Muhammad Hanif’s scientific contributions

This case study investigates the forecasting of Netflix stock prices using various regression and machine learning models, aimed at enhancing predictive accuracy in a dynamic financial environment. As one of the leading streaming services globally, Netflix's stock performance is influenced by numerous factors, including subscriber growth, content investments, and market competition. To analyze these influences, we employ a range of models, including Generalized Linear Models (GLM), Ridge Regression, Lasso Regression, Elastic Net, and advanced machine learning techniques such as Random Forest and Support Vector Regression (SVR). The study begins by preprocessing historical stock price data, extracting relevant features that may impact price movements, including macroeconomic indicators and company-specific metrics. We then implement the selected models and compare their predictive performance using various metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared values. Preliminary results indicate that machine learning models, particularly Random Forest and SVR, outperform traditional regression techniques in terms of predictive accuracy, highlighting their ability to capture complex, non-linear relationships in the data. Furthermore, the study examines the importance of feature selection and engineering, demonstrating how tailored predictors can significantly enhance model performance. This research provides valuable insights into the efficacy of different forecasting methods for stock price prediction in the rapidly evolving entertainment sector. By leveraging advanced analytical techniques, investors and analysts can make more informed decisions regarding Netflix stock, ultimately contributing to more effective investment strategies.