Amazon Stock Price Prediction

This repository contains a Jupyter notebook AMZNStockPrediction.ipynb demonstrating the process of predicting Amazon's stock prices using Rolling Ordinary Least Squares (RollingOLS) model.

Project Structure

• Data Importing and Visualizing: The data is imported, cleaned, and visualized to understand the trends in Amazon's stock prices over time.
• Data Preprocessing: The dataset is split into a training set (2010-2015) and a testing set (2016).
• Model Building: A RollingOLS model is trained.
• Performance Evaluation: The model's performance is evaluated on the test set. RMSE is used as the performance metric.
• Conclusion: Final remarks and directions for future work.

Usage

• Clone this repository
• Unzip the prices-split-adjusted.zip file
• Open the AMZNStockPrediction.ipynb notebook
• See appendix for required packages
• Run the notebook cells

Note

This project is a demonstration of time series prediction and should not be used for making actual investment decisions.