Dataset Description

This dataset includes 1,000 rows of synthetic data representing car insurance premiums calculated using a realistic linear formula. It features key variables such as driver age, driving experience, accident history, annual mileage, and car manufacturing year, making it suitable for predictive modeling and exploratory analysis. The data reflects real-world patterns influencing insurance premiums, providing meaningful insights for research in the insurance industry. It is particularly useful for training and testing linear regression models, analyzing feature importance, and conducting predictive analytics.

Dataset is available here : https://www.kaggle.com/datasets/govindaramsriram/car-insurance-premium-dataset

Power BI Dashboard Ideas for the Dataset

Here are some creative ideas for designing an insightful Power BI dashboard using this dataset:

1. Overview Dashboard

• Title: Insurance Premium Analysis Overview
• Visuals:
  • A card displaying the average insurance premium.
  • A clustered bar chart showing the distribution of premiums by driver age.
  • A line chart analyzing annual mileage trends across car manufacturing years.
  • A pie chart showing the percentage of drivers with and without accident history.
• Key Insights:
  • Highlight the average premium value.
  • Identify the driver demographics contributing most to high premiums.

2. Driver Demographics Impact

• Title: Impact of Demographics on Insurance Premiums
• Visuals:
  • A scatter plot showing the relationship between driver age and premium.
  • A heatmap of driving experience vs. accident history with corresponding premiums.
  • A table with drill-through functionality summarizing premiums by driver age groups.
• Key Insights:
  • Spot trends like whether younger or less experienced drivers pay more.

3. Risk Factor Analysis

• Title: Risk Factors Influencing Premiums
• Visuals:
  • A stacked bar chart comparing premiums for drivers with/without accidents.
  • A decomposition tree showing the influence of accident history, age, and mileage on premiums.
  • A slicer to filter data by car manufacturing year.
• Key Insights:
  • Visualize how accident history impacts premiums.
  • Identify risk factors that drive the most variation in premiums.

4. Predictive Insights

• Title: Predictive Modeling in Insurance Premiums
• Visuals:
  • A scatter plot with a trendline showing predicted premiums vs. actual premiums.
  • KPI cards displaying the R² value, MAE, and RMSE from the regression model.
  • A line chart showing the change in predicted premiums over time.
• Key Insights:
  • Evaluate how well the model predicts premiums and identify areas for improvement.

5. Driver Segmentation Dashboard

• Title: Driver Segmentation for Insurance Analysis
• Visuals:
  • Clustered bar chart segmenting drivers into categories based on age, experience, and mileage.
  • A radar chart comparing premium factors for different driver segments.
  • A tree map displaying premium values for drivers grouped by accident history and car manufacturing year.
• Key Insights:
  • Provide actionable segmentation insights for personalized premium strategies.

6. Geographic Premium Analysis (if location data is available)

• Title: Geographic Patterns in Insurance Premiums
• Visuals:
  • A map displaying average premiums by region or city (if included in data).
  • A bubble chart showing accident frequencies per region.
• Key Insights:
  • Uncover regional patterns and variations in premiums.

7. Premium Trends Over Time

• Title: Insurance Premium Trends
• Visuals:
  • A line chart showing the average premium trend across manufacturing years.
  • A combo chart displaying accident frequency alongside average premiums over time.
• Key Insights:
  • Analyze trends in premiums over car manufacturing years or other time factors.

Let me know if you need help creating DAX measures, building visuals, or interpreting data for these dashboards!