为什么值得关注

该项目强调业务优先的分析流程，从数据验证到流失洞察，并刻意使用 NumPy 来夯实数值计算和向量化操作基础，而不是依赖高级库。

适合谁使用

数据分析学习者
产品分析学习者
数据科学作品集准备者
商业智能从业者

典型使用场景

学习使用 NumPy 搭建端到端分析流程
在合成数据上练习流失分析与参与度分析
为求职打造数据科学作品集案例
以业务问题为导向进行产品分析教学

项目优势

业务优先的结构清晰，将产品问题与分析结合
涵盖数据验证、探索性分析和统计推理
使用 NumPy 强化向量化操作与数值计算基础
使用包含 5 万条记录、20 个特征的合成数据集，便于实验

使用前须知

使用合成数据，不能反映真实 Netflix 用户行为
项目仍在进行中，数据预处理尚未完成
目前主要依赖 NumPy，可能限制更高级分析的实现

README 快速开始

🎬 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

项目描述

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

Netflix-User-Engagement-Analysis