Data Analysis Customization

1. **Executive Summary:** A concise overview of the key findings and recommendations tailored for decision-makers. 2. **Exploratory Data Analysis (EDA):** Visualizations and statistical summaries exploring the distribution and relationships of variables. This includes demographic profiles of churned vs. retained customers, service usage patterns, and correlation analysis. 3. **Predictive Modeling Results:** Detailed analysis of predictive models, including logistic regression, decision trees, random forests, and gradient boosting. Evaluation metrics such as accuracy, precision, recall, and ROC-AUC will be presented, along with insights into model performance and key drivers of churn. 4. **Feature Importance Analysis:** Visualizations and insights into the most influential factors affecting customer churn, derived from feature importance analysis. 5. **Interactive Dashboard:** A dynamic dashboard allowing stakeholders to explore the data and model results interactively. Users can filter by demographic variables, services subscribed, or tenure to understand churn patterns better. This dashboard will be built using Plotly or similar visualization tools for maximum interactivity. 6. **Recommendations:** Actionable recommendations based on the analysis, tailored to different stakeholders. This includes targeted marketing strategies, service improvements, and customer engagement initiatives. 7. **Appendix:** Technical details including data preprocessing steps, model hyperparameters, and code snippets (in Python) for reproducibility. This versatile final output ensures that stakeholders across different departments can easily digest and act upon the insights provided. From high-level strategic recommendations to detailed technical analysis, the report caters to a diverse audience, facilitating informed decision-making to address customer churn effectively.