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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

  1. Problem Definition and Research
  2. 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.

  3. Data Mining and Preprocessing
  4. 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.


We provide high quality data mining & preprocessing services pertaining to audio, video, text & image data. Our AI/ML experts are also highly qualified to handle data for complex ML models.

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Frequently Asked Questions on Data Mining

    A. Yes, we assist customers in converting raw data to “smart” data to train the machine learning model, through data annotation.
    Data annotation and labeling are both part of data preprocessing. Data cleaning and labeling fall under the annotation process.

    Data curation is a type of cleaning process for the data. If there are some unexpected spikes in data, this information is removed before the data is fed to the ML algorithm. For instance, temperature data may come within a standard range. But one or two values may be zero or a very high value. This may be due to sensor errors, a sudden unexpected spike in temperature, etc. This data is removed before it is provided as training data to the algorithm.

    A. Structural tagging and data enrichment specific to the domain is done as part of our data transformation activities.

    Vital pieces of information are added to the raw data and it is structured in such a way that the ML algorithm can process it. Data is also linked or cross-referenced, as needed by the algorithm.

    Data may be recorded in an environment with some specific settings, but for the algorithm, we may need it in different settings. So, we need to transform that data to a suitable format. An example is the conversion of data from Fahrenheit to Celsius to suit the algorithm. Another example is the data collected by a sensor that was mounted horizontally. If the algorithm expects data from the sensor in vertical alignment, we transform the collected data to suit the algorithm.