🤖 Machine Learning Projects

A collection of Machine Learning projects built using Python, Scikit-learn, Streamlit, and various data visualization libraries. These projects demonstrate classification, prediction, recommendation, and detection models for solving real-world problems.

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📂 Projects Included

• 🧠 Brainstroke Predictor
• 🌾 Crop Recommendation
• 🚗 Insurance Fraud Prediction
• ⛰️ Landslide Prediction
• 🫁 Lung Cancer Prediction
• 🏥 Multi Disease Predictor
• 🌧️ Rainfall Prediction
• 🔐 URL Malware Detector

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🛠️ Technologies Used

• Python
• Scikit-learn
• Pandas
• NumPy
• Streamlit
• Matplotlib
• Seaborn

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📦 Machine Learning Algorithms

The repository contains projects built using different machine learning algorithms, including:

• Logistic Regression
• Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• K-Nearest Neighbors (KNN)
• XGBoost (where applicable)

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📁 Repository Structure

Machine-Learning-Projects/
│
├── Brainstroke-Predictor/
├── Crop-Recommendation/
├── Insurance-Fraud-Prediction/
├── Landslide-Prediction/
├── Lung-Cancer-Prediction/
├── Multi-Disease-Predictor/
├── Rainfall-Prediction/
├── URL-Malware-Detector/
│
├── requirements.txt
└── README.md

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/Vijaypn87/Machine-Learning-Projects.git

2. Navigate to the Repository

cd Machine-Learning-Projects

3. Install Required Libraries

pip install -r requirements.txt

4. Run a Project

Example:

cd Heart-Disease-Prediction
streamlit run app.py

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📊 Features

• Interactive Streamlit applications
• Machine Learning model training and prediction
• Data preprocessing and feature engineering
• Data visualization
• Classification and prediction models
• Real-world datasets

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📌 Learning Objectives

These projects were developed to practice:

• Data preprocessing
• Feature scaling
• Label encoding
• Model training
• Model evaluation
• Streamlit application development
• Data visualization
• Machine Learning workflows

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📚 Future Improvements

• Improve UI/UX of Streamlit applications
• Add deep learning projects
• Docker support
• Cloud deployment
• REST API integration
• Model performance comparison
• Better documentation for each project

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👨‍💻 Author

Vijay P N

• GitHub: https://github.com/Vijaypn87
• LinkedIn: https://www.linkedin.com/in/vijaypnin/

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⭐ If you found these projects helpful, consider giving this repository a star.