Machine Learning Project (Regression, Classification & Clustering)

Developed multiple machine learning models using different datasets to perform regression,

classification, and clustering tasks. The project included data cleaning, preprocessing, feature

engineering, and exploratory data analysis to prepare datasets for modeling.

Applied dimensionality reduction techniques and appropriate encoding methods for categorical features,

followed by training and evaluating several machine learning algorithms.

Models Implemented

• Regression: Linear Regression, XGBoost

• Classification: Support Vector Machine (SVM)

• Clustering: K-Means, DBSCAN

Model Evaluation

• Regression: RMSE, R²

• Classification: Accuracy, Precision, Recall

• Clustering: Silhouette Score

Technologies

Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Jupyter Notebook