{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "# This Julia environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/julia docker image: https://github.com/kaggle/docker-julia\n# For example, here's a helpful package to load in \n\n# using DataFrames # data processing, CSV file I/O - e.g. readtable(\"../input/MyTable.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\n#run(`ls ../input`)\n\n# Any results you write to the current directory are saved as output.\n\ntrain<-read.csv('../input/train.csv')\ntest<-read.csv('../input/train.csv')"
 }
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