Project 2 - EDA on Vehicle Insurance Customer Data

Project 2 - EDA on Vehicle Insurance Customer Data

The "Project 2 - EDA on Vehicle Insurance Customer Data" is an exploratory data analysis project focused on analyzing and gaining insights from a dataset containing customer data in the vehicle insurance domain. The objective of this project is to explore and understand the characteristics and patterns present in the data, and to uncover valuable information that can support decision-making and future growth strategies for the insurance company.

The dataset used in this project consists of customer details and policy information. The customer details table includes columns such as customer ID, gender, age, driving license presence, region code, previously insured, vehicle age, and vehicle damage. The customer policy table contains columns such as customer ID, annual premium, sales channel code, vintage, and response.

The project begins with data quality checks and cleaning processes. The null values are examined and handled by either dropping or replacing them with appropriate values. Outliers in numeric columns are identified using the interquartile range (IQR) method and replaced with mean values. Additionally, whitespace removal and case correction are performed to ensure data consistency.

Following the data cleaning steps, a master table is created by merging the customer details table and customer policy table based on the common customer ID. This master table serves as a consolidated dataset for further analysis.

The main goals of the project include investigating key information such as gender-wise and age-wise average annual premium, evaluating the balance of data between genders, and examining the relationship between age and annual premium. These insights can provide valuable information for decision-making and strategy development within the insurance company.

Overall, the "Project 2 - EDA on Vehicle Insurance Customer Data" presents an opportunity to delve into the dataset, conduct comprehensive exploratory data analysis, and extract valuable insights that can contribute to informed decision-making and drive the success of the insurance company in the dynamic vehicle insurance market.