https://github.com/archie-cm/churn-analysis-for-bank-customer

The objective from this project are to predict customer churn and provide recommendations to the business team
https://github.com/archie-cm/churn-analysis-for-bank-customer

feature-engineering machine-learning python scikit-learn

Last synced: 2 days ago
JSON representation

The objective from this project are to predict customer churn and provide recommendations to the business team

• Host: GitHub
• URL: https://github.com/archie-cm/churn-analysis-for-bank-customer
• Owner: archie-cm
• Created: 2022-09-26T08:17:20.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-08T07:22:48.000Z (about 2 years ago)
• Last Synced: 2023-04-07T08:56:15.775Z (almost 2 years ago)
• Topics: feature-engineering, machine-learning, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 11.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## Classification - Churn For Bank Customer

**Problem Statement**

![4](https://user-images.githubusercontent.com/108534539/206376148-34a51aee-2994-442f-ba5b-d729eb7f0244.jpg)

**Goal & Obejctive**

![5](https://user-images.githubusercontent.com/108534539/206376218-cf68a962-07a8-49d1-80bb-f9c30d2aff94.jpg)

**Tools**: Python, JupyterLab, Git

**Libraries**: Pandas, Numpy, Feature-engine, Scikit-learn, Imbalanced-learn, SHAP-learn, Gain & Lift Analysis

**Dataset**: Predicting Churn for Bank Customers [[source]](https://www.kaggle.com/datasets/adammaus/predicting-churn-for-bank-customers)

**Summary of the analysis**

* This dataset has 10000 observations and 14 variables with 11 numerical variables, 3 categorical variables and one target variable.

* All numerical variables have a right-skewed distribution and contain a lot of outliers. 

* Exited is the target variable that labels a 0 (not churn) and 1 (churn). The current condition is 20% of customer churn 

* From exploratory data analysis, customer who use num of products > 2 have trend churn, The older the customer, the higher the churn rate

* Based on data characteristics, the selected algorithm to build a classification model is tree-based or ensemble. The classification model with the xgboost algorithm is able to correctly predict 75% of visitors who make a purchase.

* Age, NumOfProduct, Gender Male, Geography France and IsActiveMember are the biggest impact on churn rate.

* Percentage Saving cost with model have 69%

![17](https://user-images.githubusercontent.com/108534539/206376346-866c1ee0-85a7-461b-a8ad-c68f483a7498.jpg)

![18](https://user-images.githubusercontent.com/108534539/206376392-b9f6fedb-44b5-4f3c-bf3c-7f40f0162376.jpg)

**What I have learned**

* Framing the business problem. 

* Create a machine learning model and extract insight from it to make an actionable recommendation for the business team.

* Make a business simulation from insights that decrease churn rate.

**File Dictionaries**

* [EDA_2Pendo (1).ipynb](https://github.com/archie-cm/churn-for-bank-customer/blob/main/EDA_2Pendo%20(1).ipynb): this notebook contains all of project details, such as business understanding, exploratory data analysis & insights from dataset and external data.

* [Supervised_2pendo.ipynb](https://github.com/archie-cm/churn-for-bank-customer/blob/main/Supervised_2pendo.ipynb) : data preprocessing, modeling, lift & gain analysis, feature importance with SHAP, business recommendation

* [2pendo-presentation_final_project.pdf](https://github.com/archie-cm/churn-for-bank-customer/blob/main/2pendo-presentation_final_project.pdf): summary of the project.