This project implements two fundamental machine learning algorithms:
- Linear Regression: Models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.
- Weighted Linear Regression: An extension of linear regression that assigns different weights to data points based on their variance, improving the fit for datasets with heteroscedasticity.
The project includes experimental data analyses, with detailed comparisons and results presented in the accompanying documentation.
- Implementation of linear regression and weighted linear regression algorithms.
- Analysis of experimental data using both methods.
- Comprehensive documentation detailing the methodology, results, and comparisons.
For detailed analyses, charts, and comparisons, refer to the Charts And Analysis.pdf file in the repository.