Understanding Customer Churn: A Step-by-Step Guide to Predictive Analysis

Learn the essentials of customer churn analysis using a real-world telecom dataset. This step-by-step guide covers how to inspect, clean, and visualize customer data to uncover patterns that drive customer loss. No prior experience is required, making this tutorial ideal for beginners interested in data science and business analytics.

You will explore key features such as tenure, contract type, payment methods, and charges to understand their impact on churn. The lesson demonstrates practical techniques for handling missing values, correcting data types, and using plots to reveal actionable insights. By the end, you will know how to prepare clean data and identify the most influential factors for customer retention.

00:00 Introduction to Customer Churn Analysis
00:18 Why Churn Analysis Matters
00:32 Setting Up the Analysis Environment
01:19 Loading and Previewing the Dataset
01:35 Exploring Data Columns and Features
02:18 Summary Statistics and Data Overview
03:11 Checking for Missing Values
03:55 Cleaning and Preparing Data
05:06 Selecting Features for Churn Analysis
05:54 Analyzing Churn Distribution
06:42 Visualizing Churn Counts
07:43 Exploring Tenure and Churn Relationship
08:49 Examining Contract Type and Churn
09:56 Investigating Total Charges and Churn
10:53 Internet Service Type Impact on Churn
11:52 Correlation Analysis of Numeric Features
12:53 Mini Project: Exploring Churn Factors
14:38 Payment Methods and Churn Rates
15:38 Monthly Charges and Churn Insights
16:34 Saving Cleaned Data for Future Use
17:28 Recap and Next Steps

#DataScience #CustomerChurn #BusinessAnalytics