https://github.com/rickydoan/python-explore-hypothesis-statistics-for-bank-domain

This project analyzes customer and transaction data for a bank’s credit card division. It covers data cleaning, customer segmentation, and statistical testing to design and evaluate targeted marketing campaigns.
https://github.com/rickydoan/python-explore-hypothesis-statistics-for-bank-domain

hypothesis-testing python

Last synced: 3 months ago
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This project analyzes customer and transaction data for a bank’s credit card division. It covers data cleaning, customer segmentation, and statistical testing to design and evaluate targeted marketing campaigns.

• Host: GitHub
• URL: https://github.com/rickydoan/python-explore-hypothesis-statistics-for-bank-domain
• Owner: RickyDoan
• Created: 2024-11-12T07:15:26.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-24T20:40:11.000Z (over 1 year ago)
• Last Synced: 2025-07-01T03:03:29.369Z (about 1 year ago)
• Topics: hypothesis-testing, python
• Language: Jupyter Notebook
• Homepage:
• Size: 8.39 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
### Credit Card Customer Insights and Campaign Analysis

* This project analyzes customer and transaction data for a bank’s credit card division. It covers data cleaning, customer segmentation, and statistical testing to design and evaluate targeted marketing campaigns.

**Table of Contents**

* Project Overview

* Data Analysis & Key Insights

* Campaign Design & Testing

* Results and Findings

* Usage Instructions

* License

------------------------------------

**Project Overview**

* The project includes two phases:

 * Phase 1: Data preparation and segmentation based on age, income, and spending behavior.

 * Phase 2: Campaign design for a target age group (18-24), including A/B testing to measure campaign success.

  

**Data Analysis & Key Insights**

Phase 1: Exploratory Analysis

* Data Cleaning: Managed missing values, outliers, and duplicates.

* Segmentation: Analyzed income, spending behavior, and preferences by age group.

* Visualization: Used charts to represent income distribution, categories, and demographics.

  

Phase 2: Campaign & Hypothesis Testing

* Focus group: Targeted age group.

* A/B Testing: Compared transaction amounts between test and control groups.

* Sample Size Optimization: Adjusted for budget constraints.

  

**Results and Findings**

* Usage Instructions

  **Phase 1: Review customer segmentation and visualization for insights into customer demographics**

  **Phase 2: Explore campaign testing results to understand the A/B testing approach and insights gained.**

**License**

* This project is licensed under the MIT License.

**Contact**

* For questions or feedback, feel free to reach out!