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Python: Master House Price Prediction with Linear Regression

EDUCBA via Coursera

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Overview

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Master the complete workflow of house price prediction with linear regression in Python through a practical, project-based learning experience. In this course, you will learn how to prepare housing datasets, apply data preprocessing and transformation techniques, engineer meaningful features, perform exploratory data analysis, and build predictive models using the Ames Housing dataset. You will also evaluate multicollinearity using Variance Inflation Factor (VIF) and assess prediction accuracy with established model evaluation practices. Designed for beginners in data science as well as learners looking to strengthen their machine learning skills, this course guides you step by step from project setup and dataset understanding to feature engineering, correlation analysis, regression modeling, and model evaluation. Each module builds on the previous one, helping you develop a structured approach to predictive analytics using real housing data. What sets this course apart is its end-to-end, hands-on approach that mirrors a real-world predictive modeling workflow. Rather than focusing on isolated concepts, you will apply each technique within a complete house price prediction project, gaining practical experience that can be applied to similar regression-based machine learning tasks. Enroll to build a strong foundation in data preparation, linear regression, and predictive modeling with Python.

Syllabus

Building the Foundation

This module introduces learners to the core principles of house price prediction using linear regression. Students will gain hands-on experience in project setup, data preprocessing, transformation, and target variable preparation while developing an understanding of the Ames Housing dataset. By the end of this module, learners will have a solid foundation in preparing data for predictive modeling.

Advanced Analysis & Prediction

This module equips learners with advanced techniques for feature engineering, handling missing values, and performing exploratory data analysis. Students will explore correlation, evaluate multicollinearity, and build predictive models to generate accurate house price predictions. The module concludes with best practices in model evaluation and project takeaways.