Linear Regression using sklearn

Lecture 33:- Linear Regression using sklearn

Using scikit-learn, a popular Python library for machine learning, implementing linear regression becomes significantly more straightforward and efficient. Scikit-learn provides a high-level API that streamlines the entire process, including data preprocessing, model training, and evaluation. Here's how to implement linear regression using scikit-learn:

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import numpy as np from sklearn.linear_model import LinearRegression import matplotlib.pyplot as plt # Example data x = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)  # Reshape to a column vector y = np.array([2, 3, 4, 2, 3]) # Create and fit the linear regression model model = LinearRegression() model.fit(x, y) # Get the coefficients (slope and intercept) slope = model.coef_[0] intercept = model.intercept_ # Make predictions on new data x_new = np.array([6, 7, 8]).reshape(-1, 1) predictions = model.predict(x_new) # Calculate R-squared (coefficient of determination) to evaluate the model's performance r_squared = model.score(x, y) # Visualize the Model's Line of Best Fit and Data Points plt.scatter(x, y, color='blue', label='Data Points') plt.plot(x, model.predict(x), color='red', label='Model') plt.scatter(x_new, predictions, color='green', label='Predictions') plt.xlabel('Independent Variable (x)') plt.ylabel('Dependent Variable (y)') plt.title('Linear Regression using scikit-learn') plt.legend() plt.show() print("Slope (m):", slope) print("Y-Intercept (b):", intercept) print("R-squared:", r_squared) print("Predictions on new data:", predictions)

In this implementation, we used scikit-learn's LinearRegression class to create and train the model. The process is as follows:

We import the necessary libraries, including numpy for numerical operations, LinearRegression from sklearn.linear_model for the linear regression model, and matplotlib.pyplot for visualization.

We define the example data x and y.

We create an instance of LinearRegression as model and fit it with the data using model.fit(x, y).

The model's slope and y-intercept are obtained using model.coef_ and model.intercept_.

We can then use the trained model to make predictions on new data using model.predict(x_new).

The R-squared value is calculated using model.score(x, y) to evaluate the model's performance.

Finally, we use matplotlib to visualize the model's line of best fit, data points, and predictions.

Scikit-learn simplifies the process of implementing linear regression and provides many additional functionalities for advanced model evaluation, feature selection, and regularization, making it a powerful tool for various machine learning tasks.