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talivia
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Netflix-User-Engagement-Analysis
An end-to-end analytics portfolio project that uses NumPy to analyze user engagement and churn for a subscription streaming platform, turning synthetic Netflix user data into actionable business insights.
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2026-07-30
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FR-AI / ANALYSISWhy it is worth attention
It demonstrates a business-first analytics workflow from data validation to churn insights, deliberately using NumPy to build a strong foundation in numerical computing and vectorized operations rather than relying on higher-level libraries.
Who it is for
- Aspiring data analysts
- Product analytics students
- Data science portfolio builders
- Business intelligence professionals
Use cases
- Learning an end-to-end analytics workflow with NumPy
- Practicing churn and engagement analysis on synthetic data
- Building a portfolio case study for job applications
- Teaching product analytics with a business-first methodology
Strengths
- Clear business-first structure linking product questions to analysis
- Covers data validation, exploratory analysis, and statistical reasoning
- Uses NumPy to reinforce understanding of vectorized operations
- Leverages a realistic synthetic dataset with 50,000 records and 20 features
Considerations
- Uses synthetic data, so insights are not derived from real Netflix users
- Project is still in progress with data preprocessing incomplete
- Currently relies almost exclusively on NumPy, which may limit advanced analysis without additional libraries
README quick start
🎬 Netflix User Engagement & Churn Analysis
📌 Overview
This project is an end-to-end analytics case study focused on understanding user engagement and customer churn for a subscription-based streaming platform. It combines data analysis, product analytics, business intelligence, and data science concepts to transform raw user data into actionable business insights.
The project follows a business-first approach where every analysis begins with a product question, is supported by statistical reasoning, implemented using NumPy, and concludes with data-driven recommendations.
🎯 Business Problem
This project investigates user engagement and churn by addressing real-world business problems commonly faced by streaming platforms such as Netflix.
📈 Product Analytics
- Which factors increase or decrease user engagement?
- Which user segments spend the most time watching content?
- Does recommendation click rate translate into higher watch time?
- Which subscription plans drive stronger engagement?
- How do viewing habits differ across devices, regions, genders, and time of day?
- Which user behaviors indicate an increased risk of churn?
- What product improvements can improve user retention?
📊 Data Analytics
- Measure key engagement KPIs such as average watch time, session count, watch frequency, and recommendation click rate.
- Identify trends and patterns across customer demographics.
- Compare user behavior across regions, subscription plans, genres, and devices.
- Perform exploratory data analysis (EDA) to uncover hidden insights.
- Validate data quality through missing value detection, duplicate identification, and consistency checks.
🤖 Data Science
- Identify the variables most strongly associated with user engagement.
- Discover behavioral patterns that distinguish churned and retained users.
- Engineer meaningful analytical features for predictive modeling.
- Prepare clean, validated data suitable for machine learning workflows.
- Build a foundation for churn prediction and customer segmentation models.
🌟 The objective of this project is to transform raw user activity data into actionable business insights that support product decisions, customer retention strategies, and data-driven decision making.
🚀 Key Objectives
- Analyze user engagement across different customer segments.
- Identify behavior
Description
An end-to-end, business-first analytics project evaluating streaming retention and churn drivers. Built from the ground up using Python & NumPy to demonstrate low-level numerical computing, vectorized operations, and core product analytics workflows, including data science concepts and algos
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