Explaned Line by Line : Linear Regression Model for House Price Prediction in Python

 import numpy as np

from sklearn.linear_model import LinearRegression


This imports the necessary libraries: NumPy for numerical computations and the scikit-learn library's LinearRegression class for training a linear regression model.

size = np.array([[100], [150], [200], [250], [300], [350], [400], [450], [500], [550]]) price = np.array([150000, 200000, 250000, 300000, 350000, 400000, 450000, 500000, 550000, 600000])


These two lines create the training data. size represents the square footage of each house, and price represents the corresponding selling price of each house. They are NumPy arrays.


model = LinearRegression() model.fit(size, price)

These two lines create a LinearRegression object and then train it on the size and price data. This means the model will find a linear equation that predicts the selling price of a house based on its size.

new_size = np.array([[400]]) predicted_price = model.predict(new_size) print(predicted_price)

These lines create a new size value to test the trained model's prediction, with a value of 400 sq.ft, then use the trained model to predict the corresponding selling price, and finally, print the predicted price.

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