In this stock price prediction project repository, we'll be utilizing deep learning, with Long Short-Term Memory (LSTM) networks, to forecast the stock prices of Microsoft Corporation.
The importance of this is that accurate prediction of stock prices aids investors, traders, and financial analysts to make informed decisions and devise effective strategies in the dynamic stock market environment.
Historical stock price data of Microsoft Corporation is gathered from reliable financial sources, ensuring a comprehensive dataset for training the predictive model.
The preprocessing pipeline involves handling missing values, scaling the features, and splitting the dataset into training, validation, and testing sets to prepare the data for training and evaluation.
Construction of LSTM neural networks, a specialized type of recurrent neural network (RNN), capable of capturing long-term dependencies in sequential data, making them well-suited for time series forecasting tasks like stock price prediction.
The LSTM model is then trained using historical stock price data, optimizing model parameters to minimize prediction errors and improve forecast accuracy.
The performance of the LSTM model is evaluated using appropriate metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). This is to assess the accuracy of stock price predictions.
To enhance the performance of the LSTM model, we conduct hyperparameter tuning, adjusting parameters such as the number of LSTM units, learning rate, and dropout rate to optimize model performance.
The trained LSTM model enables generation of future stock price predictions, providing valuable insights for investors and traders to anticipate market trends and make informed decisions.
Detailed documentation accompanies the project, including explanations of LSTM architecture, code comments, and usage guidelines, facilitating easy understanding and replication of the predictive model.
Thank you.