Data cleaning prepares information for reliable analysis by detecting and correcting missing, incorrect, duplicated, or inconsistent values. For clustering, even consistently logged behavioral data still needs review before modeling. section 4.1.2
A practical cleaning workflow includes:
- Handle missing values rather than allowing gaps to distort analysis. section 4.1.2
- Remove duplicate events and correct inaccurate data. section 4.1.2
- Constrain values to valid ranges and transform inconsistent records into a uniform format. section 4.1.2
- Revisit the data after training and evaluation because hidden problems can surface later and cascade through the project. section 4.1.2
Behavioral clustering
A product team removes duplicate Mixpanel events, handles occasional missing values, and excludes unhelpful fields such as visit time of day before clustering users by engagement. CRM and call-center records may require more extensive noise removal and normalization. section 4.1.2
Table 4.1 Examples of data points for clustering
| Features |
|||||
| user_id |
purchased_items |
purchase_value |
last_active |
n_visits |
|
| Examples |
abhj3k |
2 |
908 |
2024-04-30 08:36:24 |
48 |
| shj67d |
0 |
0 |
2023-12-26 12:56:24 |
24 |
|
| i963gh |
12 |
673 |
2024-05-15 23:22:11 |
156 |
|
Inspect example clustering fields such as visits, purchases, purchase value, and last-active timestamps to identify records requiring validation. section 4.1.2
