Now that our data is ready, we can start building a classification model.
There are many classification techniques or classifiers available which can be used to predict a qualitative
or categorical type of responsible evil.
Here's a list of all the classification techniques we are going to learn in the coming set of lectures.
These three techniques are not very computationally heavy on the system, and they give fairly good
accuracy in predictions.
What we are going to do now is we will pick up one technique.
Understand the intuition behind it.
A few short lectures.
And then we will learn how to implement it.
And this software package.
Lastly, we will also discuss the output and the quality of the model that we have created in this process.
I will alter they knew when to prefer and when north to preffered.
Each of these techniques.
For practice purposes, you have one data set on which you must be working side by side after every
two related.
By now.
That would also be preprocessed and ready for further analysis.
I will share with you one more data set at the end.
Once we have discussed all these techniques, the point of that data set is that you should test your
intuition of which classification method is best suited for which problem.
So just take that data set and figure out the best predictive model for it.
So before we close this lecture and start with these techniques, let us put down the two title business
questions that you may want to answer from.
Building this model.
The first US prediction question.
Prediction question is will the House be sold within three months of getting listed?
So if you remember, we had the data of houses, the asking price and some of the attributes and the
response variable for which we want to predict the value is whether it is whether it will be sold in
three months or not.
So that is a prediction question.
And the prediction question, we do not worry about the effect of individual variables on the output.
Our aim is to predict as accurately as possible the value of the response variable.
The second type of question is inferential question.
Here we are trying to infer the importance and impact of each independent variables.
So these all variables.
What is the impact of each of these variables on the response variables?
So the inferential question here is how accurately I can estimate the effect of each of these variables
on the response of even so, we will try to find the answers to these two questions using all our different
classifiers.