1. Introduction

House price prediction is a classical regression problem that is of great practical importance in the field of house sales. The purpose of this experiment is to predict the SalePrice of a house in order to provide a scientific basis for valuation. The experiment is based on the house price prediction dataset provided by Kaggle, and the linear regression algorithm is used to complete the prediction task. The experiment not only focuses on the predictive ability of the model, but also aims to understand the importance of data analysis, feature selection, and model performance evaluation.

1. 数据分析与预处理

1.1 数据来源与字段选择

本实验的数据来自 Kaggle 房价预测数据集。训练数据集包含 81 个字段，其中 LotArea、BsmtUnfSF 和 GarageArea 被选定为模型输入特征，SalePrice 作为目标变量。选择这些字段的原因在于：

• LotArea：反映房屋的地块面积；
• BsmtUnfSF：反映房屋地下未装修面积；
• GarageArea：反映车库面积。

Question 1

What is the difference between supervised and unsupervised machine learning?

The main difference between supervised and unsupervised learning is the presence or absence of data labeling. Supervised learning relies on labeled datasets to find out the relationship between inputs and outputs for new data output prediction.

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​ MQTT – Class 2 - Group 12

​ Name：XianYue Xiao