LSTM Stock Predictor

Table of Contents

1. Data Prep for Training and Testing
2. Build and train custom LSTM RNNS
3. Evaluate Model Performance

Files: Stock Predictor Files: Stock Predictor usinf fng sentiment

Data Prep for Training and Testing

• Use the FNG values to predict future closing prices.
• Use the past closing prices to predict future closing prices.
• Apply the MinMaxScaler to the X and Y values to scale the data for the model.
• Reshape X_train and X_test to fit the model requirements (samples, time steps, features).

Build and train custom LSTM RNNS

• Create a notebook to fit the data using FNG Values.
• Create a notebook to fit the data using closing prices.

Evaluate Model Performance

• Determine which model had the lowest loss.
  • Model using closing prices
• Determine which model tracks the actual values best over time.
  • Model using closing prices
• Determine the appropriate Window Size for the model.