Travel insurance — Decision tree classifier

using the decision tree classifier model we will make a model that will predict whether a person will buy travel insurance or not (with source code)

As in the previous article, I have given you an introduction to the decision tree model now in this article, I will tell you how to make a decision tree classifier model with some lines of codes.

if you want to know about the decision tree model click on this link-

what is Decision tree ?- an introduction to machine learning algorithms

so let’s start

the first step is we need to download the dataset and then apply the dataset to the model. you can download or copy data from the URL — https://raw.githubusercontent.com/aviralb13/codes/main/datas/travel%20insurance(dt).csv

codes/travel insurance(dt).csv at main · aviralb13/codes

Importing the libraries

now we will import pandas as given below if your system has not installed these libraries you can download them by pip command.

Data preparation

now we will read the data by pandas and store it in a variable named data so we don’t need to call it again and again, by the head command we can see the first 5 elements of the data if you wish to see more you can enter the number inside the bracket.

now as some data columns are strings(words) so first we have to convert them into integers and we will do it by encoding techniques by using a label encoder

so we have to encode 4 columns (Employment Type, Graduate Or Not, Frequent Flyer, and Ever Travelled Abroad)

as you can see in our new dataset the values are changed to

Employment Type : Government Sector = 0 and Private Sector/Self Employed = 1

Graduate or not : Yes = 1 and no = 1

Frequent Flyer : Yes = 1 and no = 1

Ever travelled abroad : Yes = 1 and no = 1

and now I have created a list in which I think it will be the deciding factor(Age, Employment type, Graduate or not, Annual income, Family members, Chronic diseases, Frequent flyer, Ever travelled abroad) for a person to buy insurance or not and assign it to variable features now I will pass features in the dataset and store it as x and the Travel insurance whether a person has bought or not as y

Making the model

Splitting the model

first, we have to import the test train split from sklearn model selection then we will split our model dataset into a train and test dataset

Model

here we will be making a decision tree classifier because we have to classify our values into 0 or 1 for making that we have to import the decision tree classifier from the sklearn tree and then fit our train data into the model to train the model

and finally, our model is ready now we are all set to predict from our model

Predicting

nowhere I have made a variable named prediction which will predict the values of our test data

we also can predict entered values simply by adding the values to the list manually

here I have entered
Age = 31
Employment type = 0 (Government Sector)
Graduate or not = 1 (yes)
Annual income = 400000
Family members = 6
Chronic diseases = 1 (yes)
Frequent flyer = 0 (no)
Ever travelled abroad = 0 (no)

and the answer is that the person has not bought the insurance

Accuracy

now we will see how to get our model accuracy here our model is 81% accurate which means it has guessed 81 values correct out of 100 which is a good accuracy

source code

you can go check the link for full code

codes/travel(dt).ipynb at main · aviralb13/codes

Conclusion

in the article I have given you information and codes on how to make a decision tree model with source code I would be making more exciting models for you so stay connected