This particular project is relevant to the automobile industry, so here he will be walking on the save
driver classification.
So this is regarding the insurance companies who place a lot of importance as data science and predictive
algorithms have them keep the premium low.
So the data is always been at the core of what insurance companies do for analyzing and they claim find
out what claim is relevant, which claim is not relevant.
So here this particular data set contains around 30000 Rusal be done, 17 different features related
to the driver itself so that the insurance company can actually predict if the driver would be a safe
driver or not, and then make sure if they should be doing out the short interval.
So this is the particular data set on here.
You need to perform a classification.
So here we have this data good value, which is zero and one one being the driver to save on insurance
would be provided.
Abdelal being the driver is unsafe and relevant to this.
There are different golomb such as genda engine details, credit history.
Then we have years of experience and we claim marital status.
Wacol dipen different other values and these are different range type of columns.
So these old columns you need to convert into numeric type.
You need to find out a good way to convert these into numeric.
Then there are different other informations like credit history, buckett.
So these are different columns which you have for this particular data.
And your target here is to find the if the driver is safe or not.
So the main concern here would be to convert these different categorical variables into numeric variables
that doesn't do dungee variables and how you handle this each criteria.
So maybe, first of all, you will have to find out different age criteria and then see what are the
ranges present.
And then maybe you can create dummies for each range or something like that.
So this is something which you need to think about on how you handle this.
So thank you.