ML exercise-Hiring-Multiple linear regression algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import linear_model \n",
    "from word2number import w2n\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>60000</td>\n",
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       "      <td>70000</td>\n",
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       "      <td>62000</td>\n",
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       "      <th>6</th>\n",
       "      <td>ten</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>72000</td>\n",
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       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0        NaN                    8.0                           9      50000\n",
       "1        NaN                    8.0                           6      45000\n",
       "2       five                    6.0                           7      60000\n",
       "3        two                   10.0                          10      65000\n",
       "4      seven                    9.0                           6      70000\n",
       "5      three                    7.0                          10      62000\n",
       "6        ten                    NaN                           7      72000\n",
       "7     eleven                    7.0                           8      80000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=pd.read_csv('hiring (1).csv')\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['experience', 'test_score(out of 10)', 'interview_score(out of 10)',\n",
       "       'salary($)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.columns"
   ]
  },
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   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
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       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
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       "      <td>eleven</td>\n",
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       "      <td>80000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  experience test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0       zero                     8                           9      50000\n",
       "1       zero                     8                           6      45000\n",
       "2       five                     6                           7      60000\n",
       "3        two                    10                          10      65000\n",
       "4      seven                     9                           6      70000\n",
       "5      three                     7                          10      62000\n",
       "6        ten                  zero                           7      72000\n",
       "7     eleven                     7                           8      80000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=d.fillna('zero')\n",
    "c"
   ]
  },
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   "execution_count": 63,
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       "      <th>experience</th>\n",
       "      <th>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
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       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
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      ],
      "text/plain": [
       "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0           0                    8.0                           9      50000\n",
       "1           0                    8.0                           6      45000\n",
       "2           5                    6.0                           7      60000\n",
       "3           2                   10.0                          10      65000\n",
       "4           7                    9.0                           6      70000\n",
       "5           3                    7.0                          10      62000\n",
       "6          10                    NaN                           7      72000\n",
       "7          11                    7.0                           8      80000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.experience=c.experience.apply(w2n.word_to_num)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math \n",
    "median_test_score=math.floor(d['test_score(out of 10)'].mean())\n",
    "median_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
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       "      <th>experience</th>\n",
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       "      <th>salary($)</th>\n",
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       "      <td>72000</td>\n",
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       "      <td>80000</td>\n",
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      "text/plain": [
       "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0           0                    8.0                           9      50000\n",
       "1           0                    8.0                           6      45000\n",
       "2           5                    6.0                           7      60000\n",
       "3           2                   10.0                          10      65000\n",
       "4           7                    9.0                           6      70000\n",
       "5           3                    7.0                          10      62000\n",
       "6          10                    7.0                           7      72000\n",
       "7          11                    7.0                           8      80000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['test_score(out of 10)']=d['test_score(out of 10)'].fillna(median_test_score)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=linear_model.LinearRegression()\n",
    "reg.fit(d[['experience','test_score(out of 10)','interview_score(out of 10)']],d['salary($)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([53713.86677124])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2,9,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([93747.79628651])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[12,10,10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
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     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "reg.predict([[5,5,5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.predict([[2,9,6]])"
   ]
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