SQL Task: Credit Cards Dataset

About the Project

Medium Article

I've written a detailed Medium article outlining this project. Read the article here.

Dataset Information

Name: sql_task_credit_cards.sqlite

Structure:

• Columns: [index], City, Date, CardType, ExpType, Gender, Amount

This SQLite dataset contains credit card transaction details including city, date, card type, expenditure type, gender, and amount.

SQL Insights

Total Amount Spent

• Query: 1.txt
• Description: Calculates the total amount spent by everyone.

City-wise Expenditure Analysis

• Queries: 2.txt
• Insights:
  • City with the highest and lowest total expenditure.
  • City with the highest and lowest average expenditure.

Platinum Card Expenditure

• Query: 3.txt
• Insight: Determines the expenditure type most used with the "Platinum" card.

Additional Insights (4.txt)

Several insights have been generated including:

• Comparison of spending among different card types.
• Gender-based spending analysis.
• Top expenditure categories.
• Customer segmentation by city and card type.
• Customer loyalty assessment.
• Refunds and chargebacks analysis.
• Detection of outliers in spending.
• Demographic trends in spending.
• Geographical trends in spending.

How to Use

1. Clone the Repository:

   git clone https://github.com/your-username/sql_credit_card_insights.git
   cd sql_credit_card_insights

2. Access the Database and Run Queries:

• Load the SQLite database:

  sqlite3 sql_task_credit_cards.sqlite

• Execute queries:

  sqlite3 sql_task_credit_cards.sqlite < queries/1.txt

3. Review Insights:

• Examine the results generated by each query to gain insights into the credit card dataset.

Contribution

If you'd like to contribute additional SQL queries, improve the dataset, or enhance the insights, feel free to fork this repository and submit a pull request.

Note

• The dataset is for educational purposes and contains fictional data.
• Respect data privacy and legal considerations when working with real datasets.