In this article we list all the zones that lead to the Brand Adoption Predictive Analytics.

![brand adoption zones.png](znMveUmiAmUS)

[Total Sales and Marketing by HCP (Account ID)](flow_zone:EaK09Ij)
- Filter recipe enable user to select multple brands and a time period for the training dataset. 
- Group by recipe [compute_Sales_Marketing_aggr_by_account_id](recipe:compute_Sales_Marketing_aggr_by_account_id) aggregates all the weekly transactions and communication channel activities at an individual HCP level and removed the aspect of time from the data (date, date_year and date_week). 
- [compute_Sales_marketing_feature_format](recipe:compute_Sales_marketing_feature_format) standardizes the column names to the initial input format. 
-  [Join recipe](recipe:compute_Sales_Marketing_by_account_id_joined) adds the HCP characteristics to the aggragated data. 

[Brand Adoption Modelling](flow_zone:5pVrkLJ)
- [compute_Brand_adoption_train](recipe:compute_Brand_adoption_train) recipe generate the target column feature ```brand_adoption = if(product_quantity_weekly>0,1,0)``` based on whether the product purchase sum of all the transactions for an HCP account is greater than 0 or not. 

- [Predict brand_adopter (binary)](saved_model:syf8gDwr) machine learning session trains a classification model using XGboost algorithm on the input dataset.
- [Score](recipe:score_to_score) recipe predict the probability of new data records at HCP level adopting a new brand or not. 