Submission

Inspiration

We used a hybrid model combining Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BiGRU), after reading a promising research paper on it.

1. Methodology The proposed stock market prediction model follows a two-stage approach:

Data Preprocessing: This includes:

Web scraping for historical stock data Handling missing values Feature selection Data normalization Prediction Model: A hybrid deep learning model combining LSTM and BiGRU was used to predict future closing prices of stocks. This method was chosen due to its effectiveness in capturing long-term dependencies in sequential financial data.

Built With

• python