{
  "id": 585585,
  "title": "Insights from Visualizing Acceleration-Based Feature Distributions by Target Class",
  "url": "/competitions/cmi-detect-behavior-with-sensor-data/discussion/585585",
  "author_name": "",
  "post_date": "2025-06-21T13:26:37.426152100Z",
  "votes": 3,
  "comment_count": 1,
  "views": 0,
  "content": "<p>Summary</p>\n<ul>\n<li>Clip the data in the range of 0 to 30 and aggregate it</li>\n<li>The feature motion_count_gt_0.2g seems to be effective for the target variable</li>\n</ul>\n<p>I investigated features derived from acceleration variables that might influence the target variable.<br>\nThe notebook named by \"eda-acc-feature\" below was helpful as a reference. Please note that it was inspired by \"EDA:Detect Behavior with Sensor Data\" author by \"kakuteki\"-san, and parts of the code have been adapted from it.</p>",
  "messages": [
    {
      "id": "3229428",
      "postDate": "06/21/2025 13:26:37",
      "content": "<p>Summary</p>\n<ul>\n<li>Clip the data in the range of 0 to 30 and aggregate it</li>\n<li>The feature motion_count_gt_0.2g seems to be effective for the target variable</li>\n</ul>\n<p>I investigated features derived from acceleration variables that might influence the target variable.<br>\nThe notebook named by \"eda-acc-feature\" below was helpful as a reference. Please note that it was inspired by \"EDA:Detect Behavior with Sensor Data\" author by \"kakuteki\"-san, and parts of the code have been adapted from it.</p>",
      "rawMarkdown": "Summary\n- Clip the data in the range of 0 to 30 and aggregate it\n- The feature motion_count_gt_0.2g seems to be effective for the target variable\n\nI investigated features derived from acceleration variables that might influence the target variable.\nThe notebook named by \"eda-acc-feature\" below was helpful as a reference. Please note that it was inspired by \"EDA:Detect Behavior with Sensor Data\" author by \"kakuteki\"-san, and parts of the code have been adapted from it.",
      "votes": null
    },
    {
      "id": "3229599",
      "postDate": "06/21/2025 18:48:36",
      "content": "<p><a href=\"https://www.kaggle.com/sakaken\" target=\"_blank\">@sakaken</a> Your analysis of acceleration-based features is insightful, particularly the data clipping (0-30 range) and the effectiveness of motion_count_gt_0.2g. These are crucial insights for our model development. Your collaborative approach is also appreciated. Thanks!🙂</p>",
      "rawMarkdown": "sakaken Your analysis of acceleration-based features is insightful, particularly the data clipping (0-30 range) and the effectiveness of motion_count_gt_0.2g. These are crucial insights for our model development. Your collaborative approach is also appreciated. Thanks!🙂",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3229599,
      "author_name": "abdulrehmanchatha",
      "author_url": "",
      "post_date": "06/21/2025 18:48:36",
      "content": "<p><a href=\"https://www.kaggle.com/sakaken\" target=\"_blank\">@sakaken</a> Your analysis of acceleration-based features is insightful, particularly the data clipping (0-30 range) and the effectiveness of motion_count_gt_0.2g. These are crucial insights for our model development. Your collaborative approach is also appreciated. Thanks!🙂</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3229428": "Summary\n- Clip the data in the range of 0 to 30 and aggregate it\n- The feature motion_count_gt_0.2g seems to be effective for the target variable\n\nI investigated features derived from acceleration variables that might influence the target variable.\nThe notebook named by \"eda-acc-feature\" below was helpful as a reference. Please note that it was inspired by \"EDA:Detect Behavior with Sensor Data\" author by \"kakuteki\"-san, and parts of the code have been adapted from it.",
    "3229599": "sakaken Your analysis of acceleration-based features is insightful, particularly the data clipping (0-30 range) and the effectiveness of motion_count_gt_0.2g. These are crucial insights for our model development. Your collaborative approach is also appreciated. Thanks!🙂"
  },
  "source": "meta"
}