employee-eda-analysis-beginner-level.ipynb

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   "source": [
    "# Import all the libraries..\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0723b97e",
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    "tags": []
   },
   "source": [
    "#  **Importing the CSV File**"
   ]
  },
  {
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Education</th>\n",
       "      <th>JoiningYear</th>\n",
       "      <th>City</th>\n",
       "      <th>PaymentTier</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>EverBenched</th>\n",
       "      <th>ExperienceInCurrentDomain</th>\n",
       "      <th>LeaveOrNot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bachelors</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bachelors</td>\n",
       "      <td>2013</td>\n",
       "      <td>Pune</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bachelors</td>\n",
       "      <td>2014</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Masters</td>\n",
       "      <td>2016</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Masters</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Education  JoiningYear       City  PaymentTier  Age  Gender EverBenched  \\\n",
       "0  Bachelors         2017  Bangalore            3   34    Male          No   \n",
       "1  Bachelors         2013       Pune            1   28  Female          No   \n",
       "2  Bachelors         2014  New Delhi            3   38  Female          No   \n",
       "3    Masters         2016  Bangalore            3   27    Male          No   \n",
       "4    Masters         2017       Pune            3   24    Male         Yes   \n",
       "\n",
       "   ExperienceInCurrentDomain  LeaveOrNot  \n",
       "0                          0           0  \n",
       "1                          3           1  \n",
       "2                          2           0  \n",
       "3                          5           1  \n",
       "4                          2           1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/employee-dataset/Employee.csv')\n",
    "df.head()"
   ]
  },
  {
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   "source": [
    "# **Here we check about datatypes of each column**"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4653 entries, 0 to 4652\n",
      "Data columns (total 9 columns):\n",
      " #   Column                     Non-Null Count  Dtype \n",
      "---  ------                     --------------  ----- \n",
      " 0   Education                  4653 non-null   object\n",
      " 1   JoiningYear                4653 non-null   int64 \n",
      " 2   City                       4653 non-null   object\n",
      " 3   PaymentTier                4653 non-null   int64 \n",
      " 4   Age                        4653 non-null   int64 \n",
      " 5   Gender                     4653 non-null   object\n",
      " 6   EverBenched                4653 non-null   object\n",
      " 7   ExperienceInCurrentDomain  4653 non-null   int64 \n",
      " 8   LeaveOrNot                 4653 non-null   int64 \n",
      "dtypes: int64(5), object(4)\n",
      "memory usage: 327.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28364c2f",
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    "tags": []
   },
   "source": [
    "**It is a good practice to check your data before any visualization or training for any null values.**"
   ]
  },
  {
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   "id": "17b4dfe4",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Education                    0\n",
       "JoiningYear                  0\n",
       "City                         0\n",
       "PaymentTier                  0\n",
       "Age                          0\n",
       "Gender                       0\n",
       "EverBenched                  0\n",
       "ExperienceInCurrentDomain    0\n",
       "LeaveOrNot                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c6530d",
   "metadata": {
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     "duration": 0.012098,
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   },
   "source": [
    "![](<iframe src=\"https://giphy.com/embed/yJFeycRK2DB4c\" width=\"480\" height=\"384\" frameBorder=\"0\" class=\"giphy-embed\" allowFullScreen></iframe><p><a href=\"https://giphy.com/gifs/yJFeycRK2DB4c\">via GIPHY</a></p>)\n",
    "**Noiice!! our data doesn't contain any null values now we can do further processing.\n",
    "For starters lets get the better understanding of our data by using .describe().**"
   ]
  },
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   "cell_type": "markdown",
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   "source": []
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   "cell_type": "code",
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JoiningYear</th>\n",
       "      <th>PaymentTier</th>\n",
       "      <th>Age</th>\n",
       "      <th>ExperienceInCurrentDomain</th>\n",
       "      <th>LeaveOrNot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4653.000000</td>\n",
       "      <td>4653.000000</td>\n",
       "      <td>4653.000000</td>\n",
       "      <td>4653.000000</td>\n",
       "      <td>4653.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015.062970</td>\n",
       "      <td>2.698259</td>\n",
       "      <td>29.393295</td>\n",
       "      <td>2.905652</td>\n",
       "      <td>0.343864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.863377</td>\n",
       "      <td>0.561435</td>\n",
       "      <td>4.826087</td>\n",
       "      <td>1.558240</td>\n",
       "      <td>0.475047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       JoiningYear  PaymentTier          Age  ExperienceInCurrentDomain  \\\n",
       "count  4653.000000  4653.000000  4653.000000                4653.000000   \n",
       "mean   2015.062970     2.698259    29.393295                   2.905652   \n",
       "std       1.863377     0.561435     4.826087                   1.558240   \n",
       "min    2012.000000     1.000000    22.000000                   0.000000   \n",
       "25%    2013.000000     3.000000    26.000000                   2.000000   \n",
       "50%    2015.000000     3.000000    28.000000                   3.000000   \n",
       "75%    2017.000000     3.000000    32.000000                   4.000000   \n",
       "max    2018.000000     3.000000    41.000000                   7.000000   \n",
       "\n",
       "        LeaveOrNot  \n",
       "count  4653.000000  \n",
       "mean      0.343864  \n",
       "std       0.475047  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff307165",
   "metadata": {
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     "start_time": "2023-10-08T18:04:35.026470",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**So when a employee leaves his/her job their can be many reasons like, Salary, Promotion, Room to grow, peers, Work Culture, so there are many reasons but according to our data we can shortlist some of the accurate reasons that why employee quits his/her job!**\n",
    "\n",
    "**The very first thing that comes to mind is salary, salary on the basis of degree, age, gender etc.**\n",
    "\n",
    "**Now we check how many unique degrees people have in the dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a72bbc1",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachelors', 'Masters', 'PHD'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Education.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9403282",
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    "tags": []
   },
   "source": [
    "# **Now lets visualize the number of different degree holder in the form of a pie-chart**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c5272fe",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Diagram from Degree Convention')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(x = df['Education'].value_counts(), labels = df['Education'].value_counts().index, autopct = '%1.1f%%' ,  explode = [0.05, 0.05,0 ], startangle = 360)\n",
    "plt.title(\"Diagram from Degree Convention\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923c0133",
   "metadata": {
    "papermill": {
     "duration": 0.013299,
     "end_time": "2023-10-08T18:04:35.387595",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.374296",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Now we have an idea how much distribution is there in our data on the basis of degree**\n",
    "\n",
    "**Now lets get the gender distribution using pie-chart**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b1d2e25",
   "metadata": {
    "papermill": {
     "duration": 0.013083,
     "end_time": "2023-10-08T18:04:35.414268",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.401185",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6d61e04d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:35.444329Z",
     "iopub.status.busy": "2023-10-08T18:04:35.443935Z",
     "iopub.status.idle": "2023-10-08T18:04:35.544432Z",
     "shell.execute_reply": "2023-10-08T18:04:35.543354Z"
    },
    "papermill": {
     "duration": 0.119235,
     "end_time": "2023-10-08T18:04:35.547595",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.428360",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gender Distribution within Workforce')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df['Gender'].value_counts(), labels = df['Gender'].value_counts().index, autopct = '%1.1f%%',)\n",
    "plt.title(\"Gender Distribution within Workforce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb66358d",
   "metadata": {
    "papermill": {
     "duration": 0.026509,
     "end_time": "2023-10-08T18:04:35.600781",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.574272",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Now we have gender distribution as well now lets try to get the co-relation between different columns so that we can predict which factors are affecting the leaveRate of Employees.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b26ebc",
   "metadata": {
    "papermill": {
     "duration": 0.019072,
     "end_time": "2023-10-08T18:04:35.646033",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.626961",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**df.corr() is not working on kaggle but it runs on jupyter notebook so I am going to label encode the string values.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966aaefb",
   "metadata": {
    "papermill": {
     "duration": 0.013468,
     "end_time": "2023-10-08T18:04:35.673231",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.659763",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Label Encoder**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3bf4c9d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:35.702783Z",
     "iopub.status.busy": "2023-10-08T18:04:35.702103Z",
     "iopub.status.idle": "2023-10-08T18:04:35.720850Z",
     "shell.execute_reply": "2023-10-08T18:04:35.720149Z"
    },
    "papermill": {
     "duration": 0.03604,
     "end_time": "2023-10-08T18:04:35.722971",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.686931",
     "status": "completed"
    },
    "tags": []
   },
   "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>Education</th>\n",
       "      <th>JoiningYear</th>\n",
       "      <th>City</th>\n",
       "      <th>PaymentTier</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>EverBenched</th>\n",
       "      <th>ExperienceInCurrentDomain</th>\n",
       "      <th>LeaveOrNot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Education  JoiningYear  City  PaymentTier  Age  Gender  EverBenched  \\\n",
       "0          0         2017     0            3   34       1            0   \n",
       "1          0         2013     2            1   28       0            0   \n",
       "2          0         2014     1            3   38       0            0   \n",
       "3          1         2016     0            3   27       1            0   \n",
       "4          1         2017     2            3   24       1            1   \n",
       "\n",
       "   ExperienceInCurrentDomain  LeaveOrNot  \n",
       "0                          0           0  \n",
       "1                          3           1  \n",
       "2                          2           0  \n",
       "3                          5           1  \n",
       "4                          2           1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "df['Education'] = le.fit_transform(df['Education'])\n",
    "df['City'] = le.fit_transform(df['City'])\n",
    "df['Gender'] = le.fit_transform(df['Gender'])\n",
    "df['EverBenched'] = le.fit_transform(df['EverBenched'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7214deb2",
   "metadata": {
    "papermill": {
     "duration": 0.014043,
     "end_time": "2023-10-08T18:04:35.751376",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.737333",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Now we can use .corr()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f895c62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:35.782096Z",
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    },
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     "end_time": "2023-10-08T18:04:35.801604",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.765896",
     "status": "completed"
    },
    "tags": []
   },
   "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>Education</th>\n",
       "      <th>JoiningYear</th>\n",
       "      <th>City</th>\n",
       "      <th>PaymentTier</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>EverBenched</th>\n",
       "      <th>ExperienceInCurrentDomain</th>\n",
       "      <th>LeaveOrNot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142670</td>\n",
       "      <td>0.149903</td>\n",
       "      <td>-0.140741</td>\n",
       "      <td>-0.010611</td>\n",
       "      <td>-0.010889</td>\n",
       "      <td>-0.052249</td>\n",
       "      <td>-0.004463</td>\n",
       "      <td>0.080497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoiningYear</th>\n",
       "      <td>0.142670</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051441</td>\n",
       "      <td>-0.096078</td>\n",
       "      <td>0.013165</td>\n",
       "      <td>-0.012213</td>\n",
       "      <td>0.049353</td>\n",
       "      <td>-0.036525</td>\n",
       "      <td>0.181705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>0.149903</td>\n",
       "      <td>0.051441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.295884</td>\n",
       "      <td>-0.030706</td>\n",
       "      <td>-0.168546</td>\n",
       "      <td>-0.007046</td>\n",
       "      <td>-0.009925</td>\n",
       "      <td>0.201058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentTier</th>\n",
       "      <td>-0.140741</td>\n",
       "      <td>-0.096078</td>\n",
       "      <td>-0.295884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007631</td>\n",
       "      <td>0.235119</td>\n",
       "      <td>0.019207</td>\n",
       "      <td>0.018314</td>\n",
       "      <td>-0.197638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.010611</td>\n",
       "      <td>0.013165</td>\n",
       "      <td>-0.030706</td>\n",
       "      <td>0.007631</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003866</td>\n",
       "      <td>-0.016135</td>\n",
       "      <td>-0.134643</td>\n",
       "      <td>-0.051126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>-0.010889</td>\n",
       "      <td>-0.012213</td>\n",
       "      <td>-0.168546</td>\n",
       "      <td>0.235119</td>\n",
       "      <td>-0.003866</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019653</td>\n",
       "      <td>0.008745</td>\n",
       "      <td>-0.220701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EverBenched</th>\n",
       "      <td>-0.052249</td>\n",
       "      <td>0.049353</td>\n",
       "      <td>-0.007046</td>\n",
       "      <td>0.019207</td>\n",
       "      <td>-0.016135</td>\n",
       "      <td>0.019653</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001408</td>\n",
       "      <td>0.078438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExperienceInCurrentDomain</th>\n",
       "      <td>-0.004463</td>\n",
       "      <td>-0.036525</td>\n",
       "      <td>-0.009925</td>\n",
       "      <td>0.018314</td>\n",
       "      <td>-0.134643</td>\n",
       "      <td>0.008745</td>\n",
       "      <td>0.001408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.030504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LeaveOrNot</th>\n",
       "      <td>0.080497</td>\n",
       "      <td>0.181705</td>\n",
       "      <td>0.201058</td>\n",
       "      <td>-0.197638</td>\n",
       "      <td>-0.051126</td>\n",
       "      <td>-0.220701</td>\n",
       "      <td>0.078438</td>\n",
       "      <td>-0.030504</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Education  JoiningYear      City  PaymentTier  \\\n",
       "Education                   1.000000     0.142670  0.149903    -0.140741   \n",
       "JoiningYear                 0.142670     1.000000  0.051441    -0.096078   \n",
       "City                        0.149903     0.051441  1.000000    -0.295884   \n",
       "PaymentTier                -0.140741    -0.096078 -0.295884     1.000000   \n",
       "Age                        -0.010611     0.013165 -0.030706     0.007631   \n",
       "Gender                     -0.010889    -0.012213 -0.168546     0.235119   \n",
       "EverBenched                -0.052249     0.049353 -0.007046     0.019207   \n",
       "ExperienceInCurrentDomain  -0.004463    -0.036525 -0.009925     0.018314   \n",
       "LeaveOrNot                  0.080497     0.181705  0.201058    -0.197638   \n",
       "\n",
       "                                Age    Gender  EverBenched  \\\n",
       "Education                 -0.010611 -0.010889    -0.052249   \n",
       "JoiningYear                0.013165 -0.012213     0.049353   \n",
       "City                      -0.030706 -0.168546    -0.007046   \n",
       "PaymentTier                0.007631  0.235119     0.019207   \n",
       "Age                        1.000000 -0.003866    -0.016135   \n",
       "Gender                    -0.003866  1.000000     0.019653   \n",
       "EverBenched               -0.016135  0.019653     1.000000   \n",
       "ExperienceInCurrentDomain -0.134643  0.008745     0.001408   \n",
       "LeaveOrNot                -0.051126 -0.220701     0.078438   \n",
       "\n",
       "                           ExperienceInCurrentDomain  LeaveOrNot  \n",
       "Education                                  -0.004463    0.080497  \n",
       "JoiningYear                                -0.036525    0.181705  \n",
       "City                                       -0.009925    0.201058  \n",
       "PaymentTier                                 0.018314   -0.197638  \n",
       "Age                                        -0.134643   -0.051126  \n",
       "Gender                                      0.008745   -0.220701  \n",
       "EverBenched                                 0.001408    0.078438  \n",
       "ExperienceInCurrentDomain                   1.000000   -0.030504  \n",
       "LeaveOrNot                                 -0.030504    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3235d3f9",
   "metadata": {
    "papermill": {
     "duration": 0.014107,
     "end_time": "2023-10-08T18:04:35.830233",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.816126",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Here We can see that Education, JoiningYear, City, EverBenched affect the LeaveOrNot values**\n",
    "\n",
    "**So now we can plot graphs to check the relations between different columns and how they affect the target column which is LeaveOrNot**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b580a535",
   "metadata": {
    "papermill": {
     "duration": 0.013965,
     "end_time": "2023-10-08T18:04:35.858479",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.844514",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Education V/S LeaveOrNot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bde6d94f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:35.889210Z",
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     "shell.execute_reply": "2023-10-08T18:04:36.137930Z"
    },
    "papermill": {
     "duration": 0.268023,
     "end_time": "2023-10-08T18:04:36.140706",
     "exception": false,
     "start_time": "2023-10-08T18:04:35.872683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Education', hue = 'LeaveOrNot', data = df)\n",
    "plt.figure(figsize = (14, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f03e6ae3",
   "metadata": {
    "papermill": {
     "duration": 0.014257,
     "end_time": "2023-10-08T18:04:36.169612",
     "exception": false,
     "start_time": "2023-10-08T18:04:36.155355",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "***Here***\n",
    "\n",
    "***'0' = Bachelor's Degree***\n",
    "\n",
    "***'1' = Master's Degree***\n",
    "\n",
    "***'2' = PHD***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cc9cc6",
   "metadata": {
    "papermill": {
     "duration": 0.014221,
     "end_time": "2023-10-08T18:04:36.198538",
     "exception": false,
     "start_time": "2023-10-08T18:04:36.184317",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **1. Insights**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d35a7cb",
   "metadata": {
    "papermill": {
     "duration": 0.014723,
     "end_time": "2023-10-08T18:04:36.227819",
     "exception": false,
     "start_time": "2023-10-08T18:04:36.213096",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**From the above graph we can get that employees who have bachelor's Degree have low chances of leaving & the employees who have master's degree are more likely to quit the job**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac2b4cf3",
   "metadata": {
    "papermill": {
     "duration": 0.014225,
     "end_time": "2023-10-08T18:04:36.256928",
     "exception": false,
     "start_time": "2023-10-08T18:04:36.242703",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Lets see how many employee having bachelor degree with payement tier = 1 have left the job.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e0b8387f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:36.288729Z",
     "iopub.status.busy": "2023-10-08T18:04:36.287721Z",
     "iopub.status.idle": "2023-10-08T18:04:36.506140Z",
     "shell.execute_reply": "2023-10-08T18:04:36.504893Z"
    },
    "papermill": {
     "duration": 0.23604,
     "end_time": "2023-10-08T18:04:36.508042",
     "exception": false,
     "start_time": "2023-10-08T18:04:36.272002",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Education', hue = 'LeaveOrNot', data = df.loc[(df.Education == 0) & (df.PaymentTier == 1)])\n",
    "plt.figure(figsize = (14, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cab76936",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:36.540725Z",
     "iopub.status.busy": "2023-10-08T18:04:36.540360Z",
     "iopub.status.idle": "2023-10-08T18:04:36.778695Z",
     "shell.execute_reply": "2023-10-08T18:04:36.777593Z"
    },
    "papermill": {
     "duration": 0.257201,
     "end_time": "2023-10-08T18:04:36.780846",
     "exception": false,
     "start_time": "2023-10-08T18:04:36.523645",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Education', hue = 'LeaveOrNot', data = df.loc[(df.Education == 1) & (df.PaymentTier == 1)])\n",
    "plt.figure(figsize = (14, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c892850",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:36.815114Z",
     "iopub.status.busy": "2023-10-08T18:04:36.814306Z",
     "iopub.status.idle": "2023-10-08T18:04:37.034585Z",
     "shell.execute_reply": "2023-10-08T18:04:37.033483Z"
    },
    "papermill": {
     "duration": 0.24011,
     "end_time": "2023-10-08T18:04:37.036607",
     "exception": false,
     "start_time": "2023-10-08T18:04:36.796497",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Education', hue = 'LeaveOrNot', data = df.loc[(df.Education == 1) & (df.PaymentTier == 3)])\n",
    "plt.figure(figsize = (14, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022814bb",
   "metadata": {
    "papermill": {
     "duration": 0.018491,
     "end_time": "2023-10-08T18:04:37.071485",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.052994",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**1.1 Insight**\n",
    "\n",
    "**From the above graph we can see that Employees who have done PHD and receiving 3rd Payment Tier are more likely to leave the job**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46cb37b",
   "metadata": {
    "papermill": {
     "duration": 0.016387,
     "end_time": "2023-10-08T18:04:37.104176",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.087789",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f5aab84d",
   "metadata": {
    "papermill": {
     "duration": 0.016409,
     "end_time": "2023-10-08T18:04:37.137144",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.120735",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# JoiningYear V/S LeaveOrNot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bb00e14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:37.171552Z",
     "iopub.status.busy": "2023-10-08T18:04:37.171086Z",
     "iopub.status.idle": "2023-10-08T18:04:37.480836Z",
     "shell.execute_reply": "2023-10-08T18:04:37.479744Z"
    },
    "papermill": {
     "duration": 0.329193,
     "end_time": "2023-10-08T18:04:37.482996",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.153803",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'JoiningYear' , hue = 'LeaveOrNot', data = df)\n",
    "plt.figure(figsize = (14, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa2f333c",
   "metadata": {
    "papermill": {
     "duration": 0.016603,
     "end_time": "2023-10-08T18:04:37.516351",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.499748",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **2. Insights**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d3d006",
   "metadata": {
    "papermill": {
     "duration": 0.017053,
     "end_time": "2023-10-08T18:04:37.550546",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.533493",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**From the above graph we can say that the joining/Leaveornot rate was high in 2012, 2013, 2014, 2016, 2017 and the number of employee who left the job are very high in 2018**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f18ed4",
   "metadata": {
    "papermill": {
     "duration": 0.016532,
     "end_time": "2023-10-08T18:04:37.583970",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.567438",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **City V/S LeaveOrNot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de89f434",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:37.619850Z",
     "iopub.status.busy": "2023-10-08T18:04:37.618782Z",
     "iopub.status.idle": "2023-10-08T18:04:37.877036Z",
     "shell.execute_reply": "2023-10-08T18:04:37.876069Z"
    },
    "papermill": {
     "duration": 0.278356,
     "end_time": "2023-10-08T18:04:37.879355",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.600999",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'City' , hue = 'LeaveOrNot', data = df)\n",
    "plt.figure(figsize = (14, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be08b51b",
   "metadata": {
    "papermill": {
     "duration": 0.0171,
     "end_time": "2023-10-08T18:04:37.913888",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.896788",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **3. Insights**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc7707ac",
   "metadata": {
    "papermill": {
     "duration": 0.01685,
     "end_time": "2023-10-08T18:04:37.947812",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.930962",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Here** \n",
    "\n",
    "**'0' = Bangalore**\n",
    "\n",
    "**'1' = New Delhi**\n",
    "\n",
    "**'2' = Pune**\n",
    "\n",
    "**Form the above graph we can see that employee which are from Bangalore are less likely to leave the job as compared to New Delhi and Pune**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11479dcf",
   "metadata": {
    "papermill": {
     "duration": 0.016659,
     "end_time": "2023-10-08T18:04:37.981897",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.965238",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Payment Tier V/S Leave Or Not**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "234da41b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:38.018150Z",
     "iopub.status.busy": "2023-10-08T18:04:38.017487Z",
     "iopub.status.idle": "2023-10-08T18:04:38.258355Z",
     "shell.execute_reply": "2023-10-08T18:04:38.257236Z"
    },
    "papermill": {
     "duration": 0.261682,
     "end_time": "2023-10-08T18:04:38.260563",
     "exception": false,
     "start_time": "2023-10-08T18:04:37.998881",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'PaymentTier' , hue = 'LeaveOrNot', data = df)\n",
    "plt.figure(figsize = (14, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02dd8b62",
   "metadata": {
    "papermill": {
     "duration": 0.017403,
     "end_time": "2023-10-08T18:04:38.295608",
     "exception": false,
     "start_time": "2023-10-08T18:04:38.278205",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **4. Insights**\n",
    "\n",
    "**From the above fraph we can see that employee who recieve payment of 2nd tier leave the company more as compared to employees who get 1 & 3 tier payment.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca390ec9",
   "metadata": {
    "papermill": {
     "duration": 0.017334,
     "end_time": "2023-10-08T18:04:38.331153",
     "exception": false,
     "start_time": "2023-10-08T18:04:38.313819",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Gender V/S Leave Or Not**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fbd566ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:38.368589Z",
     "iopub.status.busy": "2023-10-08T18:04:38.367875Z",
     "iopub.status.idle": "2023-10-08T18:04:38.615160Z",
     "shell.execute_reply": "2023-10-08T18:04:38.614090Z"
    },
    "papermill": {
     "duration": 0.269174,
     "end_time": "2023-10-08T18:04:38.618169",
     "exception": false,
     "start_time": "2023-10-08T18:04:38.348995",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x= 'Gender', hue= 'LeaveOrNot', data = df)\n",
    "plt.figure(figsize = (14,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "157e7184",
   "metadata": {
    "papermill": {
     "duration": 0.017796,
     "end_time": "2023-10-08T18:04:38.654599",
     "exception": false,
     "start_time": "2023-10-08T18:04:38.636803",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Here\n",
    "\n",
    "**'1' = Male**\n",
    "\n",
    "**'0' = Female**\n",
    "\n",
    "**From the above graph we can see that number of male leaving is less than number of females leaving**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3de77fd",
   "metadata": {
    "papermill": {
     "duration": 0.017912,
     "end_time": "2023-10-08T18:04:38.690855",
     "exception": false,
     "start_time": "2023-10-08T18:04:38.672943",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Age V/S Leave or not**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d89bfa15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:38.729968Z",
     "iopub.status.busy": "2023-10-08T18:04:38.729427Z",
     "iopub.status.idle": "2023-10-08T18:04:39.268101Z",
     "shell.execute_reply": "2023-10-08T18:04:39.267106Z"
    },
    "papermill": {
     "duration": 0.561702,
     "end_time": "2023-10-08T18:04:39.270571",
     "exception": false,
     "start_time": "2023-10-08T18:04:38.708869",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Age' , hue = 'LeaveOrNot', data = df)\n",
    "plt.figure(figsize = (14, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c9a3b9",
   "metadata": {
    "papermill": {
     "duration": 0.019048,
     "end_time": "2023-10-08T18:04:39.310265",
     "exception": false,
     "start_time": "2023-10-08T18:04:39.291217",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**From the above graph we don't get any meaning full insights so lets try modify our search**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9965f0ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:39.349653Z",
     "iopub.status.busy": "2023-10-08T18:04:39.348972Z",
     "iopub.status.idle": "2023-10-08T18:04:39.785247Z",
     "shell.execute_reply": "2023-10-08T18:04:39.784093Z"
    },
    "papermill": {
     "duration": 0.458217,
     "end_time": "2023-10-08T18:04:39.787395",
     "exception": false,
     "start_time": "2023-10-08T18:04:39.329178",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Age' , hue = 'LeaveOrNot', data = df.loc[df.Education == 1])\n",
    "plt.figure(figsize = (14, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac45f93",
   "metadata": {
    "papermill": {
     "duration": 0.0184,
     "end_time": "2023-10-08T18:04:39.824717",
     "exception": false,
     "start_time": "2023-10-08T18:04:39.806317",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**The above graph is made on Employee who have masters**\n",
    "\n",
    "# **5. Insight**\n",
    "**So we can get insight from the above graph that employee who have master's degree and are in between 24 - 27 are more likely to leave the job**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296c1e22",
   "metadata": {
    "papermill": {
     "duration": 0.018631,
     "end_time": "2023-10-08T18:04:39.862301",
     "exception": false,
     "start_time": "2023-10-08T18:04:39.843670",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Conclusion**\n",
    "\n",
    "**So here we can conclude our research that what are the reasons when an employee leaves**\n",
    "\n",
    "**The main reasons are :-**\n",
    "\n",
    "**1. Education** \n",
    "\n",
    "**2. Age**\n",
    "\n",
    "**3. Gender**\n",
    "\n",
    "**4. Payment Tier**\n",
    "\n",
    "# **Now next part is model building**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1b29485",
   "metadata": {
    "papermill": {
     "duration": 0.018565,
     "end_time": "2023-10-08T18:04:39.899815",
     "exception": false,
     "start_time": "2023-10-08T18:04:39.881250",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Training and Spliting data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2c4f1822",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:39.938911Z",
     "iopub.status.busy": "2023-10-08T18:04:39.938564Z",
     "iopub.status.idle": "2023-10-08T18:04:39.948114Z",
     "shell.execute_reply": "2023-10-08T18:04:39.947384Z"
    },
    "papermill": {
     "duration": 0.031401,
     "end_time": "2023-10-08T18:04:39.950078",
     "exception": false,
     "start_time": "2023-10-08T18:04:39.918677",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('LeaveOrNot', axis = 1), df.LeaveOrNot, test_size = 0.2, random_state = 23)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b120b7",
   "metadata": {
    "papermill": {
     "duration": 0.018415,
     "end_time": "2023-10-08T18:04:39.987229",
     "exception": false,
     "start_time": "2023-10-08T18:04:39.968814",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **SMOTE Oversampling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3bc54594",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:40.027520Z",
     "iopub.status.busy": "2023-10-08T18:04:40.026543Z",
     "iopub.status.idle": "2023-10-08T18:04:40.046950Z",
     "shell.execute_reply": "2023-10-08T18:04:40.046191Z"
    },
    "papermill": {
     "duration": 0.043144,
     "end_time": "2023-10-08T18:04:40.049351",
     "exception": false,
     "start_time": "2023-10-08T18:04:40.006207",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sm = SMOTE(sampling_strategy=\"minority\", random_state=42)\n",
    "x_train_smote, y_train_smote= sm.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ed44c64",
   "metadata": {
    "papermill": {
     "duration": 0.018965,
     "end_time": "2023-10-08T18:04:40.087684",
     "exception": false,
     "start_time": "2023-10-08T18:04:40.068719",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Grid Search CV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bc01bb81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:40.129543Z",
     "iopub.status.busy": "2023-10-08T18:04:40.128907Z",
     "iopub.status.idle": "2023-10-08T18:04:40.134481Z",
     "shell.execute_reply": "2023-10-08T18:04:40.133537Z"
    },
    "papermill": {
     "duration": 0.02917,
     "end_time": "2023-10-08T18:04:40.136892",
     "exception": false,
     "start_time": "2023-10-08T18:04:40.107722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_params = {\n",
    "    'svm' : {\n",
    "        'model' : svm.SVC(gamma = 'auto'),\n",
    "             'params' : {\n",
    "                 'C' : [1,10, 20, 30, 40],\n",
    "                 'kernel' : ['rbf', 'linear', 'sigmoid']\n",
    "             }\n",
    "        }, \n",
    "     'random_forest' : {\n",
    "         'model' : RandomForestClassifier(),\n",
    "         'params': {\n",
    "             'n_estimators' : [1,100,500,1000]\n",
    "         }\n",
    "     }, \n",
    "    'gaussian_nb' : { \n",
    "        'model' : GaussianNB(), \n",
    "        'params' : {} \n",
    "        \n",
    "    },\n",
    "    'multinomial_nb': {\n",
    "        'model' : MultinomialNB(),\n",
    "        'params' : {}\n",
    "        \n",
    "},\n",
    "    \n",
    "    'decision_tree_classifier' : {\n",
    "        'model' : DecisionTreeClassifier(),\n",
    "        'params' : {}\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15cb2463",
   "metadata": {
    "papermill": {
     "duration": 0.018706,
     "end_time": "2023-10-08T18:04:40.174491",
     "exception": false,
     "start_time": "2023-10-08T18:04:40.155785",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Applying Grid Search CV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e3cdd875",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:04:40.215300Z",
     "iopub.status.busy": "2023-10-08T18:04:40.214587Z",
     "iopub.status.idle": "2023-10-08T18:07:44.871105Z",
     "shell.execute_reply": "2023-10-08T18:07:44.869856Z"
    },
    "papermill": {
     "duration": 184.698361,
     "end_time": "2023-10-08T18:07:44.892185",
     "exception": false,
     "start_time": "2023-10-08T18:04:40.193824",
     "status": "completed"
    },
    "tags": []
   },
   "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>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.816024</td>\n",
       "      <td>{'C': 40, 'kernel': 'rbf'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>0.832320</td>\n",
       "      <td>{'n_estimators': 1000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gaussian_nb</td>\n",
       "      <td>0.651185</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>multinomial_nb</td>\n",
       "      <td>0.605330</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>decision_tree_classifier</td>\n",
       "      <td>0.811945</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      model  best_score                 best_params\n",
       "0                       svm    0.816024  {'C': 40, 'kernel': 'rbf'}\n",
       "1             random_forest    0.832320      {'n_estimators': 1000}\n",
       "2               gaussian_nb    0.651185                          {}\n",
       "3            multinomial_nb    0.605330                          {}\n",
       "4  decision_tree_classifier    0.811945                          {}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = []\n",
    "\n",
    "for model_name, mp, in model_params.items():\n",
    "    clf = GridSearchCV(mp['model'], mp['params'], cv = 5, return_train_score = False)\n",
    "    clf.fit(x_train_smote, y_train_smote)\n",
    "    score.append({\n",
    "        'model' : model_name,\n",
    "        'best_score' : clf.best_score_,\n",
    "        'best_params' : clf.best_params_\n",
    "    })\n",
    "    \n",
    "df3 = pd.DataFrame(score, columns=['model', 'best_score', 'best_params'])\n",
    "df3    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f2e81f1",
   "metadata": {
    "papermill": {
     "duration": 0.018859,
     "end_time": "2023-10-08T18:07:44.930321",
     "exception": false,
     "start_time": "2023-10-08T18:07:44.911462",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Applying Support Vector Machine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89e039cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:07:44.969798Z",
     "iopub.status.busy": "2023-10-08T18:07:44.969184Z",
     "iopub.status.idle": "2023-10-08T18:07:46.087498Z",
     "shell.execute_reply": "2023-10-08T18:07:46.086274Z"
    },
    "papermill": {
     "duration": 1.140674,
     "end_time": "2023-10-08T18:07:46.089825",
     "exception": false,
     "start_time": "2023-10-08T18:07:44.949151",
     "status": "completed"
    },
    "tags": []
   },
   "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>SVC(C=40)</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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=40)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=40)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv = svm.SVC(C = 40, kernel = 'rbf')\n",
    "sv.fit(x_train_smote , y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0fbfd80e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:07:46.132197Z",
     "iopub.status.busy": "2023-10-08T18:07:46.131408Z",
     "iopub.status.idle": "2023-10-08T18:07:46.391265Z",
     "shell.execute_reply": "2023-10-08T18:07:46.390073Z"
    },
    "papermill": {
     "duration": 0.283507,
     "end_time": "2023-10-08T18:07:46.393346",
     "exception": false,
     "start_time": "2023-10-08T18:07:46.109839",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4672395273899033"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae7e24fb",
   "metadata": {
    "papermill": {
     "duration": 0.0204,
     "end_time": "2023-10-08T18:07:46.434557",
     "exception": false,
     "start_time": "2023-10-08T18:07:46.414157",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Support vector machine gives 46% Accuracy, Not Good!**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432fa5cd",
   "metadata": {
    "papermill": {
     "duration": 0.019759,
     "end_time": "2023-10-08T18:07:46.474374",
     "exception": false,
     "start_time": "2023-10-08T18:07:46.454615",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Applying Random Forest Algorithm**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9ea1cc71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:07:46.516359Z",
     "iopub.status.busy": "2023-10-08T18:07:46.515928Z",
     "iopub.status.idle": "2023-10-08T18:07:48.362319Z",
     "shell.execute_reply": "2023-10-08T18:07:48.361316Z"
    },
    "papermill": {
     "duration": 1.869656,
     "end_time": "2023-10-08T18:07:48.364179",
     "exception": false,
     "start_time": "2023-10-08T18:07:46.494523",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=500)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=500)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=500)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators = 500)\n",
    "rf.fit(x_train_smote, y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5a68b998",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:07:48.408603Z",
     "iopub.status.busy": "2023-10-08T18:07:48.407923Z",
     "iopub.status.idle": "2023-10-08T18:07:48.521606Z",
     "shell.execute_reply": "2023-10-08T18:07:48.520641Z"
    },
    "papermill": {
     "duration": 0.136683,
     "end_time": "2023-10-08T18:07:48.523547",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.386864",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8184747583243824"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec39d2ed",
   "metadata": {
    "papermill": {
     "duration": 0.019833,
     "end_time": "2023-10-08T18:07:48.563817",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.543984",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Random Forest Algorithm Gives 82% Score**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "353920e3",
   "metadata": {
    "papermill": {
     "duration": 0.020373,
     "end_time": "2023-10-08T18:07:48.605745",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.585372",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Gaussian Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cf3807d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:07:48.648088Z",
     "iopub.status.busy": "2023-10-08T18:07:48.647679Z",
     "iopub.status.idle": "2023-10-08T18:07:48.659420Z",
     "shell.execute_reply": "2023-10-08T18:07:48.658364Z"
    },
    "papermill": {
     "duration": 0.035288,
     "end_time": "2023-10-08T18:07:48.661415",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.626127",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GNB = GaussianNB()\n",
    "GNB.fit(x_train_smote, y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "38a64076",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:07:48.704262Z",
     "iopub.status.busy": "2023-10-08T18:07:48.703600Z",
     "iopub.status.idle": "2023-10-08T18:07:48.711376Z",
     "shell.execute_reply": "2023-10-08T18:07:48.710629Z"
    },
    "papermill": {
     "duration": 0.031152,
     "end_time": "2023-10-08T18:07:48.713236",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.682084",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6949516648764769"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GNB.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d2504f",
   "metadata": {
    "papermill": {
     "duration": 0.021828,
     "end_time": "2023-10-08T18:07:48.756640",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.734812",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Gaussian Naive Bayes model gives 69% Accuracy**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff3228a",
   "metadata": {
    "papermill": {
     "duration": 0.019609,
     "end_time": "2023-10-08T18:07:48.796743",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.777134",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Multinomial Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3dc6ac44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:07:48.841867Z",
     "iopub.status.busy": "2023-10-08T18:07:48.841192Z",
     "iopub.status.idle": "2023-10-08T18:07:48.854055Z",
     "shell.execute_reply": "2023-10-08T18:07:48.853300Z"
    },
    "papermill": {
     "duration": 0.039744,
     "end_time": "2023-10-08T18:07:48.856584",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.816840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MNB = MultinomialNB()\n",
    "MNB.fit(x_train_smote, y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5066f834",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:07:48.908659Z",
     "iopub.status.busy": "2023-10-08T18:07:48.907972Z",
     "iopub.status.idle": "2023-10-08T18:07:48.915711Z",
     "shell.execute_reply": "2023-10-08T18:07:48.915066Z"
    },
    "papermill": {
     "duration": 0.035086,
     "end_time": "2023-10-08T18:07:48.919052",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.883966",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5993555316863588"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MNB.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3abda4da",
   "metadata": {
    "papermill": {
     "duration": 0.020349,
     "end_time": "2023-10-08T18:07:48.964129",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.943780",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Multinomial Naive Bayes gives 59.9% accuracy**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad73f4a2",
   "metadata": {
    "papermill": {
     "duration": 0.020133,
     "end_time": "2023-10-08T18:07:49.004722",
     "exception": false,
     "start_time": "2023-10-08T18:07:48.984589",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Decision Tree Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "be82697a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:07:49.047813Z",
     "iopub.status.busy": "2023-10-08T18:07:49.047197Z",
     "iopub.status.idle": "2023-10-08T18:07:49.061991Z",
     "shell.execute_reply": "2023-10-08T18:07:49.061240Z"
    },
    "papermill": {
     "duration": 0.038706,
     "end_time": "2023-10-08T18:07:49.063926",
     "exception": false,
     "start_time": "2023-10-08T18:07:49.025220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTC = DecisionTreeClassifier()\n",
    "DTC.fit(x_train_smote, y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c92eb365",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-08T18:07:49.107033Z",
     "iopub.status.busy": "2023-10-08T18:07:49.106391Z",
     "iopub.status.idle": "2023-10-08T18:07:49.114621Z",
     "shell.execute_reply": "2023-10-08T18:07:49.113529Z"
    },
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     "end_time": "2023-10-08T18:07:49.116614",
     "exception": false,
     "start_time": "2023-10-08T18:07:49.084567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8045112781954887"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTC.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a902697f",
   "metadata": {
    "papermill": {
     "duration": 0.020662,
     "end_time": "2023-10-08T18:07:49.158185",
     "exception": false,
     "start_time": "2023-10-08T18:07:49.137523",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Decision Tree gives 80% accuracy**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b5a3de",
   "metadata": {
    "papermill": {
     "duration": 0.01999,
     "end_time": "2023-10-08T18:07:49.198721",
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     "start_time": "2023-10-08T18:07:49.178731",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Conclusion On Models**\n",
    "\n",
    "# **The best model is Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2edf0cc6",
   "metadata": {
    "papermill": {
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     "end_time": "2023-10-08T18:07:49.241300",
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     "start_time": "2023-10-08T18:07:49.220580",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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