Customer Churn Prediction Analysis using Ensemble Techniques

Customer Churn Prediction Analysis using Ensemble Techniques

In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

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What will you learn

Structuring and framing a business need into a Machine Learning problem statement
Defining relevant metrics and setting the right expectations with business teams
Exploratory Data Analysis - Univariate, Bivariate analysis
Missing value and outlier treatment
Label Encoder/One Hot Encoder and handling new categorical levels in test/production data
Target encoding and avoiding data leakage
Feature transforms (scaling and normalization)
Feature engineering and Feature selection (RFE)
Solving class imbalance
Model explainability and interpretability through Tree visualizations and SHAP
Hyperparameter tuning using RandomSearch and GridSearch
Ensembling multiple models
Error analysis
Wrapping up code using Pipelines for production run

Project Description

A well-known bank has been observing a lot of customers closing their accounts or switching to competitor banks over the past couple of quarters. This has caused a huge dent in their quarterly revenues and might drastically affect annual revenues for the ongoing financial year, causing stocks to plunge and market cap to reduce significantly. The idea is to be able to predict which customers are going to churn so that necessary actions/interventions can be taken by the bank to retain such customers.

In this machine learning churn prediction project, we are provided with customer data pertaining to his past transactions with the bank and some demographic information. We use this to establish relations/associations between data features and customer's propensity to churn and build a classification model to predict whether the customer will leave the bank or not. We also go about explaining model predictions through multiple visualizations and give insight into which factor(s) are responsible for the churn of the customers.

This project walks you through a complete end-to-end cycle of a data science project in the banking industry, right from the deliberations during formation of the problem statement to making the model deployment-ready.

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Curriculum For This Mini Project

Introduction to the Churn Business Problem
08m
Setting metric targets
03m
Show Me The Data
11m
Questioning The Data
05m
Splitting The Data
06m
Univariate Analysis
06m
Outliers
05m
Missing Value Treatment
08m
Label Encoding
08m
One-hot Encoding
08m
Target Encoding
11m
Bivariate Analysis
09m
Feature Engineering
06m
Feature Scaling And Normalization
09m
Feature Selection - Rfe
09m
Baseline Models - Logistic Regression
08m
Baseline Models - Svm
06m
Plotting Decision Boundaries - Linear Models
07m
Decision Trees
09m
Decision Boundaries - Decision Tree
05m
Decision Tree Rule Engine Visualization
06m
Spot Checking - Intro
07m
Spot Checking Part 1
09m
Spot Checking Part 2
05m
Spot Checking Part 3 - Pipelines
04m
Spot Checking Part 4 - Model Zoo
12m
Spot Checking Part 5 - Evaluation Using Kfoldcv
07m
Hyperparameter Tuning - Randomsearch
10m
Gridsearch
08m
Ensembles - Model Averaging
08m
Ensembles -stacking
07m
Error Analysis - Part 1
08m
Error Analysis - Part 2
07m
Error Analysis - Part 3
06m
Training Final Model
08m
Shap
09m
Predicting On Unseen Data
06m
Conveying Results To Business Teams
04m
Test Script
09m
Ending Notes
07m