Overview

This repository contains Python code implementing linear regression and logistic regression models. Please note that this is for educational purposes only(to demonstrate understanding of linear regression)

Files

1. linear_regression.py: Contains the implementation of the LinearRegression class.
2. logistic_regression.py: Contains the implementation of the LogisticRegression class.
3. main.py: The main imports the datasets and runs the linear and logistic models.
4. utility.py: Contains functions to handle the data(generating and splitting the data) and to run the linear and logistic models.

Dependencies

To run the code, you need to install the following dependencies:

1. numpy: For matrix computations.
2. scikit-learn: For the classification dataset in logistic regression.
3. matplotlib: For visualization.

You can install these dependencies by creating a virtual environment and running the command:

    pip install -r requirements.txt

Usage

1. Clone the repository to your local machine.
2. Navigate to the repository folder.
3. Create a virtual environment and install dependencies using the command mentioned above.
4. Run the command:

    python main.py

Functionality

1. Linear Regression: Numpy was used to generate sample data. The code includes options for updating weights with momentum and without momentum.
2. Logistic Regression: Uses data generated from make_classification function in scikit-learn's datasets module.

You can see the usage of both regression models in the main.py file.