Sklearn, Numpy, Pandas, Matplotlib, Seaborn.
From the visualizations,most of the policy holders have a bmi of 30. Most the smokers are of between the age 30 and 50 ,male taking a bigger portion of the cohort.On top of that,smoker are found to have a higher bmi than non smokers,which is considered to be unhealthy.
Using least squares method, the model projected was found to be 79.23% accurate. The features used for this analysis were age,sex, bmi(body mass index),children(number of children of the policy holders) and Smokers. Age, smoker and bmi proved to be significant after standardizing the dataset. Smoking proved to have more weight within the significant cohort followed by BMI then age. Children and sex proved to have so much weight but still insignificant.
For best premium projections, marketing team should disregard both number of children and sex/gender while conversing with any prospect.