In this project, we analyze a Credit Card Customer Dataset containing customer details such as age, income, card type, credit limit, payment ratio, current balance, utilization rate, and monthly spending across different categories. The dataset also includes essential (“Needs”) and non-essential (“Wants/Lifestyle”) spend categories, allowing deeper behavioral segmentation. Using R programming for data cleaning, transformation, and visualization, the project aims to identify hidden patterns in how customers spend and repay their credit card dues. The analysis helps classify customers into various groups such as high spenders, low-risk users, lifestyle-driven users, and potential defaulters. Spending categories, repayment behavior, age groups, and credit utilization are explored to understand how different customer segments exhibit distinct financial habits.This study not only highlights the financial behavior of customers but also supports decision-making for credit risk assessment. By analyzing spending patterns and repayment discipline, the project offers insights that can help financial institutions reduce default risk, understand customer needs, and improve credit product strategies

Analysis on NBA Player stats from 1996 - 2024

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The Supermarket Sales dataset provides a detailed record of transactions made across three major branches of a retail store. The dataset includes information such as customer demographics, type of products purchased, pricing, quantity, taxes, total sales amount, gross income, and customer ratings. Descriptive analysis helps summarize and interpret these variables so that important trends and behaviors can be understood.