Linear Regression Model in Python – Sklearn

Linear regression is a simple yet powerful technique used for predicting continuous variables. In Python, the popular machine learning library Sklearn provides a simple and efficient way to implement linear regression models.

Importing Sklearn and Loading Data

To start with linear regression in Sklearn, you need to import the necessary libraries and load your dataset. Below is an example code snippet to do so:


import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

Once you have imported the required libraries, you can load your dataset using np.loadtxt() or any other suitable method.

Building and Training the Model

After loading the data, you can split it into training and testing sets using train_test_split() function. Then, you can create an instance of the LinearRegression class and fit the model to your training data as shown below:


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

lr = LinearRegression()
lr.fit(X_train, y_train)

Here, X represents the input features and y the target variable. The fit() method trains the model on the training set.

Making Predictions and Evaluating the Model

Once the model is trained, you can make predictions on the test set using the predict() method and evaluate the model’s performance using metrics like mean squared error (MSE) or R-squared score.


y_pred = lr.predict(X_test)

from sklearn.metrics import mean_squared_error, r2_score

mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

You can use the calculated metrics to assess the model’s accuracy and make any necessary adjustments to improve its performance.

Conclusion

Sklearn provides a convenient way to implement linear regression models in Python. By following the steps outlined above, you can build and train a linear regression model, make predictions, and evaluate its performance effectively.