Description:

This project focuses on analyzing and visualizing data related to Amazon Prime users' demographics, subscription plans, device usage, renewal status, and engagement metrics. The dataset used for analysis contains information about 2500 Amazon Prime users, including their personal details, membership start and end dates, subscription plans, payment information, usage frequency, purchase history, favorite genres, devices used, engagement metrics, feedback/ratings, and customer support interactions.

Key Steps:

Data Wrangling: The project starts with data wrangling, including reducing memory usage and exploring the dataset's structure and content.

Data Visualization: Various visualization techniques are employed to gain insights into the data. This includes bar plots, pie charts, and count plots to visualize distributions and patterns across different attributes such as gender, subscription plans, renewal status, usage frequency, purchase history, favorite genres, devices used, and customer support interactions.

Insights and Analysis: Through visualizations, we analyze trends and patterns in user behavior, subscription preferences, and engagement metrics. Insights are drawn regarding the popularity of different subscription plans, the distribution of device usage among users, renewal patterns based on gender, and customer support interactions across different devices.

Data Preprocessing: Data preprocessing steps involve converting data types, handling missing values, and deriving additional features such as average membership time.

Conclusion: This project provides a comprehensive analysis of Amazon Prime user data, offering valuable insights into user behavior and preferences. The visualizations and analysis can be utilized by Amazon to optimize subscription plans, improve customer engagement, and tailor services to better meet the needs of their users.