Intro to Data Mining: Project 2
Predicting a car’s brand from its specs, and comparing the classifiers that try.
Project_2_Classification.ipynb ↓Project_2_Classification.html ↓
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