Tools Used:
Database: MySQL Programming: Python (Jupyter Notebook) Libraries: Pandas, NumPy, Matplotlib, Seaborn Database Interaction: MySQL Connector for Python Contributions:
Data Extraction and Cleaning:
Connected to the e-commerce platform's MySQL database using Python’s Open DATABASE Connecter (ODBC). Extracted transactional data (order history, customer information, product details) from multiple tables using SQL queries. Cleaned and preprocessed the data in Pandas, dealing with missing values, outliers, and ensuring data consistency.
Sales Performance Analysis: Analyzed sales trends over time, identifying peak periods, low-performance products, and high-demand product categories. Conducted a year-over-year and month-over-month sales comparison to pinpoint seasonal trends and promotional success. Visualized sales data in Jupyter Notebook using Matplotlib, creating line charts, bar charts, and pie charts.
Key Achievements: Improved understanding of customer behavior, leading to a targeted marketing strategy that could potentially increase retention by 10%. Identified high-demand products, enabling the business to focus on the most profitable categories, leading to a 5% increase in sales. Provided data-backed insights into promotional periods, helping the business to optimize future marketing campaigns.