Working with data is an everyday reality for millions of computer scientists, businesses & marketers. Data can encompass a wide set of information, which might pertain to a project or problem specifically or in general. For instance, a retail fashion organization might be interested to know how economic conditions affect the sales of high-priced branded clothing vs low-priced economy brands. This information, if it could be accurately correlated, can offer valuable insight to the retailer on how to balance their stock purchasing, based on current economic predictions. However, the data required for this type of project is not widely on offer & if it is, it’s often not in a usable format or it might have a heavy price tag attached to its availability.
Data sets
OpenBlender offers data sets that have a systematic threshold over a wide range of categories. Due to its formatting, this data can be correlated into a project (based on location & timestamp) very conveniently.
Correlating
Data may be formatted in a different ways. It could be available as a CSV(Comma Separated Values), XML, JSON file or as a compounded database such as Oracle, MYSQL or SQLite.
OpenBlender’s system works with CSV files on the import (download) or export (upload) interface, & offers an API using Json, which can easily integrate with two major data-centric programming languages, namely R & Python. Users can upload their data via CSV & employ OpenBlender’s tools to correlate this data with suitable matches ‘pulled’ from a ‘universe’ of data that suits the users prediction-project’s objectives. In the example above of a fashion retailer, their own data might be their sales figures for the past two or so years, clearly separated by date, type of item sold & volume of sales.
To set up a predictive Machine Learning program, the retailer might use the gold price, dollar price or newspapers’ economic forecast data to correlate with their own to create a predictive model, which they can use, based on their needs. OpenBlender’s correlation system, which matches the users’ data to their own, presents a new dataset, which clearly demonstrates where negative & positive influences occur in the data used, based on sales. Thereby, offering the retailer a model that they can employ to predict future buying patterns.
Result
Once a ML or analysis model has been developed, the data can be retrieved from OpenBlender using a convenient API (Application Programming Interface), which melds with the users own needs without much ado. Data sent to the OpenBlender system is encrypted & secure, offering consumers peace of mind when uploading sensitive information. Data can be kept private, or may be offered to other users on the platform. The program & its tools are free to use, & easily accessible via OpenBlender’s web interface.
Conclusion
Analyzing data is a broad, complex & problematic issue, but it is a major advantage if handled correctly—OpenBlender aims to provide the tools to handle many aspects of Machine Learning & analysis, easily & effectively.
Image Credit: OpenBlender
