In this ML Algorithms course tutorial, we are going to learn ” R-Squared in detail in Hindi. we covered it by practically and theoretically.
- What is R-Squared?
- Why should we use it?
- How to calculate r2_score?
- How to implement it using Python?
# -*- coding: utf-8 -*- """R-Squared -Bangalore House Price Prediction.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/17fZwXZWj7KaioLZlG9dVLmYiRpRnAEVr # Business Problem - Predict the Price of Bangalore House Using Linear Regression - Supervised Machine Learning Algorithm ### Load Libraries """ import pandas as pd """### Load Data""" path = r"https://drive.google.com/uc?export=download&id=1xxDtrZKfuWQfl-6KA9XEd_eatitNPnkB" df = pd.read_csv(path) df.head() """### 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() sc.fit(X_train) X_train = sc.transform(X_train) X_test = sc.transform(X_test) """## Linear Regression - ML Model Training""" from sklearn.linear_model import LinearRegression lr = LinearRegression() lr.fit(X_train, y_train) lr.coef_ lr.intercept_ """## Predict the value of Home and Test""" X_test[0, :] lr.predict([X_test[0, :]]) lr.predict(X_test) y_test lr.score(X_test, y_test,) y_pred = lr.predict(X_test) """# R-sqaure""" from sklearn.metrics import r2_score r2_score(y_test, y_pred)