Stock Trend Prediction using Machine Learning
Published:
Developed a robust machine learning model to predict stock market trends using historical data from 300 companies across 11 sectors. Employed advanced data preprocessing techniques, including handling missing values with methods like median imputation and missForest. Various machine learning models, such as Random Forest, LightGBM, and Histogram Gradient Boosting, were utilized. The final model, enhanced with hyperparameter tuning using Optuna, achieved a notable error score of 0.7921.
Keywords: Stock Trend Prediction, Machine Learning, Data Preprocessing, Time Series Analysis, Random Forest, LightGBM, Histogram Gradient Boosting, Optuna, Financial Indicators, Imputation Techniques
