Snapshot Instagram

Snapshot done by https://github.com/amzar96, total size,

instagram.jsonl, 90469 rows. text-only-instragram.jsonl, 695571 rows.

  Example data

{'_index': 'instagram-v2', '_type': '_doc', '_id': 'instagram-v2+6+9042', '_score': 1, '_source': {'datetime': '2021-12-19T15:52:32', 'comment': {'date': 'Nov 26, 2021', 'url_to_reply': 'https://www.instagram.com/p/CWukPPBJ2sz/c/17943481405632363/r/17929092841798589/', 'like': 0, 'mention_to':… See the full description on the dataset page: https://huggingface.co/datasets/malaysia-ai/snapshot-instagram.

Saudi Arabian Oil Company Aramco, Stocks

👨‍💻 Author: Azhar Saleem

"https://github.com/azharsaleem18" target="_blank"> https://img.shields.io/badge/GitHub-Profile-blue?style=for-the-badge&logo=github" alt="GitHub Profile"> "https://www.kaggle.com/azharsaleem" target="_blank"> https://img.shields.io/badge/Kaggle-Profile-blue?style=for-the-badge&logo=kaggle" alt="Kaggle Profile"> "https://www.linkedin.com/in/azhar-saleem/" target="_blank"> https://img.shields.io/badge/LinkedIn-Profile-blue?style=for-the-badge&logo=linkedin" alt="LinkedIn Profile">
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Dataset Description

Welcome to the Enhanced Saudi Arabian Oil Company (Aramco) Stock Dataset! This dataset has been meticulously prepared from Yahoo Finance and further enriched with several engineered features to elevate your data analysis, machine learning, and financial forecasting projects. It captures the daily trading figures of Aramco stocks, presented in Saudi Riyal (SAR), providing a robust foundation for comprehensive market analysis.

Columns in the Dataset

• Date: The trading day for the data recorded (ISO 8601 format).
• Open: The price at which the stock first traded upon the opening of an exchange on a given trading day.
• High: The highest price at which the stock traded during the trading day.
• Low: The lowest price at which the stock traded during the trading day.
• Close: The price at which the stock last traded upon the close of an exchange on a given trading day.
• Volume: The total number of shares traded during the trading day.
• Dividends: The dividend value paid out per share on the trading day.
• Stock Splits: The number of stock splits occurring on the trading day.
• Lag Features (Lag_Close, Lag_High, Lag_Low): Previous day's closing, highest, and lowest prices.
• Rolling Window Statistics (e.g., Rolling_Mean_7, Rolling_Std_7): 7-day and 30-day moving averages and standard deviations of the Close price.
• Technical Indicators (RSI, MACD, Bollinger Bands): Key metrics used in trading to analyze short-term price movements.
• Change Features (Change_Close, Change_Volume): Day-over-day changes in Close price and trading volume.
• Date-Time Features (Weekday, Month, Year, Quarter): Extracted components of the trading day.
• Volume_Normalized: The standardized trading volume using z-score normalization to adjust for scale differences.

Potential Uses

This dataset is tailored for a wide array of applications:

• Financial Analysis: Explore historical performance, volatility, and market trends.
• Forecasting Models: Utilize features like lagged prices and rolling statistics to predict future stock prices.
• Machine Learning: Develop regression models or classification frameworks to predict market movements.
• Deep Learning: Leverage LSTM networks for more sophisticated time-series forecasting.
• Time-Series Analysis: Dive deep into trend analysis, seasonality, and cyclical behavior of stock prices.

Whether you are a data scientist, a financial analyst, or a hobbyist interested in the stock market, this dataset provides a rich playground for analysis and model building. Its comprehensive feature set allows for the development of robust predictive models and offers unique insights into one of the world’s most significant oil companies. Unlock the potential of financial data with this carefully crafted dataset.

This dataset provides comprehensive social media profile links discovered through real-time web search. It includes profiles from major social networks like Facebook, TikTok, Instagram, Twitter, LinkedIn, Youtube, Pinterest, Github and more. The data is gathered through intelligent search algorithms and pattern matching. Users can leverage this dataset for social media research, influencer discovery, social presence analysis, and social media marketing. The API enables efficient discovery of social profiles across multiple platforms. The dataset is delivered in a JSON format via REST API.