Machine-Learning-Hiring

Exercise

This file contains hiring statics for a firm such as experience of candidate, his written test score and personal interview score. Based on these 3 factors, HR will decide the salary. Given this data, you need to build a machine learning model for HR department that can help them decide salaries for future candidates. Using this predict salaries for following candidates,

2 yr experience, 9 test score, 6 interview score

12 yr experience, 10 test score, 10 interview score

!pip install word2number

import pandas as pd import numpy as np from sklearn import linear_model from word2number import w2n

df = pd.read_csv(r'C:/Users/Jaydeep Patel/Downloads/hiring.csv') df

df.info()

df.experience = df.experience.fillna('zero') df

df.experience = df.experience.apply(w2n.word_to_num) df

import math median_test_score = math.floor(df['test_score(out of 10)'].mean()) median_test_score

df['test_score(out of 10)'] = df['test_score(out of 10)'].fillna(median_test_score) df

model = linear_model.LinearRegression() model.fit(df[['experience','test_score(out of 10)','interview_score(out of 10)']],df['salary($)'])

model.predict([[2,9,6]])

model.predict([[12,10,10]])

model.coef_

model.intercept_

2922.2690150212+2221.3090995910+2147.48256637*10+14992.65144669314