Datasets:

mayadeeb08
/

car-insurance-eda

ID int64 101 1,000k	AGE stringclasses 4 values	GENDER stringclasses 2 values	RACE stringclasses 2 values	DRIVING_EXPERIENCE stringclasses 4 values	EDUCATION stringclasses 3 values	INCOME stringclasses 4 values	CREDIT_SCORE float64 0.05 0.96 ⌀	VEHICLE_OWNERSHIP float64 0 1	VEHICLE_YEAR stringclasses 2 values	MARRIED float64 0 1	CHILDREN float64 0 1	POSTAL_CODE int64 10.2k 92.1k	ANNUAL_MILEAGE float64 2k 22k ⌀	VEHICLE_TYPE stringclasses 2 values	SPEEDING_VIOLATIONS int64 0 22	DUIS int64 0 6	PAST_ACCIDENTS int64 0 15	OUTCOME float64 0 1
569,520	65+	female	majority	0-9y	high school	upper class	0.629027	1	after 2015	0	1	10,238	12,000	sedan	0	0	0	0
750,365	16-25	male	majority	0-9y	none	poverty	0.357757	0	before 2015	0	0	10,238	16,000	sedan	0	0	0	1
199,901	16-25	female	majority	0-9y	high school	working class	0.493146	1	before 2015	0	0	10,238	11,000	sedan	0	0	0	0
478,866	16-25	male	majority	0-9y	university	working class	0.206013	1	before 2015	0	1	32,765	11,000	sedan	0	0	0	0
731,664	26-39	male	majority	10-19y	none	working class	0.388366	1	before 2015	0	0	32,765	12,000	sedan	2	0	1	1
877,557	40-64	female	majority	20-29y	high school	upper class	0.619127	1	after 2015	0	1	10,238	13,000	sedan	3	0	3	0
930,134	65+	male	majority	30y+	high school	upper class	0.492944	0	after 2015	1	1	10,238	13,000	sedan	7	0	3	0
461,006	26-39	female	majority	0-9y	university	working class	0.468689	0	after 2015	0	1	10,238	14,000	sedan	0	0	0	1
68,366	40-64	female	majority	20-29y	university	working class	0.521815	0	before 2015	1	0	10,238	13,000	sedan	0	0	0	0
445,911	40-64	female	majority	0-9y	high school	upper class	0.561531	1	before 2015	0	1	32,765	11,000	sedan	0	0	0	1
275,820	65+	male	majority	30y+	high school	upper class	0.620361	1	after 2015	1	1	10,238	10,000	sedan	6	2	7	0
521,399	65+	female	majority	30y+	high school	upper class	0.729831	1	after 2015	1	0	32,765	12,000	sedan	4	0	0	0
429,728	40-64	male	majority	20-29y	high school	upper class	0.637045	1	before 2015	1	1	10,238	8,000	sedan	4	1	2	0
569,640	16-25	female	majority	0-9y	university	upper class	0.59126	1	before 2015	0	1	10,238	null	sedan	0	0	0	0
980,181	26-39	male	majority	10-19y	high school	middle class	0.461568	1	before 2015	1	1	10,238	12,000	sedan	0	2	1	0
906,223	26-39	female	majority	0-9y	high school	upper class	0.762798	0	after 2015	1	0	10,238	null	sedan	0	0	0	0
517,747	65+	male	majority	30y+	university	upper class	0.796175	1	before 2015	1	1	32,765	null	sedan	10	2	1	0
24,851	16-25	male	majority	0-9y	none	poverty	null	0	before 2015	1	0	32,765	12,000	sedan	0	0	0	1
104,086	26-39	female	majority	0-9y	university	upper class	0.680594	1	before 2015	0	1	32,765	null	sedan	0	0	0	1
240,658	16-25	female	majority	0-9y	high school	working class	0.417714	1	before 2015	0	1	10,238	18,000	sedan	0	0	0	1
484,399	16-25	female	majority	0-9y	high school	working class	0.409514	0	before 2015	0	0	10,238	17,000	sedan	0	0	0	1
912,828	16-25	male	majority	0-9y	high school	middle class	0.55326	1	after 2015	1	1	32,765	8,000	sedan	0	0	0	1
892,754	40-64	male	majority	20-29y	none	poverty	0.33095	1	before 2015	1	1	10,238	13,000	sedan	3	1	1	0
217	16-25	male	majority	0-9y	none	poverty	null	0	before 2015	0	0	10,238	17,000	sedan	0	0	0	0
633,896	65+	male	majority	30y+	high school	upper class	0.500631	1	before 2015	1	1	32,765	7,000	sedan	13	2	3	0
193,110	26-39	female	majority	10-19y	university	middle class	0.55197	1	before 2015	1	0	10,238	14,000	sports car	0	0	0	0
98,515	40-64	male	majority	10-19y	high school	middle class	0.626996	1	before 2015	0	0	10,238	13,000	sedan	1	1	0	0
165,623	26-39	male	majority	10-19y	none	poverty	0.294797	1	before 2015	1	0	10,238	15,000	sedan	0	0	1	0
493,590	26-39	male	majority	10-19y	university	middle class	0.55195	0	after 2015	0	0	10,238	17,000	sedan	0	0	1	0
389,792	65+	male	majority	0-9y	university	upper class	0.64982	1	after 2015	1	1	32,765	9,000	sedan	0	0	0	1
642,614	40-64	male	majority	20-29y	high school	middle class	0.521804	1	after 2015	1	1	92,101	7,000	sedan	2	0	1	0
951,465	65+	female	majority	30y+	university	upper class	0.701268	1	before 2015	1	1	10,238	10,000	sedan	3	1	1	0
496,987	16-25	male	majority	0-9y	none	poverty	0.171244	1	before 2015	1	1	10,238	9,000	sedan	0	0	0	0
732,437	16-25	male	majority	0-9y	none	poverty	0.318442	1	before 2015	0	1	32,765	10,000	sedan	0	0	0	1
146,897	26-39	female	majority	0-9y	high school	middle class	0.604684	1	before 2015	1	1	21,217	11,000	sedan	0	0	0	1
316,173	16-25	male	majority	0-9y	university	working class	0.504995	0	after 2015	0	0	32,765	8,000	sedan	0	0	0	1
9,013	26-39	male	majority	10-19y	high school	working class	0.625147	1	before 2015	0	1	10,238	17,000	sedan	3	0	2	0
511,757	40-64	female	majority	10-19y	none	middle class	null	1	before 2015	1	1	10,238	11,000	sedan	2	0	1	0
429,947	65+	male	majority	30y+	university	upper class	null	0	after 2015	0	1	10,238	12,000	sports car	6	0	5	0
485,660	40-64	male	majority	10-19y	university	upper class	0.578131	1	before 2015	0	1	92,101	14,000	sedan	1	0	1	0
80,496	65+	male	majority	30y+	high school	upper class	0.705454	1	before 2015	1	1	10,238	10,000	sedan	5	1	3	0
478,336	40-64	male	majority	20-29y	university	upper class	0.659525	1	after 2015	0	1	10,238	16,000	sedan	4	0	2	0
10,843	40-64	male	majority	20-29y	university	upper class	0.587384	0	after 2015	1	1	32,765	9,000	sedan	10	2	0	0
397,446	16-25	male	majority	0-9y	none	poverty	0.406482	0	before 2015	0	0	10,238	16,000	sedan	0	0	0	1
101,678	26-39	female	majority	10-19y	high school	poverty	0.285747	0	before 2015	0	1	10,238	13,000	sedan	1	0	0	1
231,661	40-64	male	majority	20-29y	high school	middle class	0.502285	0	before 2015	0	0	10,238	14,000	sports car	3	0	4	0
173,782	65+	male	majority	20-29y	high school	upper class	0.625626	1	after 2015	1	1	10,238	7,000	sedan	7	0	5	0
921,097	40-64	female	majority	20-29y	university	upper class	null	1	after 2015	1	1	92,101	11,000	sedan	3	0	2	0
98,839	26-39	female	minority	10-19y	university	working class	0.393153	1	before 2015	0	1	32,765	12,000	sedan	1	0	0	0
105,372	26-39	female	minority	10-19y	none	working class	0.50395	0	before 2015	0	1	10,238	14,000	sedan	1	0	0	0
888,828	40-64	male	majority	20-29y	university	upper class	0.658627	1	before 2015	1	0	10,238	14,000	sedan	3	0	2	0
37,454	26-39	female	majority	0-9y	high school	working class	0.524146	0	before 2015	0	0	10,238	17,000	sedan	0	0	0	1
370,044	65+	male	majority	20-29y	university	upper class	0.553101	0	after 2015	1	1	32,765	7,000	sedan	9	0	1	0
768,282	16-25	female	majority	0-9y	university	working class	0.447551	0	before 2015	1	1	10,238	8,000	sedan	0	0	0	1
738,729	16-25	female	majority	0-9y	none	working class	0.510733	1	before 2015	1	1	10,238	7,000	sedan	0	0	0	1
437,535	26-39	female	majority	10-19y	high school	middle class	null	1	after 2015	1	0	10,238	15,000	sedan	0	0	0	0
329,536	16-25	female	majority	0-9y	high school	poverty	0.461374	0	before 2015	0	0	32,765	9,000	sedan	0	0	0	1
328,531	40-64	female	majority	20-29y	university	middle class	0.387557	0	after 2015	0	1	32,765	12,000	sedan	2	0	1	0
123,941	40-64	female	majority	20-29y	university	middle class	0.570157	1	after 2015	1	1	10,238	null	sedan	0	0	0	0
131,881	26-39	male	majority	10-19y	university	working class	0.550125	1	before 2015	0	0	10,238	16,000	sedan	0	1	0	0
839,572	40-64	male	majority	20-29y	high school	working class	0.452944	0	before 2015	1	1	10,238	12,000	sedan	3	0	1	0
171,578	26-39	male	majority	10-19y	university	upper class	null	1	before 2015	1	1	10,238	8,000	sedan	2	0	1	0
589,757	40-64	female	majority	20-29y	high school	upper class	0.535274	1	before 2015	1	1	92,101	7,000	sedan	2	0	0	0
336,859	16-25	female	majority	0-9y	university	middle class	0.614736	1	before 2015	0	1	10,238	15,000	sedan	0	0	0	1
788,111	40-64	female	minority	20-29y	high school	poverty	0.454447	1	before 2015	1	1	10,238	null	sedan	1	0	1	0
407,453	40-64	female	majority	0-9y	university	upper class	0.613741	1	before 2015	0	0	10,238	null	sedan	0	0	0	0
367,026	40-64	female	minority	10-19y	high school	working class	0.532661	1	before 2015	1	1	10,238	11,000	sedan	0	0	0	0
334,687	40-64	female	minority	0-9y	university	middle class	0.427729	1	before 2015	0	0	10,238	15,000	sedan	0	0	0	1
623,367	65+	male	majority	20-29y	university	upper class	0.685946	1	before 2015	1	1	32,765	5,000	sedan	4	1	4	0
688,440	65+	male	majority	10-19y	university	upper class	0.693612	1	before 2015	1	1	10,238	10,000	sedan	2	0	0	0
547,974	65+	male	majority	20-29y	high school	upper class	0.597991	0	before 2015	1	1	10,238	12,000	sedan	2	1	7	1
989,064	26-39	male	majority	0-9y	none	poverty	0.330819	0	after 2015	1	1	10,238	12,000	sedan	0	0	0	1
422,816	40-64	male	majority	20-29y	high school	working class	null	1	before 2015	0	0	10,238	12,000	sedan	5	0	7	0
307,408	26-39	female	minority	10-19y	none	working class	null	1	before 2015	0	1	10,238	9,000	sedan	2	0	0	0
675,898	16-25	male	majority	0-9y	high school	middle class	0.508304	1	before 2015	0	1	10,238	16,000	sedan	0	0	0	0
480,452	26-39	female	majority	0-9y	university	upper class	0.764133	1	before 2015	1	1	10,238	12,000	sedan	0	0	0	0
93,199	40-64	female	majority	0-9y	university	middle class	0.477517	1	after 2015	1	1	10,238	14,000	sedan	0	0	0	0
125,802	40-64	female	majority	20-29y	university	upper class	0.681233	1	before 2015	1	1	10,238	10,000	sedan	0	0	1	0
616,663	40-64	male	majority	0-9y	university	upper class	0.467156	1	after 2015	0	0	10,238	16,000	sedan	0	0	0	0
423,497	16-25	female	majority	0-9y	high school	working class	0.394163	1	before 2015	0	0	10,238	16,000	sedan	0	0	0	1
492,837	40-64	female	majority	10-19y	university	upper class	0.661061	1	after 2015	1	1	10,238	14,000	sedan	0	0	0	0
422,563	40-64	male	majority	20-29y	university	upper class	0.665188	0	before 2015	0	1	10,238	14,000	sedan	2	0	1	0
185,231	16-25	female	majority	0-9y	high school	middle class	0.56077	1	before 2015	0	1	10,238	11,000	sedan	0	0	0	0
949,152	26-39	female	majority	10-19y	university	upper class	0.745433	0	after 2015	0	0	32,765	9,000	sedan	0	0	0	0
859,453	26-39	male	majority	10-19y	university	middle class	0.532574	1	before 2015	1	1	10,238	13,000	sedan	1	0	2	0
466,578	26-39	female	majority	10-19y	high school	middle class	0.526747	1	after 2015	0	1	10,238	14,000	sedan	0	0	0	0
998,214	16-25	female	minority	0-9y	none	poverty	0.412452	0	before 2015	0	0	10,238	14,000	sedan	0	0	0	1
828,462	40-64	male	majority	20-29y	high school	working class	0.387107	0	before 2015	0	1	10,238	16,000	sedan	0	0	6	0
511,837	65+	male	majority	10-19y	high school	middle class	0.549994	1	before 2015	1	1	32,765	9,000	sedan	3	1	1	0
554,995	16-25	male	majority	0-9y	university	upper class	0.585342	1	before 2015	1	1	10,238	12,000	sports car	0	0	0	1
680,717	65+	male	majority	30y+	none	middle class	0.59874	1	before 2015	1	1	10,238	14,000	sedan	6	1	3	0
978,021	40-64	male	majority	20-29y	university	upper class	0.602656	1	after 2015	1	1	10,238	7,000	sedan	4	0	6	0
509,815	26-39	female	majority	0-9y	high school	working class	0.511457	1	after 2015	1	1	32,765	null	sedan	0	0	0	0
6,676	40-64	female	majority	20-29y	high school	upper class	0.558659	0	before 2015	0	1	10,238	15,000	sedan	3	0	2	0
362,280	40-64	male	majority	10-19y	high school	working class	0.628407	0	before 2015	0	0	10,238	13,000	sedan	0	0	0	1
883,018	40-64	male	majority	0-9y	high school	working class	0.50579	1	before 2015	0	1	10,238	13,000	sedan	0	0	0	0
889,355	26-39	female	majority	10-19y	university	upper class	0.726818	1	before 2015	1	1	10,238	15,000	sedan	2	0	0	0
198,273	65+	male	majority	20-29y	university	upper class	0.690442	1	after 2015	1	1	32,765	9,000	sedan	8	0	2	0
197,763	40-64	male	majority	20-29y	university	upper class	0.479677	0	after 2015	1	0	10,238	14,000	sedan	1	0	2	0
55,670	26-39	male	majority	10-19y	university	upper class	null	1	after 2015	1	1	10,238	11,000	sedan	3	1	0	0

End of preview. Expand in Data Studio

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Check out the documentation for more information.

Car Insurance Claim Analysis

Project Presentation

▶️ Watch the video presentation

Overview

This project presents an Exploratory Data Analysis (EDA) of the Car Insurance Claim dataset.
The goal of the analysis is to explore which factors may influence whether an individual files an insurance claim.

The target variable in this dataset is OUTCOME, where:

0 = No claim
1 = Claim

Dataset Information

Dataset name: Car Insurance Claim Dataset
Source: Kaggle
Dataset link: https://www.kaggle.com/datasets/sagnik1511/car-insurance-data

This dataset contains demographic, financial, and driving-related information about individuals, including variables such as:

Age group
Credit score
Driving experience
Annual mileage
Speeding violations
DUIs
Past accidents
Vehicle year
Vehicle type
Children
Marital status

The dataset includes mostly numeric features, which makes it suitable for statistical analysis and visualization.

Research Goal

The main question explored in this project is:

What factors influence whether a person files an insurance claim?

Data Cleaning

The dataset was examined for data quality issues before analysis.

Missing Values Missing values were checked across all columns. Most columns did not contain missing values, while a few columns had some missing entries.
Duplicate Rows Duplicate entries were checked, and no duplicate rows were found.
Inconsistencies Categorical columns were reviewed for inconsistencies such as typos or irregular values. The categories appeared consistent and well-structured.
Date Parsing The dataset was reviewed for date or time-related features. No date columns were found, so no date parsing was required.
Scaling Issues Numeric features were reviewed for scaling differences. Some variables, such as annual mileage and credit score, are measured on different scales. This does not affect EDA directly, but it may be relevant in future modeling.

Outlier Detection & Handling

Outliers were examined using distribution plots for the following variables:

ANNUAL_MILEAGE
SPEEDING_VIOLATIONS
DUIS
PAST_ACCIDENTS

The distributions showed that most values are concentrated near the lower range, especially for DUIs and past accidents. However, some extreme values appeared in the higher ranges, indicating possible outliers.

Decision

The extreme values were not removed, because they may represent real-world risky driving behavior rather than errors. Keeping them makes the dataset more realistic and informative.

Descriptive Statistics

Descriptive statistics were used to summarize the numeric variables in the dataset.

ID and POSTAL_CODE were removed before calculating descriptive statistics because they are not meaningful analytical features.
Summary statistics included mean, standard deviation, minimum, maximum, and quartiles.
A correlation heatmap was used to examine relationships between numeric variables.

Main Insight

Most variables showed weak to moderate correlations, suggesting that no single feature strongly dominates the prediction. Instead, multiple factors may contribute to insurance claim behavior.

Visualizations, Questions, and Insights

1. Distribution of Insurance Claims

What was done:
A count plot was used to visualize the distribution of the target variable (OUTCOME).

Question:
Is the dataset balanced in terms of insurance claims?

Answer / Insight:
The visualization shows that there are significantly more individuals who did not file a claim (OUTCOME = 0) than individuals who did (OUTCOME = 1).
This indicates that the dataset is imbalanced.

2. Age Group Among Claimants

What was done:
A pie chart was used to visualize the distribution of age groups among individuals who filed an insurance claim.

Question:
Does age affect the likelihood of filing an insurance claim?

Answer / Insight:
The analysis suggests that younger individuals are more likely to file insurance claims compared to older age groups.

3. Vehicle Year vs Insurance Claim

What was done:
A count plot was used to compare vehicle year and insurance claim outcomes.

Question:
Does vehicle age affect the likelihood of filing an insurance claim?

Answer / Insight:
Individuals with older vehicles (before 2015) are more likely to file insurance claims compared to those with newer vehicles.
This suggests that vehicle age may influence claim behavior.

4. Credit Score vs Insurance Claim

What was done:
A histogram was used to compare credit score distributions across claim outcomes.

Question:
Does credit score affect the likelihood of filing an insurance claim?

Answer / Insight:
Individuals who filed insurance claims tend to have lower credit scores, while those who did not file claims generally have higher credit scores.
This suggests that credit score is an important factor in predicting insurance claims.

5. Children and Driving Experience

What was done:
A heatmap was used to examine claim rates based on the combination of having children and driving experience.

Question:
How do having children and driving experience together affect the likelihood of filing an insurance claim?

Answer / Insight:
The heatmap shows that individuals with less driving experience tend to have higher claim rates, regardless of whether they have children.
Among more experienced drivers, those with children tend to have slightly lower claim rates.
This suggests that driving experience is a stronger factor, while having children may be associated with more cautious driving among experienced individuals.

6. Credit Score and Driving Experience

What was done:
A heatmap was used to examine how credit score and driving experience together influence insurance claim rates. Credit score was grouped into categories to better visualize patterns across different levels.

Question:
How do credit score and driving experience together affect the likelihood of filing an insurance claim?

Answer / Insight:
The heatmap shows that individuals with both low credit scores and low driving experience have the highest claim rates. In contrast, individuals with high credit scores and more driving experience tend to have significantly lower claim rates. This suggests that the combination of financial responsibility and driving experience is a strong predictor of insurance claims.

Key Decisions Made

During the analysis, the following decisions were made:

ID and POSTAL_CODE were excluded from descriptive statistics and correlation analysis because they are not meaningful predictive features.
Outliers were kept because they likely represent realistic extreme cases rather than data errors.
No date parsing was performed because the dataset does not include date-related features.
Categorical values were checked and found to be consistent.
Missing values were identified in a small number of entries; however, they were not handled as their proportion was minimal and not expected to significantly affect the analysis results.
The age variable was converted from categorical ranges into ordinal numeric values (0–3) to preserve the natural order of age groups and simplify analysis and visualization.

Main Findings

The analysis demonstrates that insurance claim behavior is influenced by a combination of demographic, financial, and behavioral factors, rather than a single variable.

Key findings include:

Age: Younger individuals are significantly more likely to file insurance claims. This may be due to lower driving experience and higher risk-taking behavior compared to older drivers.
Credit Score: Individuals with lower credit scores tend to file more claims. This suggests a potential link between financial responsibility and driving behavior, where lower credit scores may be associated with higher risk profiles.
Vehicle Year: Drivers with older vehicles are more likely to file claims. This may be explained by increased mechanical issues, lower safety standards, or higher likelihood of damage in older cars.
Driving Experience: Less experienced drivers show higher claim rates, indicating that experience plays a critical role in reducing risk and improving driving behavior over time.
Children and Driving Experience: When combining family status with driving experience, it was observed that drivers with less experience tend to have higher claim rates regardless of having children. However, among more experienced drivers, those with children tend to have slightly lower claim rates, suggesting more cautious driving behavior.
Credit Score and Driving Experience: The combination of low credit score and low driving experience is associated with the highest claim rates, while high credit score and extensive driving experience are associated with lower risk. This highlights the importance of combining multiple factors when analyzing insurance behavior.

Overall, these findings indicate that insurance risk is shaped by multiple interacting factors, highlighting the importance of analyzing variables both individually and in combination.

Conclusion

This analysis reveals clear and meaningful patterns in insurance claim behavior. The results show that younger individuals, drivers with lower credit scores, and owners of older vehicles are significantly more likely to file insurance claims.

Additionally, driving experience plays a critical role, with less experienced drivers showing higher claim rates. In contrast, factors such as speeding violations and annual mileage were found to have a weaker and less consistent impact on claim behavior. In particular, the combination of credit score and driving experience provides a strong distinction between high-risk and low-risk individuals.

These findings highlight that insurance claims are influenced by a combination of demographic, financial, and behavioral factors, rather than a single dominant variable.

Overall, the dataset provides valuable insights into risk patterns and demonstrates strong potential for predictive modeling and decision-making in insurance analytics.

Files Included

This Hugging Face dataset repository includes:

The original dataset file
The Jupyter Notebook (.ipynb)
The README file
The presentation video

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