Home Forums Kamanja Forums Use Cases & Samples Remove anomalous data from the dataset in preprocessing phase

This topic contains 0 replies, has 1 voice, and was last updated by  Yasser Deeb 1 year, 4 months ago.

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    Yasser Deeb

    Removing anomalous data from the dataset often results in a statistically significant increase in accuracy.

    For example any statistical dataset that is being analysed (like weather data ), could have outliers (observation points that are distant from other observations). This could happen for many reasons like incorrectly entered or measured data, and it could affect the results. Kamanja can be useful in finding these anomalies/outliers before actually processing the data.

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