Data Mining and Preprocessing Services
Our ML experts assist you in data labeling, data annotation & data transformation, at scale.
As Machine Learning (ML) undergoes constant evolution, the steps involved in ML projects are constantly changing. However, one of the most crucial stages in a machine learning project has been that of data mining and preprocessing.
It is important for humans to identify and annotate data accurately so that the machine learning model can learn to classify information and hone its prediction capabilities. Embitel specializes in data mining and preprocessing activities related to various industries.
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Best Practices in Data Mining and Preprocessing
- Problem Definition and Research
- Data Mining and Preprocessing
While embarking on a machine learning project, it is crucial to define the problem at the initial stage. This includes gaining a clear understanding of the problem that you are attempting to solve using ML. You need to break down the steps involved in the problem and also identify the ideal solution to the problem.
At the end of this problem definition stage, you will have a document that clarifies the problem statement, the ideal solution to the problem defined, insights into the problem and the technical requirements to solve the problem through ML.
You will also have a clear understanding of the problem, proposed solutions, and type of data that will be collected. This will help you identify a suitable ML model to achieve the solution. You should also delve deeper into the hardware and software requirements for the implementation of the ML algorithm.
Data mining is a precursor to all activities related to ML algorithm development. It actually sets a precedent for the successful training of the model.
Data mining broadly consists of the following activities:
- Data should be sourced from various avenues carefully.
- The data that is sourced should be examined carefully and analyzed, to discover patterns and trends.
- It is also important that the data used is diverse enough so that all possible conditions are captured and fed to the ML algorithm.
- There should be abundant data for accurate training of the ML model.
- The data is also expected to be unbiased for effective performance of the algorithm.
Data preprocessing is a data mining technique that helps in converting raw data into efficient data that is used to train the machine learning model. It is based on the selected model’s input requirements.
At the end of the Data Mining and Preprocessing stage, data is cleaned and split out into training and validation data.
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