Insurance Cost Prediction with Linear Regression

Implemetation of cost prediction for medical insurance using linear regression with python.

Dataset

you can see the data set here:

age	sex	bmi	children	smoker	region	charges
19	female	27.9	0	yes	southwest	16884.924
18	male	33.77	1	no	southeast	1725.5523
28	male	33	3	no	southeast	4449.462
33	male	22.705	0	no	northwest	21984.47061
32	male	28.88	0	no	northwest	3866.8552
31	female	25.74	0	no	southeast	3756.6216
46	female	33.44	1	no	southeast	8240.5896
37	female	27.74	3	no	northwest	7281.5056
...	...	...	...	...	...	...

overview of what our dataset is.

Data visualization

you can analyze data using the following code:

from predictor import InsuranceCostPredictor

icp = InsuranceCostPredictor('dataset/insurance.csv')
icp.analyze(hue='smoker', based_on=['age', 'bmi', 'smoker', 'charges'])

see the example file to undrestand exactly how to use it.

Libraries

to implement this project this libraries have been used:

• pandas
• matplotlib
• sklearn
• seaborn

Installation

1. clone project using this code in you shell:

   git clone https://github.com/Amir-Shamsi/insurance-cost-prediction-LR/.git

2. install requirements

   pip install -r requirements.txt

3. Done 👾