Team solutions

The SAP Case using KNIME and Multiple Linear Regression Method


5 thoughts on “The SAP Case using KNIME and Multiple Linear Regression Method

  1. 0

    Hi team Honeybadger,
    Very well performed EDA!
    Nice plots! You have captured the essence of the problem!
    Rsqr of more than 0.9 is excellent!
    Move it forward and make it to the final goal.
    — zenpanik

  2. 2

    Great great work team! 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 is on the right path and you managed to create the new features that will help you identify the volume uplift drivers and increase the accuracy of the model. This is a great accuracy for this specific dataset! This comes as a result of your great business understanding of the case and the dataset as well

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