# Planet X Model Selection

*English translation by SOTA – AI Community of the Georgian original. Organisers who would like this translation removed can email sota.ai.community@gmail.com.*

*Source: Georgian AI League I, a practice contest of the Georgian Artificial Intelligence Association (GAIA) on the Nitro AI judge, 18 January 2026, task 2: [original statement](https://judge.nitro-ai.org/competitions/gaia/ai-league-i/2/view).*

## 🪐 Task: Planet X Model Selection

## Overview

On planet X, scientists were trying to predict the mysterious phenomenon Y on the basis of X features. However, planet X has unusual gravitational anomalies that sometimes damage their measuring instruments during data collection!

Over the years they collected data and trained 1000 regression models. Unfortunately, only a small number of these models (**exactly 5**) were trained while the instruments were working correctly. The rest were trained on corrupted labels!

## Task

You are given:

1. **Tabular data** (`train_data.csv`)

**Your goal:** identify the indices of the 5 correctly trained models.

## Input Files

### `train_data.csv`
Tabular data containing:
* the first 15 columns — the features on which the models were trained
* the remaining 1000 columns — the predictions of the 1000 models on the training data

You can load the data as follows:

```python
import pandas as pd

data = pd.read_csv("train_data.csv")
features = data.iloc[:, :15]
predictions = data.iloc[:, 15:]
```

## Output Format

You must create a CSV file containing exactly **three columns and 1 row**:

```csv
subtaskID,datapointID,answer
1,0,"1,4,3,16,246"
```

where:

* `subtaskID` is exactly 1 (because of the platform's format)
* `datapointID` is exactly 0
* `answer` — a string containing the comma-separated indices of the 5 models (0-999)

**Under no circumstances change the column names.**

## Evaluation

Your work will be scored by accuracy.

### Scoring Rules

The final score is given on a 100-point scale. Accordingly, if you correctly identify:
* 1 model, you get 20 points
* 2 models, you get 40 points
* 3 models, you get 60 points
* 4 models, you get 80 points
* 5 models, you get 100 points


Good luck, Earthling scientist! 🚀