VIVENDO FOOD CLAIM ANALYTIC REPORT

Based on the information provided from our dataset, we can draw several insights about the number of individuals on claim in these four states in the country:

1. The state with the highest number of individuals on claim is RECIPE with 7,101, followed by FORTALEZA with 2,508, NATAL with 2,329, and SAOLUIS with 4,161.
2. The number of individuals on claim in a state could be an indication of the level of economic hardship or poverty in that area. Higher numbers of individuals on claim could suggest that there is a higher level of unemployment, or that the cost of living is higher than the income earned by the population.
3. The difference in the number of individuals on claim across these states could suggest that there are variations in the economic development and opportunities in each state, with some states having more job opportunities than others.
4. It would be helpful to understand the specific types of claims being made by these individuals, as this could provide insight into the underlying factors driving the need for support or assistance in these regions.
5. Further analysis and comparison with other regions in the country could provide a more comprehensive picture of the economic situation in the country and help identify potential areas for policy intervention.
6. The data indicates that time_to_close is the dependent variable, while claim_id is the independent variable. This means that time_to_close is influenced by claim_id.
7. The list of top ten claim_ids with the highest total time_to_close values suggests that these claims took longer to close compared to other claims. This could be due to various reasons, such as complexity of the claim, issues with documentation, or delays in processing.
8. There may be outliers in the dataset, which could be skewing the data. Further analysis would be needed to confirm this and determine how to handle the outliers.
9. It is unclear what units the time_to_close values are in (e.g. hours, days, weeks, etc.), which could impact the interpretation of the data.
10. The top ten claim_ids with the highest total time_to_close values could be prioritized for further investigation to identify ways to reduce the time it takes to close such claims. This could help improve the overall efficiency of the claims process.
11. It would be helpful to compare the time_to_close values across different types of claims to see if there are any patterns or trends. For example, claims related to certain products or services may take longer to close than others.
12. Further analysis of the data could help identify factors that contribute to longer closing times, such as the geographic location of the claim or the complexity of the claim. This could help develop strategies to reduce closing times and improve customer satisfaction.
13. The list of top ten claim_ids with the highest total time_to_close values suggests that these claims took longer to close compared to other claims.
14. The claim_id with the highest time_to_close is claim_id number 827, with a total time_to_close value of 518.
15. The claim_id with the lowest time_to_close is claim_id number 454, with a total time_to_close value of 370.
16. There may be outliers in the dataset, which could be skewing the data. Further analysis would be needed to confirm this and determine how to handle the outliers.
17. It is unclear what units the time_to_close values are in (e.g. hours, days, weeks, etc.), which could impact the interpretation of the data.
18. The top ten claim_ids with the highest total time_to_close values could be prioritized for further investigation to identify ways to reduce the time it takes to close such claims. This could help improve the overall efficiency of the claims process.
19. It would be helpful to compare the time_to_close values across different types of claims to see if there are any patterns or trends. For example, claims related to certain products or services may take longer to close than others.
20. Further analysis of the data could help identify factors that contribute to longer closing times, such as the geographic location of the claim or the complexity of the claim. This could help develop strategies to reduce closing times and improve customer satisfaction