Campus Placement Prediction Using ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Placement.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Display Top 5 Rows of The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sl_no</th>\n",
       "      <th>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>ssc_b</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>hsc_b</th>\n",
       "      <th>hsc_s</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>degree_t</th>\n",
       "      <th>workex</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>status</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>91.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>58.80</td>\n",
       "      <td>Placed</td>\n",
       "      <td>270000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>79.33</td>\n",
       "      <td>Central</td>\n",
       "      <td>78.33</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>77.48</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>Yes</td>\n",
       "      <td>86.5</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>66.28</td>\n",
       "      <td>Placed</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>68.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>Arts</td>\n",
       "      <td>64.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>57.80</td>\n",
       "      <td>Placed</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>Science</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>59.43</td>\n",
       "      <td>Not Placed</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>85.80</td>\n",
       "      <td>Central</td>\n",
       "      <td>73.60</td>\n",
       "      <td>Central</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>73.30</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>96.8</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>55.50</td>\n",
       "      <td>Placed</td>\n",
       "      <td>425000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sl_no  gender  ssc_p    ssc_b  hsc_p    hsc_b     hsc_s  degree_p  \\\n",
       "0      1       0  67.00   Others  91.00   Others  Commerce     58.00   \n",
       "1      2       0  79.33  Central  78.33   Others   Science     77.48   \n",
       "2      3       0  65.00  Central  68.00  Central      Arts     64.00   \n",
       "3      4       0  56.00  Central  52.00  Central   Science     52.00   \n",
       "4      5       0  85.80  Central  73.60  Central  Commerce     73.30   \n",
       "\n",
       "    degree_t workex  etest_p specialisation  mba_p      status    salary  \n",
       "0   Sci&Tech     No     55.0         Mkt&HR  58.80      Placed  270000.0  \n",
       "1   Sci&Tech    Yes     86.5        Mkt&Fin  66.28      Placed  200000.0  \n",
       "2  Comm&Mgmt     No     75.0        Mkt&Fin  57.80      Placed  250000.0  \n",
       "3   Sci&Tech     No     66.0         Mkt&HR  59.43  Not Placed       NaN  \n",
       "4  Comm&Mgmt     No     96.8        Mkt&Fin  55.50      Placed  425000.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Check Last 5 Rows of The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sl_no</th>\n",
       "      <th>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>ssc_b</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>hsc_b</th>\n",
       "      <th>hsc_s</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>degree_t</th>\n",
       "      <th>workex</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>status</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>80.6</td>\n",
       "      <td>Others</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>77.6</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>74.49</td>\n",
       "      <td>Placed</td>\n",
       "      <td>400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Others</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>53.62</td>\n",
       "      <td>Placed</td>\n",
       "      <td>275000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Others</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>Yes</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>69.72</td>\n",
       "      <td>Placed</td>\n",
       "      <td>295000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Others</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>60.23</td>\n",
       "      <td>Placed</td>\n",
       "      <td>204000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>215</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>89.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>60.22</td>\n",
       "      <td>Not Placed</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sl_no  gender  ssc_p    ssc_b  hsc_p   hsc_b     hsc_s  degree_p  \\\n",
       "210    211       0   80.6   Others   82.0  Others  Commerce      77.6   \n",
       "211    212       0   58.0   Others   60.0  Others   Science      72.0   \n",
       "212    213       0   67.0   Others   67.0  Others  Commerce      73.0   \n",
       "213    214       1   74.0   Others   66.0  Others  Commerce      58.0   \n",
       "214    215       0   62.0  Central   58.0  Others   Science      53.0   \n",
       "\n",
       "      degree_t workex  etest_p specialisation  mba_p      status    salary  \n",
       "210  Comm&Mgmt     No     91.0        Mkt&Fin  74.49      Placed  400000.0  \n",
       "211   Sci&Tech     No     74.0        Mkt&Fin  53.62      Placed  275000.0  \n",
       "212  Comm&Mgmt    Yes     59.0        Mkt&Fin  69.72      Placed  295000.0  \n",
       "213  Comm&Mgmt     No     70.0         Mkt&HR  60.23      Placed  204000.0  \n",
       "214  Comm&Mgmt     No     89.0         Mkt&HR  60.22  Not Placed       NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Find Shape of Our Dataset (Number of Rows And Number of Columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(215, 15)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows 215\n",
      "Number of Columns 15\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Rows\",data.shape[0])\n",
    "print(\"Number of Columns\",data.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Get Information About Our Dataset Like the Total Number of Rows, Total Number of Columns, Datatypes of Each Column And Memory Requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 215 entries, 0 to 214\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   sl_no           215 non-null    int64  \n",
      " 1   gender          215 non-null    int64  \n",
      " 2   ssc_p           215 non-null    float64\n",
      " 3   ssc_b           215 non-null    object \n",
      " 4   hsc_p           215 non-null    float64\n",
      " 5   hsc_b           215 non-null    object \n",
      " 6   hsc_s           215 non-null    object \n",
      " 7   degree_p        215 non-null    float64\n",
      " 8   degree_t        215 non-null    object \n",
      " 9   workex          215 non-null    object \n",
      " 10  etest_p         215 non-null    float64\n",
      " 11  specialisation  215 non-null    object \n",
      " 12  mba_p           215 non-null    float64\n",
      " 13  status          215 non-null    object \n",
      " 14  salary          148 non-null    float64\n",
      "dtypes: float64(6), int64(2), object(7)\n",
      "memory usage: 25.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Check Null Values In The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sl_no              0\n",
       "gender             0\n",
       "ssc_p              0\n",
       "ssc_b              0\n",
       "hsc_p              0\n",
       "hsc_b              0\n",
       "hsc_s              0\n",
       "degree_p           0\n",
       "degree_t           0\n",
       "workex             0\n",
       "etest_p            0\n",
       "specialisation     0\n",
       "mba_p              0\n",
       "status             0\n",
       "salary            67\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Get Overall Statistics About The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>sl_no</th>\n",
       "      <th>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>215.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>148.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.353488</td>\n",
       "      <td>67.303395</td>\n",
       "      <td>66.333163</td>\n",
       "      <td>66.370186</td>\n",
       "      <td>72.100558</td>\n",
       "      <td>62.278186</td>\n",
       "      <td>288655.405405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>62.209324</td>\n",
       "      <td>0.479168</td>\n",
       "      <td>10.827205</td>\n",
       "      <td>10.897509</td>\n",
       "      <td>7.358743</td>\n",
       "      <td>13.275956</td>\n",
       "      <td>5.833385</td>\n",
       "      <td>93457.452420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.890000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>51.210000</td>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.600000</td>\n",
       "      <td>60.900000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>57.945000</td>\n",
       "      <td>240000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>265000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>161.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.700000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>83.500000</td>\n",
       "      <td>66.255000</td>\n",
       "      <td>300000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>215.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>89.400000</td>\n",
       "      <td>97.700000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>77.890000</td>\n",
       "      <td>940000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sl_no      gender       ssc_p       hsc_p    degree_p     etest_p  \\\n",
       "count  215.000000  215.000000  215.000000  215.000000  215.000000  215.000000   \n",
       "mean   108.000000    0.353488   67.303395   66.333163   66.370186   72.100558   \n",
       "std     62.209324    0.479168   10.827205   10.897509    7.358743   13.275956   \n",
       "min      1.000000    0.000000   40.890000   37.000000   50.000000   50.000000   \n",
       "25%     54.500000    0.000000   60.600000   60.900000   61.000000   60.000000   \n",
       "50%    108.000000    0.000000   67.000000   65.000000   66.000000   71.000000   \n",
       "75%    161.500000    1.000000   75.700000   73.000000   72.000000   83.500000   \n",
       "max    215.000000    1.000000   89.400000   97.700000   91.000000   98.000000   \n",
       "\n",
       "            mba_p         salary  \n",
       "count  215.000000     148.000000  \n",
       "mean    62.278186  288655.405405  \n",
       "std      5.833385   93457.452420  \n",
       "min     51.210000  200000.000000  \n",
       "25%     57.945000  240000.000000  \n",
       "50%     62.000000  265000.000000  \n",
       "75%     66.255000  300000.000000  \n",
       "max     77.890000  940000.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sl_no', 'gender', 'ssc_p', 'ssc_b', 'hsc_p', 'hsc_b', 'hsc_s',\n",
       "       'degree_p', 'degree_t', 'workex', 'etest_p', 'specialisation', 'mba_p',\n",
       "       'status', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How Many Students Got Placed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Placed', 'Not Placed'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Placed        148\n",
       "Not Placed     67\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Could you display the top 5 sci&tech students placed according to their salary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sl_no', 'gender', 'ssc_p', 'ssc_b', 'hsc_p', 'hsc_b', 'hsc_s',\n",
       "       'degree_p', 'degree_t', 'workex', 'etest_p', 'specialisation', 'mba_p',\n",
       "       'status', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>sl_no</th>\n",
       "      <th>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>ssc_b</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>hsc_b</th>\n",
       "      <th>hsc_s</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>degree_t</th>\n",
       "      <th>workex</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>status</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>71.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>58.66</td>\n",
       "      <td>Central</td>\n",
       "      <td>Science</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>Yes</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>61.30</td>\n",
       "      <td>Placed</td>\n",
       "      <td>690000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>80.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>65.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>Yes</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>57.65</td>\n",
       "      <td>Placed</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>63.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>67.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>64.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>66.46</td>\n",
       "      <td>Placed</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>73.24</td>\n",
       "      <td>Others</td>\n",
       "      <td>50.83</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>64.27</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>Yes</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>66.23</td>\n",
       "      <td>Placed</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>70.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>72.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>71.04</td>\n",
       "      <td>Placed</td>\n",
       "      <td>450000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sl_no  gender  ssc_p    ssc_b  hsc_p    hsc_b    hsc_s  degree_p  \\\n",
       "150    151       0  71.00  Central  58.66  Central  Science     58.00   \n",
       "77      78       0  64.00   Others  80.00   Others  Science     65.00   \n",
       "163    164       0  63.00   Others  67.00   Others  Science     64.00   \n",
       "174    175       0  73.24   Others  50.83   Others  Science     64.27   \n",
       "53      54       0  80.00   Others  70.00   Others  Science     72.00   \n",
       "\n",
       "     degree_t workex  etest_p specialisation  mba_p  status    salary  \n",
       "150  Sci&Tech    Yes     56.0        Mkt&Fin  61.30  Placed  690000.0  \n",
       "77   Sci&Tech    Yes     69.0        Mkt&Fin  57.65  Placed  500000.0  \n",
       "163  Sci&Tech     No     75.0        Mkt&Fin  66.46  Placed  500000.0  \n",
       "174  Sci&Tech    Yes     64.0        Mkt&Fin  66.23  Placed  500000.0  \n",
       "53   Sci&Tech     No     87.0         Mkt&HR  71.04  Placed  450000.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['degree_t']==\"Sci&Tech\") & (data['status']==\"Placed\")].sort_values(by=\"salary\",ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>sl_no</th>\n",
       "      <th>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>ssc_b</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>hsc_b</th>\n",
       "      <th>hsc_s</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>degree_t</th>\n",
       "      <th>workex</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>status</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>91.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>58.80</td>\n",
       "      <td>Placed</td>\n",
       "      <td>270000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>79.33</td>\n",
       "      <td>Central</td>\n",
       "      <td>78.33</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>77.48</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>Yes</td>\n",
       "      <td>86.5</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>66.28</td>\n",
       "      <td>Placed</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>68.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>Arts</td>\n",
       "      <td>64.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>57.80</td>\n",
       "      <td>Placed</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>Science</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>59.43</td>\n",
       "      <td>Not Placed</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>85.80</td>\n",
       "      <td>Central</td>\n",
       "      <td>73.60</td>\n",
       "      <td>Central</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>73.30</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>96.8</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>55.50</td>\n",
       "      <td>Placed</td>\n",
       "      <td>425000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sl_no  gender  ssc_p    ssc_b  hsc_p    hsc_b     hsc_s  degree_p  \\\n",
       "0      1       0  67.00   Others  91.00   Others  Commerce     58.00   \n",
       "1      2       0  79.33  Central  78.33   Others   Science     77.48   \n",
       "2      3       0  65.00  Central  68.00  Central      Arts     64.00   \n",
       "3      4       0  56.00  Central  52.00  Central   Science     52.00   \n",
       "4      5       0  85.80  Central  73.60  Central  Commerce     73.30   \n",
       "\n",
       "    degree_t workex  etest_p specialisation  mba_p      status    salary  \n",
       "0   Sci&Tech     No     55.0         Mkt&HR  58.80      Placed  270000.0  \n",
       "1   Sci&Tech    Yes     86.5        Mkt&Fin  66.28      Placed  200000.0  \n",
       "2  Comm&Mgmt     No     75.0        Mkt&Fin  57.80      Placed  250000.0  \n",
       "3   Sci&Tech     No     66.0         Mkt&HR  59.43  Not Placed       NaN  \n",
       "4  Comm&Mgmt     No     96.8        Mkt&Fin  55.50      Placed  425000.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['sl_no','salary'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>ssc_b</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>hsc_b</th>\n",
       "      <th>hsc_s</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>degree_t</th>\n",
       "      <th>workex</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Others</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>58.8</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  ssc_p   ssc_b  hsc_p   hsc_b     hsc_s  degree_p  degree_t workex  \\\n",
       "0       0   67.0  Others   91.0  Others  Commerce      58.0  Sci&Tech     No   \n",
       "\n",
       "   etest_p specialisation  mba_p  status  \n",
       "0     55.0         Mkt&HR   58.8  Placed  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Encoding the Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Others', 'Central'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ssc_b'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['ssc_b'] = data['ssc_b'].map({'Central':1,'Others':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>ssc_b</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>hsc_b</th>\n",
       "      <th>hsc_s</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>degree_t</th>\n",
       "      <th>workex</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>58.80</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>79.33</td>\n",
       "      <td>1</td>\n",
       "      <td>78.33</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>77.48</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>Yes</td>\n",
       "      <td>86.5</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>66.28</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  ssc_p  ssc_b  hsc_p   hsc_b     hsc_s  degree_p  degree_t workex  \\\n",
       "0       0  67.00      0  91.00  Others  Commerce     58.00  Sci&Tech     No   \n",
       "1       0  79.33      1  78.33  Others   Science     77.48  Sci&Tech    Yes   \n",
       "\n",
       "   etest_p specialisation  mba_p  status  \n",
       "0     55.0         Mkt&HR  58.80  Placed  \n",
       "1     86.5        Mkt&Fin  66.28  Placed  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Others', 'Central'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['hsc_b'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['hsc_b'] = data['hsc_b'].map({'Central':1,'Others':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   gender  ssc_p  ssc_b  hsc_p  hsc_b     hsc_s  degree_p  degree_t workex  \\\n",
       "0       0  67.00      0  91.00      0  Commerce     58.00  Sci&Tech     No   \n",
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       "\n",
       "   etest_p specialisation  mba_p  status  \n",
       "0     55.0         Mkt&HR  58.80  Placed  \n",
       "1     86.5        Mkt&Fin  66.28  Placed  "
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     "execution_count": 41,
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    "data.head(2)"
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   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "array(['Commerce', 'Science', 'Arts'], dtype=object)"
      ]
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     "execution_count": 43,
     "metadata": {},
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    "data['hsc_s'].unique()"
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   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['hsc_s'] = data['hsc_s'].map({'Science':2,'Commerce':1,'Arts':0})"
   ]
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   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   gender  ssc_p  ssc_b  hsc_p  hsc_b  hsc_s  degree_p   degree_t workex  \\\n",
       "0       0  67.00      0  91.00      0      1     58.00   Sci&Tech     No   \n",
       "1       0  79.33      1  78.33      0      2     77.48   Sci&Tech    Yes   \n",
       "2       0  65.00      1  68.00      1      0     64.00  Comm&Mgmt     No   \n",
       "3       0  56.00      1  52.00      1      2     52.00   Sci&Tech     No   \n",
       "4       0  85.80      1  73.60      1      1     73.30  Comm&Mgmt     No   \n",
       "\n",
       "   etest_p specialisation  mba_p      status  \n",
       "0     55.0         Mkt&HR  58.80      Placed  \n",
       "1     86.5        Mkt&Fin  66.28      Placed  \n",
       "2     75.0        Mkt&Fin  57.80      Placed  \n",
       "3     66.0         Mkt&HR  59.43  Not Placed  \n",
       "4     96.8        Mkt&Fin  55.50      Placed  "
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     "execution_count": 45,
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    "data.head()"
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  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sci&Tech', 'Comm&Mgmt', 'Others'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data['degree_t'].unique()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['degree_t'] = data['degree_t'].map({'Sci&Tech':2,'Comm&Mgmt':1,'Others':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "   gender  ssc_p  ssc_b  hsc_p  hsc_b  hsc_s  degree_p  degree_t workex  \\\n",
       "0       0  67.00      0  91.00      0      1     58.00         2     No   \n",
       "1       0  79.33      1  78.33      0      2     77.48         2    Yes   \n",
       "\n",
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       "0     55.0         Mkt&HR  58.80  Placed  \n",
       "1     86.5        Mkt&Fin  66.28  Placed  "
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     "execution_count": 49,
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   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mkt&HR', 'Mkt&Fin'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
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   "source": [
    "data['specialisation'].unique()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['specialisation'] =data['specialisation'].map({'Mkt&HR':1,'Mkt&Fin':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "   gender  ssc_p  ssc_b  hsc_p  hsc_b  hsc_s  degree_p  degree_t workex  \\\n",
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       "\n",
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       "0     55.0               1  58.80  Placed  \n",
       "1     86.5               0  66.28  Placed  "
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     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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    "data.head(2)"
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   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['workex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['workex'] = data['workex'].map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>ssc_b</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>hsc_b</th>\n",
       "      <th>hsc_s</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>degree_t</th>\n",
       "      <th>workex</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>58.80</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>79.33</td>\n",
       "      <td>1</td>\n",
       "      <td>78.33</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>77.48</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0</td>\n",
       "      <td>66.28</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  ssc_p  ssc_b  hsc_p  hsc_b  hsc_s  degree_p  degree_t  workex  \\\n",
       "0       0  67.00      0  91.00      0      1     58.00         2       0   \n",
       "1       0  79.33      1  78.33      0      2     77.48         2       1   \n",
       "\n",
       "   etest_p  specialisation  mba_p  status  \n",
       "0     55.0               1  58.80  Placed  \n",
       "1     86.5               0  66.28  Placed  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Placed', 'Not Placed'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['status'] = data['status'].map({'Placed':1,'Not Placed':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>ssc_b</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>hsc_b</th>\n",
       "      <th>hsc_s</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>degree_t</th>\n",
       "      <th>workex</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>58.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>79.33</td>\n",
       "      <td>1</td>\n",
       "      <td>78.33</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>77.48</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0</td>\n",
       "      <td>66.28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>1</td>\n",
       "      <td>68.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>1</td>\n",
       "      <td>52.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>52.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>85.80</td>\n",
       "      <td>1</td>\n",
       "      <td>73.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>73.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96.8</td>\n",
       "      <td>0</td>\n",
       "      <td>55.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  ssc_p  ssc_b  hsc_p  hsc_b  hsc_s  degree_p  degree_t  workex  \\\n",
       "0       0  67.00      0  91.00      0      1     58.00         2       0   \n",
       "1       0  79.33      1  78.33      0      2     77.48         2       1   \n",
       "2       0  65.00      1  68.00      1      0     64.00         1       0   \n",
       "3       0  56.00      1  52.00      1      2     52.00         2       0   \n",
       "4       0  85.80      1  73.60      1      1     73.30         1       0   \n",
       "\n",
       "   etest_p  specialisation  mba_p  status  \n",
       "0     55.0               1  58.80       1  \n",
       "1     86.5               0  66.28       1  \n",
       "2     75.0               0  57.80       1  \n",
       "3     66.0               1  59.43       0  \n",
       "4     96.8               0  55.50       1  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Store Feature Matrix In X and Response(Target) In Vector y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'ssc_p', 'ssc_b', 'hsc_p', 'hsc_b', 'hsc_s', 'degree_p',\n",
       "       'degree_t', 'workex', 'etest_p', 'specialisation', 'mba_p', 'status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('status',axis=1)\n",
    "y= data['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "210    1\n",
       "211    1\n",
       "212    1\n",
       "213    1\n",
       "214    0\n",
       "Name: status, Length: 215, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Splitting The Dataset Into The Training Set And Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Import The models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "\n",
    "svm = svm.SVC()\n",
    "svm.fit(X_train,y_train)\n",
    "\n",
    "knn=KNeighborsClassifier()\n",
    "knn.fit(X_train,y_train)\n",
    "\n",
    "dt=DecisionTreeClassifier()\n",
    "dt.fit(X_train,y_train)\n",
    "\n",
    "rf=RandomForestClassifier()\n",
    "rf.fit(X_train,y_train)\n",
    "\n",
    "gb=GradientBoostingClassifier()\n",
    "gb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13. Prediction on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = lr.predict(X_test)\n",
    "y_pred2 = svm.predict(X_test)\n",
    "y_pred3 = knn.predict(X_test)\n",
    "y_pred4 = dt.predict(X_test)\n",
    "y_pred5 = rf.predict(X_test)\n",
    "y_pred6 = gb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14. Evaluating the Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "score1=accuracy_score(y_test,y_pred1)\n",
    "score2=accuracy_score(y_test,y_pred2)\n",
    "score3=accuracy_score(y_test,y_pred3)\n",
    "score4=accuracy_score(y_test,y_pred4)\n",
    "score5=accuracy_score(y_test,y_pred5)\n",
    "score6=accuracy_score(y_test,y_pred6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8837209302325582 0.7674418604651163 0.7906976744186046 0.8372093023255814 0.7906976744186046 0.813953488372093\n"
     ]
    }
   ],
   "source": [
    "print(score1,score2,score3,score4,score5,score6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.DataFrame({'Models':['LR','SVC','KNN','DT','RF','GB'],\n",
    "            'ACC':[score1*100,\n",
    "                  score2*100,\n",
    "                  score3*100,\n",
    "                  score4*100,\n",
    "                  score5*100,score6*100]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Models</th>\n",
       "      <th>ACC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>88.372093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>76.744186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN</td>\n",
       "      <td>79.069767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>83.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RF</td>\n",
       "      <td>79.069767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GB</td>\n",
       "      <td>81.395349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Models        ACC\n",
       "0     LR  88.372093\n",
       "1    SVC  76.744186\n",
       "2    KNN  79.069767\n",
       "3     DT  83.720930\n",
       "4     RF  79.069767\n",
       "5     GB  81.395349"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Models', ylabel='ACC'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.barplot(final_data['Models'],final_data['ACC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15. Prediction on New Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.DataFrame({\n",
    "    'gender':0,\n",
    "    'ssc_p':67.0,\n",
    "    'ssc_b':0,\n",
    "    'hsc_p':91.0,\n",
    "    'hsc_b':0,\n",
    "    'hsc_s':1,\n",
    "    'degree_p':58.0,\n",
    "    'degree_t':2,\n",
    "    'workex':0,\n",
    "    'etest_p':55.0,\n",
    "     'specialisation':1,\n",
    "    'mba_p':58.8,   \n",
    "},index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr= LogisticRegression()\n",
    "lr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Placed\n",
      "You will be placed with probability of 0.96\n"
     ]
    }
   ],
   "source": [
    "p=lr.predict(new_data)\n",
    "prob=lr.predict_proba(new_data)\n",
    "if p==1:\n",
    "    print('Placed')\n",
    "    print(f\"You will be placed with probability of {prob[0][1]:.2f}\")\n",
    "else:\n",
    "    print(\"Not-placed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04186191, 0.95813809]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16. Save Model Using Joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_campus_placement']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(lr,'model_campus_placement')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load('model_campus_placement')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(new_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import *\n",
    "import joblib\n",
    "import numpy as np\n",
    "from sklearn import *\n",
    "import tkinter.font as font\n",
    "import pandas as pd\n",
    "\n",
    "def show_entry_fields():\n",
    "    text = clicked.get()\n",
    "    if text == \"Male\":\n",
    "        p1=1\n",
    "        print(p1)\n",
    "    else:\n",
    "        p1=0\n",
    "        print(p1)\n",
    "    p2=float(e2.get())\n",
    "    text = clicked1.get()\n",
    "    if text == \"Central\":\n",
    "        p3=1\n",
    "        print(p3)\n",
    "    else:\n",
    "        p3=0\n",
    "        print(p3)\n",
    "    p4=float(e4.get())\n",
    "    text = clicked6.get()\n",
    "    if text == \"Central\":\n",
    "        p5=1\n",
    "        print(p3)\n",
    "    else:\n",
    "        p5=0\n",
    "        print(p3)\n",
    "    text = clicked2.get()\n",
    "    if text == \"Science\":\n",
    "        p6=2\n",
    "        print(p6)\n",
    "    elif text == \"Commerce\":\n",
    "        p6=1\n",
    "        print(p6)\n",
    "    else:\n",
    "        p6=0\n",
    "        print(p6)\n",
    "    p7=float(e7.get())\n",
    "    text = clicked3.get()\n",
    "    if text == \"Sci&Tech\":\n",
    "        p8=2\n",
    "        print(p8)\n",
    "    elif text==\"Comm&Mgmt\":\n",
    "        p8=1\n",
    "        print(p8)\n",
    "    else:\n",
    "        p8=0\n",
    "        print(p8)\n",
    "    text = clicked4.get()\n",
    "    if text == \"Yes\":\n",
    "        p9=1\n",
    "        print(p3)\n",
    "    else:\n",
    "        p9=0\n",
    "        print(p3)\n",
    "    p10=float(e10.get())\n",
    "    text = clicked5.get()\n",
    "    if text == \"Mkt&HR\":\n",
    "        p11=1\n",
    "        print(p11)\n",
    "    else:\n",
    "        p11=0\n",
    "        print(p11)\n",
    "    p12=float(e12.get())\n",
    "\n",
    "    model = joblib.load('model_campus_placement')\n",
    "    new_data = pd.DataFrame({\n",
    "    'gender':p1,\n",
    "    'ssc_p':p2,\n",
    "    'ssc_b':p3,\n",
    "    'hsc_p':p4,\n",
    "    'hsc_b':p5,\n",
    "    'hsc_s':p6,\n",
    "    'degree_p':p7,\n",
    "    'degree_t':p8,\n",
    "    'workex':p9,\n",
    "    'etest_p':p10,\n",
    "     'specialisation':p11,\n",
    "    'mba_p':p12,   \n",
    "},index=[0])\n",
    "    result=model.predict(new_data)\n",
    "    result1=model.predict_proba(new_data)\n",
    "    \n",
    "    if result[0] == 0:\n",
    "        Label(master, text=\"Can't Placed\").grid(row=31)\n",
    "    else:\n",
    "        Label(master, text=\"Student Will be Placed With Probability of\",font=(\"Arial\", 15)).grid(row=31)\n",
    "        Label(master, text=round(result1[0][1],2)*100,font=(\"Arial\", 15)).grid(row=33)\n",
    "        Label(master, text=\"Percent\",font=(\"Arial\", 15)).grid(row=34)\n",
    "\n",
    "master = Tk()\n",
    "master.title(\"Campus Placement Prediction System\")\n",
    "\n",
    "\n",
    "label = Label(master, text = \"Campus Placement Prediction System\"\n",
    "                          , bg = \"green\", fg = \"white\",font=(\"Arial\", 20)) \\\n",
    "                               .grid(row=0,columnspan=2)\n",
    "\n",
    "\n",
    "Label(master, text=\"Gender\",font=(\"Arial\", 15)).grid(row=1)\n",
    "Label(master, text=\"Secondary Education percentage- 10th Grade\",font=(\"Arial\", 15)).grid(row=2)\n",
    "Label(master, text=\"Board of Education\",font=(\"Arial\", 15)).grid(row=3)\n",
    "Label(master, text=\"Higher Secondary Education percentage- 12th Grade\",font=(\"Arial\", 15)).grid(row=4)\n",
    "Label(master, text=\"Board of Education\",font=(\"Arial\", 15)).grid(row=5)\n",
    "Label(master, text=\"Specialization in Higher Secondary Education\",font=(\"Arial\", 15)).grid(row=6)\n",
    "Label(master, text=\"Degree Percentage\",font=(\"Arial\", 15)).grid(row=7)\n",
    "Label(master, text=\"Under Graduation(Degree type)- Field of degree education\",font=(\"Arial\", 15)).grid(row=8)\n",
    "Label(master, text=\"Work Experience\",font=(\"Arial\", 15)).grid(row=9)\n",
    "Label(master, text=\"Enter test percentage\",font=(\"Arial\", 15)).grid(row=10)\n",
    "Label(master, text=\"branch specialization\",font=(\"Arial\", 15)).grid(row=11)\n",
    "Label(master, text=\"MBA percentage\",font=(\"Arial\", 15)).grid(row=12)\n",
    "clicked = StringVar()\n",
    "options = [\"Male\",\"Female\"]\n",
    "\n",
    "clicked1 = StringVar()\n",
    "options1 = [\"Central\",\"Others\"]\n",
    "\n",
    "clicked2 = StringVar()\n",
    "options2 = [\"Science\",\"Commerce\",\"Arts\"]\n",
    "\n",
    "clicked3 = StringVar()\n",
    "options3 = [\"Sci&Tech\",\"Comm&Mgmt\",\"Others\"]\n",
    "\n",
    "clicked4 = StringVar()\n",
    "options4 = [\"Yes\",\"No\"]\n",
    "\n",
    "clicked5 = StringVar()\n",
    "options5 = [\"Mkt&HR\",\"Mky&Fin\"]\n",
    "\n",
    "clicked6 = StringVar()\n",
    "options6 = [\"Central\",\"Others\"]\n",
    "e1 = OptionMenu(master , clicked , *options )\n",
    "e1.configure(width=13)\n",
    "e2 = Entry(master)\n",
    "e3 = OptionMenu(master , clicked1 , *options1 )\n",
    "e3.configure(width=13)\n",
    "e4 = Entry(master)\n",
    "e5 = OptionMenu(master , clicked6 , *options6)\n",
    "e5.configure(width=13)\n",
    "e6 = OptionMenu(master , clicked2 , *options2)\n",
    "e6.configure(width=13)\n",
    "e7 = Entry(master)\n",
    "e8 = OptionMenu(master , clicked3 , *options3)\n",
    "e8.configure(width=13)\n",
    "e9 = OptionMenu(master , clicked4 , *options4)\n",
    "e9.configure(width=13)\n",
    "e10 = Entry(master)\n",
    "e11 = OptionMenu(master , clicked5 , *options5)\n",
    "e11.configure(width=13)\n",
    "e12 = Entry(master)\n",
    "\n",
    "\n",
    "e1.grid(row=1, column=1)\n",
    "e2.grid(row=2, column=1)\n",
    "e3.grid(row=3, column=1)\n",
    "e4.grid(row=4, column=1)\n",
    "e5.grid(row=5, column=1)\n",
    "e6.grid(row=6, column=1)\n",
    "e7.grid(row=7, column=1)\n",
    "e8.grid(row=8, column=1)\n",
    "e9.grid(row=9, column=1)\n",
    "e10.grid(row=10, column=1)\n",
    "e11.grid(row=11, column=1)\n",
    "e12.grid(row=12, column=1)\n",
    "buttonFont = font.Font(family='Helvetica', size=16, weight='bold')\n",
    "Button(master, text='Predict',height= 1, width=8,activebackground='#00ff00',font=buttonFont,bg='black', fg='white',command=show_entry_fields).grid()\n",
    "\n",
    "mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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