About this project:
The Data Science Stock Price Prediction Model using LSTM is an advanced machine learning model that uses historical stock data to predict future prices. The model utilizes a recurrent neural network architecture to analyze and interpret patterns in the stock data, and the LSTM layer helps the model capture long-term dependencies in the data. The model can predict future stock prices with high accuracy and can be used by investors to make informed decisions about their investments. The project involves preprocessing the data, training the LSTM model, and evaluating its performance. The results of this project can assist investors in making informed decisions about their investments.
Scope:
The scope of the code is to predict stock prices using an LSTM model based on historical price data, focusing on data preprocessing, model training, and evaluation. It does not incorporate external factors or advanced techniques, and further refinement may be needed for real-world applications.
Conclusion:
In conclusion, the developed stock price prediction model using LSTM demonstrates promising results with an R-squared accuracy of 91.6%. This indicates that the model is able to capture and learn from the historical price patterns effectively, leading to reasonably accurate predictions. However, it is important to note that further testing and validation on diverse datasets and consideration of external factors are necessary to ensure its reliability and applicability in real-world scenarios.
Check out Pravithra M G (https://github.com/PavithraMadhan) who contributed to this project.