{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "import numpy as np \nimport pandas as pd\nimport time\nimport logging \nlog_format='%(asctime)s %(levelname)s %(message)s'\nlogging.basicConfig(format=log_format, level=logging.INFO)\n        \n\nfrom subprocess import check_output\n#print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n## a peek on the train data \ntrain = pd.read_csv(\"../input/train.csv\", nrows=10000)\n#print(train.head()) \n#print(train.shape) \ntrain.describe().transpose()"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "train.head(10)"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "train[\"hotel_cluster\"].value_counts()"
 },
 {
  "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}