Santander Customer Satisfaction Machine Learning Project in R

Santander Customer Satisfaction Machine Learning Project in R

In this machine learning project, we will use hundreds of anonymized features to predict if customers are satisfied or dissatisfied for one of the biggest banks - Santander


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Project Experience

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What will you learn

Understanding the Problem Statement and Importing the Dataset
Performing basic EDA to get Insights into the data
Understanding Balanced and Unbalanced dataset
Understanding different types of models and it's importance in different scenarios
Identifying similar features and constant feature using Standard Deviation method
Imputing missing values and removing redundant features
Plotting box plot for identifying outliers
Using min and max values of the train to limit the number of variables
Splitting the dependent and Independent columns
Applying Random Forest as the model for training and understanding it's parameters
Using the summary function and understanding the output
Plotting and understanding confusion matrix
Hyperparameter tuning Random forest model and getting the best result
Calculating the probability score for the model

Project Description

Customer satisfaction is a key measure of success. Unhappy customers don't stick around. What's more, unhappy customers rarely voice their dissatisfaction before leaving.

Santander Bank is asking to help them identify dissatisfied customers early in their relationship. Doing so would allow Santander to take proactive steps to improve a customer's happiness before it's too late.

In this machine learning project, you'll work with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.

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Curriculum For This Mini Project

05h 15m