MSFT Stock Price Prediction
Deep Learning
About The Project
This project aims to predict Microsoft (MSFT) stock prices using the Long Short-Term Memory (LSTM) model, a Recurrent Neural Network (RNN) architecture highly effective for handling and forecasting time series data.
Tools & Libraries : Python, Streamlit, VSCode, Numpy, Pandas, Scikit-Learn, Tensorflow, Matplotlib, Joblib, Os, Plotly, PIL.
Achievements
Achieved high model accuracy in forecasting Microsoft stock prices, with MSE < 0.002, indicating strong predictive performance.
Successfully handled and prepared real-world time-series data (6 years, 1,500+ records) for deep learning applications using industry-standard tools.
Built a robust LSTM-based model that learned temporal dependencies effectively over long historical sequences, showing strong convergence within 75 epochs.
Links
azzriala@gmail.com
+6287872788220
