📊 Kaggle-Projects

A collection of end-to-end machine learning notebooks built for classic Kaggle competitions. Each project lives in its own folder with its own notebook, data, generated artifacts, and README — covering the full pipeline from raw data to a Kaggle-ready submission file.

📂 Projects

Project	Task	Approach	Notebook
🏠 House Prices - Advanced Regression Techniques	Predict residential home sale prices (regression)	Tuned XGBoost Regressor + Keras ANN, with missing-value imputation, one-hot encoding, and outlier removal	code.ipynb
🚢 Titanic - Machine Learning from Disaster	Predict passenger survival (classification)	Custom scikit-learn Pipeline (imputation, encoding, feature dropping) + tuned Random Forest Classifier	Titanic - Machine Learning from Disaster.ipynb

Each folder is self-contained: open its own README for a full breakdown of features, project structure, requirements, and usage instructions.

🛠️ Tech Stack

Across the repo, the notebooks make use of:

• Language: Python 3
• Environment: Jupyter Notebook
• Data handling: pandas, NumPy
• Visualization: Matplotlib, Seaborn
• Classical ML: scikit-learn (Pipelines, GridSearchCV / RandomizedSearchCV, StandardScaler)
• Gradient Boosting: XGBoost
• Deep Learning: TensorFlow / Keras

🚀 Getting Started

Each project is independent and has its own data and dependencies. To run one:

# Clone the repository
git clone https://github.com/Usf132/Kaggle-Projects.git
cd Kaggle-Projects

# Move into the project you want to run
cd "House Prices - Advanced Regression Techniques"   # or "Titanic - Machine Learning from Disaster"

# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate      # Windows: venv\Scripts\activate

# Install dependencies (see each project's README for the full list)
pip install numpy pandas matplotlib seaborn scikit-learn jupyter

# Launch Jupyter and open the notebook
jupyter notebook

⚠️ Neither project currently includes a requirements.txt or environment file — see each project's README for the exact packages to install.

📁 Repository Structure

Kaggle-Projects/
├── House Prices - Advanced Regression Techniques/
│   ├── code.ipynb
│   ├── data/
│   ├── images/
│   ├── model/
│   └── outputs/
├── Titanic - Machine Learning from Disaster/
│   ├── Titanic - Machine Learning from Disaster.ipynb
│   ├── data/
│   └── images/
├── LICENSE
└── README.md

This repository is licensed under the MIT License.