{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "553ed458-53cc-5e55-ec94-5d009ae1e886"
      },
      "outputs": [],
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "from sklearn import linear_model\n",
        "from numpy import genfromtxt,savetxt\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",
        "dataset=pd.read_csv(\"../input/train.csv\")\n",
        "train_Y=dataset.iloc[:,0]\n",
        "train_X=dataset.iloc[:,1:]\n",
        "test_X=pd.read_csv(\"../input/test.csv\")\n",
        "model=linear_model.SGDClassifier()\n",
        "model.fit(train_X,train_Y)\n",
        "\n",
        "from subprocess import check_output\n",
        "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
        "\n",
        "\n",
        "\n",
        "# Any results you write to the current directory are saved as output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "9b84040f-7962-8852-8e67-c883b842798b"
      },
      "outputs": [],
      "source": [
        "train_X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "56532506-a074-9430-ea2b-62a2c5f150fc"
      },
      "outputs": [],
      "source": [
        "rs=model.predict(test_X)\n",
        "sm=pd.DataFrame({'ImageId':range(1,len(rs)+1),'label':rs})\n",
        "sm.to_csv('mine_submission.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "e515a878-6d36-9f20-79e9-c5e8d05ac548"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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