Project Overview
This repository features a deep Exploratory Data Analysis (EDA) on customer churn within the telecommunications industry. The main focus is to translate raw data into actionable insights for customer retention departments.
Key Objectives
- Pattern Recognition: Identify which customer profiles are at the highest risk of leaving.
- Data Quality: Perform technical data cleaning, justifying every decision (nulls, data types, outliers).
- Business Value: Suggest data-driven strategies to reduce churn rates.
📈 Key Findings Summary
- Contract Risk: Month-to-month contracts show a +40% churn rate.
- Critical Period: Most churn occurs within the first 6 months of tenure.
- Service Anchors: Tech Support and Online Security significantly increase retention.