This repository contains the code and projects from FIZ 437E: Statistical Learning from Data: Applications in Physics during the Fall 2023 semester. The aim of this course was to explore various machine learning techniques and their applications in physics. The project includes multiple assignments and a term project focused on classification problems using real-world physics data.

• Implemented KNN for classification tasks, comparing its performance with SGD on various datasets.
• Explored distance-based algorithms and gradient descent optimization for classification and regression.

• Applied Naive Bayes classifier to predict Breast Cancer detection using real datasets.
• Focused on probabilistic models and feature extraction techniques for medical data.

• Used machine learning models for classifying Hadron and Gamma particles.
• Implemented classification algorithms and feature engineering for particle physics datasets.
• Evaluated model performance using confusion matrix, accuracy, and precision-recall metrics.

• K-Nearest Neighbors (KNN): Distance-based classification algorithm.
• Stochastic Gradient Descent (SGD): Optimization algorithm used for training models.
• Naive Bayes: Probabilistic classification technique based on Bayes’ Theorem.
• Particle Physics Classification: Hadron vs Gamma particle classification using supervised learning models.

• Breast Cancer Detection dataset: Real-world medical dataset used for testing the Naive Bayes classifier.
• Hadron and Gamma classification data: Physics-based datasets for particle detection.

• Machine Learning in Physics: Applying statistical learning methods to real-world physics problems.
• Data Classification: Classifying particles in physics experiments using supervised learning models.
• Medical Data Analysis: Using machine learning for medical diagnostics and cancer detection.

K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Naive Bayes, Breast Cancer Detection, Hadron vs Gamma Classification, Supervised Learning, Machine Learning, Particle Physics Classification, Feature Engineering, Optimization Algorithms, Classification Models, Data Classification, Statistical Learning, Physics Applications, Medical Data Analysis, Cancer Detection.