中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.
Python: Master House Price Prediction with Linear Regression
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
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最近已更新！
September 2025
作业
8 项作业
授课语言：英语（English）
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该课程共有2个模块
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1个插件
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.
涵盖的内容
11个视频4个作业
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