Deep_Learning_Machine_Learning_Stock

Why we included this project

This repo is a hands-on study of applying machine learning and deep learning to stock data, with everything worked through in Jupyter notebooks rather than abstract theory. It covers the whole forecasting path, from collecting and preparing market data through model selection, training, parameter tuning, and prediction, and it treats both technical and fundamental analysis across regression and classification problems. Along the way it digs into feature engineering and the bias-variance tradeoff, including why models overfit, so you see not just what works but why. A data scientist or quant developer gets a concrete reference for how classical ML and neural networks behave on real market data before building their own system, and someone newer to the field can use the explanations to pick an approach for a given forecasting task.