The primary objective of this project is to develop predictive models for stock market movements using two distinct regression approaches: Simple Regression and Bayesian Regression.
The goal of Simple Regression is to establish a linear relationship between a single independent variable and a dependent variable, providing a straightforward model for prediction. In this section our workflow involves collecting and exploring data, building and evaluating models, and ultimately interpreting the results to derive meaningful insights.
Bayesian Regression aims to account for uncertainty in predictions by incorporating prior knowledge and updating it with new data. We will follow theses steps:
- Prior Knowledge: - 2. Data Update: - Model Inference: - Uncertainty Quantification: - Interpretation:
We are utilizing Apple stock price data obtained from Yahoo Finance, focusing specifically on the period from January 1, 2020, to December 1, 2023.