Machine Learning Approach For Accurate Stock Predication Using Lstm

Abstract

Stock Price Prediction Is A Critical Task For Investors, Traders, And Financial Analysts To Make Informed Decisions. Traditional Statistical Methods Often Struggle To Capture The Complex, Non-linear Patterns And Long-term Dependencies Inherent In Stock Market Data. This Project Proposes A Machine Learning-based Solution Using Long Short-Term Memory (LSTM) Networks To Analyze Historical Stock Data, Identify Trends, And Provide Accurate Predictions. By Leveraging Features Such As Opening Price, Closing Price, And Volume, The LSTM Model Aims To Deliver Actionable Insights For Optimizing Investment Strategies And Minimizing Risks. The Study Involves Data Collection, Preprocessing, Feature Engineering, Model Development, And Evaluation Using Metrics Like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), And Root Mean Square Error (RMSE). The Results Demonstrate The Model's Ability To Capture Seasonal Trends And Long-term Dependencies, Outperforming Traditional Methods. A User-friendly Dashboard Is Also Developed To Visualize Predictions In Real-time.

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