Project 2 - EDA on Vehicle Insurance Customer Data

EDA on Vehicle Insurance Customer Data

Aim:

The aim of this project is to perform exploratory data analysis (EDA) and data cleaning on two datasets containing customer details and policy details. The objective is to prepare the data for future analysis and modeling, identify patterns, and derive insights to aid business decisions.

Introduction:

The datasets provided contain information about customers and their insurance policies. The data analysis aims to uncover relationships between different variables, identify any data quality issues, clean the data, and derive actionable insights for the company.

Procedure:

Loading the Datasets:

• Two CSV files, customer_details.csv and customer_policy_details.csv, were provided.
• The datasets were loaded into Pandas DataFrames.

Data Quality Check:

• Checked for null values in both datasets and generated a summary of null values column-wise.
• Null values were handled by dropping rows with null customer IDs and replacing null values for numeric columns with the mean, and for categorical columns with the mode.

Outlier Detection and Treatment:

• Outliers were identified using the Interquartile Range (IQR) method.
• Outliers in numeric columns were replaced with the mean.

White Spaces and Case Correction:

• Removed leading and trailing white spaces from string columns.
• Converted string columns to lowercase for consistency.

Categorical Data Encoding: