Polynomial Linear Regression

In this ML Algorithms course tutorial, we are going to learn “Polynomial Linear Regression in detail. we covered it by practically and theoretical intuition.

  1. What is a Linear Regression?
  2. What is Multiple Linear Regression?
  3. What is Polynomial Linear Regression?
  4. What is the R^2 score?
  5. Why need to use it?
  6. How to implement Polynomial Regression in python?
# Business Problem - Predict the Price of Bangalore House
#Using Linear Regression - Supervised Machine Learning Algorithm

### Load Libraries
import pandas as pd
import numpy as np

"""### Load Data"""

path = r"https://drive.google.com/uc?export=download&id=1xxDtrZKfuWQfl-6KA9XEd_eatitNPnkB" 
df = pd.read_csv(path)


"""### Split Data"""

X = df.drop('price', axis=1)
y = df['price']

print('Shape of X = ', X.shape)
print('Shape of y = ', y.shape)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=51)

print('Shape of X_train = ', X_train.shape)
print('Shape of y_train = ', y_train.shape)
print('Shape of X_test = ', X_test.shape)
print('Shape of y_test = ', y_test.shape)

"""### Feature Scaling"""

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.transform(X_train)
X_test = sc.transform(X_test)

"""## Polynomial Linear Regression - ML Model Training"""

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

poly_reg = PolynomialFeatures(degree=2)
X_train_poly = poly_reg.transform(X_train)
X_test_poly = poly_reg.transform(X_test)

X_train_poly.shape, X_test_poly.shape

lr = LinearRegression()

lr.fit(X_train_poly, y_train)

lr.score(X_test_poly, y_test,)


y_pred = lr.predict(X_test_poly)


from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)

mse, rmse

"""Ab milenge next tutorial me,Tab tak ke liye SIKHATE SIKHATE kuch IMPLEMENT karte raho,\nThank You.....-:)"""

Leave a Reply