Customer Churn Analysis Using Python | A Beginner Data Analytics Project
Customer retention is a major challenge in the telecom industry. Losing existing customers not only impacts revenue but also increases the cost of acquiring new ones. As a beginner in data analytics, I wanted to understand why customers leave and how data can help identify churn patterns. This motivated me to build a Customer Churn Analysis project using Python.
The goal of this project was to analyze telecom customer data and identify key factors that influence customer churn. Understanding these patterns can help businesses take early actions to retain customers and improve long-term customer relationships.
For this project, I used:
The dataset used was the Telco Customer Churn dataset, which contains information about customer demographics, contract types, tenure, monthly charges, and churn status. For this project, I used a Kaggle telecom customer churn dataset.
I began by cleaning the data and handling missing values to ensure accuracy. Feature engineering was then performed by creating tenure bands to better understand customer behavior across different time periods. The analysis focused on churn patterns based on contract type, tenure, monthly charges, and payment methods. To support the findings, multiple visualizations were created, including histograms, box plots, count plots, and a correlation heatmap.
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One of the most important insights from this analysis was that a high percentage of churned customers were on month-to-month contracts with less than three months of tenure. This highlights the importance of early customer engagement strategies to reduce churn during the initial customer lifecycle.
Through this project, I improved my understanding of data cleaning, feature engineering, and exploratory data analysis using Python. More importantly, I learned how data-driven insights can support business decision-making. This project strengthened my interest in data analytics and motivated me to explore more real-world datasets.
This project was built as part of my learning journey in data analytics.Also this was a valuable learning experience, and I look forward to building more data analytics projects and sharing my journey. Feedback and suggestions are always welcome!
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