Data Quality Testing Lab

Inspect ML training data for missing values, duplicates, outliers, and other quality issues that could compromise model performance.

Your Mission

You are a QA tester reviewing a loan approval dataset that will be used to train an ML model. Your job is to find data quality issues that could affect model performance. Click on any suspicious cell to report an issue.

Approval rules: The model is expected to approve applicants with credit score > 650 AND income > $50,000.

Issue Types

Missing ValueDuplicate RecordOutlier/AnomalyMislabeled DataData Type ErrorSample BiasFormatting IssueInvalid Value

Found Issues:0

ID	Name	Age	Income	Credit Score	City	Approved
1	Alice Johnson	28	$94,424	579	Chicago	No
2	Bob Smith	49	$36,688	624	New York	No
3	Carlos Garcia	24	$100,467	706	Los Angeles	Yes
4	Diana Chen	23	$91,218	579	New York	No
5	Emma Wilson	49	—	735	New York	Yes
6	Frank Brown	50	$63,958	523	New York	No
7	Grace Lee	43	$60,248	790	Houston	Yes
8	Grace Lee	43	$60,248	790	Houston	Yes
9	Grace Lee	43	$60,248	790	Houston	Yes
10	James Anderson	67	$102,004	735	Chicago	Yes
11	Karen White	57	$41,746	715	Phoenix	No
12	Liam Harris	49	$101,213	—	Los Angeles	No
13	Maria Rodriguez	63	$59,917	563	New York	No
14	Nathan Clark	30	$65,424	696	Los Angeles	Yes
15	Olivia Lewis	150	$116,176	603	Phoenix	No

Showing rows 1–15 of 50