Data Quality

Pristine Data Cleansing

Garbage in, garbage out. We sanitize, standardize, and de-duplicate your raw datasets to maximize model performance and reliability.

Clean My Data Our Methods

De-Duplication

Identify and remove exact or fuzzy duplicates to prevent model overfitting.

Outlier Removal

Statistical detection of anomalies that could skew training results.

Standardization

Normalizing formats (dates, units, currencies) for consistent processing.

Transforming Chaos into Clarity

RAW DATA
{
  "id": "u_1", 
  "date": "01/02/2023",
  "val": "NaN"
},
{
  "id": "u_1", // Duplicate
  "date": "2023-02-01",
  "val": null
}
CLEANSED
{
  "id": "u_1",
  "date": "2023-02-01T00:00:00Z",
  "status": "active",
  "value": 0.0 // Imputed
}