Capstone project applys topics provided throughout the Nanodegree program to solve a real problem by applying machine learning algorithms and techniques.
Santander Bank, started in Spain and it has been serving customers in the Northeast since 2013, cares about its customers satisfaction and work to take proactive steps to improve a customer’s happiness before it’s too late . From frontline support teams to C-suites, customer satisfaction is a key measure of success. Unhappy customers don't stick around, so Santander Bank posted a competition in Kaggle.com to identify dissatisfied customers. As an input, Santander Bank provides hundreds of anonymized features to predict if a customer is satisfied or not based on their banking experience. This task is a classification problem since customers should be classified as happy or not.
It is a Kaggle competition. Dataset link: link
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Python 3.
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Jupyter Notebook.
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Pandas.
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Numpy.
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Scikit-learn: open-source machine learning library for Python.
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Xgboost: package for gradient boosted decision trees implementation.
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Keras: open-source neural network library written in Python.
Kaggle, Santander Customer Satisfaction competition: link
Santander web site: link
Supervised learning Wikipedia page: link