A Python package for comprehensive telecom data analytics, focusing on four key areas: User Overview, User Engagement, User Experience, and User Satisfaction. Designed to provide actionable insights to improve telecom services and customer retention through detailed data analysis.
End-to-end telco customer churn analysis data analyst project: raw Kaggle dataset ingestion, MySQL data warehousing, Python-based cleaning/modeling, and Power BI dashboard. Project includes actionable business insights, model-driven recommendations, and revenue impact.
Derive insights of factors contributing to customer churn in the Telecom Industry.
In this notebook, We will evaluate the five best known machine learning algorithms to determine which one is the best fit for this given telecom churn binary classification.
KPIs for understanding the decline in Sales, Revenue and Market Shares of AtliQo Telecom Pre and Post 5G Launch using the Data Modelling and Power BI
Telecom Data Analysis
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