This Insurance Claim Analysis Dashboard is a data visualization tool designed to offer insights into the correlation between demographic factors, health indicators, and insurance claims. Leveraging a rich dataset, the dashboard provides an interactive experience for exploring how different variables impact insurance claim amounts.

• Age Distribution: Showcases claim distribution across different age groups.

• Gender Distribution: Analyzes claims in relation to gender demographics.

• BMI and Blood Pressure Distribution: Displays the health profile of claimants through BMI and blood pressure statistics.

• Regional Analysis: Identifies geographical patterns in insurance claims.

• Smoking Status: Compares claim frequency and amounts between smokers and non-smokers.

• Family Size: Examines the influence of the number of children on claim amounts.

Included visualizations:

• Line charts correlating age with claim amounts.

• Pie charts detailing the distribution of claims by region and smoking status.

• Bar charts comparing claim amounts across genders, BMI categories, blood pressure categories, and family sizes.

• A composite bar chart illustrating the relationship between claim amounts, gender, smoking status, and diabetic status.

  

• Power BI
• Python Libraries: Matplotlib, Seaborn, Pandas

The data utilized in this dashboard was obtained from Kaggle, under the dataset titled "Insurance Claim Analysis (Demographic and Health)". Access the dataset here.

To use this dashboard:

1. Clone the GitHub repository.
2. Download the dataset from the Kaggle link provided.
3. Load the dataset into Power BI Desktop.
4. Open the .pbix file from the cloned repository to interact with the pre-built visualizations.

Contributions to enhance this dashboard are welcomed. Please initiate a discussion through an issue before submitting any major changes.

Should you have any questions or suggestions, feel free to reach out.