Projects

The purpose of this Credit Card Regression Data Insights is to analyze and model the relationships between variables such as Age, Income, Loan Amount, Ethnicity, Education, Gender, and Income status, and how they influence credit card usage and financial behavior. By applying regression techniques, the analysis aims to identify key predictors of spending habits, debt accumulation, and payment patterns. These insights are essential for predicting customer behavior, assessing credit risk, segmenting customers for targeted marketing, and optimizing financial products, ultimately enhancing decision-making processes within financial institutions. 

The purpose of this Dress Rental Pricing Data Insights is to analyze and model the relationships between variables such as Price, Color, Brand, and Name to determine optimal rental pricing strategies. By examining these factors, the analysis aims to identify how each variable influences the perceived value and demand for dress rentals. These insights are crucial for setting competitive prices, understanding customer preferences, and maximizing profitability in the dress rental market. Ultimately, the goal is to enhance pricing strategies by leveraging data-driven insights into consumer behavior and market trends. 

The purpose of this HR Attrition Data Insights is to analyze and understand the factors contributing to employee turnover within an organization. By examining variables such as employee demographics, job roles, tenure, performance metrics, and other relevant factors, the analysis aims to identify patterns and predictors of attrition. These insights are essential for developing strategies to improve employee retention, enhance workplace satisfaction, and reduce turnover costs. Ultimately, the goal is to support HR decision-making with data-driven insights to create a more stable and engaged workforce.