Qamro/predicting-smartphone-addiction-binary-classification

Playground Series S6E8 - Screen Addiction Prediction

Binary classification solution for Kaggle's Playground Series - Season 6, Episode 8, predicting whether an individual is "addicted" to screen usage from self-reported digital habits and lifestyle indicators.

Metric: ROC AUC

Best OOF AUC: 0.9628 (v2) · Leaderboard score: 0.96323

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Repo Structure

.
├── data/
│   ├── train.csv                # Training data (691,369 rows × 14 cols)
│   ├── test.csv                 # Test data (296,302 rows, no target)
│   └── sample_submission.csv    # Required submission format
├── results/
│   ├── submission.csv           # v1 output - single LightGBM
│   └── submission_v2.csv        # v2 output - engineered features + blended ensemble
├── train_model_v1.py            # v1 training script
├── train_model_v2.py            # v2 training script
└── README.md

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Dataset

Each row represents an individual with self-reported screen-time and lifestyle data. Nearly every column contains missing values by design.

Column	Type	Description
id	int	Row identifier
age	float	Age
daily_screen_time_hours	float	Avg. daily screen time
social_media_hours	float	Daily social media time
gaming_hours	float	Daily gaming time
work_study_hours	float	Daily work/study time
sleep_hours	float	Avg. nightly sleep
notifications_per_day	float	Notifications received per day
app_opens_per_day	float	App opens per day
weekend_screen_time	float	Weekend screen time
gender	category	Male / Female / Other
stress_level	category	Low / Medium / High
academic_work_impact	category	Yes / No
addicted_label	int (target)	1 = addicted, 0 = not addicted

Target is imbalanced: ~71% positive class, ~29% negative.

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Approach

v1 - Baseline (train_model_v1.py)

• Single LightGBM classifier, 5-fold stratified CV.
• Native handling of missing values and categorical features — no imputation or one-hot encoding.
• Result: OOF AUC 0.9620, leaderboard 0.96323.

v2 - Feature Engineering + Ensemble (train_model_v2.py)

• Adds:
  • Missingness indicator columns for every feature (missingness itself is predictive).
  • Ratio/interaction features: social_to_screen, gaming_to_screen, sleep_deficit, weekend_to_screen, screen_per_age, weekday_weekend_diff, and others.
• Trains LightGBM + XGBoost + CatBoost, 3-fold stratified CV (reduced from 5 due to single-CPU compute constraints).
• Blends the three models using an OOF-AUC-maximizing weight search.
• Result: OOF AUC 0.9628. On this run the blend search settled on 100% LightGBM weight, the lighter XGB/CatBoost configs (needed to fit the compute budget) didn't outperform LightGBM individually, but the added features still improved LightGBM itself over v1.

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Usage

pip install lightgbm xgboost catboost scikit-learn pandas numpy

# Baseline
python train_model_v1.py

# Feature-engineered ensemble
python train_model_v2.py

Each script reads train.csv / test.csv from the working directory and writes a submission CSV (submission.csv or submission_v2.csv) in the same format as sample_submission.csv:

id,addicted_label
691369,0.99
691370,0.03
...

Where addicted_label is the predicted probability of addiction, not a hard 0/1 label.

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Notes / Next Steps

• Training was constrained to a single CPU core; with more compute, restoring 5-fold CV and deeper/longer XGBoost & CatBoost runs would likely let the ensemble outperform LightGBM alone.
• Further gains are more likely to come from additional feature engineering (binning, clustering, higher-order interactions) than from adding more model types.