{
  "id": 580067,
  "title": "Baseline Starter LB:0.058 Wow!",
  "url": "/competitions/drw-crypto-market-prediction/discussion/580067",
  "author_name": "yunsuxiaozi",
  "post_date": "2025-05-22T08:20:57.387000",
  "votes": 14,
  "comment_count": 0,
  "views": 0,
  "content": "<pre><code> pandas  pd\n polars  pl\n lightgbm  LGBMRegressor\ntrain=pl.read_parquet()\ntrain=train.to_pandas()\ntest=pl.read_parquet()\ntest=test.to_pandas()\n()\ntrain.head()\n\nNUNIQUE1=[c  c  train.columns  train[c].nunique()==]\ntrain.drop(NUNIQUE1+[],axis=,inplace=)\ntest.drop(NUNIQUE1+[],axis=,inplace=)\ntrain.head()\n\nFEATURES=[c  c  train.columns  c!=]\nmodel=LGBMRegressor()\nmodel.fit(train[FEATURES].values,train[].values)\ntest_preds=model.predict(test[FEATURES].values)\n(test_preds[:])\n\nsub=pd.read_csv()\nsub[]=test_preds\nsub.to_csv(,index=)\nsub.head()\n</code></pre>",
  "messages": [
    {
      "id": 3207090,
      "postDate": "2025-05-22T08:20:57.387Z",
      "content": "<pre><code> pandas  pd\n polars  pl\n lightgbm  LGBMRegressor\ntrain=pl.read_parquet()\ntrain=train.to_pandas()\ntest=pl.read_parquet()\ntest=test.to_pandas()\n()\ntrain.head()\n\nNUNIQUE1=[c  c  train.columns  train[c].nunique()==]\ntrain.drop(NUNIQUE1+[],axis=,inplace=)\ntest.drop(NUNIQUE1+[],axis=,inplace=)\ntrain.head()\n\nFEATURES=[c  c  train.columns  c!=]\nmodel=LGBMRegressor()\nmodel.fit(train[FEATURES].values,train[].values)\ntest_preds=model.predict(test[FEATURES].values)\n(test_preds[:])\n\nsub=pd.read_csv()\nsub[]=test_preds\nsub.to_csv(,index=)\nsub.head()\n</code></pre>",
      "rawMarkdown": "```python\nimport pandas as pd\nimport polars as pl\nfrom lightgbm import LGBMRegressor\ntrain=pl.read_parquet(\"/kaggle/input/drw-crypto-market-prediction/train.parquet\")\ntrain=train.to_pandas()\ntest=pl.read_parquet('/kaggle/input/drw-crypto-market-prediction/test.parquet')\ntest=test.to_pandas()\nprint(f\"train.shape:{train.shape},test.shape:{test.shape}\")\ntrain.head()\n\nNUNIQUE1=[c for c in train.columns if train[c].nunique()==1]\ntrain.drop(NUNIQUE1+['timestamp'],axis=1,inplace=True)\ntest.drop(NUNIQUE1+['label'],axis=1,inplace=True)\ntrain.head()\n\nFEATURES=[c for c in train.columns if c!='label']\nmodel=LGBMRegressor()\nmodel.fit(train[FEATURES].values,train['label'].values)\ntest_preds=model.predict(test[FEATURES].values)\nprint(test_preds[:10])\n\nsub=pd.read_csv('/kaggle/input/drw-crypto-market-prediction/sample_submission.csv')\nsub['prediction']=test_preds\nsub.to_csv(\"base.csv\",index=None)\nsub.head()\n```\n",
      "votes": 14
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "3207090": "```python\nimport pandas as pd\nimport polars as pl\nfrom lightgbm import LGBMRegressor\ntrain=pl.read_parquet(\"/kaggle/input/drw-crypto-market-prediction/train.parquet\")\ntrain=train.to_pandas()\ntest=pl.read_parquet('/kaggle/input/drw-crypto-market-prediction/test.parquet')\ntest=test.to_pandas()\nprint(f\"train.shape:{train.shape},test.shape:{test.shape}\")\ntrain.head()\n\nNUNIQUE1=[c for c in train.columns if train[c].nunique()==1]\ntrain.drop(NUNIQUE1+['timestamp'],axis=1,inplace=True)\ntest.drop(NUNIQUE1+['label'],axis=1,inplace=True)\ntrain.head()\n\nFEATURES=[c for c in train.columns if c!='label']\nmodel=LGBMRegressor()\nmodel.fit(train[FEATURES].values,train['label'].values)\ntest_preds=model.predict(test[FEATURES].values)\nprint(test_preds[:10])\n\nsub=pd.read_csv('/kaggle/input/drw-crypto-market-prediction/sample_submission.csv')\nsub['prediction']=test_preds\nsub.to_csv(\"base.csv\",index=None)\nsub.head()\n```\n"
  }
}