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Python: Master House Price Prediction with Linear Regression

Instructor: EDUCBA

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2 modules

Gain insight into a topic and learn the fundamentals.

5 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Details to know

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There are 2 modules in this course

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.

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.

What's included

7 videos4 assignments1 plugin

7 videosTotal 63 minutes

Introduction of Projects11 minutes
Import Packages7 minutes
Data Preprocessing7 minutes
Data Transformation8 minutes
Target Variable Splitting6 minutes
Dataset Explanation11 minutes
Dataset Explanation Continue10 minutes

4 assignmentsTotal 60 minutes

Getting Started with the Project10 minutes
Preparing and Transforming Data10 minutes
Understanding the Dataset10 minutes
Building the Foundation30 minutes

1 pluginTotal 30 minutes

From Raw Housing Data to Ready Dataset: Foundations for Prediction30 minutes

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.

What's included

11 videos4 assignments

11 videosTotal 102 minutes

Feature Engineering9 minutes
Feature Engineering Continue8 minutes
Handling Missing Values9 minutes
Handling Missing Values Continue8 minutes
Exploratory Data Analysis7 minutes
Exploratory Data Analysis Continue9 minutes
Correlation10 minutes
Predicting Result9 minutes
Calculating Variance Inflation Factor11 minutes
Calculating Variance Inflation Factor Continue11 minutes
Conclusion7 minutes

4 assignmentsTotal 60 minutes

Feature Engineering & Data Cleaning10 minutes
Exploratory Data Analysis & Correlation10 minutes
Prediction & Model Evaluation10 minutes
Advanced Analysis & Prediction30 minutes

Instructor

EDUCBA

341 Courses113,215 learners

Offered by

EDUCBA

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