ADITYA PACHLANGIA

Conducted in-depth credit risk analysis on a complex dataset, identifying key patterns and correlations to enhance predictive modeling for loan defaults.

• Managed a complex dataset by effectively addressing missing values, dropping over 50% of sparse columns, and accurately imputing numerical and categorical data using median and mode methods.
• Identified and mitigated outliers within the dataset using the Interquartile Range (IQR) method, significantly enhancing data quality and ensuring the reliability of subsequent analyses.
• Performed comprehensive univariate and bivariate analysis utilizing advanced Python libraries (Pandas, NumPy, Matplotlib, Seaborn) to uncover critical patterns in loan default risk.
• Analyzed intricate correlations between key features such as 'DAYS_BIRTH' and 'EXIT_SOURCE_3' and the target variable, providing actionable insights crucial for robust risk assessment and strategic decision-making.

Developed a Python-based e-commerce data management system to streamline product information handling and enhance data accessibility for business operations.

• Developed a comprehensive Python-based e-commerce data management system, efficiently handling product information across CSV, JSON, and TXT file formats to significantly enhance data organization and accessibility.
• Implemented robust file handling functions utilizing `os`, `csv`, and `json` modules to seamlessly load, modify, and save diverse product sales data, details, and descriptions, ensuring high data processing efficiency.
• Designed and integrated rigorous input validation mechanisms for SKUs (13-character length) and sales data (14 integers), substantially improving data integrity and optimizing user experience in administrative tasks.
• Created modular functions (e.g., `load_data`, `update`, `dump_data`) to streamline data operations, enabling fluid addition and updating of product information with intuitive, user-friendly prompts.