Intro to Data Mining: Project 2

Project Overview

This project tackles a multi-class classification problem: predicting car brands based on vehicle characteristics. It demonstrates the complete machine learning workflow from data preprocessing to model deployment.

Problem Statement

Given a dataset of vehicles with various features (engine size, horsepower, fuel efficiency, price, etc.), build a classification model that can accurately predict the manufacturer brand.

Approach

Data Preprocessing

• Feature engineering and selection
• Handling categorical variables
• Scaling numerical features
• Addressing class imbalance

Model Development

Multiple classification algorithms are implemented and compared:

• Logistic Regression: Baseline linear model
• Decision Trees: Non-linear decision boundaries
• Random Forest: Ensemble learning approach
• Support Vector Machines: Maximum margin classifier
• k-Nearest Neighbors: Instance-based learning

Evaluation Strategy

• Accuracy: Overall correctness
• Precision & Recall: Per-class performance
• F1-Score: Harmonic mean of precision and recall
• Confusion Matrix: Detailed error analysis
• Cross-Validation: Generalization performance

Results & Insights

The project includes:

• Comparative analysis of algorithm performance
• Feature importance rankings
• Visualization of decision boundaries
• Discussion of model trade-offs

Key Learnings

• When to use different classification algorithms
• How to handle multi-class problems
• Techniques for improving model performance
• Best practices for model evaluation and selection

Real-World Applications

Classification skills are applicable to:

• Customer segmentation
• Fraud detection
• Medical diagnosis
• Image recognition
• Sentiment analysis

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