In this project, I applied advanced machine learning techniques to predict graduate admissions using a given dataset. Initially, I developed a linear regression model, processed the data, and evaluated the model's performance using Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics. Subsequently, I improved the model's performance by incorporating K-Means clustering as a pre-processing step. I determined the optimal number of clusters using the Silhouette Coefficient. I then developed individual linear regression models for each cluster, improving the predictive accuracy. This project demonstrated my strong grasp of machine learning principles, including linear regression and K-Means clustering.
Download the jupyter notebook file and then you can directly view it using jupyter notebook