Acomparative study of Neural Network Architectures for stock market prediction in the era of accessible machine learning

Designed the project using Tensorflow & Keras in Jupyter Notebooks. ● Developed and tested LSTM neural networks to predict stock prices using time series analysis, comparing performance against major index funds like Fidelity 500 (FXAIX) and Vanguard 500 (VFIAX). ● Applied K-Means clustering to optimize a diversified investment portfolio, leveraging financial indicators such as RSI and ATR to maximize returns and minimize risk. ● https://github.com/gino23odar/ThesisMLmodels

Designed the project using Tensorflow & Keras in Jupyter Notebooks

Developed and tested LSTM neural networks to predict stock prices using time series analysis, comparing performance against major index funds like Fidelity 500 (FXAIX) and Vanguard 500 (VFIAX)

Applied K-Means clustering to optimize a diversified investment portfolio, leveraging financial indicators such as RSI and ATR to maximize returns and minimize risk