The main goals throughout the analysis includes the following:
- Price products based on policy holder behaviour
- Gain customer insight and assess their experience
- Gain customer insight and assess their experience
In this project, I performed the following tasks:
- Queried a public dataset
- Created a custom table
- Loaded data into a table
- Queried a table
- Presented findings in a dashboard
This is a data analytics project that:
- Discovered the underlying structure of the data.
- Looked for trends, patterns, and anomalies in the data.
- Tested hypotheses and validated assumptions about the data.
Team Included :
- Data Engineer
- Data Scientist
- Data Analyst
- Business Analyst/Ops Analyst
- Head of Data Analytics
This data set was taken from Here
The data has been anonymised to comply with privacy regulations.
The data has the following features:
- Age - The customer's age
- Sex - The customer's sex
- BMI - The customer's BMI
- Smoker - Whether the customer is or is not a smoker
- Region - The region that the customer lives in
- Charges - The customer's insurance charges
- Children - Whether the customer has children & How many
In this stage of the project, I cleaned the data and made specific changes like:
- Removed duplicate or irrelevant observations
- Fixed the structural errors (naming convention, typos etc.)
- Filtered unwanted outliers
- Handled missing data
- Validated the data and performed QA
I asked the following questions:
- Does the data make sense?
- Does the data follow the appropriate rules for its field?
- Does it prove or disprove your working theory or bring any insight to light?
- Can you find trends in the data to help you form your next theory?
- If not, is that because of a data quality issue?
In this stage, I performed Exploratory Data Analysis on the cleaned data and got some insights:
The data insights were presented to stakeholders using Tableau.
