{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "# This R environment comes with all of CRAN preinstalled, as well as many other helpful packages\n# The environment is defined by the kaggle/rstats docker image: https://github.com/kaggle/docker-rstats\n# For example, here's several helpful packages to load in \n\nlibrary(ggplot2) # Data visualization\nlibrary(readr) # CSV file I/O, e.g. the read_csv function\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\nsystem(\"ls ../input\")\n\n# Any results you write to the current directory are saved as output.\n\nlibrary('xgboost')\n\ntrain <- read.csv(\"../input/train.csv\")\ntrain$hour <- train$time/(60*60)\ntrain$hour <- (train$hour%%24)%/%1)\ntrain$place_id<- as.factor(train$place_id)\n\nindex <- sample (c(1:nrow(train)), round(1*nrow(train)))\n\ntraintrain <- train\n#traintest <- train[-index,]\n\nstr(traintrain)\nsummary(traintrain)\nmodel <- xgboost(data = traintrain[ ,c(2, 3, 4, 7)], label = as.numeric(traintrain$place_id)-1, nrounds = 2, objective = \"multi:softmax\")\n\n\n(traintrain$place_id~.,data=traintrain[ ,c(2, 3, 4, 7)])\nrf.fit\n\nprediction <- predict(rf.fit, traintrain)\n\nt<-table(prediction, traintrain$place_id)\n\nprint(t)\ntraintrain$rightPred <- prediction == traintrain$place_id\n\naccuracy <- sum(traintest$rightPred)/nrow(traintrain)\n#       accuracies <- c(accuracies,accuracy)\n       print(accuracy)\n\n\n"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "library('xgboost')\n\ntrain <- read.csv(\"../input/train.csv\")\ntrain$hour <- train$time/(60*60)\ntrain$hour <- (train$hour%%24)%/%1)\ntrain$place_id<- as.factor(train$place_id)\n"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "library('xgboost')\n\ntrain <- read.csv(\"../input/train.csv\")\ntrain$hour <- train$time/(60*60)\ntrain$hour <- (train$hour%%24)%/%1))\ntrain$place_id<- as.factor(train$place_id)"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "library('xgboost')\n\ntrain <- read.csv(\"../input/train.csv\")\ntrain$hour <- train$time/(60*60)\ntrain$hour <- (train$hour%%24)%/%1\ntrain$place_id<- as.factor(train$place_id)"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "traintrain <- train\n#traintest <- train[-index,]\n\nstr(traintrain)\nsummary(traintrain)\nmodel <- xgboost(data = traintrain[ ,c(2, 3, 4, 7)], label = as.numeric(traintrain$place_id)-1, nrounds = 2, objective = \"multi:softmax\")"
 }
],"metadata":{"kernelspec":{"display_name":"R","language":"R","name":"ir"}}, "nbformat": 4, "nbformat_minor": 0}