So we have learned all these steps and all the models in our cause, I am summarizing all the steps
that we took and what you should do when you face a business problem in which you have to classify the
results.
The first step is to do data collection.
You have to identify all the relevant variables and collect data for D.C..
Once you've collected all the relevant data.
You have to pre-process it.
You learned how to do data preprocessing.
Few of the major steps that we took would outline a treatment in which we found out the outlying values
and tested values so that they do not harmfully impact our analysis.
Then we have a missing value in imputation where we replaced black values with harmless values such
as mean or medians.
We also did variable transformation.
We combined four different distance variables into one variable and so on.
So data preprocessing is a very important part.
You have to clean your data.
You have to put it into a tabular format that all the values of the variables in proper format so that
your model can work on it.
Next year's model training.
If you have only one data set.
You have to split it into test and train data set.
You will use the training data set to train the model and we will use the test it to test its performance.
We have created the template for logistic regression, linear discriminant, analysis and Ganon.
Same those template.
Whenever you face any business problem, just replace the dataset and you are a good tool.
You can train your model with those same templates.
That point I agree that it is do I iterations.
The point is, when we trained our model before that we have taken some decisions on our data.
For example, we decided that we will impute the missing values using mean.
What will be the impact of using median?
Will that perform better?
We decided in variable transformation that we will replace these four distances by average distance.
Well, maybe it would make more business sense if we do basic by the largest distance or the smallest
distance.
So we should do a iterations of all these changes wherever we make our decision.
Lastly, when we are training the model, we should also compare the performance of different methods.
For example, here we learned three methods.
So we should compare the performance of all these three methods using dissect.
He's only last we know how to do that.
We use the confusion metrics for classification problems.
Draw the classification metrics of data set for all the different models that you have created and select
the best one.
So that's the last point.
We have to select the best model.
As I told you, there are two types of business problems.
One is prediction problem.
Their aim is to have maximum prediction accuracy in such a case.
We should use the model with best accuracy.
And the second type of problem is interpretation problem.
That is, we want to identify the relationship between a particular prediction very well.
And the response we're able.
For that, we can use decode fishing values or deep parametric models.
Once we have selected the best model, for example, say linear discriminant analysis is giving us the
best prediction results.
And we have selected that model.
Now, whenever we get new data or new observations, we can feed those observations as a test set to
our model and find out the predicted classes for those of deletions.
So this is the whole process for a given data.
We train the model to start predicting.
We identified a model which is giving us the best predictions.
And once we have that model, which is giving us the best predictions, we use it to predict for future
observations.
Thank you for being with us.
And this goes.