HR Analytics .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "983be2a9",
   "metadata": {},
   "source": [
    "#  $$ Hiring Process Analytics $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5e8cc6",
   "metadata": {},
   "source": [
    "## Below are contents of project "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ba2eab",
   "metadata": {},
   "source": [
    "1. Description\n",
    "2. Importing Python Packages \n",
    "3. Describing Dataset\n",
    "4. Data Preprocessing \n",
    "5. Required Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03181a43",
   "metadata": {},
   "source": [
    "### Description :\n",
    "*Hiring process is the fundamental and the most important function of a company. Here, the MNCs get to know about the major underlying trends about the hiring process. Trends such as- number of rejections, number of interviews, types of jobs, vacancies etc. are important for a company to analyse before hiring freshers or any other individual. Thus, making an opportunity for a Data Analyst job here too!*\n",
    "\n",
    "*Being a Data Analyst, your job is to go through these trends and draw insights out of it for hiring department to work upon.*\n",
    "\n",
    "### You are required to provide a detailed report for the below data record mentioning the answers of the questions that follows: \n",
    "*You are given a dataset of a company where the details about people who registered for a particular post in a department of this company. You are required to use your knowledge in statistics and use different formulas in excel and draw necessary conclusions about the company.*\n",
    "\n",
    "***\n",
    "\n",
    " **Note**:The entire Analysis was done with Python Packages and MS Excel \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6c9d81",
   "metadata": {},
   "source": [
    "### Importing Python Packages :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ccacdca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b95136fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>application_id</th>\n",
       "      <th>Interview Taken on</th>\n",
       "      <th>Status</th>\n",
       "      <th>event_name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Post Name</th>\n",
       "      <th>Offered Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>383422</td>\n",
       "      <td>2014-05-01 11:40:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c8</td>\n",
       "      <td>56553.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>907518</td>\n",
       "      <td>2014-05-06 08:08:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>22075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176719</td>\n",
       "      <td>2014-05-06 08:08:00</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>70069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>429799</td>\n",
       "      <td>2014-05-02 16:28:00</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>Female</td>\n",
       "      <td>Operations Department</td>\n",
       "      <td>i4</td>\n",
       "      <td>3207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>253651</td>\n",
       "      <td>2014-05-02 16:32:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Operations Department</td>\n",
       "      <td>i4</td>\n",
       "      <td>29668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7163</th>\n",
       "      <td>493131</td>\n",
       "      <td>2014-08-28 17:32:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c9</td>\n",
       "      <td>49282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7164</th>\n",
       "      <td>214261</td>\n",
       "      <td>2014-08-31 01:36:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>57742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7165</th>\n",
       "      <td>932441</td>\n",
       "      <td>2014-08-31 01:37:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>69932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7166</th>\n",
       "      <td>39010</td>\n",
       "      <td>2014-08-31 01:38:00</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>14489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7167</th>\n",
       "      <td>686055</td>\n",
       "      <td>2014-08-26 12:14:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Operations Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>54201.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7168 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      application_id  Interview Taken on    Status event_name  \\\n",
       "0             383422 2014-05-01 11:40:00     Hired       Male   \n",
       "1             907518 2014-05-06 08:08:00     Hired     Female   \n",
       "2             176719 2014-05-06 08:08:00  Rejected       Male   \n",
       "3             429799 2014-05-02 16:28:00  Rejected     Female   \n",
       "4             253651 2014-05-02 16:32:00     Hired       Male   \n",
       "...              ...                 ...       ...        ...   \n",
       "7163          493131 2014-08-28 17:32:00     Hired       Male   \n",
       "7164          214261 2014-08-31 01:36:00     Hired     Female   \n",
       "7165          932441 2014-08-31 01:37:00     Hired       Male   \n",
       "7166           39010 2014-08-31 01:38:00  Rejected       Male   \n",
       "7167          686055 2014-08-26 12:14:00     Hired       Male   \n",
       "\n",
       "                 Department Post Name  Offered Salary  \n",
       "0        Service Department        c8         56553.0  \n",
       "1        Service Department        c5         22075.0  \n",
       "2        Service Department        c5         70069.0  \n",
       "3     Operations Department        i4          3207.0  \n",
       "4     Operations Department        i4         29668.0  \n",
       "...                     ...       ...             ...  \n",
       "7163     Service Department        c9         49282.0  \n",
       "7164     Service Department        c5         57742.0  \n",
       "7165     Service Department        c5         69932.0  \n",
       "7166     Service Department        c5         14489.0  \n",
       "7167  Operations Department        c5         54201.0  \n",
       "\n",
       "[7168 rows x 7 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import, open and view the dataset \n",
    "data=pd.read_csv(r\"C:\\Users\\sarfaraaz\\OneDrive\\Desktop\\Trainity\\Hiring Analytics\\Statistics.csv\",parse_dates=[\"Interview Taken on\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe628a4",
   "metadata": {},
   "source": [
    "### Describing Dataset : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6e4b5d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50176\n",
      "Total Rows are :  7168\n",
      "Total Columns are : 7\n"
     ]
    }
   ],
   "source": [
    "# understand the structure(dimension) of dataset i.e: size,row,columns\n",
    "print(data.size) #prints the size of dataset\n",
    "row,col = data.shape #assigns the row and column to variable row,col\n",
    "print(\"Total Rows are : \",row)\n",
    "print(\"Total Columns are :\",col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d8aee2ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7168 entries, 0 to 7167\n",
      "Data columns (total 7 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   application_id      7168 non-null   int64         \n",
      " 1   Interview Taken on  7168 non-null   datetime64[ns]\n",
      " 2   Status              7168 non-null   object        \n",
      " 3   event_name          7168 non-null   object        \n",
      " 4   Department          7168 non-null   object        \n",
      " 5   Post Name           7168 non-null   object        \n",
      " 6   Offered Salary      7167 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(4)\n",
      "memory usage: 392.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# describe the columns of dataset\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ac9a94b5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>Rejected</td>\n",
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       "      <th>4</th>\n",
       "      <td>Hired</td>\n",
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       "      <td>Operations Department</td>\n",
       "      <td>i4</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7164</th>\n",
       "      <td>Hired</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7165</th>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7166</th>\n",
       "      <td>Rejected</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7167</th>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Operations Department</td>\n",
       "      <td>c5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7168 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Status event_name             Department Post Name\n",
       "0        Hired       Male     Service Department        c8\n",
       "1        Hired     Female     Service Department        c5\n",
       "2     Rejected       Male     Service Department        c5\n",
       "3     Rejected     Female  Operations Department        i4\n",
       "4        Hired       Male  Operations Department        i4\n",
       "...        ...        ...                    ...       ...\n",
       "7163     Hired       Male     Service Department        c9\n",
       "7164     Hired     Female     Service Department        c5\n",
       "7165     Hired       Male     Service Department        c5\n",
       "7166  Rejected       Male     Service Department        c5\n",
       "7167     Hired       Male  Operations Department        c5\n",
       "\n",
       "[7168 rows x 4 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gather all the object type columns\n",
    "o_data = data.select_dtypes('object')\n",
    "o_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "af337797",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_id</th>\n",
       "      <th>Offered Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>907518</td>\n",
       "      <td>22075.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>176719</td>\n",
       "      <td>70069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>429799</td>\n",
       "      <td>3207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>253651</td>\n",
       "      <td>29668.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>7163</th>\n",
       "      <td>493131</td>\n",
       "      <td>49282.0</td>\n",
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       "    <tr>\n",
       "      <th>7164</th>\n",
       "      <td>214261</td>\n",
       "      <td>57742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7165</th>\n",
       "      <td>932441</td>\n",
       "      <td>69932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7166</th>\n",
       "      <td>39010</td>\n",
       "      <td>14489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7167</th>\n",
       "      <td>686055</td>\n",
       "      <td>54201.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7168 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      application_id  Offered Salary\n",
       "0             383422         56553.0\n",
       "1             907518         22075.0\n",
       "2             176719         70069.0\n",
       "3             429799          3207.0\n",
       "4             253651         29668.0\n",
       "...              ...             ...\n",
       "7163          493131         49282.0\n",
       "7164          214261         57742.0\n",
       "7165          932441         69932.0\n",
       "7166           39010         14489.0\n",
       "7167          686055         54201.0\n",
       "\n",
       "[7168 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gather all the numerical column from the dataset\n",
    "n_data = data.select_dtypes(['int64','float64'])\n",
    "n_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af9a3f6",
   "metadata": {},
   "source": [
    "### Data Preprocessing :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "45741b9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_id</th>\n",
       "      <th>Interview_Taken_on</th>\n",
       "      <th>Status</th>\n",
       "      <th>event_name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Post_Name</th>\n",
       "      <th>Offered_Salary</th>\n",
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       "      <td>383422</td>\n",
       "      <td>2014-05-01 11:40:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c8</td>\n",
       "      <td>56553.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>907518</td>\n",
       "      <td>2014-05-06 08:08:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>22075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176719</td>\n",
       "      <td>2014-05-06 08:08:00</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>70069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>429799</td>\n",
       "      <td>2014-05-02 16:28:00</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>Female</td>\n",
       "      <td>Operations Department</td>\n",
       "      <td>i4</td>\n",
       "      <td>3207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>253651</td>\n",
       "      <td>2014-05-02 16:32:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Operations Department</td>\n",
       "      <td>i4</td>\n",
       "      <td>29668.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7163</th>\n",
       "      <td>493131</td>\n",
       "      <td>2014-08-28 17:32:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c9</td>\n",
       "      <td>49282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7164</th>\n",
       "      <td>214261</td>\n",
       "      <td>2014-08-31 01:36:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>57742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7165</th>\n",
       "      <td>932441</td>\n",
       "      <td>2014-08-31 01:37:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>69932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7166</th>\n",
       "      <td>39010</td>\n",
       "      <td>2014-08-31 01:38:00</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>14489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7167</th>\n",
       "      <td>686055</td>\n",
       "      <td>2014-08-26 12:14:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Operations Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>54201.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7168 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      application_id  Interview_Taken_on    Status event_name  \\\n",
       "0             383422 2014-05-01 11:40:00     Hired       Male   \n",
       "1             907518 2014-05-06 08:08:00     Hired     Female   \n",
       "2             176719 2014-05-06 08:08:00  Rejected       Male   \n",
       "3             429799 2014-05-02 16:28:00  Rejected     Female   \n",
       "4             253651 2014-05-02 16:32:00     Hired       Male   \n",
       "...              ...                 ...       ...        ...   \n",
       "7163          493131 2014-08-28 17:32:00     Hired       Male   \n",
       "7164          214261 2014-08-31 01:36:00     Hired     Female   \n",
       "7165          932441 2014-08-31 01:37:00     Hired       Male   \n",
       "7166           39010 2014-08-31 01:38:00  Rejected       Male   \n",
       "7167          686055 2014-08-26 12:14:00     Hired       Male   \n",
       "\n",
       "                 Department Post_Name  Offered_Salary  \n",
       "0        Service Department        c8         56553.0  \n",
       "1        Service Department        c5         22075.0  \n",
       "2        Service Department        c5         70069.0  \n",
       "3     Operations Department        i4          3207.0  \n",
       "4     Operations Department        i4         29668.0  \n",
       "...                     ...       ...             ...  \n",
       "7163     Service Department        c9         49282.0  \n",
       "7164     Service Department        c5         57742.0  \n",
       "7165     Service Department        c5         69932.0  \n",
       "7166     Service Department        c5         14489.0  \n",
       "7167  Operations Department        c5         54201.0  \n",
       "\n",
       "[7168 rows x 7 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since some of the column names in the dataset may cause problem in the analysis it is necessary to keep standard type/format\n",
    "# in the column name which can be done as,\n",
    "data.rename(columns={\"Interview Taken on\":\"Interview_Taken_on\",\"Post Name\":\"Post_Name\",\"Offered Salary\":\"Offered_Salary\"},\n",
    "           inplace = True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7f94c0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data cleaning\n",
    "#replacing the not available salary values with the mean of salary values available\n",
    "data.fillna({\"Offered_Salary\":data[\"Offered_Salary\"].mean()},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ce04d374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_id        0\n",
       "Interview_Taken_on    0\n",
       "Status                0\n",
       "event_name            0\n",
       "Department            0\n",
       "Post_Name             0\n",
       "Offered_Salary        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if any column of dataset contains null values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c9ce92e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      7168.000000\n",
       "mean      49983.029022\n",
       "std       28852.163831\n",
       "min         100.000000\n",
       "25%       25463.750000\n",
       "50%       49628.000000\n",
       "75%       74429.000000\n",
       "max      400000.000000\n",
       "Name: Offered_Salary, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#learn and understand the column using the given command\n",
    "data[\"Offered_Salary\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "71fe270d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove the duplicate/repeated values in the dataset\n",
    "data.drop_duplicates(subset=\"application_id\",keep=\"first\",inplace=True)\n",
    "#check if all the duplicate values are removed \n",
    "data[\"application_id\"].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bcdbacc8",
   "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>application_id</th>\n",
       "      <th>Interview_Taken_on</th>\n",
       "      <th>Status</th>\n",
       "      <th>event_name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Post_Name</th>\n",
       "      <th>Offered_Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>383422</td>\n",
       "      <td>2014-05-01 11:40:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c8</td>\n",
       "      <td>56553.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>907518</td>\n",
       "      <td>2014-05-06 08:08:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>22075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176719</td>\n",
       "      <td>2014-05-06 08:08:00</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>70069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>429799</td>\n",
       "      <td>2014-05-02 16:28:00</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>Female</td>\n",
       "      <td>Operations Department</td>\n",
       "      <td>i4</td>\n",
       "      <td>3207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>253651</td>\n",
       "      <td>2014-05-02 16:32:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Operations Department</td>\n",
       "      <td>i4</td>\n",
       "      <td>29668.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7163</th>\n",
       "      <td>493131</td>\n",
       "      <td>2014-08-28 17:32:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c9</td>\n",
       "      <td>49282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7164</th>\n",
       "      <td>214261</td>\n",
       "      <td>2014-08-31 01:36:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>57742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7165</th>\n",
       "      <td>932441</td>\n",
       "      <td>2014-08-31 01:37:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>69932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7166</th>\n",
       "      <td>39010</td>\n",
       "      <td>2014-08-31 01:38:00</td>\n",
       "      <td>Rejected</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>14489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7167</th>\n",
       "      <td>686055</td>\n",
       "      <td>2014-08-26 12:14:00</td>\n",
       "      <td>Hired</td>\n",
       "      <td>Male</td>\n",
       "      <td>Operations Department</td>\n",
       "      <td>c5</td>\n",
       "      <td>54201.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7141 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      application_id  Interview_Taken_on    Status event_name  \\\n",
       "0             383422 2014-05-01 11:40:00     Hired       Male   \n",
       "1             907518 2014-05-06 08:08:00     Hired     Female   \n",
       "2             176719 2014-05-06 08:08:00  Rejected       Male   \n",
       "3             429799 2014-05-02 16:28:00  Rejected     Female   \n",
       "4             253651 2014-05-02 16:32:00     Hired       Male   \n",
       "...              ...                 ...       ...        ...   \n",
       "7163          493131 2014-08-28 17:32:00     Hired       Male   \n",
       "7164          214261 2014-08-31 01:36:00     Hired     Female   \n",
       "7165          932441 2014-08-31 01:37:00     Hired       Male   \n",
       "7166           39010 2014-08-31 01:38:00  Rejected       Male   \n",
       "7167          686055 2014-08-26 12:14:00     Hired       Male   \n",
       "\n",
       "                 Department Post_Name  Offered_Salary  \n",
       "0        Service Department        c8         56553.0  \n",
       "1        Service Department        c5         22075.0  \n",
       "2        Service Department        c5         70069.0  \n",
       "3     Operations Department        i4          3207.0  \n",
       "4     Operations Department        i4         29668.0  \n",
       "...                     ...       ...             ...  \n",
       "7163     Service Department        c9         49282.0  \n",
       "7164     Service Department        c5         57742.0  \n",
       "7165     Service Department        c5         69932.0  \n",
       "7166     Service Department        c5         14489.0  \n",
       "7167  Operations Department        c5         54201.0  \n",
       "\n",
       "[7141 rows x 7 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#after all the corrections the final dataset looks like,\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "454cf36c",
   "metadata": {},
   "source": [
    "### Analysis : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f3658871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hired       4679\n",
       "Rejected    2462\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating the status count in dataset\n",
    "data.Status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f46a75ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male                 4070\n",
       "Female               2664\n",
       "Don’t want to say     392\n",
       "Name: event_name, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the rows in dataset where event name is invalid/'-'\n",
    "data.drop(data[data[\"event_name\"]==\"-\"].index,inplace=True)\n",
    "data.event_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2ff38df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>85914.000000</td>\n",
       "      <td>85914.0</td>\n",
       "      <td>85914.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b9</th>\n",
       "      <td>49847.287912</td>\n",
       "      <td>1105.0</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c-10</th>\n",
       "      <td>51244.359307</td>\n",
       "      <td>1817.0</td>\n",
       "      <td>99891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5</th>\n",
       "      <td>50241.313003</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>99948.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c8</th>\n",
       "      <td>50747.257862</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>99967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c9</th>\n",
       "      <td>50210.546884</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>99953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i1</th>\n",
       "      <td>49937.954545</td>\n",
       "      <td>1519.0</td>\n",
       "      <td>99939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i4</th>\n",
       "      <td>48877.840909</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i5</th>\n",
       "      <td>49467.559949</td>\n",
       "      <td>1304.0</td>\n",
       "      <td>98926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i6</th>\n",
       "      <td>48839.248577</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>99762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i7</th>\n",
       "      <td>50073.575849</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m6</th>\n",
       "      <td>34521.333333</td>\n",
       "      <td>800.0</td>\n",
       "      <td>68466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m7</th>\n",
       "      <td>41402.000000</td>\n",
       "      <td>41402.0</td>\n",
       "      <td>41402.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n10</th>\n",
       "      <td>26990.000000</td>\n",
       "      <td>26990.0</td>\n",
       "      <td>26990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n6</th>\n",
       "      <td>44700.000000</td>\n",
       "      <td>44700.0</td>\n",
       "      <td>44700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n9</th>\n",
       "      <td>46219.000000</td>\n",
       "      <td>46219.0</td>\n",
       "      <td>46219.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean      min       max\n",
       "Post_Name                                 \n",
       "-          85914.000000  85914.0   85914.0\n",
       "b9         49847.287912   1105.0  200000.0\n",
       "c-10       51244.359307   1817.0   99891.0\n",
       "c5         50241.313003   1038.0   99948.0\n",
       "c8         50747.257862   1035.0   99967.0\n",
       "c9         50210.546884   1007.0   99953.0\n",
       "i1         49937.954545   1519.0   99939.0\n",
       "i4         48877.840909   1212.0  400000.0\n",
       "i5         49467.559949   1304.0   98926.0\n",
       "i6         48839.248577   1074.0   99762.0\n",
       "i7         50073.575849   1022.0  300000.0\n",
       "m6         34521.333333    800.0   68466.0\n",
       "m7         41402.000000  41402.0   41402.0\n",
       "n10        26990.000000  26990.0   26990.0\n",
       "n6         44700.000000  44700.0   44700.0\n",
       "n9         46219.000000  46219.0   46219.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Differentiating salary based on mean,minimum and maximum for each post \n",
    "data.groupby([\"Post_Name\"]).Offered_Salary.agg([\"mean\",\"min\",\"max\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6e3dcc70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c9      1781\n",
       "c5      1738\n",
       "i7       976\n",
       "i5       784\n",
       "i6       527\n",
       "b9       455\n",
       "c8       318\n",
       "c-10     231\n",
       "i1       220\n",
       "i4        88\n",
       "m6         3\n",
       "-          1\n",
       "n10        1\n",
       "m7         1\n",
       "n9         1\n",
       "n6         1\n",
       "Name: Post_Name, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total Post Count on the basis of Post_Name\n",
    "\n",
    "data.Post_Name.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e763094",
   "metadata": {},
   "source": [
    "*** \n",
    "**Note** : `Below are the visualization done on the basis of questions provided on dataset`\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bcf154c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total males and females in the dataset\n",
    "Gender=\"Male\",\"Female\",\"Don’t want to say\"\n",
    "total=data.event_name.value_counts()\n",
    "colors=[\"Red\",\"Pink\",\"Yellow\"]\n",
    "explode=(0,0,0)\n",
    "plt.pie(total)\n",
    "explode=(0,0,0)\n",
    "plt.pie(total,explode=explode,labels=Gender,colors=colors,autopct='%1.1f%%',shadow=True,startangle=360)\n",
    "plt.title('Male and Female % in Dataset')\n",
    "plt.legend(loc=\"upper right\",handlelength=2, borderpad=2, labelspacing=1.2,bbox_to_anchor=(0.1, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edc91be3",
   "metadata": {},
   "source": [
    "**Insight:**\n",
    "1. *This is on the Gender from the entire dataset*\n",
    "2. *Male has more occurence upto 57.1%*\n",
    "3. *Female has occurence upto 37.4%*\n",
    "4. *Don't want to say are 5.5%*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a6a2117",
   "metadata": {},
   "source": [
    "***\n",
    "##  $$ TASK - 1 $$\n",
    "**Hiring**: Process of intaking of people into an organization for different kinds of positions. \\\n",
    "**Your task**: How many males and females are Hired ? \n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cc33dfc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0kaShwNaStpQ0NN3age26urN0DaLiY0Dlk1IzgUmSNpS0I8WF7HsiYimwUtJe6VNQhwPXdXW/Zma2bgZnln8WOI4iGO7jjW6hF4GzOltR0uXAeIqgWQx8HRgvaQxFV9LCtH0iYr6kGcBDwCrg6IhYnTZ1FMUnqzYGbkg3MzPrRZ2GRUScDpwu6diI+GFXNhwRk2sUX9BJ/WnAtBrls4Fdu7JvMzPrWbkzCwAi4oeS3gm0l9eJiEsa1C4zM2shdYWFpEspPsU0B6h0D1W+92BmZv1cXWEBjAVGpy/GmZnZAFPv9yweBP6ukQ0xM7PWVe+ZxdbAQ5LuAV6rFEbERxvSKjMzayn1hsVJjWyEmZm1tno/DfW/jW6ImZm1rno/DbWSN8Zk2gBYH3g5IjZrVMPMzKx11HtmMaQ8L2kib4zrZGZm/Vy3Rp2NiGuB9/VsU8zMrFXV2w318dLsehTfu/B3LszMBoh6Pw31kdL0KopBACf0eGvMzKwl1XvN4tONboiZmbWuen/8aISkayQtl7RM0lWSRuTXNDOz/qDeC9wXUfxA0XYUv4F9fSozM7MBoN6waIuIiyJiVbpNB/y7pWZmA0S9YfGMpEMlDUq3Q4FnG9kwMzNrHfWGxf8DDgL+CCwFPgH4oreZ2QBR70dnTwamRMTzAJKGAt+lCBEzM+vn6j2z2K0SFAAR8Rzw9sY0yczMWk29YbGepC0rM+nMot6zEjMz6+Pq/Yf/PeAOSVdSDPNxEDCtYa0yM7OWUu83uC+RNJti8EABH4+IhxraMjMzaxl1dyWlcHBAmJkNQN0aotzMzAYWh4WZmWU5LMzMLMthYWZmWQ4LMzPLcliYmVmWw8LMzLIaFhaSLky/rPdgqWyopJslPZruy0OInChpgaRHJO1fKt9d0ry07AxJalSbzcystkaeWUwHDqgqOwGYFRGjgFlpHkmjgUnALmmdsyUNSuucA0wFRqVb9TbNzKzBGhYWEXEb8FxV8QTg4jR9MTCxVH5FRLwWEU8AC4BxkoYBm0XEnRERwCWldczMrJf09jWLbSNiKUC63yaVDwcWleotTmXD03R1uZmZ9aJWucBd6zpEdFJeeyPSVEmzJc1esWJFjzXOzGyg6+2wWJa6lkj3y1P5YmBkqd4IYEkqH1GjvKaIOC8ixkbE2La2th5tuJnZQNbbYTETmJKmpwDXlconSdpQ0o4UF7LvSV1VKyXtlT4FdXhpHTMz6yUN+7U7SZcD44GtJS0Gvg6cCsyQdCTwFPBJgIiYL2kGxRDoq4CjI2J12tRRFJ+s2hi4Id3MzKwXNSwsImJyB4v27aD+NGr8+l5EzAZ27cGmmZlZF7XKBW4zM2thDgszM8tyWJiZWZbDwszMshwWZmaW5bAwM7Msh4WZmWU5LMzMLMthYWZmWQ4LMzPLcliYmVmWw8LMzLIcFmZmluWwMDOzLIeFmZllOSzMzCzLYWFmZlkOCzMzy3JYmJlZlsPCzMyyHBZmZpblsDAzsyyHhZmZZTkszMwsy2FhZmZZDgszM8tyWJiZWZbDwszMshwWZmaW5bAwM7Msh4WZmWU5LMzMLKspYSFpoaR5kuZImp3Khkq6WdKj6X7LUv0TJS2Q9Iik/ZvRZjOzgayZZxb/FBFjImJsmj8BmBURo4BZaR5Jo4FJwC7AAcDZkgY1o8FmZgNVK3VDTQAuTtMXAxNL5VdExGsR8QSwABjX+80zMxu4mhUWAdwk6T5JU1PZthGxFCDdb5PKhwOLSusuTmVmZtZLBjdpv/tExBJJ2wA3S/p9J3VVoyxqViyCZyrA9ttvv+6tNDMzoElnFhGxJN0vB66h6FZaJmkYQLpfnqovBkaWVh8BLOlgu+dFxNiIGNvW1tao5puZDTi9HhaSNpE0pDIN/DPwIDATmJKqTQGuS9MzgUmSNpS0IzAKuKd3W21mNrA1oxtqW+AaSZX9/yQibpR0LzBD0pHAU8AnASJivqQZwEPAKuDoiFjdhHabmQ1YvR4WEfE48LYa5c8C+3awzjRgWoObZmZmHWilj86amVmLcliYmVmWw8LMzLIcFmZmluWwMDOzLIeFmZllOSzMzCzLYWFmZlkOCzMzy3JYmJlZlsPCzMyyHBZmZpblsDAzsyyHhZmZZTXrZ1XNzFi4cMdmN6Hfa29/oke24zMLMzPLcliYmVmWw8LMzLIcFmZmluWwMDOzLIeFmZllOSzMzCzLYWFmZlkOCzMzy3JYmJlZlsPCzMyyHBZmZpblsDAzsyyHhZmZZTkszMwsy2FhZmZZDgszM8vqM2Eh6QBJj0haIOmEZrfHzGwg6RNhIWkQcBbwAWA0MFnS6Oa2ysxs4OgTYQGMAxZExOMR8RfgCmBCk9tkZjZgDG52A+o0HFhUml8M7FldSdJUYGqafUnSI73QtmbZGnim2Y2om9TsFrSSvvXcWbU+9vx1+W9vh1qFfSUsah1trFUQcR5wXuOb03ySZkfE2Ga3w7rOz13fNlCfv77SDbUYGFmaHwEsaVJbzMwGnL4SFvcCoyTtKGkDYBIws8ltMjMbMPpEN1RErJJ0DPBLYBBwYUTMb3Kzmm1AdLf1U37u+rYB+fwpYq2ufzMzszX0lW4oMzNrIoeFmZllOSxaiKSQdGlpfrCkFZJ+nllvfK6O9QxJqyXNKd3aG7ivhZK2btT2W0npcZ0v6QFJ/19St/8/SbpU0uQ0fZykN/Vcazvdb7ukT3V1WV/gsGgtLwO7Sto4zb8feLqJ7bG1vRIRY0q3hc1uUD9ReVx3oXjdfxD4+jpsbyRvfGLyOKBXwgJoBzoKhM6WtTyHReu5AfhQmp4MXF5ZIGmcpDsk3Z/u31y9sqRNJF0o6d5Uz8OiNJik3SX9r6T7JP1S0rBUfquk0yTdJulhSXtIulrSo5K+VVr/2rTu/DQKQa19HCrpnvTu+7/TeGn9UkQspxiJ4RgVNpJ0kaR56TX9TwCSjkiP543pMf1OaTOTI+JlSV8AtgNukXRLeT/p7+nqND1B0iuSNkj7ezyVfyb9LT0g6arKGYqk6ZLOSH+Hj0v6RNrsqcC70/P0L1WHtsayjo6rqo3D0utnjqQHJb07lZ8jaXZ6zXwjle0r6ZrSuu+vHF+PiAjfWuQGvATsBlwJbATMAcYDP0/LNwMGp+n9gKvSdLnOfwKHpuktgD8AmzT72PrLDVidnpc5wDXA+sAdQFtafjDFR7sBbgW+naa/SPFF0mHAhhRfNN0qLRua7jcGHiyVL6QYWuKtwPXA+qn8bODwZj8WPfy4vlSj7HlgW+B44KJU9hbgqfT3cQTwOLB5mn8SGFljOwuBrWuUDwaeSNPfpfg+1z7Ae4HLU/lWpfrfAo5N09OBn1G84R5NMXbdGn+LNfa3xrKOjqtqneOBr6bpQcCQqtfMoPQ6241ipIvfl16LPwE+0lPPUZ/4nsVAEhFzUz/4ZOAXVYs3By6WNIpiuJP1a2zin4GPSvpSmt8I2B54uDEtHnBeiYgxlRlJuwK7AjerGP9qELC0VL/SFTIPmB8RS9N6j1N0lTwLfEHSx1K9kcCoVF6xL7A7cG/ax8bA8h49qtZUGebnXcAPASLi95KeBHZOy2ZFxAsAkh6iGNdoUfWGaoni+1sLJL2VYrDS7wPvoXgOf5Oq7ZrOArcANqX4rlfFtRHxOvCQpG27cXwdHdfcUp17gQslrZ/2NyeVH5TOQgdTvAEZnf53XAocKukiYG/g8G60qyaHRWuaSfFOZzywVan8ZOCWiPhYCpRba6wr4MCI6M+DKLYSUYTA3h0sfy3dv16arswPljSe4ixx74j4s6RbKQK+eh8XR8SJPdXoVifp7ynO4pbT+Uh45cd0NV3/n/Ybip8++CvwK4ozhkFA5c3WdGBiRDwg6QiKv8la++7OSJnZdSLiNknvoeiavlTSf6U2fwnYIyKelzSdN14zF1Gchb4K/CwiVnWjXTX5mkVruhD4ZkTMqyrfnDcueB/Rwbq/BI5Vegsq6e0NaaFVPAK0SdobQNL6knbpwvqbA8+noHgLsFeNOrOAT0jaJu1jqKSaI4P2B5LagHOBM6PoT7kNOCQt25niTLkrb4ZWAkM6WHYbxQXwOyNiBcWbs7cAlREihgBL0zv7Q9ZxX9XLsseVnuflEXE+cAHwDoru6JeBF9IZzQcq9SNiCUV359cogq7HOCxaUEQsjojTayz6DnCKpN9SvPup5WSK7qm5kh5M89YgUfy+yieAb0t6gOJaxju7sIkbKc4w5lI8V3fV2MdDFH/8N6V6N1N0PfQnG6eLuPMp3uHfBHwjLTsbGCRpHvBT4IiIeK2D7dRyHnBD9QXu5G6K6yK3pfm5wNwUUgD/nurcTHE9IGcusCpdEK++wF29rJ7jGg/MkXQ/cCBwekQ8ANxPEWgXAr+tWucyYFF63fQYD/dhZtaPSDoTuD8iLujR7ToszMz6B0n3UXRRvb+LZ1/5bTsszMwsx9cszMwsy2FhZmZZDguzBpE0UdLoZrfDrCc4LMwaZyLFUBBmfZ7DwgY0rT1A39HlAenSYHU/7KDuoFT+kqRp6fPzd0naVtI7gY8C/5Xq79TB/m+V9O203T+UBoprl/QbSb9Lt3em8vEqBi2ckeqfKumQtP68yn4ktakY+O7edNunsY+k9XcOCxuw0phABwP7pPGeVlMM5vjxUrWDgZ92ULfyjd5NgLsi4m0UX+76TETcQTFsy5ejGHr7sU6aMjgixlF8k7gyLPdyio8/viPt94xS/bdRDEz4j8BhwM5p/R8Bx6Y6pwOnRcQeFF/m+lG9j4tZLR4bygayjgboe1zSXsCjwJspviF7dAd1Af4CVH586j6K32Poisow0vdR/OYBFN/CP1PSGIpg2rlU/97SgISPUXzbGYrBCivDXO8HjE5tBdhM0pCIWNnFtpkBDgsb2GoO0CfpSOAgiuEdromISGNtdTSY319Lw0N0ZzC7ypenyuv+C7CM4ixiPYqB4arrw5oDFL5eWn89isEJX+liW8xqcjeUDWQdDdB3NcXF6ckUY/Z0VrcznQ0ql7M5sDQNgX0YHY8F1pGbgGMqM+kMxazbHBY2YHU0QF9EPA88BOwQEfd0VjeziyuAL6v4FbSaF7g7cTYwRdJdFF1QL3dx/S8AYyXNVfE7D5/r4vpma/BwH2ZmluUzCzMzy/IFbrNeIOksit93Ljs9Ii5qRnvMusrdUGZmluVuKDMzy3JYmJlZlsPCzMyyHBZmZpblsDAzsyyHhZmZZf0fkF9KlDpj5d0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting data for all Male and Female.\n",
    "\n",
    "plt.title(\"Representation of all the Male and Female\")\n",
    "p=[\"Red\",\"Pink\",\"Yellow\"]\n",
    "sns.countplot(x =data.event_name, data = data,palette=p)\n",
    "plt.show()\n",
    "\n",
    "# Filtering only Hired People and plotting it\n",
    "c=data[data[\"Status\"]==\"Hired\"]\n",
    "plt.title(\"Data for the Only Hired Male and Female\")\n",
    "sns.countplot(x =c.event_name, data = data,palette=p)\n",
    "plt.show()\n",
    "\n",
    "# According to Department\n",
    "c=data[data[\"Status\"]==\"Hired\"]\n",
    "plt.title(\"Data for the Only Hired Male and Female Department wise\")\n",
    "sns.countplot(x =c.event_name, data = data,hue=\"Department\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c44ca7",
   "metadata": {},
   "source": [
    "**Insight:**\n",
    "1. *In the provided dataset we can see that more males are being hired than females*\n",
    "2. *Department wise most males are hired in Operation Departments*\n",
    "3. *Department wise most females are hired in Operation Departments*\n",
    "4. *Finance and General Management Department are hiring more females than males*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11b77e8e",
   "metadata": {},
   "source": [
    "***\n",
    "##  $$ TASK - 2 $$\n",
    "**Average Salary**: Adding all the salaries for a select group of employees and then dividing the sum by the number of employees in the group. \\\n",
    "**Your task**: What is the average salary offered in this company ?\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2095193e",
   "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>Average_Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post_Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>85914.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b9</th>\n",
       "      <td>49847.287912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c-10</th>\n",
       "      <td>51244.359307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5</th>\n",
       "      <td>50241.313003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c8</th>\n",
       "      <td>50747.257862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c9</th>\n",
       "      <td>50210.546884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i1</th>\n",
       "      <td>49937.954545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i4</th>\n",
       "      <td>48877.840909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i5</th>\n",
       "      <td>49467.559949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i6</th>\n",
       "      <td>48839.248577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i7</th>\n",
       "      <td>50073.575849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m6</th>\n",
       "      <td>34521.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m7</th>\n",
       "      <td>41402.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n10</th>\n",
       "      <td>26990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n6</th>\n",
       "      <td>44700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n9</th>\n",
       "      <td>46219.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Average_Salary\n",
       "Post_Name                \n",
       "-            85914.000000\n",
       "b9           49847.287912\n",
       "c-10         51244.359307\n",
       "c5           50241.313003\n",
       "c8           50747.257862\n",
       "c9           50210.546884\n",
       "i1           49937.954545\n",
       "i4           48877.840909\n",
       "i5           49467.559949\n",
       "i6           48839.248577\n",
       "i7           50073.575849\n",
       "m6           34521.333333\n",
       "m7           41402.000000\n",
       "n10          26990.000000\n",
       "n6           44700.000000\n",
       "n9           46219.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=data.groupby([\"Post_Name\"]).Offered_Salary.agg([\"mean\"])\n",
    "df.rename(columns={\"mean\":\"Average_Salary\"},inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5d0c2946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Average_Salary    85914.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max() #this will provide the maximum salary offered for an post "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5bfea017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting average salary for all the post's provided in the dataset\n",
    "plt.title(\"Average Salary for each Post Offered\")\n",
    "z=data.Post_Name\n",
    "zp=data.groupby([\"Post_Name\"]).Offered_Salary.mean()\n",
    "sns.barplot(x=data.Post_Name,y=data.Offered_Salary,data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92e9468",
   "metadata": {},
   "source": [
    "**Insight:**\n",
    "1. *Highest Average Salary was for the post of '-'*\n",
    "2. *Second highest Average salary was for the post of 'c-10'*\n",
    "3. *Least/Minimum Average Salary was for the post of 'n10'*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9501f49e",
   "metadata": {},
   "source": [
    "***\n",
    "## $$ TASK - 3 $$\n",
    "**Class Intervals** : The class interval is the difference between the upper class limit and the lower class limit. \\\n",
    "**Your task** : Draw the class intervals for salary in the company ?\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e0c803b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      7126.000000\n",
       "mean      50018.106515\n",
       "std       28828.268238\n",
       "min         800.000000\n",
       "25%       25605.750000\n",
       "50%       49658.500000\n",
       "75%       74418.500000\n",
       "max      400000.000000\n",
       "Name: Offered_Salary, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#try understanding all the statistical aspects of salaries \n",
    "data[\"Offered_Salary\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "397986a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting an figure consisting of number of people working at different salary ranges \n",
    "plt.figure(figsize=(8,6))\n",
    "sns.displot(data[\"Offered_Salary\"],bins=[0,25000,50000,75000,100000,125000,150000,175000,200000],color=\"red\")\n",
    "plt.title(\"Number of People working in different Department\")\n",
    "locs,labels=plt.xticks()\n",
    "plt.setp(labels, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f0248c",
   "metadata": {},
   "source": [
    "**Insights**:\n",
    "1. *Most People are working with salaries ranging from 25000 to 50000*\n",
    "2. *Minimum People are working with salaries ranging from 0 to 25000*\n",
    "3. *From above stats we can conclude the group of people/place are under development stage(i.e:Moderate Lifestyle)*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d61a66",
   "metadata": {},
   "source": [
    "***\n",
    "## $$ TASK - 4 $$ \n",
    "**Charts and Plots**: This is one of the most important part of analysis to visualize the data \\\n",
    "**Your task**: Draw Pie Chart / Bar Graph ( or any other graph ) to show proportion of people working different department \n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ed58fdfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting an pie chart to show the percentage of people working in different departments \n",
    "Dep=[\"Service Department\",\"Operations Department\",\"Sales Department\",\"Finance Department\",\n",
    "     \"Production Department\",\"Purchase Department\",\"Marketing Department\", \"Human Resource Depatment\",\n",
    "     \"Generaal Management\"]     \n",
    "total=c.Department.value_counts()\n",
    "explode=(0,0,0,0,0,0,0,0,0)\n",
    "plt.pie(total)\n",
    "explode=(0,0,0,0,0,0,0,0,0)\n",
    "plt.pie(total,explode=explode,labels=Dep,autopct='%12.0f%%',shadow=True,radius = 2,startangle=360)\n",
    "plt.title('Number of People working in different Department',fontsize = 20,pad=100.0)\n",
    "plt.legend(loc=\"upper right\",handlelength=2, borderpad=2, labelspacing=1.5,bbox_to_anchor=(3.2,1.2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6150c3c",
   "metadata": {},
   "source": [
    "**Insights:**\n",
    "1. *Operation Dept. have maximum number of workers*\n",
    "2. *General Management Dept. have minimum number of workers*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3cd0f54",
   "metadata": {},
   "source": [
    "***\n",
    "## $$ TASK - 5 $$\n",
    "**Charts**: Use different charts and graphs to perform the task representing the data  \\\n",
    "**Your task**: Represent different post tiers using chart/graph?\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8a3b11c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 844.125x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot different relations in the dataset \n",
    "sns.pairplot(c,hue=\"event_name\",kind=\"kde\",height=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "824110ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}