This project involves a comprehensive analysis of e-commerce transactions using a combination of Python and SQL. The primary focus areas include data cleaning, cohort analysis, and leveraging SQL for insightful queries. The objective is to gain valuable insights from the transactional data, enabling informed decision-making for the success of the e-commerce platform in a competitive market.
Meticulously clean and preprocessed raw data to ensure accuracy and reliability. Handle missing values, outliers, and any inconsistencies in the dataset. Standardize and format data for consistency and ease of analysis.
Conduct a cohort analysis to understand customer behavior over time. Segment customers based on their transactional patterns and cohort characteristics. Extract meaningful insights to optimize marketing strategies and enhance customer retention.
Utilize Python for data manipulation, analysis, and visualization. Implement algorithms and functions to perform specific tasks related to data analysis and cohort segmentation.
Leverage SQL for querying the database to extract relevant information. Formulate SQL queries to obtain key metrics, trends, and patterns from the e-commerce transactions dataset.
With every line of code and SQL query, ABC not only uncovers answers but also paves the way for smarter decisions. This project transcends mere numerical analysis; it is about empowering the e-commerce business to thrive in a rapidly evolving and competitive landscape.
We extend our gratitude to the open-source community, Python, and SQL for providing the tools that make this analysis possible. This project is a testament to collaborative efforts in leverage data for business success.