Data
Intel-Stock-Prices-Historical-Data-(INTC)

Intel-Stock-Prices-Historical-Data-(INTC)

active ARFF CC BY-SA 4.0 Visibility: public Uploaded 23-03-2022 by Elif Ceren Gok
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  • Machine Learning Manufacturing
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Context Intel Corporation designs, manufactures, and sells essential technologies for the cloud, smart, and connected devices worldwide. The company operates through DCG, IOTG, Mobileye, NSG, PSG, CCG, and All Other segments. It offers platform products, such as central processing units and chipsets, and system-on-chip and multichip packages; and non-platform or adjacent products comprising accelerators, boards and systems, connectivity products, and memory and storage products. The company also provides Internet of things products, including high-performance compute solutions for targeted verticals and embedded applications; and computer vision and machine learning-based sensing, data analysis, localization, mapping, and driving policy technology. It serves original equipment manufacturers, original design manufacturers, and cloud service providers. The company has collaborations with UC San Francisco's Center for Digital Health Innovation, Fortanix, and Microsoft Azure to establish a computing platform with privacy-preserving analytics to accelerate the development and validation of clinical algorithms; and Inventec Corporation. Intel Corporation was founded in 1968 and is headquartered in Santa Clara, California. Content Here is a simple code used to download data. import yfinance as yf df = yf.download(tickers="INTC") df.to_csv('INTC.csv') Acknowledgements This data collected with one line code using yahoo finance and yfinance library.

7 features

Datestring10361 unique values
0 missing
Opennumeric4275 unique values
0 missing
Highnumeric4335 unique values
0 missing
Lownumeric4281 unique values
0 missing
Closenumeric4543 unique values
0 missing
Adj_Closenumeric7128 unique values
0 missing
Volumenumeric9910 unique values
0 missing

19 properties

10361
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
6
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
85.71
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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