This article was published as a part of the Data Science Blogathon.
Introduction
Any company in existence today thrives to make a profit. Insurance companies are profitable when the claims that they issue are lesser than the premiums they receive.
This is the real-world problem we are going to tackle today. If there is a method by which the insurance company can predict a person’s hospital bills, they would be able to generate a financial gain.
This article aims to provide a good starter for someone who wants to solve a real-world problem using classical machine learning.
We have data of about 1338 observations(rows) and 7 features(columns) including age, gender, BMI(Body Mass Index), number of children they have, the region they belong to, and if they are a smoker or a non-smoker. Our task is to uncover a relationship that might exist between a person’s hospital bills and their family conditions, health factors, or location of residence.
With this introduction, we start exploring the data. Remember that garbage in is garbage out. You could use the best possible machine learning model but if your data is garbage your results would not be good as well. This makes it imperative to have a thorough data analysis. Let’s begin.
Data Exploration in Machine Learning
From the count plot on the left, we know that the data has more non-smokers than smokers. Even then from the plot on the right, it is evident that the hospital bills of a smoker are higher than those of non-smokers. We can certainly see a pattern between the feature ‘smoker’ and our target ‘hospital bills’.
The last feature we need to look into is ‘gender’.
Data Preprocessing in Machine Learning
from sklearn.preprocessing import LabelEncoder label = LabelEncoder() df.iloc[:,1] = label.fit_transform(df.iloc[:,1]) df.iloc[:,5] = label.fit_transform(df.iloc[:,5]) df.iloc[:,4] = label.fit_transform(df.iloc[:,4])
Above is a code snippet displaying the use of LabelEncoder for encoding categorical features region, smoker, and sex. At the end of this article, I would also link my entire notebook for your reference.
Fitting a Model and Making Predictions
Machine learning involves a lot of experimentation, I tried using a combination of different features and I found that the gender and region features were not contributing much to the target(expense) variable. Considering the same we divide our data frame into X and Y as below.
X = df[['bmi','age','smoker','children']] Y = df['expenses']
X_train,X_test,y_train,y_test = sklearn.<a onclick="parent.postMessage({'referent':'.sklearn.model_selection'}, '*')">model_selection.train_test_split(X,Y,test_size=0.25)
Our problem is a regression problem as we are trying to predict expense which is a real-valued number. We would use DecisionTreeRegressor and LinearRegression models for the above task.
from sklearn.tree import DecisionTreeRegressor from sklearn.model_selection import cross_val_score regressor = DecisionTreeRegressor(random_state=0) #cross_val_score(regressor, X_train, y_train, cv=10) regressor.fit(X_train, y_train) y_predict = regressor.predict(X_test) mse_dt = mean_squared_error(y_test,y_predict,squared=False) print(mse_dt)
6900.60117589873
from sklearn.linear_model import LinearRegression regressor = LinearRegression() #cross_val_score(regressor, X_train, y_train, cv=10) regressor.fit(X_train, y_train) y_predict = regressor.predict(X_test) mse_dt = mean_squared_error(y_test,y_predict,squared=False) print(mse_dt)
5908.41334052373
!pip install gradio import gradio as gr def greet(<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..bmi'}, '*')">bmi,<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..age'}, '*')">age,<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..smoker'}, '*')">smoker,<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..children'}, '*')">children): if <a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..smoker'}, '*')">smoker: <a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..is_smoker'}, '*')">is_smoker = 1 else: <a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..is_smoker'}, '*')">is_smoker = 0 <a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..X_test'}, '*')">X_test = pd.<a onclick="parent.postMessage({'referent':'.pandas.DataFrame'}, '*')">DataFrame.from_dict({'bmi':[<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..bmi'}, '*')">bmi],'age':[<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..age'}, '*')">age],'smoker':[<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..is_smoker'}, '*')">is_smoker],'children':[<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..children'}, '*')">children]}) print(<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..X_test'}, '*')">X_test) <a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..y_predict'}, '*')">y_predict = regressor.predict(<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..X_test'}, '*')">X_test) print(<a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..y_predict'}, '*')">y_predict) return <a onclick="parent.postMessage({'referent':'.kaggle.usercode.19256245.71583406.greet..y_predict'}, '*')">y_predict[0] iface = gr.<a onclick="parent.postMessage({'referent':'.gradio.Interface'}, '*')">Interface( fn=greet, inputs=['text','text','checkbox','text'], outputs="number") iface.launch(share=True)
As promised, link to my code.
Conclusion
The key takeaways from the above article are as follow:
1) A simpler model like Linear Regression can perform better than Decision trees for some datasets.
2) Being a smoker or having more number of children increases the possibility of hospital expenses.
About the Author
Hello Everyone!
My name is Alifia G and I am a Data Science Enthusiast.
Please feel free to leave a remark below if you have any questions or concerns about the blog. Thank you.
You can know more about me here!
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