Data preparation and feature engineering steps should be done in a separate project to match the expected input datasets format. Respecting the expected data model when loading new data helps ensure the results are reliable and easy to interpret.

## Input datasets format

The input data should be separated into 4 different datasets: 

![Screenshot 2024-10-01 at 13.41.52.png](dN4OkOsLmqWf)
- (1) lead_touchpoints : 

![Screenshot 2024-10-01 at 13.40.36.png](9lx5mwS51zcy)

- (2) historical_lead_information : 

![Screenshot 2024-10-01 at 13.40.48.png](OSRP5EGFZ9nh)

**Note**: In order to build the customer value prediction model, at  **least one optional column**  must be added to the historical_lead_information, to_score_lead_information, and the customers_value datasets. 

- (3) to_score_lead_information : 

![Screenshot 2024-10-01 at 13.40.57.png](lrKhTw2KoFQc)

**Note**: In order to build the customer value prediction model, at  **least one optional column**  must be added to the historical_lead_information, to_score_lead_information, and the customers_value datasets. 

- (4) customers_value : 

![Screenshot 2024-10-01 at 13.41.07.png](dRZBYRRZyuej)
**Note**: In order to build the customer value prediction model, at  **least one optional column**  must be added to the historical_lead_information, to_score_lead_information, and the customers_value datasets. 