About the Google Stock Price Dataset
The Google Stock Price Dataset consists of two CSV (Comma Separated Values) files containing historical stock price data for training and evaluation. Each row in the dataset represents a trading day, and the columns provide various information related to Google's stock for that day.
Columns:
Date: The date of the trading day in the format "YYYY-MM-DD."
Open: The opening price of Google's stock on that trading day.
High: The highest price reached during the trading day.
Low: The lowest price reached during the trading day.
Close: The closing price of Google's stock on that trading day.
Adj Close: The adjusted closing price, accounting for any corporate actions (e.g., stock splits, dividends) that may affect the stock's value.
Volume: The trading volume, representing the number of shares traded on that trading day.
Time Period:
The train dataset spans from January 1, 2010, to December 31, 2022, providing twelve years of daily stock price information for model training. The test dataset spans from January 1, 2023, to July 30, 2023, providing seven month of daily stock price data for model evaluation.
Data Source:
The dataset was collected from Yahoo Finance (finance.yahoo.com), a reputable and widely-used financial data platform.
Use Case:
The Google Stock Price Dataset can be utilized for various purposes, such as predicting future stock prices, analyzing historical stock trends, and building machine learning models for financial forecasting.
Potential Applications:
Time Series Analysis: Explore stock price patterns and seasonality.
Financial Modeling: Develop predictive models to forecast stock prices.
Algorithmic Trading: Create trading strategies based on historical stock data.
Risk Management: Assess potential risks and volatilities in the stock market.
Citation:
If you use this dataset in your research or analysis, please provide proper attribution and citation to acknowledge the source.
License:
This dataset is provided under the Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication, making it freely available for use without any restrictions or attribution requirements.