https://github.com/haroldeustaquio/machine-learning-projects

This repository contains Machine Learning mini-projects focused on different predictive models, from linear regression to more advanced techniques. It also includes more comprehensive end-to-end projects covering the entire ML workflow, from data preparation to model deployment.
https://github.com/haroldeustaquio/machine-learning-projects

adaptive-boosting-algorithm boosting-algorithms boostrap-aggregating end-to-end machine-learning python ramdom-forest regression-models tree-classification xgboost-algorithm xgboost-classifier xgboost-models xgboost-regression

Last synced: 5 months ago
JSON representation

This repository contains Machine Learning mini-projects focused on different predictive models, from linear regression to more advanced techniques. It also includes more comprehensive end-to-end projects covering the entire ML workflow, from data preparation to model deployment.

• Host: GitHub
• URL: https://github.com/haroldeustaquio/machine-learning-projects
• Owner: haroldeustaquio
• License: mit
• Created: 2024-08-30T16:24:36.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-10-24T11:54:32.000Z (6 months ago)
• Last Synced: 2024-10-24T12:53:10.868Z (6 months ago)
• Topics: adaptive-boosting-algorithm, boosting-algorithms, boostrap-aggregating, end-to-end, machine-learning, python, ramdom-forest, regression-models, tree-classification, xgboost-algorithm, xgboost-classifier, xgboost-models, xgboost-regression
• Language: Jupyter Notebook
• Homepage:
• Size: 94.7 MB
• Stars: 6
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: License.txt

Awesome Lists containing this project

README

        
# 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](https://github.com/haroldeustaquio/Machine-Learning-Projects/tree/main/Linear-Regression)  

   - **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](https://github.com/haroldeustaquio/Machine-Learning-Projects/tree/main/Logistic-Regression)  

   - **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](https://github.com/haroldeustaquio/Machine-Learning-Projects/tree/main/Naive-Bayes)  

   - **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](https://github.com/haroldeustaquio/Machine-Learning-Projects/tree/main/Trees_and_Ensemble)

   - **Classification**: Projects using decision trees and ensemble models to classify datasets. [See more](https://github.com/haroldeustaquio/Machine-Learning-Projects/tree/main/Trees_and_Ensemble/Classification)  

     - **Projects**:

       - Basic Classification with Synthetic Data  

       - Cirrhosis Patient Survival Prediction  

   - **Regression**: Projects using decision trees and ensemble models for regression tasks. [See more](https://github.com/haroldeustaquio/Machine-Learning-Projects/tree/main/Trees_and_Ensemble/Regression)  

     - **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](https://github.com/haroldeustaquio/Machine-Learning-Projects/tree/main/Clustering-DimReduction)  

   - **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!