# ML prediction


Classification model to predict the future CLV cluster. We run a random forest and an XGBoost to choose the best-performing model after a bayesian hyperparameter search.

![Classification.png](wxf6OyQZ3CnV)


Regression model to predict the future CLV, we also apply the clustering algorithm of the predicted CLV to compare the performances with the classification model. We use an XGBoost as a regression model.
![Regression.png](KNcLpKVpAVuv)

On both cases we compare the predicted CLV groups but depending on the data and the optimization strategy, the classification or the regression model can give better results. (On the UCI dataset, the classification algorythm gives better performances)

We automatically include customer metadata (as numerical or categorical features) and RFM information when they are available.

Models and hyperparameters can be modified and will stay at each retraining when you want to customize the solution.
