Multivariate Linear Regression Program in Python

 Multivariate Linear Regression Program in Python

Here,
There are many dependent variables and only one independent variable

# Importing packages

import pandas as pd

import numpy as np

import math

from word2number import w2n

from sklearn import linear_model

# Read the csv File (Input File/Train data)

df = pd.read_csv("D:\hiring.csv")

df

# Filling the experience column considering that experience as zero

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

df

# To fill the Test score column, we will take mean of all test_score and replace NaN with that value

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

# Converting the experience column from string(str) to Value(num)

df.experience = df.experience.astype(str)

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

df

# Train the Model

reg = linear_model.LinearRegression()

reg.fit(df[['experience','test_score(out of 10)','interview_score(out of 10)']],df['salary($)'])    # reg.fit(dependent variable, independent variable)

reg.predict([[10,10,10]])

reg.predict([[0,5,5]])

For reference:

Input Data File : https://drive.google.com/file/d/1BGDw1G_pJChEhUBtBR5XSXFHg96dt9K5/view?usp=sharing