ML Exercises Repository

Overview

This repository contains Jupyter notebooks for various machine learning exercises and demonstrations, completed as part of an ML course. Each exercise focuses on different techniques and algorithms in data science and machine learning.

Exercises

Exercise 1: Visualization, K-means, Elbow, and Silhouette

• Topics Covered: Data visualization, clustering with K-means, Elbow method, Silhouette analysis.
• Technologies Used: Python, NumPy, Matplotlib, Scikit-learn.

Exercise 2: Linear Regression, Naive Bayes, Decision Trees

• Topics Covered: Linear regression, Naive Bayes classification, Decision Trees with entropy and Gini impurity.
• Technologies Used: Python, Pandas, Scikit-learn.

Exercise 3: EDA, Visualization, Preprocessing, SHAP, Ensemble Methods

• Topics Covered: Exploratory Data Analysis, advanced visualization techniques, data preprocessing, SHAP values interpretation, ensemble methods including SVMs.
• Technologies Used: Python, Pandas, Seaborn, Matplotlib, SHAP, Scikit-learn.

Getting Started

To use these notebooks:

git clone https://github.com/ABbgu1995/ML.git
cd ML
# Run Jupyter Notebook or Jupyter Lab
jupyter notebook