When a person hears about machine learning, they think it is about getting answers for complex questions fast and with fewer instructions. Well, this is true to a point. Yes, the ML algorithms are capable of giving answers to questions. But, all this requires the right data sets to be fed to the algorithm to get answers to the questions asked.
One of the biggest and most crucial steps of data processing and visualization in machine learning is to gather data. Here are some things that help in gathering the right machine learning datasets CSV.
Well, there are two sources of data; external data and internal data. But at times, companies may not have the required data sets in their archive. In such a case, they will have to use an external source of data. One can use the public data sets to get data from. Also, one can use open-source data sets that are kept for ML specifically. In other companies and concerns, the data is collected and kept in the cloud, used as data sets.
Articulating the data
In collecting and gathering the data, classification is highly crucial. Data tend to be in the raw form, consisting of numbers, characters, audio, images, etc. classifying the data makes the processing easy. In data classification, clustering, regression, and ranking are some steps that stand to be useful and effective.
Data collection and warehousing
After, it is decided from where the data is to be collected. One will have to set up a collection process and mechanisms. After the data is collected, now is the time to store them. This is where data warehouses are required, which helps in storing the data.
After the data is collected and stored in data frames, data processing becomes much of an easier process. This helps remove the data errors and duplication and makes the data clean enough for the ML algorithm.