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phone_hours
float64
computer_hours
float64
device_count
int64
sleep_quality
string
use_before_bed
int64
sleep_time
int64
sleep_hours
float64
3.5
5
3
good
1
23
7
4.2
6.5
3
medium
1
0
6.5
5
4
4
bad
1
1
6
2
7.5
3
good
0
22
7.5
3.8
6
3
medium
1
0
6
4.5
5.5
3
good
1
1
7
6
8
5
bad
1
2
5.5
3
4
3
medium
1
23
7
2.5
3.5
2
good
0
22
8
4.8
6.2
3
medium
1
1
6
3.6
7
3
bad
1
0
5.5
2.2
5.5
3
medium
1
23
7.5
5.5
6.5
4
good
1
1
6
3.1
4.8
3
medium
0
22
7
4
7.2
3
good
1
0
6.5
2.8
5
3
bad
1
1
6
3.9
6.8
3
medium
1
0
7
4.7
7.5
4
good
1
2
6
5.2
6
3
medium
1
1
5.5
3.3
5.7
3
good
1
0
7
2.7
4.2
3
medium
0
23
7.5
3.5
5
3
good
1
0
6.5
4.1
6.3
3
medium
1
1
6
2.9
5.8
3
bad
1
0
5.5
5.8
7.2
5
good
1
2
6.5
3.4
4.9
3
medium
1
23
7
2.6
6.5
3
bad
1
0
6
4.9
7.8
4
good
1
1
6
3.7
5.4
3
medium
0
22
7.5
5.3
6.7
3
bad
1
0
5.5

Dataset Summary

This dataset records daily electronic device usage and sleep patterns of students.
It is designed for exploring the relationship between screen time, device behavior, and average sleep duration.

  • Original size: 30 samples
  • Augmented size: 300 samples
  • Task type: Regression (predicting daily sleep hours)
  • Goal: Predict sleep_hours from usage and sleep-related features

Data Splits

  • No fixed train/test split is provided.
  • Users can apply their own strategy (e.g., 80/20 split).

Intended Uses

  • Regression Task: Predict sleep_hours from device usage.
  • Correlation Analysis: Study relationships between screen time and sleep quality.
  • Education: Demonstrates dataset augmentation (30 → 300 samples).
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