Machine Learning Projects

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Project Overview

Welcome to the Machine Learning Projects Repository! This collection encompasses various projects demonstrating core concepts in machine learning, deep learning, natural language processing (NLP), and computer vision. It includes both deployed applications (built using Flask) and GUI-based apps (using Tkinter). These projects illustrate the potential of machine learning across domains, including medical diagnosis, human activity recognition, image processing, and more.

Project List

Here’s a detailed list of all projects included in this repository:

Project Name	Description	Link
AI Room Booking Chatbot	An intelligent chatbot built with IBM Watson Assistant to facilitate room bookings.	AI Room Booking Chatbot
Brain Tumor Detection (Flask App)	A deep learning-based Flask app for detecting brain tumors in MRI scans using PyTorch. Medical professionals can upload scans to receive predictions.	Brain Tumor Detection
Arrhythmia Classification	Predict and classify arrhythmias using machine learning algorithms and ECG data. Dataset from the UCI Repository.	Arrhythmia Classification
Image Colorization	A deep learning-based solution for colorizing black-and-white images using OpenCV and deep neural networks.	Image Colorization
Diabetes Prediction (Flask App)	A web application for predicting the likelihood of diabetes based on health parameters. Built using Flask and scikit-learn.	Diabetes Prediction
Distracted Driver Detection	Detect different distracted behaviors of drivers (e.g., texting, eating) using CNN and image classification techniques.	Distracted Driver Detection
Driver Drowsiness Detection	Detect drowsiness in drivers using OpenCV and CNN models based on eye status, with real-time alerts for safety.	Driver Drowsiness Detection
Emoji Generator Based on Emotions (Tkinter)	A Tkinter GUI application that detects facial expressions in real time and generates emojis based on detected emotions.	Emoji Generator
Gender and Age Detection	A deep learning-based app that predicts the gender and age of a person using facial images and OpenCV.	Gender and Age Detection
Heart Disease Prediction	Predict the likelihood of heart disease based on medical attributes. Built using scikit-learn models, with 92% accuracy.	Heart Disease Prediction
Human Activity Recognition (LSTM)	Classify human activities using 2D pose estimation and LSTM. Explore the application of limited dataset inputs for behavior prediction.	Human Activity Recognition
Human Detection & Counting	An OpenCV project that detects humans in images/videos and counts the number of people present.	Human Detection & Counting
IPL Score Prediction	Predict first-inning scores in IPL matches using EDA and various regression models (Linear, Decision Tree, Random Forest, etc.).	IPL Score Prediction
Iris Flower Classification	Classify iris flowers into different species based on petal and sepal measurements using classic machine learning algorithms.	Iris Flower Classification
Medical Chatbot (NLP)	A medical chatbot built with NLP that uses a dataset of disease symptoms and responds with probable diagnoses.	Medical Chatbot
Predict Employee Turnover	Predict employee turnover using scikit-learn decision trees and random forest models.	Predict Employee Turnover
Wine Quality Prediction	Predict wine quality using physicochemical features like acidity, sugar, and pH with machine learning models.	Wine Quality Prediction

Technologies Used

This repository includes a wide range of technologies and tools used in various machine learning and data science projects:

• Programming Languages: Python
• Libraries/Frameworks:
  • Machine Learning: scikit-learn, TensorFlow, PyTorch, Keras
  • NLP: IBM Watson, Natural Language Toolkit (NLTK), SpaCy
  • Web Development: Flask
  • Image Processing: OpenCV
  • GUI Development: Tkinter
  • Deep Learning: CNN, LSTM, DNN
• Tools & Platforms:
  • IBM Watson, Google Colab, Jupyter Notebooks
  • Deployed apps using Flask
  • Git and GitHub for version control

Contributing 🌱

We welcome contributions to this project! If you would like to improve the existing codebase or contribute new features, feel free to submit a pull request. Before submitting, please ensure that you adhere to the following:

1. **Fork the repository** and create your feature branch:  
   ```bash
   git checkout -b feature/YourFeature

2. Commit your changes:

   git commit -m "Add your feature description"

3. Push your branch to GitHub:

   git push origin feature/YourFeature

4. Open a pull request to the main branch.

For major changes, please open an issue first to discuss what you would like to change.

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📊 Project Structure

Each project follows a consistent structure for easy navigation and understanding:

ProjectName/
│
├── data/                  # Data files and datasets
├── notebooks/             # Jupyter notebooks for experimentation and prototyping
├── models/                # Trained machine learning models (if applicable)
├── static/                # Static files (CSS, JS, images for Flask-based projects)
├── templates/             # HTML templates (for Flask-based projects)
├── src/                   # Core Python scripts for data preprocessing, model training, etc.
├── app.py                 # Main application file for Flask-based projects
├── README.md              # Project-specific readme file
└── requirements.txt       # List of dependencies for the project

Feel free to explore individual projects to understand the data flow and code structure.

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🌍 Deployment

Some of the projects can be easily deployed on cloud platforms like Heroku, AWS, or Azure. The following steps outline a generic approach for deploying a Flask-based web app on Heroku:

1. Install Heroku CLI:
   Follow the instructions here.

2. Login to Heroku:

   heroku login

3. Create a new Heroku app:

   heroku create your-app-name

4. Push to Heroku:
   Ensure your Procfile is correctly set up for Flask:

   web: gunicorn app:app
   

   Then push the project to Heroku:

   git push heroku main

5. View your deployed app:

   heroku open

You can follow similar steps for AWS (using Elastic Beanstalk) or Azure (using App Services).

🎯 Roadmap

Future Enhancements:

• Integrate Explainable AI (XAI) models for better understanding of predictions in complex models.
• Add Docker support for easy containerization of all projects.
• Incorporate CI/CD pipelines using GitHub Actions for automated testing and deployment.
• Migrate some projects to use streamlit for interactive dashboards.
• Explore Reinforcement Learning for game-based AI projects.
• Expand the NLP section to include text summarization, translation, and more chatbot capabilities.

📚 Resources and References

• Official Python Documentation: Python.org
• Flask Documentation: Flask.palletsprojects.com
• Scikit-learn User Guide: Scikit-learn.org
• Keras Documentation: Keras.io
• TensorFlow Documentation: Tensorflow.org
• PyTorch Documentation: Pytorch.org

For a deeper understanding of AI, machine learning, and data science, I recommend the following courses:

• Coursera - Machine Learning by Andrew Ng
• Udacity - AI for Everyone
• Kaggle Learn - Data Science

⭐ Acknowledgments

• The wonderful Kaggle community, which provided open datasets and insightful discussions.
• Udemy, Coursera, and edX instructors who have helped me build a solid foundation in AI.

License

Distributed under the MIT License. See LICENSE for more information.

Maintained By

Sarvesh Sharma