{
  "id": 81143,
  "title": "Earthquake Prediction Tutorial [Youtube Video 🔴]",
  "url": "/competitions/LANL-Earthquake-Prediction/discussion/81143",
  "author_name": "",
  "post_date": "2019-02-19T12:39:16.473998Z",
  "votes": 7,
  "comment_count": 2,
  "views": 0,
  "content": "<h2><a href=\"https://www.youtube.com/watch?v=TffGdSsWKlA\">Link</a></h2>\n\n<p>Lecture Content <br>\nStep 1 - Installing dependencies <br>\nStep 2 - Importing dataset <br>\nStep 3 - Exploratory data analysis <br>\nStep 4 - Feature engineering (statistical features added) <br>\nStep 5 - Implement \"Catboost\" model <br>\nStep 6 - Implement Support Vector Machine + Radial Basis Function model <br>\nStep 7 - Future Directions (Genetic Programming, Recurrent Networks, etc.) <br>\nStep 8 - Freestyle Rap <br></p>\n\n<p>Thanks For Reading (✌◠▽◠) <br></p>",
  "messages": [
    {
      "id": "474493",
      "postDate": "02/19/2019 12:39:16",
      "content": "<h2><a href=\"https://www.youtube.com/watch?v=TffGdSsWKlA\">Link</a></h2>\n\n<p>Lecture Content <br>\nStep 1 - Installing dependencies <br>\nStep 2 - Importing dataset <br>\nStep 3 - Exploratory data analysis <br>\nStep 4 - Feature engineering (statistical features added) <br>\nStep 5 - Implement \"Catboost\" model <br>\nStep 6 - Implement Support Vector Machine + Radial Basis Function model <br>\nStep 7 - Future Directions (Genetic Programming, Recurrent Networks, etc.) <br>\nStep 8 - Freestyle Rap <br></p>\n\n<p>Thanks For Reading (✌◠▽◠) <br></p>",
      "rawMarkdown": "[Link][1]\n----\n\nLecture Content <br>\nStep 1 - Installing dependencies <br>\nStep 2 - Importing dataset <br>\nStep 3 - Exploratory data analysis <br>\nStep 4 - Feature engineering (statistical features added) <br>\nStep 5 - Implement \"Catboost\" model <br>\nStep 6 - Implement Support Vector Machine + Radial Basis Function model <br>\nStep 7 - Future Directions (Genetic Programming, Recurrent Networks, etc.) <br>\nStep 8 - Freestyle Rap <br>\n\nThanks For Reading (✌◠▽◠) <br>\n\n\n  [1]: https://www.youtube.com/watch?v=TffGdSsWKlA",
      "votes": null
    },
    {
      "id": "478845",
      "postDate": "02/26/2019 17:44:19",
      "content": "<p>I wrote a kernel based roughly on Siraj's steps. My intention is to use this as a template for exploring different models in this competition. My kernel uses an LTSM model. It can easily be adapted to use the CatBoost model (Step 5 above), or the SVM + RBF model (Step 6 above).</p>\n\n<p>You may find my kernel here:\n<a href=\"https://www.kaggle.com/devilears/siraj-s-steps-lstm\">Siraj's Steps: LSTM</a>.</p>",
      "rawMarkdown": "I wrote a kernel based roughly on Siraj's steps. My intention is to use this as a template for exploring different models in this competition. My kernel uses an LTSM model. It can easily be adapted to use the CatBoost model (Step 5 above), or the SVM + RBF model (Step 6 above).\n\nYou may find my kernel here:\n[Siraj's Steps: LSTM](https://www.kaggle.com/devilears/siraj-s-steps-lstm).",
      "votes": null
    },
    {
      "id": "478982",
      "postDate": "02/26/2019 21:59:21",
      "content": "<p>Okk, I will see.</p>",
      "rawMarkdown": "Okk, I will see.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 478845,
      "author_name": "devilears",
      "author_url": "",
      "post_date": "02/26/2019 17:44:19",
      "content": "<p>I wrote a kernel based roughly on Siraj's steps. My intention is to use this as a template for exploring different models in this competition. My kernel uses an LTSM model. It can easily be adapted to use the CatBoost model (Step 5 above), or the SVM + RBF model (Step 6 above).</p>\n\n<p>You may find my kernel here:\n<a href=\"https://www.kaggle.com/devilears/siraj-s-steps-lstm\">Siraj's Steps: LSTM</a>.</p>",
      "votes": null,
      "replies": [
        {
          "id": 478982,
          "author_name": "ishivinal",
          "author_url": "",
          "post_date": "02/26/2019 21:59:21",
          "content": "<p>Okk, I will see.</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "474493": "[Link][1]\n----\n\nLecture Content <br>\nStep 1 - Installing dependencies <br>\nStep 2 - Importing dataset <br>\nStep 3 - Exploratory data analysis <br>\nStep 4 - Feature engineering (statistical features added) <br>\nStep 5 - Implement \"Catboost\" model <br>\nStep 6 - Implement Support Vector Machine + Radial Basis Function model <br>\nStep 7 - Future Directions (Genetic Programming, Recurrent Networks, etc.) <br>\nStep 8 - Freestyle Rap <br>\n\nThanks For Reading (✌◠▽◠) <br>\n\n\n  [1]: https://www.youtube.com/watch?v=TffGdSsWKlA",
    "478845": "I wrote a kernel based roughly on Siraj's steps. My intention is to use this as a template for exploring different models in this competition. My kernel uses an LTSM model. It can easily be adapted to use the CatBoost model (Step 5 above), or the SVM + RBF model (Step 6 above).\n\nYou may find my kernel here:\n[Siraj's Steps: LSTM](https://www.kaggle.com/devilears/siraj-s-steps-lstm).",
    "478982": "Okk, I will see."
  },
  "source": "meta"
}