insurance claim prediction machine learning. Insurance companies are extremely interested in the prediction of the future. Accurate prediction gives a chance to reduce financial loss for the company. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Furthermore, because of the payment errors, processing the claims again accounts for a significant portion of administrative costs.
This dataset contains 7 features as shown below:
age: age of the policyholder
sex: gender of policyholder (female=0, male=1)
BMI: Body mass index, providing an understanding of the body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 25
steps: average walking steps per day of the policyholder
children: number of children/dependents of the policyholder
smoker: smoking state of policyholder (non-smoke=0;smoker=1)
region: the residential area of the policyholder in the US (northeast=0, northwest=1, southeast=2, southwest=3)
charges: individual medical costs billed by health insurance.
Installation Steps :-
- Install Python 3.7.0
- Install all dependencies cmd -python -m pip install –-user -r requirements.txt
- Finally run cmd - python app.py