license: cc0-1.0
Intel Stock Data (1980-2024)
This is a dataset copied from Kaggle. You can see the original dataset here: https://www.kaggle.com/datasets/mhassansaboor/intel-stock-data-1980-2024
The following is the original readme of this dataset:
About Dataset
📊 Intel Stock Dataset (1980-2024)
🌟 This dataset contains daily stock trading data for Intel Corporation (ticker: INTC) from 1980 to 2024, sourced from Yahoo Finance. It provides a comprehensive view of Intel's stock performance over four decades, including key metrics like opening/closing prices, trading volume, dividends, and stock splits.
📄 Dataset Overview
- 🗓️ Time Period: 1980 to 2024
- 📈 Total Records: 11,289 rows
- 📂 File Size: ~989.25 KB
This dataset is ideal for financial analysis, stock trend forecasting, machine learning models, and portfolio optimization studies.
📋 Columns and Descriptions
| 🏷️ Column | 🔍 Description |
|---|---|
| 📅 Date | The trading date in YYYY-MM-DD format. |
| 🔓 Open | The opening price of Intel's stock on the given day. |
| 📈 High | The highest price of the stock during the trading session. |
| 📉 Low | The lowest price of the stock during the trading session. |
| 🔒 Close | The closing price of the stock on the given day. |
| 🔄 Volume | The total number of shares traded on the given day. |
| 💰 Dividends | The dividend payouts, if applicable, on the given day. |
| 📊 Stock Splits | The ratio of stock splits (if applicable) on the given day (e.g., 2-for-1 split = 2.0). |
🌟 Key Features
- Clean and Complete: No missing values across all columns.
- Rich Historical Data: Captures Intel's stock trends and major events over the years.
- Ready for Analysis: Ideal for time-series analysis, regression models, and financial forecasting.
🚀 Applications
- 📈 Trend Analysis: Identify long-term trends and patterns in Intel's stock performance.
- 🤖 Machine Learning: Train predictive models for stock price forecasting.
- 💼 Portfolio Insights: Analyze Intel's stock as part of an investment portfolio.
- 🧮 Statistical Research: Study correlations between market events and stock performance.
Feel free to dive into the dataset and unlock its potential! Let me know if you need help with analysis or visualization. 😄