https://github.com/xmen3em/kaggle-competitions

This collection contains various projects and notebooks developed to tackle a range of Kaggle competitions, showcasing different machine learning techniques, data preprocessing methods, and model optimizations.
https://github.com/xmen3em/kaggle-competitions

data data-science data-visualization deep-learning deployment ensemble-learning machine-learning-algorithms python streamlit

Last synced: about 21 hours ago
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This collection contains various projects and notebooks developed to tackle a range of Kaggle competitions, showcasing different machine learning techniques, data preprocessing methods, and model optimizations.

• Host: GitHub
• URL: https://github.com/xmen3em/kaggle-competitions
• Owner: Xmen3em
• License: mit
• Created: 2024-02-02T18:12:46.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-08-19T13:15:43.000Z (3 months ago)
• Last Synced: 2024-08-20T00:18:04.872Z (3 months ago)
• Topics: data, data-science, data-visualization, deep-learning, deployment, ensemble-learning, machine-learning-algorithms, python, streamlit
• Language: Jupyter Notebook
• Homepage:
• Size: 18.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Kaggle Competitions Repository

Welcome to my Kaggle Competitions repository! This collection contains various projects and notebooks developed to tackle a range of Kaggle competitions, showcasing different machine-learning techniques, data preprocessing methods, and model optimizations.

## 📂 Repository Structure

The repository is organized into folders, each dedicated to a specific Kaggle competition. Inside each folder, you will find:

- **Notebooks**: Jupyter notebooks containing data exploration, preprocessing, model training, evaluation, and predictions.

- **Datasets**: Links to the datasets used, often hosted on Kaggle.

- **Models**: Saved models, including various machine learning algorithms like Random Forest, XGBoost, and Neural Networks.

- **Results**: Visualizations, predictions, and final competition submissions.

- **Documentation**: Detailed README files explaining the approach taken for each competition, including any unique challenges and solutions.

## 📊 Competitions Covered

- **House Prices - Advanced Regression Techniques**: A deep dive into predicting home prices using regression models, including feature engineering, and model stacking.

- **Academic Success Prediction**: A Streamlit app developed to predict student academic success, integrating exploratory data analysis, model training, and hyperparameter tuning with a user-friendly interface.

- **[Other Competitions]**: Various other competitions that involve classification, regression, and deep learning tasks.

## 🚀 How to Use

1. **Clone the repository**:

   ```bash

   git clone https://github.com/Xmen3em/Kaggle-Competitions.git

2. Navigate to a specific competition folder:

```bash

cd [competition-name]

```

3. Run the Jupyter notebooks to explore the data and models.