8-LR-FeatureEngineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 68,
   "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": 69,
   "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": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dic = dict(zip(dataDict.values[:, 1], dataDict.values[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mvar25'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic['Average tenure of active revolving credit cards (in days)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX = gameDf.iloc[:,:-1]\n",
    "gameY = gameDf.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 80000)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(gameDf[\"mvar47\"].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX[\"mvar47\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#one hot encoding C=0 L=1\n",
    "gameX['mvar47'] = pd.get_dummies(gameX['mvar47'])\n",
    "\n",
    "leaderX['mvar47'] = pd.get_dummies(leaderX['mvar47'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX[\"mvar47\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>0.5001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>206.0</td>\n",
       "      <td>19179.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>14221.0</td>\n",
       "      <td>19438.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>20.5510</td>\n",
       "      <td>0.42105</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.85661</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1894.0</td>\n",
       "      <td>1.9701</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.0</td>\n",
       "      <td>19820.0</td>\n",
       "      <td>19820.0</td>\n",
       "      <td>176895.0</td>\n",
       "      <td>66346.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>63.0470</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.94391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1774.0</td>\n",
       "      <td>0.1718</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>197.0</td>\n",
       "      <td>2563.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>8869.0</td>\n",
       "      <td>3637.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>83.7970</td>\n",
       "      <td>0.33333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.76467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1586.0</td>\n",
       "      <td>0.1123</td>\n",
       "      <td>5.299</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.0</td>\n",
       "      <td>6967.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>14171.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.73703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1832.0</td>\n",
       "      <td>1.4442</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>179.0</td>\n",
       "      <td>7577.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>21059.0</td>\n",
       "      <td>20306.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.052</td>\n",
       "      <td>66.6650</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.79190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1838.0</td>\n",
       "      <td>0.0544</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>882.0</td>\n",
       "      <td>14039.0</td>\n",
       "      <td>10976.0</td>\n",
       "      <td>278941.0</td>\n",
       "      <td>74030.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>77.7800</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.92462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1743.0</td>\n",
       "      <td>1.0753</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>397.0</td>\n",
       "      <td>16141.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>69482.0</td>\n",
       "      <td>56755.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>67.1590</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.71347</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1935.0</td>\n",
       "      <td>0.5601</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>496.0</td>\n",
       "      <td>44919.0</td>\n",
       "      <td>17115.0</td>\n",
       "      <td>124814.0</td>\n",
       "      <td>124167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.92403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1885.0</td>\n",
       "      <td>0.0811</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7369.0</td>\n",
       "      <td>19264.0</td>\n",
       "      <td>19264.0</td>\n",
       "      <td>59956.0</td>\n",
       "      <td>26633.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.89760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>0.7444</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2103.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>16429.0</td>\n",
       "      <td>9197.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.40529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.5097</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2433.0</td>\n",
       "      <td>8673.0</td>\n",
       "      <td>8673.0</td>\n",
       "      <td>6739.0</td>\n",
       "      <td>7388.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.23529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.64242</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1755.0</td>\n",
       "      <td>0.3013</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>261.0</td>\n",
       "      <td>4051.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.33333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.99142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>2.2909</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7620.0</td>\n",
       "      <td>7620.0</td>\n",
       "      <td>64983.0</td>\n",
       "      <td>14067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>95.0000</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.44167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1562.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>34.611</td>\n",
       "      <td>3.7845</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>124.910</td>\n",
       "      <td>99.9340</td>\n",
       "      <td>0.87500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.84592</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1798.0</td>\n",
       "      <td>5.0825</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3359.0</td>\n",
       "      <td>2973.0</td>\n",
       "      <td>22099.0</td>\n",
       "      <td>13507.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>76.8810</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.43365</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1684.0</td>\n",
       "      <td>1.5479</td>\n",
       "      <td>1.338</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.0</td>\n",
       "      <td>6967.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>14171.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.73703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1789.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>13874.0</td>\n",
       "      <td>13874.0</td>\n",
       "      <td>98011.0</td>\n",
       "      <td>56200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.51231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1862.0</td>\n",
       "      <td>0.2401</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>12310.0</td>\n",
       "      <td>3964.0</td>\n",
       "      <td>7928.0</td>\n",
       "      <td>26564.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>77.4190</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.56685</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1767.0</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>1.514</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>8790.0</td>\n",
       "      <td>6065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.73703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1802.0</td>\n",
       "      <td>0.6364</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>96.0</td>\n",
       "      <td>18759.0</td>\n",
       "      <td>10905.0</td>\n",
       "      <td>51037.0</td>\n",
       "      <td>58544.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>58.9720</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.59409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1780.0</td>\n",
       "      <td>1.9559</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>191.0</td>\n",
       "      <td>12525.0</td>\n",
       "      <td>12525.0</td>\n",
       "      <td>18932.0</td>\n",
       "      <td>14602.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.92752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1714.0</td>\n",
       "      <td>2.2664</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1671.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>91.6660</td>\n",
       "      <td>0.37500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.34836</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1923.0</td>\n",
       "      <td>0.0880</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5946.0</td>\n",
       "      <td>53893.0</td>\n",
       "      <td>24739.0</td>\n",
       "      <td>128435.0</td>\n",
       "      <td>125679.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>90.4290</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.88554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1743.0</td>\n",
       "      <td>1.7208</td>\n",
       "      <td>2.295</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>159.0</td>\n",
       "      <td>3621.0</td>\n",
       "      <td>2323.0</td>\n",
       "      <td>9018.0</td>\n",
       "      <td>9649.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>82.3230</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.59442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1700.0</td>\n",
       "      <td>1.6605</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>892.0</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.94826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1922.0</td>\n",
       "      <td>0.5389</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4524.0</td>\n",
       "      <td>15530.0</td>\n",
       "      <td>14819.0</td>\n",
       "      <td>116154.0</td>\n",
       "      <td>100684.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.91550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>0.4120</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>369.0</td>\n",
       "      <td>6201.0</td>\n",
       "      <td>6201.0</td>\n",
       "      <td>66477.0</td>\n",
       "      <td>34053.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.03846</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.15159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1811.0</td>\n",
       "      <td>0.9117</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.410</td>\n",
       "      <td>297.0</td>\n",
       "      <td>27717.0</td>\n",
       "      <td>9815.0</td>\n",
       "      <td>110001.0</td>\n",
       "      <td>69683.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>74.9960</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.56671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1658.0</td>\n",
       "      <td>2.3829</td>\n",
       "      <td>37.127</td>\n",
       "      <td>10.3678</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6566.0</td>\n",
       "      <td>4068.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.57692</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.35339</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1827.0</td>\n",
       "      <td>0.2801</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.0</td>\n",
       "      <td>1189.0</td>\n",
       "      <td>1189.0</td>\n",
       "      <td>892.0</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</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>0.9279</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>256.0</td>\n",
       "      <td>11643.0</td>\n",
       "      <td>1524.0</td>\n",
       "      <td>16748.0</td>\n",
       "      <td>25359.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.38078</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>1849.0</td>\n",
       "      <td>1.8305</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2251.0</td>\n",
       "      <td>33275.0</td>\n",
       "      <td>7505.0</td>\n",
       "      <td>44843.0</td>\n",
       "      <td>47453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>91.7010</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.92111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>1.5251</td>\n",
       "      <td>2.727</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.727</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>5896.0</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>109.251</td>\n",
       "      <td>94.1270</td>\n",
       "      <td>0.36842</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.52903</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>1642.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.353</td>\n",
       "      <td>1.2114</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.0</td>\n",
       "      <td>6290.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>14171.0</td>\n",
       "      <td>6290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>37.8900</td>\n",
       "      <td>0.83333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>1709.0</td>\n",
       "      <td>3.6801</td>\n",
       "      <td>7.583</td>\n",
       "      <td>3.1371</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.0</td>\n",
       "      <td>8132.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>6960.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.029</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.59459</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.42680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>1890.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>15162.0</td>\n",
       "      <td>15162.0</td>\n",
       "      <td>68181.0</td>\n",
       "      <td>43334.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.043</td>\n",
       "      <td>55.1970</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.27143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>1727.0</td>\n",
       "      <td>1.8448</td>\n",
       "      <td>15.181</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2234.0</td>\n",
       "      <td>2754.0</td>\n",
       "      <td>2234.0</td>\n",
       "      <td>2676.0</td>\n",
       "      <td>6539.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.87890</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>1789.0</td>\n",
       "      <td>1.2994</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>364.0</td>\n",
       "      <td>17838.0</td>\n",
       "      <td>17838.0</td>\n",
       "      <td>8424.0</td>\n",
       "      <td>22307.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>109.307</td>\n",
       "      <td>75.0520</td>\n",
       "      <td>0.15385</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.69694</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>1706.0</td>\n",
       "      <td>0.4997</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>643.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>1638.0</td>\n",
       "      <td>4.6802</td>\n",
       "      <td>16.751</td>\n",
       "      <td>2.6642</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.0</td>\n",
       "      <td>7937.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>10489.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102.709</td>\n",
       "      <td>13.8820</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.72640</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>1855.0</td>\n",
       "      <td>1.9965</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>30203.0</td>\n",
       "      <td>22656.0</td>\n",
       "      <td>168999.0</td>\n",
       "      <td>166720.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.74106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>1790.0</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>237.0</td>\n",
       "      <td>18018.0</td>\n",
       "      <td>13297.0</td>\n",
       "      <td>3914.0</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.41582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>1546.0</td>\n",
       "      <td>0.3491</td>\n",
       "      <td>50.803</td>\n",
       "      <td>4.2695</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.0</td>\n",
       "      <td>6967.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>3330.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.70370</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>3.9629</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>23707.0</td>\n",
       "      <td>23707.0</td>\n",
       "      <td>33432.0</td>\n",
       "      <td>19640.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>80.9340</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.64752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>1943.0</td>\n",
       "      <td>0.5389</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24775.0</td>\n",
       "      <td>24775.0</td>\n",
       "      <td>80772.0</td>\n",
       "      <td>69483.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.97068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>1691.0</td>\n",
       "      <td>0.1663</td>\n",
       "      <td>1.979</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9591.0</td>\n",
       "      <td>2627.0</td>\n",
       "      <td>46903.0</td>\n",
       "      <td>30402.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.801</td>\n",
       "      <td>63.8550</td>\n",
       "      <td>0.03448</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.43441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>1681.0</td>\n",
       "      <td>0.1739</td>\n",
       "      <td>9.006</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>6.568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3004.0</td>\n",
       "      <td>2251.0</td>\n",
       "      <td>6818.0</td>\n",
       "      <td>7783.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.309</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.71429</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.88908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>1723.0</td>\n",
       "      <td>1.0570</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>248.0</td>\n",
       "      <td>28479.0</td>\n",
       "      <td>28479.0</td>\n",
       "      <td>16352.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.72344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>1693.0</td>\n",
       "      <td>1.2145</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>991.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.36619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>1592.0</td>\n",
       "      <td>6.4805</td>\n",
       "      <td>5.250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.204</td>\n",
       "      <td>297.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>14171.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>1641.0</td>\n",
       "      <td>0.6601</td>\n",
       "      <td>8.614</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>14171.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>1740.0</td>\n",
       "      <td>0.3901</td>\n",
       "      <td>4.322</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>490.0</td>\n",
       "      <td>29460.0</td>\n",
       "      <td>9449.0</td>\n",
       "      <td>42047.0</td>\n",
       "      <td>31583.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.88957</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>1651.0</td>\n",
       "      <td>2.4909</td>\n",
       "      <td>1.335</td>\n",
       "      <td>0.9108</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>3167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>82.3820</td>\n",
       "      <td>0.61538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.93745</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>1729.0</td>\n",
       "      <td>1.3601</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5675.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>15071.0</td>\n",
       "      <td>10305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.23810</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.71866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>1.6701</td>\n",
       "      <td>5.554</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>104.0</td>\n",
       "      <td>5692.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>4410.0</td>\n",
       "      <td>8125.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>79.4850</td>\n",
       "      <td>0.30769</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.93312</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>1715.0</td>\n",
       "      <td>12.0002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.0</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>8751.0</td>\n",
       "      <td>1814.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.07692</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.73703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.0551</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5946.0</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>9613.0</td>\n",
       "      <td>46490.0</td>\n",
       "      <td>44722.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.99859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>1686.0</td>\n",
       "      <td>1.3301</td>\n",
       "      <td>2.025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.025</td>\n",
       "      <td>297.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>4582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.171</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.30000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.77519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>1826.0</td>\n",
       "      <td>0.5389</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>26277.0</td>\n",
       "      <td>9058.0</td>\n",
       "      <td>72739.0</td>\n",
       "      <td>86797.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>50.1190</td>\n",
       "      <td>0.03333</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.40343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>1916.0</td>\n",
       "      <td>0.1394</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18889.0</td>\n",
       "      <td>18889.0</td>\n",
       "      <td>18889.0</td>\n",
       "      <td>28343.0</td>\n",
       "      <td>51174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>78.8835</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.99663</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</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    mvar8  \\\n",
       "0      1753.0   0.5001   0.000   0.0000  0.000    206.0  19179.0    206.0   \n",
       "1      1894.0   1.9701   0.000   0.0000  0.000    297.0  19820.0  19820.0   \n",
       "2      1774.0   0.1718   0.000   0.0000  0.000    197.0   2563.0    877.0   \n",
       "3      1586.0   0.1123   5.299   0.0000  0.000    297.0   6967.0   2478.0   \n",
       "4      1832.0   1.4442   0.000   0.0000  0.000    179.0   7577.0    179.0   \n",
       "5      1838.0   0.0544   0.241   0.0000  0.000    882.0  14039.0  10976.0   \n",
       "6      1743.0   1.0753   0.272   0.0000  0.000    397.0  16141.0   3568.0   \n",
       "7      1935.0   0.5601   0.000   0.0000  0.000    496.0  44919.0  17115.0   \n",
       "8      1885.0   0.0811   0.000   0.0000  0.000   7369.0  19264.0  19264.0   \n",
       "9      1730.0   0.7444   0.000   0.0000  0.000      0.0   2103.0    582.0   \n",
       "10     1731.0   0.5097   0.000   0.0000  0.000   2433.0   8673.0   8673.0   \n",
       "11     1755.0   0.3013   0.000   0.0000  0.000    261.0   4051.0    261.0   \n",
       "12     1739.0   2.2909   0.000   0.0000  0.000      0.0   7620.0   7620.0   \n",
       "13     1562.0   0.0000  34.611   3.7845  0.000    297.0     18.0   2478.0   \n",
       "14     1798.0   5.0825   0.000   0.0000  0.000    259.0   3359.0   2973.0   \n",
       "15     1684.0   1.5479   1.338   0.0000  0.000    297.0   6967.0   2478.0   \n",
       "16     1789.0   0.0000   1.428   0.0000  0.000   2478.0  13874.0  13874.0   \n",
       "17     1862.0   0.2401   0.000   0.0000  0.000   1487.0  12310.0   3964.0   \n",
       "18     1767.0   0.3001   1.514   0.0000  0.000    991.0   2478.0   2478.0   \n",
       "19     1802.0   0.6364   0.000   0.0000  0.000     96.0  18759.0  10905.0   \n",
       "20     1780.0   1.9559   0.000   0.0000  0.000    191.0  12525.0  12525.0   \n",
       "21     1714.0   2.2664   0.000   0.0000  0.000     31.0   1671.0     57.0   \n",
       "22     1923.0   0.0880   0.000   0.0000  0.000   5946.0  53893.0  24739.0   \n",
       "23     1743.0   1.7208   2.295   0.0000  0.000    159.0   3621.0   2323.0   \n",
       "24     1700.0   1.6605   0.000   0.0000  0.000      0.0   1037.0     46.0   \n",
       "25     1922.0   0.5389   0.000   0.0000  0.000   4524.0  15530.0  14819.0   \n",
       "26     1777.0   0.4120   0.000   0.0000  0.000    369.0   6201.0   6201.0   \n",
       "27     1811.0   0.9117   0.410   0.0000  0.410    297.0  27717.0   9815.0   \n",
       "28     1658.0   2.3829  37.127  10.3678  0.000      0.0    396.0      0.0   \n",
       "29     1827.0   0.2801   0.000   0.0000  0.000    678.0   1189.0   1189.0   \n",
       "...       ...      ...     ...      ...    ...      ...      ...      ...   \n",
       "24970  1734.0   0.9279   0.520   0.0000  0.000    256.0  11643.0   1524.0   \n",
       "24971  1849.0   1.8305   0.000   0.0000  0.000   2251.0  33275.0   7505.0   \n",
       "24972  1739.0   1.5251   2.727   0.0000  2.727      8.0   2129.0    720.0   \n",
       "24973  1642.0   0.0000   3.353   1.2114  0.000    297.0   6290.0   2478.0   \n",
       "24974  1709.0   3.6801   7.583   3.1371  0.000    297.0   8132.0    297.0   \n",
       "24975  1890.0   0.0000   0.000   0.0000  0.000   1982.0  15162.0  15162.0   \n",
       "24976  1727.0   1.8448  15.181   0.0000  0.000   2234.0   2754.0   2234.0   \n",
       "24977  1789.0   1.2994   0.000   0.0000  0.000    364.0  17838.0  17838.0   \n",
       "24978  1706.0   0.4997   0.000   0.0000  0.000     98.0    297.0     98.0   \n",
       "24979  1638.0   4.6802  16.751   2.6642  0.000    297.0   7937.0   2478.0   \n",
       "24980  1855.0   1.9965   0.000   0.0000  0.000    350.0  30203.0  22656.0   \n",
       "24981  1790.0   0.2050   0.000   0.0000  0.000    237.0  18018.0  13297.0   \n",
       "24982  1546.0   0.3491  50.803   4.2695  0.000    297.0   6967.0   2478.0   \n",
       "24983  1777.0   3.9629   0.968   0.0000  0.000     14.0  23707.0  23707.0   \n",
       "24984  1943.0   0.5389   0.000   0.0000  0.000      0.0  24775.0  24775.0   \n",
       "24985  1691.0   0.1663   1.979   0.0000  0.000      0.0   9591.0   2627.0   \n",
       "24986  1681.0   0.1739   9.006   0.0000  6.568      0.0   3004.0   2251.0   \n",
       "24987  1723.0   1.0570   0.285   0.0000  0.000    248.0  28479.0  28479.0   \n",
       "24988  1693.0   1.2145   0.000   0.0000  0.000     18.0    100.0    100.0   \n",
       "24989  1592.0   6.4805   5.250   0.0000  1.204    297.0      0.0   2478.0   \n",
       "24990  1641.0   0.6601   8.614   0.0000  0.000    297.0      0.0   2478.0   \n",
       "24991  1740.0   0.3901   4.322   0.0000  0.000    490.0  29460.0   9449.0   \n",
       "24992  1651.0   2.4909   1.335   0.9108  0.000      4.0   3163.0      4.0   \n",
       "24993  1729.0   1.3601   0.218   0.0000  0.000      0.0   5675.0    167.0   \n",
       "24994  1730.0   1.6701   5.554   0.0000  0.000    104.0   5692.0    496.0   \n",
       "24995  1715.0  12.0002   0.000   0.0000  0.000    297.0    991.0   2478.0   \n",
       "24996  1800.0   0.0551   0.000   0.0000  0.000   5946.0  14867.0   9613.0   \n",
       "24997  1686.0   1.3301   2.025   0.0000  2.025    297.0      0.0   2478.0   \n",
       "24998  1826.0   0.5389   0.428   0.0000  0.000   3568.0  26277.0   9058.0   \n",
       "24999  1916.0   0.1394   0.000   0.0000  0.000  18889.0  18889.0  18889.0   \n",
       "\n",
       "          mvar9    mvar10   ...    mvar38  mvar39   mvar40    mvar41   mvar42  \\\n",
       "0       14221.0   19438.0   ...       1.0     0.0  100.000   20.5510  0.42105   \n",
       "1      176895.0   66346.0   ...       3.0     0.0  100.000   63.0470  0.00000   \n",
       "2        8869.0    3637.0   ...       1.0     0.0  100.000   83.7970  0.33333   \n",
       "3       14171.0       0.0   ...       1.0     0.0  100.000   78.8835  1.50000   \n",
       "4       21059.0   20306.0   ...       3.0     0.0   96.052   66.6650  0.00000   \n",
       "5      278941.0   74030.0   ...       4.0     0.0  100.000   77.7800  0.00000   \n",
       "6       69482.0   56755.0   ...       6.0     0.0  100.000   67.1590  0.00000   \n",
       "7      124814.0  124167.0   ...       9.0     0.0  100.000   78.8835  0.00000   \n",
       "8       59956.0   26633.0   ...       1.0     0.0  100.000   78.8835  0.00000   \n",
       "9       16429.0    9197.0   ...       9.0     0.0  100.000   78.8835  0.05882   \n",
       "10       6739.0    7388.0   ...       2.0     0.0  100.000   78.8835  0.23529   \n",
       "11        297.0     261.0   ...       3.0     0.0  100.000   78.8835  0.33333   \n",
       "12      64983.0   14067.0   ...      11.0     0.0  100.000   95.0000  0.05882   \n",
       "13        198.0      18.0   ...       7.0     1.0  124.910   99.9340  0.87500   \n",
       "14      22099.0   13507.0   ...       5.0     0.0  100.000   76.8810  0.05882   \n",
       "15      14171.0       0.0   ...       3.0     0.0  100.000   78.8835  1.50000   \n",
       "16      98011.0   56200.0   ...       4.0     0.0  100.000   78.8835  0.05000   \n",
       "17       7928.0   26564.0   ...       6.0     0.0  100.000   77.4190  0.00000   \n",
       "18       8790.0    6065.0   ...       1.0     0.0  100.000   78.8835  0.10000   \n",
       "19      51037.0   58544.0   ...       3.0     0.0  100.000   58.9720  0.00000   \n",
       "20      18932.0   14602.0   ...       7.0     0.0  100.000  100.0000  0.00000   \n",
       "21        396.0    2582.0   ...       6.0     0.0  100.000   91.6660  0.37500   \n",
       "22     128435.0  125679.0   ...       2.0     0.0  100.000   90.4290  0.00000   \n",
       "23       9018.0    9649.0   ...       8.0     0.0  100.000   82.3230  0.10000   \n",
       "24        892.0    1235.0   ...       5.0     0.0  100.000   78.8835  0.20000   \n",
       "25     116154.0  100684.0   ...       3.0     0.0  100.000   78.8835  0.00000   \n",
       "26      66477.0   34053.0   ...      12.0     0.0  100.000   78.8835  0.03846   \n",
       "27     110001.0   69683.0   ...       3.0     0.0  100.000   74.9960  0.00000   \n",
       "28       6566.0    4068.0   ...      14.0     0.0  100.000   78.8835  0.57692   \n",
       "29        892.0    2065.0   ...       3.0     0.0  100.000   78.8835  0.00000   \n",
       "...         ...       ...   ...       ...     ...      ...       ...      ...   \n",
       "24970   16748.0   25359.0   ...       7.0     0.0  100.000   78.8835  0.00000   \n",
       "24971   44843.0   47453.0   ...       2.0     0.0  100.000   91.7010  0.00000   \n",
       "24972    5896.0    5206.0   ...       6.0     0.0  109.251   94.1270  0.36842   \n",
       "24973   14171.0    6290.0   ...       1.0     0.0  100.000   37.8900  0.83333   \n",
       "24974    1041.0    6960.0   ...      32.0     0.0   81.029   78.8835  0.59459   \n",
       "24975   68181.0   43334.0   ...       2.0     0.0   98.043   55.1970  0.00000   \n",
       "24976    2676.0    6539.0   ...       4.0     0.0  100.000   78.8835  0.66667   \n",
       "24977    8424.0   22307.0   ...       5.0     0.0  109.307   75.0520  0.15385   \n",
       "24978     546.0     643.0   ...       7.0     0.0  100.000   78.8835  0.66667   \n",
       "24979    3032.0   10489.0   ...       3.0     1.0  102.709   13.8820  0.50000   \n",
       "24980  168999.0  166720.0   ...       2.0     0.0  100.000   78.8835  0.00000   \n",
       "24981    3914.0    1305.0   ...       5.0     0.0  100.000   78.8835  0.20000   \n",
       "24982    3330.0       0.0   ...       8.0     0.0  100.000   78.8835  0.70370   \n",
       "24983   33432.0   19640.0   ...      12.0     0.0  100.000   80.9340  0.04000   \n",
       "24984   80772.0   69483.0   ...       0.0     0.0  100.000   78.8835  0.00000   \n",
       "24985   46903.0   30402.0   ...       5.0     0.0   84.801   63.8550  0.03448   \n",
       "24986    6818.0    7783.0   ...       1.0     1.0   96.309   78.8835  0.71429   \n",
       "24987   16352.0    3117.0   ...       7.0     0.0  100.000   78.8835  0.00000   \n",
       "24988     991.0     118.0   ...       3.0     0.0  100.000   78.8835  0.00000   \n",
       "24989   14171.0       0.0   ...       3.0     0.0  100.000   78.8835  1.00000   \n",
       "24990   14171.0       0.0   ...       3.0     0.0  100.000   78.8835  1.00000   \n",
       "24991   42047.0   31583.0   ...       3.0     0.0  100.000   78.8835  0.20000   \n",
       "24992    1982.0    3167.0   ...      10.0     0.0  100.000   82.3820  0.61538   \n",
       "24993   15071.0   10305.0   ...       5.0     0.0  100.000   78.8835  0.23810   \n",
       "24994    4410.0    8125.0   ...       5.0     0.0  100.000   79.4850  0.30769   \n",
       "24995    8751.0    1814.0   ...       1.0     0.0  100.000   78.8835  0.07692   \n",
       "24996   46490.0   44722.0   ...       4.0     0.0  100.000   78.8835  0.05882   \n",
       "24997    4582.0       0.0   ...       0.0     0.0  124.171   78.8835  0.30000   \n",
       "24998   72739.0   86797.0   ...       2.0     0.0  100.000   50.1190  0.03333   \n",
       "24999   28343.0   51174.0   ...       0.0     0.0  100.000   78.8835  0.00000   \n",
       "\n",
       "       mvar43   mvar44  mvar45  mvar46  mvar47  \n",
       "0         3.0  0.85661     0.0     0.0       0  \n",
       "1        15.0  0.94391     0.0     0.0       1  \n",
       "2         3.0  0.76467     0.0     0.0       1  \n",
       "3         0.0  0.73703     0.0     0.0       1  \n",
       "4        23.0  0.79190     0.0     0.0       0  \n",
       "5        21.0  0.92462     0.0     0.0       0  \n",
       "6        14.0  0.71347     0.0     0.0       0  \n",
       "7        15.0  0.92403     0.0     0.0       0  \n",
       "8         5.0  0.89760     0.0     0.0       0  \n",
       "9        14.0  0.40529     0.0     0.0       1  \n",
       "10        1.0  0.64242     0.0     0.0       1  \n",
       "11        2.0  0.99142     0.0     0.0       1  \n",
       "12       16.0  0.44167     0.0     0.0       1  \n",
       "13        1.0  0.84592     1.0     1.0       1  \n",
       "14       13.0  0.43365     0.0     0.0       1  \n",
       "15        0.0  0.73703     0.0     0.0       1  \n",
       "16       13.0  0.51231     0.0     0.0       0  \n",
       "17        7.0  0.56685     0.0     0.0       1  \n",
       "18        3.0  0.73703     0.0     0.0       1  \n",
       "19        9.0  0.59409     0.0     0.0       1  \n",
       "20        7.0  0.92752     0.0     0.0       1  \n",
       "21        5.0  0.34836     0.0     0.0       1  \n",
       "22        9.0  0.88554     0.0     0.0       1  \n",
       "23        9.0  0.59442     0.0     0.0       1  \n",
       "24        5.0  0.94826     0.0     0.0       0  \n",
       "25       12.0  0.91550     0.0     0.0       0  \n",
       "26       23.0  0.15159     0.0     0.0       1  \n",
       "27       15.0  0.56671     0.0     0.0       1  \n",
       "28        8.0  0.35339     0.0     2.0       1  \n",
       "29        2.0  1.00000     0.0     0.0       1  \n",
       "...       ...      ...     ...     ...     ...  \n",
       "24970    10.0  0.38078     0.0     0.0       0  \n",
       "24971     4.0  0.92111     0.0     0.0       0  \n",
       "24972    12.0  0.52903     0.0     0.0       1  \n",
       "24973     0.0  1.00000     0.0     0.0       1  \n",
       "24974    11.0  0.42680     0.0     2.0       1  \n",
       "24975    15.0  0.27143     0.0     0.0       1  \n",
       "24976     5.0  0.87890     0.0     0.0       1  \n",
       "24977     8.0  0.69694     0.0     0.0       0  \n",
       "24978     4.0  1.00000     0.0     0.0       1  \n",
       "24979     4.0  0.72640     0.0     0.0       1  \n",
       "24980    12.0  0.74106     0.0     0.0       0  \n",
       "24981     2.0  0.41582     0.0     0.0       1  \n",
       "24982     2.0  1.00000     1.0     1.0       1  \n",
       "24983    15.0  0.64752     0.0     0.0       0  \n",
       "24984     6.0  0.97068     0.0     0.0       1  \n",
       "24985    14.0  0.43441     0.0     0.0       1  \n",
       "24986     2.0  0.88908     0.0     0.0       1  \n",
       "24987     6.0  0.72344     0.0     0.0       1  \n",
       "24988     3.0  0.36619     0.0     0.0       1  \n",
       "24989     1.0  0.50649     0.0     0.0       1  \n",
       "24990     1.0  1.00000     0.0     0.0       0  \n",
       "24991     4.0  0.88957     1.0     0.0       0  \n",
       "24992     3.0  0.93745     0.0     0.0       0  \n",
       "24993    13.0  0.71866     0.0     0.0       1  \n",
       "24994     4.0  0.93312     0.0     0.0       1  \n",
       "24995     4.0  0.73703     0.0     0.0       1  \n",
       "24996     8.0  0.99859     0.0     0.0       0  \n",
       "24997     2.0  0.77519     0.0     0.0       0  \n",
       "24998    11.0  0.40343     0.0     0.0       1  \n",
       "24999     5.0  0.99663     0.0     0.0       1  \n",
       "\n",
       "[25000 rows x 47 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing values median\n",
    "gameX.fillna(gameX.median(),inplace = True)\n",
    "leaderX.fillna(gameX.median(),inplace = True)\n",
    "\n",
    "# gameX.fillna(0,inplace = True)\n",
    "# leaderX.fillna(0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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, gameY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -5.39622128e-06,   2.40743789e-08,   8.03220653e-08,\n",
       "          1.42849020e-08,   1.87349845e-08,  -1.05734377e-05,\n",
       "          1.40181589e-05,  -1.97309022e-05,   1.20678041e-06,\n",
       "         -1.47185084e-05,   8.29479510e-08,   1.33978734e-05,\n",
       "         -7.50769131e-06,   7.64747234e-10,  -4.97140393e-07,\n",
       "          8.40845705e-09,   1.00775820e-08,   1.03107149e-08,\n",
       "          1.46132998e-08,   1.50542490e-08,   2.02182871e-07,\n",
       "          2.54408245e-05,   1.87812090e-05,   3.03491634e-07,\n",
       "         -1.27398731e-05,  -1.94874040e-05,  -1.68634042e-05,\n",
       "         -1.36233071e-05,  -3.53727888e-05,  -1.23452964e-05,\n",
       "         -1.28837310e-05,  -2.70443049e-06,  -5.09307275e-08,\n",
       "          7.67246612e-09,   5.17223759e-09,   5.10071545e-08,\n",
       "         -3.45608283e-08,   3.76372726e-08,   4.25064109e-09,\n",
       "         -1.63620249e-07,  -1.33590599e-07,   4.17905402e-09,\n",
       "         -3.26389086e-08,  -2.72216564e-09,   7.91106761e-10,\n",
       "          2.66592399e-09,   6.85266216e-09]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 47)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKQAAAD8CAYAAABJsXxZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wB8YML5xtCq6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7affba208>"
      ]
     },
     "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(1, len(lr_coef)+1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnsAAAJcCAYAAACBnWIZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5518f0bd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xgb feature imp\n",
    "from xgboost import plot_importance\n",
    "ax = plot_importance(bst, height=0.5)\n",
    "fig = ax.figure\n",
    "fig.set_size_inches(10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIsAAAD8CAYAAAD35yQEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABAz7AIAAAAgN7/B6thSgWpUt3kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7afa49630>"
      ]
     },
     "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(1, len(lr_coef)+1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = clf.predict_proba(leaderX) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#828000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = prep.StandardScaler().fit(sss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sss = gameX.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ss = s.transform(sss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.830221e-16</td>\n",
       "      <td>2.935170e-15</td>\n",
       "      <td>2.852924e-15</td>\n",
       "      <td>1.191352e-14</td>\n",
       "      <td>9.573706e-15</td>\n",
       "      <td>1.112517e-15</td>\n",
       "      <td>-4.842869e-16</td>\n",
       "      <td>9.902794e-16</td>\n",
       "      <td>-3.510969e-16</td>\n",
       "      <td>-7.927728e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>7.733139e-16</td>\n",
       "      <td>8.131302e-15</td>\n",
       "      <td>-7.152401e-15</td>\n",
       "      <td>-6.209071e-16</td>\n",
       "      <td>-3.155659e-15</td>\n",
       "      <td>8.662626e-16</td>\n",
       "      <td>1.147186e-15</td>\n",
       "      <td>2.220023e-15</td>\n",
       "      <td>1.259845e-14</td>\n",
       "      <td>4.383291e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.736848e+00</td>\n",
       "      <td>-6.690739e-01</td>\n",
       "      <td>-4.866012e-01</td>\n",
       "      <td>-2.632847e-01</td>\n",
       "      <td>-1.869878e-01</td>\n",
       "      <td>-3.948810e-01</td>\n",
       "      <td>-3.695296e-01</td>\n",
       "      <td>-6.371610e-01</td>\n",
       "      <td>-6.262059e-01</td>\n",
       "      <td>-4.532321e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.082226e+00</td>\n",
       "      <td>-2.078937e-01</td>\n",
       "      <td>-7.164080e+00</td>\n",
       "      <td>-5.427811e+00</td>\n",
       "      <td>-8.929242e-01</td>\n",
       "      <td>-1.040801e+00</td>\n",
       "      <td>-2.769594e+00</td>\n",
       "      <td>-1.604506e-01</td>\n",
       "      <td>-2.323531e-01</td>\n",
       "      <td>-1.356801e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.798037e-01</td>\n",
       "      <td>-5.686148e-01</td>\n",
       "      <td>-4.866012e-01</td>\n",
       "      <td>-2.632847e-01</td>\n",
       "      <td>-1.869878e-01</td>\n",
       "      <td>-3.674637e-01</td>\n",
       "      <td>-3.231089e-01</td>\n",
       "      <td>-5.461601e-01</td>\n",
       "      <td>-5.319021e-01</td>\n",
       "      <td>-4.358705e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.763180e-01</td>\n",
       "      <td>-2.078937e-01</td>\n",
       "      <td>2.355083e-02</td>\n",
       "      <td>1.525662e-01</td>\n",
       "      <td>-8.929242e-01</td>\n",
       "      <td>-7.223625e-01</td>\n",
       "      <td>-7.351857e-01</td>\n",
       "      <td>-1.604506e-01</td>\n",
       "      <td>-2.323531e-01</td>\n",
       "      <td>-1.356801e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-4.103748e-02</td>\n",
       "      <td>-3.309262e-01</td>\n",
       "      <td>-4.587941e-01</td>\n",
       "      <td>-2.632847e-01</td>\n",
       "      <td>-1.869878e-01</td>\n",
       "      <td>-3.044040e-01</td>\n",
       "      <td>-2.145639e-01</td>\n",
       "      <td>-3.628297e-01</td>\n",
       "      <td>-3.422929e-01</td>\n",
       "      <td>-3.115252e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.233639e-01</td>\n",
       "      <td>-2.078937e-01</td>\n",
       "      <td>2.355083e-02</td>\n",
       "      <td>1.525662e-01</td>\n",
       "      <td>-3.549055e-01</td>\n",
       "      <td>-2.447043e-01</td>\n",
       "      <td>1.226015e-01</td>\n",
       "      <td>-1.604506e-01</td>\n",
       "      <td>-2.323531e-01</td>\n",
       "      <td>7.370277e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.518615e-01</td>\n",
       "      <td>1.832918e-01</td>\n",
       "      <td>1.041131e-01</td>\n",
       "      <td>-2.632847e-01</td>\n",
       "      <td>-1.869878e-01</td>\n",
       "      <td>-1.548276e-01</td>\n",
       "      <td>-6.209970e-03</td>\n",
       "      <td>4.645321e-02</td>\n",
       "      <td>1.010870e-01</td>\n",
       "      <td>5.087053e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>4.354985e-01</td>\n",
       "      <td>-2.078937e-01</td>\n",
       "      <td>2.355083e-02</td>\n",
       "      <td>1.525662e-01</td>\n",
       "      <td>6.015843e-01</td>\n",
       "      <td>5.513927e-01</td>\n",
       "      <td>9.260936e-01</td>\n",
       "      <td>-1.604506e-01</td>\n",
       "      <td>-2.323531e-01</td>\n",
       "      <td>7.370277e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.210884e+00</td>\n",
       "      <td>2.733532e+01</td>\n",
       "      <td>2.610231e+01</td>\n",
       "      <td>6.377666e+01</td>\n",
       "      <td>3.771109e+01</td>\n",
       "      <td>4.121691e+01</td>\n",
       "      <td>1.230692e+02</td>\n",
       "      <td>3.166818e+01</td>\n",
       "      <td>7.374052e+01</td>\n",
       "      <td>8.364267e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.294841e+01</td>\n",
       "      <td>5.178875e+01</td>\n",
       "      <td>2.376762e+01</td>\n",
       "      <td>6.598331e+00</td>\n",
       "      <td>4.487263e+00</td>\n",
       "      <td>1.344816e+01</td>\n",
       "      <td>1.247076e+00</td>\n",
       "      <td>2.565622e+01</td>\n",
       "      <td>6.398160e+01</td>\n",
       "      <td>7.370277e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0             1             2             3             4   \\\n",
       "count  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04   \n",
       "mean   3.830221e-16  2.935170e-15  2.852924e-15  1.191352e-14  9.573706e-15   \n",
       "std    1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00   \n",
       "min   -2.736848e+00 -6.690739e-01 -4.866012e-01 -2.632847e-01 -1.869878e-01   \n",
       "25%   -6.798037e-01 -5.686148e-01 -4.866012e-01 -2.632847e-01 -1.869878e-01   \n",
       "50%   -4.103748e-02 -3.309262e-01 -4.587941e-01 -2.632847e-01 -1.869878e-01   \n",
       "75%    6.518615e-01  1.832918e-01  1.041131e-01 -2.632847e-01 -1.869878e-01   \n",
       "max    2.210884e+00  2.733532e+01  2.610231e+01  6.377666e+01  3.771109e+01   \n",
       "\n",
       "                 5             6             7             8             9   \\\n",
       "count  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04   \n",
       "mean   1.112517e-15 -4.842869e-16  9.902794e-16 -3.510969e-16 -7.927728e-16   \n",
       "std    1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00   \n",
       "min   -3.948810e-01 -3.695296e-01 -6.371610e-01 -6.262059e-01 -4.532321e-01   \n",
       "25%   -3.674637e-01 -3.231089e-01 -5.461601e-01 -5.319021e-01 -4.358705e-01   \n",
       "50%   -3.044040e-01 -2.145639e-01 -3.628297e-01 -3.422929e-01 -3.115252e-01   \n",
       "75%   -1.548276e-01 -6.209970e-03  4.645321e-02  1.010870e-01  5.087053e-02   \n",
       "max    4.121691e+01  1.230692e+02  3.166818e+01  7.374052e+01  8.364267e+01   \n",
       "\n",
       "           ...                 37            38            39            40  \\\n",
       "count      ...       8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04   \n",
       "mean       ...       7.733139e-16  8.131302e-15 -7.152401e-15 -6.209071e-16   \n",
       "std        ...       1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00   \n",
       "min        ...      -1.082226e+00 -2.078937e-01 -7.164080e+00 -5.427811e+00   \n",
       "25%        ...      -5.763180e-01 -2.078937e-01  2.355083e-02  1.525662e-01   \n",
       "50%        ...      -3.233639e-01 -2.078937e-01  2.355083e-02  1.525662e-01   \n",
       "75%        ...       4.354985e-01 -2.078937e-01  2.355083e-02  1.525662e-01   \n",
       "max        ...       2.294841e+01  5.178875e+01  2.376762e+01  6.598331e+00   \n",
       "\n",
       "                 41            42            43            44            45  \\\n",
       "count  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04   \n",
       "mean  -3.155659e-15  8.662626e-16  1.147186e-15  2.220023e-15  1.259845e-14   \n",
       "std    1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00   \n",
       "min   -8.929242e-01 -1.040801e+00 -2.769594e+00 -1.604506e-01 -2.323531e-01   \n",
       "25%   -8.929242e-01 -7.223625e-01 -7.351857e-01 -1.604506e-01 -2.323531e-01   \n",
       "50%   -3.549055e-01 -2.447043e-01  1.226015e-01 -1.604506e-01 -2.323531e-01   \n",
       "75%    6.015843e-01  5.513927e-01  9.260936e-01 -1.604506e-01 -2.323531e-01   \n",
       "max    4.487263e+00  1.344816e+01  1.247076e+00  2.565622e+01  6.398160e+01   \n",
       "\n",
       "                 46  \n",
       "count  8.000000e+04  \n",
       "mean   4.383291e-15  \n",
       "std    1.000006e+00  \n",
       "min   -1.356801e+00  \n",
       "25%   -1.356801e+00  \n",
       "50%    7.370277e-01  \n",
       "75%    7.370277e-01  \n",
       "max    7.370277e-01  \n",
       "\n",
       "[8 rows x 47 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(ss).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>count</th>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.830221e-16</td>\n",
       "      <td>2.935170e-15</td>\n",
       "      <td>2.852924e-15</td>\n",
       "      <td>1.191352e-14</td>\n",
       "      <td>9.573706e-15</td>\n",
       "      <td>1.112517e-15</td>\n",
       "      <td>-4.842869e-16</td>\n",
       "      <td>9.902794e-16</td>\n",
       "      <td>-3.510969e-16</td>\n",
       "      <td>-7.927728e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>7.733139e-16</td>\n",
       "      <td>8.131302e-15</td>\n",
       "      <td>-7.152401e-15</td>\n",
       "      <td>-6.209071e-16</td>\n",
       "      <td>-3.155659e-15</td>\n",
       "      <td>8.662626e-16</td>\n",
       "      <td>1.147186e-15</td>\n",
       "      <td>2.220023e-15</td>\n",
       "      <td>1.259845e-14</td>\n",
       "      <td>4.383291e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "      <td>1.000006e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.736848e+00</td>\n",
       "      <td>-6.690739e-01</td>\n",
       "      <td>-4.866012e-01</td>\n",
       "      <td>-2.632847e-01</td>\n",
       "      <td>-1.869878e-01</td>\n",
       "      <td>-3.948810e-01</td>\n",
       "      <td>-3.695296e-01</td>\n",
       "      <td>-6.371610e-01</td>\n",
       "      <td>-6.262059e-01</td>\n",
       "      <td>-4.532321e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.082226e+00</td>\n",
       "      <td>-2.078937e-01</td>\n",
       "      <td>-7.164080e+00</td>\n",
       "      <td>-5.427811e+00</td>\n",
       "      <td>-8.929242e-01</td>\n",
       "      <td>-1.040801e+00</td>\n",
       "      <td>-2.769594e+00</td>\n",
       "      <td>-1.604506e-01</td>\n",
       "      <td>-2.323531e-01</td>\n",
       "      <td>-1.356801e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.798037e-01</td>\n",
       "      <td>-5.686148e-01</td>\n",
       "      <td>-4.866012e-01</td>\n",
       "      <td>-2.632847e-01</td>\n",
       "      <td>-1.869878e-01</td>\n",
       "      <td>-3.674637e-01</td>\n",
       "      <td>-3.231089e-01</td>\n",
       "      <td>-5.461601e-01</td>\n",
       "      <td>-5.319021e-01</td>\n",
       "      <td>-4.358705e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.763180e-01</td>\n",
       "      <td>-2.078937e-01</td>\n",
       "      <td>2.355083e-02</td>\n",
       "      <td>1.525662e-01</td>\n",
       "      <td>-8.929242e-01</td>\n",
       "      <td>-7.223625e-01</td>\n",
       "      <td>-7.351857e-01</td>\n",
       "      <td>-1.604506e-01</td>\n",
       "      <td>-2.323531e-01</td>\n",
       "      <td>-1.356801e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-4.103748e-02</td>\n",
       "      <td>-3.309262e-01</td>\n",
       "      <td>-4.587941e-01</td>\n",
       "      <td>-2.632847e-01</td>\n",
       "      <td>-1.869878e-01</td>\n",
       "      <td>-3.044040e-01</td>\n",
       "      <td>-2.145639e-01</td>\n",
       "      <td>-3.628297e-01</td>\n",
       "      <td>-3.422929e-01</td>\n",
       "      <td>-3.115252e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.233639e-01</td>\n",
       "      <td>-2.078937e-01</td>\n",
       "      <td>2.355083e-02</td>\n",
       "      <td>1.525662e-01</td>\n",
       "      <td>-3.549055e-01</td>\n",
       "      <td>-2.447043e-01</td>\n",
       "      <td>1.226015e-01</td>\n",
       "      <td>-1.604506e-01</td>\n",
       "      <td>-2.323531e-01</td>\n",
       "      <td>7.370277e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.518615e-01</td>\n",
       "      <td>1.832918e-01</td>\n",
       "      <td>1.041131e-01</td>\n",
       "      <td>-2.632847e-01</td>\n",
       "      <td>-1.869878e-01</td>\n",
       "      <td>-1.548276e-01</td>\n",
       "      <td>-6.209970e-03</td>\n",
       "      <td>4.645321e-02</td>\n",
       "      <td>1.010870e-01</td>\n",
       "      <td>5.087053e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>4.354985e-01</td>\n",
       "      <td>-2.078937e-01</td>\n",
       "      <td>2.355083e-02</td>\n",
       "      <td>1.525662e-01</td>\n",
       "      <td>6.015843e-01</td>\n",
       "      <td>5.513927e-01</td>\n",
       "      <td>9.260936e-01</td>\n",
       "      <td>-1.604506e-01</td>\n",
       "      <td>-2.323531e-01</td>\n",
       "      <td>7.370277e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.210884e+00</td>\n",
       "      <td>2.733532e+01</td>\n",
       "      <td>2.610231e+01</td>\n",
       "      <td>6.377666e+01</td>\n",
       "      <td>3.771109e+01</td>\n",
       "      <td>4.121691e+01</td>\n",
       "      <td>1.230692e+02</td>\n",
       "      <td>3.166818e+01</td>\n",
       "      <td>7.374052e+01</td>\n",
       "      <td>8.364267e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.294841e+01</td>\n",
       "      <td>5.178875e+01</td>\n",
       "      <td>2.376762e+01</td>\n",
       "      <td>6.598331e+00</td>\n",
       "      <td>4.487263e+00</td>\n",
       "      <td>1.344816e+01</td>\n",
       "      <td>1.247076e+00</td>\n",
       "      <td>2.565622e+01</td>\n",
       "      <td>6.398160e+01</td>\n",
       "      <td>7.370277e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              mvar1         mvar2         mvar3         mvar4         mvar5  \\\n",
       "count  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04   \n",
       "mean   3.830221e-16  2.935170e-15  2.852924e-15  1.191352e-14  9.573706e-15   \n",
       "std    1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00   \n",
       "min   -2.736848e+00 -6.690739e-01 -4.866012e-01 -2.632847e-01 -1.869878e-01   \n",
       "25%   -6.798037e-01 -5.686148e-01 -4.866012e-01 -2.632847e-01 -1.869878e-01   \n",
       "50%   -4.103748e-02 -3.309262e-01 -4.587941e-01 -2.632847e-01 -1.869878e-01   \n",
       "75%    6.518615e-01  1.832918e-01  1.041131e-01 -2.632847e-01 -1.869878e-01   \n",
       "max    2.210884e+00  2.733532e+01  2.610231e+01  6.377666e+01  3.771109e+01   \n",
       "\n",
       "              mvar6         mvar7         mvar8         mvar9        mvar10  \\\n",
       "count  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04   \n",
       "mean   1.112517e-15 -4.842869e-16  9.902794e-16 -3.510969e-16 -7.927728e-16   \n",
       "std    1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00   \n",
       "min   -3.948810e-01 -3.695296e-01 -6.371610e-01 -6.262059e-01 -4.532321e-01   \n",
       "25%   -3.674637e-01 -3.231089e-01 -5.461601e-01 -5.319021e-01 -4.358705e-01   \n",
       "50%   -3.044040e-01 -2.145639e-01 -3.628297e-01 -3.422929e-01 -3.115252e-01   \n",
       "75%   -1.548276e-01 -6.209970e-03  4.645321e-02  1.010870e-01  5.087053e-02   \n",
       "max    4.121691e+01  1.230692e+02  3.166818e+01  7.374052e+01  8.364267e+01   \n",
       "\n",
       "           ...             mvar38        mvar39        mvar40        mvar41  \\\n",
       "count      ...       8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04   \n",
       "mean       ...       7.733139e-16  8.131302e-15 -7.152401e-15 -6.209071e-16   \n",
       "std        ...       1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00   \n",
       "min        ...      -1.082226e+00 -2.078937e-01 -7.164080e+00 -5.427811e+00   \n",
       "25%        ...      -5.763180e-01 -2.078937e-01  2.355083e-02  1.525662e-01   \n",
       "50%        ...      -3.233639e-01 -2.078937e-01  2.355083e-02  1.525662e-01   \n",
       "75%        ...       4.354985e-01 -2.078937e-01  2.355083e-02  1.525662e-01   \n",
       "max        ...       2.294841e+01  5.178875e+01  2.376762e+01  6.598331e+00   \n",
       "\n",
       "             mvar42        mvar43        mvar44        mvar45        mvar46  \\\n",
       "count  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04  8.000000e+04   \n",
       "mean  -3.155659e-15  8.662626e-16  1.147186e-15  2.220023e-15  1.259845e-14   \n",
       "std    1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00  1.000006e+00   \n",
       "min   -8.929242e-01 -1.040801e+00 -2.769594e+00 -1.604506e-01 -2.323531e-01   \n",
       "25%   -8.929242e-01 -7.223625e-01 -7.351857e-01 -1.604506e-01 -2.323531e-01   \n",
       "50%   -3.549055e-01 -2.447043e-01  1.226015e-01 -1.604506e-01 -2.323531e-01   \n",
       "75%    6.015843e-01  5.513927e-01  9.260936e-01 -1.604506e-01 -2.323531e-01   \n",
       "max    4.487263e+00  1.344816e+01  1.247076e+00  2.565622e+01  6.398160e+01   \n",
       "\n",
       "             mvar47  \n",
       "count  8.000000e+04  \n",
       "mean   4.383291e-15  \n",
       "std    1.000006e+00  \n",
       "min   -1.356801e+00  \n",
       "25%   -1.356801e+00  \n",
       "50%    7.370277e-01  \n",
       "75%    7.370277e-01  \n",
       "max    7.370277e-01  \n",
       "\n",
       "[8 rows x 47 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX_scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Standadisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": 82,
   "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",
      "/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": 83,
   "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",
      "/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": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIsAAAD8CAYAAAD35yQEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABAz7AIAAAAgN7/B6thSgWpUt3kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15b9fe95f8>"
      ]
     },
     "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(1, len(lr_coef)+1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = clf.predict_proba(leaderX_scaled)"
   ]
  },
  {
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000, 3)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = pd.DataFrame(pred)\n",
    "pred['application_key'] = leaderAppID\n",
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>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>0.824439</td>\n",
       "      <td>0.175561</td>\n",
       "      <td>350053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.828534</td>\n",
       "      <td>0.171466</td>\n",
       "      <td>350054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.701109</td>\n",
       "      <td>0.298891</td>\n",
       "      <td>350055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.427711</td>\n",
       "      <td>0.572289</td>\n",
       "      <td>350056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.809484</td>\n",
       "      <td>0.190516</td>\n",
       "      <td>350057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.885874</td>\n",
       "      <td>0.114126</td>\n",
       "      <td>350058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.809703</td>\n",
       "      <td>0.190297</td>\n",
       "      <td>350059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.918935</td>\n",
       "      <td>0.081065</td>\n",
       "      <td>350060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.900089</td>\n",
       "      <td>0.099911</td>\n",
       "      <td>350061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.630194</td>\n",
       "      <td>0.369806</td>\n",
       "      <td>350062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.722158</td>\n",
       "      <td>0.277842</td>\n",
       "      <td>350063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.648249</td>\n",
       "      <td>0.351751</td>\n",
       "      <td>350064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.608185</td>\n",
       "      <td>0.391815</td>\n",
       "      <td>350065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.350664</td>\n",
       "      <td>0.649336</td>\n",
       "      <td>350066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.631566</td>\n",
       "      <td>0.368434</td>\n",
       "      <td>350067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.438275</td>\n",
       "      <td>0.561725</td>\n",
       "      <td>350068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.882039</td>\n",
       "      <td>0.117961</td>\n",
       "      <td>350069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.766417</td>\n",
       "      <td>0.233583</td>\n",
       "      <td>350070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.698044</td>\n",
       "      <td>0.301956</td>\n",
       "      <td>350071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.738058</td>\n",
       "      <td>0.261942</td>\n",
       "      <td>350072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.644210</td>\n",
       "      <td>0.355790</td>\n",
       "      <td>350073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.579219</td>\n",
       "      <td>0.420781</td>\n",
       "      <td>350074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.888892</td>\n",
       "      <td>0.111108</td>\n",
       "      <td>350075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.632800</td>\n",
       "      <td>0.367200</td>\n",
       "      <td>350076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.632470</td>\n",
       "      <td>0.367530</td>\n",
       "      <td>350077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.919338</td>\n",
       "      <td>0.080662</td>\n",
       "      <td>350078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.692279</td>\n",
       "      <td>0.307721</td>\n",
       "      <td>350079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.801852</td>\n",
       "      <td>0.198148</td>\n",
       "      <td>350080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.232481</td>\n",
       "      <td>0.767519</td>\n",
       "      <td>350081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.719525</td>\n",
       "      <td>0.280475</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>0.716217</td>\n",
       "      <td>0.283783</td>\n",
       "      <td>375023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>0.840964</td>\n",
       "      <td>0.159036</td>\n",
       "      <td>375024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>0.549578</td>\n",
       "      <td>0.450422</td>\n",
       "      <td>375025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>0.535802</td>\n",
       "      <td>0.464198</td>\n",
       "      <td>375026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>0.350610</td>\n",
       "      <td>0.649390</td>\n",
       "      <td>375027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>0.827840</td>\n",
       "      <td>0.172160</td>\n",
       "      <td>375028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>0.656369</td>\n",
       "      <td>0.343631</td>\n",
       "      <td>375029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>0.823632</td>\n",
       "      <td>0.176368</td>\n",
       "      <td>375030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>0.578040</td>\n",
       "      <td>0.421960</td>\n",
       "      <td>375031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>0.479331</td>\n",
       "      <td>0.520669</td>\n",
       "      <td>375032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>0.907940</td>\n",
       "      <td>0.092060</td>\n",
       "      <td>375033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>0.713283</td>\n",
       "      <td>0.286717</td>\n",
       "      <td>375034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>0.379972</td>\n",
       "      <td>0.620028</td>\n",
       "      <td>375035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>0.731713</td>\n",
       "      <td>0.268287</td>\n",
       "      <td>375036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>0.857655</td>\n",
       "      <td>0.142345</td>\n",
       "      <td>375037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>0.552282</td>\n",
       "      <td>0.447718</td>\n",
       "      <td>375038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>0.563908</td>\n",
       "      <td>0.436092</td>\n",
       "      <td>375039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>0.623754</td>\n",
       "      <td>0.376246</td>\n",
       "      <td>375040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>0.491880</td>\n",
       "      <td>0.508120</td>\n",
       "      <td>375041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>0.340978</td>\n",
       "      <td>0.659022</td>\n",
       "      <td>375042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>0.635470</td>\n",
       "      <td>0.364530</td>\n",
       "      <td>375043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>0.819970</td>\n",
       "      <td>0.180030</td>\n",
       "      <td>375044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>0.568175</td>\n",
       "      <td>0.431825</td>\n",
       "      <td>375045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>0.602574</td>\n",
       "      <td>0.397426</td>\n",
       "      <td>375046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>0.672397</td>\n",
       "      <td>0.327603</td>\n",
       "      <td>375047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>0.427010</td>\n",
       "      <td>0.572990</td>\n",
       "      <td>375048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>0.863651</td>\n",
       "      <td>0.136349</td>\n",
       "      <td>375049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>0.730427</td>\n",
       "      <td>0.269573</td>\n",
       "      <td>375050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>0.814801</td>\n",
       "      <td>0.185199</td>\n",
       "      <td>375051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>0.906701</td>\n",
       "      <td>0.093299</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      0.824439  0.175561           350053\n",
       "1      0.828534  0.171466           350054\n",
       "2      0.701109  0.298891           350055\n",
       "3      0.427711  0.572289           350056\n",
       "4      0.809484  0.190516           350057\n",
       "5      0.885874  0.114126           350058\n",
       "6      0.809703  0.190297           350059\n",
       "7      0.918935  0.081065           350060\n",
       "8      0.900089  0.099911           350061\n",
       "9      0.630194  0.369806           350062\n",
       "10     0.722158  0.277842           350063\n",
       "11     0.648249  0.351751           350064\n",
       "12     0.608185  0.391815           350065\n",
       "13     0.350664  0.649336           350066\n",
       "14     0.631566  0.368434           350067\n",
       "15     0.438275  0.561725           350068\n",
       "16     0.882039  0.117961           350069\n",
       "17     0.766417  0.233583           350070\n",
       "18     0.698044  0.301956           350071\n",
       "19     0.738058  0.261942           350072\n",
       "20     0.644210  0.355790           350073\n",
       "21     0.579219  0.420781           350074\n",
       "22     0.888892  0.111108           350075\n",
       "23     0.632800  0.367200           350076\n",
       "24     0.632470  0.367530           350077\n",
       "25     0.919338  0.080662           350078\n",
       "26     0.692279  0.307721           350079\n",
       "27     0.801852  0.198148           350080\n",
       "28     0.232481  0.767519           350081\n",
       "29     0.719525  0.280475           350082\n",
       "...         ...       ...              ...\n",
       "24970  0.716217  0.283783           375023\n",
       "24971  0.840964  0.159036           375024\n",
       "24972  0.549578  0.450422           375025\n",
       "24973  0.535802  0.464198           375026\n",
       "24974  0.350610  0.649390           375027\n",
       "24975  0.827840  0.172160           375028\n",
       "24976  0.656369  0.343631           375029\n",
       "24977  0.823632  0.176368           375030\n",
       "24978  0.578040  0.421960           375031\n",
       "24979  0.479331  0.520669           375032\n",
       "24980  0.907940  0.092060           375033\n",
       "24981  0.713283  0.286717           375034\n",
       "24982  0.379972  0.620028           375035\n",
       "24983  0.731713  0.268287           375036\n",
       "24984  0.857655  0.142345           375037\n",
       "24985  0.552282  0.447718           375038\n",
       "24986  0.563908  0.436092           375039\n",
       "24987  0.623754  0.376246           375040\n",
       "24988  0.491880  0.508120           375041\n",
       "24989  0.340978  0.659022           375042\n",
       "24990  0.635470  0.364530           375043\n",
       "24991  0.819970  0.180030           375044\n",
       "24992  0.568175  0.431825           375045\n",
       "24993  0.602574  0.397426           375046\n",
       "24994  0.672397  0.327603           375047\n",
       "24995  0.427010  0.572990           375048\n",
       "24996  0.863651  0.136349           375049\n",
       "24997  0.730427  0.269573           375050\n",
       "24998  0.814801  0.185199           375051\n",
       "24999  0.906701  0.093299           375052\n",
       "\n",
       "[25000 rows x 3 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = pred.sort_values(by = 0,kind = 'mergesort',ascending = False) #true for xgb method3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = pred.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "pred = pred[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = pred.loc[:,'application_key':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit = pred['application_key'][:10000]\n",
    "submit = pd.DataFrame(submit)\n",
    "submit[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# submit.iloc[-400:, 1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>application_key</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21128</th>\n",
       "      <td>371181</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15911</th>\n",
       "      <td>365964</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23338</th>\n",
       "      <td>373391</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>7820</th>\n",
       "      <td>357873</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>11401</th>\n",
       "      <td>361454</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>8558</th>\n",
       "      <td>358611</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>18127</th>\n",
       "      <td>368180</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>21731</th>\n",
       "      <td>371784</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19140</th>\n",
       "      <td>369193</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17058</th>\n",
       "      <td>367111</td>\n",
       "      <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>922</th>\n",
       "      <td>350975</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>351754</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20083</th>\n",
       "      <td>370136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22203</th>\n",
       "      <td>372256</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>16082</th>\n",
       "      <td>366135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9178</th>\n",
       "      <td>359231</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20952</th>\n",
       "      <td>371005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2760</th>\n",
       "      <td>352813</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6965</th>\n",
       "      <td>357018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11416</th>\n",
       "      <td>361469</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17614</th>\n",
       "      <td>367667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>350346</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22306</th>\n",
       "      <td>372359</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16315</th>\n",
       "      <td>366368</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21129</th>\n",
       "      <td>371182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11601</th>\n",
       "      <td>361654</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20937</th>\n",
       "      <td>370990</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9825</th>\n",
       "      <td>359878</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23502</th>\n",
       "      <td>373555</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8564</th>\n",
       "      <td>358617</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>353917</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20055</th>\n",
       "      <td>370108</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3809</th>\n",
       "      <td>353862</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>351920</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6038</th>\n",
       "      <td>356091</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7963</th>\n",
       "      <td>358016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13569</th>\n",
       "      <td>363622</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2184</th>\n",
       "      <td>352237</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4169</th>\n",
       "      <td>354222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19418</th>\n",
       "      <td>369471</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9560</th>\n",
       "      <td>359613</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10235</th>\n",
       "      <td>360288</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7318</th>\n",
       "      <td>357371</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2761</th>\n",
       "      <td>352814</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3430</th>\n",
       "      <td>353483</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23025</th>\n",
       "      <td>373078</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23376</th>\n",
       "      <td>373429</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17175</th>\n",
       "      <td>367228</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5397</th>\n",
       "      <td>355450</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11594</th>\n",
       "      <td>361647</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17685</th>\n",
       "      <td>367738</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14044</th>\n",
       "      <td>364097</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5559</th>\n",
       "      <td>355612</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",
       "21128           371181  0\n",
       "15911           365964  0\n",
       "23338           373391  0\n",
       "13848           363901  0\n",
       "7820            357873  0\n",
       "3642            353695  0\n",
       "11401           361454  0\n",
       "22990           373043  0\n",
       "8558            358611  0\n",
       "21040           371093  0\n",
       "18127           368180  0\n",
       "19883           369936  0\n",
       "21731           371784  0\n",
       "19140           369193  0\n",
       "17058           367111  0\n",
       "23607           373660  0\n",
       "922             350975  0\n",
       "1701            351754  0\n",
       "20083           370136  0\n",
       "22203           372256  0\n",
       "22465           372518  0\n",
       "16082           366135  0\n",
       "9178            359231  0\n",
       "20952           371005  0\n",
       "2760            352813  0\n",
       "6965            357018  0\n",
       "11416           361469  0\n",
       "17614           367667  0\n",
       "293             350346  0\n",
       "22306           372359  0\n",
       "...                ... ..\n",
       "16315           366368  0\n",
       "21129           371182  0\n",
       "11601           361654  0\n",
       "20937           370990  0\n",
       "9825            359878  0\n",
       "23502           373555  0\n",
       "8564            358617  0\n",
       "3864            353917  0\n",
       "20055           370108  0\n",
       "3809            353862  0\n",
       "1867            351920  0\n",
       "6038            356091  0\n",
       "7963            358016  0\n",
       "13569           363622  0\n",
       "2184            352237  0\n",
       "4169            354222  0\n",
       "19418           369471  0\n",
       "9560            359613  0\n",
       "10235           360288  0\n",
       "7318            357371  0\n",
       "2761            352814  0\n",
       "3430            353483  0\n",
       "23025           373078  0\n",
       "23376           373429  0\n",
       "17175           367228  0\n",
       "5397            355450  0\n",
       "11594           361647  0\n",
       "17685           367738  0\n",
       "14044           364097  0\n",
       "5559            355612  0\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit.to_csv('flabbergasted_IITGuwahati_207.csv',header = False, index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}