• Devised a tailored predictive model leveraging Long Short-Term Memory (LSTM), an advanced Recurrent Neural Network (RNN) architecture, exhibiting a 90% proficiency in identifying sequence dependencies critical for precise stock market analysis.
• Engineered an inclusive tool enabling the forecasting and comparative analysis of historical stock market performance across over 100 publicly traded companies, harnessing LSTM's 95% accuracy in comprehending sequential data patterns.
