Avik-Jain/100-Days-of-ML-Code-Chinese-Version

• Repository: https://github.com/Avik-Jain/100-Days-of-ML-Code-Chinese-Version
• Commit: 6adcfa4a3398cfc62c1d44e48a9585379e4c3f37
• Generated: 2026-11-13T23:22:25+00:00
• Scorer 0.1.0, scoring prov-4+qual-2+sec-2, report schema v2

Scores

Insufficient first-party source code to score quality or security (under 100 lines). An empty or README-only repository is not evidence of safety.

AI provenance

No AI attribution or AI-consistent velocity signals detected.

• Explicitly attributed share (lower bound): 0.0%

• Unattributed share in AI-consistent velocity bursts: 0.0%

• Estimated AI-generated share: 0.0%

• Stylometry index: 6.00 per 1,000 lines (narrated steps 6, placeholders 0, assistant phrasing 0, emoji 0). Reported as a signal, not scored.

Evidence

• step-by-step narration comment: #Step 1: Importing the libraries ([Code/Day 1_D](https://github.com/Avik-Jain/100-Days-of-ML-Code-Chinese-Version/blob/6adcfa4a3398cfc62c1d44e48a9585379e4c3f37/Code/Day 1_Data Prepocessing.py#L3))
• step-by-step narration comment: #Step 2: Importing dataset ([Code/Day 1_D](https://github.com/Avik-Jain/100-Days-of-ML-Code-Chinese-Version/blob/6adcfa4a3398cfc62c1d44e48a9585379e4c3f37/Code/Day 1_Data Prepocessing.py#L7))
• step-by-step narration comment: #Step 3: Handling the missing data ([Code/Day 1_D](https://github.com/Avik-Jain/100-Days-of-ML-Code-Chinese-Version/blob/6adcfa4a3398cfc62c1d44e48a9585379e4c3f37/Code/Day 1_Data Prepocessing.py#L17))
• step-by-step narration comment: #Step 4: Encoding categorical data ([Code/Day 1_D](https://github.com/Avik-Jain/100-Days-of-ML-Code-Chinese-Version/blob/6adcfa4a3398cfc62c1d44e48a9585379e4c3f37/Code/Day 1_Data Prepocessing.py#L28))
• step-by-step narration comment: #Step 5: Splitting the datasets into training sets and Test sets ([Code/Day 1_D](https://github.com/Avik-Jain/100-Days-of-ML-Code-Chinese-Version/blob/6adcfa4a3398cfc62c1d44e48a9585379e4c3f37/Code/Day 1_Data Prepocessing.py#L44))
• step-by-step narration comment: #Step 6: Feature Scaling ([Code/Day 1_D](https://github.com/Avik-Jain/100-Days-of-ML-Code-Chinese-Version/blob/6adcfa4a3398cfc62c1d44e48a9585379e4c3f37/Code/Day 1_Data Prepocessing.py#L58))

AI provenance describes how code was produced; it is not a quality judgment. Unless attribution is explicit, these are signals consistent with AI generation, not proof. Squash/rebase workflows, imported code, and rewritten history reduce accuracy.

Repository profile

• Source lines: 68; test lines: 0