Domain: Science, Environment & Research

Using the local file `machine_sensor_final_data.csv`, clean the noisy machine-sensor values and return the sums for the requested machines.

Column-to-machine mapping:

1. The columns use zero-based machine indexing.
2. `Unnamed: 0` is machine 1.
3. Therefore:
   - machine 4 -> `Unnamed: 3`
   - machine 5 -> `Unnamed: 4`
   - machine 200 -> `Unnamed: 199`
   - machine 367 -> `Unnamed: 366`
   - machine 530 -> `Unnamed: 529`

Cleaning rule:

1. Treat each target cell as a noisy string that contains exactly one real signed decimal number plus arbitrary surrounding junk.
2. Recover the numeric value by extracting the first regex match of:

```python
[+-]?\d+\.\d+
```

3. Parse that extracted substring as a float.
4. Sum the cleaned numeric values down each target column.

Return exactly one JSON object with this structure:

```json
{
  "machine_4_sum": 0.0,
  "machine_5_sum": 0.0,
  "machine_200_sum": 0.0,
  "machine_367_sum": 0.0,
  "machine_530_sum": 0.0
}
```

Output rules:

1. Round all five sums to 7 decimal places using round-half-to-even.
2. Print only the final JSON object.

Grading rubric:
criterion: The final answer is a single valid JSON object with exactly the keys `machine_4_sum`, `machine_5_sum`, `machine_200_sum`, `machine_367_sum`, and `machine_530_sum`.
weight: 10
criterion: `machine_4_sum` is exactly `-0.0959857`.
weight: 15
criterion: `machine_5_sum` is exactly `-0.0693898`.
weight: 15
criterion: `machine_200_sum` is exactly `0.1017913`.
weight: 15
criterion: `machine_367_sum` is exactly `-0.0000518`.
weight: 15
criterion: `machine_530_sum` is exactly `840.2493`.
weight: 15

Golden answer:
# Code

```python
import csv
import json
import re
from pathlib import Path


DATA_PATH = Path(__file__).with_name("machine_sensor_final_data.csv")
NUMBER_RE = re.compile(r"[+-]?\d+\.\d+")


targets = {
    "machine_4_sum": "Unnamed: 3",
    "machine_5_sum": "Unnamed: 4",
    "machine_200_sum": "Unnamed: 199",
    "machine_367_sum": "Unnamed: 366",
    "machine_530_sum": "Unnamed: 529",
}


with DATA_PATH.open(newline="", encoding="utf-8") as handle:
    rows = list(csv.DictReader(handle))

result = {}
for key, column in targets.items():
    total = 0.0
    for row in rows:
        match = NUMBER_RE.search(row[column])
        total += float(match.group(0))
    result[key] = round(total, 7)

json_text = (
    "{"
    f'"machine_4_sum":{result["machine_4_sum"]:.7f},'
    f'"machine_5_sum":{result["machine_5_sum"]:.7f},'
    f'"machine_200_sum":{result["machine_200_sum"]:.7f},'
    f'"machine_367_sum":{result["machine_367_sum"]:.7f},'
    f'"machine_530_sum":{result["machine_530_sum"]:.7f}'
    "}"
)

print(json_text)
```

# Reasoning

The local file is `machine_sensor_final_data.csv`, not the draft filename. The target columns are the zero-based machine mappings described in the prompt. The actual noisy cells contain one embedded signed decimal number plus surrounding junk text, tags, comments, or mixed Unicode fragments. The code therefore uses a single deterministic extraction rule: recover the first match of `[+-]?\d+\.\d+` from each target cell, parse it as float, and sum the cleaned values down each requested machine column.

This prompt-faithful cleaning rule reproduces the real local sums directly from the uploaded CSV without needing any broader column cleaning or dataset-wide heuristics.

# Verified Result

```json
{"machine_4_sum":-0.0959857,"machine_5_sum":-0.0693898,"machine_200_sum":0.1017913,"machine_367_sum":-0.0000518,"machine_530_sum":840.2493000}
```