{
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      "metadata": {
        "_cell_guid": "2dfd08f7-5338-2732-2500-23c2351a6b43"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "train_dataset = pd.read_csv(\"../input/train.csv\")\n",
        "test_dataset  = pd.read_csv(\"../input/test.csv\")\n",
        "\n",
        "train_dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "e589bdb7-db16-c30a-9074-d3897f1dae0c"
      },
      "outputs": [],
      "source": [
        "seed = 42\n",
        "\n",
        "X = train_dataset.drop(\"label\", axis=1)\n",
        "y = train_dataset[\"label\"]\n",
        "\n",
        "classifier = MLPClassifier(solver=\"lbfgs\", alpha=1e-5, random_state=seed)\n",
        "classifier.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "8d1b0292-45f2-472f-4b37-ca5468d7d8c4"
      },
      "outputs": [],
      "source": [
        "predictions = classifier.predict(test_dataset)\n",
        "out = pd.DataFrame(predictions, columns=[\"Label\"])\n",
        "out.to_csv(\"result.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "c4635d53-02c9-572e-d8d1-870b06530c4d"
      },
      "outputs": [],
      "source": ""
    }
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