End-to-end time series forecasting using both Machine Learning and Deep Learning models. Includes data preprocessing, EDA, feature scaling, and performance evaluation on real-world datasets.

Code for classifying hand-sign image dataset using a supervised learning approach: Neural Network with some technique to improve the accuracy metric.

Deep Learning

This project involves developing a Traffic Sign Detection and Recognition system using a deep learning model built with Keras. Its goal is interpreting traffic signs in real-time.