Addressable market challange

The solution was developed in a juputer notebook with the following structure:

• Load libs and modules
• Load raw data sets
• Split actual and addressable customers
• EDA
  • Descriptive statistics analsysis
  • Customer type size analysis
  • Outlier analysis
  • Univariate distribution analysis
• Model building
  • Customer segmentation
  • Decision-tree classifier
  • Customer scorer (pearson similiarity)
• Generate Deliverables
  • Deliverable 1
  • Deliverable 2
  • Sanity Check
• Improvements (TODO)

The notebook source artifacts:

• Addressable Market.ipynb (source)
• Addressable Market.html (please, open in a chrome web browser)

Raw Data

• customer_CRM_2019-05-17.csv
• Neoway_database_2019-05-17.csv

Python modules

• config.py
• utils.py
• viz.py
• clustering.py
• classifier.py

Deliverables

• Addressable customers' ids: addressable_ids.csv
• Training dataset: training_ids.csv
• Validation dataset: testing_ids.csv
• Addressable Market in the format id/score, ordered by score: addressable_ranking.csv

Environment

Powered by IBM Watson Studio using the following hadware and software config:

• Environment Default Spark Python 3.6 XS
• Creator IBM
• Hardware configuration (Driver) 1 vCPU and 4 GB RAM
• Hardware configuration (Executor) 1 vCPU and 4 GB RAM
• Number of executors 2
• Spark version 2.3
• Software version Python 3.6

Link: https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/89a5651d-87f7-440d-b6b4-7b831cefcad4/view?projectid=8d5fdbe5-355b-4c65-bbf1-1d1abba0077f&context=wdp

Author

Fernando Felix do Nascimento Junior