salary_prediction_LR_to_PR

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dataset = pd.read_csv('dataset.csv')
print(dataset.shape)
print(dataset.head(10))
# Separating dataset into X and Y
X= dataset.iloc[:,:-1].values
X
Y = dataset.iloc[:,-1].values
Y
# training dataset into Linear Regression
from sklearn.linear_model import LinearRegression
modelLR = LinearRegression()
modelLR.fit(X,Y)
plt.scatter(X,Y,color = 'blue',marker='*')
plt.plot(X,modelLR.predict(X))
plt.title("Linear regression")
plt.xlabel("Level")
plt.ylabel("Salary")
plt.show()
# training dataset into Polynomial Regression
from sklearn.preprocessing import PolynomialFeatures
modelPR = PolynomialFeatures(degree = 2)
xPoly = modelPR.fit_transform(X)
modelPLR = LinearRegression()
modelPLR.fit(xPoly,Y)
modelLR = LinearRegression()
modelLR.fit(X,Y)
plt.scatter(X,Y,color = 'black',marker='*')
plt.plot(X,modelPLR.predict(modelPR.fit_transform(X)))
plt.title("Polynomial regression")
plt.xlabel("Level")
plt.ylabel("Salary")
plt.show()
#Prediction
a = 9.2
salaryPred = modelPLR.predict(modelPR.fit_transform([[a]]))
print("salary of person with ",a,"years of experience is:",salaryPred,"INR")