{
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      "cell_type": "markdown",
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        "_cell_guid": "ea18632d-bb75-4b1c-e8f0-e5043bff6ccb"
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      "source": [
        "This is just a test"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "f801273c-0ccf-c265-5949-b6b0ec3ad614"
      },
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      "source": [
        "%%time\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "from sklearn import linear_model\n",
        "\n",
        "from numpy import genfromtxt,savetxt\n",
        "\n",
        "dataset = pd.read_csv(\"../input/train.csv\")\n",
        "target = dataset.iloc[:,0]\n",
        "train = dataset.iloc[:,1:]\n",
        "\n",
        "test = pd.read_csv(\"../input/test.csv\")\n",
        "\n",
        "clf = linear_model.SGDClassifier()\n",
        "clf.fit(train,target) # 784\n",
        "rs = clf.predict(test)\n",
        "sm = pd.DataFrame({'ImageId': range(1,len(rs)+1), 'Label':rs})\n",
        "\n",
        "sm.to_csv('mine_submission.csv',index=False)\n",
        "\n",
        "from subprocess import check_output\n",
        "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))"
      ]
    }
  ],
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