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Project Overview
A curated portfolio of 26 end-to-end machine learning projects — spanning healthcare AI, real-time computer vision, NLP chatbots, time series forecasting, and classical ML prediction tasks.
📊 Repository at a Glance
| 📁 Projects | 🏷️ Domains | 🚀 Deployed Apps | 🖥️ GUI Apps | ⭐ GitHub Stars |
|---|---|---|---|---|
| 26 | 6 | 5 | 3 | 1.3k+ |
📚 Table of Contents
Click to expand / collapse
- 🗂️ All Projects by Category
- 🛠️ Tech Stack
- 📁 Project Structure
- 🚀 Getting Started
- 🤝 Contribution
- 📜 License
All Projects by Category
Legend: 🟢 Beginner 🟡 Intermediate 🔴 Advanced | 🌐 Web App 🖥️ GUI App 📓 Notebook
🏥 Healthcare & Medical AI
6 Projects — click to collapse
| Project | Description | Tools & Algorithms | Level | Type |
|---|---|---|---|---|
| Brain Tumor Detection | Detects tumors in MRI scans using a CNN. Upload a scan and get a prediction with confidence score. | CNN, Transfer Learning, Flask | 🔴 | 🌐 |
| Diabetes Prediction | Predicts diabetes likelihood from 8 health markers (glucose, BMI, age, etc.). | Random Forest, SVM, Logistic Regression | 🟢 | 📓 |
| Heart Disease Prediction | Predicts cardiac risk from 13 clinical features using ensemble models. | XGBoost, Logistic Regression, Random Forest | 🟢 | 📓 |
| Arrhythmia Classification | Classifies 16 arrhythmia types from 279 ECG features. | Random Forest, XGBoost, SVM | 🟡 | 📓 |
| Medical Chatbot | NLP chatbot mapping user-described symptoms to diagnosis suggestions. | NLP, NLTK, Flask, ML models | 🔴 | 🌐 |
| MoA Prediction | Predicts drug biological activity from gene expression and cell viability. | Deep Learning, TabNet, PyTorch | 🔴 | 📓 |
🎥 Computer Vision & OpenCV
9 Projects — click to collapse
| Project | Description | Tools & Algorithms | Level | Type |
|---|---|---|---|---|
| Driver Drowsiness Detection | Monitors driver eye state via Eye Aspect Ratio (EAR) and triggers alerts on drowsiness. | OpenCV, Dlib, Flask | 🟡 | 🌐 |
| Distracted Driver Detection | Classifies 10 distracted behaviors (texting, eating, phone call, etc.) from images. | CNN, TensorFlow/Keras | 🔴 | 📓 |
| Lane Line Detection | Overlays detected road lane lines on images/video using Canny edge detection and Hough transform. | OpenCV, Hough Transform | 🟢 | 📓 |
| Human Detection & Counting | Detects and counts people in live video using HOG descriptor. | OpenCV, HOG, NMS | 🟡 | 📓 |
| Gender & Age Detection | Predicts gender and age group from a face image. | OpenCV, DNN, Caffe Models | 🟡 | 📓 |
| Image Colorization | Adds realistic color to grayscale photos using deep learning. | OpenCV, Deep Learning | 🟡 | 📓 |
| Smile Selfie Capture | Auto-captures a photo the instant a smile is detected in the webcam stream. | OpenCV, Haar Cascades | 🟢 | 📓 |
| Emoji Creator from Emotions | Detects real-time facial emotions via webcam and overlays emoji reactions. | OpenCV, Haar Cascades | 🟢 | 📓 |
| Human Activity Recognition | Classifies activities (walking, sitting, standing) from pose estimation features. | OpenCV, Pose Estimation | 🟡 | 📓 |
📈 Classical ML & Prediction
7 Projects — click to collapse
| Project | Description | Tools & Algorithms | Level | Type |
|---|---|---|---|---|
| Iris Flower Classification | Classic benchmark — classifies iris species from petal/sepal measurements. | Logistic Regression, KNN, SVM | 🟢 | 📓 |
| Wine Quality Prediction | Predicts wine quality score (3–8) from 11 physicochemical properties. | XGBoost, Random Forest, SVM | 🟢 | 📓 |
| Loan Repayment Prediction | Predicts whether a LendingClub borrower will repay based on credit profile. | Logistic Regression, XGBoost | 🟡 | 📓 |
| College Admission Prediction | Estimates graduate admission probability from GRE, TOEFL, GPA, and research experience. | Linear Regression, Lasso, Random Forest | 🟢 | 📓 |
| Employee Turnover Prediction | Identifies employees at high risk of leaving the company. | Random Forest, XGBoost, CatBoost | 🟡 | 📓 |
| Property Maintenance Fines | Predicts fine compliance from Detroit's blight data. | Gradient Boosting, XGBoost | 🔴 | 📓 |
| Research Topic Prediction | Classifies academic papers into topic categories using NLP-based features. | TF-IDF, Naive Bayes, SVM | 🟡 | 📓 |
💬 NLP & Conversational AI
2 Projects — click to collapse
| Project | Description | Tools & Algorithms | Level | Type |
|---|---|---|---|---|
| AI Room Booking Chatbot | Hotel room booking chatbot using IBM Watson. Handles booking requests and sends email notifications. | IBM Watson, Flask, Python | 🟡 | 🌐 |
| Medical Chatbot | Symptom-to-diagnosis NLP chatbot with multi-turn conversation support. | NLP, LSTM, Flask | 🔴 | 🌐 |
📊 Time Series & Business Analytics
2 Projects — click to collapse
| Project | Description | Tools & Algorithms | Level | Type |
|---|---|---|---|---|
| Multi-Store Sales Prediction | Forecasts daily sales for 50 items across 10 stores using time series features. | XGBoost, Lag Features, Seasonal Patterns | 🟡 | 📓 |
| IPL Score Prediction | Predicts first-innings T20 scores from ball-by-ball match data with deep EDA. | Random Forest, XGBoost | 🟡 | 📓 |
🗺️ Geospatial & Data Science
1 Project — click to collapse
| Project | Description | Tools & Algorithms | Level | Type |
|---|---|---|---|---|
| The Battle of Neighborhoods | IBM Capstone — clusters city neighborhoods to recommend better business locations. | K-Means, Folium, Foursquare API | 🟡 | 📓 |
🛠️ Tech Stack
Languages & Environments :
Machine Learning & Deep Learning :
Computer Vision & NLP :
Data & Visualization :
Deployment :
📁 Project Structure
Every project follows a consistent layout for easy navigation and reuse:
ProjectName/
│
├── 📂 data/ # Raw and processed datasets
├── 📂 notebooks/ # Jupyter notebooks (EDA → Training → Evaluation)
├── 📂 models/ # Saved weights (.pkl / .h5 / .pt)
├── 📂 static/ # CSS, JS, images (Flask apps)
├── 📂 templates/ # Jinja2 HTML templates (Flask apps)
├── 📂 src/
│ ├── preprocess.py # Data cleaning & feature engineering
│ ├── train.py # Model training pipeline
│ └── predict.py # Inference logic
├── app.py # Flask entry point (web apps)
├── requirements.txt # Python dependencies
└── README.md # Project-specific documentation
🚀 Getting Started
Prerequisites
Python 3.7+ | pip | Git
Clone & Run
# Clone the repository
git clone https://github.com/shsarv/Machine-Learning-Projects.git
cd Machine-Learning-Projects
# Navigate to any project
cd "Heart Disease Prediction [END 2 END]"
# (Recommended) Create a virtual environment
python -m venv venv
source venv/bin/activate # Linux / macOS
venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txt
# For Flask web apps
python app.py
# → Open http://127.0.0.1:5000
# For notebooks
jupyter notebook
Deploy to Heroku
heroku login
heroku create your-app-name
echo "web: gunicorn app:app" > Procfile
git push heroku main
heroku open
Contributions 🌱
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 your changes align with the repository's style and structure.
- Fork this repo
- Branch:
git checkout -b feature/YourProjectName - Structure your folder with a
README.mdandrequirements.txt - Commit:
git commit -m "Add: YourProjectName" - Push:
git push origin feature/YourProjectName - Open a Pull Request → target
main
Please read CONTRIBUTING.md and follow the Code of Conduct.
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.