9-XGBSklearn-Synthesis-Copy2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "game_csv = pd.read_csv('./data/Training_dataset_Original.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "gameDf = pd.DataFrame(game_csv)\n",
    "\n",
    "dataDict = pd.read_csv('./data/Data_Dictionary.csv',na_values=\"NA\")\n",
    "\n",
    "#Leaderboard\n",
    "leader_csv = pd.read_csv('./data/Leaderboard_dataset.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "leaderDf = pd.DataFrame(leader_csv)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 49)\n",
      "(49, 2)\n"
     ]
    }
   ],
   "source": [
    "print(gameDf.shape)\n",
    "print(dataDict.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dic = dict(zip(dataDict.values[:, 1], dataDict.values[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mvar25'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic['Average tenure of active revolving credit cards (in days)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#remove ID column\n",
    "del gameDf[dic['Application ID (primary key)']]\n",
    "del dic['Application ID (primary key)']\n",
    "\n",
    "leaderAppID = leaderDf['application_key']\n",
    "del leaderDf['application_key']\n",
    "leaderX = leaderDf.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX = gameDf.iloc[:,:-1]\n",
    "gameY = gameDf.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 80000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(gameDf[\"mvar47\"].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX[\"mvar47\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#one hot encoding C=0 L=1\n",
    "gameX['mvar47'] = pd.get_dummies(gameX['mvar47'])\n",
    "leaderX['mvar47'] = pd.get_dummies(leaderX['mvar47'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX[\"mvar47\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthesis begins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 47)\n",
      "(25000, 47)\n"
     ]
    }
   ],
   "source": [
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "gameX = gameX.assign(var345 = (1+gameX['mvar3'])*(1+gameX['mvar4'])*(1+gameX['mvar5']))\n",
    "leaderX = leaderX.assign(var345 = (1+leaderX['mvar3'])*(1+leaderX['mvar4'])*(1+leaderX['mvar5']) )\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX  = gameX.assign(mvar678 = (gameX['mvar7'] + gameX['mvar8'])/(1+gameX['mvar6']))\n",
    "leaderX = leaderX.assign(mvar678 = (leaderX['mvar7'] + leaderX['mvar8'])/(1+leaderX['mvar6']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 49)\n",
      "(25000, 49)\n"
     ]
    }
   ],
   "source": [
    "print(gameX.shape)\n",
    "print(leaderX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar36, 37, 38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = gameX.copy()\n",
    "\n",
    "temp37 = df['mvar37'].copy()\n",
    "\n",
    "temp37[temp37.isnull()] = -1\n",
    "\n",
    "temp37[temp37<0] = 0.458344\n",
    "temp37[temp37==0] = 0.381028\n",
    "temp37[temp37==1] = 0.309995\n",
    "temp37[temp37==2] = 0.239528\n",
    "temp37[temp37==3] = 0.186923\n",
    "temp37[temp37==4] = 0.159852\n",
    "temp37[temp37==5] = 0.14485\n",
    "temp37[temp37==6] = 0.131449\n",
    "temp37[temp37==7] = 0.118592\n",
    "temp37[temp37==8] = 0.111612\n",
    "temp37[temp37==9] = 0.102641\n",
    "temp37[temp37>=10] = 0.0904\n",
    "\n",
    "df = df.assign(var37 = temp37)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp37 = df['mvar37'].copy()\n",
    "\n",
    "temp37[temp37.isnull()] = -1\n",
    "\n",
    "temp37[temp37<0] = 0.458344\n",
    "temp37[temp37==0] = 0.381028\n",
    "temp37[temp37==1] = 0.309995\n",
    "temp37[temp37==2] = 0.239528\n",
    "temp37[temp37==3] = 0.186923\n",
    "temp37[temp37==4] = 0.159852\n",
    "temp37[temp37==5] = 0.14485\n",
    "temp37[temp37==6] = 0.131449\n",
    "temp37[temp37==7] = 0.118592\n",
    "temp37[temp37==8] = 0.111612\n",
    "temp37[temp37==9] = 0.102641\n",
    "temp37[temp37>=10] = 0.0904\n",
    "\n",
    "df = df.assign(var37 = temp37)\n",
    "leaderX = df.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 50)\n",
      "(25000, 50)\n"
     ]
    }
   ],
   "source": [
    "print(gameX.shape)\n",
    "print(leaderX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX = gameX.assign(var363738 = (gameX['mvar36']*gameX['mvar37']/(1+gameX['mvar38'])) )\n",
    "leaderX = leaderX.assign(var363738 = (leaderX['mvar36']*leaderX['mvar37']/(1+leaderX['mvar38']) ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 51)\n",
      "(25000, 51)\n"
     ]
    }
   ],
   "source": [
    "print(gameX.shape)\n",
    "print(leaderX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#drop\n",
    "df = gameX.copy() \n",
    "leader_df = leaderX.copy()\n",
    "drop_target = ['mvar27']\n",
    "#number of misssing values\n",
    "# for i in df.columns:\n",
    "#     if (100*(80000-gameDf[i].describe()['count'])/80000) > "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in drop_target:\n",
    "    del df[i]\n",
    "    del leader_df[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mvar1', 'mvar2', 'mvar3', 'mvar4', 'mvar5', 'mvar6', 'mvar7', 'mvar8',\n",
       "       'mvar9', 'mvar10', 'mvar11', 'mvar12', 'mvar13', 'mvar14', 'mvar15',\n",
       "       'mvar16', 'mvar17', 'mvar18', 'mvar19', 'mvar20', 'mvar21', 'mvar22',\n",
       "       'mvar23', 'mvar24', 'mvar25', 'mvar26', 'mvar28', 'mvar29', 'mvar30',\n",
       "       'mvar31', 'mvar32', 'mvar33', 'mvar34', 'mvar35', 'mvar36', 'mvar37',\n",
       "       'mvar38', 'mvar39', 'mvar40', 'mvar41', 'mvar42', 'mvar43', 'mvar44',\n",
       "       'mvar45', 'mvar46', 'mvar47'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leader_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX = df\n",
    "leaderX = leader_df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthesis Ends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 47)\n",
      "(25000, 47)\n"
     ]
    }
   ],
   "source": [
    "print(gameX.shape)\n",
    "print(leaderX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "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>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar6</th>\n",
       "      <th>mvar7</th>\n",
       "      <th>mvar8</th>\n",
       "      <th>mvar9</th>\n",
       "      <th>mvar10</th>\n",
       "      <th>...</th>\n",
       "      <th>mvar38</th>\n",
       "      <th>mvar39</th>\n",
       "      <th>mvar40</th>\n",
       "      <th>mvar41</th>\n",
       "      <th>mvar42</th>\n",
       "      <th>mvar43</th>\n",
       "      <th>mvar44</th>\n",
       "      <th>mvar45</th>\n",
       "      <th>mvar46</th>\n",
       "      <th>mvar47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1753.0</td>\n",
       "      <td>0.500100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>19179.000000</td>\n",
       "      <td>206.00000</td>\n",
       "      <td>14221.000000</td>\n",
       "      <td>19438.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>20.551000</td>\n",
       "      <td>0.42105</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.856610</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1894.0</td>\n",
       "      <td>1.970100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>19820.000000</td>\n",
       "      <td>19820.00000</td>\n",
       "      <td>176895.000000</td>\n",
       "      <td>66346.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>63.047000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.943910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1774.0</td>\n",
       "      <td>0.171800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>2563.000000</td>\n",
       "      <td>877.00000</td>\n",
       "      <td>8869.000000</td>\n",
       "      <td>3637.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>83.797000</td>\n",
       "      <td>0.33333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.764670</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1586.0</td>\n",
       "      <td>0.112300</td>\n",
       "      <td>5.299</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.090819</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.705285</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1832.0</td>\n",
       "      <td>1.444200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>7577.000000</td>\n",
       "      <td>179.00000</td>\n",
       "      <td>21059.000000</td>\n",
       "      <td>20306.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.052000</td>\n",
       "      <td>66.665000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.791900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1838.0</td>\n",
       "      <td>0.054400</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>882.000000</td>\n",
       "      <td>14039.000000</td>\n",
       "      <td>10976.00000</td>\n",
       "      <td>278941.000000</td>\n",
       "      <td>74030.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>77.780000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.924620</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1743.0</td>\n",
       "      <td>1.075300</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>16141.000000</td>\n",
       "      <td>3568.00000</td>\n",
       "      <td>69482.000000</td>\n",
       "      <td>56755.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>67.159000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.713470</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1935.0</td>\n",
       "      <td>0.560100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>44919.000000</td>\n",
       "      <td>17115.00000</td>\n",
       "      <td>124814.000000</td>\n",
       "      <td>124167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.924030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1885.0</td>\n",
       "      <td>0.081100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7369.000000</td>\n",
       "      <td>19264.000000</td>\n",
       "      <td>19264.00000</td>\n",
       "      <td>59956.000000</td>\n",
       "      <td>26633.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.897600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>0.744400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2103.000000</td>\n",
       "      <td>582.00000</td>\n",
       "      <td>16429.000000</td>\n",
       "      <td>9197.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.405290</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.509700</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2433.000000</td>\n",
       "      <td>8673.000000</td>\n",
       "      <td>8673.00000</td>\n",
       "      <td>6739.000000</td>\n",
       "      <td>7388.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.23529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.642420</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1755.0</td>\n",
       "      <td>0.301300</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>4051.000000</td>\n",
       "      <td>261.00000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>261.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.33333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.991420</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>2.290900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "      <td>7620.00000</td>\n",
       "      <td>64983.000000</td>\n",
       "      <td>14067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.441670</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1562.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.611</td>\n",
       "      <td>3.7845</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>124.910000</td>\n",
       "      <td>99.934000</td>\n",
       "      <td>0.87500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.845920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1798.0</td>\n",
       "      <td>5.082500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>3359.000000</td>\n",
       "      <td>2973.00000</td>\n",
       "      <td>22099.000000</td>\n",
       "      <td>13507.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>76.881000</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.433650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1684.0</td>\n",
       "      <td>1.547900</td>\n",
       "      <td>1.338</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.090819</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.705285</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1789.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>13874.000000</td>\n",
       "      <td>13874.00000</td>\n",
       "      <td>98011.000000</td>\n",
       "      <td>56200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.512310</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1862.0</td>\n",
       "      <td>0.240100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1487.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>3964.00000</td>\n",
       "      <td>7928.000000</td>\n",
       "      <td>26564.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>77.419000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.566850</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1767.0</td>\n",
       "      <td>0.300100</td>\n",
       "      <td>1.514</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.00000</td>\n",
       "      <td>8790.000000</td>\n",
       "      <td>6065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.705285</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1802.0</td>\n",
       "      <td>0.636400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>18759.000000</td>\n",
       "      <td>10905.00000</td>\n",
       "      <td>51037.000000</td>\n",
       "      <td>58544.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>58.972000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.594090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1780.0</td>\n",
       "      <td>1.955900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>12525.000000</td>\n",
       "      <td>12525.00000</td>\n",
       "      <td>18932.000000</td>\n",
       "      <td>14602.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.927520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1714.0</td>\n",
       "      <td>2.266400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1671.000000</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>91.666000</td>\n",
       "      <td>0.37500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.348360</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1923.0</td>\n",
       "      <td>0.088000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5946.000000</td>\n",
       "      <td>53893.000000</td>\n",
       "      <td>24739.00000</td>\n",
       "      <td>128435.000000</td>\n",
       "      <td>125679.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>90.429000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.885540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1743.0</td>\n",
       "      <td>1.720800</td>\n",
       "      <td>2.295</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>3621.000000</td>\n",
       "      <td>2323.00000</td>\n",
       "      <td>9018.000000</td>\n",
       "      <td>9649.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>82.323000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.594420</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1700.0</td>\n",
       "      <td>1.660500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1037.000000</td>\n",
       "      <td>46.00000</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.948260</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1922.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4524.000000</td>\n",
       "      <td>15530.000000</td>\n",
       "      <td>14819.00000</td>\n",
       "      <td>116154.000000</td>\n",
       "      <td>100684.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.915500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>0.412000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>369.000000</td>\n",
       "      <td>6201.000000</td>\n",
       "      <td>6201.00000</td>\n",
       "      <td>66477.000000</td>\n",
       "      <td>34053.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.03846</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.151590</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1811.0</td>\n",
       "      <td>0.911700</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.410</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>27717.000000</td>\n",
       "      <td>9815.00000</td>\n",
       "      <td>110001.000000</td>\n",
       "      <td>69683.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>74.996000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.566710</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1658.0</td>\n",
       "      <td>2.382900</td>\n",
       "      <td>37.127</td>\n",
       "      <td>10.3678</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6566.000000</td>\n",
       "      <td>4068.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.57692</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.353390</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1827.0</td>\n",
       "      <td>0.280100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>1189.000000</td>\n",
       "      <td>1189.00000</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>1734.0</td>\n",
       "      <td>0.927900</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>11643.000000</td>\n",
       "      <td>1524.00000</td>\n",
       "      <td>16748.000000</td>\n",
       "      <td>25359.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.380780</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>1849.0</td>\n",
       "      <td>1.830500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2251.000000</td>\n",
       "      <td>33275.000000</td>\n",
       "      <td>7505.00000</td>\n",
       "      <td>44843.000000</td>\n",
       "      <td>47453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>91.701000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.921110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>1.525100</td>\n",
       "      <td>2.727</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.727</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>720.00000</td>\n",
       "      <td>5896.000000</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109.251000</td>\n",
       "      <td>94.127000</td>\n",
       "      <td>0.36842</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.529030</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>1642.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.353</td>\n",
       "      <td>1.2114</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>6290.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>6290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>37.890000</td>\n",
       "      <td>0.83333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>1709.0</td>\n",
       "      <td>3.680100</td>\n",
       "      <td>7.583</td>\n",
       "      <td>3.1371</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>8132.000000</td>\n",
       "      <td>297.00000</td>\n",
       "      <td>1041.000000</td>\n",
       "      <td>6960.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>81.029000</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.59459</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>1890.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>15162.000000</td>\n",
       "      <td>15162.00000</td>\n",
       "      <td>68181.000000</td>\n",
       "      <td>43334.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.043000</td>\n",
       "      <td>55.197000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.271430</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>1727.0</td>\n",
       "      <td>1.844800</td>\n",
       "      <td>15.181</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2234.000000</td>\n",
       "      <td>2754.000000</td>\n",
       "      <td>2234.00000</td>\n",
       "      <td>2676.000000</td>\n",
       "      <td>6539.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.878900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>1789.0</td>\n",
       "      <td>1.299400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>17838.000000</td>\n",
       "      <td>17838.00000</td>\n",
       "      <td>8424.000000</td>\n",
       "      <td>22307.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109.307000</td>\n",
       "      <td>75.052000</td>\n",
       "      <td>0.15385</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.696940</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>1706.0</td>\n",
       "      <td>0.499700</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>98.00000</td>\n",
       "      <td>546.000000</td>\n",
       "      <td>643.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>1638.0</td>\n",
       "      <td>4.680200</td>\n",
       "      <td>16.751</td>\n",
       "      <td>2.6642</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>7937.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>3032.000000</td>\n",
       "      <td>10489.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>102.709000</td>\n",
       "      <td>13.882000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.726400</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>1855.0</td>\n",
       "      <td>1.996500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>30203.000000</td>\n",
       "      <td>22656.00000</td>\n",
       "      <td>168999.000000</td>\n",
       "      <td>166720.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.741060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>1790.0</td>\n",
       "      <td>0.205000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>18018.000000</td>\n",
       "      <td>13297.00000</td>\n",
       "      <td>3914.000000</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.415820</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>1546.0</td>\n",
       "      <td>0.349100</td>\n",
       "      <td>50.803</td>\n",
       "      <td>4.2695</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>3330.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.70370</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>3.962900</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>23707.000000</td>\n",
       "      <td>23707.00000</td>\n",
       "      <td>33432.000000</td>\n",
       "      <td>19640.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>80.934000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.647520</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>1943.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24775.000000</td>\n",
       "      <td>24775.00000</td>\n",
       "      <td>80772.000000</td>\n",
       "      <td>69483.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.970680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>1691.0</td>\n",
       "      <td>0.166300</td>\n",
       "      <td>1.979</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9591.000000</td>\n",
       "      <td>2627.00000</td>\n",
       "      <td>46903.000000</td>\n",
       "      <td>30402.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.801000</td>\n",
       "      <td>63.855000</td>\n",
       "      <td>0.03448</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.434410</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>1681.0</td>\n",
       "      <td>0.173900</td>\n",
       "      <td>9.006</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>6.568</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3004.000000</td>\n",
       "      <td>2251.00000</td>\n",
       "      <td>6818.000000</td>\n",
       "      <td>7783.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.309000</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.71429</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.889080</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>1723.0</td>\n",
       "      <td>1.057000</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>28479.000000</td>\n",
       "      <td>28479.00000</td>\n",
       "      <td>16352.000000</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.723440</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>1693.0</td>\n",
       "      <td>1.214500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.366190</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>1592.0</td>\n",
       "      <td>6.480500</td>\n",
       "      <td>5.250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.204</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.506490</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>1641.0</td>\n",
       "      <td>0.660100</td>\n",
       "      <td>8.614</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>1740.0</td>\n",
       "      <td>0.390100</td>\n",
       "      <td>4.322</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>29460.000000</td>\n",
       "      <td>9449.00000</td>\n",
       "      <td>42047.000000</td>\n",
       "      <td>31583.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.889570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>1651.0</td>\n",
       "      <td>2.490900</td>\n",
       "      <td>1.335</td>\n",
       "      <td>0.9108</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3163.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>3167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>82.382000</td>\n",
       "      <td>0.61538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.937450</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>1729.0</td>\n",
       "      <td>1.360100</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5675.000000</td>\n",
       "      <td>167.00000</td>\n",
       "      <td>15071.000000</td>\n",
       "      <td>10305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.23810</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.718660</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>1.670100</td>\n",
       "      <td>5.554</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>5692.000000</td>\n",
       "      <td>496.00000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>8125.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>79.485000</td>\n",
       "      <td>0.30769</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.933120</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>1715.0</td>\n",
       "      <td>12.000200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>8751.000000</td>\n",
       "      <td>1814.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.07692</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.705285</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.055100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5946.000000</td>\n",
       "      <td>14867.000000</td>\n",
       "      <td>9613.00000</td>\n",
       "      <td>46490.000000</td>\n",
       "      <td>44722.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.998590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>1686.0</td>\n",
       "      <td>1.330100</td>\n",
       "      <td>2.025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.025</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>4582.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>124.171000</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.30000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.775190</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>1826.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3568.000000</td>\n",
       "      <td>26277.000000</td>\n",
       "      <td>9058.00000</td>\n",
       "      <td>72739.000000</td>\n",
       "      <td>86797.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>50.119000</td>\n",
       "      <td>0.03333</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.403430</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>1916.0</td>\n",
       "      <td>0.139400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18889.000000</td>\n",
       "      <td>18889.000000</td>\n",
       "      <td>18889.00000</td>\n",
       "      <td>28343.000000</td>\n",
       "      <td>51174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.996630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mvar1      mvar2   mvar3    mvar4  mvar5         mvar6         mvar7  \\\n",
       "0      1753.0   0.500100   0.000   0.0000  0.000    206.000000  19179.000000   \n",
       "1      1894.0   1.970100   0.000   0.0000  0.000    297.000000  19820.000000   \n",
       "2      1774.0   0.171800   0.000   0.0000  0.000    197.000000   2563.000000   \n",
       "3      1586.0   0.112300   5.299   0.0000  0.000   1606.702372  17595.807537   \n",
       "4      1832.0   1.444200   0.000   0.0000  0.000    179.000000   7577.000000   \n",
       "5      1838.0   0.054400   0.241   0.0000  0.000    882.000000  14039.000000   \n",
       "6      1743.0   1.075300   0.272   0.0000  0.000    397.000000  16141.000000   \n",
       "7      1935.0   0.560100   0.000   0.0000  0.000    496.000000  44919.000000   \n",
       "8      1885.0   0.081100   0.000   0.0000  0.000   7369.000000  19264.000000   \n",
       "9      1730.0   0.744400   0.000   0.0000  0.000      0.000000   2103.000000   \n",
       "10     1731.0   0.509700   0.000   0.0000  0.000   2433.000000   8673.000000   \n",
       "11     1755.0   0.301300   0.000   0.0000  0.000    261.000000   4051.000000   \n",
       "12     1739.0   2.290900   0.000   0.0000  0.000      0.000000   7620.000000   \n",
       "13     1562.0   0.000000  34.611   3.7845  0.000   1606.702372     18.000000   \n",
       "14     1798.0   5.082500   0.000   0.0000  0.000    259.000000   3359.000000   \n",
       "15     1684.0   1.547900   1.338   0.0000  0.000   1606.702372  17595.807537   \n",
       "16     1789.0   0.000000   1.428   0.0000  0.000   2478.000000  13874.000000   \n",
       "17     1862.0   0.240100   0.000   0.0000  0.000   1487.000000  12310.000000   \n",
       "18     1767.0   0.300100   1.514   0.0000  0.000    991.000000   2478.000000   \n",
       "19     1802.0   0.636400   0.000   0.0000  0.000     96.000000  18759.000000   \n",
       "20     1780.0   1.955900   0.000   0.0000  0.000    191.000000  12525.000000   \n",
       "21     1714.0   2.266400   0.000   0.0000  0.000     31.000000   1671.000000   \n",
       "22     1923.0   0.088000   0.000   0.0000  0.000   5946.000000  53893.000000   \n",
       "23     1743.0   1.720800   2.295   0.0000  0.000    159.000000   3621.000000   \n",
       "24     1700.0   1.660500   0.000   0.0000  0.000      0.000000   1037.000000   \n",
       "25     1922.0   1.105629   0.000   0.0000  0.000   4524.000000  15530.000000   \n",
       "26     1777.0   0.412000   0.000   0.0000  0.000    369.000000   6201.000000   \n",
       "27     1811.0   0.911700   0.410   0.0000  0.410    297.000000  27717.000000   \n",
       "28     1658.0   2.382900  37.127  10.3678  0.000      0.000000    396.000000   \n",
       "29     1827.0   0.280100   0.000   0.0000  0.000    678.000000   1189.000000   \n",
       "...       ...        ...     ...      ...    ...           ...           ...   \n",
       "24970  1734.0   0.927900   0.520   0.0000  0.000    256.000000  11643.000000   \n",
       "24971  1849.0   1.830500   0.000   0.0000  0.000   2251.000000  33275.000000   \n",
       "24972  1739.0   1.525100   2.727   0.0000  2.727      8.000000   2129.000000   \n",
       "24973  1642.0   0.000000   3.353   1.2114  0.000   1606.702372   6290.000000   \n",
       "24974  1709.0   3.680100   7.583   3.1371  0.000    297.000000   8132.000000   \n",
       "24975  1890.0   0.000000   0.000   0.0000  0.000   1982.000000  15162.000000   \n",
       "24976  1727.0   1.844800  15.181   0.0000  0.000   2234.000000   2754.000000   \n",
       "24977  1789.0   1.299400   0.000   0.0000  0.000    364.000000  17838.000000   \n",
       "24978  1706.0   0.499700   0.000   0.0000  0.000     98.000000    297.000000   \n",
       "24979  1638.0   4.680200  16.751   2.6642  0.000   1606.702372   7937.000000   \n",
       "24980  1855.0   1.996500   0.000   0.0000  0.000    350.000000  30203.000000   \n",
       "24981  1790.0   0.205000   0.000   0.0000  0.000    237.000000  18018.000000   \n",
       "24982  1546.0   0.349100  50.803   4.2695  0.000   1606.702372  17595.807537   \n",
       "24983  1777.0   3.962900   0.968   0.0000  0.000     14.000000  23707.000000   \n",
       "24984  1943.0   1.105629   0.000   0.0000  0.000      0.000000  24775.000000   \n",
       "24985  1691.0   0.166300   1.979   0.0000  0.000      0.000000   9591.000000   \n",
       "24986  1681.0   0.173900   9.006   0.0000  6.568      0.000000   3004.000000   \n",
       "24987  1723.0   1.057000   0.285   0.0000  0.000    248.000000  28479.000000   \n",
       "24988  1693.0   1.214500   0.000   0.0000  0.000     18.000000    100.000000   \n",
       "24989  1592.0   6.480500   5.250   0.0000  1.204   1606.702372      0.000000   \n",
       "24990  1641.0   0.660100   8.614   0.0000  0.000   1606.702372      0.000000   \n",
       "24991  1740.0   0.390100   4.322   0.0000  0.000    490.000000  29460.000000   \n",
       "24992  1651.0   2.490900   1.335   0.9108  0.000      4.000000   3163.000000   \n",
       "24993  1729.0   1.360100   0.218   0.0000  0.000      0.000000   5675.000000   \n",
       "24994  1730.0   1.670100   5.554   0.0000  0.000    104.000000   5692.000000   \n",
       "24995  1715.0  12.000200   0.000   0.0000  0.000   1606.702372    991.000000   \n",
       "24996  1800.0   0.055100   0.000   0.0000  0.000   5946.000000  14867.000000   \n",
       "24997  1686.0   1.330100   2.025   0.0000  2.025   1606.702372      0.000000   \n",
       "24998  1826.0   1.105629   0.428   0.0000  0.000   3568.000000  26277.000000   \n",
       "24999  1916.0   0.139400   0.000   0.0000  0.000  18889.000000  18889.000000   \n",
       "\n",
       "             mvar8          mvar9    mvar10   ...    mvar38    mvar39  \\\n",
       "0        206.00000   14221.000000   19438.0   ...       1.0  0.000000   \n",
       "1      19820.00000  176895.000000   66346.0   ...       3.0  0.000000   \n",
       "2        877.00000    8869.000000    3637.0   ...       1.0  0.000000   \n",
       "3       6773.68666   34097.436013       0.0   ...       1.0  0.090819   \n",
       "4        179.00000   21059.000000   20306.0   ...       3.0  0.000000   \n",
       "5      10976.00000  278941.000000   74030.0   ...       4.0  0.000000   \n",
       "6       3568.00000   69482.000000   56755.0   ...       6.0  0.000000   \n",
       "7      17115.00000  124814.000000  124167.0   ...       9.0  0.000000   \n",
       "8      19264.00000   59956.000000   26633.0   ...       1.0  0.000000   \n",
       "9        582.00000   16429.000000    9197.0   ...       9.0  0.000000   \n",
       "10      8673.00000    6739.000000    7388.0   ...       2.0  0.000000   \n",
       "11       261.00000     297.000000     261.0   ...       3.0  0.000000   \n",
       "12      7620.00000   64983.000000   14067.0   ...      11.0  0.000000   \n",
       "13      6773.68666     198.000000      18.0   ...       7.0  1.000000   \n",
       "14      2973.00000   22099.000000   13507.0   ...       5.0  0.000000   \n",
       "15      6773.68666   34097.436013       0.0   ...       3.0  0.090819   \n",
       "16     13874.00000   98011.000000   56200.0   ...       4.0  0.000000   \n",
       "17      3964.00000    7928.000000   26564.0   ...       6.0  0.000000   \n",
       "18      2478.00000    8790.000000    6065.0   ...       1.0  0.000000   \n",
       "19     10905.00000   51037.000000   58544.0   ...       3.0  0.000000   \n",
       "20     12525.00000   18932.000000   14602.0   ...       7.0  0.000000   \n",
       "21        57.00000     396.000000    2582.0   ...       6.0  0.000000   \n",
       "22     24739.00000  128435.000000  125679.0   ...       2.0  0.000000   \n",
       "23      2323.00000    9018.000000    9649.0   ...       8.0  0.000000   \n",
       "24        46.00000     892.000000    1235.0   ...       5.0  0.000000   \n",
       "25     14819.00000  116154.000000  100684.0   ...       3.0  0.000000   \n",
       "26      6201.00000   66477.000000   34053.0   ...      12.0  0.000000   \n",
       "27      9815.00000  110001.000000   69683.0   ...       3.0  0.000000   \n",
       "28         0.00000    6566.000000    4068.0   ...      14.0  0.000000   \n",
       "29      1189.00000     892.000000    2065.0   ...       3.0  0.000000   \n",
       "...            ...            ...       ...   ...       ...       ...   \n",
       "24970   1524.00000   16748.000000   25359.0   ...       7.0  0.000000   \n",
       "24971   7505.00000   44843.000000   47453.0   ...       2.0  0.000000   \n",
       "24972    720.00000    5896.000000    5206.0   ...       6.0  0.000000   \n",
       "24973   6773.68666   34097.436013    6290.0   ...       1.0  0.000000   \n",
       "24974    297.00000    1041.000000    6960.0   ...      32.0  0.000000   \n",
       "24975  15162.00000   68181.000000   43334.0   ...       2.0  0.000000   \n",
       "24976   2234.00000    2676.000000    6539.0   ...       4.0  0.000000   \n",
       "24977  17838.00000    8424.000000   22307.0   ...       5.0  0.000000   \n",
       "24978     98.00000     546.000000     643.0   ...       7.0  0.000000   \n",
       "24979   6773.68666    3032.000000   10489.0   ...       3.0  1.000000   \n",
       "24980  22656.00000  168999.000000  166720.0   ...       2.0  0.000000   \n",
       "24981  13297.00000    3914.000000    1305.0   ...       5.0  0.000000   \n",
       "24982   6773.68666    3330.000000       0.0   ...       8.0  0.000000   \n",
       "24983  23707.00000   33432.000000   19640.0   ...      12.0  0.000000   \n",
       "24984  24775.00000   80772.000000   69483.0   ...       0.0  0.000000   \n",
       "24985   2627.00000   46903.000000   30402.0   ...       5.0  0.000000   \n",
       "24986   2251.00000    6818.000000    7783.0   ...       1.0  1.000000   \n",
       "24987  28479.00000   16352.000000    3117.0   ...       7.0  0.000000   \n",
       "24988    100.00000     991.000000     118.0   ...       3.0  0.000000   \n",
       "24989   6773.68666   34097.436013       0.0   ...       3.0  0.000000   \n",
       "24990   6773.68666   34097.436013       0.0   ...       3.0  0.000000   \n",
       "24991   9449.00000   42047.000000   31583.0   ...       3.0  0.000000   \n",
       "24992      4.00000    1982.000000    3167.0   ...      10.0  0.000000   \n",
       "24993    167.00000   15071.000000   10305.0   ...       5.0  0.000000   \n",
       "24994    496.00000    4410.000000    8125.0   ...       5.0  0.000000   \n",
       "24995   6773.68666    8751.000000    1814.0   ...       1.0  0.000000   \n",
       "24996   9613.00000   46490.000000   44722.0   ...       4.0  0.000000   \n",
       "24997   6773.68666    4582.000000       0.0   ...       0.0  0.000000   \n",
       "24998   9058.00000   72739.000000   86797.0   ...       2.0  0.000000   \n",
       "24999  18889.00000   28343.000000   51174.0   ...       0.0  0.000000   \n",
       "\n",
       "           mvar40      mvar41   mvar42  mvar43    mvar44    mvar45    mvar46  \\\n",
       "0       98.497452   20.551000  0.42105     3.0  0.856610  0.070736  0.000000   \n",
       "1       98.497452   63.047000  0.00000    15.0  0.943910  0.000000  0.000000   \n",
       "2       98.497452   83.797000  0.33333     3.0  0.764670  0.000000  0.000000   \n",
       "3       98.497452   72.096758  1.50000     0.0  0.705285  0.070736  0.096135   \n",
       "4       96.052000   66.665000  0.00000    23.0  0.791900  0.000000  0.000000   \n",
       "5       98.497452   77.780000  0.00000    21.0  0.924620  0.000000  0.000000   \n",
       "6       98.497452   67.159000  0.00000    14.0  0.713470  0.000000  0.000000   \n",
       "7       98.497452   72.096758  0.00000    15.0  0.924030  0.000000  0.000000   \n",
       "8       98.497452   72.096758  0.00000     5.0  0.897600  0.000000  0.096135   \n",
       "9       98.497452   72.096758  0.05882    14.0  0.405290  0.070736  0.000000   \n",
       "10      98.497452   72.096758  0.23529     1.0  0.642420  0.000000  0.096135   \n",
       "11      98.497452   72.096758  0.33333     2.0  0.991420  0.070736  0.000000   \n",
       "12      98.497452   95.000000  0.05882    16.0  0.441670  0.070736  0.000000   \n",
       "13     124.910000   99.934000  0.87500     1.0  0.845920  1.000000  1.000000   \n",
       "14      98.497452   76.881000  0.05882    13.0  0.433650  0.000000  0.000000   \n",
       "15      98.497452   72.096758  1.50000     0.0  0.705285  0.070736  0.096135   \n",
       "16      98.497452   72.096758  0.05000    13.0  0.512310  0.070736  0.096135   \n",
       "17      98.497452   77.419000  0.00000     7.0  0.566850  0.000000  0.000000   \n",
       "18      98.497452   72.096758  0.10000     3.0  0.705285  0.070736  0.096135   \n",
       "19      98.497452   58.972000  0.00000     9.0  0.594090  0.000000  0.000000   \n",
       "20      98.497452  100.000000  0.00000     7.0  0.927520  0.000000  0.000000   \n",
       "21      98.497452   91.666000  0.37500     5.0  0.348360  0.070736  0.000000   \n",
       "22      98.497452   90.429000  0.00000     9.0  0.885540  0.000000  0.000000   \n",
       "23      98.497452   82.323000  0.10000     9.0  0.594420  0.070736  0.000000   \n",
       "24      98.497452   72.096758  0.20000     5.0  0.948260  0.070736  0.000000   \n",
       "25      98.497452   72.096758  0.00000    12.0  0.915500  0.000000  0.000000   \n",
       "26      98.497452   72.096758  0.03846    23.0  0.151590  0.070736  0.096135   \n",
       "27      98.497452   74.996000  0.00000    15.0  0.566710  0.000000  0.000000   \n",
       "28      98.497452   72.096758  0.57692     8.0  0.353390  0.070736  2.000000   \n",
       "29      98.497452   72.096758  0.00000     2.0  1.000000  0.070736  0.096135   \n",
       "...           ...         ...      ...     ...       ...       ...       ...   \n",
       "24970   98.497452   72.096758  0.00000    10.0  0.380780  0.070736  0.000000   \n",
       "24971   98.497452   91.701000  0.00000     4.0  0.921110  0.000000  0.000000   \n",
       "24972  109.251000   94.127000  0.36842    12.0  0.529030  0.070736  0.000000   \n",
       "24973   98.497452   37.890000  0.83333     0.0  1.000000  0.070736  0.000000   \n",
       "24974   81.029000   72.096758  0.59459    11.0  0.426800  0.070736  2.000000   \n",
       "24975   98.043000   55.197000  0.00000    15.0  0.271430  0.070736  0.000000   \n",
       "24976   98.497452   72.096758  0.66667     5.0  0.878900  0.000000  0.000000   \n",
       "24977  109.307000   75.052000  0.15385     8.0  0.696940  0.000000  0.000000   \n",
       "24978   98.497452   72.096758  0.66667     4.0  1.000000  0.070736  0.000000   \n",
       "24979  102.709000   13.882000  0.50000     4.0  0.726400  0.070736  0.000000   \n",
       "24980   98.497452   72.096758  0.00000    12.0  0.741060  0.000000  0.096135   \n",
       "24981   98.497452   72.096758  0.20000     2.0  0.415820  0.070736  0.000000   \n",
       "24982   98.497452   72.096758  0.70370     2.0  1.000000  1.000000  1.000000   \n",
       "24983   98.497452   80.934000  0.04000    15.0  0.647520  0.070736  0.000000   \n",
       "24984   98.497452   72.096758  0.00000     6.0  0.970680  0.000000  0.096135   \n",
       "24985   84.801000   63.855000  0.03448    14.0  0.434410  0.070736  0.000000   \n",
       "24986   96.309000   72.096758  0.71429     2.0  0.889080  0.000000  0.000000   \n",
       "24987   98.497452   72.096758  0.00000     6.0  0.723440  0.070736  0.000000   \n",
       "24988   98.497452   72.096758  0.00000     3.0  0.366190  0.070736  0.096135   \n",
       "24989  100.000000   72.096758  1.00000     1.0  0.506490  0.070736  0.096135   \n",
       "24990  100.000000   72.096758  1.00000     1.0  1.000000  0.070736  0.096135   \n",
       "24991   98.497452   72.096758  0.20000     4.0  0.889570  1.000000  0.000000   \n",
       "24992   98.497452   82.382000  0.61538     3.0  0.937450  0.070736  0.000000   \n",
       "24993   98.497452   72.096758  0.23810    13.0  0.718660  0.070736  0.000000   \n",
       "24994   98.497452   79.485000  0.30769     4.0  0.933120  0.070736  0.000000   \n",
       "24995   98.497452   72.096758  0.07692     4.0  0.705285  0.070736  0.000000   \n",
       "24996   98.497452   72.096758  0.05882     8.0  0.998590  0.000000  0.000000   \n",
       "24997  124.171000   72.096758  0.30000     2.0  0.775190  0.070736  0.096135   \n",
       "24998   98.497452   50.119000  0.03333    11.0  0.403430  0.070736  0.000000   \n",
       "24999   98.497452   72.096758  0.00000     5.0  0.996630  0.000000  0.000000   \n",
       "\n",
       "       mvar47  \n",
       "0           1  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           1  \n",
       "5           1  \n",
       "6           1  \n",
       "7           1  \n",
       "8           1  \n",
       "9           0  \n",
       "10          0  \n",
       "11          0  \n",
       "12          0  \n",
       "13          0  \n",
       "14          0  \n",
       "15          0  \n",
       "16          1  \n",
       "17          0  \n",
       "18          0  \n",
       "19          0  \n",
       "20          0  \n",
       "21          0  \n",
       "22          0  \n",
       "23          0  \n",
       "24          1  \n",
       "25          1  \n",
       "26          0  \n",
       "27          0  \n",
       "28          0  \n",
       "29          0  \n",
       "...       ...  \n",
       "24970       1  \n",
       "24971       1  \n",
       "24972       0  \n",
       "24973       0  \n",
       "24974       0  \n",
       "24975       0  \n",
       "24976       0  \n",
       "24977       1  \n",
       "24978       0  \n",
       "24979       0  \n",
       "24980       1  \n",
       "24981       0  \n",
       "24982       0  \n",
       "24983       1  \n",
       "24984       0  \n",
       "24985       0  \n",
       "24986       0  \n",
       "24987       0  \n",
       "24988       0  \n",
       "24989       0  \n",
       "24990       1  \n",
       "24991       1  \n",
       "24992       1  \n",
       "24993       0  \n",
       "24994       0  \n",
       "24995       0  \n",
       "24996       1  \n",
       "24997       1  \n",
       "24998       0  \n",
       "24999       0  \n",
       "\n",
       "[25000 rows x 47 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing values median\n",
    "gameX.fillna(gameX.mean(),inplace = True)\n",
    "leaderX.fillna(gameX.mean(),inplace = True)\n",
    "\n",
    "# gameX.fillna(0,inplace = True)\n",
    "# leaderX.fillna(0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing as prep\n",
    "\n",
    "scaler_list = []\n",
    "for i in gameX.columns:\n",
    "    scaler_list.append(prep.StandardScaler().fit(gameX[i]))\n",
    "    \n",
    "# prep.StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "gameX_scaled = gameX.copy()\n",
    "\n",
    "for i in enumerate(gameX_scaled.columns):\n",
    "    gameX_scaled[i[1]] = scaler_list[i[0]].transform(gameX_scaled[i[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "leaderX_scaled = leaderX.copy()\n",
    "\n",
    "for i in enumerate(leaderX_scaled.columns):\n",
    "    leaderX_scaled[i[1]] = scaler_list[i[0]].transform(leaderX_scaled[i[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression(random_state=1, solver='lbfgs', multi_class='multinomial').fit(gameX_scaled, gameY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred1 = clf.predict_proba(leaderX_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.23484095,  0.12925587, -0.03234188, -0.02144982, -0.02070771,\n",
       "        -0.04387564,  0.10068508, -0.00258864,  0.08702531, -0.20954754,\n",
       "        -0.00553855,  0.02480015, -0.08919082,  0.00787917, -0.03957864,\n",
       "         0.01494694, -0.03505678,  0.09207162, -0.04312696,  0.09812068,\n",
       "         0.04529579,  0.02637607,  0.03143128, -0.00630004, -0.07612773,\n",
       "        -0.0168728 ,  0.0019322 ,  0.00884536, -0.04090837, -0.02698291,\n",
       "        -0.02253847,  0.12478947, -0.02117119,  0.06629332,  0.01968629,\n",
       "         0.08239639, -0.16754376,  0.04072226, -0.00274787,  0.02462847,\n",
       "         0.02413357,  0.13003694, -0.12340559, -0.02959807,  0.03283271,\n",
       "         0.05622059,  0.2413942 ]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 47)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AC2YWAXBMmPUAAADzYGYRAAAAAAuaRQAAAAAsaBYBAAAAsKBZBAAAAMCCE1wDAHDMjXHS\neye8B4BpaBYBzJg/tgAAgLFpFgEA7EJDFgA4EWkWAbPljzgAAIDxDTrBdVWdXVXvqKqrq+qCHR6v\nqnph//hfVtX9hj4XAAAAgP1jz2ZRVR1I8qIk5yS5R5Jvrqp7bNvsnCRn9pfzkrxkhecCAAAAsE8M\nWYZ2/yRXt9belSRVdUmSRyV529I2j0ryitZaS/KmqvrsqrpjksMDnguswRIsAI4Fxxtgi98HcOIY\nsgzt1CTvWbr93v6+IdsMeS4AAAAA+0R1k4GOskHVNyQ5u7X2Hf3txyV5QGvtKUvb/E6SC1trf9Lf\n/v0kz0g3s+ioz12KcV66JWxJ8gVJ3rHZjza62yX5wAked065ThV3TrlOFXdOuU4Vd065ThV3TrnO\nLe6ccp0q7pxynSrunHKdKu6ccp0q7pxynSrunHKdKu6ccp0q7pxynSrunHKdKu5UuZ5o7tJaO7TX\nRkOWoV2b5M5Lt0/r7xuyzc0HPDdJ0lq7OMnFA/I5LqrqqtbakRM57pxynSrunHKdKu6ccp0q7pxy\nnSrunHKdW9w55TpV3DnlOlXcOeU6Vdw55TpV3DnlOlXcOeU6Vdw55TpV3DnlOlXcOeU6VdypcmVn\nQ5ahXZnkzKo6o6pOSvKYJJdt2+ayJI/vvxXtgUn+tbX29wOfCwAAAMA+sefMotba9VX1lCRXJDmQ\n5GWttbdW1fn94xcluTzJVye5Osm/J3nC0Z47yU8CAAAAwMaGLENLa+3ydA2h5fsuWrrekjx56HNn\naqolcnOKO6dcp4o7p1ynijunXKeKO6dcp4o7p1znFndOuU4Vd065ThV3TrlOFXdOuU4Vd065ThV3\nTrlOFXdOuU4Vd065ThV3TrlOFXffnrbm09GeJ7gGAAAA4MQx5JxFAAAAAJwgNIuAY6Kq6njnMNRU\nuc4t7tiq6uYTxf2sKeL2sUcf2xN9P0jmNwZzGdu51djcXq+57AfJvMZgZuM6eo3N7Rg2Vdw57QfJ\nvMZgTmM7txr7dKdZNKKq+sz+39HGtapOr6qT++ujFXpV3bWqPmeCuEeq6ovGitfHfGBV/acxY/Zx\nH1pVPzBB3K+oqouq6pYjx/2iCcb2Llt5TnTQO1xVj6yqaiOuea2qQ/2/Y+67p/ev3di5Hq6qr62q\nW4wc985V9YPJ4rxxY8Q8s6p+tqpuNUa8pbhfUFUvTfLQkePetapekeTpI+8LZ1TVk6vq1CSjxO33\nr4cl471efVw1psZmVWNT1FcfdzY1NkV99fFmU2NT1FcfdzY1NqdjWB939Bqb0zGsjzd6jc3pGNbH\nPeFr7ESiWTSCqrpjVV2S5PlVdevW2g2b7pBVdaeqenmS30jym1X1la21NkLcU6vqZUl+LcmvV9VX\njRG3j31Kkjcl+fb++ka/pPtcX5rk59N9y94oqupgVV2c5KeTfLiqbjbSz3/qUtzzknxRf/+mr9lt\nq+pXkvxKkour6nGbxl2K+RtJXl5VDxnzQNL/Hz+S5I+S/Nd0tfEVI8S8WVX9TJJ/rKr/2O+7BzaM\nebCqnpfuWxt/IMkvVNV9Rsj1YFX9eJLfSfJfkvzcGHGX/FCS51bVt/T/39q/z6vqQJ/rq5L8a2vt\nIyP+YfioJL+Z5N1J/rSqBn2xwh4xb76U7/2SnNzvCxsf06rqOUkuS/LgJM9JcvYIMX8sye8lOa+q\nnlVVD9k0Zh9XjamxWdXYFPXVx51FjU1RX33cOdbYaPXVP382NTa3Y1gfd/Qam8sxrI87eo3N6RjW\nP1+NnYAM2Ib6nflHk9w+yYeTfGOyWQe3qk5L8tIkf9tae0C6b5P7hRHi3j7JLyb5u9balyb57XQH\nkrE6zqcleUOSDyXZ6JOH6ma7vDbJnVtrD2ytXTFCfls+N8mB1tqXtNZ+obV2w6Y/f1XdO8kr0o3t\n/ZM8M90vpzHG9slJ/r219sVJfi7Jd44Q90lJPtTnekm6jvsDN8xzoapOSnKXJF+W5Nwkb0/ylKo6\nvGHoh6b7FscfSjcWaa19csOD1ROT3K219oXp6zfJ3TfKsnN+kjsmuV9r7euTnJFk49lmSwe6P0/y\nE0meVps3qZ+Y5JuSPKa19uxk8/12KZe7JHlxa+25rbUPt9au3yRu75l93AemeyP86Kq6eWvthk2C\n9vvnF7bW7tVae0yS65L884YxT0/yRa21L0jyLUnem+Sp1X3iu0ncW2SaGntYxq+x8zNNjT0p09TY\n1h8WY9bY+ZlXjf1oRq6xKeqrj3uXTFNjt8z4NTZFfSXT1tidMmKNLf0ROGZ9JRPU2JKxa+xZmeYY\ndkZmUmMT1VeSPDzzqbHvyjTHsKlq7Ek5wWvsRKRZtKa+c32zfmd+bpLHJrkqyQOr6q5b26was7/6\nwSTPa609M0laay9K8k+15hKkrbittX9M8oTW2g/3D906yWv7P2bWiVvbfun8W5L/L90soPuucxBZ\nGtePJfmpJJ/s7390dVM07zlCrndJcs/+/sdU1XOr6txaYzrl0mt2dZKvb609q799ZpKb99us/IlG\nn+7WL/uPJdn6pXmnJH9eVffdMObNk7w1SVpr/yNds/M7NjlIV78Ms3dSkockuVX/i/l/JHlnkqdu\nGPcNSX6+tfaTSf6l+um1WfF32baYlyR5epK01j6U5CPp3rysbFvcl7TWvq21dl3/admhJHevqtus\nG7evja0D3SOT/F/pZvM9o89/8EF7W66vTPLXST6nqh5e3fTib62qu62ba3e1bp7kS5JcVVV3r6rL\nq+rZVfXYNeLeeunmc1pr/7n/eT+Z5I39/7OybePw4SRfV91U6P+Q5EFJHlJV998gZktypKpu31r7\nZJL3J7lbujddq+a6WJac5BZJviLj1NjpVfUZ/c0/zjg1thzz1zJejZ2+9Lv6ohFrbDG2/euUbF5j\ny7m+KuPV2OlVtfUp6S0yXo0tv2bPHaPGtsX8aEaorx3iJl2N3WGEGrtrVX1uf/OkjFBjfczb9jdH\nqa8d4o5ZY3etqtv1Ny9qrT1h0xqrpVMfLP0RuFF97ZDrmDW2fKqGUWps2+s15jFseZ/9SMarseW4\nyQjHsepOVbH1Pv7mGe994pGquld/848yXo0txx3lvWIf8979zRePeAxbnAZk5BpbznfM94qLfKv7\ngHmMGlt+vZ49Vo2d6DSL1lBV1c9GuWGrgFprf5fkzUk+kBtnFw3uXm7F7G+eka5rv/XYmUlOTtd1\nXyvX/vq9c2MD4+npOto3JLm0qr54jbitf7N676q6Q5LT+7x/Pl1z48n9L5FbrJLr0ri+IclHq+pf\n0s18uH+S36mqlYp9W65b0zv/sKouSvda/W2S70vyxG1/5A3Kt7+5aA713pDu05flPzxWzff6qrpH\nun3q+qp6Y5LvSfIPSX61qr6u/wW7asy7p5tddbuqOqu6ZuG7k3xWumbUSmrbMswkaa19OMmlSb63\n3+wf070RuFMNbPjtEve6dK9X0u0TT6/u05JBnxrtFDPJB1tr76obG38tyduG5LhH3K26u0e6pYm/\nku6N0Y/WwGnG2+P2tbH1mr8tyd+n+/TkG6rqt6vqfuvk2lr7lyS/le7N0I+ne8P21Um+f+lNwsq5\nttY+keRvkrw43ZvKX07XpPzuqvqa/nlHfd2W4v7c0thev/S865PcIckn+u0HHdd2GYcPpHsz+PR0\nU81fkK4p80M1YMr9LvvBB9JNrb6ougb62emWCNylqj5vYK7blyU/orX2b32cTWpsOe5vVNVD+0b9\n3/WbrFNj22Oe1Vp7/wg1thz31/u4n6jOJjW2fWzPWnp43RrbnusjWmv/nOTV6f6oX7fGtuf68Nba\nx5O8J5vV2E6v2cfqxpOMrlxjO8R8ZP+B1Q9mzfraJe7DWmvvTrfvv2SDGlteov9rVfU1m9bYtpiX\nVNU5fX1d22+ycn3tEvfskWpsOe6r+rgb1Vjd9NQHy0ui1qqvXXL9mqUa2+Q4tj3fczetsV1er43q\na4e4W/vs+7J5jW2Pe85SjW1yHNs6VcW3VdWd+obLq7PBMWxb3CdU18z6eG78G2qtGtsh7imttQ+M\nUGNbMb+1j3l9f//ax7BtcRenAemtXWM75HunvsYuzQbHsR3yvWP/3v7d2ew4tv31+njd+GH9WjVG\nx2CtoW86nFxdw+VF6WaqpLX29iRXJjmzqo4kw3fIbTFfkm6mx5aDSd69atNht1z7gvvl1trh1tqP\npVvu9X19voN+ke6Q76F0fxR9LMlnpFsr/V+T3K4v2D3HYVvMFyc5Jd3some21s5p3YyoX0ny3Wvm\n+owkz0/XEPlIuubT01prF6drcN0ryWcPibkt7vcn+W/pGkZb/leSa2uNbvtS3Av6uP9vkh9L9wfc\nvVprF6Zblvjo9A2JFWL+QLppuX+abgbb96R70/2LSa5J8nWr5Fq7LMPsvTLd/vZlfWf//enGfc/m\n4dHi9g2TA621P0/yunT7X5Kjn8Rxt5hLDb+tg8rd0jXkBtljDNJae1uSB7TWXpDk2enq4/PXjdsf\nVJNuP759unXupyT5gn5M1sq1tfbSdMtSv7K19ovp3mzekG481sq199NJbpPklq21V7XWfi3dks1z\n+/9310+5jvaa9ft0tdben642vnnrsU3yba39TLo3HE/qx+SF6aZyH/UTxKPk+tH+/r9N93vmA+ne\nCB3or++V607Lkp/fP/zfs36N7RT3BX3O11XVwTVqbKeYL1zaZGtm46o1tmvcvgm+bo3tle+pWb3G\ndor5832uL8n6NXa0XH8iXbN/nRo72th+orrZjCvV2NH22f749b+yYn3tEve1ubEWfjDdH/Tr1NhO\nS/R/tH/4FVmjxnaJ+WP9GHxinfo6StxnL22ybo3tGnfdGhuQ68r1dZS4z+pzfUm62f5nrVFjR8t3\nq8ZOXqXG9hjXteprwBhsUmO7xk23rGutGustn6riK/v7fjnJ4XWOYbvEfWjSzaxZt8Z2ifuwpfvX\nqrGjxVz3GLZL3Icv3b9Wje0S96w+11/MmsexbXltH4cLs+ZxbIdct06F8sl1a4wlrTWXPS5Jbrbt\n9uckeV66xtCpy9ukK4Anp+u4PivJQzeIWf2/5yf54f7605Lce5Nct2+b5N7pDggnbTgGj0/yZ+mW\nZP1i//j3pZtiumrM0/r7Dmwbiy9O9yn9zdfMdSvuQ5K8JslT+9ufneR3k3z2hmOwled9+ni3HWn/\nOiXJzyY50t++U5LXJ/nMFWNetTUGS3Fu0V9/dJIfGloTS/vPaX2cxya5ON2a7iS5VbrG3h8sPe+K\ndGuz14l7160xXv750r1ReW6Slyf5vHVj9v/eJt0Mm1sk+YZ+zO60Qa432+F5l6Z7U7DJGBxI14T5\nUJJf7/e3v0zyNevGXB6HpduvTvKlm+TaP3Z+kmuXbv90kseNsB9svW5fl66BevIG++1yvo9L8sql\n2y9P8uhNYvaP33Lp+u/utm8t7zvp6ugh2x77o3TnSjgpa9TYUeL+YZJ7bu1jq9TYkJjr1Ngecb9o\nl+ftWWN7xL330n46uMYGxly5xgbGXbnGhoxtVqixFXIdXF8D495nkxrrrx9auv70dH+wbR1/n5rk\nfw6psT1i/liS0/vbB4fW1ypx+/s+KyvW2IB8t++3u9bYgJiH+9s/k66xvmd9DYx7xvbtVqmxAfk+\nKV0TZc8aW2Fct/btwcewgbk+PsmrVq2xAWNbGVhjWTpG97c/L93fBs9I90HwHfr7n5zkD4fU14C4\nz8u2v3VWqLHBcTOwxlaMubzdXsewveLeub//wnTNt6E1tlfc03d53l41tte+sJXvdyb5+4E1ttJ+\nkIE15nLTi5lFe+g/ud5aTvLlVfU5rbUPJvmDdAe5I/2mLUlaa9emW4713em6sO/cIOZWB/zBST6/\nql6Xrhv/vk1yrc6B1s3Q+Nokv5Tk9e3GGQurxt1aC31Fkj9J8lWttSf21z+8NTYrxtxaanZDP66t\nz/XiJL/fuuUt6+T6pf2mV6b7JfQt/WybP0jy/6Rb9naTTx5Wfc1aa3+R7g/H/7hTnmvE/US6P7Ke\nWFVPSfeL+U/SzeRaJeaH8qlrdv++dTO/vjHJD/djcFRb8duNywVbu3EZ5j+lO3CmtfaR1tovJPlY\nVV1cVVdll6LXAAALm0lEQVSmO9nitWvGXZw8funnu0O65X/nJHlZa+3qdWP2T7lruvNZvS5dDf92\nv/26ud7Q19vNquqhVfXqPsQ1G47BJ9N9K8mDW2vf1O9vz0w3jXetmMvjsJTrDblx2d8mcS9K8ntV\n9VNV9YdJ7pvk/95wDNrS63YoyZmtm8Wzo1XyTXdekS+oqouq6oo+/l9tGDOtW3pwVj8G12SXT2SX\nazjJGek+zd167PPTnQDzna216/oa++gqNXaUuCenm8m4tY8NrrEhMXsr1dgecd++tW1vcI3tEfct\n/V2/mxVqbI+YW+eIW7nGBsZducb2iPv25XyzR42tkmu6T38/f6/6WiHu1uu1Vo31tXuwv768RP/V\nVXXf1toLM6DGBsRs6Zb937fduPzkqPW1atylcVqpxobEHXIcGxjzN6tbYnNFBtTXinHvu7TdSjV2\nlLi/VVVf3LpZS3vW2Ir7wda+PfgYNiDX+6b7QGHlGhswti3Jx/eqsT5ma21xqorb58ZTVbwg3bKg\np1bVt6b7A/8jKxzD9or7KafA6LcZUmND456U5HC6bzzetcZWjHly/5yhx7C94j6pqv5LulUEX75C\nje0V9/xtY3vWwBo7WtxPJPmuqvrW1s2Eu6KqnjegxoaO7dYJw/esMXbR9kHHaj9e8qmfqp6Z7gRh\nb0g3Y+i70jUFnpPu6xNvu7Ttg9MVzNkjxnxtuj/izxor13Sffj8rXRE+YoS4p+zw/IMj5fq56TrF\nV46U6+2Wtr1nkm9Lcs6IY3uw//dxSe47QtxD/XZfmOQJSX51rP0r3QH0/0j3Jv6rVqiPk9N94vTH\n6Q5EW/f/p3TNxy9duu/W6WaEPWaEuFszq6qP+/x0U603jtnf9x/SnaD9m0bM9WC6JY5XJvm2EeLe\nf2sMlsbiJjOYNhjXu6absj5Grstje8t0B/OvHXNs+39vNdL+tbzfnpHud8PjRxyD26Zb67/q2H7Z\n0v1fmOTXt217m6xXY0Pirlpje8V8UNarsb3i3iddjT1hzLhr1theud4tXUNx7P3g5DVrbMgY7Flj\nK+Z61yH1tUbcQ+vWWP8633Hp8R9PPwMq3UyCPWtsQMznpjsNQNLNaN6zvlaI+6v99QevW2O7xH1F\nf/2+Q2psQMyfTPLyVeprjbH9vKxZY7vEfVV//TMyoMZWyXVofQ2M+xNLr9fdsmaN7TG2p2RAjS3F\n/NN074HuneT70810f0O6DzufsVQLqx7Ddov70ST/Z7/t52T1Gjta3O/vt31IBtTYirl+cVY/hu0W\n9+NJvneDGhuS75lZvcaOFveCpVpYpcaOFvNpq9aYyw5jfbwT2M+XdH/knZxuxsWj0035/7W+OO6V\n7s3pC5N8w5Qxs/THxshx7zZR3L3eVK8T8y7H4/XaJ3G/ceL96xZ7xNxkGebDJor78P7+7VPjx4h5\nYIox2B5jhLibLHHdawzGznWq/eCh2/eBDeP+xPI4jJzrI3baZ1eIO8my5KPEve8GNbZbzPtsWGN7\nxV13v11pbEca12OS6z4dg/tMlOuO++zQuNu3zY1L9Hc8Rm4Y85ZT5po1a2xA3O0xjtupDwaM7Vo1\ntmq++3QMdn1fN8V+OyRmdj9VxY6nUxgh7q03qbEBcQ+MEPOnlsdg3X32KLnueBqQEcdg7HxXPbXG\nRmPgMuxiGVqvtp2Aubqv4vyzdJ+GPz/dtL0/SXcys0vTdSv/It2SsAfWjV/dOWbMQ0nSWrtqorh/\nPcUYtKUTh42Y67u3xRn99drHcR9QN37d6ui5tu6bI3a0bXryOssw3zFR3O1LJMaM+Sknkh9rDJbr\nYqS4myxx3WsMxs51qv3gncv7wAhxn7I8DiPnurW8q60Zd5JlyUeJ+w/b890w5lnpvu1mkxrbLdet\nuOvut4PHdsRxnTzXfTwG/zhRrjfZZ1eJW52dlujf5Bg5QsyPTZDr72/lum6NDYh7w0gxNzn1wdCx\nXavGVsl3H4/Bju/rpthvB8Tc61QVn/I6jRj3k9tzHSHuR5bifnKEmH/cx7yhj7nqPrtXrjd5fzTy\nGIyZ7477wggxdxwDVtD2QcfqeF7SnZx0t0+mfzPJN/fXn5rkif31b0+31Ozr0y0ruMPUMecWd065\nzi3uVLluizP6Msyp4s4pV2Mwv7j7MNfRlyXvFXdOuRoDY7BJ3OyxRH+KmHOLO6dcjcG+GYNBp6qY\nW9w55Tq3uFPl6rL65bgnsF8u6Zbv/ED6r1bv7/vB9OtR0y3l+ckkd0n31a0/kqW188cq5tzizinX\nucWdKtdt8WexrG9OuRqD+cXdT7lmumXJR407p1yNgTHYMO5Rl+hPEXNuceeUqzHYd2Mw5Nw5s4k7\np1znFneqXF1Wu5yQy9Cq6sDS9aqqF6X7OsWT002tu6CqDqc7QdjD+k2fk26N5OuSXNNae05r7Y1T\nxpxb3DnlOre4U+W6FHM/Lr+bannnnJYgGgNje6yWJV81Zcy5xZ1TrsZg9Lh/PWXMucWdU67GYF+P\nwU1OVTG3uHPKdW5xp8qVERzvbtWxvKSbslZLt++Z7tuPnpfkkUlemW5622PTfS312enWRH5Wv/0t\nknzG1DHnFndOuc4t7lS5LsU74ZffzSnunHKdW9w55WoMjIExMAbGwBgYA2Mw97hT5eoy3uVgTiCt\n38Oq6u5JXpDkdkmuS/JDrbU/qKqPJvlvSX40yWnpvl3jbum+bu9f2w4njJsi5tzizinXucWdKtel\n+FsnjTuY7isnT0ny0621v0v39elbJ/D78iTvqqq7JHlgumVtf9da+6djFXdOuRqD+cWdU67GwBgY\nA2NgDIyBMTAGc487Va6M54RbhlZVj0v39Y8/m+ThST6Y5LSqulVr7Y+TvDHJ81trz0v3Nco/0u+w\nxzTm3OLOKde5xR07Zll+N6u4c8p1bnHnlKsxMAbGwBgYA2NgDIzB3ONOlSvTOKFmFvXelm7mxQdb\nax+sqt9Mt6znLek6mD+b5Leq6tTW2h8cx5hzizunXOcWd5SYVVXJjV/3WVX3THKbdF8t+eok35rk\nb9J9u8xL+7jnVtVntdbeUlXfk+7bCf596rhzytUYzC/unHI1BsbAGBgDY2AMjIExmHvcqXJlYm0f\nrIU71pckP53kkv76SUlenOSC3Ph1kZ+5H2LOLe6ccp1b3DFjJrl7kivSfZvAG5M8tL//wUn+Il0T\n+RlJXp/uhHJ3Ol5x55SrMZhf3DnlagyMgTEwBsbAGBgDYzD3uFPl6jLN5bgncFx+6OT2Sd6U5Oz+\n9qOTPDv9SYH3S8y5xZ1TrnOLO1bMJI9LN1Ppkemmc16e5PFJbtU/flGSX+ivPzTJ449X3Dnlagzm\nF3dOuRoDY2AMjIExMAbGwBjMPe5UubpMdznuCRy3Hzx5YpK37feYc4s7p1znFneMmEm+JN0a4C/t\nb39b/4v5fv3tM5P8ZZJTj3fcOeVqDOYXd065GgNjYAyMgTEwBsbAGMw97lS5ukx3Oe4JHLcfvPtK\n8e9Md5Lvm+3XmHOLO6dc5xZ3rJg5QZffzTXunHKdW9w55WoMjIExMAbGwBgYA2Mw97hT5eoyzeWE\n+za0La21j7fWXtpau6H1X9u3H2POLe6ccp1b3BFj/kySw1V1dmvtuiS/m+Qzklzf/z8f3kdx55Tr\nVHHnlOvc4s4p16nizinXqeLOKdep4s4p16nizinXqeLOKdep4s4p16nizinXqeLOKde5xZ0qV6aw\nabfJxcVlfpecYMvv5h53TrnOLe6ccjUGxsAYGANjYAyMgTGYe9ypcnUZ/3Jwe/MIOCG8PMkNVXWz\nJGnjzaqaIu4UMecWd4qY4k4Xc25xp4g5t7hTxJxb3Clizi3uFDHnFneKmHOLO0XMucWdIubc4k4R\nU9zpYjKBal13DwAAAABO3HMWAQAAAHBTmkUAAAAALGgWAQAAALCgWQQAAADAgmYRAAAAAAuaRQAA\nAAAsaBYBAAAAsKBZBAAAAMDC/w9OjErm2QOg+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f249dc47048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#LR feature imp\n",
    "lr_coef = clf.coef_.reshape(-1, 1)\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.bar(range(1, len(lr_coef)+1), np.abs(lr_coef) )\n",
    "plt.xticks(range(len(gameX.columns)), gameX.columns, rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Method 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn import cross_validation, metrics   #Additional scklearn functions\n",
    "from sklearn.grid_search import GridSearchCV   #Perforing grid search\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def modelfit(alg, dtrainX, dtrainY, predictors, useTrainCV=True, cv_folds=4, early_stopping_rounds=50):\n",
    "    \n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "#         xgb_param['num_class']=2\n",
    "        xgtrain = xgb.DMatrix(dtrainX[predictors].values, label=dtrainY.values)\n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
    "             early_stopping_rounds=early_stopping_rounds,stratified=True)\n",
    "        alg.set_params(n_estimators=cvresult.shape[0])\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(dtrainX[predictors], dtrainY ,eval_metric='error')\n",
    "        \n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(dtrainX[predictors])\n",
    "    dtrain_predprob = alg.predict_proba(dtrainX[predictors])[:,1]\n",
    "        \n",
    "    #Print model report:\n",
    "    print (\"\\nModel Report\")\n",
    "    print (\"Accuracy : %.4g\" % metrics.accuracy_score(dtrainY.values, dtrain_predictions))\n",
    "    #print (\"AUC Score (Train): %f\" % metrics.roc_auc_score(dtrain[target], dtrain_predprob))\n",
    "                    \n",
    "    #feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)\n",
    "    #feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "    #plt.ylabel('Feature Importance Score')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8243\n",
      "time 69.31128191947937\n"
     ]
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "t=time.time()\n",
    "predictors = [x for x in gameX.columns]\n",
    "xgb1 = XGBClassifier(\n",
    " learning_rate =0.1,\n",
    " n_estimators=1000,\n",
    " max_depth=4, #4\n",
    " min_child_weight=1,\n",
    " gamma=0,\n",
    "reg_alpha = 0.05,\n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'binary:logistic',\n",
    " nthread=4,\n",
    " scale_pos_weight=1,\n",
    " seed=27)\n",
    "\n",
    "modelfit(xgb1, gameX, gameY, predictors)\n",
    "\n",
    "print('time',time.time()-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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YgDfffJOzzjqLgw46iEGDBrFggd9N7Sw9slhMLCT9POO0jLZKYJGkLcCTwLFZxl4FDAPm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\n2v0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f746d7bcfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xgb feature imp\n",
    "from xgboost import plot_importance\n",
    "ax = plot_importance(xgb1, height=0.5)\n",
    "fig = ax.figure\n",
    "fig.set_size_inches(10,20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+udes4qJFi+jfvz+vvPIKl19+OZ/5zGcKGKGZmXXm6191ZO9nnJSzrRpYKOlt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\nZmZJLhbNzMzMLOn/A0l2PGtem6LMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0627fb83c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xgb feature imp\n",
    "from xgboost import plot_importance\n",
    "ax = plot_importance(xgb1, height=0.5)\n",
    "fig = ax.figure\n",
    "fig.set_size_inches(10,20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IkrTuLFnoCkiSJEmSJGnDYc8iSdIGzx4Es8NhMpIkSbPPZJEkSRsgky6SJElaKCaLJEmS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\nSZIkSZIkdf1/mqNLdL62lLoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06292af0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGB feature imp\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.bar(range(1, len(xgb1.feature_importances_)+1), xgb1.feature_importances_)\n",
    "plt.xticks(range(1, len(xgb1.feature_importances_)), gameX.columns, rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IkrTuLFnoCkiSJEmSJGnDYc8iSdIGzx4Es8NhMpIkSbPPZJEkSRsgky6SJElaKCaLJEmS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\nSZIkSZIkdf1/mqNLdL62lLoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06292af0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGB feature imp\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.bar(range(1, len(xgb1.feature_importances_)+1), xgb1.feature_importances_)\n",
    "plt.xticks(range(1, len(xgb1.feature_importances_)), gameX.columns, rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FmP1Jrq2Jy5M83Vp7ovuUtLckebi19tOjzhwAAACA0a15ZFFr7bmqujHJ3Um2JLm9tXao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\nAAAA6GkWAQAAAND7/85sLAccanVdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7f7231c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGB feature imp\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.bar(range(1, len(xgb1.feature_importances_)+1), xgb1.feature_importances_)\n",
    "plt.xticks(range(1, len(xgb1.feature_importances_)+1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred2 = xgb1.predict_proba(leaderX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(25000, 3)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred1 = pd.DataFrame(pred1)\n",
    "pred1['application_key'] = leaderAppID\n",
    "print(pred1.shape)\n",
    "\n",
    "pred2= pd.DataFrame(pred2)\n",
    "pred2['application_key'] = leaderAppID\n",
    "pred2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>application_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.744983</td>\n",
       "      <td>0.255017</td>\n",
       "      <td>350053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.884746</td>\n",
       "      <td>0.115254</td>\n",
       "      <td>350054</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.800212</td>\n",
       "      <td>0.199788</td>\n",
       "      <td>350055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.543523</td>\n",
       "      <td>0.456477</td>\n",
       "      <td>350056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724498</td>\n",
       "      <td>0.275502</td>\n",
       "      <td>350057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.799576</td>\n",
       "      <td>0.200424</td>\n",
       "      <td>350058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.727787</td>\n",
       "      <td>0.272213</td>\n",
       "      <td>350059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.872432</td>\n",
       "      <td>0.127568</td>\n",
       "      <td>350060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.833368</td>\n",
       "      <td>0.166632</td>\n",
       "      <td>350061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.746011</td>\n",
       "      <td>0.253989</td>\n",
       "      <td>350062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.811678</td>\n",
       "      <td>0.188322</td>\n",
       "      <td>350063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.764865</td>\n",
       "      <td>0.235135</td>\n",
       "      <td>350064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.699396</td>\n",
       "      <td>0.300604</td>\n",
       "      <td>350065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.459662</td>\n",
       "      <td>0.540338</td>\n",
       "      <td>350066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.744996</td>\n",
       "      <td>0.255004</td>\n",
       "      <td>350067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.553626</td>\n",
       "      <td>0.446374</td>\n",
       "      <td>350068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.813239</td>\n",
       "      <td>0.186761</td>\n",
       "      <td>350069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.850321</td>\n",
       "      <td>0.149679</td>\n",
       "      <td>350070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.786510</td>\n",
       "      <td>0.213490</td>\n",
       "      <td>350071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.829889</td>\n",
       "      <td>0.170111</td>\n",
       "      <td>350072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.755381</td>\n",
       "      <td>0.244619</td>\n",
       "      <td>350073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.698933</td>\n",
       "      <td>0.301067</td>\n",
       "      <td>350074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.926595</td>\n",
       "      <td>0.073405</td>\n",
       "      <td>350075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.744882</td>\n",
       "      <td>0.255118</td>\n",
       "      <td>350076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.515867</td>\n",
       "      <td>0.484133</td>\n",
       "      <td>350077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.863504</td>\n",
       "      <td>0.136496</td>\n",
       "      <td>350078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.784807</td>\n",
       "      <td>0.215193</td>\n",
       "      <td>350079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.869487</td>\n",
       "      <td>0.130513</td>\n",
       "      <td>350080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.306657</td>\n",
       "      <td>0.693343</td>\n",
       "      <td>350081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.816062</td>\n",
       "      <td>0.183938</td>\n",
       "      <td>350082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>0.605488</td>\n",
       "      <td>0.394512</td>\n",
       "      <td>375023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>0.762881</td>\n",
       "      <td>0.237119</td>\n",
       "      <td>375024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>0.670963</td>\n",
       "      <td>0.329037</td>\n",
       "      <td>375025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>0.658155</td>\n",
       "      <td>0.341845</td>\n",
       "      <td>375026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>0.458687</td>\n",
       "      <td>0.541313</td>\n",
       "      <td>375027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>0.885356</td>\n",
       "      <td>0.114644</td>\n",
       "      <td>375028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>0.767991</td>\n",
       "      <td>0.232009</td>\n",
       "      <td>375029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>0.731963</td>\n",
       "      <td>0.268037</td>\n",
       "      <td>375030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>0.706490</td>\n",
       "      <td>0.293510</td>\n",
       "      <td>375031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>0.605235</td>\n",
       "      <td>0.394765</td>\n",
       "      <td>375032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>0.849900</td>\n",
       "      <td>0.150100</td>\n",
       "      <td>375033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>0.804009</td>\n",
       "      <td>0.195991</td>\n",
       "      <td>375034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>0.499410</td>\n",
       "      <td>0.500590</td>\n",
       "      <td>375035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>0.603884</td>\n",
       "      <td>0.396116</td>\n",
       "      <td>375036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>0.901664</td>\n",
       "      <td>0.098336</td>\n",
       "      <td>375037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>0.660963</td>\n",
       "      <td>0.339037</td>\n",
       "      <td>375038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>0.690172</td>\n",
       "      <td>0.309828</td>\n",
       "      <td>375039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>0.718698</td>\n",
       "      <td>0.281302</td>\n",
       "      <td>375040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>0.613261</td>\n",
       "      <td>0.386739</td>\n",
       "      <td>375041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>0.460924</td>\n",
       "      <td>0.539076</td>\n",
       "      <td>375042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>0.513496</td>\n",
       "      <td>0.486504</td>\n",
       "      <td>375043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>0.719726</td>\n",
       "      <td>0.280274</td>\n",
       "      <td>375044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>0.446084</td>\n",
       "      <td>0.553916</td>\n",
       "      <td>375045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>0.718854</td>\n",
       "      <td>0.281146</td>\n",
       "      <td>375046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>0.782102</td>\n",
       "      <td>0.217898</td>\n",
       "      <td>375047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>0.570882</td>\n",
       "      <td>0.429118</td>\n",
       "      <td>375048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>0.794564</td>\n",
       "      <td>0.205436</td>\n",
       "      <td>375049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>0.620337</td>\n",
       "      <td>0.379663</td>\n",
       "      <td>375050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>0.866956</td>\n",
       "      <td>0.133044</td>\n",
       "      <td>375051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>0.928740</td>\n",
       "      <td>0.071260</td>\n",
       "      <td>375052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1  application_key\n",
       "0      0.744983  0.255017           350053\n",
       "1      0.884746  0.115254           350054\n",
       "2      0.800212  0.199788           350055\n",
       "3      0.543523  0.456477           350056\n",
       "4      0.724498  0.275502           350057\n",
       "5      0.799576  0.200424           350058\n",
       "6      0.727787  0.272213           350059\n",
       "7      0.872432  0.127568           350060\n",
       "8      0.833368  0.166632           350061\n",
       "9      0.746011  0.253989           350062\n",
       "10     0.811678  0.188322           350063\n",
       "11     0.764865  0.235135           350064\n",
       "12     0.699396  0.300604           350065\n",
       "13     0.459662  0.540338           350066\n",
       "14     0.744996  0.255004           350067\n",
       "15     0.553626  0.446374           350068\n",
       "16     0.813239  0.186761           350069\n",
       "17     0.850321  0.149679           350070\n",
       "18     0.786510  0.213490           350071\n",
       "19     0.829889  0.170111           350072\n",
       "20     0.755381  0.244619           350073\n",
       "21     0.698933  0.301067           350074\n",
       "22     0.926595  0.073405           350075\n",
       "23     0.744882  0.255118           350076\n",
       "24     0.515867  0.484133           350077\n",
       "25     0.863504  0.136496           350078\n",
       "26     0.784807  0.215193           350079\n",
       "27     0.869487  0.130513           350080\n",
       "28     0.306657  0.693343           350081\n",
       "29     0.816062  0.183938           350082\n",
       "...         ...       ...              ...\n",
       "24970  0.605488  0.394512           375023\n",
       "24971  0.762881  0.237119           375024\n",
       "24972  0.670963  0.329037           375025\n",
       "24973  0.658155  0.341845           375026\n",
       "24974  0.458687  0.541313           375027\n",
       "24975  0.885356  0.114644           375028\n",
       "24976  0.767991  0.232009           375029\n",
       "24977  0.731963  0.268037           375030\n",
       "24978  0.706490  0.293510           375031\n",
       "24979  0.605235  0.394765           375032\n",
       "24980  0.849900  0.150100           375033\n",
       "24981  0.804009  0.195991           375034\n",
       "24982  0.499410  0.500590           375035\n",
       "24983  0.603884  0.396116           375036\n",
       "24984  0.901664  0.098336           375037\n",
       "24985  0.660963  0.339037           375038\n",
       "24986  0.690172  0.309828           375039\n",
       "24987  0.718698  0.281302           375040\n",
       "24988  0.613261  0.386739           375041\n",
       "24989  0.460924  0.539076           375042\n",
       "24990  0.513496  0.486504           375043\n",
       "24991  0.719726  0.280274           375044\n",
       "24992  0.446084  0.553916           375045\n",
       "24993  0.718854  0.281146           375046\n",
       "24994  0.782102  0.217898           375047\n",
       "24995  0.570882  0.429118           375048\n",
       "24996  0.794564  0.205436           375049\n",
       "24997  0.620337  0.379663           375050\n",
       "24998  0.866956  0.133044           375051\n",
       "24999  0.928740  0.071260           375052\n",
       "\n",
       "[25000 rows x 3 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>application_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.863583</td>\n",
       "      <td>0.136417</td>\n",
       "      <td>350053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.981587</td>\n",
       "      <td>0.018413</td>\n",
       "      <td>350054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.956605</td>\n",
       "      <td>0.043395</td>\n",
       "      <td>350055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.770681</td>\n",
       "      <td>0.229319</td>\n",
       "      <td>350056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.766061</td>\n",
       "      <td>0.233939</td>\n",
       "      <td>350057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.973787</td>\n",
       "      <td>0.026213</td>\n",
       "      <td>350058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.867007</td>\n",
       "      <td>0.132993</td>\n",
       "      <td>350059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.990688</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>350060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.985968</td>\n",
       "      <td>0.014032</td>\n",
       "      <td>350061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.870953</td>\n",
       "      <td>0.129047</td>\n",
       "      <td>350062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.974984</td>\n",
       "      <td>0.025016</td>\n",
       "      <td>350063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.894807</td>\n",
       "      <td>0.105193</td>\n",
       "      <td>350064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.658621</td>\n",
       "      <td>0.341379</td>\n",
       "      <td>350065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.420447</td>\n",
       "      <td>0.579553</td>\n",
       "      <td>350066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.859153</td>\n",
       "      <td>0.140847</td>\n",
       "      <td>350067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.712096</td>\n",
       "      <td>0.287904</td>\n",
       "      <td>350068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.964098</td>\n",
       "      <td>0.035902</td>\n",
       "      <td>350069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.956422</td>\n",
       "      <td>0.043578</td>\n",
       "      <td>350070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.918092</td>\n",
       "      <td>0.081908</td>\n",
       "      <td>350071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.957719</td>\n",
       "      <td>0.042281</td>\n",
       "      <td>350072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.869839</td>\n",
       "      <td>0.130161</td>\n",
       "      <td>350073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.780847</td>\n",
       "      <td>0.219153</td>\n",
       "      <td>350074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.993685</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>350075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.863975</td>\n",
       "      <td>0.136025</td>\n",
       "      <td>350076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.536229</td>\n",
       "      <td>0.463771</td>\n",
       "      <td>350077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.987937</td>\n",
       "      <td>0.012063</td>\n",
       "      <td>350078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.955527</td>\n",
       "      <td>0.044473</td>\n",
       "      <td>350079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.975975</td>\n",
       "      <td>0.024025</td>\n",
       "      <td>350080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.132166</td>\n",
       "      <td>0.867834</td>\n",
       "      <td>350081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.952478</td>\n",
       "      <td>0.047523</td>\n",
       "      <td>350082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>0.736351</td>\n",
       "      <td>0.263649</td>\n",
       "      <td>375023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>0.926254</td>\n",
       "      <td>0.073746</td>\n",
       "      <td>375024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>0.719047</td>\n",
       "      <td>0.280953</td>\n",
       "      <td>375025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>0.588850</td>\n",
       "      <td>0.411150</td>\n",
       "      <td>375026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>0.569073</td>\n",
       "      <td>0.430927</td>\n",
       "      <td>375027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>0.989228</td>\n",
       "      <td>0.010772</td>\n",
       "      <td>375028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>0.900266</td>\n",
       "      <td>0.099734</td>\n",
       "      <td>375029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>0.887100</td>\n",
       "      <td>0.112900</td>\n",
       "      <td>375030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>0.823408</td>\n",
       "      <td>0.176592</td>\n",
       "      <td>375031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>0.713192</td>\n",
       "      <td>0.286808</td>\n",
       "      <td>375032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>0.978932</td>\n",
       "      <td>0.021068</td>\n",
       "      <td>375033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>0.955217</td>\n",
       "      <td>0.044783</td>\n",
       "      <td>375034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>0.662148</td>\n",
       "      <td>0.337852</td>\n",
       "      <td>375035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>0.577523</td>\n",
       "      <td>0.422477</td>\n",
       "      <td>375036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>0.994438</td>\n",
       "      <td>0.005562</td>\n",
       "      <td>375037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>0.762321</td>\n",
       "      <td>0.237679</td>\n",
       "      <td>375038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>0.868770</td>\n",
       "      <td>0.131230</td>\n",
       "      <td>375039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>0.906996</td>\n",
       "      <td>0.093004</td>\n",
       "      <td>375040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>0.703727</td>\n",
       "      <td>0.296272</td>\n",
       "      <td>375041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>0.310627</td>\n",
       "      <td>0.689373</td>\n",
       "      <td>375042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>0.298910</td>\n",
       "      <td>0.701090</td>\n",
       "      <td>375043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>0.874864</td>\n",
       "      <td>0.125136</td>\n",
       "      <td>375044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>0.304955</td>\n",
       "      <td>0.695045</td>\n",
       "      <td>375045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>0.812849</td>\n",
       "      <td>0.187151</td>\n",
       "      <td>375046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>0.864805</td>\n",
       "      <td>0.135195</td>\n",
       "      <td>375047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>0.676842</td>\n",
       "      <td>0.323158</td>\n",
       "      <td>375048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>0.938926</td>\n",
       "      <td>0.061074</td>\n",
       "      <td>375049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>0.680997</td>\n",
       "      <td>0.319003</td>\n",
       "      <td>375050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>0.983911</td>\n",
       "      <td>0.016089</td>\n",
       "      <td>375051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>0.994953</td>\n",
       "      <td>0.005047</td>\n",
       "      <td>375052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1  application_key\n",
       "0      0.863583  0.136417           350053\n",
       "1      0.981587  0.018413           350054\n",
       "2      0.956605  0.043395           350055\n",
       "3      0.770681  0.229319           350056\n",
       "4      0.766061  0.233939           350057\n",
       "5      0.973787  0.026213           350058\n",
       "6      0.867007  0.132993           350059\n",
       "7      0.990688  0.009312           350060\n",
       "8      0.985968  0.014032           350061\n",
       "9      0.870953  0.129047           350062\n",
       "10     0.974984  0.025016           350063\n",
       "11     0.894807  0.105193           350064\n",
       "12     0.658621  0.341379           350065\n",
       "13     0.420447  0.579553           350066\n",
       "14     0.859153  0.140847           350067\n",
       "15     0.712096  0.287904           350068\n",
       "16     0.964098  0.035902           350069\n",
       "17     0.956422  0.043578           350070\n",
       "18     0.918092  0.081908           350071\n",
       "19     0.957719  0.042281           350072\n",
       "20     0.869839  0.130161           350073\n",
       "21     0.780847  0.219153           350074\n",
       "22     0.993685  0.006315           350075\n",
       "23     0.863975  0.136025           350076\n",
       "24     0.536229  0.463771           350077\n",
       "25     0.987937  0.012063           350078\n",
       "26     0.955527  0.044473           350079\n",
       "27     0.975975  0.024025           350080\n",
       "28     0.132166  0.867834           350081\n",
       "29     0.952478  0.047523           350082\n",
       "...         ...       ...              ...\n",
       "24970  0.736351  0.263649           375023\n",
       "24971  0.926254  0.073746           375024\n",
       "24972  0.719047  0.280953           375025\n",
       "24973  0.588850  0.411150           375026\n",
       "24974  0.569073  0.430927           375027\n",
       "24975  0.989228  0.010772           375028\n",
       "24976  0.900266  0.099734           375029\n",
       "24977  0.887100  0.112900           375030\n",
       "24978  0.823408  0.176592           375031\n",
       "24979  0.713192  0.286808           375032\n",
       "24980  0.978932  0.021068           375033\n",
       "24981  0.955217  0.044783           375034\n",
       "24982  0.662148  0.337852           375035\n",
       "24983  0.577523  0.422477           375036\n",
       "24984  0.994438  0.005562           375037\n",
       "24985  0.762321  0.237679           375038\n",
       "24986  0.868770  0.131230           375039\n",
       "24987  0.906996  0.093004           375040\n",
       "24988  0.703727  0.296272           375041\n",
       "24989  0.310627  0.689373           375042\n",
       "24990  0.298910  0.701090           375043\n",
       "24991  0.874864  0.125136           375044\n",
       "24992  0.304955  0.695045           375045\n",
       "24993  0.812849  0.187151           375046\n",
       "24994  0.864805  0.135195           375047\n",
       "24995  0.676842  0.323158           375048\n",
       "24996  0.938926  0.061074           375049\n",
       "24997  0.680997  0.319003           375050\n",
       "24998  0.983911  0.016089           375051\n",
       "24999  0.994953  0.005047           375052\n",
       "\n",
       "[25000 rows x 3 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred1 = pred1.sort_values(by = 0,kind = 'mergesort',ascending = False)\n",
    "\n",
    "pred2 = pred2.sort_values(by = 0,kind = 'mergesort',ascending = False)\n",
    "# pred2 = pred2.sort_values(by = 0,kind = 'mergesort',ascending = True) #true for xgb method3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = pred1.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "pred1 = pred1[cols]\n",
    "\n",
    "cols = pred2.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "pred2 = pred2[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred1 = pred1.loc[:,'application_key':]\n",
    "\n",
    "pred2 = pred2.loc[:,'application_key':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit1 = pred1['application_key'][:10000]\n",
    "submit1 = pd.DataFrame(submit1)\n",
    "submit1[0] = 0\n",
    "\n",
    "submit2 = pred2['application_key'][:10000]\n",
    "submit2 = pd.DataFrame(submit2)\n",
    "submit2[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# submit.iloc[-400:, 1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_key</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23338</th>\n",
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       "      <th>15911</th>\n",
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       "    <tr>\n",
       "      <th>21128</th>\n",
       "      <td>371181</td>\n",
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       "      <th>19140</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23607</th>\n",
       "      <td>373660</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3642</th>\n",
       "      <td>353695</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7820</th>\n",
       "      <td>357873</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24868</th>\n",
       "      <td>374921</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22990</th>\n",
       "      <td>373043</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21731</th>\n",
       "      <td>371784</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21040</th>\n",
       "      <td>371093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>358611</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19883</th>\n",
       "      <td>369936</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>350975</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13848</th>\n",
       "      <td>363901</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22647</th>\n",
       "      <td>372700</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393</th>\n",
       "      <td>351446</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11401</th>\n",
       "      <td>361454</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>365041</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16123</th>\n",
       "      <td>366176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17352</th>\n",
       "      <td>367405</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22362</th>\n",
       "      <td>372415</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22465</th>\n",
       "      <td>372518</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18127</th>\n",
       "      <td>368180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3051</th>\n",
       "      <td>353104</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3747</th>\n",
       "      <td>353800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22265</th>\n",
       "      <td>372318</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6076</th>\n",
       "      <td>356129</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15669</th>\n",
       "      <td>365722</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5673</th>\n",
       "      <td>355726</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21963</th>\n",
       "      <td>372016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>353439</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17014</th>\n",
       "      <td>367067</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>351173</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24674</th>\n",
       "      <td>374727</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19747</th>\n",
       "      <td>369800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12936</th>\n",
       "      <td>362989</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21218</th>\n",
       "      <td>371271</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2590</th>\n",
       "      <td>352643</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9110</th>\n",
       "      <td>359163</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21059</th>\n",
       "      <td>371112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4128</th>\n",
       "      <td>354181</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8713</th>\n",
       "      <td>358766</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9739</th>\n",
       "      <td>359792</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12629</th>\n",
       "      <td>362682</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9206</th>\n",
       "      <td>359259</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7294</th>\n",
       "      <td>357347</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17746</th>\n",
       "      <td>367799</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>351778</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15612</th>\n",
       "      <td>365665</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14729</th>\n",
       "      <td>364782</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16797</th>\n",
       "      <td>366850</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18293</th>\n",
       "      <td>368346</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>350471</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15808</th>\n",
       "      <td>365861</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13401</th>\n",
       "      <td>363454</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8836</th>\n",
       "      <td>358889</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17942</th>\n",
       "      <td>367995</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4649</th>\n",
       "      <td>354702</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16615</th>\n",
       "      <td>366668</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key  0\n",
       "23338           373391  0\n",
       "15911           365964  0\n",
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       "3642            353695  0\n",
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       "21040           371093  0\n",
       "8558            358611  0\n",
       "19883           369936  0\n",
       "922             350975  0\n",
       "13848           363901  0\n",
       "22647           372700  0\n",
       "1393            351446  0\n",
       "11401           361454  0\n",
       "14988           365041  0\n",
       "16123           366176  0\n",
       "17352           367405  0\n",
       "22362           372415  0\n",
       "22465           372518  0\n",
       "18127           368180  0\n",
       "3051            353104  0\n",
       "3747            353800  0\n",
       "22265           372318  0\n",
       "6076            356129  0\n",
       "15669           365722  0\n",
       "5673            355726  0\n",
       "...                ... ..\n",
       "21963           372016  0\n",
       "3386            353439  0\n",
       "17014           367067  0\n",
       "1120            351173  0\n",
       "24674           374727  0\n",
       "19747           369800  0\n",
       "12936           362989  0\n",
       "21218           371271  0\n",
       "2590            352643  0\n",
       "9110            359163  0\n",
       "21059           371112  0\n",
       "4128            354181  0\n",
       "8713            358766  0\n",
       "9739            359792  0\n",
       "12629           362682  0\n",
       "9206            359259  0\n",
       "7294            357347  0\n",
       "17746           367799  0\n",
       "1725            351778  0\n",
       "15612           365665  0\n",
       "14729           364782  0\n",
       "16797           366850  0\n",
       "18293           368346  0\n",
       "418             350471  0\n",
       "15808           365861  0\n",
       "13401           363454  0\n",
       "8836            358889  0\n",
       "17942           367995  0\n",
       "4649            354702  0\n",
       "16615           366668  0\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>5667</th>\n",
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       "      <th>23414</th>\n",
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       "      <th>17393</th>\n",
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       "      <th>19448</th>\n",
       "      <td>369501</td>\n",
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       "      <th>7033</th>\n",
       "      <td>357086</td>\n",
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       "      <th>3071</th>\n",
       "      <td>353124</td>\n",
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       "      <th>20538</th>\n",
       "      <td>370591</td>\n",
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       "    <tr>\n",
       "      <th>23870</th>\n",
       "      <td>373923</td>\n",
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       "      <th>20007</th>\n",
       "      <td>370060</td>\n",
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       "      <th>10735</th>\n",
       "      <td>360788</td>\n",
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       "    <tr>\n",
       "      <th>23586</th>\n",
       "      <td>373639</td>\n",
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       "    <tr>\n",
       "      <th>21895</th>\n",
       "      <td>371948</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>18916</th>\n",
       "      <td>368969</td>\n",
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       "      <td>356129</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>365047</td>\n",
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       "      <th>18143</th>\n",
       "      <td>368196</td>\n",
       "      <td>0</td>\n",
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       "      <th>24535</th>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>19901</th>\n",
       "      <td>369954</td>\n",
       "      <td>0</td>\n",
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       "      <th>13626</th>\n",
       "      <td>363679</td>\n",
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       "      <th>10964</th>\n",
       "      <td>361017</td>\n",
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       "      <th>11718</th>\n",
       "      <td>361771</td>\n",
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       "      <th>10873</th>\n",
       "      <td>360926</td>\n",
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       "    <tr>\n",
       "      <th>21935</th>\n",
       "      <td>371988</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3933</th>\n",
       "      <td>353986</td>\n",
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       "      <th>2806</th>\n",
       "      <td>352859</td>\n",
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       "      <th>21474</th>\n",
       "      <td>371527</td>\n",
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       "      <th>1023</th>\n",
       "      <td>351076</td>\n",
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       "    <tr>\n",
       "      <th>17887</th>\n",
       "      <td>367940</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>20034</th>\n",
       "      <td>370087</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12825</th>\n",
       "      <td>362878</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>354229</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13367</th>\n",
       "      <td>363420</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21923</th>\n",
       "      <td>371976</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>350775</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22337</th>\n",
       "      <td>372390</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24870</th>\n",
       "      <td>374923</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3003</th>\n",
       "      <td>353056</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>351339</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23400</th>\n",
       "      <td>373453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>359757</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22662</th>\n",
       "      <td>372715</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2952</th>\n",
       "      <td>353005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2360</th>\n",
       "      <td>352413</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11806</th>\n",
       "      <td>361859</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7227</th>\n",
       "      <td>357280</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17110</th>\n",
       "      <td>367163</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key  0\n",
       "15263           365316  0\n",
       "24638           374691  0\n",
       "14078           364131  0\n",
       "3302            353355  0\n",
       "562             350615  0\n",
       "21478           371531  0\n",
       "21506           371559  0\n",
       "20481           370534  0\n",
       "12773           362826  0\n",
       "21597           371650  0\n",
       "5522            355575  0\n",
       "5667            355720  0\n",
       "23414           373467  0\n",
       "16233           366286  0\n",
       "9417            359470  0\n",
       "17393           367446  0\n",
       "19448           369501  0\n",
       "7033            357086  0\n",
       "3071            353124  0\n",
       "20538           370591  0\n",
       "23870           373923  0\n",
       "20007           370060  0\n",
       "10735           360788  0\n",
       "23586           373639  0\n",
       "21895           371948  0\n",
       "18916           368969  0\n",
       "6076            356129  0\n",
       "14994           365047  0\n",
       "18143           368196  0\n",
       "24535           374588  0\n",
       "...                ... ..\n",
       "19901           369954  0\n",
       "13626           363679  0\n",
       "10964           361017  0\n",
       "14992           365045  0\n",
       "11718           361771  0\n",
       "10873           360926  0\n",
       "21935           371988  0\n",
       "3933            353986  0\n",
       "2806            352859  0\n",
       "21474           371527  0\n",
       "1023            351076  0\n",
       "17887           367940  0\n",
       "20034           370087  0\n",
       "12825           362878  0\n",
       "4176            354229  0\n",
       "13367           363420  0\n",
       "21923           371976  0\n",
       "722             350775  0\n",
       "22337           372390  0\n",
       "24870           374923  0\n",
       "3003            353056  0\n",
       "1286            351339  0\n",
       "23400           373453  0\n",
       "9704            359757  0\n",
       "22662           372715  0\n",
       "2952            353005  0\n",
       "2360            352413  0\n",
       "11806           361859  0\n",
       "7227            357280  0\n",
       "17110           367163  0\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "no = 278\n",
    "\n",
    "# submit1.to_csv('flabbergasted_IITGuwahati_'+str(no)+'.csv',header = False, index = False)\n",
    "submit2.to_csv('flabbergasted_IITGuwahati_'+str(no+1)+'.csv',header = False, index = False)\n",
    "\n",
    "no+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "279"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wh6G11p6uqhuT3JVkW5LbW2uHq+r6bvn+JAeTXJXkSJInk1zXPfxLk3xjko9U1X3dfd/f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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c2f9f5198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGB feature imp\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.bar(range(1, len(xgb1.feature_importances_)+1), xgb1.feature_importances_)\n",
    "plt.xticks(range(1, len(xgb1.feature_importances_)+1))\n",
    "plt.show()"
   ]
  }
 ],
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