# Lera Andronova (@lerabyte) on tiktok

- id: koinon:creator:tiktok_lerabyte
- url: https://koinonlink.com/kol/tiktok/lerabyte
- schema_version: 1.3

## Identity
- platform: tiktok
- handle: lerabyte
- platform_url: https://www.tiktok.com/@lerabyte
- country: United States
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 49463
- following: 62
- posts_observed: 61
- verified: false
- size_tier: koc

## Engagement
- status: not_collected
- status_note: Engagement has not been collected for this creator yet. This is a gap in our collection, NOT a statement that the creator has no engagement. Follower count is known, so it is measurable.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: software
- subcategory: ai_tools
- category_confidence: 0.9
- category_source: derived_from_recent_content
- topics: building ML models from scratch, Python for machine learning, supervised/unsupervised/reinforcement learning explanations, data science project walkthroughs, ML model selection and evaluation, Monte Carlo simulations and time series

## Audience (inferred)
- status: present
- description: aspiring data scientists, ML students and junior practitioners learning practical machine learning with Python
- roles: data
- seniority: learner
- pain_points: struggling to move from theory to building real ML models, unclear on which algorithms to choose for different problems, difficulty turning notebook code into usable projects or GUIs, lack of structured roadmaps and beginner-friendly project ideas
- posts_analysed: 100
- confidence: 0.9

## Commercial (inferred)
- audience_buyer_fit: 0.6
- promotion_intent: 0.9
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 53
- authenticity_basis: profile_shape_only
- credibility_score: 53
- flagged_for_review: false
- koinon_bd_grade: B — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [tiktok_lerabyte#f3fef95b] Check it out: https://www.udemy.com/course/supervisedml/?couponCode=DISCOUNT The course is also hyperlinked in my TikTok Bio! Discounted to $19.99 for the next week ONLY! #machinelearning #udemy #STEM #python #techtok (https://www.tiktok.com/@lerabyte)
- [tiktok_lerabyte#329a1ab3] original sound - lerabyte
- [tiktok_lerabyte#ae21b4a0] It is a becoming a big trend on TikTok now! Click here: machinelearning
- [tiktok_lerabyte#0a96c7fe] Check out Lera Andronova’s video! #TikTok >
- [tiktok_lerabyte#6b7e645e] Search:
- [tiktok_lerabyte#b015f802] udemy course discount
- [tiktok_lerabyte#e2983cde] The Thought Process of a Data Scientist 👩‍💻  I didn’t start by picking a neural network - I started by understanding the data and the problem it represents. Because fraud is rare, every design choice had to focus on stability, generalization, and meaningful evaluation instead o

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.9, freshness_days=25, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.9, freshness_days=25, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.9, freshness_days=25, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=0, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=0, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=1, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.25, freshness_days=0, method=Profile-shape heuristic from a single snapshot (follower count, following ratio, account age, posting volume, verification), reduced for engagement red flags where post engagement was collected. Where only a follower count is available the score saturates at 58. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=1, method=llm_partner_fit_classifier + fit_score >= 0.6 AND authenticity >= 60 => A; fit_score >= 0.4 AND authenticity >= 45 => B; else C. authenticity < 30 or a promotional-pattern flag => quarantine. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=1, method=platform_profile

## Freshness
- profile_checked_days_ago: 1
- content_analysed_days_ago: 0
- audience_updated_days_ago: 25

## AI retrieval
- indexing_requests: 1
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=tiktok_lerabyte
- Request removal: https://koinonlink.com/claim?k=tiktok_lerabyte
- Terms: https://koinonlink.com/for-ai