Santander Customer Satisfaction — A Self Case Study using Python

Santander Customer Satisfaction — A Self Case Study using Python

Santander Customer Satisfaction — A Self Case Study using Python. Classification modeling on the Santander Customer Satisfaction dataset in Kaggle as part of self case study — Applied AI Course using Python (github link and Linkedin)

Table of Contents

  1. Business problem
  2. Use of Machine learning to solve the business problem
  3. Evaluation metric (Area Under the Curve)
  4. Exploratory data analysis
  5. Feature engineering
  6. Existing solutions
  7. My Experiments with models
  8. Summary, results and conclusions
  9. Future work
  10. Link to my profile — Github code and Linkedin
  11. References

1. Business Problem

This case study is based on Kaggle competition conducted in the year 2016. Customer satisfaction is one of the most important key performance indicators in every company today and is seen as a key element of a company’s success. Unhappy customers don’t stick around. What’s more, unhappy customers rarely voice their dissatisfaction before leaving. Santander is a Spanish multinational corporation bank and financial based company which operates in Europe, North and South America, and also Asia. In this Kaggle competition that is conducted by Santander we need to predict whether a customer is dissatisfied with their services early on based on the features provided by the company. This will help them to take proactive steps to improve the customer satisfaction before the customer leaves.

kaggle customer-satisfaction classification machine-learning data-science

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