Customer Churn Analysis using Python
I conducted a detailed analysis of telecom customer churn, uncovering key insights: 24.54% churn rate, with higher churn among senior citizens and short-tenure customers. Month-to-month contracts and lack of value-added services drive higher churn. Fiber optic internet users show dissatisfaction, leading to increased churn. Using impactful visualizations, I provided actionable recommendations like promoting long-term contracts, improving service satisfaction, and offering retention incentives. Looking for data-driven insights to reduce churn? Let’s collaborate! link: https://github.com/Weebhavv/Customer-Churn-Analysis
About Me
Data Analyst | Power BI Specialist | Python & SQL I help businesses make sense of their data with interactive Power BI dashboards, smart analytics, and clear insights. With skills in SQL, Python, and data visualization, I clean, analyze, and transform data into valuable business solutions. ✅ Custom Power BI Dashboards ✅ Data Cleaning & Transformation (SQL, Power Query, Pandas) ✅ Business Insights & Reporting Let’s turn your data into decisions! ?
$ 2 USD/hr
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