I hope you did well in the last project, which was, again, a classification project, now in this
particular project we would again be applying classification but on an industry level project.
So this project is regarding flooding of junk properties.
So flooding the property as possibly junk beforehand can help businesses prioritize their efforts and
focus them on more probable success rather than bogged down with the weight of unsaleable portfolios.
So these property sellers, they are trying to fly certain properties which don't really get sold easily
so that they can target on something which is more beneficial and could give success faster.
So in this project, we will be making use of the data from the past operations where many properties
were found to be junk after their porches for renovations.
So this particular dataset contains around sixty two thousand drawers of data and it has around 31 various
attributes regarding the location of the property and different details.
So your task here is to build a predictive model for predicting whether a property should be marked
as junk on the basis of the listing details.
And the different preliminary assessment is now here.
You how you will be evaluating your model is on the basis of the U.S. score, which has to be more than
zero point six five.
What do you do for this particular project?
So let's have a look at the data set.
So here you can see this particular poll over just junk.
This is the target column.
So you need to classify these properties so that you get a value, either zero or one here.
And here are different columns with different types of values, such as impedance style, price details,
listing the material, the channel, the zip code, the insurance, the blood type architecture and
different different information represented.
So what you will have to do is, again, you can see there are a lot of categorical variables.
So your main focus here would be looking for these variables into dummy variables and then to find out
the variables which are actually important.
So I hope you will be working on this particular project and would be so able to solve this very easily
as this is not a very complex problem, but again, has a lot of tasks which you would have to perform
so that you can achieve a good result.
Thank you.