Predictive Modeling with Linear Regression

Summary

This project demonstrates how to build a predictive model using linear regression to predict a numerical outcome based on one or more features. Using a dataset with a continuous target variable, the project covers data preprocessing, model building, training, evaluation, and visualization of results.

Introduction

Linear regression is a fundamental statistical technique used to model the relationship between a dependent variable and one or more independent variables. It is widely used in predictive modeling due to its simplicity and interpretability. This project utilizes linear regression to predict a continuous target variable from a given dataset.

Project Overview

Data Collection

For this project, we'll use a dataset from a publicly available source, such as the Boston Housing dataset, which contains information about various factors influencing house prices in Boston.

Data Preprocessing

Data preprocessing involves:

Handling missing values (if any).

Encoding categorical variables (if any).

Normalizing or scaling numerical features to improve model performance.

Splitting the dataset into training and testing sets.

Model Building

We use the LinearRegression class from the scikit-learn library to build the linear regression model. The model will learn the relationship between the features and the target variable during training.

Training and Prediction

The model is trained on the training dataset and used to make predictions on the testing dataset. Training involves finding the best-fit line that minimizes the error between the predicted and actual values.

Evaluation

The model's performance is evaluated using metrics such as:

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

R-squared (R²) score

These metrics provide insights into the accuracy and reliability of the model.

Results Visualization

The project visualizes the model's performance by plotting the actual vs. predicted values and the residual errors. This helps in understanding the model's accuracy and identifying any patterns in the prediction errors.

Conclusion

This project demonstrates how to build and evaluate a linear regression model for predicting a continuous target variable. By understanding the relationship between the features and the target, linear regression provides a straightforward approach to predictive modeling.

Requirements

Python 3.x

Jupyter Notebook

Libraries: numpy, pandas, matplotlib, scikit-learn, seaborn

Contributions

Contributions are welcome! Please fork the repository and submit a pull request with your enhancements.

License

This project is licensed under the MIT License.

Contact

Alolika Bhowmik

[email protected]