https://github.com/itsmeyogesh22/case-study-3-insurance-claims-case-study

This repository features a detailed case study on exploratory data analysis (EDA) and hypothesis testing for insurance claims data. Provided as part of Data Science course (DS360) by Analytix Labs.
https://github.com/itsmeyogesh22/case-study-3-insurance-claims-case-study

Last synced: 5 days ago
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This repository features a detailed case study on exploratory data analysis (EDA) and hypothesis testing for insurance claims data. Provided as part of Data Science course (DS360) by Analytix Labs.

• Host: GitHub
• URL: https://github.com/itsmeyogesh22/case-study-3-insurance-claims-case-study
• Owner: itsmeyogesh22
• Created: 2024-12-19T14:08:39.000Z (6 days ago)
• Default Branch: main
• Last Pushed: 2024-12-19T14:18:29.000Z (6 days ago)
• Last Synced: 2024-12-19T15:28:49.452Z (6 days ago)
• Language: Jupyter Notebook
• Size: 744 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        





 Overview

This repository features a detailed case study on exploratory data analysis (EDA) and hypothesis testing for insurance claims data. The project provides insights into customer demographics, claim patterns, fraudulent activities, and other key factors impacting the insurance industry. The dataset and Python scripts allow for an in-depth analysis and visualization of trends and patterns in insurance claims.







 Skills Demonstrated


  - **Data Preprocessing:**

    

      - Data type auditing and adjustments.

      - Handling missing values with appropriate imputation techniques.

      - Creating unique customer records and alert flags for unreported claims.

      - Age calculation and categorization into groups (Children, Youth, Adult, Senior).

  - **Analysis & Insights:**

      - Aggregation and visualization of claim amounts by gender, age category, and segments.

      - Identification of fraudulent policy claims by age group and other factors.

      - Monthly trend analysis of claimed amounts in chronological order.

   - **Hypothesis Testing:**

      - Statistical testing to explore relationships between variables like age, gender, claim amount, and insurance segments.

      - Comparing claim amounts between genders.

      - Assessing the rise in claim amounts over different fiscal periods.

  - **Visualizations:**

    

      - Pie charts showing claim amount percentages by gender and segments.

      - Bar charts and faceted bar charts for fraudulent and non-fraudulent claims by age and gender.

      - Chronological trend charts for monthly claim amounts.






 Analytical Questions Addressed

  

  - Average claim amounts by customer segments.

  - Total claim amount analysis based on incident causes.

  - Age and gender distribution for driver-related claims.

  - Relationships and trends in fraudulent claims.