Chief Science Officer at DataPrime, Inc.
Senior Data Scientist, Mawdoo3 Ltd
Senior Data Engineer, National Bank of Belgium
Head of Data Science, Slated
In this project, we are going to talk about insurance forecast by using linear and xgboost regression techniques.
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Overview
Insurance companies cover expenses the policyholder incurs from damages to health or property policies commonly offered: medical bills, house, motor vehicle, and fire insurance, and financial losses such as a loss of income against a fee or premium paid by the client. Traditional approaches to premium calculation require a lot of time-consuming human labor and are getting more complicated daily to capture the increasingly complex interactions in the data.
Insurance firms should normally collect a higher premium than the amount given to the insured individual if that person files a valid claim to generate a profit. Since profitability is the fundamental factor that helps the insurance firm survive, they need a mechanism for reliably forecasting healthcare expenses.
Hence, our goal is to build a machine learning model that helps establish the rates by predicting the charges or payouts done by the health insurance firm to maintain profitability.
In this project, we will primarily focus on building an XGBoost Regressor to determine healthcare expenses based on features such as age, BMI, smoking, etc. We will also learn about categorical correlation, build a linear regression model as a baseline and compare it with the results of the XGBoost Regressor. We will eventually learn how to communicate technical results to stakeholders who are not technical.
Aim
This data science project aims to build and evaluate linear and xgboost regression models and determine the healthcare charges of each customer. This analysis will help the insurance firm to strategize a premium plan that will help maximize the profits.
Data Description
The insurance price forecast dataset contains historical records for 1338 insured customers. The column definitions are below
age: Age of the primary beneficiary.
sex: Gender of the primary beneficiary.
BMI: Body mass index of primary beneficiary
children: Number of children the primary beneficiary has.
smoker: Whether the primary beneficiary smokes.
region: The primary beneficiary's residential area in the US.
charges: Individual medical costs billed by health insurance.
Tech Stack
Language: Python
Libraries: pandas, numpy, matplotlib, plotly, statsmodels, sklearn, xgboost, skopt
Approach
Exploratory Data Analysis (EDA)
Distributions
Univariate Analysis
Bivariate Analysis
Correlation
Pearson Correlation
Chi-squared Tests
ANOVA
Build and evaluate a baseline linear model
Linear regression assumptions
Data preprocessing
Model training
Model evaluation
RMSE
Improve on the baseline linear model
Introduction to a non-linear model - XGBoost
Data preprocessing
Using Sklearn's `Pipeline` to optimize the model training process
Model evaluation
RMSE
Comparison to the baseline model
Presenting the results to non-technical stakeholders
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