https://github.com/easonlai/eda_for_prudential_life_insurance_sample_data

Notebook sample of Exploratory Data Analysis (EDA) for Prudential Life Insurance Sample Data
https://github.com/easonlai/eda_for_prudential_life_insurance_sample_data

azure-databricks azuredatabricks data-analysis data-analysis-python data-analytics databricks databricks-notebooks eda exploratory-data-analysis insurance insurance-sample-data jupyter-notebook python python3

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Notebook sample of Exploratory Data Analysis (EDA) for Prudential Life Insurance Sample Data

• Host: GitHub
• URL: https://github.com/easonlai/eda_for_prudential_life_insurance_sample_data
• Owner: easonlai
• Created: 2021-07-30T05:55:26.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-07-31T15:44:12.000Z (over 3 years ago)
• Last Synced: 2024-11-10T23:39:03.095Z (2 months ago)
• Topics: azure-databricks, azuredatabricks, data-analysis, data-analysis-python, data-analytics, databricks, databricks-notebooks, eda, exploratory-data-analysis, insurance, insurance-sample-data, jupyter-notebook, python, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 4.18 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Exploratory Data Analysis (EDA) for Prudential Life Insurance Sample Data

I find great Insurance sample data from [Kaggle](https://www.kaggle.com/) which is about ["Prudential Life Insurance Assessment - Can you make buying life insurance easier?"](https://www.kaggle.com/c/prudential-life-insurance-assessment). This sample data is great for practice data analysis. As usual, I use [Jupyter Notebook](https://jupyter.org/) & [Azure Databricks](https://docs.microsoft.com/en-us/azure/databricks/scenarios/what-is-azure-databricks) notebook to perform analysis.

Data Fields Description

* Id, A unique identifier associated with an application.

* Product_Info_1-7, A set of normalized variables relating to the product applied for

* Ins_Age, Normalized age of applicant

* Ht, Normalized height of applicant

* Wt, Normalized weight of applicant

* BMI, Normalized BMI of applicant

* Employment_Info_1-6, A set of normalized variables relating to the employment history of the applicant.

* InsuredInfo_1-6, A set of normalized variables providing information about the applicant.

* Insurance_History_1-9, A set of normalized variables relating to the insurance history of the applicant.

* Family_Hist_1-5, A set of normalized variables relating to the family history of the applicant.

* Medical_History_1-41, A set of normalized variables relating to the medical history of the applicant.

* Medical_Keyword_1-48, A set of dummy variables relating to the presence of/absence of a medical keyword being associated with the application.

* Response, This is the target variable, an ordinal variable relating to the final decision associated with an application

File Content Description

* data/prudential_life_insurance_sample_data.csv <-- Sample data from Kaggle

* eda_for_prudential_life_insurance_sample_data.ipynb <-- Notebook sample of EDA

* eda_for_prudential_life_insurance_sample_data_databricks.ipynb <-- Notebook sample for Databricks

* eda_for_prudential_life_insurance_sample_data_databricks.html <-- Notebook HTML export from Databricks