Machine Learning Projects

This repository contains a comprehensive collection of machine learning mini-projects, covering a variety of tasks including classification, regression, clustering, dimensionality reduction, and sentiment analysis. Each category demonstrates the application of specific machine learning techniques to solve real-world problems, providing a practical introduction to various models and methodologies.

---

Repository Structure

The projects are organized into the following main categories:

1. Linear Regression
   Regression projects applying linear regression techniques to various datasets. See more

   • Projects:
     • Beer Consumption Prediction
     • Personal Insurance Cost Prediction
     • Water Temperature Prediction Using Oceanographic Data
     • Weather Prediction During World War II
     • Weather Prediction in Szeged (2006-2016)

2. Logistic Regression
   Classification projects focused on logistic regression models. See more

   • Projects:
     • Fake Bills Detector
     • Halloween Candy Power Ranking
     • Heart Disease Prediction
     • Predicting MBTI Personality Types
     • Titanic Survival Prediction

3. Naive Bayes
   Sentiment analysis projects applying Naive Bayes models to classify text data. See more

   • Projects:
     • Sentiment Analysis of Airline Tweets
     • Sentiment Classification on 1,600,000 Tweets

4. Trees and Ensemble
   Projects using decision trees and ensemble models for both classification and regression tasks. See more

   • Classification: Projects using decision trees and ensemble models to classify datasets. See more

     • Projects:
       • Basic Classification with Synthetic Data
       • Cirrhosis Patient Survival Prediction

   • Regression: Projects using decision trees and ensemble models for regression tasks. See more

     • Projects:
       • Car Price Prediction
       • Boston Housing Price Prediction

5. Clustering and Dimensionality Reduction
   Projects focusing on clustering and dimensionality reduction techniques, such as K-Means and PCA. See more

   • Projects:
     • Breast Cancer Wisconsin Diagnostic Clustering using PCA
     • Clustering on the Iris Dataset

Each subfolder contains a detailed README with project descriptions, dataset information, and specific results.

---

Feel free to explore each project to understand the methodologies and results in more detail!