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  3. 房屋租金预测学习赛
OpenAtomGeneral MLBeginnerRegistration unverified

房屋租金预测学习赛

A beginner general ml competition requiring roughly 6–16 hours for an end-to-end practice run.

Difficulty
Beginner
Estimated effort
6–16 hours
Registration deadline
To be confirmed
Compute
CPU only
OverviewPreparationSource

Overview

Who this competition fits

适合已具备 Python 和基础机器学习回归知识的学习者,尤其是正在学习数据挖掘、梯度下降和多因子房价预测的人。参赛者需要能处理表格型房屋特征数据,并训练模型输出租金预测结果。

Read the original official blurb
给定房屋租金价格的各个影响因素数据,建立模型预测国内某城市房屋的租金价格。 Official category: 训练学习赛. Organizers: DataCastle数据城堡. Participants: 999999.

Preparation

From registration to a first submission

  1. 01

    Python

  2. 02

    pandas 数据处理

  3. 03

    scikit-learn 回归模型

  4. 04

    均方误差计算

  5. 05

    CSV 文件生成

  6. 06

    多特征回归分析

Before you commit: 核心工作是把多个影响租金的特征转化为误差较小的回归预测;页面以平均预测误差评估结果,因此需要围绕回归模型效果进行调参与验证。提交还要求生成指定命名格式的 CSV 结果文件,并将 Python 代码另行发送。

Source

How this page was assembled

Competition information is structured from the official page. Scores are platform estimates for decision support; official rules take precedence.

Official competition page
OpenAtom
Last checked
Mar 29, 2024

Entry assessment

est.
72

Worth shortlisting

Overall fit estimate

Beginner fit72/100
Learning value70/100

Confirm eligibility and time commitment before moving into formal preparation.

Entry eligibility needs official confirmation

Open official pageSign in to saveOpen guide

Data and review state

Separate what is confirmed from what still needs review. Official rules and deadlines win.

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Source page verifiedMetric confirmedReviewed
Source published
Mar 29, 2024
Source created
Mar 29, 2024
Published on this site
Jul 21, 2026

Official metric is not published on the OpenAtom competition listing; verify scoring on the competition page.