Machine Learning- Recommendation System for Personalized Investment Portfolios

This study uses Long Short-Term Memory (LSTM) networks to propose a machine learning-driven recommendation system for customised investment portfolios.Individual investors have a variety of financial goals and risk tolerances, which traditional portfolio management techniques frequently find difficult to accommodate.Our suggested methodology uses cutting-edge machine learning methods-specifically, LSTM networks-to examine investor profiles and historical market data in order to close this gap.Investment portfolios are dynamically tailored by the system to conform to individual preferences and change in the market.The procedure includes gathering data, preprocessing, training the LSTM model, and ongoing adaption.The system's effectiveness is demonstrated by evaluation criteria such as Mean Squared Error (MSE) and Mean Absolute Error (MAE), and comparative research shows that it outperforms conventional portfolio management.