Financial markets are a complex hive of data stretching back hundreds of years. In recent years, the financial market has developed into a digital center that can be widely used by anyone through the Internet. With the ease of market access, market participants have grown exponents ents ents essentially , not only from large institutional players such as banks, hedge funds, and financial companies, but also retail investors and anyone with an interest in the market. As a result, transaction volumes are constantly increasing, with billions of dollars tra deed every day .With the massive amounts of data generated over the years and the development of technology, the market has evolved and relied on automation to meet high data demands and ever-changing complexity.The highly competitive nature of financial market participants drives the quantitative analysis of markets through the use of algorithms that may help in calculating and predicting future market directions. This study explores financial market data using multiple machine learning algorithms such as linear regression, random forest, gradient boosted tree regression, K nearest neighbors, and artificial neural networks to identify the best performing predictive models. This model is then further improved by using ensemble learning techniques (Bagging, Boosting) to increase its predictive accuracy, thereby providing actionable signals that will greatly assist participants in their trading decisions. This study addresses common problems in financial data processing, such as handling complex financial market data, model selection,hyperparameter optimization, and model improvement, which have a great impact on the accuracy of stock price forecasting. The proposed solution will help improve the profitability and financial market analysis capabilities of traders.

The financial markets are a complicated hive of data dating back hundreds of years ago. Over the years they have evolved to be digitally centered carriers widely available for anyone through the use of the internet. With the ease of access, market participants have exponentially in increased not only from big institutional players like banks, hedge funds, and financial companies but more recently retail investors together with anyone with an interest in the markets. This has increased the trade volume daily with several billion dollars being transacted with each passing day. As a As a result of the massive data generated and the development of technology over the years, the markets have evolved and relied on automation to keep up with the high data demands and forever changing complexity.The highly competitive nature of the financial markets participants has fueled several developments to quantitatively analyze the markets through the use of algorithms that may help calculate and predict future market direction. This research explores financial market data with several m machine learning algorithms named Linear Regression, Random Forest , Gradient Boosting Tree Regressor, K-NN and Artificial Neural Networks in order to identify the best performing hyperparameter optimized predictive model. The model is then further developed and improved using ensemble learning techniques, Bagging and Boosting in order to increase ase its predictive accuracy and aid in providing actionable signals which will greatly help inform trading decisions. This research tackles prevalent existing problems like handling complex financial market data,model selection, hyperparameter optimization and finally focuses on model improvement which has a great influence on the efficiency of stock price prediction. The solutions proposed will aid in the profitability of traders and overall financial market analysis.

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