{
  "id": 393487,
  "title": "Aggregate Features EDA",
  "url": "/competitions/predict-student-performance-from-game-play/discussion/393487",
  "author_name": "Shashwat Raman",
  "post_date": "2023-03-09T15:54:25.018000",
  "votes": 9,
  "comment_count": 0,
  "views": 0,
  "content": "<h1>Aggregate Features EDA</h1>\n<p>I published an EDA notebook for the aggregate features. It provides many interesting insights like the distribution of these features, which features are highly correlated with each other, which features are helpful in predicting the target, the outliners, etc. <br>\nAggregate Features are the ones which are used to train the gradient boosting models in this competition, so I think getting to know and developing a better intuition about them will really help in building better models.</p>\n<p>You can find the notebook <a href=\"https://www.kaggle.com/code/shashwatraman/eda-of-aggregated-features\" target=\"_blank\">here.</a></p>\n<p>I hope you find it helpful 😊</p>",
  "messages": [
    {
      "id": 2175038,
      "postDate": "2023-03-09T15:54:25.017Z",
      "content": "<h1>Aggregate Features EDA</h1>\n<p>I published an EDA notebook for the aggregate features. It provides many interesting insights like the distribution of these features, which features are highly correlated with each other, which features are helpful in predicting the target, the outliners, etc. <br>\nAggregate Features are the ones which are used to train the gradient boosting models in this competition, so I think getting to know and developing a better intuition about them will really help in building better models.</p>\n<p>You can find the notebook <a href=\"https://www.kaggle.com/code/shashwatraman/eda-of-aggregated-features\" target=\"_blank\">here.</a></p>\n<p>I hope you find it helpful 😊</p>",
      "rawMarkdown": "# Aggregate Features EDA\n\nI published an EDA notebook for the aggregate features. It provides many interesting insights like the distribution of these features, which features are highly correlated with each other, which features are helpful in predicting the target, the outliners, etc. \nAggregate Features are the ones which are used to train the gradient boosting models in this competition, so I think getting to know and developing a better intuition about them will really help in building better models.\n\nYou can find the notebook [here.](https://www.kaggle.com/code/shashwatraman/eda-of-aggregated-features)\n\nI hope you find it helpful 😊",
      "votes": 7
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "2175038": "# Aggregate Features EDA\n\nI published an EDA notebook for the aggregate features. It provides many interesting insights like the distribution of these features, which features are highly correlated with each other, which features are helpful in predicting the target, the outliners, etc. \nAggregate Features are the ones which are used to train the gradient boosting models in this competition, so I think getting to know and developing a better intuition about them will really help in building better models.\n\nYou can find the notebook [here.](https://www.kaggle.com/code/shashwatraman/eda-of-aggregated-features)\n\nI hope you find it helpful 😊"
  }
}