_cat = encoder.fit_transform(X_train[['Experience', 'Interview Level']])\n",
    "X_test_cat = encoder.transform(X_test[['Experience', 'Interview Level']])\n",
    "\n",
    "X_train = pd.concat([pd.DataFrame(X_train_text.toarray()), pd.DataFrame(X_train_cat.toarray())], axis=1)\n",
    "X_test = pd.concat([pd.DataFrame(X_test_text.toarray()), pd.DataFrame(X_test_cat.toarray())], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f0779df",
   "metadata": {},
   "source": [
    "### We used Logistic Regression, Random Forest Regressor, SVM, XGBoost, ABA Boost, KNN, Decision Tree etc along with GridSearchCV and RandomizedSearchCV for hyperparameter tuning.\n",
    "### In the end, Logistic Regression gives us the best result with an Accuracy of 78% and an F1 Score of 0.84."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "194080b7",
   "metadata": {},
   "source": [
    "### LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "64c4b942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cace765b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "46178f70",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.78003003003003\n",
      "Precision: 0.7997977755308392\n",
      "Recall: 0.8927765237020316\n",
      "F1 Score: 0.8437333333333333\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, pos_label=1)\n",
    "recall = recall_score(y_test, y_pred, pos_label=1)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=1)\n",
    "\n",
    "print(f'Accuracy: {accuracy}')\n",
    "print(f'Precision: {precision}')\n",
    "print(f'Recall: {recall}')\n",
    "print(f'F1 Score: {f1}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "028563bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "y_pred = model.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred,pos_label=1)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label='Logistic Regression (AUC = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e1e0a2",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f419f049",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7717717717717718\n",
      "Precision: 0.7635869565217391\n",
      "Recall: 0.9514672686230248\n",
      "F1 Score: 0.8472361809045226\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, pos_label=1)\n",
    "recall = recall_score(y_test, y_pred, pos_label=1)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=1)\n",
    "\n",
    "print(f'Accuracy: {accuracy}')\n",
    "print(f'Precision: {precision}')\n",
    "print(f'Recall: {recall}')\n",
    "print(f'F1 Score: {f1}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6c87a081",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = rf.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred,pos_label=1)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label='Random Forest (AUC = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de4543da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4e9eb5ac",
   "metadata": {},
   "source": [
    "### ADA Boost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "321de65a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7612612612612613\n",
      "Precision: 0.8021276595744681\n",
      "Recall: 0.8510158013544018\n",
      "F1 Score: 0.8258488499452356\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada = AdaBoostClassifier(n_estimators=50, random_state=42)\n",
    "ada.fit(X_train, y_train)\n",
    "y_pred = ada.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, pos_label=1)\n",
    "recall = recall_score(y_test, y_pred, pos_label=1)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=1)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "print(f'Precision: {precision}')\n",
    "print(f'Recall: {recall}')\n",
    "print(f'F1 Score: {f1}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c00702b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "ada = AdaBoostClassifier(random_state=42)\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'learning_rate': [0.1, 0.5, 1.0]\n",
    "}\n",
    "grid_search = GridSearchCV(estimator=ada, param_grid=param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n",
    "best_ada = grid_search.best_estimator_\n",
    "y_pred = best_ada.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, pos_label=1)\n",
    "recall = recall_score(y_test, y_pred, pos_label=1)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=1)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "print(f'Precision: {precision}')\n",
    "print(f'Recall: {recall}')\n",
    "print(f'F1 Score: {f1}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}