Multi-Stock-Prediction

• Integrated historical stock data for multiple companies:

  • Consolidated data for various companies including Apple, Google, Microsoft, and Amazon into a single dataframe.
  • Ensured consistent formatting and handled missing values to maintain data integrity.

• Comprehensive data preprocessing:

  • Converted dates to datetime objects, calculated daily returns, and normalized features using MinMaxScaler.
  • Arranged data into sequences with appropriate dimensions: ( x ) (input sequences) and ( y ) (predicted values).

• Developed multi-company workflow:

  • Utilized a loop to preprocess and train models for each company's stock data separately.
  • Implemented a function to handle model training and evaluation for different stocks, ensuring scalability and modularity.

• Model training and prediction:

  • Constructed and trained LSTM models using TensorFlow and Keras for each company.
  • Implemented early stopping and model checkpoints to optimize performance and prevent overfitting.
  • Predicted stock prices with the model, achieving predictions with dimensions matching the input sequences.

• Performance evaluation:

  • Evaluated model performance using metrics such as R-squared and Mean Squared Error (MSE), achieving high accuracy in predictions.
  • Visualized predictions against actual stock prices to validate model effectiveness.