Data Preprocessing
Raw data is rarely trainable as collected. We clean, normalise and deduplicate it, and tell you honestly what had to be discarded.
What is included
Data Preprocessing, in detail
Cleaning and normalisation
Encoding, formatting, units and date handling made consistent across the whole set.
Deduplication
Exact and near-duplicate detection, which matters more than most teams expect.
PII handling
Personal data detected and redacted or pseudonymised according to your policy.
Splits and stratification
Train, validation and test splits built to preserve distribution.
Data documentation
A datasheet describing what is in the set, what was removed, and why.
How we work
Four steps, no surprises
- ConsultationWe learn the business and what success looks like.
- Audit & scopeA written plan: what we build, in what order, at what cost.
- BuildDelivered in stages you review as we go.
- SupportMonitoring and iteration once it is live.
Questions
Frequently asked
How much data typically gets discarded?
It varies enormously by source. Scraped data often loses a large share; curated data much less. We report the number rather than quietly dropping rows.
Can you work with our existing pipeline?
Yes — we can deliver as files or run the steps inside your own tooling.
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