This repository is a curated collection of machine learning and deep learning projects covering a wide range of real-world applications including regression, classification, computer vision, and generative models.
The goal of this repository is to demonstrate practical implementation of machine learning concepts, strong feature engineering, model optimization, and deployment-ready workflows.
End-to-end ML pipelines (data → preprocessing → modeling → evaluation)
Real-world datasets and problem statements
Advanced feature engineering techniques
Model comparison and hyperparameter tuning
Explainable AI (SHAP, feature importance)
Clean and modular project structure
Languages: Python
Libraries:
NumPy, Pandas
Scikit-learn
TensorFlow / Keras
XGBoost
OpenCV
Tools: Jupyter Notebook, Google Colab, GitHub
Data Collection
Data Cleaning & Preprocessing
Feature Engineering
Model Selection
Hyperparameter Tuning
Model Evaluation
Model Interpretation