shsarv/Machine-Learning-Projects
shsarv/Machine-Learning-Projects has 2,031 GitHub stars and 616 forks. GitHot measured +1 stars in 24 hours. Last checked 2026-11-07 23:28 UTC. Last push 2026-10-13.
This repository showcases a selection of machine learning projects undertaken to understand and master various ML concepts. Each project reflects commitment to applying theoretical knowledge to practical scenarios, demonstrating proficiency in machine learning techniques and tools.
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README.md
Original
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
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.
Maintained By
Sarvesh Sharma |
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