Project Overview
This project aims to build a predictive system that serves as a valuable tool for estimating medical insurance expenses based on relevant factors. It assists in decision-making and planning for both individuals and insurance providers.
The predictive system utilizes several libraries, including numpy, pandas, matplotlib, seaborn, scikit-learn, and a Linear Regression model. These libraries enable the system to predict the cost of medical insurance effectively. By harnessing the power of these libraries, the system analyzes a comprehensive dataset and applies machine learning techniques to accurately estimate the insurance cost, taking into account factors such as age, BMI, number of children, and smoking habits.
Technical aspects of the predictive system:
- • Data manipulation and preprocessing tasks performed using numpy and pandas libraries.
- • Visualizations created using matplotlib.pyplot and seaborn libraries to understand variable relationships.
- • Data split into training and testing sets using train_test_split from scikit-learn library.
- • Linear Regression used as the predictive model.
- • Performance of the model assessed using metrics from scikit-learn library.
- • Evaluation of accuracy of predicted medical insurance costs.