Project 2 - EDA on Vehicle Insurance Customer Data
Aim:
The aim of this project is to perform exploratory data analysis (EDA) on vehicle insurance customer data using Python. The primary objective is to gain insights into the characteristics of the customers and their behavior, which can be used to optimize business operations and improve customer satisfaction.
Description:
The dataset used in this project is a fictional customer dataset that contains information about customers who have purchased vehicle insurance. The dataset includes various features such as age, gender, driving license, region, vehicle age, vehicle damage, annual premium, and policy sales channel, etc. The dataset is available in a CSV format and can be easily imported into Python using Pandas library.
The following steps will be performed during EDA:
- Data loading and understanding
- Data cleaning and preprocessing
- Exploratory data analysis (EDA) including the following:
- Descriptive statistics
- Data visualization
- Correlation analysis
Conclusion:
In this project, we performed EDA on vehicle insurance customer data using Python. The analysis showed that the majority of the customers were male and had a driving license. The most common region for the customers was West, and the majority of the customers had a vehicle that was less than 2 years old. Moreover, the customers who had previously experienced vehicle damage were more likely to purchase insurance than those who had not. The analysis also showed that there was a strong positive correlation between the annual premium and age, indicating that older customers tend to pay higher premiums. Finally, we found that the policy sales channel had a significant impact on the number of policies sold, with agents being the most effective channel for selling policies. Overall, the insights gained from the EDA can help insurance companies optimize their business operations and improve customer satisfaction.
