{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "import pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\n\n# The competition datafiles are in the directory ../input\n# Read competition data files:\ntrain = pd.read_csv(\"../input/train.csv\")\ntest  = pd.read_csv(\"../input/test.csv\")\n\n# Write to the log:\nprint(\"Training set has {0[0]} rows and {0[1]} columns\".format(train.shape))\nprint(\"Test set has {0[0]} rows and {0[1]} columns\".format(test.shape))\n# Any files you write to the current directory get shown as outputs\ndf = train\ntdf = test"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "X = df.drop('label', axis=1)\nY = df.label\ncls = DecisionTreeClassifier()\ncls.fit(X, Y)\npredict = cls.predict(tdf)\nsubmission = pd.DataFrame({'ImageId': tdf.index + 1,\n                           'Label':predict})\nsubmission.to_csv('decision_tree.csv', index=False)"
 }
],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}}, "nbformat": 4, "nbformat_minor": 0}