Project 2 - EDA on Vehicle Insurance Customer Data

EDA on Vehicle Insurance Customer Data

Aim:A company has customer data that contains 8 columns of customer details and another table having name customer_policy data contains the policy details of the customer. The company intends to offer some discount in premium for certain customers. To do that they ask their Data scientist team to get some information. Hence, following tasks DS team decided to perform:

1. Add the column names to both dataset
2. Checking and Cleaning Data Quality
3. Create a Master table for future use.
4. Company needs some important information from the master table to make decisions for future growth.
5. To find any relation between Person Age and annual premium

all are done in .ipynb format

Here are the simplified conclusions from each point:

1. Gender-wise Average Annual Premium:
- If males or females have higher average premiums, it could indicate different risk levels based on gender. If the averages are close, gender likely doesn’t affect the premium much.

2. Age-wise Average Annual Premium:
- Younger customers may have higher premiums because they are considered riskier drivers. Middle- aged customers usually have lower premiums.

3. Gender Balance:
- If the number of male and female customers is roughly equal, the data is balanced. If one gender dominates, the analysis might be skewed.

4. Vehicle Age-wise Average Annual Premium:
- Newer vehicles tend to have higher premiums due to their higher value, while older vehicles usually have lower premiums.

5. Correlation Between Age and Annual Premium:
- A strong positive correlation means older customers pay higher premiums.
- A strong negative correlation means younger customers pay higher premiums.
- A weak correlation means age doesn’t affect premiums much.

Additional Conclusions:

- Null Values: After removing missing values, the data is cleaner and more reliable for analysis.
- Outliers: After correcting outliers, the data is more consistent, and extreme values don’t distort the results.
- Dummies for Nominal Data: The data is now ready for machine learning models and future analysis.

This provides a clearer and more straightforward summary of the data analysis outcomes.