Predict Hospital Costs Using Python | Multiple Linear Regression + PCA (Full ML Project)

This tutorial demonstrates how to build a hospital cost prediction model using Python and machine learning. Using the scikit-learn library, we apply multiple linear regression to analyze healthcare data and estimate hospital expenses. The dataset includes 200 patient records with numerical and categorical variables such as gender, marital status, body weight, body height, pulse rate, and respiratory rate. In this step-by-step tutorial you will learn: • Data preprocessing in Python • Handling categorical variables with One-Hot Encoding • Feature scaling using StandardScaler • Identifying and handling multicollinearity • Applying Principal Component Analysis (PCA) • Building multiple regression models • Comparing model performance using RMSE • Creating a full machine learning pipeline with Scikit-Learn We compare four different models and identify the best one for healthcare cost forecasting. This project is ideal for: • Data Science Students • Machine Learning Beginners • Business Analytics learners • Healthcare Analytics researchers Tools used: Python Pandas NumPy Scikit-Learn PCA Machine Learning Pipelines By the end of this tutorial, you will understand how to build a real-world predictive analytics model in healthcare. Code & Data file: https://github.com/hakeemrehman/Multiple-Linear-Regression-Case-Study- #MachineLearning #PythonTutorial #DataScience #HealthcareAnalytics #Regression #PCA #ScikitLearn #BusinessAnalytics What is hospital cost prediction in machine learning? How do you predict hospital costs using Python? What is multiple linear regression in healthcare analytics? Why is PCA used in machine learning models? How do you handle multicollinearity in regression? What is one-hot encoding in machine learning? How do you build a machine learning pipeline in scikit-learn? What you will learn in this video • Build a machine learning model in Python • Predict hospital costs using regression • Handle categorical and numerical data • Use PCA to solve multicollinearity • Compare multiple ML models If you're learning Data Science, Machine Learning, or Business Analytics, this tutorial will help you build real-world projects.