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Santander Product Recommendation ML Project in Python

The goal of this machine learning project is to predict which products existing customers will use next month based on their past behaviour and that of similar customers.

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What will you learn

  • BFSI Domain Understanding
  • Recommendation Engine Building
  • Advanced Exploratory Data Analysis
  • Visualization using multiple Advanced Functions
  • Classification Models for Parallel Processing
  • Ensemble models

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.


  • Language used: Python

Project Description

Ready to make a down payment on your first house? Or looking to leverage the equity in the home you have? To support needs for a range of financial decisions, Santander Bank offers a lending hand to their customers through personalized product recommendations

Under their current system, a small number of Santander’s customers receive many recommendations while many others rarely see any resulting in an uneven customer experience. In this machine learning project in Python, Santander is challenging to predict which products their existing customers will use in the next month based on their past behavior and that of similar customers.

With a more effective recommendation system in place, Santander can better meet the individual needs of all customers and ensure their satisfaction no matter where they are in life.



Data Scientist / Business Consultant at GE

3 years of rich working experience in BIG Data, Business Intelligence & Analytics with CMMI Level 5 Organizations in BFSI, Manufacturing Sector. Excellent written and oral communications, strong analytical and problem solving capabilities. Constantly learning and experimenting emerging open source tools and technologie see more...