Vehicle insurance — Random forest classifier
using the random forest classifier model we will make a model that will predict whether a person will buy insurance for his or her vehicle or not (with source code)
As in the previous article, I have given you an introduction to the random forest model now in this article, I will tell you how to make a random forest classifier model with some lines of codes.
if you want to know about the random forest model click on this link-
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/vehicle%20insursnce.csv
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 3 columns (gender, vehicle damage, and vehicle age)
as you can see in our new dataset the values are changed to
gender : 1 = male and 0 = female
vehicle age : > 2 = 2 Years , 0 =1–2 Year and 1= < 1 Year
vehicle damage : 1 = yes and 0 = no
and now I have created a list in which I think it will be the deciding factor 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 response 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 random forest classifier because we have to classify our values into 0 or 1 for making that we have to import random forest classifier from sklearn ensemble 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
gender = 1 (male)
age = 44
vehicle age = 2 (>2 Years)
vehicle damage = 1 (yes)
annual premium = 40454.0
and the answer is that the person has bought the insurance
Accuracy
now we will see how to get our model accuracy here our model is 84% accurate which means it has guessed 84 values correct out of 100 which is a good accuracy
source code
you can go check the link for full code
Conclusion
in the article I have given you information and codes on how to make a random forest model with source code I would be making more exciting models for you so stay connected