Customers_segmentation
The goal of this project is to understand the different types of users through their behavior over time, in order to detect the most likely to make purchases.
3 parts :
- Cleaning and Exploratory analysis
- Feature engineering and clustering
- Classification
The main algorithms implemented :
- Latent Dirichlet Allocation (LDA)
- Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
- K-means
- Random Forests Clasifier (RFC)
- Gradient Boosting Clasifier (GBC)
- eXtreme Gradiengt Boosting Clasifier (XGBC)
Result:
Excellent accuracy score for the final classification model (Gradient Boosting (target variable:Customer_Category_km13 )) : 0.987