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SAP-iens team, SAP case

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    The most difficult part of this challenge is to understand the data, create new features and rerun the predictive models till you achieve a good accuracy.
    As you may mentioned if you run a predictive model with the initial dataset you will get an extremely low modelling accuracy.

    I will vote based on the below criteria:

    1. business understanding
    2. feature engineering
    3. modelling accuracy
    4. insights & final results

    Your approach was on the right path but you didn’t managed to create the new features that will help you identify the volume uplift drivers and increase the accuracy of the model.

    However I think that your approach was different than the usual and the adstock effect is a good idea.
    You could increase the accuracy of the model by implementing a base price algorithm and then by taking the % of difference between the actual and the base price you could extract the weekly promotional price reduction and use it as input parameters for your regression model.

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