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Python: Master House Price Prediction with Linear Regression

位教师：EDUCBA

包含在中

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2个模块

深入了解一个主题并学习基础知识。

6 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

6 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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.

您将获得的技能

要了解的详细信息

可分享的证书

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作业

8 项作业

授课语言：英语（English）

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有2个模块

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.

单元详情

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.

涵盖的内容

7个视频4个作业

7个视频总计57分钟

Introduction of Projects6分钟
Import Packages7分钟
Data Preprocessing7分钟
Data Transformation8分钟
Target Variable Splitting6分钟
Dataset Explanation12分钟
Dataset Explanation Continue10分钟

4个作业总计60分钟

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

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.