This project is part of the GlobalAIHub's Regression Bootcamp assignment. In this project, we will use regression analysis to predict medical insurance costs.

In our project, we will follow these steps:

Data Loading and Exploration: In the first step, we will load a dataset containing information about medical insurance costs and explore the data to understand important features.

Data Preprocessing: We will clean, scale, and encode the data, addressing missing values, categorical variables, and outliers.

Data Analysis and Visualization: We will perform statistical analysis and visualizations to better understand the data and visualize relationships.

Building the Regression Model: We will use regression algorithms to model the relationship between features and the target variable. In this project, we will particularly consider using the XGBoost Regression model.

Hyperparameter Optimization: We will optimize the model's hyperparameters to achieve the best model performance.

Results and Predictions: Using the best model, we will make insurance cost predictions for new observations.

By completing this project, you will demonstrate your ability to apply data analysis, preprocessing, and regression techniques to predict medical insurance costs. The goal of this project is to enhance your practical skills in using regression analysis to approach real-world problems.