In the rapidly evolving world of healthcare, the importance of making informed decisions about medical insurance cannot be overstated. Medical insurance is a critical tool that protects individuals and families from exorbitant healthcare costs. However, understanding and predicting the cost of medical insurance premiums can be a complex task due to the myriad of factors that influence it.
This project was born out of the desire to leverage the power of machine learning to make this task easier and more transparent. By creating a model that accurately predicts the yearly medical cover cost, we aim to empower individuals to make better-informed decisions about their healthcare.
Moreover, this project also serves as a valuable exploration into how health-related parameters can influence insurance costs. Through this, we hope to shed light on the importance of maintaining good health and its potential financial benefits in terms of lower insurance premiums.
In essence, the motivation behind this project is to use technology and data to improve people's understanding of medical insurance costs, thereby promoting better healthcare decisions and financial planning.
This project aims to predict the yearly medical cover cost for customers of a medical insurance company. The data used for this project is voluntarily provided by the customers and contains various health-related parameters. The model built in this project can help in making crucial financial decisions and potentially impact many people.
The dataset used in this project can be found here. It contains health-related parameters of almost 1000 customers. The premium price is in INR (₹) currency and showcases prices for a whole year.
The project involves exploratory data analysis (EDA) and the creation of a prediction model using Python. The libraries used include pandas, numpy, matplotlib, seaborn, and scikit-learn. The project involves the following steps:
- Data loading and exploration
- Data visualization
- Feature engineering
- Model creation and training
- Model evaluation
The model's performance is evaluated using accuracy score, confusion matrix, and classification report. The feature importance is also visualized to understand the impact of various features on the prediction.
This project successfully demonstrates the application of machine learning in predicting medical insurance premiums. The model's performance indicates its potential in aiding individuals to make informed healthcare decisions. While the model is a powerful tool, it complements, not replaces, professional advice. Future work could expand this model, exploring more variables and sophisticated techniques, further enhancing its predictive power. Ultimately, this project highlights the transformative potential of data science in healthcare and insurance industries.