📊Data Analysis

Telco Customer Churn Analysis

A comprehensive data analysis project that loads, cleans, and analyzes the Telco customer dataset to uncover actionable insights. The project involves data cleaning by handling duplicates and converting data types , followed by extensive Exploratory Data Analysis (EDA) using libraries like Matplotlib and Seaborn. Key findings reveal that churn is highest among new customers (0-12 months tenure) , those with high monthly charges , Fiber Optic internet service users , and customers on month-to-month contracts. The analysis concludes with strategic recommendations to improve customer retention.

📅1/31/2025
⏱️
👤Data Analyst
completed
Project screenshot 1

Technologies Used

Pythonpandasnumpymatplotlibseaborn

Features

Data cleaning and preprocessing, including handling duplicates and null values
Univariate and Bivariate Exploratory Data Analysis (EDA)
Analysis of churn rate, which is approximately 26.5%
Correlation heatmap for numerical features like tenure and monthly charges
Identified that customers on month-to-month contracts are more likely to churn
Found a high churn rate among customers using Fiber Optic internet service
Analysis shows customers paying by electronic check have a higher churn rate
Discovered that new customers (0-12 months tenure) are the most likely to churn