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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. Each project applies theory to a practical problem, with several fully deployed as web and GUI applications.

📊 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
  • 🏥 Healthcare & Medical AI
  • 🎥 Computer Vision & OpenCV
  • 📈 Classical ML & Prediction
  • 💬 NLP & Conversational AI
  • 📊 Time Series & Business Analytics
  • 🗺️ Geospatial & Data Science
• 🛠️ 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 real-time prediction.	PyTorch · CNN · Flask	🔴	🌐
Diabetes Prediction	Predicts diabetes likelihood from 8 health markers (glucose, BMI, insulin, age) using the Pima Indians dataset.	scikit-learn · SVM · Flask	🟡	🌐
Heart Disease Prediction	Predicts cardiac risk from 13 clinical features with ~92% accuracy.	scikit-learn · Logistic Reg. · Flask	🟡	🌐
Arrhythmia Classification	Classifies 16 arrhythmia types from 279 ECG features (UCI dataset).	SVM · KNN · Decision Tree	🟡	📓
Medical Chatbot	NLP chatbot mapping user-described symptoms to diagnoses via a curated medical knowledge base.	NLTK · TF-IDF · Flask	🔴	🌐
MoA Prediction	Predicts drug biological activity from gene expression and cell viability data (Kaggle competition).	PyTorch · TabNet · Multi-label	🔴	📓

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🎥 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 an audio alert on drowsiness.	OpenCV · dlib · EAR	🟡	🖥️
Distracted Driver Detection	Classifies 10 distracted behaviors (texting, eating, phone call, etc.) from dashboard camera images.	CNN · Keras · ImageDataGenerator	🔴	📓
Lane Line Detection	Overlays detected road lane lines on images/video using Canny edge detection and Hough transforms.	OpenCV · Canny · Hough Transform	🟢	🖥️
Human Detection & Counting	Detects and counts people in live video or images using HOG + SVM.	OpenCV · HOG · SVM	🟢	🖥️
Gender & Age Detection	Predicts gender and age group from a face image using pre-trained Caffe models.	OpenCV DNN · Caffe Models	🟡	🖥️
Image Colorization	Adds realistic color to grayscale photos using the Zhang et al. deep colorization network.	OpenCV DNN · Zhang et al. · LAB space	🟡	📓
Smile Selfie Capture	Auto-captures a photo the instant a smile is detected in the webcam feed. No button needed.	OpenCV · Haar Cascades	🟢	🖥️
Emoji Creator from Emotions	Detects real-time facial emotions via webcam and overlays the matching emoji on screen.	OpenCV · CNN · FER dataset	🟡	🖥️
Human Activity Recognition	Classifies activities (walking, sitting, standing) from pose estimation keypoints over time.	LSTM · Keras · 2D Pose Estimation	🔴	📓

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📈 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. Ideal for comparing classifiers side-by-side.	KNN · SVM · Decision Tree · Naive Bayes	🟢	📓
Wine Quality Prediction	Predicts wine quality score (3–8) from 11 physicochemical properties like acidity, sulfates, and alcohol.	Random Forest · XGBoost	🟡	📓
Loan Repayment Prediction	Predicts whether a LendingClub borrower will repay based on credit history, income, and loan purpose.	Random Forest · XGBoost · Class Balancing	🟡	📓
College Admission Prediction	Estimates graduate admission probability from GRE, TOEFL, GPA, and research experience.	Linear Reg. · Ridge · Lasso · SVR	🟢	📓
Employee Turnover Prediction	Identifies employees at high risk of leaving using HR data (satisfaction, evaluations, workload, promotions).	Decision Tree · Random Forest	🟡	📓
Property Maintenance Fines	Predicts fine compliance from Detroit's blight dataset — a real-world class-imbalance problem (Michigan Data Science Team).	Gradient Boosting · SMOTE · AUC optimization	🔴	📓
Research Topic Prediction	Classifies academic papers into topic categories using NLP-based feature extraction on titles/abstracts.	TF-IDF · Naive Bayes · SVM · NLTK	🟡	📓

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💬 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 slot-filling, availability queries, and booking confirmations through a web interface.	IBM Watson Assistant · Watson Discovery	🟡	🌐
Medical Chatbot	Symptom-to-diagnosis NLP chatbot with multi-turn conversation support. (Also listed under Healthcare.)	NLTK · Flask · TF-IDF · Cosine Similarity	🔴	🌐

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📊 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 three time series approaches and model ensembling.	ARIMA · Facebook Prophet · LSTM (Keras)	🔴	📓
IPL Score Prediction	Predicts first-innings T20 scores from ball-by-ball match data with deep EDA and multiple regression models.	Linear/Ridge Reg. · Random Forest · ANN	🟡	📓

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🗺️ Geospatial & Data Science

1 Project — click to collapse

Project	Description	Tools & Algorithms	Level	Type
The Battle of Neighborhoods	IBM Capstone — clusters city neighborhoods using Foursquare API data to recommend optimal business locations.	K-Means · Foursquare API · Folium · Geopy	🟡	📓

🛠️ 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 you adhere to the following:

1. Fork this repo
2. Branch: git checkout -b feature/YourProjectName
3. Structure your folder with a README.md and requirements.txt
4. Commit: git commit -m "Add: YourProjectName"
5. Push: git push origin feature/YourProjectName
6. 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