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Insurance Pricing Forecast Using XGBoost Regressor

The project, Insurance Pricing Forecast Using XGBoost Regressor focuses on leveraging machine learning to accurately predict healthcare costs for insurance companies. This helps insurance companies forecast future expenses. The goal is to set accurate premiums. Insurance companies need accurate methods to forecast future expenses. This helps them set premiums profitably. Traditional methods often struggle with complex data interactions. Machine learning, especially XGBoost, offers a valuable solution. This project develops a machine learning model. It helps insurers establish rates based on features like age, BMI, and smoking status. The goal is to ensure profitability while providing fair coverage.

Project Overview

In this project, we build a machine learning model using XGBoost Regressor. This XGBoost Regressor predicts healthcare expenses. It considers factors like age, BMI, smoking status, and region. These factors help estimate healthcare costs accurately. We also build a linear regression model as a baseline for comparison. By the end of this project, insurance companies will have a reliable tool. This tool helps set premiums based on predicted expenses. It reduces reliance on manual calculations and improves profitability.


Prerequisites

Before starting this project, understand Python, statistics, and machine learning. Familiarity with libraries like NumPy, Pandas, Matplotlib, and Scikit-learn. These libraries help with data manipulation, visualization, and model building. Also, be familiar with XGBoost Regressor, linear regression, and regression analysis. This knowledge will help you understand the modeling process.


Approach

We focus on building an XGBoost Regressor to predict healthcare costs. The model uses several features for forecasting. Additionally, we will compare this XGBoost Regressor with a linear regression model. This comparison helps evaluate the model's effectiveness. We select the XGBoost Regressor for its ability to handle non-linear relationships. Furthermore, the XGBoost Regressor excels with complex datasets. It provides high predictive power and efficiency. Although other machine learning techniques could be used, the XGBoost Regressor stands out.


Workflow and Methodology

The overall workflow of this project includes:

  • Problem Definition: Predict healthcare expenses using various features like age and smoking status.

  • Data Collection: Gather data from healthcare records, including patient demographics and medical expenses.

  • Data Preparation: Clean, transform, and encode the data for modeling.

  • Modeling: Build a baseline linear regression model first. Then, use an XGBoost regressor to achieve better accuracy.

  • Evaluation: Use evaluation metrics to assess model performance. Check Root Mean Squared Error (RMSE). Also, calculate Mean Absolute Percentage Error (MAPE).

  • Conclusion: Analyze results and finalize the best model for predicting healthcare expenses.

The methodology involves:

  • Exploratory Data Analysis (EDA): Understanding feature distributions, correlations, and trends in the data.

  • Data Preprocessing: Handle missing values in the dataset. Encode categorical variables appropriately. Transform the target variable as needed. Ensure data suitability for modeling.

  • Feature Engineering: Creating or refining features that improve model performance.

  • Hyperparameter Tuning: Using Bayesian Optimization to fine-tune the XGBoost Regressor for optimal results.

  • Model Comparison: Compare the linear regression model with the XGBoost Regressor. Determine which model performs better. Assess their accuracy in predicting healthcare costs.


Data Collection

Data Preparation

We use a dataset with healthcare records to train the XGBoost Regressor. Specifically, features include age, BMI, smoking status, region, and costs. This data represents real-world medical expenses from diverse health profiles. Our goal is to identify features that impact costs. Therefore, we use the XGBoost Regressor. We then use this information to predict future expenses. We do this accurately with the XGBoost Regressor.

Data Preparation Workflow

  • Data Cleaning: We start by checking for missing values and outliers in the dataset. This ensures that the data is clean and consistent for modeling.

  • Feature Encoding: We one-hot encode categorical variables like 'sex' and 'region.' This process converts them into numerical values.

  • Target Transformation: Healthcare costs often have a skewed distribution. We apply a Yeo-Johnson transformation. This makes the target variable more normally distributed. As a result, model performance improves.

  • Data Splitting: The dataset is split into training and test sets, typically using a ratio of 80:20. This allows us to train the model on one portion of the data and evaluate it on the remaining portion.


Code Explanation

STEP 1:

You can mount your Google Drive in a Google Colab notebook with this block of code. This lets users easily view files saved in Google Drive within Colab. You can modify and analyze your data or even train models using the files.

from google.colab import drive
drive.mount('/content/drive')

Import required packages

We import essential libraries such as numpy, pandas, and matplotlib. We also include seaborn, plotly, and xgboost. These libraries help with data manipulation, visualization, and building machine learning models.

!pip install numpy
!pip install pandas
!pip install plotly
!pip install scikit-learn
!pip install scikit-optimize
!pip install statsmodels
!pip install category_encoders
!pip install xgboost
!pip install nbformat
!pip install matplotlib

Import libraries

Code Editor