This project implements a linear regression model from scratch to predict exam scores based on study hours and other relevant features. The goal is to provide a comprehensive understanding of the linear regression algorithm and its application in educational data analysis.
- Data Collection and Preprocessing
- Exploratory Data Analysis (EDA) to Visualize Relationships
- Implementation of Linear Regression Algorithm from Scratch
- Model Evaluation Using Metrics like Mean Squared Error (MSE) and R-squared
To get started with the project, clone the repository and navigate to the project directory. You can run the main script to view the predictions and evaluations.
The dataset used in this project consists of study hours and corresponding exam scores. It can be found in the data directory.
The linear regression model is implemented in the (link unavailable) file. It includes functions for calculating the coefficients, making predictions, and fitting the model to the training data.
Model performance is evaluated using the following metrics:
- Mean Squared Error (MSE): Measures the average of the squares of the errors.
- R-squared: Indicates how well the model explains the variability of the data.
Contributions are welcome! If you have suggestions for improvements or new features, please feel free to open an issue or submit a pull request.
License
This project is licensed under the MIT License. See the LICENSE file for details.