Credit-Card-Transaction-India

Data set Link :- https://www.kaggle.com/datasets/thedevastator/analyzing-credit-card-spending-habits-in-india

Credit Card Transactions in India

This dataset provides a detailed view of credit card transactions across various cities in India. It encompasses critical information to understand spending patterns and financial behavior. The dataset includes the following columns:

Index: A unique identifier for each transaction. City: The city where the transaction occurred, including Delhi, Greater Mumbai, Bengaluru, Ahmedabad, and more. Date: The date of each transaction, spanning from 2014 to 2018. Card Type: The type of credit card used, such as Gold, Platinum, Signature, and Silver. Expense Type: Categorized types of expenses (e.g., bills, dining, shopping). Gender: The gender of the cardholder. Amount: The amount of money spent in each transaction, ranging from modest sums to significant expenditures.

Key Analyses and Solutions:

Data Cleaning and Preparation:

Ensure Data Integrity: Verify there are no missing or incorrect values in the dataset. Columns include index, City, Date, Card Type, Expense Type, Gender, and Amount.

Standardize Data Format:

Format dates and categorical data consistently.

Descriptive Statistics:

Calculate the Total Number of Transactions: Method: Use the COUNTA function to count non-empty cells in a selected column to ensure each row represents a transaction.

Calculate the Total Amount Spent:

Method: Utilize the SUM function to sum all values in the "Amount" column, providing the total spending.

#Find the Average Transaction Amount: Method: Apply the AVERAGE function to the "Amount" column to determine the average spending per transaction.

Determine the Number of Transactions per City:

Method: Create a PivotTable with the City field in the Rows area and count the number of transactions using the COUNTA function for a relevant column.

Trend Analysis:

Analyze Spending Trends Over Time (Monthly, Quarterly, Yearly): Method: Extract months, quarters, and years from the Date column using Excel functions. Create PivotTables and charts to visualize spending trends over time intervals.

Identify Peak Spending Periods:

Method: Use PivotTables to analyze total spending per month, quarter, and year to identify the periods with the highest spending.

Category Analysis:

Compare Spending Habits Based on Card Type (e.g., Gold, Platinum, Silver, Signature): Method: Use a PivotTable to compare total and average spending amounts for each card type, and create relevant visualizations.

Compare Spending Habits Based on Expense Type (e.g., bills, dining, shopping):

Method: Configure a PivotTable to analyze spending patterns across different expense types.

Demographic Analysis:

Analyze Spending Habits Based on Gender: Method: Create a PivotTable with the Gender field in the Rows area and summarize the Amount field to compare total and average spending between genders.

Determine if There Are Significant Differences in Spending Between Different Cities:

Method: Create a PivotTable to compare total and average spending by city, and consider statistical analyses for deeper insights.