Employee Turnover Rate Prediction using Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f96f5946",
   "metadata": {},
   "source": [
    "# Employees Turnover rate Prediction using ML model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56905272",
   "metadata": {},
   "source": [
    "__Employee turnover__ refers to the total number of employees who leave a company over a certain time period. It includes those who exit voluntarily as well as employees who are fired or laid off—that is, involuntary turnover. High turnover can be costly and disruptive to a business, so it's important to understand what causes it and how to reduce it.\n",
    "\n",
    "This project aims to build a Machine Learning model to analyze various factors that contribute to Employee Turnover including job satisfaction, last evaluation, number of projects, average monthly hours, time spent in company, accidents at workplace, promotion in 5 years, department and salary.\n",
    "\n",
    "Here the variable 'Left' is used to indicate whether an employee stay or leave an organization.\n",
    "\n",
    "__Steps performed to build the model:-__\n",
    "\n",
    "1.\tPerform data quality check by checking for missing values if any.                                                       \n",
    "2.\tUnderstand what factors contributed most to employee turnover by EDA.                                                   \n",
    "3.\tPerform clustering of Employees who left based on their satisfaction and evaluation.                                   \n",
    "4.\tHandle the left Class Imbalance using SMOTE technique.                                                                 \n",
    "5.\tPerform k-fold cross-validation model training and evaluate performance.                                               \n",
    "6.\tIdentify the best model and justify the evaluation metrics used.                                                      \n",
    "7.\tSuggest various retention strategies for targeted employees.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4a0cd41",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aad50c11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years department  \\\n",
       "0                   3              0     1                      0      sales   \n",
       "1                   6              0     1                      0      sales   \n",
       "2                   4              0     1                      0      sales   \n",
       "3                   5              0     1                      0      sales   \n",
       "4                   3              0     1                      0      sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('empp1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67218f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5d2e72b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   satisfaction_level     14999 non-null  float64\n",
      " 1   last_evaluation        14999 non-null  float64\n",
      " 2   number_project         14999 non-null  int64  \n",
      " 3   average_montly_hours   14999 non-null  int64  \n",
      " 4   time_spend_company     14999 non-null  int64  \n",
      " 5   Work_accident          14999 non-null  int64  \n",
      " 6   left                   14999 non-null  int64  \n",
      " 7   promotion_last_5years  14999 non-null  int64  \n",
      " 8   department             14999 non-null  object \n",
      " 9   salary                 14999 non-null  object \n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25cccd52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       0\n",
       "last_evaluation          0\n",
       "number_project           0\n",
       "average_montly_hours     0\n",
       "time_spend_company       0\n",
       "Work_accident            0\n",
       "left                     0\n",
       "promotion_last_5years    0\n",
       "department               0\n",
       "salary                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065c01f5",
   "metadata": {},
   "source": [
    "We can infer from above output that there is no null value present in our data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4ca70c",
   "metadata": {},
   "source": [
    "# Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "23bcef95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 5, 7, 6, 4, 3], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['number_project'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0167ea8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['number_project'])\n",
    "plt.xlabel('Number of Project')\n",
    "plt.ylabel('Frequency of Projects')\n",
    "plt.title(' Project done Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f07e0457",
   "metadata": {},
   "source": [
    "Number of projects seems to be almost normally distributed with mean projects near to 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8dcf0cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  6,  4,  5,  2,  8, 10,  7], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['time_spend_company'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1616b9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['time_spend_company'])\n",
    "plt.xlabel('Number of Years')\n",
    "plt.ylabel('Frequency of Employees')\n",
    "plt.title(' Employees Time spent Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c01a78ee",
   "metadata": {},
   "source": [
    "From the above plot we can see that there are only few people who have worked for more than 5 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c5330e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Work_accident'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f3b0b77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['left'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8962d43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['promotion_last_5years'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6209a199",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sales' 'accounting' 'hr' 'technical' 'support' 'management' 'IT'\n",
      " 'product_mng' 'marketing' 'RandD']\n",
      "The number of Departments in the organization are:  10\n"
     ]
    }
   ],
   "source": [
    "print(df['department'].unique())\n",
    "print('The number of Departments in the organization are: ',df['department'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0a3b817a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(df['department'])\n",
    "plt.xlabel('Department Name')\n",
    "plt.ylabel('Frequency of Employees')\n",
    "plt.title(' Employees Distribution within Departments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e335a1",
   "metadata": {},
   "source": [
    "Above distribution shows that maximum people work in Sales Department and minimum in Management."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a64147f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['low', 'medium', 'high'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['salary'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "231b4686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['salary'])\n",
    "plt.xlabel('Type of Salary')\n",
    "plt.ylabel('Frequency of Employees')\n",
    "plt.title(' Salary Distribution within Organization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b6bd5f",
   "metadata": {},
   "source": [
    "Above distribution shows that maximum people working in organization have low salary and small portion of employees have High salary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66fc4155",
   "metadata": {},
   "source": [
    "# Bi-Variate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8608317e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='satisfaction_level', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=df,x='satisfaction_level',hue='left',fill=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e41eab67",
   "metadata": {},
   "source": [
    "Above plot shows that employees having low satisfaction tends to leave organization more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "26938d0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=df,x='last_evaluation',hue='left',fill=True)\n",
    "plt.xlabel('rating in last evaluation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a142e0",
   "metadata": {},
   "source": [
    "Above plot shows as the ratings in last evaluation increases the tendency of employee to leave organiztion decreases. But employees getting very high ratings also tends to leave."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f8b958dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='average_montly_hours', ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=df,x='average_montly_hours',hue='left',fill=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e603fc8",
   "metadata": {},
   "source": [
    "Above plot shows the employees who are leaving are usually working for longer hours than those who are staying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb86c4fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time_spend_company', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=df,x='time_spend_company',hue='left',fill=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef221ade",
   "metadata": {},
   "source": [
    "Above plot shows that employees who are leaving have usually spent lesser years than those who are staying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5a2306ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Work_accident  left\n",
       "0              0       73.484022\n",
       "               1       26.515978\n",
       "1              0       92.208391\n",
       "               1        7.791609\n",
       "Name: left, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Work_accident'],hue=df['left'])\n",
    "df.groupby(['Work_accident']).left.value_counts(sum)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "811317bb",
   "metadata": {},
   "source": [
    "Above plot shows that more accidents are done by employees who are not leaving the organization than those who are leaving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1fb52375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "promotion_last_5years  left\n",
       "0                      0       75.803815\n",
       "                       1       24.196185\n",
       "1                      0       94.043887\n",
       "                       1        5.956113\n",
       "Name: left, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pqVFNTY2zXFVV1YizBAAALUFAzzS98soreuGFF/TSSy/pgw8+0LPPPqvf/e53evbZZ33qXC6Xz7Ixpt7YqU6taaj+bPvJzc11Lhx3u92Kj4+3OS0AANACBXRomjp1qh588EH9+Mc/VlJSkjIzM/XAAw8oNzdXkuTxeCSp3mxQRUWFM/vk8XhUW1urysrKM9Z8+eWX9Y6/b9++erNYJ5sxY4a8Xq/z2r17d+NPFgAABLSADk1HjhzRZZf5thgUFOQ8cqBbt27yeDwqKChw1tfW1qqwsFB9+vSRJCUnJys4ONinpqysTNu2bXNqUlNT5fV6tXnzZqdm06ZN8nq9Tk1DQkNDFRUV5fMCAAAXp4C+pun222/XrFmz1LlzZ33nO9/R1q1bNX/+fP385z+XdPwjtezsbOXk5CghIUEJCQnKyclReHi4Ro4cKUlyu90aM2aMJk+erOjoaLVv315TpkxRUlKSBgwYIEnq3r27Bg8erLFjx2rJkiWSpPvuu09Dhw61vnMOAABc3AI6ND355JN65JFHlJWVpYqKCsXFxWncuHF69NFHnZpp06apurpaWVlZqqysVEpKilavXq3IyEinZsGCBWrVqpUyMjJUXV2t/v37a/ny5QoKCnJqXnzxRU2cONG5y27YsGHKy8u7cCcLAAACWkA/p6ml4TlNlx6e0wQALd9F8ZwmAACAQEFoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsEBoAgAAsNCo0HTrrbfqwIED9carqqp06623nm9PAAAAAadRoWnNmjWqra2tN/7NN99o7dq1590UAABAoGl1LsUffvih898ff/yxysvLneW6ujrl5+fr8ssvb7ruAAAAAsQ5habrrrtOLpdLLperwY/hwsLC9OSTTzZZcwAAAIHinEJTaWmpjDG68sortXnzZnXs2NFZFxISopiYGAUFBTV5kwAAAP52TqGpS5cukqRjx441SzMAAACB6pxC08l27typNWvWqKKiol6IevTRR8+7MQAAgEDSqND09NNP6xe/+IU6dOggj8cjl8vlrHO5XIQmAABw0WnUIwd++9vfatasWSovL1dxcbG2bt3qvD744IMmbfD//u//9NOf/lTR0dEKDw/Xddddp6KiIme9MUaPP/644uLiFBYWprS0NG3fvt1nHzU1NZowYYI6dOigNm3aaNiwYdqzZ49PTWVlpTIzM+V2u+V2u5WZmdngs6gAAMClqVGhqbKyUnfddVdT99Lgcfr27avg4GC99dZb+vjjjzVv3jy1bdvWqZkzZ47mz5+vvLw8bdmyRR6PRwMHDtTBgwedmuzsbK1cuVIrVqzQunXrdOjQIQ0dOlR1dXVOzciRI1VcXKz8/Hzl5+eruLhYmZmZzX6OAACgZXAZY8y5bjRmzBjdeOONGj9+fHP05HjwwQf13nvvnfaBmcYYxcXFKTs7W9OnT5d0fFYpNjZWs2fP1rhx4+T1etWxY0c9//zzGjFihCRp7969io+P16pVqzRo0CDt2LFDPXr00MaNG5WSkiJJ2rhxo1JTU/XJJ58oMTHRqt+qqiq53W55vV5FRUU1wTtQX/LU55plv2icormj/N0CAOA82f79btQ1TVdddZUeeeQRbdy4UUlJSQoODvZZP3HixMbstp4///nPGjRokO666y4VFhbq8ssvV1ZWlsaOHSvp+CMQysvLlZ6e7mwTGhqqfv36af369Ro3bpyKiop09OhRn5q4uDj17NlT69ev16BBg7Rhwwa53W4nMElS79695Xa7tX79euvQBAAALl6NCk1Lly5VRESECgsLVVhY6LPO5XI1WWj63//9Xy1evFiTJk3Sr3/9a23evFkTJ05UaGioRo0a5TyRPDY21me72NhYffHFF5Kk8vJyhYSEqF27dvVqTmxfXl6umJiYesePiYnxeer5qWpqalRTU+MsV1VVNe5EAQBAwGtUaCotLW3qPhp07Ngx9erVSzk5OZKk66+/Xtu3b9fixYs1atT//1jk5Lv3pOMf2506dqpTaxqqP9t+cnNz9cQTT1idCwAAaNkadSH4hdKpUyf16NHDZ6x79+7atWuXJMnj8UhSvdmgiooKZ/bJ4/GotrZWlZWVZ6z58ssv6x1/37599WaxTjZjxgx5vV7ntXv37nM8QwAA0FI0aqbp5z//+RnX/+lPf2pUM6fq27evSkpKfMZ27tzpPJm8W7du8ng8Kigo0PXXXy9Jqq2tVWFhoWbPni1JSk5OVnBwsAoKCpSRkSFJKisr07Zt2zRnzhxJUmpqqrxerzZv3qzvfve7kqRNmzbJ6/WqT58+p+0vNDRUoaGhTXKuAAAgsDUqNJ06a3P06FFt27ZNBw4caPCLfBvrgQceUJ8+fZSTk6OMjAxt3rxZS5cu1dKlSyUd/0gtOztbOTk5SkhIUEJCgnJychQeHq6RI0dKktxut8aMGaPJkycrOjpa7du315QpU5SUlKQBAwZIOj57NXjwYI0dO1ZLliyRJN13330aOnQoF4EDAABJjQxNK1eurDd27NgxZWVl6corrzzvpk648cYbtXLlSs2YMUMzZ85Ut27dtHDhQt19991OzbRp01RdXa2srCxVVlYqJSVFq1evVmRkpFOzYMECtWrVShkZGaqurlb//v21fPlyny8XfvHFFzVx4kTnLrthw4YpLy+vyc4FAAC0bI16TtPplJSUKC0tTWVlZU21yxaF5zRdenhOEwC0fLZ/v5v0QvDPPvtM//73v5tylwAAAAGhUR/PTZo0yWfZGKOysjL97W9/0z333NMkjQEAAASSRoWmrVu3+ixfdtll6tixo+bNm3fWO+sAAABaokaFpnfeeaep+wAAAAhojQpNJ+zbt08lJSVyuVy6+uqr1bFjx6bqCwAAIKA06kLww4cP6+c//7k6deqkm2++WTfddJPi4uI0ZswYHTlypKl7BAAA8LtGhaZJkyapsLBQf/nLX3TgwAEdOHBAb775pgoLCzV58uSm7hEAAMDvGvXx3GuvvaZXX31VaWlpzthtt92msLAwZWRkaPHixU3VHwAAQEBo1EzTkSNHGvwi25iYGD6eAwAAF6VGhabU1FQ99thj+uabb5yx6upqPfHEE0pNTW2y5gAAAAJFoz6eW7hwoYYMGaIrrrhC1157rVwul4qLixUaGqrVq1c3dY8AAAB+16jQlJSUpE8//VQvvPCCPvnkExlj9OMf/1h33323wsLCmrpHAAAAv2tUaMrNzVVsbKzGjh3rM/6nP/1J+/bt0/Tp05ukOQAAgEDRqGualixZom9/+9v1xr/zne/oqaeeOu+mAAAAAk2jQlN5ebk6depUb7xjx44qKys776YAAAACTaNCU3x8vN5777164++9957i4uLOuykAAIBA06hrmu69915lZ2fr6NGjuvXWWyVJb7/9tqZNm8YTwQEAwEWpUaFp2rRp+vrrr5WVlaXa2lpJUuvWrTV9+nTNmDGjSRsEAAAIBI0KTS6XS7Nnz9YjjzyiHTt2KCwsTAkJCQoNDW3q/gAAAAJCo0LTCREREbrxxhubqhcAAICA1agLwQEAAC41hCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALLSo05ebmyuVyKTs72xkzxujxxx9XXFycwsLClJaWpu3bt/tsV1NTowkTJqhDhw5q06aNhg0bpj179vjUVFZWKjMzU263W263W5mZmTpw4MAFOCsAANAStJjQtGXLFi1dulTXXHONz/icOXM0f/585eXlacuWLfJ4PBo4cKAOHjzo1GRnZ2vlypVasWKF1q1bp0OHDmno0KGqq6tzakaOHKni4mLl5+crPz9fxcXFyszMvGDnBwAAAluLCE2HDh3S3Xffraefflrt2rVzxo0xWrhwoR566CH94Ac/UM+ePfXss8/qyJEjeumllyRJXq9XzzzzjObNm6cBAwbo+uuv1wsvvKCPPvpIf//73yVJO3bsUH5+vv74xz8qNTVVqampevrpp/XXv/5VJSUlfjlnAAAQWFpEaPrlL3+p73//+xowYIDPeGlpqcrLy5Wenu6MhYaGql+/flq/fr0kqaioSEePHvWpiYuLU8+ePZ2aDRs2yO12KyUlxanp3bu33G63U9OQmpoaVVVV+bwAAMDFqZW/GzibFStWqKioSO+//369deXl5ZKk2NhYn/HY2Fh98cUXTk1ISIjPDNWJmhPbl5eXKyYmpt7+Y2JinJqG5Obm6oknnji3EwIAAC1SQM807d69W7/61a/04osvqnXr1qetc7lcPsvGmHpjpzq1pqH6s+1nxowZ8nq9zmv37t1nPCYAAGi5Ajo0FRUVqaKiQsnJyWrVqpVatWqlwsJC/f73v1erVq2cGaZTZ4MqKiqcdR6PR7W1taqsrDxjzZdfflnv+Pv27as3i3Wy0NBQRUVF+bwAAMDFKaBDU//+/fXRRx+puLjYefXq1Ut33323iouLdeWVV8rj8aigoMDZpra2VoWFherTp48kKTk5WcHBwT41ZWVl2rZtm1OTmpoqr9erzZs3OzWbNm2S1+t1agAAwKUtoK9pioyMVM+ePX3G2rRpo+joaGc8OztbOTk5SkhIUEJCgnJychQeHq6RI0dKktxut8aMGaPJkycrOjpa7du315QpU5SUlORcWN69e3cNHjxYY8eO1ZIlSyRJ9913n4YOHarExMQLeMYAACBQBXRosjFt2jRVV1crKytLlZWVSklJ0erVqxUZGenULFiwQK1atVJGRoaqq6vVv39/LV++XEFBQU7Niy++qIkTJzp32Q0bNkx5eXkX/HwAAEBgchljjL+buFhUVVXJ7XbL6/U22/VNyVOfa5b9onGK5o7ydwsAgPNk+/c7oK9pAgAACBSEJgAAAAuEJgAAAAuEJgAAAAuEJgAAAAuEJgAAAAuEJgAAAAst/uGWgD/tmpnk7xbwH50f/cjfLQC4yDHTBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYIHQBAAAYCGgQ1Nubq5uvPFGRUZGKiYmRsOHD1dJSYlPjTFGjz/+uOLi4hQWFqa0tDRt377dp6ampkYTJkxQhw4d1KZNGw0bNkx79uzxqamsrFRmZqbcbrfcbrcyMzN14MCB5j5FAADQQgR0aCosLNQvf/lLbdy4UQUFBfr3v/+t9PR0HT582KmZM2eO5s+fr7y8PG3ZskUej0cDBw7UwYMHnZrs7GytXLlSK1as0Lp163To0CENHTpUdXV1Ts3IkSNVXFys/Px85efnq7i4WJmZmRf0fAEAQOByGWOMv5uwtW/fPsXExKiwsFA333yzjDGKi4tTdna2pk+fLun4rFJsbKxmz56tcePGyev1qmPHjnr++ec1YsQISdLevXsVHx+vVatWadCgQdqxY4d69OihjRs3KiUlRZK0ceNGpaam6pNPPlFiYqJVf1VVVXK73fJ6vYqKimqW9yB56nPNsl80zsrIuf5uAf/R+dGP/N0CgBbK9u93QM80ncrr9UqS2rdvL0kqLS1VeXm50tPTnZrQ0FD169dP69evlyQVFRXp6NGjPjVxcXHq2bOnU7Nhwwa53W4nMElS79695Xa7nZqG1NTUqKqqyucFAAAuTi0mNBljNGnSJH3ve99Tz549JUnl5eWSpNjYWJ/a2NhYZ115eblCQkLUrl27M9bExMTUO2ZMTIxT05Dc3FznGii32634+PjGnyAAAAhoLSY03X///frwww/18ssv11vncrl8lo0x9cZOdWpNQ/Vn28+MGTPk9Xqd1+7du892GgAAoIVqEaFpwoQJ+vOf/6x33nlHV1xxhTPu8Xgkqd5sUEVFhTP75PF4VFtbq8rKyjPWfPnll/WOu2/fvnqzWCcLDQ1VVFSUzwsAAFycAjo0GWN0//336/XXX9c//vEPdevWzWd9t27d5PF4VFBQ4IzV1taqsLBQffr0kSQlJycrODjYp6asrEzbtm1zalJTU+X1erV582anZtOmTfJ6vU4NAAC4tLXydwNn8stf/lIvvfSS3nzzTUVGRjozSm63W2FhYXK5XMrOzlZOTo4SEhKUkJCgnJwchYeHa+TIkU7tmDFjNHnyZEVHR6t9+/aaMmWKkpKSNGDAAElS9+7dNXjwYI0dO1ZLliyRJN13330aOnSo9Z1zAADg4hbQoWnx4sWSpLS0NJ/xZcuWafTo0ZKkadOmqbq6WllZWaqsrFRKSopWr16tyMhIp37BggVq1aqVMjIyVF1drf79+2v58uUKCgpyal588UVNnDjRuctu2LBhysvLa94TBAAALUaLek5ToOM5TZcentMUOHhOE4DGuiif0wQAAOAvhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALrfzdAADguOSpz/m7BZykaO4of7eAAMNMEwAAgAVCEwAAgAVCEwAAgAVCEwAAgAVC0ykWLVqkbt26qXXr1kpOTtbatWv93RIAAAgAhKaTvPLKK8rOztZDDz2krVu36qabbtKQIUO0a9cuf7cGAAD8jNB0kvnz52vMmDG699571b17dy1cuFDx8fFavHixv1sDAAB+Rmj6j9raWhUVFSk9Pd1nPD09XevXr/dTVwAAIFDwcMv/+Oqrr1RXV6fY2Fif8djYWJWXlze4TU1NjWpqapxlr9crSaqqqmq2Putqqptt3zh3B4Pr/N0C/qM5f+8uFH6/A8v2X/fwdwv4j/gHNzbr/k/8+2GMOWMdoekULpfLZ9kYU2/shNzcXD3xxBP1xuPj45ulNwSenv5uAP9frtvfHeAiw+93ALlAv98HDx6U2336YxGa/qNDhw4KCgqqN6tUUVFRb/bphBkzZmjSpEnO8rFjx/T1118rOjr6tEELF4+qqirFx8dr9+7dioqK8nc7AJoQv9+XFmOMDh48qLi4uDPWEZr+IyQkRMnJySooKNCdd97pjBcUFOiOO+5ocJvQ0FCFhob6jLVt27Y520QAioqK4h9V4CLF7/el40wzTCcQmk4yadIkZWZmqlevXkpNTdXSpUu1a9cujR8/3t+tAQAAPyM0nWTEiBHav3+/Zs6cqbKyMvXs2VOrVq1Sly5d/N0aAADwM0LTKbKyspSVleXvNtAChIaG6rHHHqv3ES2Alo/fbzTEZc52fx0AAAB4uCUAAIANQhMAAIAFQhMAAIAFQhPQCIsWLVK3bt3UunVrJScna+3atf5uCUATePfdd3X77bcrLi5OLpdLb7zxhr9bQgAhNAHn6JVXXlF2drYeeughbd26VTfddJOGDBmiXbt2+bs1AOfp8OHDuvbaa5WXl+fvVhCAuHsOOEcpKSm64YYbtHjxYmese/fuGj58uHJzc/3YGYCm5HK5tHLlSg0fPtzfrSBAMNMEnIPa2loVFRUpPT3dZzw9PV3r16/3U1cAgAuB0AScg6+++kp1dXX1vsQ5Nja23pc9AwAuLoQmoBFcLpfPsjGm3hgA4OJCaALOQYcOHRQUFFRvVqmioqLe7BMA4OJCaALOQUhIiJKTk1VQUOAzXlBQoD59+vipKwDAhcAX9gLnaNKkScrMzFSvXr2UmpqqpUuXateuXRo/fry/WwNwng4dOqR//etfznJpaamKi4vVvn17de7c2Y+dIRDwyAGgERYtWqQ5c+aorKxMPXv21IIFC3TzzTf7uy0A52nNmjW65ZZb6o3fc889Wr58+YVvCAGF0AQAAGCBa5oAAAAsEJoAAAAsEJoAAAAsEJoAAAAsEJoAAAAsEJoAAAAsEJoAAAAsEJoAAAAsEJoAtEjLly9X27ZtL9jx1qxZI5fLpQMHDlywYwIILIQmAAGva9euWrhwoc/YiBEjtHPnTv801ARGjx6t4cOHn9M2Xbt2lcvl8nk9+OCDzdMggHr4wl4AZ1VbW6uQkBB/t+EjLCxMYWFh/m7jgps5c6bGjh3rLEdERPixm+MC8ecDaA7MNAGXoLS0NN1///26//771bZtW0VHR+vhhx/Wia+i7Nq1q377299q9OjRcrvdzh/p1157Td/5zncUGhqqrl27at68eT77PbHdqFGjFBERoS5duujNN9/Uvn37dMcddygiIkJJSUl6//33fbY7037T0tL0xRdf6IEHHnBmV6SGP55bvHixvvWtbykkJESJiYl6/vnnfda7XC798Y9/1J133qnw8HAlJCToz3/+c6Pew/379+snP/mJrrjiCoWHhyspKUkvv/yyT82rr76qpKQkhYWFKTo6WgMGDNDhw4f1+OOP69lnn9Wbb77pnNOaNWusjhsZGSmPx+O8ToQmY4yuuuoq/e53v/Op37Ztmy677DJ99tlnkiSv16v77rtPMTExioqK0q233qp//vOfTv1nn32mO+64Q7GxsYqIiNCNN96ov//97z77bOjno7a2Vvfff786deqk1q1bq2vXrsrNzT3XtxUIbAbAJadfv34mIiLC/OpXvzKffPKJeeGFF0x4eLhZunSpMcaYLl26mKioKDN37lzz6aefmk8//dS8//775rLLLjMzZ840JSUlZtmyZSYsLMwsW7bM2W+XLl1M+/btzVNPPWV27txpfvGLX5jIyEgzePBg81//9V+mpKTEDB8+3HTv3t0cO3bMGGPOut/9+/ebK664wsycOdOUlZWZsrIyY4wxy5YtM2632zn266+/boKDg80f/vAHU1JSYubNm2eCgoLMP/7xD6dGkrniiivMSy+9ZD799FMzceJEExERYfbv33/W9+ydd94xkkxlZaUxxpg9e/aYuXPnmq1bt5rPPvvM/P73vzdBQUFm48aNxhhj9u7da1q1amXmz59vSktLzYcffmj+8Ic/mIMHD5qDBw+ajIwMM3jwYOecampqztpDly5djMfjMe3btzfXXnut+e1vf+uz3axZs0yPHj18tnnggQfMzTffbIwx5tixY6Zv377m9ttvN1u2bDE7d+40kydPNtHR0c57UFxcbJ566inz4Ycfmp07d5qHHnrItG7d2nzxxRc+fZz68zF37lwTHx9v3n33XfP555+btWvXmpdeeums5wS0JIQm4BLUr18/n+BijDHTp0833bt3N8Yc/6M4fPhwn21GjhxpBg4c6DM2depUnz/SXbp0MT/96U+d5bKyMiPJPPLII87Yhg0bjCQn/Njud8GCBT41p4amPn36mLFjx/rU3HXXXea2225zliWZhx9+2Fk+dOiQcblc5q233jJnc2poashtt91mJk+ebIwxpqioyEgyn3/+eYO199xzj7njjjvOetyTzZ8/36xZs8b885//NE8//bTp0KGDGTNmjLN+7969JigoyGzatMkYY0xtba3p2LGjWb58uTHGmLfffttERUWZb775xme/3/rWt8ySJUtOe9wePXqYJ5980llu6OdjwoQJ5tZbb/X5mQIuNnw8B1yievfu7XzUJUmpqan69NNPVVdXJ0nq1auXT/2OHTvUt29fn7G+ffv6bCNJ11xzjfPfsbGxkqSkpKR6YxUVFee037M53X527NjhM3Zyf23atFFkZKTTy7moq6vTrFmzdM011yg6OloRERFavXq1du3aJUm69tpr1b9/fyUlJemuu+7S008/rcrKynM+zskeeOAB9evXT9dcc43uvfdePfXUU3rmmWe0f/9+SVKnTp30/e9/X3/6058kSX/961/1zTff6K677pIkFRUV6dChQ06/J16lpaXOx3eHDx/WtGnT1KNHD7Vt21YRERH65JNPnPM64dSfj9GjR6u4uFiJiYmaOHGiVq9efV7nCgQiQhOABrVp08Zn2RjjE7JOjJ0qODjY+e8T9Q2NHTt27Jz2a6Oh/Zw6dnIvJ7Y50cu5mDdvnhYsWKBp06bpH//4h4qLizVo0CDV1tZKkoKCglRQUKC33npLPXr00JNPPqnExESVlpae87FOp3fv3pKkf/3rX87YvffeqxUrVqi6ulrLli3TiBEjFB4eLun4e96pUycVFxf7vEpKSjR16lRJ0tSpU/Xaa69p1qxZWrt2rYqLi5WUlOSc1wmn/nzccMMNKi0t1W9+8xtVV1crIyNDP/rRj5rsXIFAwN1zwCVq48aN9ZYTEhIUFBTUYH2PHj20bt06n7H169fr6quvPu02Nmz2GxISctZZp+7du2vdunUaNWqUz366d+/e6N7OZO3atbrjjjv005/+VNLxQPLpp5/6HM/lcqlv377q27evHn30UXXp0kUrV67UpEmTrM7pbLZu3Srp+AzTCbfddpvatGmjxYsX66233tK7777rrLvhhhtUXl6uVq1aqWvXrqc9r9GjR+vOO++UJB06dEiff/65VT9RUVEaMWKERowYoR/96EcaPHiwvv76a7Vv375xJwgEGEITcInavXu3Jk2apHHjxumDDz7Qk08+We9uuJNNnjxZN954o37zm99oxIgR2rBhg/Ly8rRo0aLz6sNmv127dtW7776rH//4xwoNDVWHDh3q7Wfq1KnKyMjQDTfcoP79++svf/mLXn/99Xp3fjWVq666Sq+99prWr1+vdu3aaf78+SovL3dC06ZNm/T2228rPT1dMTEx2rRpk/bt2+es79q1q/7nf/5HJSUlio6OltvtrjcLdrINGzZo48aNuuWWW+R2u7VlyxY98MADGjZsmDp37uzUBQUFafTo0ZoxY4auuuoqpaamOusGDBig1NRUDR8+XLNnz1ZiYqL27t2rVatWafjw4erVq5euuuoqvf7667r99tvlcrn0yCOPWM3ELViwQJ06ddJ1112nyy67TP/93/8tj8dzQR9ACjQ7v15RBcAv+vXrZ7Kyssz48eNNVFSUadeunXnwwQedi3gbuvDaGGNeffVV06NHDxMcHGw6d+5s5s6d67O+oe0kmZUrVzrLpaWlRpLZunWr9X43bNhgrrnmGhMaGmpO/LN16oXgxhizaNEic+WVV5rg4GBz9dVXm+eee+6MvRhjjNvt9rkD8HROvRB8//795o477jAREREmJibGPPzww2bUqFHOxd0ff/yxGTRokOnYsaMJDQ01V199tc/F1BUVFWbgwIEmIiLCSDLvvPPOGY9fVFRkUlJSjNvtNq1btzaJiYnmscceM4cPH65X+9lnnxlJZs6cOfXWVVVVmQkTJpi4uDgTHBxs4uPjzd1332127dpljDn+/+eWW24xYWFhJj4+3uTl5Zl+/fqZX/3qV84+Gvr/vHTpUnPdddeZNm3amKioKNO/f3/zwQcfnPV9BVoSlzGNvHgAQIuVlpam6667rt5TtnFxeO+995SWlqY9e/Y4F94DOH98PAcAF4mamhrt3r1bjzzyiDIyMghMQBPj7jkAkDR+/Hif2/BPfo0fP77Zj5+Tk3Pa4w8ZMsRqHy+//LISExPl9Xo1Z86cZu4YuPTw8RwA6Phzo6qqqhpcFxUVpZiYmGY9/tdff62vv/66wXVhYWG6/PLLm/X4AM6O0AQAAGCBj+cAAAAsEJoAAAAsEJoAAAAsEJoAAAAsEJoAAAAsEJoAAAAsEJoAAAAsEJoAAAAs/D/Oh2/ItLisFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['promotion_last_5years'],hue=df['left'])\n",
    "df.groupby(['promotion_last_5years']).left.value_counts(sum)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee30dba",
   "metadata": {},
   "source": [
    "Above plot shows that there is greater number of employees leaving who have not received promotion in the last 5 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c1444efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "department   left\n",
       "IT           0       77.750611\n",
       "             1       22.249389\n",
       "RandD        0       84.625159\n",
       "             1       15.374841\n",
       "accounting   0       73.402868\n",
       "             1       26.597132\n",
       "hr           0       70.906631\n",
       "             1       29.093369\n",
       "management   0       85.555556\n",
       "             1       14.444444\n",
       "marketing    0       76.340326\n",
       "             1       23.659674\n",
       "product_mng  0       78.048780\n",
       "             1       21.951220\n",
       "sales        0       75.507246\n",
       "             1       24.492754\n",
       "support      0       75.100942\n",
       "             1       24.899058\n",
       "technical    0       74.375000\n",
       "             1       25.625000\n",
       "Name: left, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(df['department'],hue=df['left'])\n",
    "df.groupby(['department']).left.value_counts(sum)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1437a99d",
   "metadata": {},
   "source": [
    "Above plot shows maximum percentage of Employees leaving are from HR department and minimum are from Management."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "894af36e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "salary  left\n",
       "high    0       93.371059\n",
       "        1        6.628941\n",
       "low     0       70.311646\n",
       "        1       29.688354\n",
       "medium  0       79.568725\n",
       "        1       20.431275\n",
       "Name: left, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['salary'],hue=df['left'])\n",
    "df.groupby(['salary']).left.value_counts(sum)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "292b706c",
   "metadata": {},
   "source": [
    "Above plot shows maximum percentage of Employees leaving are having LOW salaries and minimum are from employees with HIGH salaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c42e63c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='number_project',hue='left')\n",
    "plt.xlabel('project_count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e8acad",
   "metadata": {},
   "source": [
    "From the above plot we can infer that people who have low project counts seems to be leaving organization more than those having higher number of projects. But also when number of projects exceed beyond 5 tendency to leave again start increasing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5ee082",
   "metadata": {},
   "source": [
    "# Multi Variate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e7302720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c8f4e2",
   "metadata": {},
   "source": [
    "From the above plot we can infer that:  \n",
    "\n",
    "1) There is good correlation between Average montly Hours and Number of Projects Done.           \n",
    "2) There is good correlation between employee who left the company and Satisfaction Level.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4266891e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "left                     1.00\n",
       "satisfaction_level       0.39\n",
       "Work_accident            0.15\n",
       "time_spend_company       0.14\n",
       "average_montly_hours     0.07\n",
       "promotion_last_5years    0.06\n",
       "number_project           0.02\n",
       "last_evaluation          0.01\n",
       "Name: left, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The below data shows factors responsible for Employee turnover in descending order according to pearson correlation.\n",
    "a=df.corr()\n",
    "a['left'].round(2).abs().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30abd866",
   "metadata": {},
   "source": [
    "# Clustering of Employees who left Oragnzation Using K Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6af0ba65",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecting only those employees who have left the company.\n",
    "df2=df[df['left']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6996a8f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=df2[['satisfaction_level','last_evaluation']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c745c6d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scaling the data\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "74266569",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "df3=scaler.fit_transform(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7154eb49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.22773328, -0.9517667 ],\n",
       "       [ 1.36379989,  0.71788783],\n",
       "       [-1.25086175,  0.81907901],\n",
       "       ...,\n",
       "       [-0.26562693, -0.9517667 ],\n",
       "       [-1.25086175,  1.22384375],\n",
       "       [-0.26562693, -1.00236229]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c9c17964",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "37c0f61b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'WCSS')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finding optimal number of Clusters using Elbow method.\n",
    "wcss=[]\n",
    "for i in range(1,11):\n",
    "    kmeans=KMeans(n_clusters=i,init='k-means++',random_state=99)\n",
    "    kmeans.fit(df3)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.xlabel('No of Cluster')\n",
    "plt.ylabel('WCSS')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7dd7bf",
   "metadata": {},
   "source": [
    "Here from the above plot we can say that optimal number of Clusters is 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2c7ab287",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans=KMeans(n_clusters=3,init='k-means++',random_state=99)\n",
    "y_means=kmeans.fit_predict(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cff4d3f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a71dfb20d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df3[y_means==0,0],df3[y_means==0,1],s=50,c='orange',label='Cluster1')\n",
    "plt.scatter(df3[y_means==1,0],df3[y_means==1,1],s=50,c='b',label='Cluster2')\n",
    "plt.scatter(df3[y_means==2,0],df3[y_means==2,1],s=50,c='g',label='Cluster3')\n",
    "plt.xlabel('Satisfaction_level')\n",
    "plt.ylabel('Ratings_in_last_evaluation')\n",
    "plt.legend(bbox_to_anchor=(1.05,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f807e189",
   "metadata": {},
   "source": [
    "From the above plot we can infer that:                                                                                      \n",
    "1) Cluster1 consists of Employees who have received high ratings in last evaluation and are highly satisfed but still left the company. These are our target employees as they have received very high rating due to their high performance and focus should be made on retaining these employees.            \n",
    "\n",
    "2) Cluster2 consists of Employees who have received medium ratings in last evaluation and are mediumly satisfed but still left the company. These sholud also come in our Target employees because these employees can improve and reach high performance in future with proper guidance and training.\n",
    "\n",
    "3) Cluster3 consists of Employees who have received high ratings in last evaluation and are not much satisfed. These are less important employees as they might be looking for better opportunities outside the Organization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd30e007",
   "metadata": {},
   "source": [
    "# Data Preparation For Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "97196abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='left', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking class imbalance in Target variable ('left')\n",
    "sns.countplot(df['left'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a39237f",
   "metadata": {},
   "source": [
    "Clearly there is class imbalance. We will correct it using SMOTE technique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "565faeef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       float64\n",
       "last_evaluation          float64\n",
       "number_project             int64\n",
       "average_montly_hours       int64\n",
       "time_spend_company         int64\n",
       "Work_accident              int64\n",
       "left                       int64\n",
       "promotion_last_5years      int64\n",
       "department                object\n",
       "salary                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checkinh datatype of all the columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0eb03ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating Categorical Variable from Numerical Variable\n",
    "import numpy as np\n",
    "numeric_data=df.select_dtypes(include=[np.number])\n",
    "categorical_data=df.select_dtypes(exclude=[np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ce5a4e19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  \n",
       "0                   3              0     1                      0  \n",
       "1                   6              0     1                      0  \n",
       "2                   4              0     1                      0  \n",
       "3                   5              0     1                      0  \n",
       "4                   3              0     1                      0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f7ac7d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  department  salary\n",
       "0      sales     low\n",
       "1      sales  medium\n",
       "2      sales  medium\n",
       "3      sales     low\n",
       "4      sales     low"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "833d9267",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department_RandD</th>\n",
       "      <th>department_accounting</th>\n",
       "      <th>department_hr</th>\n",
       "      <th>department_management</th>\n",
       "      <th>department_marketing</th>\n",
       "      <th>department_product_mng</th>\n",
       "      <th>department_sales</th>\n",
       "      <th>department_support</th>\n",
       "      <th>department_technical</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   department_RandD  department_accounting  department_hr  \\\n",
       "0                 0                      0              0   \n",
       "1                 0                      0              0   \n",
       "2                 0                      0              0   \n",
       "3                 0                      0              0   \n",
       "4                 0                      0              0   \n",
       "\n",
       "   department_management  department_marketing  department_product_mng  \\\n",
       "0                      0                     0                       0   \n",
       "1                      0                     0                       0   \n",
       "2                      0                     0                       0   \n",
       "3                      0                     0                       0   \n",
       "4                      0                     0                       0   \n",
       "\n",
       "   department_sales  department_support  department_technical  salary_low  \\\n",
       "0                 1                   0                     0           1   \n",
       "1                 1                   0                     0           0   \n",
       "2                 1                   0                     0           0   \n",
       "3                 1                   0                     0           1   \n",
       "4                 1                   0                     0           1   \n",
       "\n",
       "   salary_medium  \n",
       "0              0  \n",
       "1              1  \n",
       "2              1  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting Categorical data to Numerical by creating Dummies.\n",
    "categorical_data=pd.get_dummies(data=categorical_data,columns=['department','salary'],drop_first=True)\n",
    "categorical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "72f92740",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  \n",
       "0                   3  \n",
       "1                   6  \n",
       "2                   4  \n",
       "3                   5  \n",
       "4                   3  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sca_data=numeric_data.drop(['Work_accident','left','promotion_last_5years'],axis=1)\n",
    "sca_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "043fb89b",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   Work_accident  left  promotion_last_5years\n",
       "0              0     1                      0\n",
       "1              0     1                      0\n",
       "2              0     1                      0\n",
       "3              0     1                      0\n",
       "4              0     1                      0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_sca=numeric_data[['Work_accident','left','promotion_last_5years']]\n",
    "non_sca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "13484c38",
   "metadata": {},
   "outputs": [
    {
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       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
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       "      <td>-0.882040</td>\n",
       "      <td>-0.341235</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.752814</td>\n",
       "      <td>0.840707</td>\n",
       "      <td>0.971113</td>\n",
       "      <td>1.220423</td>\n",
       "      <td>1.713436</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.022479</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.431041</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.971113</td>\n",
       "      <td>0.439508</td>\n",
       "      <td>1.028546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.976716</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>-1.462863</td>\n",
       "      <td>-0.841993</td>\n",
       "      <td>-0.341235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0           -0.936495        -1.087275       -1.462863             -0.882040   \n",
       "1            0.752814         0.840707        0.971113              1.220423   \n",
       "2           -2.022479         0.957554        2.593763              1.420657   \n",
       "3            0.431041         0.899131        0.971113              0.439508   \n",
       "4           -0.976716        -1.145699       -1.462863             -0.841993   \n",
       "\n",
       "   time_spend_company  \n",
       "0           -0.341235  \n",
       "1            1.713436  \n",
       "2            0.343655  \n",
       "3            1.028546  \n",
       "4           -0.341235  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying scaling on required data.\n",
    "scaler=StandardScaler()\n",
    "sca_data=pd.DataFrame(scaler.fit_transform(sca_data),columns=sca_data.columns)\n",
    "sca_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "810bb39c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.976716</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>-1.462863</td>\n",
       "      <td>-0.841993</td>\n",
       "      <td>-0.341235</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0           -0.936495        -1.087275       -1.462863             -0.882040   \n",
       "1            0.752814         0.840707        0.971113              1.220423   \n",
       "2           -2.022479         0.957554        2.593763              1.420657   \n",
       "3            0.431041         0.899131        0.971113              0.439508   \n",
       "4           -0.976716        -1.145699       -1.462863             -0.841993   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  \\\n",
       "0           -0.341235              0     1                      0   \n",
       "1            1.713436              0     1                      0   \n",
       "2            0.343655              0     1                      0   \n",
       "3            1.028546              0     1                      0   \n",
       "4           -0.341235              0     1                      0   \n",
       "\n",
       "   department_RandD  department_accounting  department_hr  \\\n",
       "0                 0                      0              0   \n",
       "1                 0                      0              0   \n",
       "2                 0                      0              0   \n",
       "3                 0                      0              0   \n",
       "4                 0                      0              0   \n",
       "\n",
       "   department_management  department_marketing  department_product_mng  \\\n",
       "0                      0                     0                       0   \n",
       "1                      0                     0                       0   \n",
       "2                      0                     0                       0   \n",
       "3                      0                     0                       0   \n",
       "4                      0                     0                       0   \n",
       "\n",
       "   department_sales  department_support  department_technical  salary_low  \\\n",
       "0                 1                   0                     0           1   \n",
       "1                 1                   0                     0           0   \n",
       "2                 1                   0                     0           0   \n",
       "3                 1                   0                     0           1   \n",
       "4                 1                   0                     0           1   \n",
       "\n",
       "   salary_medium  \n",
       "0              0  \n",
       "1              1  \n",
       "2              1  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Concatinating numerical and categorical dataframes.\n",
    "df_final=pd.concat([sca_data,non_sca,categorical_data],axis=1)\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d310572",
   "metadata": {},
   "source": [
    "# Using Logistics Regression without applying SMOTE technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "db33e748",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating Features and Target variable\n",
    "X=df_final.drop(['left'],axis=1)\n",
    "Y=df_final['left']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "200a0f46",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "13ba1db3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11999, 18)\n",
      "(11999,)\n",
      "(3000, 18)\n",
      "(3000,)\n"
     ]
    }
   ],
   "source": [
    "#Done stratified split of the dataset to train and test in the ratio 80:20 with random_state=123.\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,train_size=0.8,random_state=123,stratify=Y)\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "794a3d54",
   "metadata": {},
   "source": [
    "Stratified splitting is done to keep same percentage of each class in each split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2aaf6cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "004c367c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm=LogisticRegression()\n",
    "lm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "557f5214",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lm=lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5d639838",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6d4ef706",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy score is: 78.96666666666667 %\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression Accuracy score is:',metrics.accuracy_score(pred_lm,y_test)*100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "32e000af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2108,  453],\n",
       "       [ 178,  261]], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(pred_lm,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "81373265",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.82      0.87      2561\n",
      "           1       0.37      0.59      0.45       439\n",
      "\n",
      "    accuracy                           0.79      3000\n",
      "   macro avg       0.64      0.71      0.66      3000\n",
      "weighted avg       0.84      0.79      0.81      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(pred_lm,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb8e480",
   "metadata": {},
   "source": [
    "We will use recall as a metrics to evaluate our model as we want all the employees who will be leaving to be identified correctly. We are doing it at cost of sacrificing precision so that most of the Employees who would be leaving can be identified correctly even if some employees who would not leave would be wrongly tagged as leaving.\n",
    "\n",
    "As we can see our Simple Logistic model is having very low recall for Employees leaving so we will try to improve it in upcoming models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d08753",
   "metadata": {},
   "source": [
    "# Applying SMOTE to Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "93c3e3d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "smote=SMOTE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "38c84b98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11999, 18)\n",
      "(11999,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e2bd6179",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Resampling data to balance class with less data.\n",
    "x_train_smote,y_train_smote=smote.fit_resample(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "11f2ee6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18284, 18)\n",
      "(18284,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train_smote.shape)\n",
    "print(y_train_smote.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c061e302",
   "metadata": {},
   "source": [
    "Clearly some data has been added after applying SMOTE to handle class imbalance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "afce01cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm1=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a0f96726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Again fitting the data in Logistics Regression model.\n",
    "lm1.fit(x_train_smote,y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "221f01d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy score is: 76.93333333333334 %\n"
     ]
    }
   ],
   "source": [
    "pred_lm1=lm1.predict(x_test)\n",
    "print('Logistic Regression Accuracy score is:',metrics.accuracy_score(pred_lm1,y_test)*100,\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e32e68d9",
   "metadata": {},
   "source": [
    "In this case the efficiency of the model has been slightly decreased after applying SMOTE technique. Without SMOTE it was 78.96 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "72a548d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1728,  134],\n",
       "       [ 558,  580]], dtype=int64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(pred_lm1,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "91043eea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.76      0.83      2286\n",
      "           1       0.51      0.81      0.63       714\n",
      "\n",
      "    accuracy                           0.77      3000\n",
      "   macro avg       0.72      0.78      0.73      3000\n",
      "weighted avg       0.83      0.77      0.78      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred_lm1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d80150f",
   "metadata": {},
   "source": [
    "Although the efficiency got reduced after applying SMOTE but recall of class with less data has been considerably increased. Earlier it was 0.59."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "543640fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Examining the coefficient of variables to check their contribution in classification.\n",
    "r=lm1.coef_.flatten()\n",
    "coeffs=r.tolist()\n",
    "coeffs.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ec6c7586",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0922d3bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.barh(a,coeffs)\n",
    "plt.xlabel('Value of Coeffs')\n",
    "plt.ylabel('Features')\n",
    "plt.title(' Featues Contribution in Classification')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9801797",
   "metadata": {},
   "source": [
    "From the above plot we can infer that top factors contributing in the employee turnover are:                                   \n",
    "1) Satisfaction Level                                                                                                       \n",
    "2) Last Evaluation                                                                                                        \n",
    "3) Medium Salary                                                                                                           \n",
    "4) Number of Projects done                                                                                                 \n",
    "5) Low salary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8bcfaac",
   "metadata": {},
   "source": [
    "# Applying Random Forest Algorithm to data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a38d7672",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8c56bf85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=7, n_estimators=200, random_state=77)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier(criterion='gini',max_depth=7,n_estimators=200,random_state=77)\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5d8d6be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_rf=rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9eff7dd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2275   11]\n",
      " [  64  650]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cfa7ed52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy score is: 97.5 %\n"
     ]
    }
   ],
   "source": [
    "print('Random Forest Accuracy score is:',metrics.accuracy_score(y_test,pred_rf)*100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9e0b8283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98      2286\n",
      "           1       0.98      0.91      0.95       714\n",
      "\n",
      "    accuracy                           0.97      3000\n",
      "   macro avg       0.98      0.95      0.96      3000\n",
      "weighted avg       0.98      0.97      0.97      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34a232b",
   "metadata": {},
   "source": [
    "We have improved our recall from last instance. In Logistic model with smote data it was 0.8 for class represented by '1'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1963538",
   "metadata": {},
   "source": [
    "# Applying XG Boost Algorithm to data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "47be8b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "bebb9add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=0, ...)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boost=XGBClassifier(random_state=0)\n",
    "boost.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f42b86cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_boost=boost.predict(x_test)\n",
    "pred_boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c4dde80c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2269   17]\n",
      " [  20  694]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred_boost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b5ab2a78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Accuracy score is: 98.76666666666667 %\n"
     ]
    }
   ],
   "source": [
    "print('XGBoost Accuracy score is:',metrics.accuracy_score(y_test,pred_boost)*100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7e3cb539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      2286\n",
      "           1       0.98      0.97      0.97       714\n",
      "\n",
      "    accuracy                           0.99      3000\n",
      "   macro avg       0.98      0.98      0.98      3000\n",
      "weighted avg       0.99      0.99      0.99      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred_boost))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4e27220",
   "metadata": {},
   "source": [
    "We have receieved the highest recall for Employees leaving using XGBoost algorithm. So we will be finalising this model to classify our data.The accuracy is also highest in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a676cd",
   "metadata": {},
   "source": [
    "# Plotting ROC-AUC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "70c5c10e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "07b890c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_fpr,log_tpr,threshold=roc_curve(y_test,pred_lm)\n",
    "auc_log=auc(log_fpr,log_tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f063c4e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_fpr1,log_tpr1,threshold1=roc_curve(y_test,pred_lm1)\n",
    "auc_log1=auc(log_fpr1,log_tpr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b7ab63d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_fpr,rf_tpr,threshold=roc_curve(y_test,pred_rf)\n",
    "auc_rf=auc(rf_fpr,rf_tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e1cd7f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "boost_fpr1,boost_tpr1,threshold1=roc_curve(y_test,pred_boost)\n",
    "auc_boost1=auc(boost_fpr1,boost_tpr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "adb51912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(log_fpr,log_tpr,marker='.',label='Logistic without SMOTE (auc=%0.3f)' %auc_log)\n",
    "plt.plot(log_fpr1,log_tpr1,marker='.',label='Logistic with SMOTE (auc=%0.3f)' %auc_log1)\n",
    "plt.plot(rf_fpr,rf_tpr,linestyle='-',label='Random Forest (auc=%0.3f)' %auc_rf)\n",
    "plt.plot(boost_fpr1,boost_tpr1,linestyle='-',label='XGBoost (auc=%0.3f)' %auc_boost1)\n",
    "plt.xlabel('False +ve Rate')\n",
    "plt.ylabel('True +ve Rate')\n",
    "plt.title('ROC-AUC curve')\n",
    "plt.legend(bbox_to_anchor=(1.05,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a21b71",
   "metadata": {},
   "source": [
    "The value of Area Under Curve (AUC) shows the performance of the model over different threshold values used by model for classification. So greater the AOC better would be the model. As the Area Under Curve (AUC) is greatest for model with XGBoost algorithm so we can say XGBoost is the best Classification model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "44cbc0ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158d9e2a",
   "metadata": {},
   "source": [
    "# Applying K fold on all models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2e03d09d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "45c55ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_models=[]\n",
    "classification_models.append(('Logistic',LogisticRegression()))\n",
    "classification_models.append(('Random Forest',RandomForestClassifier(criterion='gini',max_depth=7,n_estimators=200,random_state=77)))\n",
    "classification_models.append(('XGBoost',XGBClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e6595fc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic: Mean Accuracy = 79.16% - SD Accuracy = 0.25%\n",
      "Random Forest: Mean Accuracy = 97.57% - SD Accuracy = 0.27%\n",
      "XGBoost: Mean Accuracy = 98.69% - SD Accuracy = 0.16%\n"
     ]
    }
   ],
   "source": [
    "for name,models in classification_models:\n",
    "    kfold =KFold(n_splits=5,random_state=9,shuffle=True)\n",
    "    result=cross_val_score(models,X,Y,cv=kfold,scoring='accuracy')\n",
    "    print(\"%s: Mean Accuracy = %.2f%% - SD Accuracy = %.2f%%\" % (name, result.mean()*100, result.std()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb0f0e0",
   "metadata": {},
   "source": [
    "From K-Fold technique we can conclude that XGBoost has the best performance among the three models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aae88b38",
   "metadata": {},
   "source": [
    "# Separating Employees according to Risk associated with them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2060c6ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.1709332e-03, 9.9882907e-01],\n",
       "       [3.3697188e-02, 9.6630281e-01],\n",
       "       [1.5676022e-04, 9.9984324e-01],\n",
       "       ...,\n",
       "       [5.6954026e-03, 9.9430460e-01],\n",
       "       [1.3293028e-03, 9.9867070e-01],\n",
       "       [1.2331426e-02, 9.8766857e-01]], dtype=float32)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Obtaining probability values for all employees using XGBoost model.\n",
    "pred_pr=boost.predict_proba(X)\n",
    "pred_pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1d539613",
   "metadata": {},
   "outputs": [],
   "source": [
    "pb=pred_pr\n",
    "proab=pb.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8ac86895",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['probability']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "84ecb3d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>0.998829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.966303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.999843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>0.828187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>0.993935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "      <td>0.997210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "      <td>0.995534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "      <td>0.994305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "      <td>0.998671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "      <td>0.987669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994                   151                   3              0     1   \n",
       "14995                   160                   3              0     1   \n",
       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years department  salary  probability  \n",
       "0                          0      sales     low     0.998829  \n",
       "1                          0      sales  medium     0.966303  \n",
       "2                          0      sales  medium     0.999843  \n",
       "3                          0      sales     low     0.828187  \n",
       "4                          0      sales     low     0.993935  \n",
       "...                      ...        ...     ...          ...  \n",
       "14994                      0    support     low     0.997210  \n",
       "14995                      0    support     low     0.995534  \n",
       "14996                      0    support     low     0.994305  \n",
       "14997                      0    support     low     0.998671  \n",
       "14998                      0    support     low     0.987669  \n",
       "\n",
       "[14999 rows x 11 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding probability column in dataset.\n",
    "for i in range(14999):\n",
    "    df['probability'][i]=proab[i][1]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5db34025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting distribution of probability among employees\n",
    "df['probability'].hist()\n",
    "plt.xlabel('Probabilities')\n",
    "plt.ylabel('Frequency of Employees')\n",
    "plt.title(' Distribution of probabilities')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2754cbd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Zone']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "9647be8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Segragating employees in various risk zone according to probabilities.\n",
    "for i in range (14999):\n",
    "    h=float(df['probability'][i])\n",
    "    if h<=0.2:\n",
    "        df['Zone'][i]='Safe Zone'\n",
    "    elif h>0.2 and h<=0.6:\n",
    "        df['Zone'][i]='Low Risk Zone'\n",
    "    elif h>0.6 and h<=0.9:\n",
    "        df['Zone'][i]='Medium Risk Zone'\n",
    "    else:\n",
    "        df['Zone'][i]='High Risk Zone'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "16591f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "      <th>probability</th>\n",
       "      <th>Zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>0.998829</td>\n",
       "      <td>High Risk Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.966303</td>\n",
       "      <td>High Risk Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.999843</td>\n",
       "      <td>High Risk Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>0.828187</td>\n",
       "      <td>Medium Risk Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>0.993935</td>\n",
       "      <td>High Risk Zone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years department  \\\n",
       "0                   3              0     1                      0      sales   \n",
       "1                   6              0     1                      0      sales   \n",
       "2                   4              0     1                      0      sales   \n",
       "3                   5              0     1                      0      sales   \n",
       "4                   3              0     1                      0      sales   \n",
       "\n",
       "   salary  probability              Zone  \n",
       "0     low     0.998829    High Risk Zone  \n",
       "1  medium     0.966303    High Risk Zone  \n",
       "2  medium     0.999843    High Risk Zone  \n",
       "3     low     0.828187  Medium Risk Zone  \n",
       "4     low     0.993935    High Risk Zone  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8bf6d198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11428\n",
       "1     3571\n",
       "Name: left, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['left'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "9d5ad821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Safe Zone           11366\n",
       "High Risk Zone       3308\n",
       "Medium Risk Zone      201\n",
       "Low Risk Zone         124\n",
       "Name: Zone, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Zone'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2440e81b",
   "metadata": {},
   "source": [
    "Initially there were 11428 employees in Safe Zone or who were not leaving and our model has identified 99.45% among them as safe. Rest of the employees have been placed in various risk categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "bb064d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Zone'],palette=['red','orange','yellow','green'])\n",
    "plt.xlabel('Risk Zones')\n",
    "plt.title('Grouping of Employees according to Risk Zones')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bff20f95",
   "metadata": {},
   "source": [
    "# Employees Retention Stratergies:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1f5a7c6",
   "metadata": {},
   "source": [
    "Salary - Most of the time an employee leave an organization is because he/she is getting salary boost by joining other Organization. So Focus should be made on providing competitive salaries to employees."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba91ae36",
   "metadata": {},
   "source": [
    "Average Monthly Hours - Every employee needs a work life balance. So focus should be made to keep check on employees whose working hours are exceeding beyond certain optimal level. Proper training and guidance should be provided to such employees so that they complete their work on time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8123224",
   "metadata": {},
   "source": [
    "Recognition (Promotion) - Lack of recognition also plays a major role in Employee turover. So appropriate salary hikes and promotions should be done to keep employee satisfied and recognised. Also continuous appreciation by Manager would be a boost for employee's motivation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351564e9",
   "metadata": {},
   "source": [
    "Work Load (Project Count) - It is the utmost responsibilty of the manager to know the capability of his team members and divide the workload accordingly. Many time employees feel burn out due to high work load which later become reason of them leaving the company."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "166b033d",
   "metadata": {},
   "source": [
    "Opportunity to grow (Time spent) - Every person joins an organization with a goal to grow both Financially and Technically. So ample amount of growth opportunities should be provided to employees so that they stay in organization for longer time."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
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}