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.
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Age Distribution: Showcases claim distribution across different age groups.
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Gender Distribution: Analyzes claims in relation to gender demographics.
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BMI and Blood Pressure Distribution: Displays the health profile of claimants through BMI and blood pressure statistics.
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Regional Analysis: Identifies geographical patterns in insurance claims.
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Smoking Status: Compares claim frequency and amounts between smokers and non-smokers.
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Family Size: Examines the influence of the number of children on claim amounts.
Included visualizations:
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Line charts correlating age with claim amounts.
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Pie charts detailing the distribution of claims by region and smoking status.
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Bar charts comparing claim amounts across genders, BMI categories, blood pressure categories, and family sizes.
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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:
- Clone the GitHub repository.
- Download the dataset from the Kaggle link provided.
- Load the dataset into Power BI Desktop.
- Open the
.pbixfile 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.