Stock price prediction has long been a challenging problem in the financial industry, with researchers and practitioners continuously seeking more accurate and robust methods. This study explores the application of hyperparameter-tuned machine learning (ML) models for stock price prediction. Leveraging the power of advanced ML algorithms and hyperparameter optimization techniques, this research aims to improve the accuracy of stock price forecasts and provide insights into the effectiveness of different models.

Algorithms Used

Decision Tree algorithm

KNN algorithm

Random forest algorithm

The research begins by collecting historical stock price data, along with relevant financial indicators and market sentiment features. Hyperparameter optimization techniques such as grid search optimization are then applied to fine-tune the models and enhance their predictive performance.

Python Demo

The stock price prediction project also implemented using

Clustering Analysis

Stock price prediction using deep learning project demo can be seen here

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