Linear Regression Mini Project

This mini project focuses on implementing a simple linear regression model from scratch and structuring the project to make it more maintainable. It also includes unit tests to ensure the correctness of the implementation.

Project Structure

• data/: Directory for storing dataset(s).

  • salary_data.csv: Dataset containing salary information.

• models/: Directory for model-related code.

  • __init__.py: Initialization file for the models package.
  • linear_regression.py: Implementation of the LinearRegressionModel class.

• utils/: Directory for utility functions.

  • __init__.py: Initialization file for the utils package.
  • data_preprocessing.py: Data preprocessing functions.

• tests/: Directory for unit tests.

  • __init__.py: Initialization file for the tests package.
  • test_linear_regression.py: Unit tests for the LinearRegressionModel.

• main.py: Main script to run the project.

Linear Regression Implementation

The linear regression model is implemented in the LinearRegressionModel class in models/linear_regression.py. The class includes methods for fitting the model to the training data (fit), making predictions (predict), and updating weights using gradient descent (update_weights).

Data Preprocessing

Data preprocessing, including loading and splitting the dataset, is done in the utils/data_preprocessing.py module. The load_and_preprocess_data function reads the dataset from a CSV file and splits it into training and test sets using train_test_split from sklearn.

Unit Tests

Unit tests for the LinearRegressionModel class are defined in tests/test_linear_regression.py. The tests cover the fit and predict methods, ensuring that the model behaves as expected.

Conclusion

Through this mini project, I have learned how to structure a machine learning project, make it more maintainable, and use unit tests to validate the implementation. I have gained a deeper understanding of linear regression and the importance of testing in machine learning development. These skills are essential for building robust and reliable machine learning systems.

# root dictory e.g: cd RegressionProject
# for testing the project
python -m unittest tests.test_linear_regression


## Running the Project 
python main.py