Data Visualisation of Time Tradable Assets Using Machine Learning

Forecasting financial instruments accurately is a complex task due to the volatile, non-linear and dynamic nature of financial markets. Numerous factors, including political events, global economic conditions, and company performance, contribute to the difficulty of predicting outcomes. However, this challenge also provides a wealth of data that can be leveraged to uncover patterns. With the advancement of AI and increased computational power, programmable prediction techniques have emerged as more effective tools for forecasting tradable assets. This paper explores the application of various regression techniques, such as Linear, Support Vector, Polynomial, and Tree Regression, in financial prediction. Additionally, the study incorporates Long Short-Term Memory (LSTM), a method of recurrent neural network model, to analyse an asset's historical behaviour and generate reliable conclusions. The proposed approach aims to address the practical implementation of prediction systems in real-world scenarios while addressing challenges related to the accuracy of the provided values. Furthermore, appropriate visualization techniques are employed to enhance the interpretation and understanding of the results. By combining advanced prediction models and visualization methods, this research endeavours to improve the accuracy of financial predictions and provide valuable insights for decision-making infinancial markets.