{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "import xgboost as xgb\nimport numpy as np\nimport pandas as pd\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "train = pd.read_csv('../input/train.csv')\ntest = pd.read_csv('../input/test.csv')"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "train['wkday'] = train.time // 1440 % 7\ntrain['dtime'] = train.time % 1440"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "from sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\ny = le.fit_transform(train['place_id'])\nX = train[['x', 'y', 'accuracy', 'wkday', 'dtime']]"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "param = {'bst:max_depth':10, 'bst:eta':1, 'silent':1, 'objective':'multi:softmax', 'eval_metric':'auc' }"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "goal = xgb.DMatrix(X, label = y)\n"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": ""
 }
],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}}, "nbformat": 4, "nbformat_minor": 0}