PDSH_Bike sharing Demand_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# to draw graph in notebook\n",
    "%matplotlib inline\n",
    "\n",
    "#solve problem about korean minus font in graph\n",
    "mpl.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10886, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train =pd.read_csv(\"data/train.csv\",parse_dates=[\"datetime\"])\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=pd.read_csv(\"data/test.csv\",parse_dates=[\"datetime\"])\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10886, 18)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"year\"]=train[\"datetime\"].dt.year\n",
    "train[\"month\"]=train[\"datetime\"].dt.month\n",
    "train[\"day\"]=train[\"datetime\"].dt.day\n",
    "train[\"hour\"]=train[\"datetime\"].dt.hour\n",
    "train[\"minute\"]=train[\"datetime\"].dt.minute\n",
    "train[\"second\"]=train[\"datetime\"].dt.second\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10886, 19)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"dayofweek\"]=train[\"datetime\"].dt.dayofweek\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 16)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"year\"]=test[\"datetime\"].dt.year\n",
    "test[\"month\"]=test[\"datetime\"].dt.month\n",
    "test[\"day\"]=test[\"datetime\"].dt.day\n",
    "test[\"hour\"]=test[\"datetime\"].dt.hour\n",
    "test[\"minute\"]=test[\"datetime\"].dt.minute\n",
    "test[\"second\"]=test[\"datetime\"].dt.second\n",
    "test[\"dayofweek\"]=test[\"datetime\"].dt.dayofweek\n",
    "test[\"dayofweek\"]=test[\"datetime\"].dt.dayofweek\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119dc5320>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M/Aa4LCK+DdzWW5iZ7xlKVJIkSZIkaYkw0cTEX5r/YN7gl5IkSZIkSffLhBITmXn4wreS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\nDyjf4xMF/R79AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6e9630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#widdspeed\n",
    "\n",
    "fig, axes =plt.subplots(nrows=2)\n",
    "fig.set_size_inches(18,10)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.xticks(rotation=30,ha='right')\n",
    "axes[0].set(ylabel='Count',title=\"train windspeed\")\n",
    "sns.countplot(data=train, x=\"windspeed\", ax=axes[0])\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.xticks(rotation=30,ha='right')\n",
    "axes[1].set(ylabel='Count',title=\"test windspeed\")\n",
    "sns.countplot(data=test, x=\"windspeed\", ax=axes[1])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think zero values were recorded when there were no data.\n",
    "so i should take machine learning method to predict the values of zero value in windspeed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1313, 19)\n",
      "(9573, 19)\n"
     ]
    }
   ],
   "source": [
    "# separate windspeed is 0 and windspeed is not 0\n",
    "trainWind0 = train.loc[train['windspeed'] == 0]\n",
    "trainWindNot0 = train.loc[train['windspeed'] != 0]\n",
    "print(trainWind0.shape)\n",
    "print(trainWindNot0.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# put on data using machine learing \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "def predict_windspeed(data):\n",
    "    \n",
    "    #separate data\n",
    "    dataWind0 = data.loc[train['windspeed'] == 0]\n",
    "    dataWindNot0 = train.loc[train['windspeed'] != 0]\n",
    "    \n",
    "    # choice feature to predict windspeed\n",
    "    wCol=[\"season\",\"weather\",\"humidity\",\"month\",\"temp\",\"year\",\"atemp\"]   \n",
    "    \n",
    "    # change type of data windspeed is not 0 to string  \n",
    "    dataWindNot0[\"windspeed\"]= dataWindNot0[\"windspeed\"].astype(\"str\")\n",
    "    \n",
    "    # using randomforestclassifier\n",
    "    rfModel_wind=RandomForestClassifier()\n",
    "    \n",
    "    \n",
    "    #be learned windspeed underlying feature values in wCol data \n",
    "    rfModel_wind.fit(dataWindNot0[wCol],dataWindNot0[\"windspeed\"])\n",
    "    \n",
    "    #predict windspeed for data with 0 recode, based on learned values\n",
    "    wind0Values = rfModel_wind.predict(X= dataWind0[wCol])\n",
    "    \n",
    "    # to compare values\n",
    "    predictWind0=dataWind0\n",
    "    predictWindNot0=dataWindNot0\n",
    "    \n",
    "    # predicted values put on data \n",
    "    predictWind0[\"windspeed\"]=wind0Values\n",
    "    \n",
    "    # to combine dataframe\n",
    "    data= predictWindNot0.append(predictWind0)\n",
    "    \n",
    "    # datatype of windspeed assign type \" float\"\n",
    "    data[\"windspeed\"]=data[\"windspeed\"].astype(\"float\")\n",
    "    \n",
    "    data.reset_index(inplace=True)\n",
    "    data.drop('index',inplace=True,axis=1)\n",
    "    \n",
    "    return data\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11da35e10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AT4EjI2J54LfA7My8KyIOpyQppgIHZuatQ45VkiRJkiR1bKiJicz8B/Cyhby11ULWPRI4\nsvOgJEmSJElSM8MeyiFJkiRJknQPExOSJEmSJKkZExOSJEmSJKkZExOSJEmSJKkZExOSJEmSJKkZ\nExOSJEmSJKkZExOSJEmSJKkZExOSJEmSJKkZExOSJEmSJKkZExOSJEmSJKkZExOSJEmSJKkZExOS\nJEmSJKmZgRITEfGZhSw7dvLDkSRJkiRJy5Kxid6MiKOADYGnRcTj+95aDli9y8AkSZIkSdLSb8LE\nBHAosD7waeCQvuV3Ar/tKCZJkiRJkrSMmDAxkZnXANcAT4qI1ShVElPq26sAf+8yOEmaLCeeuUuT\n7b5s69lNtitJkiQ9UCyuYgKAiDgAOAC4oW/xfMowD0mSJEmSpPtkoMQEsBfwyMyc02UwkiRJkiRp\n2TLo40L/hMM2JEmSJEnSJBu0YuJ3wAUR8UPg1t7CzPxAJ1FJkiRJkqRlwqCJif+t/8GCyS8lSZIk\nSZLul4ESE5l5yOLXkiRJkiRJWjKDPpXjbspTOPr9OTPXm/yQJEmSJEnSsmLQiol7JsmMiOWAHYFn\ndRWUJEmSJElaNgz6VI57ZOYdmXkS8NwO4pEkSZIkScuQQYdy7NH34xTg8cDtnUQkSZIkSZKWGYM+\nleM5fa/nA38DXj754UiSJEmSpGXJoHNM7Fnnloj6O5dn5p2dRiZJkiRJkpZ6A80xERGbAL8DjgWO\nBv4UEc/oMjBJkiRJkrT0G3Qox+HAyzPzYoCIeCbwGWDTrgKTJEmSJElLv0GfyrFKLykBkJkXASt0\nE5IkSZIkSVpWDJqY+HtE7ND7ISJ2BG7oJiRJkiRJkrSsGHQox+uA0yPiS5THhc4Hnt1ZVJIkSZIk\naZkwaMXEtsAtwCMojw6dA8zqKCZJkiRJkrSMGDQx8Tpgs8z8Z2b+CtgEeFN3YUmSJEmSpGXBoImJ\n5YDb+36+nTKcQ5IkSZIk6T4bdI6JU4GzI+LE+vNOwGndhCRJkiRJkpYVA1VMZOa7gMOBADYEDs/M\ng7sMTJIkSZIkLf0GrZggM2cDszuMRZIkSZIkLWMGnWNCkiRJkiRp0g1cMTFZIuIA4CXA8sARwLnA\nMZTJNC8H3piZd0fE3sA+wJ3AoZl5+rBjlSRJkiRJ3RpqxUREzAKeDWwGbAWsB3wSOCgztwCmADtE\nxNrA/nW9rYHDImL6MGOVJEmSJEndG3bFxNbAZcApwGrAO4C9KVUTAGcALwTuAi7MzNuA2yLiKmBj\n4CcT/fEZM1ZibGwaAHO6iH4AM2euOuH71w8pjn6Li+naIcUx3kRxXTfEOPotrq1aMKbBjGJMMLpx\nSZIkSaNi2ImJNYFHANsDGwDfBKZm5vz6/jxgdUrS4qa+3+stn9DcubdMarD3xZw581qHcC+jGBOM\nZlzGNBhjGtyoxiVJkiQN00Q37IadmLgBuCIzbwcyIm6lDOfoWRW4Ebi5vh6/XJIkSZIkLUWG/VSO\nC4BtImJKRKwLrAz8oM49AbAtcD5wCbBFRKwQEasDG1EmxpQkSZIkSUuRoVZMZObpEbElJfEwFXgj\ncDVwZEQsD/wWmJ2Zd0XE4ZQkxVTgwMy8dZixSpIkSZKk7g39caGZ+c6FLN5qIesdCRzZfUSSJEmS\nJKmVYQ/lkCRJkiRJuoeJCUmSJEmS1IyJCUmSJEmS1IyJCUmSJEmS1IyJCUmSJEmS1IyJCUmSJEmS\n1IyJCUmSJEmS1IyJCUmSJEmS1IyJCUmSJEmS1IyJCUmSJEmS1IyJCUmSJEmS1IyJCUmSJEmS1IyJ\nCUmSJEmS1IyJCUmSJEmS1MxY6wAkaVn1+bN3abLd1z93dpPtSpIkSQtjxYQkSZIkSWrGxIQkSZIk\nSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrG\nxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQk\nSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIk\nSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWpmrMVGI2It4FLgBcCdwDHAfOBy4I2ZeXdE7A3s\nU98/NDNPbxGrJEmSJEnqztArJiJiOeALwL/qok8CB2XmFsAUYIeIWBvYH9gM2Bo4LCKmDztWSZIk\nSZLUrRZDOT4OfB74c/15E+Dc+voM4PnApsCFmXlbZt4EXAVsPOxAJUmSJElSt4Y6lCMiXgPMycwz\nI+KAunhKZs6vr+cBqwOrATf1/Wpv+YRmzFiJsbFpAMyZrKCX0MyZq074/vVDiqPf4mK6dkhxjDdR\nXNcNMY5+i2urFoxpMKMYE4xmXKMYkyRJkpZdw55j4t+B+RHxfODJwHHAWn3vrwrcCNxcX49fPqG5\nc2+ZvEjvozlz5rUO4V5GMSYYzbiMaTDGNLhRjGsUY5IkSdLSbaKbY0NNTGTmlr3XEXEO8HrgPyJi\nVmaeA2wL/BC4BPhQRKwATAc2okyMKUmSJEmSliJNnsoxztuAIyNieeC3wOzMvCsiDgfOp8yDcWBm\n3toySEmSJEmSNPmaJSYyc1bfj1st5P0jgSOHFpAkiXde8NIm2/3Y5ic12a4kSZLaa/FUDkmSJEmS\nJMDEhCRJkiRJasjEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJasbE\nhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJasbEhCRJ\nkiRJasbEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJ\nasbEhCRJkiRJasbEhCRJkiRJasbEhCRJkiRJamasdQCSJE1kjwsParLd4zY7tMl2JUmSljVWTEiS\nJEmSpGZMTEiSJEmSpGZMTEiSJEmSpGZMTEiSJEmSpGZMTEiSJEmSpGZMTEiSJEmSpGZMTEiSJEmS\npGbGWgcgSdID0asv+OzQt3ns5vsNfZuSJElds2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JC\nkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1MzbMjUXEcsCXgfWB6cCh\nwG+AY4D5wOXAGzPz7ojYG9gHuBM4NDNPH2askiRJkiSpe8OumNgNuCEztwC2AT4LfBI4qC6bAuwQ\nEWsD+wObAVsDh0XE9CHHKkmSJEmSOjbUigngJGB2fT2FUg2xCXBuXXYG8ELgLuDCzLwNuC0irgI2\nBn4y3HAlSZIkSVKXhpqYyMx/AETEqpQExUHAxzNzfl1lHrA6sBpwU9+v9pZPaMaMlRgbmwbAnMkL\ne4nMnLnqhO9fP6Q4+i0upmuHFMd4E8V13RDj6Le4tmrBmAYzijHBaMZlTIMxJkmSpOEYdsUEEbEe\ncApwRGaeEBEf63t7VeBG4Ob6evzyCc2de8tkhnqfzJkzr3UI9zKKMcFoxmVMgzGmwY1iXMY0mAdi\nTK85//ghRfJ/HbPF7k22K0mSHjgmusEy1DkmIuIhwFnAuzLzy3XxzyNiVn29LXA+cAmwRUSsEBGr\nAxtRJsaUJEmSJElLkWFXTLwHmAEcHBEH12VvBg6PiOWB3wKzM/OuiDickqSYChyYmbcOOVZJkiRJ\nktSxYc8x8WZKImK8rRay7pHAkZ0HJUmSJEmSmhn240IlSZIkSZLuYWJCkiRJkiQ1Y2JCkiRJkiQ1\nY2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JC\nkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJkiQ1Y2JCkiRJ\nkiQ1M9Y6AEmStPTa87yTm2z36C13brJdSZK05KyYkCRJkiRJzZiYkCRJkiRJzZiYkCRJkiRJzZiY\nkCRJkiRJzTj5pSRJWqbsee4ZTbZ79FbbNtmuJEmjzooJSZIkSZLUjBUTkiRJjb323HObbPdLW23V\nZLuSJPWzYkKSJEmSJDVjYkKSJEmSJDVjYkKSJEmSJDVjYkKSJEmSJDXj5JeSJElaqNed97Ohb/OL\nWz516NuUJLVlxYQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQkSZIkSWrGxIQk\nSZIkSWrGxIQkSZIkSWpmrHUAkiRJ0gPZBy+4scl2D958jSbblaTJZsWEJEmSJElqxooJSZIkPWDs\ne94fmmz3c1tu2GS7krQssGJCkiRJkiQ1M7IVExExFTgCeBJwG7BXZl7VNipJkiRp9J18/i1Ntrvz\nFist8r0fn3PbECNZ4FmzpjfZrqTBjWxiAtgRWCEznxURzwQ+AezQOCZJkiRJS5Erz7h16Nt8zLYr\nDH2b0igb5cTE5sB3ATLzooh4WuN4JEmSJKlzc2a3qXiZucuiK17uPPqmIUaywNieqzfZ7tJm/tfP\nG/o2p7x8y8HXnT9/foeh3HcRcRRwcmaeUX/+E7BhZt7ZNjJJkiRJkjRZRnnyy5uBVft+nmpSQpIk\nSZKkpcsoJyYuBLYDqHNMXNY2HEmSJEmSNNlGeY6JU4AXRMSPgCnAno3jkSRJkiRJk2xk55iQJEmS\nJElLv1EeyiFJkiRJkpZyJiYkSZIkSVIzJiYkSepARExpHYOk0WGfIKnH/uDeltnERESs1DqG8SJi\n7YhYu3UckvRAFBEjc0yLiOWAR9fXnnxMICKm9D4722rRImJaRDy4dRwPJPYJDzz2B0vOdhrMKLWT\n/cHCjUyHPUwRcQhwfES8NyJmtI4HICLeDXwbeGrrWHoiYvvaVqN2cN+s/n+UYnp2RKxbX09rHU9P\nRDy2dQzjRcSjWscwXkRsEhGrtY6jX0TsEhE7t46jX0TsVvvNJ7WOpad+dqcAZObdreMBiIh/A34N\nHAiQmc1nmY6I3SPifRHxtNax9IuIfYBjgJ1gZNrqVRHxjoh4SutYeiLiVcBpwLNax9Iziv0B2CcM\nahT7BPuDwUTEnhHx2YjYHUamnUa1P3hSRLwFRqOdYGT7g5HYz0fmwm4YImK5iDgYWBN4MzAL2L6+\n1yRbFRGrRcRRwIOBzYDvRcT0FrEsxNOAt0XEw0fo4L4hcH5ERGbe3TrLGBFPj4hfAfsCZ0TEepl5\nV+ukSUQ8JSK+AfxnRHw4Ih5flzdrr76TxY9GxIci4hl1ebO2ioin1nb6APDViHhhXd6ynbaIiNnA\n84Gft4qjL54pEbF8RBwDvBi4GXh7RDy3bWT3WBfYISJeABARzR6DHRGPi4iTgR2A3wLfqstb7uPT\nIuILwIuAOcD+EbFlq3hqTFPrfnUw8HTgw8C03mfX8Hi8XEQcTdnPr6Xs5/v0Ym4U0wpRHpu+JfDq\nzDy9772W7XQMo9kfADyU0ekTNhrBPmFKRHyR0eoT7A8WH8/UiBir1wwvAE4CXhsRbxx2LOPiGvX+\n4CXAv0VEQPPzu5HqD+o+tfwo7efLRGIi6vCIzLyDcvF/YmZeS/lSb1rfG2q2KiLWqNu9GVge+B/g\nMOBkygXSxsOMp8b0kL7X61GSJd8BPjHsWCbwaODvwCGtA6l2Af4rM3cHzqUkKEbhLs3ewAWUjmYq\n8ERok5WNiBXqy9cB52bmzsBlwKdqTC3b6nXAOZn5IuBE4JU1ppbZ60OAyzLz9cBGEfHoVoFEHfKW\nmbcD/wI+kpn/CcwF/ty33tAO9HHviqQZwNGU/pPMvHNYsfTF1BsauCFwdGa+BvgqsFGNaej7eE1I\nTMnMuyjHmAMz8wjK53hD33pDPUmrbTW/fsdWpVQKvhTYjnJCtGJmzh/yPjW1bu8uYD5wcGZ+Dfgv\n4B0RsdKwP8OIWCciXpiZtwKXA78B9o6IEyLi4zWmJu1Uz6VuZwT6g7q9dSLi+VFKowHWoH2f0Du/\nfhRw3Ij0CevU5PvywDRGoE/o26fmUz6302nfH0wbwf5gpRrLXcDdwPsz81zgWGDo+3eNadq4/uCw\nEekPVuqVdqPcAAAgAElEQVR7vQnlJs8vgTdBs/Pg3nnLKPUHqwFj9fzuLkZgP4elPDERpRrhcOAr\nEfGuiHgkpVLi4rrKI4CfNohrc+B9EfG4uujLwHuBizPzJZSTkF0iYuaQ4lm9ttPxEfH2iFifcvF/\nXGa+DHhCRDy/rjuUDqa3nV6CJhZUkTwSeA6wfkTsSLkQGGZca0TE6yOiN+Tmz8CLI2LNGtsdEbFl\nRCw/jHhqTOPb6sGUk4wz6wHjBcBqUcvrFnJh12VsvWFTawLLARfWt74FrBkRe/b/GzqOZXw7rQPc\nAvygrrIO5eR/6CJi05oMBPg0sFVEnA08D/h8RPy/ut7Q+uzeZwccUJMjVwCvi4iPAa+prw+rqw/r\n+/cB4MMRsXff4m0y87XAdRFxTi/TPyy1nb4aEQcAF/bd0X4q8Ke6zlCPtb12Al5TL9a+BlwdEbsB\nrwX2jYhP1dWHecLf26cOjIgNKH3CRpS7bG8Fngx8ZphxRcSHgP8AXg2sQEnI94Z1XQ9cRzlvGOZx\n5vXAr4B31kXfBXajtMl+lCqh/6zvDbudXlPb4Tc07g9qXL22ekc93kH7PuFDwCci4jWZ+a3MPK2+\n1bJP6LXTW4A7KIn4pn1C3z7173XRGPA42vYHH6bcNHltXTQTWL2+btUf9PrNd1Ha5/vU/YhS9fK3\nYcZTtzW+nRLYJyI+Stv+oHfO+b6IWJkyXOKzlP5ylYgYepV8batPR8RrR6g/OBSYTam4WZ8R2M97\nmpW3DckelC/EHsDHgSsz85Sa5VuHMlThswD1g/ljl5m0iJhas08vplxc/zIifp+Z50TEQZTOBuDz\nlB3m8K5iGee1lH3hNZTqiP/JzK9HGaIA8BHgfcD3h5VprBnyLYATowwlua2+NRO4Bvge8HVKx/ju\nYcQUEbMomcTTgcdExNaUg+pawI+BMykXb4cBbwMuGkZcfW11UpShJDdExEcy858R8SJKadadwEUR\nsWZm/rPrmCLiZZTqg+WA6Zn5t4i4jZLE+QclKfgNSrntV2vGtlMLaafrIuLjmdnL7G9IqVgamoh4\nJvAF4EfAEyPiDZQDwm+BkzPz7Ih4MvDNiPh8Zt7ScTxTKH3Bu1kw5O144PeZ+el6MHsGJet/O3Bl\nRByRmf/TcUzLU5K3DwE+BBxV96dvApdHGX+/BvB44A3193r9bVcx9bfTm4DjgP+t/wf4BfAy4Nhh\n3HVYRDt9CbgVOKkOfbuU8t27Gci6///vEOJa2D71S+AqStXSuzPz7xGxF3BxRDwkM//acVxjlIu0\ntSjHva9QvnuXAa+PiJuB9eryp0bEyl33nTV5fATl5slbKJ8jwB+ALwKzM3NuRLwO+HFErJWZ13cc\n0/h2Oh74B+Vu3z6UIbFD6w/64hrfVmvV5SsCV0TEKxlin1D//vi2Oi4i7gC+nZk3Uvb5lzKkPqHG\ndK99qm77zPr+Txh+n3Cv715E/IVyp30H4AMN+oPlgQMo37lPUipOL6LcRNk/Iq4HHs6Q+oMJ+s0/\nZeYJUSpNHko5bzm1/tpKQNd91L3aKSJ+k5mfaHF+UGNaWFsdB1yTmcdGGToxhXITaueI+F7fNUWX\ncY1vq/N613x1laGeI/TFtTuwIrAj8LLMvCbKjbD9IuIGhnzcG2+pq5iIiMfHgjvCzwKOrxce04En\nRcQjamnrwynVEo+uH8h2dNQeNaap9cRwRcrJ46nA+sAmdbUv1vhWrLFd1UUs42LqtdNGlHL2P1MO\nDBERM3pf3Mw8Flg5It7WZUzj4lsZeAXwIODgumwG8CRKpv8RwE/oKxcbgicA/5GZ7wKOBB5DOYie\nCGRm7lfLoK6iXJAPRV9bzaC2FaU0k8z8dmbukJlfpiRyOp94MiKeSEm+HQa8n3IyBqVjvgE4CNid\nkny7lDKWdBgVE/3t9N66+Lr63mqUA/wPoszP8bKu46l2Bo7IzH0pJZn7UQ7o/0U5MSIzfwGcxYKL\nlE7Ui5z5CxnydiKweV1tOvDNejH0EEoZ/k1DiOk2YGPKBfbVlITg/pT+YG/KAXZ3yj7+HuiuNHKC\ndjqJMj6651LgdxHxmC7iWERM49vpY5S7bE+sq/6lXnQ8lHLCfcNC/+DkxzW+rWYDW1CSclcBj6z9\n+6MpVVV/6zImuKe8/xnAaZl5BaVf2oFSnfB+SkL3QEqy+cqOL0LWqi9vBF6fmf+P8l3rDen8FWVo\nwnK1r3wM5UZBZ0mJCdrpQMr50qMod9hOG1Z/0B8Xi26r5Sg3WXZiiH1C/fsLa6vnA1FX/QlD7BPq\ny/Ht9IRxq84ZVp+wmO/edpTv/rXABg36g9trTN/KzF9TjsdBrRpkyP3BBMfiZ9V476ZcQ5wErFcv\nvvdexJ+clJjqdse309HAhlEq86bToD+Y4Hj8tBrz3fW67zxKtdB+XcdUtzu+rY4BHtZ37fUT4Koh\n9wdQqqj/SDnOvTjKnCU/oHwP/8SQ9vNFWWoqJqJUQHyMcsH6q4j4NrBbvUu6MeWi4zLgc1FKbp9N\nKZNcB/hQZv5gEX96smL6RUT8JDOPj1Iu909KxvjpEfHrzLwpInYB3k4pIz0gMye9M46Ih1EqINai\nfCG+TanM2DVK2dX1wA+BL9VMZ6+KY09glcmOpy+uNSh32C+inITNp1yYfRT4YUQcm5m/j4grgN9l\n5tFRJsI8KSKO6uJOckQEJfN6FuXg+GDKnCTHZOZvI+IuysH0NOC2KFUvj6WUA18z2fH0xbW4tjo6\nM6+OMrfDtpQL3a0on/nvO47pQkpGf/e6fGPgcTXr+jtKOdtzKQf5lwMPy8x/dRzTotrpy/XiDcpF\n0vKUE9jnUKqWuorpFcAllITNTZREJJl5ZE3+/Rg4BdgtSlXAEynJlGs7imk14IPAoyLiB5R5ZfYF\n/lJXeQQLJuH8NfCMiPga5cTj6Czz5Ex2TKvXmB4eET+ktNe3KJ/NmZQKk38AKwNbZWavHPKdwNaT\nHU/924O0U//QwLuAtSll91d2FNMg7fQ3YOOI+BvwpnoStArls7u1o7gW11brUZK5d0bEZyj96Nco\nc058rp5EdhnT2ZSbA6dSkm6nZ+Z3I2IryrnBxcBfgXdQ+vSPTXY8Nabe5/eIiPg+ZTjnJfXtaygn\nsctl5h0R8QRKMu5hlIvvL3QU0+La6ayIeB7lou1iYOuIOIGyr3fSH9S4BmmrFTPz5ojYPjN/U39v\nWH3CwtrqexHxHMrx92LKza9h9QmD7FOPBfaIMqy4sz5hwO/ecyn9wmHArnTfH/T3nedSLqI/ChwU\nEa+mXOSuSrm4PZZyIdd1f7Ckx5jnU65jZlHOS7/WQUyDtNMMSqXC3yjVxJ2eH9S4lrStoNzI/Drl\n2NxFTIO01eqUc+KPUZIkD2F45wjnUhLvX6cMnzolM98ZEf9Bqdw4ntJGne7ni7M0VUxsB8zLzC2B\n/6ZcWKwCkJm/ysynZOb7KRcpDwauBvbNzFd3kZRYSEwnAodGxCqZ+afMvIGSvVuXciJEvWu6d2Zu\nlpnndRTTLsA/M/OFlLv+n6FcrJ4G/CQzn5WZ76Fk8u55Okhm/jIzL1zYH7y/ojz+8zxKdvxN9b87\ngPPrBcdJlDukUCZrOjrKhDt/AJ7eUVLieZRM8KWU5NXxlNLoLSPiDRHxCUpVwl8pZbbvoXR2Z2fm\nLtlR6dqAbfXRuvpUygnsrpS7Jjtk5j86iGkWJSGxAWWc9huiVP5AOVhdnmVYyZQoJZzzKZ/nSkAn\ns0kvYTtBuYv0GEoSZ+vMPKmDmGZR2mlD4FWUssMrKXcbdotSXnc5MCtL2e/tlBPqm4FX5ILx05Nt\nV0qS+rWU5Mz+lMkJ/xXlEbhPowydgpK0PIBSxv3CzDy+o5j2pkzOtjclebMbpbpl3Yg4jtJffYeS\nwO0lJZbLzHmZObujmAZpp+/XWDao/dOXKPthVwZpp7OAf6990vuAoyhj8L/aYVyDtNVZdd1rMvOd\nlGqvrTLzK0OIaTnKjYEVgFujDHmDUr76KsrQt+8C5wPPy74nYUyy3ue3FzCPBeN9oVzAPqr3vc/M\n3tOfPkHpo07sKKZB2ul4StL+5Pr+1+i2P4DB2upf9dygl5QYZp8w0T61e5Sq2Sspc4oNo08YZJ+6\nAjiUci7YZZ8wSDsdR9mnLq3VHe+l2/6gv++cS7kp+FPKXe1pmbkWZZ6Lx9f1f0i5sdFlf7Akx5iH\nU85NPwi8qIukRDVIO72Fkhw5lVKhN4z+YEnaav36/bsjM3+QC4ZSTLZB96mNgXXqucvRDO8cYS7l\nXHh1yr6zbl3nU5T5zOZS2qzr496EHtCJiYh4TpTx11DG0f4tIlbIzAsojfuJut5OEfHgiPh34IXA\n1Zl5amZO+h2HAWL6eN/qP6jrbBoRDwKoJUiTHdOsWPBc4amU8ZfTM/PnlEku30O5aNsuIlaKiNdS\nHq/zh8mOZRHWAz5dD0afo9wR2rvXFpn5bsqQm21rBcw9kzdmd+OyVgN+lplfyszPUbKvu1HKsm6k\ntM3bKBfeczMzKTOUf7mjeHoGaauN6h2jW+rJ656Z+eEOL2x7Q1zeSbno2YD6dAvKXAlbR8Q6Wcrt\n7szMHwK7ZuYB2d1s6QO3U13/x8AzM/NjHcbU305fphwYVqMkaTaq778CmBqlPPErlAvKSf/s4v8O\n5XoicGpm/gU4gZIx36u+tx7lwPqoekf+xcBNmXn6ZLfTuJg2o4ynn0O56/8w4DGZuQelImDX+v+z\ne7/fxf59H9rpMfWO4IvqBdJ5k91H3cd2OjcipmXmv7r47BYS16D71NnA9rWtftLxPtUf039T7oA+\nhdJH7RWldHwtSjuumJl/z8zjO/7u9X9+F1BOFHet730NeGwseKzyWD2x/t4ItNNMyk2elTPzn1km\ndOt6nxqorbJvHqwh9wmLaquHUE70V6wxnTvEPmFx+9S0ep7w7RHZp35EqYIjMy8Z4jHmAkrfuR8l\nmbNqX9xTKRe8N2bmcSNwLH5MRJxHqcJ5W2Z+ZATa6QmUJMGUeoz55ogcY3rH4+3oaALO+9BWG1Nu\nzs0HyMxzhnyOsDblfPNwyjXfwyk3y/5CmQ9ubhfHvSXxgBzKEeWxlidRJhpbJ8rQiJUpd6w3pMwW\n/RbKkI41KeOwtqHMV7BXLijfbhHTLyPikZn5+yxzThxPqar4ewcxPYxSqfF74EFRnmW/PGVCqJ0i\nIoFzgOdSsuYfpswDMJNyMXvFZMdU43ospS3OoNxBXosyO+2XKHeMZwCviIh1c8HEhEdQJjE9Izso\n61uIBwE3RMSjswxBOJkygdYJlE5mKiU7/Dvg5ljwuKtJdT/a6lWUSTp749wmM6bFDXGZAmweEWdl\n5rVRyknfVf8d1Jgmddza/Win3SmlpJOesV5MO/06ylCbzSjDNr5f3z+cMszrBpj8x9zFvYe8nUrJ\n1h9IqYq4lnKxv2tErELJoveGvB2a3Qx5Gz+87BRKmxxKaZ+plP7zoXXduynt9FDKfjXp7mc7dTU0\n8H61U1f95oi21eJi+hMlGbku8DPKMfqI+v93ZDdVeIv7/MYofdWDo8yF9ceIOI3SXhd3dKJ/f9tp\n0ivwalz3q606iun+ttWkj9WehH2qiyESI7dPDdhOv6ScG88FzomIb1DOA99fbzxNdkz3p988pNEx\nZnHt1NU1wygeY0aurQY8R0jKdeD5lIriwyjzBb07O570dlAPyMQEZcLIH2fmu6KUR/cm8ngoZQLJ\nv2SZ0fc0ygzEn4xStdDJeNoljOkbLHgkC5nZ5SSXmwOXZOZbosz+vwvli3IFJdv6Skqp0c2Ug8RH\nKRmzztopIp5NGRIxm/KleAMlm/mHiHhiZl4WEVfXmNamTm6ZpWrhcx3Esx7lgva7wG9zQcXK2ZRh\nBu+L8hifKynZ/W0oF5nPA47MzFMmO6a+2EaqrWpMz6OUDn6JBUNctqd0gm+gPDL1n5RhCL0M8QmU\nypJOLAXt9GBKgmsn4FeZ2cn8FtU2wM2ZuWVEbEkpIwzgbRGxS2bOjog/UpKqy1MudPfNDqrL+uxE\nHV4WEU+hJFM3ogyb+jxlgssvUPrYGzLz5Ii4LjN/1GFMttPgRrGtBo1pDeCvmXlYdD/z/xJ9fvV3\nPtfFTYs+o9hOYFsNynYazCDt9EXK40l/lpnnR8Rju7rQrkax3xzFdgLbajJj+gJlmMsNmfnBiFgj\ny9DhkfGAGMoRZWz69L5Fd1MurMkyVuh/KePVLqXcJX1XRBxImb32r3W9Sb3YjoixiHjqfYhpCxY8\nf7hrd1PHx2XmRZQS+ydQLqyPoQx12YqSwJiTpdS+y+QNlDa5MjP/KzM/TpkH5N8o44sPr7H+jjIh\n4D1flujgqQ1Rnrwwm3KBuAVlaAY1hqspM9R+ETgzy/wkfwR+npk3ZOaJXSUl+v6to9RWvb85yBCX\nd1CSbzfVGC/NzDMnO6Y+I9NOfZaknf5ZE2Jv7iop0fdvvY1SCTS9VolcSBnKtS8lCbcxZbjb2pTy\n1U6GvI2LaYz/O7zsH5THxe1FeaTs1pR5E1amznvT1cV2LHiW+Ci1U+8Gwsi0U41rlPepQWJ6QY1p\nBYCuLozu434+rcbUyQVkLCjzHZl2qnGNYlu5Ty1ZTA/Udvoz5WbKcjWmru7+P9CPMUNppxrXKB9j\nRqat7kN/sBILzhFGKikBD4DERETsQxkisVff4osp5Ty71Z/Polxgn0UZijCXsiO8KLt5ssUOlCd8\nfLpv8UWNY3p1RHw8Ivpnn74IuCkiPhIRO1Mm9Fq3xjGP8jztpwCvzMx5kx3TIqxA6WAeWX9+M2Vu\nhuOAORHxiYg4hzK77997X7icxGEStRODcpH4lsx8L2U82C31/bEoQzNupXyxHxYR51PubM/p6oI2\nyhMb+v+tzduqp+9v3jPEpf7cG+Iyl7JP3UQZ4vIv6hCXyY4F7pVUGIl2GhfTErdTTv44w962+/+t\nd1DuKGxQf96PMjvzHyhVJ3tQ5pd5S2bOncx4aky9yZb6Y7qJMtxux5rs/T6wVZTHW61ImZfnnZTP\ndNIPohHxxKiTtfZ9Bq3b6dkRcUiNqVfC37Sdalxr916P0D71oPsR01uzg0duLuK7tySf36Q/Yi8i\nnlHvNpILSvibtlONq//7NyptdX/28672qYXF1LqdFtaft24njzGDxeQxZvCYFnaMGaV9aiT6g8ky\nZf78Sb+GmTRRJqvcijKOeBvg5MycVz+MrSnj6F+b5fFQJwDHdnlnNiI2oExUdyswh/K4s8/XC5Ix\n4EUNYppKGYLxSMojYHalTHRyfF/M21AqJfanJFOOz8yLo8PhLVEe5bMl5SkIN0WZEffuGs9BlEqF\n87I8reGE+vN3KE9GeEhmfm+Rf/z+xfVoykz1z6B0bt/KzMsj4uXAzpn5sr51Z2bmnCjDYG6rGcgu\nYno4ZQbqn1EmRby9dVtFxCMoFQenABdleYpMb396IyUz3RvisgHlwrs3xOWO7KCaJMqwm5dQkn3X\nZn3MaON2miimlu10IGVyqHMoQ8xOr+/1Jts8D/hOZs6NMh/O0Zn5qyiToXUx7vhhwCGUyaBOo8zr\n0XuaRq8/fy7loPp2yqOrvkapUpqRZeKmyY6pN6P54yiTtn4v69N0GrbTepQ7QTtRngf/uiiTHt7Z\nqp1qXA+jPPN8LeCbwLlZqpCatVXd9o7AM4HDM/PPvW01/vwW9d1rtZ+vT+nL1wQOrv/+3j7V8rO7\n1/eP0ofOb9hW61GeXLMGZXjnJVmehtK673w/C//utWqnifpzjzELYvIYM3hcI3WMqTeL5k9wjBmZ\nfYrG/eZkG/WKiQ0pj618FWU2+A9HxIPqyf8ZwPXA0RFxFGXCxt90HM8qlHkFdqM8n3YzKNmqLDOY\nfmfYMdXs6wrACZn5dcpMtH+JBXeFr6Zc8F5O+dJvSJkMc9KHt4yzDSUR8uRenPWLfjWl4mUWdegL\n5a5xZuatWR7t2lVSYirl0UKrAu/NzMNqUmIa5eKyl8xZvmYYD4uIVTPzog6TEvtS5rM4NTOPqG1w\nN9zz2Q29rSJiG0pbXELpI+6ZiyYbDXGJiFdSvl/rAvtQqg/6Y2rRTouLqclQoBrLNZR5Nq4Gnh4R\nU+sB9WZKwmYT4N1Rhpc9mwXzbnQ1sexelAmq3kBJyjwc7qkyuTUzT6WMG/045TPcErg+y1NcujqQ\n7kiZ2+M5LHiCUq/fHHo7RcR2lEfm/ZhyN6g3FOrO2nf+q1E7Qfn8/pdSjbQu8Ob/3965R901n3n8\nk1CXxGWqLlPRFjW+RcbUtbKGNnVLJZmoW4yslARB2qZVtRhVUoq6DDILRdpUEpKibguRICKmTIdR\n1KXxGCRluaUSU0pH0Mwfz+/IcbwvbzrO3od+P2tlvfvdZ79nf/Pbv8v+Pb/neX7K/dFrKasmhpOh\niDs07lVzPf862eZa217vGp/fiaRhfU9gRUmrsywjfJ3Pbl9a2l/TZ6/XVFbfZlk9XxE4opRXnWU1\nhuw739X2qK+cxpIhya39ee8ay6k7TXWOMfuRyQY7ZYwZRE5S76JDxhhJK0j6BLkjWUeMMWVO0K/8\n2tUYU3l/XsppTXJu9Rid1W9+oHSsYaI03oVk4rjeEbEP6cpzFkBEvB4R3yD3X/1VROzWsES2i4h4\nKJZ5PzQyCr8dnxURS6rWVMrpduAASeewzBL646bL/ouML3w5IgZHG0JJipa/Lz/XJF2rVgYGFI8A\nigaAKcD9wIHKrXz+TDVbk/YiQzO+SCYk3bmcX7ucv1HSkaQHyqKIODTaFOLSKCsy+eFEcj/vSyQd\nLmlA06WVlZWWhbisB1xAGrMOAAaq5FNR7gtfWYhLk6b+pOvn8WQyza21bKtPqKecutVUViCqLKfR\nkiZLOoVMbDQx0lVvbWCVYuxqGLxuJcPLFpN9arvCyxqaTiYTMF0YEU+QKyJbSPpUMeo23PZeJScr\nW9Om8LKiaUp5sdmENBZNJHOSnEV6UQGVltPBki4gX34GR4YgbUDJj1Qm2s2ujW0vp3LfUZLOVebi\n2QiYWgxu08mwt4Ma11ZYVqMknSNpiHKLwYXkitEWkvqXyxqT7qo1fY18cZ7U2vbinWFaVdTzhqa9\nSMP3y5JuJd8PzgaOalxbVTk16TpXGVq6MTmRvJiW9tdUXlWW1QhyK7+Jkdnp7yU9UfdvXFthnZKk\nGZL6kQmdp3fV9ioup4amz5Bj36Qu+vOqx5ieaKp6jJGkm5QhCSJj+i+i3jHmc5KuBB4HhkZuRb4+\n9Y8xI0hv0+HlXl3Wc6i0rBqadlYuUC4uv79rjClUUadGkOPc/uQY02WdqrI/aCedHsqxHTABuDwi\nzivn5pOVtT9wS7R3V4uuNDXce3Ynt+wZUM5vAOxDuo89UaWmcv+xpHV2eES8KOk5YBQ5+M8Ano42\n5B5oun8jRGJQRDwtaVcyYc1gcpX4yqZrGyES/cgXt8rKS9K6EbFQ0qHA3hExuNSzmWSi0gXA+Mi9\nkduloVFWA0mX36PIvANTyGSluwEjImKRSrbqdpdVk6YdgXHky9BD5C4uW5HW4tGRO8s0yrDdIS4N\nTTuQnXBExEnKFayLyCSSx0SGbjQ0VVVOPdFUVSjQ6WQ7P51cHX0zIvYtn51MJuG8rvz+KWBYRFzQ\nDi3daDqJzL5+qHLF5jQyv8aOZMLZF8gwqovU3vCyhqYfke7aw8h2f15EzJL0j8BxZHt8mewfftzd\n933Amk4jd4i5MSJOlTQM+EZEDCrX9SITqA6KiIvbWU7lfieTfdF0YFfyxXF6RIxTbnP7RXLl5jQy\ndnVoZJLXttGk6TLyHeAmMolzP3K19L/J0MklkjYmy6oqTT8nXWgXFkNld21vaERc2OZ63tA0jVyk\n6E2OxdMj4lZJ25DPbRwV1fMWXdPJshpIegKcERE3d9H+vlpBn9D8/AaS9Wp6RBwuaSdgD3JB40zS\n03JIu+tU0bU9aVDak5xgrBIRo7tpe4Mr6hMamoYDv42IBZJ2I/v45v58IbBvRXWqp5oqGWOaNN1G\nzge+RCZlPD4ibqljjGnSNJscUxqewUOBcXWMMcrNDC4lF26OKXOWCcBKEfH1OsaYFk1Hl3fdVcn2\n/0nSE7V1jNmtnWXVTTmdQM4RfhQRM+voN9tNx3pMFO4lO51+kjYpk/+5pPvODVUbJeAdSUbmAA9J\nanYdv6YOo0ThTXISuViZ/OcWMg/GDRHxVJuNEo0QiT7kTgOQHiO/JFdE+kvavFy7IXCqMkTimarL\nK5YlW5oOvCrpQDJEZxFwbEQc3majRKOsViMHzDnAg2Tm3JnkLhKPAduXSfYP211WemeIy3eA88gX\n2j9ExCzgfOBpYIei6RS1P8SloWmNoukoYIykg0gXtadJY84aZbXklArLqaeaTm13ORXWJFf57gN+\nQq6KIGkdMtxslqS/LX3V70nX0nbTrOnicl8i4uaI2CYifkh6uLxR/j1ePm/nQNrQdD/Zzs4nXxw/\nUT6/jzRMvkxO5B5ro5ZWTQ+Qe42vXl4QFwB3S1oP3h53XqKaMDzIFceLIuJqcvJ4OLCXpM3LvX8P\nrFpWrP5IvrC1m4ama4FHiq43Ylm44kZk7iDIVa4qNV1FTsjuVO4itib5Yt3c9l5saGrz82touoZs\nV5eTk7Tbyufzyr//obp63qzravK94Dhge5ZtK93a/qroE5qf33PAaGBtSReSBtXHgRUiE+q9QjV1\nCjIM4ToyjGM8sLukLbtpe1X1CZ8mM/uPJes6EXFrF/35EqqrUz3VVNUY09B0HfmuMIH0Fvx4+ayO\nMaah6RpgP0l9y7vMfOCeOsaYiHidfF4LgVGSppE5EQ6UtFkdY0wXmq4lt3vvU94p7+XdY0xby6oL\nTT8nPSZ2JHfagXr6zbay4vtfUh+RngnnkJ3O+WTjnliHQaIL1iBfND4r6d8jkwMuep+/aSc3k67J\nV5AupJeWSUEVNEIkvgxcLGnniJhTPruJfH67SZofEQuAwyrS1S0R8ZqkyWQ84tCIUEW3bpTVl8hJ\n5IMdXO4AAAiiSURBVPYRcZwyuU/j8zWAhyPdSasoq+YQl0lknPsE0lp9EelSty4wrwZNOxVNV5Xj\n3Unj5CxyVeTliHj1r1VTecG4hsyxAbnC93A53ox8dgeQK4GXRMQd5MpJlZq+Qk4kkfTPpJfCGGA7\n4OzIjNVVa9qffHl8GBgr6QVyj+/+5GT3pRo07UG2+6WSXiefX+9yba/I8IC2amrSNQm4X5lvZjzp\nZbI+cKIyk/suwFqS+lZYVs2ajiRXsYaTnoGzyBCm7STdV2GdatW0PrnKfQb1tb1mTceSK259gSWS\n5pG5VTYivbjqqlMnkiuhfYA9Jb1Ctr2q21+rpomkAeJnwANk3PvKyu332q6piQGkkeQcMmH5qsAE\npWdspW2vRdMhZN2eLWkmaeCaTeagqaw//xBoOphlz+41cgFjMRkqVFkdb9E0hiynm0mvz+uoaYwp\nTCQXCK8kt1I/hvRiPEPSsdRTz9/WFBF7STqPLLNDyMXe7alwjGnVROZbPJosp5F19JtV0NGhHM1I\n+hzwRGSSyY5A0j+QL5LtTBC1XCjzJtwZEUsqvm9ziMReETGk6bORZEzWL8jG0zGVTiUbccX3bJTV\nGNLtaoik60mr55ZkSMkJwJ+qKqsmTYeTA8L+5KRpERkn+TDpDfNqDZoOI8MPhhbvhKXkQHEbaQio\nrE51oqYmbWuQg+c+EfFMcY0cSb5k/1sxKlVKk6a9IzNaN9xW+wDfi4hna9I0m3x+z0v6Frl7ynrk\nrgV1abqVLKdnyrmpwKyImF61niZd/ch94Z+V9CSZLb03uWvBMe30LnsfTW+VZ/c8sHNE/FYZ//tU\nZKK0OjU9S3rCfZVcUDmvprbXrOlJcsL2PJnr6biIeK5qTU26GnVqPhma05f0nBhfU/trLqunyEnA\nOuQiwRER8ULFeo4lcwLtQYZ6jSJXlL9AllXlba9J0yAy1HMUaZjfhPTY/X7Vz67DNTWe3WhyYWdH\nMlF95XW8pZw2BcZGxAxJvyB3O7y8Sj1F0+pkuMucWLZ7ym9Iw8mqpDdhpfW8VVPxXnyMDG95UtIW\nZFh8ZWNMN+X0ILkw9nHS+7qWfrNdfGgME+bDgaQ+ZNzv3CixcypbhdYqrAMpZXUZaQ29nnS7XxIR\nd3WApqsjYpqkbcnks/fUrGkqaeG/AvgasKDJK8eaUtNmRccU0oi0ARmHeEcHaJpK7gr0KHBa1YbT\nbjRNIfdAvy8iTqtLT4umn5HJk38JnFu3Ib64+S4k69LJZL36Q526mjR9klzNOqauSXYXmvqRIXq3\nk0bc297zD6vRtAHp9XIc+exqa3stuvqR4UvfLboqXSB4D00/IOv6a9GmJKDvo6UXOa78ifTi2Isy\nmYRMst4BmvYmJ91HFk2vWVO3z05kKNzSOup4F5qGAVtHxAhJK9U8Fvcm29wrpCHiWHJXjl516WrR\ntBrZd367jvrUjaY1gX8hcwT1qvsdoR3YMGE+cJTb3o0D/qnOl40PA5KGkBl1h3VKB9P0/DpJU6Oc\nOqZOdZqm4u1yIemhcFlk5u1aadF0aURMq1mSNfUQSX3JfmAncgVrakRMtqb31NSnaLqkgzR1RDlB\nZ+rqUE1/U1zFUSYC/HJkDiprsqaPhKaiZXVy4r8NGSo7KUpyTmvqbE3txIYJ0xaU2w91TIhLJ9OJ\nZWVNPaOTNEkaTa4i/2vdq6INrKlndKKmBsrEjb/qJF3W1DM6URN0pq4O1fSxTlkcaGBNPcOaeo6k\nAcCvO6ztWVNN2DBhjDEfAVS2Mq5bRzPW1DM6UZMxxhhjTJXYMGGMMcYYY4wxxpja6F23AGOMMcYY\nY4wxxvz1YsOEMcYYY4wxxhhjasOGCWOMMcYYY4wxxtSGDRPGGGOM+cCQtK2kny7n31Se8ErShpIW\nVH1fY4wxxrybFesWYIwxxpiPDhFxL3Bo3TqMMcYY8+HBhgljjDHGLBeSHgKGR8Q8SdOAlyNirKQd\ngDnAPRExUNJc4B5gJ2AdYFxEzJS0IXAZsBrwn03fuwtwJrAUeAk4oFxzA/AE8HfA74CREbFY0leA\nk4GPAfOBMRGxSNJ2wLlAH+BF4PCImC9pK2BSud1v2lQ8xhhjjFlOHMphjDHGmOVlBrBLOd4S2LEc\n7wEc3XLtShExAPgOcEo5dz4wOSI+D9zVdO33gSMiYlvSGLF1Od8fmBARWwDzgB9IWgc4HRgUEVsB\nNwNnSFoJ+CkwIiK2Bs4GflK+ZypwTDn/5P+nAIwxxhjzwWHDhDHGGGOWlxnALpI2Bx4B3pK0LmmY\n+GPLtbPKz4eBtcrxQOCKcjwNeKMcXw9cK+l8YF5E3FLOPxYRc8vxFGBn4AvAp4HbJT0AfJP0qNgU\n+CxwfTl/BrCxpLWB9SNidvmeyX/x/94YY4wxHygO5TDGGGPM8vIfpPfBrsBc4AVgX2Al4KmWa/+3\n/FwK9Go67t10/GeAiDhX0g3AUOBMSVeRhos3m76vd/l9BeDOiBgGIGkVYHVgfeDJ4o2BpBWA9Vru\nT8t3GmOMMaZG7DFhjDHGmOUiIt4C7ga+RRom5gDHAzf18CtmAyPL8d7AygCS7gZWj4gJZI6IRiiH\nJH2+HI8GZpb7D5C0aTl/AnAW8CiwlqSdyvmDgekRsQj4naQh5fyIHv+HjTHGGNNWbJgwxhhjzF/C\nDKBvRDwK3EF6JdzYw7/9JrCPpAeBwcAr5fz3gMmSfg0cBowv5xcDJ0l6BFgXOCUinieNDleWZJxb\nA9+NiNeB/YCzy/cfBBxSvmckMF7S/WS4hzHGGGM6gF5Ll1a+dbgxxhhjTI8oO3jMjYgNa5ZijDHG\nmDZhjwljjDHGGGOMMcbUhj0mjDHGGGOMMcYYUxv2mDDGGGOMMcYYY0xt2DBhjDHGGGOMMcaY2rBh\nwhhjjDHGGGOMMbVhw4QxxhhjjDHGGGNqw4YJY4wxxhhjjDHG1IYNE8YYY4wxxhhjjKmN/wNk51cT\nARj9CgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e49d780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = predict_windspeed(train)\n",
    "\n",
    "fig, ax1= plt.subplots()\n",
    "fig.set_size_inches(18,6)\n",
    "\n",
    "plt.sca(ax1)\n",
    "plt.xticks(rotation=30,ha='right')\n",
    "ax1.set(ylabel='Count', title = 'train windspeed')\n",
    "sns.countplot(data=train,x=\"windspeed\",ax=ax1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "- have to divide signal and noise\n",
    "- a large number of features don't always give good performance \n",
    "- should to add and change features one at a time, and remove any poorly performing features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# categorical data AND continuous data\n",
    "# continuous data = [\"temp\",\"humidity\",\"windspeed\",\"atemp\"]\n",
    "# type of categorical data change to \"category\"\n",
    "\n",
    "categorical_feature_names=[\"season\",\"holiday\",\"workingday\",\n",
    "                          \"weather\",\"dayofweek\",\"month\",\"year\",\"hour\"]\n",
    "\n",
    "for var in categorical_feature_names:\n",
    "    train[var]=train[var].astype(\"category\")\n",
    "    test[var]=test[var].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['season',\n",
       " 'weather',\n",
       " 'temp',\n",
       " 'atemp',\n",
       " 'humidity',\n",
       " 'windspeed',\n",
       " 'year',\n",
       " 'hour',\n",
       " 'dayofweek',\n",
       " 'holiday',\n",
       " 'workingday']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names=[\"season\",\"weather\",\"temp\",\"atemp\",\"humidity\",\n",
    "               \"windspeed\",\"year\",\"hour\",\"dayofweek\",\"holiday\",\"workingday\"]\n",
    "\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9.84</td>\n",
       "      <td>12.880</td>\n",
       "      <td>75</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.58</td>\n",
       "      <td>19.695</td>\n",
       "      <td>76</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.76</td>\n",
       "      <td>16.665</td>\n",
       "      <td>81</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>77</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>18.86</td>\n",
       "      <td>22.725</td>\n",
       "      <td>72</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>2011</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  season weather   temp   atemp  humidity  windspeed  year hour dayofweek  \\\n",
       "0      1       2   9.84  12.880        75     6.0032  2011    5         5   \n",
       "1      1       1  15.58  19.695        76    16.9979  2011   10         5   \n",
       "2      1       1  14.76  16.665        81    19.0012  2011   11         5   \n",
       "3      1       1  17.22  21.210        77    19.0012  2011   12         5   \n",
       "4      1       2  18.86  22.725        72    19.9995  2011   13         5   \n",
       "\n",
       "  holiday workingday  \n",
       "0       0          0  \n",
       "1       0          0  \n",
       "2       0          0  \n",
       "3       0          0  \n",
       "4       0          0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train=train[feature_names]\n",
    "\n",
    "print(X_train.shape)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6493, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  season weather   temp   atemp  humidity  windspeed  year hour dayofweek  \\\n",
       "0      1       1  10.66  11.365        56    26.0027  2011    0         3   \n",
       "1      1       1  10.66  13.635        56     0.0000  2011    1         3   \n",
       "2      1       1  10.66  13.635        56     0.0000  2011    2         3   \n",
       "3      1       1  10.66  12.880        56    11.0014  2011    3         3   \n",
       "4      1       1  10.66  12.880        56    11.0014  2011    4         3   \n",
       "\n",
       "  holiday workingday  \n",
       "0       0          1  \n",
       "1       0          1  \n",
       "2       0          1  \n",
       "3       0          1  \n",
       "4       0          1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test=test[feature_names]\n",
    "\n",
    "print(X_test.shape)\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1    36\n",
       "2    56\n",
       "3    84\n",
       "4    94\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_name=\"count\"\n",
    "\n",
    "y_train=train[label_name]\n",
    "print(y_train.shape)\n",
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make_scorer(rmsle)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "def rmsle(predicted_values, actual_values):\n",
    "    predicted_values = np.array(predicted_values)\n",
    "    actual_values = np.array(actual_values)\n",
    "\n",
    "    log_predict = np.log(predicted_values + 1)\n",
    "    log_actual = np.log(actual_values + 1)\n",
    "\n",
    "    difference = log_predict - log_actual\n",
    "    difference = np.square(difference)\n",
    "\n",
    "    mean_difference = difference.mean()\n",
    "\n",
    "    score = np.sqrt(mean_difference)\n",
    "\n",
    "    return score\n",
    "\n",
    "rmsle_scorer = make_scorer(rmsle)\n",
    "rmsle_scorer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=100, n_jobs=-1, oob_score=False, random_state=0,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "max_depth_list= []\n",
    "\n",
    "\n",
    "# the higher n_estomator value , the higher estimator \n",
    "model = RandomForestRegressor(n_estimators=100, \n",
    "                             n_jobs=-1,\n",
    "                             random_state=0)\n",
    "\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 47.1 s, sys: 1.34 s, total: 48.4 s\n",
      "Wall time: 15.4 s\n",
      "Score = 0.33129\n"
     ]
    }
   ],
   "source": [
    "%time score= cross_val_score(model, X_train, y_train, cv=k_fold, scoring=rmsle_scorer)\n",
    "score=score.mean()\n",
    "\n",
    "print(\"Score = {0:.5f}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=100, n_jobs=-1, oob_score=False, random_state=0,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make that learn , fitting \n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6493,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  12.66,    5.09,    4.37,    3.57,    3.14,    6.18,   38.54,\n",
       "        105.11,  232.68,  136.11])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "print(predictions.shape)\n",
    "predictions[0:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x11f1094a8>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xLnTLmll88v3L8XpcfP+ZI3z90Teoj1/JLSIil66+pZv8HO8VXTo10RzH4Zr5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\nYfxqm7rJ8rkp1oVTk8rvc/O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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dcbf400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to draw graph predicted data\n",
    "fig,(ax1,ax2)=plt.subplots(ncols=2)\n",
    "fig.set_size_inches(12,5)\n",
    "\n",
    "sns.distplot(y_train,ax=ax1,bins=50)\n",
    "ax1.set(title = \" Train\")\n",
    "sns.distplot(predictions,ax=ax2, bins=50 )\n",
    "ax2.set(title = \"Test\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.read_csv(\"data/sampleSubmission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-01-20 05:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-01-20 06:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011-01-20 07:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011-01-20 08:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011-01-20 09:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011-01-20 10:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011-01-20 11:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2011-01-20 12:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2011-01-20 13:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011-01-20 14:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2011-01-20 15:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2011-01-20 16:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2011-01-20 17:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011-01-20 18:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2011-01-20 19:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2011-01-20 20:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2011-01-20 21:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2011-01-20 22:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2011-01-20 23:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2011-01-21 00:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2011-01-21 01:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2011-01-21 02:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2011-01-21 03:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2011-01-21 04:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2011-01-21 05:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6463</th>\n",
       "      <td>2012-12-30 18:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>2012-12-30 19:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6465</th>\n",
       "      <td>2012-12-30 20:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6466</th>\n",
       "      <td>2012-12-30 21:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6467</th>\n",
       "      <td>2012-12-30 22:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6468</th>\n",
       "      <td>2012-12-30 23:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6469</th>\n",
       "      <td>2012-12-31 00:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6470</th>\n",
       "      <td>2012-12-31 01:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6471</th>\n",
       "      <td>2012-12-31 02:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6472</th>\n",
       "      <td>2012-12-31 03:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6473</th>\n",
       "      <td>2012-12-31 04:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6474</th>\n",
       "      <td>2012-12-31 05:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6475</th>\n",
       "      <td>2012-12-31 06:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6476</th>\n",
       "      <td>2012-12-31 07:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6477</th>\n",
       "      <td>2012-12-31 08:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6478</th>\n",
       "      <td>2012-12-31 09:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6479</th>\n",
       "      <td>2012-12-31 10:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6480</th>\n",
       "      <td>2012-12-31 11:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6481</th>\n",
       "      <td>2012-12-31 12:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6482</th>\n",
       "      <td>2012-12-31 13:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6483</th>\n",
       "      <td>2012-12-31 14:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6484</th>\n",
       "      <td>2012-12-31 15:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6485</th>\n",
       "      <td>2012-12-31 16:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6486</th>\n",
       "      <td>2012-12-31 17:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6487</th>\n",
       "      <td>2012-12-31 18:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6488</th>\n",
       "      <td>2012-12-31 19:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6489</th>\n",
       "      <td>2012-12-31 20:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6490</th>\n",
       "      <td>2012-12-31 21:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6491</th>\n",
       "      <td>2012-12-31 22:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>2012-12-31 23:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6493 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime  count\n",
       "0     2011-01-20 00:00:00      0\n",
       "1     2011-01-20 01:00:00      0\n",
       "2     2011-01-20 02:00:00      0\n",
       "3     2011-01-20 03:00:00      0\n",
       "4     2011-01-20 04:00:00      0\n",
       "5     2011-01-20 05:00:00      0\n",
       "6     2011-01-20 06:00:00      0\n",
       "7     2011-01-20 07:00:00      0\n",
       "8     2011-01-20 08:00:00      0\n",
       "9     2011-01-20 09:00:00      0\n",
       "10    2011-01-20 10:00:00      0\n",
       "11    2011-01-20 11:00:00      0\n",
       "12    2011-01-20 12:00:00      0\n",
       "13    2011-01-20 13:00:00      0\n",
       "14    2011-01-20 14:00:00      0\n",
       "15    2011-01-20 15:00:00      0\n",
       "16    2011-01-20 16:00:00      0\n",
       "17    2011-01-20 17:00:00      0\n",
       "18    2011-01-20 18:00:00      0\n",
       "19    2011-01-20 19:00:00      0\n",
       "20    2011-01-20 20:00:00      0\n",
       "21    2011-01-20 21:00:00      0\n",
       "22    2011-01-20 22:00:00      0\n",
       "23    2011-01-20 23:00:00      0\n",
       "24    2011-01-21 00:00:00      0\n",
       "25    2011-01-21 01:00:00      0\n",
       "26    2011-01-21 02:00:00      0\n",
       "27    2011-01-21 03:00:00      0\n",
       "28    2011-01-21 04:00:00      0\n",
       "29    2011-01-21 05:00:00      0\n",
       "...                   ...    ...\n",
       "6463  2012-12-30 18:00:00      0\n",
       "6464  2012-12-30 19:00:00      0\n",
       "6465  2012-12-30 20:00:00      0\n",
       "6466  2012-12-30 21:00:00      0\n",
       "6467  2012-12-30 22:00:00      0\n",
       "6468  2012-12-30 23:00:00      0\n",
       "6469  2012-12-31 00:00:00      0\n",
       "6470  2012-12-31 01:00:00      0\n",
       "6471  2012-12-31 02:00:00      0\n",
       "6472  2012-12-31 03:00:00      0\n",
       "6473  2012-12-31 04:00:00      0\n",
       "6474  2012-12-31 05:00:00      0\n",
       "6475  2012-12-31 06:00:00      0\n",
       "6476  2012-12-31 07:00:00      0\n",
       "6477  2012-12-31 08:00:00      0\n",
       "6478  2012-12-31 09:00:00      0\n",
       "6479  2012-12-31 10:00:00      0\n",
       "6480  2012-12-31 11:00:00      0\n",
       "6481  2012-12-31 12:00:00      0\n",
       "6482  2012-12-31 13:00:00      0\n",
       "6483  2012-12-31 14:00:00      0\n",
       "6484  2012-12-31 15:00:00      0\n",
       "6485  2012-12-31 16:00:00      0\n",
       "6486  2012-12-31 17:00:00      0\n",
       "6487  2012-12-31 18:00:00      0\n",
       "6488  2012-12-31 19:00:00      0\n",
       "6489  2012-12-31 20:00:00      0\n",
       "6490  2012-12-31 21:00:00      0\n",
       "6491  2012-12-31 22:00:00      0\n",
       "6492  2012-12-31 23:00:00      0\n",
       "\n",
       "[6493 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission[\"count\"]=predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6493, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>12.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>5.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>4.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>3.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  count\n",
       "0  2011-01-20 00:00:00  12.66\n",
       "1  2011-01-20 01:00:00   5.09\n",
       "2  2011-01-20 02:00:00   4.37\n",
       "3  2011-01-20 03:00:00   3.57\n",
       "4  2011-01-20 04:00:00   3.14"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(submission.shape)\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission.to_csv(\"data/Score_{0:.5f}_submission.csv\".format(score), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12310768351899458"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "431/3501"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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