Recommendation system (Shopping basket analysis)

The funeral services company has operations in different regions of some country, the marketing department manually analyzes sales to determine the product and service packs to sell in each region. These packs are based on sales history by region, taking into account differences in the income level of people in each area.

Problem:
The company wants to automate this process by implementing a recommendation system that optimizes the selection of product and service packs by region, using historical sales analysis and advanced machine learning techniques.

Project Phases and deliverables:
Phase 1: Dataset Analysis
Deliverable: Detailed report on the preprocessing, analysis and description of the dataset, including techniques used

Phase 2: Identification of Product Sets
Determine product sets using association rules and item combinations. Identify which products sell best together.
Deliverable: Results of association rules and item combinations. Optimized product packs, ready for implementation.

Phase 3: Clustering Analysis
Perform a clustering analysis to group regions based on sales behaviors. Identify centers or offices with similar sales patterns.
Deliverable: Results of the clustering analysis, with visualizations showing the identified clusters. Description of the common characteristics of clusters.

Phase 4: Recommendation of Packs by Clusters
Use the assembled packs and identified clusters to recommend which packs should be sold in each cluster and justify the recommendations.
Deliverable: Product pack recommendations for each cluster. Justification of recommendations based on previous analyses. Visualizations and reports that facilitate understanding of the recommendations.

Final delivery:
All source code used for data preprocessing, association analysis, clustering and recommendation generation. As well as trained Models and scripts for their deployment.

We will give you:
-Dataset with sales by region in 2023