Excellent 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

You achieved a good modeling accuracy and you created a number of new features based on your excellent understanding of the data and the business case.
Moreover you visualized all the variable in a meaningful way and you took the right decisions on creating new features.

You could further increase the accuracy of the model by implementing a better base price algorithm and by calculating the baseline volume in a better way.

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

Excellent 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

You achieved a fair modeling accuracy and you created a number of new features based on your good understanding of the data and the business case.
You could further increase the accuracy of the model by implementing a better base price algorithm and by identifying the base volume

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

You achieved a fair but not great modeling accuracy and you created a number of new features based on your good understanding of the data and the business case.
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. This was your pain point on a very nice job

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.
You understand the data and the business challenge but you didn’t manage to increase the accuracy of the 1st model by using feature engineering.
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.