Pattern Recognition

About the Project

Pattern Recognition is a machine learning project focused on image classification using multiple classical machine learning algorithms. The project implements and compares Support Vector Machines (SVM), Random Forest, and Naive Bayes classifiers to determine the most effective approach for classifying vectorized image data.

The implementation emphasizes proper data preprocessing techniques, including normalization and feature scaling, which are critical for optimal model performance. This project demonstrates fundamental machine learning principles and best practices in model development and evaluation.

Key Features

• Multiple Classifiers: Implementation and comparison of SVM, Random Forest, and Naive Bayes algorithms
• Data Preprocessing: Comprehensive normalization and feature scaling pipeline
• Vectorized Processing: Efficient handling of image data through vectorization techniques
• Performance Metrics: Detailed evaluation and comparison of model accuracy and performance
• Scalable Pipeline: Modular design allowing easy addition of new classifiers or datasets
• Data Analysis: Pandas integration for data manipulation and exploratory analysis

Technical Highlights

The project showcases expertise in applying classical machine learning algorithms to computer vision tasks. Each classifier is carefully tuned and evaluated to understand its strengths and weaknesses in the context of image classification.

Feature engineering and preprocessing are handled through scikit-learn’s robust pipeline system, ensuring consistent transformations across training and testing datasets. The implementation includes cross-validation and hyperparameter tuning to optimize model performance.

This project demonstrates proficiency in machine learning fundamentals, data preprocessing, and model evaluation methodologies essential for developing reliable classification systems.