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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!