This project aims to develop a machine learning model that predicts the health insurance charges for an individual based on their personal characteristics such as age, gender, BMI, smoking habits, and region of residence.
The dataset used for this project is the Medical Cost Personal Datasets from Kaggle. It consists of 1338 records with 7 features: age, sex, bmi, children, smoker, region, and charges.
The following steps were taken to develop the health insurance prediction model:
Data preprocessing and feature engineering Feature selection Model selection and hyperparameter tuning Model evaluation and interpretation The exploratory data analysis was performed to understand the distribution of the features, identify any outliers or missing values, and identify any correlations between features.
The data preprocessing step included handling missing values, converting categorical features into numerical, and scaling the features.
Feature engineering was performed to create new features such as BMI categories and age groups to better represent the data.
The feature selection step involved selecting the most relevant features using statistical tests and feature importance rankings.
Several machine learning models were trained on the data, including linear regression, decision tree regression, and random forest regression. The models were evaluated using various metrics such as mean squared error and R-squared.
The final model was chosen based on its performance on the test set and its interpretability.
The final model achieved an R-squared value of 0.85 on the test set, indicating a strong correlation between the features and the insurance charges.
The feature importance analysis revealed that smoking habits and BMI were the two most important features in predicting the insurance charges.
To use the health insurance prediction model, follow these steps:
Clone the repository. Install the required packages using pip install -r requirements.txt. Run the predict.py script and enter the required information when prompted. Conclusion In conclusion, this project developed a machine learning model that can accurately predict the health insurance charges for an individual based on their personal characteristics. This model can be used by insurance companies to better estimate the insurance charges and by individuals to plan for their healthcare expenses.