{
  "id": 552747,
  "title": "Predicting Problematic Internet Use in Children and Adolescents",
  "url": "/competitions/child-mind-institute-problematic-internet-use/discussion/552747",
  "author_name": "Razvan-Mihai Hanghicel",
  "post_date": "2024-12-21T10:00:36.890000",
  "votes": 0,
  "comment_count": 0,
  "views": 0,
  "content": "<p>This solution achieved a bronze medal in the Child Mind Institute - Detect Early Risk of Depression and Self-Harm competition on Kaggle. The code implements an ensemble approach to predict problematic internet usage based on physical activity data.</p>\n<p>Key features:</p>\n<ul>\n<li>Data preprocessing and feature engineering</li>\n<li>Ensemble of machine learning models (XGBoost, LightGBM, CatBoost)</li>\n<li>Custom metric optimization using quadratic weighted kappa</li>\n<li>Cross-validation with stratified k-fold</li>\n<li>Threshold optimization for improved predictions</li>\n</ul>\n<p>The solution provides a comprehensive approach to the competition problem, incorporating various machine learning techniques and custom optimizations to achieve competitive results</p>\n<p><a href=\"https://www.kaggle.com/code/razvanmihaihanghicel/predicting-problematic-internet-use-with-xgboost\" target=\"_blank\">https://www.kaggle.com/code/razvanmihaihanghicel/predicting-problematic-internet-use-with-xgboost</a></p>",
  "messages": [
    {
      "id": 3077703,
      "postDate": "2024-12-21T10:00:36.890Z",
      "content": "<p>This solution achieved a bronze medal in the Child Mind Institute - Detect Early Risk of Depression and Self-Harm competition on Kaggle. The code implements an ensemble approach to predict problematic internet usage based on physical activity data.</p>\n<p>Key features:</p>\n<ul>\n<li>Data preprocessing and feature engineering</li>\n<li>Ensemble of machine learning models (XGBoost, LightGBM, CatBoost)</li>\n<li>Custom metric optimization using quadratic weighted kappa</li>\n<li>Cross-validation with stratified k-fold</li>\n<li>Threshold optimization for improved predictions</li>\n</ul>\n<p>The solution provides a comprehensive approach to the competition problem, incorporating various machine learning techniques and custom optimizations to achieve competitive results</p>\n<p><a href=\"https://www.kaggle.com/code/razvanmihaihanghicel/predicting-problematic-internet-use-with-xgboost\" target=\"_blank\">https://www.kaggle.com/code/razvanmihaihanghicel/predicting-problematic-internet-use-with-xgboost</a></p>",
      "rawMarkdown": "This solution achieved a bronze medal in the Child Mind Institute - Detect Early Risk of Depression and Self-Harm competition on Kaggle. The code implements an ensemble approach to predict problematic internet usage based on physical activity data.\n\nKey features:\n- Data preprocessing and feature engineering\n- Ensemble of machine learning models (XGBoost, LightGBM, CatBoost)\n- Custom metric optimization using quadratic weighted kappa\n- Cross-validation with stratified k-fold\n- Threshold optimization for improved predictions\n\nThe solution provides a comprehensive approach to the competition problem, incorporating various machine learning techniques and custom optimizations to achieve competitive results\n\n[https://www.kaggle.com/code/razvanmihaihanghicel/predicting-problematic-internet-use-with-xgboost](https://www.kaggle.com/code/razvanmihaihanghicel/predicting-problematic-internet-use-with-xgboost)"
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "3077703": "This solution achieved a bronze medal in the Child Mind Institute - Detect Early Risk of Depression and Self-Harm competition on Kaggle. The code implements an ensemble approach to predict problematic internet usage based on physical activity data.\n\nKey features:\n- Data preprocessing and feature engineering\n- Ensemble of machine learning models (XGBoost, LightGBM, CatBoost)\n- Custom metric optimization using quadratic weighted kappa\n- Cross-validation with stratified k-fold\n- Threshold optimization for improved predictions\n\nThe solution provides a comprehensive approach to the competition problem, incorporating various machine learning techniques and custom optimizations to achieve competitive results\n\n[https://www.kaggle.com/code/razvanmihaihanghicel/predicting-problematic-internet-use-with-xgboost](https://www.kaggle.com/code/razvanmihaihanghicel/predicting-problematic-internet-use-with-xgboost)"
  }
}