PYTHON & ML
Project 01: Python Foundations - FoodHub
In this project for a food aggregator company, I analyzed order data to understand restaurant demand and enhance customer experience. First, I examined the data structure, identifying key variables and addressing missing values. Through univariate analysis, I summarized individual variables to uncover patterns in customer orders. Multivariate analysis revealed relationships between variables, such as correlations between order times and restaurant popularity. I ensured high-quality visualizations to clearly present insights, using charts to depict trends and interactions. Ultimately, I provided actionable recommendations to improve business strategies based on the data-driven findings.
Project 02: Machine Learning - Personal Loan Campaign
In this project, I aimed to predict liability customers' likelihood of buying personal loans, identify key customer attributes driving purchases, and determine target segments. I defined the problem, analyzed the data, treated missing values and outliers, and engineered features. After building and evaluating predictive models, I improved performance through pruning and selected the best model. Key insights and decision rules were extracted to provide actionable recommendations for the marketing team, optimizing their campaign strategies. The project was structured clearly, with concise analysis and visual appeal, leading to practical business recommendations.