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Uber Technologies Financial Market Data
Stock & Market Data
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About
Uber Technologies, Inc. is an American mobility-as-a-service provider headquartered in San Francisco, with operations spanning approximately 72 countries and 10,500 cities. The provided file contains historical stock performance data for the company, capturing market behaviour during periods where the market capitalisation exceeded 100 billion. This resource is curated to support financial analysis, investment strategy formulation, and the training of machine learning models, specifically Recurrent Neural Networks (RNN).
Columns
- Date: The specific calendar day of the trading record.
- Open: The price of the stock at the opening of the market.
- High: The highest value the stock reached during the trading session.
- Low: The lowest value the stock dropped to during the trading session.
- Close: The final trading price at the end of the market day.
- Adj Close: The closing price adjusted for corporate actions such as dividends and stock splits.
- Volume: The total number of shares traded on the recorded day.
Distribution
The information is structured as a tabular dataset in CSV format, specifically named
Uber.csv. It has a file size of approximately 53.58 kB. The dataset consists of 766 valid rows and 7 columns. Analysis indicates there are zero missing values and zero mismatched entries, ensuring 100% data validity across all records.Usage
This data is highly applicable for tasks involving financial forecasting and algorithmic trading research. It serves as an excellent resource for:
- Training and testing Recurrent Neural Networks (RNN) and other time-series prediction models.
- Conducting technical analysis on stock price volatility and trends.
- Educational exercises for beginner and intermediate data scientists learning to manipulate tabular financial data.
Coverage
The geographic scope relates to the financial performance of a US-based global corporation. Temporally, the data spans from 10 May 2019 to 23 May 2022.
License
CC0: Public Domain
Who Can Use It
- Quantitative Analysts: For modelling historical price movements.
- Machine Learning Practitioners: For developing deep learning models focused on sequence prediction.
- Students: For practising data cleaning, visualisation, and basic financial econometrics.
- Investors: For reviewing past market performance to inform future decisions.
Dataset Name Suggestions
- Uber Historical Stock Data 2019-2022
- Uber Technologies Daily Financial Metrics
- Uber Market Performance and Volume
- Uber Stock Price Time Series
Attributes
Original Data Source:Uber Technologies Financial Market Data
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