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  3. 【教学赛】金融数据分析赛题1:银行客户认购产品预测
TianchiTime SeriesBeginnerRegistration unverified

【教学赛】金融数据分析赛题1:银行客户认购产品预测

A beginner time series competition requiring roughly 8–20 hours for an end-to-end practice run.

Difficulty
Beginner
Estimated effort
8–20 hours
Registration deadline
To be confirmed
Compute
CPU only
OverviewPreparationSource

Overview

Who this competition fits

Learners who want Chinese-language Tianchi practice or daily learning style challenges.

Read the original official blurb
赛题以银行产品认购预测为背景,想让你来预测下客户是否会购买银行的产品。在和客户沟通的过程中,我们记录了和客户联系的次数,上一次联系的时长,上一次联系的时间间隔,同时在银行系统中我们保存了客户的基本信息,包括:年龄、职业、婚姻、之前是否有违约、是否有房贷等信息,此外我们还统计了当前市场的情况:就业、消费信息、银行同业拆解率等。 Official category: Daily Learning Competition (日常学习赛). Official tags: ‌金融. Teams entered: 7677.

Preparation

From registration to a first submission

  1. 01

    Python basics

  2. 02

    pandas

  3. 03

    train/validation split

Before you commit: Even learning-oriented Tianchi races can still punish weak validation or misunderstanding of submission rules.

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
Tianchi
Last checked
Jun 21, 2022

Entry assessment

est.
78

Worth shortlisting

Overall fit estimate

Beginner fit78/100
Learning value82/100

Confirm eligibility and time commitment before moving into formal preparation.

Entry eligibility needs official confirmation

Open official pageSign in to saveOpen guide

Timeline

Competition ends
Mar 31, 2026

Data and review state

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

View data stateClose
Source page verifiedMetric confirmedReviewed
Source published
Jun 21, 2022
Published on this site
Jul 18, 2026

Official metric is not published on the Tianchi competition listing; scoring rules should be verified on the competition page.

This record is a track under a Tianchi series competition; prize amounts may be series-level totals.