# How to prepare data for a neural network: dataset cleaning and labeling

> AI Release · @ai_release1 · https://ai-release.org/guides/kak-podgotovit-dannye-dlya-neyroseti-chistka-i-razmetka_en.html

Cleaning and labeling a dataset is the step that decides how well a neural network will work. Below is a practical look at how data preparation is described in the channel's posts — from collecting sources to picking the model that touches your data.

## TL;DR

- Data prep starts with collection from several places at once: in the post from the October 4, 2026 digest, an n8n SEO agent pulls Screaming Frog, Yandex Metrica and Yandex Webmaster into a single flow.
- Keep page history. The same agent stores past page states, so cleaning never erases the past.
- Rules first, neural network second: the author cleans data and counts signals with rules plus "a bit of neural networks".
- Catalogs already update themselves — AI-Stat says its model data is refreshed automatically by AI agents, not by people.
- Choosing the model for the AI step is a compromise between quality, speed and money.

## Why data preparation beats model choice

Most people start with the model. The channel's posts start with the data. In the story published in the digest for October 4, 2026, the author had a drop in organic traffic and got tired of manually merging three sources — Screaming Frog, Yandex Metrica and Yandex Webmaster.

The fix was not a bigger model. It was a pipeline: one agent on n8n that keeps page history, cleans the data, and counts signals. The neural network part is described as small. That is the whole idea of data prep — the boring layer does the work.

If you are building that layer yourself, the [AI agents for task automation guide](https://ai-release.org/guides/ii-agenty-dlya-avtomatizacii.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide) covers the same agent logic in more detail.

## Where the data comes from

The agent in the post works with three sources, and each one plays a different role:

- **Screaming Frog** — the crawler side of the picture.
- **Yandex Metrica** — behaviour data.
- **Yandex Webmaster** — search-side data.

Manually joining these three is what the author called the painful part. Automating the join is the first real step of dataset preparation, before any cleaning starts.

A useful way to describe that pipeline is a short config, so the sources are not buried in code:

```yaml
pipeline: seo-data-prep
sources: [Screaming Frog, Yandex Metrica, Yandex Webmaster]
store: page history
steps: [clean, count signals]
logic: rules + light LLM
```

## Cleaning and signal counting: a checklist

The post is clear on the order: clean, then count signals, then add the model. Here is that order as a checklist you can reuse on your own dataset.

- [ ] Collect raw data from all sources before touching anything.
- [ ] Store page history, so you can compare a page with its past self.
- [ ] Clean the data first — remove noise, duplicates and empty rows.
- [ ] Count the signals you care about using fixed rules.
- [ ] Add a neural network only at the last step, and keep it small.

Rules give you repeatable results. The network gives you flexibility where rules are not enough. Mixing them in the wrong order means you cannot tell which part made a mistake.

## Storage, history and control

Storing history is the quiet feature that makes cleaning safe. When you keep past page states, a "bad" row can be traced back to the moment it appeared, instead of being deleted forever.

Control is the other half. One post describes a typical vibe-coding story: an employee builds an app in an evening, and a month later half the department uses it. The problem is that nobody owns updates or answers for the data inside. The [AI agent security guide](https://ai-release.org/guides/bezopasnost-ii-agentov-kak-zaschitit-dannye-i-izbezhat.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide) covers exactly this layer.

Automation is spreading