Credit Card Transaction Dataset Project

Table of Contents

• Dataset Overview
• Features
• Structure of the Repository
• The App
  • Intro
  • Data
  • Structure
  • Summary
  • Plots
  • Customer Details
• Why Analyze?

Dataset Overview:

This dataset provides a treasure trove of insights into customers' spending habits, transactions, and more. Whether you're a data scientist, analyst, or just someone curious about how money moves, this dataset is for you. The dataset itself can be found from here

Features:

1. Customer ID: Unique identifiers for every customer.
2. Name: First name of the customer.
3. Surname: Last name of the customer.
4. Gender: The gender of the customer.
5. Birthdate: Date of birth for each customer.
6. Transaction Amount: The dollar amount for each transaction.
7. Date: Date when the transaction occurred.
8. Merchant Name: The name of the merchant where the transaction took place.
9. Category: Categorization of the transaction.

Structure of the Repository:

This repository has two main directories for the project:

1. Credit_card_analysis_static This directory contains the static analysis of the data where we added some relevant columns for analysis and proper conclusions to be drawn from the dataset. The R ggplot library is used for plotting in this directory.

2. Credit: This directory involved the conversion of the static previously worked on previously into an interactive web application using the Shiny web framework package available for R applications. The dynamic Plotly package was used to plot interactive plots for the application.

The App

The shiny application application can be started by running the app.R script available in the Credit-App directory.

Intro

The homepage introduction to the dataset and why it is useful.

Data

Browse and discover several entries in the initial credit dataset.

Structure

View the general structure of the the dataset.

Summary

View a summarized version of the the dataset.

Plots

Explore the provided set of plots using different categories know form the dataset and years.

Customer Details

See customer details of the adjusted dataset(more_credit) in detail.

Why Analyze?

1. Insight into Customer Behavior: Analyzing transaction frequency, amount, and categories provides insights into customer behavior and preferences.

2. Temporal Trends: Analyzing transactions over time helps identify temporal trends, seasonality, or patterns valuable for understanding customer behavior.

3. Identifying Outliers: Plots like boxplots and histograms aid in identifying outliers in transaction amounts, allowing for further investigation.

4. Demographic Analysis: Age and gender analysis helps understand the demographics of customers and their spending patterns.

5. Category Insights: Analyzing transaction categories provides insights into which types of merchants or transactions are more common among customers.