ML Case Study: Classification & Regression

This repository contains machine learning case studies focusing on both classification and regression problems, demonstrating an end-to-end workflow from data preprocessing to model evaluation.

Overview

The project includes:

• Data preprocessing and feature scaling
• Training multiple machine learning models
• Evaluating and comparing model performance

Classification Models

• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)
• Decision Tree
• Naive Bayes

Evaluation Metrics:

• Accuracy
• Precision
• Recall
• F1 Score
• ROC-AUC

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Regression Models

• Linear Regression
• Support Vector Regressor (SVR)
• Random Forest Regressor
• Gradient Boosting Regressor

Evaluation Metrics:

• RMSE
• MAE
• R² Score

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Tools & Technologies

• Python
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn

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Key Learnings

• Compared multiple models to identify the best-performing approach
• Understood trade-offs between different algorithms
• Applied appropriate evaluation metrics for model selection

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Author

Samyuktha T R