In this cohort analysis project, I harnessed the capabilities of several powerful Python libraries, including NumPy, Pandas, Matplotlib, and Seaborn, to explore and derive insights from a substantial dataset comprising over 500,000 customers. NumPy provided a robust foundation for numerical operations and efficient data manipulation, while Pandas facilitated seamless handling and manipulation of large datasets, enabling the extraction of meaningful patterns and trends.
The visualization aspect of the project was enhanced through Matplotlib and Seaborn, which are widely-used plotting libraries. These tools allowed for the creation of clear and insightful graphs, aiding in the representation of cohort-based trends and patterns. Cohort analysis involves grouping individuals based on shared characteristics and observing their behavior over time. By leveraging these technologies, the project provided a detailed understanding of customer behavior, retention rates, and other key metrics critical for informed decision-making in areas such as marketing, customer engagement, and product development. The combination of NumPy, Pandas, Matplotlib, and Seaborn not only facilitated efficient data processing but also enhanced the interpretability of complex cohort analysis results for stakeholders.
