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Thomas PIQUE 2024-04-04 18:46:38 +00:00
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@ -64,5 +64,5 @@ Generate graphics providing some descriptive statistics about the data at the ac
- `6_Segmentation_and_Marketing_Personae.py` \
The test set will be fitted with the optimal parameters computed previously, and a propensity score (probability of a future purchase) will be assigned to each customer of this dataset. Segmentation is performed according to the scores provided. Graphics describing the marketing personae associated to the segments as well as their business value are exported to location 2_Output/2_2_Segmentation_and_Marketing_Personae/.
- `7_Sales_Forecast.py` \
To ensure a decent recall, and because of the unbalancing of the target variable y (the global probabiliy of purchase is between 4 and 14 %), the probabilities of purchasing are overestimated.The scores will therefore be adjusted so that their mean approximates the overall probability of a purchase. This score adjusted is used to estimate, for each customer, the number of tickets sold and the revenue generated during the incoming year. Results are aggregated at segment level. An histogram displaying the adjusted propensity scores and 2 tables summarizing the forecast outcome are exported to location 2_Output/2_3_Sales_Forecast/.
To ensure a decent recall, and because of the unbalancing of the target variable y (the global probability of purchase is between 4 and 14 %), the probabilities of purchasing are overestimated.The scores will therefore be adjusted so that their mean approximates the overall probability of a purchase. This score adjusted is used to estimate, for each customer, the number of tickets sold and the revenue generated during the incoming year. Results are aggregated at segment level. A histogram displaying the adjusted propensity scores and 2 tables summarizing the forecast outcome are exported to location 2_Output/2_3_Sales_Forecast/.