Python: House Price Prediction with Linear Regression

By the end of this course, learners will be able to prepare housing datasets, apply preprocessing and transformation techniques, engineer meaningful features, perform exploratory data analysis, and build predictive models using linear regression in Python. You will also learn to evaluate multicollinearity with Variance Inflation Factor (VIF) and validate prediction accuracy with best practices in model evaluation. This course is designed to take you step by step through the entire workflow of predictive modeling, starting with project setup and dataset understanding, followed by advanced techniques in data cleaning, correlation analysis, and regression modeling. Through hands-on practice with the Ames Housing dataset, you will gain practical skills in transforming raw data into actionable insights. What makes this course unique is its end-to-end, project-based structure that mirrors real-world machine learning workflows. Instead of abstract theory, you will learn by applying concepts directly to a practical case study—predicting house prices with real housing data. Whether you are a beginner in data science or looking to strengthen your machine learning portfolio, this course will equip you with the skills to confidently implement regression-based predictive analytics

• Prepare and preprocess housing datasets, apply transformations, and engineer features
• Build and evaluate regression models with correlation, VIF, and accuracy metrics
• Apply an end-to-end workflow on the Ames Housing dataset for predictive analytics

• No deep prior experience is required, but basic computer and internet skills are helpful
• Ability to read course instructions in English and complete short practice activities