Machine learning heavily depends on the type of data you are using. Good quality data is a crucial aspect of algorithm training, and it further reflects the importance of ML in today’s world. Regardless of your deep learning image data science expertise and actual terabytes of information, it is useless to use machines if your data records don’t make sense.
How to collect data for machine learning if you don’t have any
For taking the initiative for ML execution, you can use open-source datasets. Here you will find plenty of data for machine learning.
Articulate the problem early
Predicting in advance helps you to know which type of data is more valuable and relevant for you. When formulating problems, it is essential to conduct data exploration and try to break your data into different categories for better understanding. The task can be categorized in the following manner:
Establish a data collection mechanism
While taking up any initiate, creating a data-driven culture is the most demanding and challenging task. If you are thinking of using machine learning for predictive strategy, it is essential first to combat data fragmentation.
Check your data quality
The quality of your data plays a vital role in the entire process. If you don’t have good quality data, even the sophisticated machine algorithms are of no use. To check data quality, you need to consider the following points
- Whether there is any technical issue while transferring data?
- How tangible is human error?
- Is your data accurate for your task?
- How many omitted values does your data have?
Format data to make it consistent
Data formatting is just like the file formatting feature. It is easy to convert a dataset into a file format so that it fits best with your machine learning system.