polyreg2.py

#!/usr/bin/env python
# coding: utf-8

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler


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filepath = ('C:/Users/USER/Desktop/coursera/ML/tutorial/machine-learning-ex1/ex1/ex1data1.txt')
data =pd.read_csv(filepath,sep=',',header=None)
X = data.values[:,:1]
y = data.values[:,1:2]


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X = X.reshape(-1,1)
scale = StandardScaler()
X = scale.fit_transform(X)
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=.2,random_state=0)


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poly = PolynomialFeatures(degree=2)


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X_poly = poly.fit_transform(X_train)


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poly.fit(X_poly,y_train)


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linreg = LinearRegression()


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linreg.fit(X_poly,y_train)


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y_Pred = linreg.predict(poly.fit_transform(X))


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plt.scatter(X,y,color="black")
plt.plot(X,y_Pred,color="yellow",label = "polynomial regression Model with degree = 2")
plt.xlabel("Population of city in 10,000s")
plt.ylabel("profit in $10,000s")
plt.legend()


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linreg.score(poly.fit_transform(X),y)


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