🚀 Machine Learning Engineering — From Scratch to Production

A high-caliber machine learning engineering library — featuring from-scratch NumPy implementations of core algorithms and full-stack production applications in Healthcare, NLP, and Real Estate.

---

✨ Key Features

• 🏗️ Math-to-Code Mastery — From-scratch implementations of Linear Regression, Logistic Regression, KNN, K-Means, and PCA using only NumPy.
• 🏥 Healthcare Solutions — Heart disease and clinical diagnosis systems.
• 🛡️ Production-Ready NLP — Full-stack Hate Speech detection and Sentiment Analysis apps.
• 🏠 Real Estate Ecosystem — End-to-end price prediction with Flask API and UI.
• 🎨 Computer Vision — OpenCV-powered image stylization and transformation.

---

📂 Project Structure

Machine-Learning-/
├── supervised/          # From-scratch Supervised Learning (NumPy)
├── unsupervised/        # From-scratch Unsupervised Learning (NumPy)
├── preprocessing/       # Feature Engineering & Scaling (NumPy)
├── evaluation/          # Model Evaluation Metrics (NumPy)
├── projects/            # Production-Ready Full-Stack Applications
│   ├── ML_Healthcare/   # Heart disease & disease prediction
│   ├── hate_speech/     # Flask Hate Speech Detector
│   ├── real_estate/     # Price Prediction Pipeline
│   └── notebooks/       # Research & Lab Archives
└── README.md

---

🧠 Master Concept Checklist (Scratch Implementations)

Category	Concept	Implementation	Status
Supervised	Linear Regression	supervised/linear_regression.py	✅
Supervised	Logistic Regression	supervised/logistic_regression.py	✅
Supervised	K-Nearest Neighbors	supervised/knn.py	✅
Unsupervised	K-Means Clustering	unsupervised/kmeans.py	✅
Unsupervised	PCA (Reduction)	unsupervised/pca.py	✅

---

⚙️ Installation & Setup

# Clone the repository
git clone https://github.com/Vaishnavi-Dubey/Machine-Learning-.git
cd Machine-Learning-

# Install core dependencies
pip install numpy pandas scikit-learn matplotlib seaborn flask opencv-python

---

📈 Impact / Highlights

• 🎯 Deep Intuition — Algorithms implemented from first principles (Gradient Descent, Sigmoid, Eigen-decomposition).
• ⚡ Framework Proficiency — Masterful use of Scikit-learn for high-level validation and NumPy for low-level logic.
• 📊 Commercial Ready — Demonstrates the ability to build, train, and deploy ML models into web interfaces.

---

📜 License

This project is licensed under the MIT License.

---

Built with ❤️ by Vaishnavi Dubey