experiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import (cross_val_score, train_test_split,\n",
    "                                    GridSearchCV, RandomizedSearchCV)\n",
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('data/train_final.csv')\n",
    "test = pd.read_csv('data/test_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "\n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      id  F1  F2        F3  F4   F5     F6  F7  F8  F9     ...      F18  \\\n",
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       "\n",
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       "0  5016.0    1    0    8  0.405964    1    2   50    0.943392  \n",
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Drop ID column now, we don't really need it.\n",
    "\n",
    "train = train.drop(['id'], axis=1)\n",
    "test = test.drop(['id'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature/Data Exploration\n",
    "## Let's explore the features and data to get a feel for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (49998, 28)\n",
      "Test shape: (50000, 27)\n",
      "Unique values of each feature in train/test set\n",
      "         0        1\n",
      "F1       9     11.0\n",
      "F10     23     23.0\n",
      "F11     43     42.0\n",
      "F12      9     10.0\n",
      "F13     10      8.0\n",
      "F14     16     14.0\n",
      "F15     10      8.0\n",
      "F16    310    307.0\n",
      "F17      9      9.0\n",
      "F18     83     83.0\n",
      "F19   8771   8802.0\n",
      "F2      12     11.0\n",
      "F20     10     10.0\n",
      "F21    334    295.0\n",
      "F22     55     50.0\n",
      "F23  42562  42460.0\n",
      "F24      9     11.0\n",
      "F25     14     13.0\n",
      "F26     83     83.0\n",
      "F27  41705  41563.0\n",
      "F3   49998  49998.0\n",
      "F4       9     11.0\n",
      "F5      12     12.0\n",
      "F6    1880   1880.0\n",
      "F7       9     11.0\n",
      "F8       9     11.0\n",
      "F9     322    312.0\n",
      "Y        2      NaN\n"
     ]
    }
   ],
   "source": [
    "print(\"Train shape:\", train.shape)\n",
    "print(\"Test shape:\", test.shape)\n",
    "print(\"Unique values of each feature in train/test set\")\n",
    "train_feat_counts = train.apply(lambda x: len(x.unique()))\n",
    "test_feat_counts = test.apply(lambda x: len(x.unique()))\n",
    "feat_counts = pd.concat([train_feat_counts, test_feat_counts], axis=1)\n",
    "print(feat_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Y  F1  F2        F3  F4   F5  F6  F7  F8  F9  F10  F11  F12  F13  F14  F15  \\\n",
       "0  0   1   0  0.107576   0  0.0   1   1   1   1    0   32    1    1    0    1   \n",
       "\n",
       "    F16  F17  F18      F19  \n",
       "0  1.77    1  104  22902.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(1).ix[:, :20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F20</th>\n",
       "      <th>F21</th>\n",
       "      <th>F22</th>\n",
       "      <th>F23</th>\n",
       "      <th>F24</th>\n",
       "      <th>F25</th>\n",
       "      <th>F26</th>\n",
       "      <th>F27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.042295</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0.02825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   F20  F21  F22       F23  F24  F25  F26      F27\n",
       "0    1    0   18  0.042295    1    0   27  0.02825"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(1).ix[:, 20:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's try to narrow down the categorical variables. Some may be ordinal, some may be nominal (and thus require one-hot encoding). Categorical variables will most certainly have less than 49998 (number of examples in training set) categories.\n",
    "\n",
    "Possible Categorical variables: Number of unique values\n",
    "- F1: 9\n",
    "- F2: 12\n",
    "- F4: 9\n",
    "- F6: 1880\n",
    "- F7: 9\n",
    "- F8: 9\n",
    "- F9: 322\n",
    "- F10: 23\n",
    "- F11: 43\n",
    "- F12: 9\n",
    "- F13: 10\n",
    "- F14: 16\n",
    "- F15: 10\n",
    "- F17: 9\n",
    "- F18: 83\n",
    "- F20: 10\n",
    "- F21: 334\n",
    "- F22: 55\n",
    "- F24: 9\n",
    "- F25: 14\n",
    "- F26: 83"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's try to print variables with < 100 unique values first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y       2\n",
      "F1      9\n",
      "F2     12\n",
      "F4      9\n",
      "F5     12\n",
      "F7      9\n",
      "F8      9\n",
      "F10    23\n",
      "F11    43\n",
      "F12     9\n",
      "F13    10\n",
      "F14    16\n",
      "F15    10\n",
      "F17     9\n",
      "F18    83\n",
      "F20    10\n",
      "F22    55\n",
      "F24     9\n",
      "F25    14\n",
      "F26    83\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Possible categorical features - all of these have < 100 unique values.\n",
    "cat_feats = train_feat_counts[train_feat_counts < 100]\n",
    "print(cat_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y: [0, 1]\n",
      "F1: [1, 2, 3, 4, 5, 6, 8, 12, 18]\n",
      "F2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 96, 98]\n",
      "F4: [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
      "F5: [0.0, 1.0, 2.0, 3.0, 5.0, nan, 4.0, 6.0, 7.0, 8.0, 9.0, 10.0]\n",
      "F7: [1, 2, 3, 4, 5, 6, 8, 10, 23]\n",
      "F8: [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "F10: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 23, 29, 54]\n",
      "F11: [18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 63]\n",
      "F12: [1, 2, 3, 4, 5, 6, 7, 11, 12]\n",
      "F13: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
      "F14: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 96, 98]\n",
      "F15: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
      "F17: [1, 2, 3, 4, 5, 6, 7, 8, 10]\n",
      "F18: [98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 178, 179, 180, 184]\n",
      "F20: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
      "F22: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 45, 46, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58]\n",
      "F24: [1, 2, 3, 4, 5, 6, 7, 8, 10]\n",
      "F25: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 96, 98]\n",
      "F26: [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 107]\n"
     ]
    }
   ],
   "source": [
    "def print_unique_values(df, feat):\n",
    "    \"\"\"Given a dataframe and feature name, \n",
    "       display all unique values of a particular feature. \"\"\"\n",
    "    print('{}: {}'.format(feat, sorted(df.ix[:, feat].unique())))\n",
    "\n",
    "for feat in cat_feats.index:\n",
    "    print_unique_values(train, feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### F5 is probably not a categorical feature, since its a floating-point value. The rest of the features are rather inconclusive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's look for any correlated features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>F3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>F4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>F6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.428354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.992715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.983338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.986766</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Y   F1        F2   F3   F4   F5   F6   F7   F8   F9 ...   F18  F19  \\\n",
       "Y    1.0  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F1   NaN  1.0       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F2   NaN  NaN  1.000000  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F3   NaN  NaN       NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F4   NaN  NaN       NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F5   NaN  NaN       NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F6   NaN  NaN       NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F7   NaN  NaN       NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN ...   NaN  NaN   \n",
       "F8   NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN ...   NaN  NaN   \n",
       "F9   NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0 ...   NaN  NaN   \n",
       "F10  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F11  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F12  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F13  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F14  NaN  NaN  0.992715  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F15  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F16  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F17  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F18  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   1.0  NaN   \n",
       "F19  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  1.0   \n",
       "F20  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F21  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F22  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F23  NaN  NaN       NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F24  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F25  NaN  NaN  0.986766  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "F26  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   1.0  NaN   \n",
       "F27  NaN  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN   \n",
       "\n",
       "     F20  F21       F22  F23  F24       F25  F26  F27  \n",
       "Y    NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F1   NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F2   NaN  NaN       NaN  NaN  NaN  0.986766  NaN  NaN  \n",
       "F3   NaN  NaN       NaN  1.0  NaN       NaN  NaN  NaN  \n",
       "F4   NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F5   NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F6   NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F7   NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F8   NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F9   NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F10  NaN  NaN  0.428354  NaN  NaN       NaN  NaN  NaN  \n",
       "F11  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F12  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F13  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F14  NaN  NaN       NaN  NaN  NaN  0.983338  NaN  NaN  \n",
       "F15  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F16  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F17  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F18  NaN  NaN       NaN  NaN  NaN       NaN  1.0  NaN  \n",
       "F19  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F20  1.0  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F21  NaN  1.0       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F22  NaN  NaN  1.000000  NaN  NaN       NaN  NaN  NaN  \n",
       "F23  NaN  NaN       NaN  1.0  NaN       NaN  NaN  NaN  \n",
       "F24  NaN  NaN       NaN  NaN  1.0       NaN  NaN  NaN  \n",
       "F25  NaN  NaN       NaN  NaN  NaN  1.000000  NaN  NaN  \n",
       "F26  NaN  NaN       NaN  NaN  NaN       NaN  1.0  NaN  \n",
       "F27  NaN  NaN       NaN  NaN  NaN       NaN  NaN  1.0  \n",
       "\n",
       "[28 rows x 28 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_corr = train.corr()\n",
    "train_corr[train_corr > 0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>F2</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.210803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.210803</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.210803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.210803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Y  F1  F2  F3  F4        F5  F6  F7  F8  F9 ...        F18  F19  F20  \\\n",
       "Y   NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F1  NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F2  NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F3  NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F4  NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F5  NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...  -0.210803  NaN  NaN   \n",
       "F6  NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F7  NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F8  NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F9  NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F10 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F11 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F12 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F13 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F14 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F15 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F16 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F17 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F18 NaN NaN NaN NaN NaN -0.210803 NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F19 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F20 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F21 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F22 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F23 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F24 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F25 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F26 NaN NaN NaN NaN NaN -0.210803 NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "F27 NaN NaN NaN NaN NaN       NaN NaN NaN NaN NaN ...        NaN  NaN  NaN   \n",
       "\n",
       "     F21  F22  F23  F24  F25       F26  F27  \n",
       "Y    NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F1   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F2   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F3   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F4   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F5   NaN  NaN  NaN  NaN  NaN -0.210803  NaN  \n",
       "F6   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F7   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F8   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F9   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F10  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F11  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F12  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F13  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F14  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F15  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F16  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F17  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F18  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F19  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F20  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F21  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F22  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F23  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F24  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F25  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F26  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F27  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "\n",
       "[28 rows x 28 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_corr[train_corr < -0.2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looks like F2, F14, and F25 are highly correlated. Is this also the case in the test dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
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       "      <th>...</th>\n",
       "      <th>F18</th>\n",
       "      <th>F19</th>\n",
       "      <th>F20</th>\n",
       "      <th>F21</th>\n",
       "      <th>F22</th>\n",
       "      <th>F23</th>\n",
       "      <th>F24</th>\n",
       "      <th>F25</th>\n",
       "      <th>F26</th>\n",
       "      <th>F27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.987521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.217611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.437405</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.993581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.984679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>F23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>F24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.987521</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F27</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      F1        F2   F3   F4   F5   F6   F7   F8   F9       F10 ...   F18  \\\n",
       "F1   1.0       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F2   NaN  1.000000  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F3   NaN       NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F4   NaN       NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F5   NaN       NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F6   NaN       NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F7   NaN       NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN       NaN ...   NaN   \n",
       "F8   NaN       NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN       NaN ...   NaN   \n",
       "F9   NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0       NaN ...   NaN   \n",
       "F10  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.000000 ...   NaN   \n",
       "F11  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F12  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F13  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F14  NaN  0.993581  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F15  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F16  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F17  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F18  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   1.0   \n",
       "F19  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  0.217611 ...   NaN   \n",
       "F20  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F21  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F22  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  0.437405 ...   NaN   \n",
       "F23  NaN       NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F24  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F25  NaN  0.987521  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "F26  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   1.0   \n",
       "F27  NaN       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN       NaN ...   NaN   \n",
       "\n",
       "          F19  F20  F21       F22  F23  F24       F25  F26  F27  \n",
       "F1        NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F2        NaN  NaN  NaN       NaN  NaN  NaN  0.987521  NaN  NaN  \n",
       "F3        NaN  NaN  NaN       NaN  1.0  NaN       NaN  NaN  NaN  \n",
       "F4        NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F5        NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F6        NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F7        NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F8        NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F9        NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F10  0.217611  NaN  NaN  0.437405  NaN  NaN       NaN  NaN  NaN  \n",
       "F11       NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F12       NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F13       NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F14       NaN  NaN  NaN       NaN  NaN  NaN  0.984679  NaN  NaN  \n",
       "F15       NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F16       NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F17       NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F18       NaN  NaN  NaN       NaN  NaN  NaN       NaN  1.0  NaN  \n",
       "F19  1.000000  NaN  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F20       NaN  1.0  NaN       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F21       NaN  NaN  1.0       NaN  NaN  NaN       NaN  NaN  NaN  \n",
       "F22       NaN  NaN  NaN  1.000000  NaN  NaN       NaN  NaN  NaN  \n",
       "F23       NaN  NaN  NaN       NaN  1.0  NaN       NaN  NaN  NaN  \n",
       "F24       NaN  NaN  NaN       NaN  NaN  1.0       NaN  NaN  NaN  \n",
       "F25       NaN  NaN  NaN       NaN  NaN  NaN  1.000000  NaN  NaN  \n",
       "F26       NaN  NaN  NaN       NaN  NaN  NaN       NaN  1.0  NaN  \n",
       "F27       NaN  NaN  NaN       NaN  NaN  NaN       NaN  NaN  1.0  \n",
       "\n",
       "[27 rows x 27 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_corr = test.corr()\n",
    "test_corr[test_corr > 0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.211963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.211963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     F1  F2  F3  F4        F5  F6  F7  F8  F9  F10 ...        F18  F19  F20  \\\n",
       "F1  NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F2  NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F3  NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F4  NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F5  NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...  -0.211963  NaN  NaN   \n",
       "F6  NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F7  NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F8  NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F9  NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F10 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F11 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F12 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F13 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F14 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F15 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F16 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F17 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F18 NaN NaN NaN NaN -0.211963 NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F19 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F20 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F21 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F22 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F23 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F24 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F25 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F26 NaN NaN NaN NaN -0.211963 NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "F27 NaN NaN NaN NaN       NaN NaN NaN NaN NaN  NaN ...        NaN  NaN  NaN   \n",
       "\n",
       "     F21  F22  F23  F24  F25       F26  F27  \n",
       "F1   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F2   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F3   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F4   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F5   NaN  NaN  NaN  NaN  NaN -0.211963  NaN  \n",
       "F6   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F7   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F8   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F9   NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F10  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F11  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F12  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F13  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F14  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F15  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F16  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F17  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F18  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F19  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F20  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F21  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F22  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F23  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F24  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F25  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F26  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "F27  NaN  NaN  NaN  NaN  NaN       NaN  NaN  \n",
       "\n",
       "[27 rows x 27 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_corr[test_corr < - 0.2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out of the 3 features, F14 probably retains the most information, seeing how it has 16 unique values that appear in the dataframe, which is more than either F2 or F25. Let's drop both of those features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Try not dropping features, see whether or not pipeline improves.\n",
    "\n",
    "# train = train.drop(['F2', 'F25'], axis=1)\n",
    "# test = test.drop(['F2', 'F25'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Are there any missing values? Missing values could break any models we'd like to train on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        0       1\n",
      "F1      0     0.0\n",
      "F10     0     0.0\n",
      "F11     0     0.0\n",
      "F12     0     0.0\n",
      "F13     0     0.0\n",
      "F14     0     0.0\n",
      "F15     0     0.0\n",
      "F16     0     0.0\n",
      "F17     0     0.0\n",
      "F18     0     0.0\n",
      "F19  9828  9984.0\n",
      "F2      0     0.0\n",
      "F20     0     0.0\n",
      "F21     0     0.0\n",
      "F22     0     0.0\n",
      "F23     0     0.0\n",
      "F24     0     0.0\n",
      "F25     0     0.0\n",
      "F26     0     0.0\n",
      "F27     0     0.0\n",
      "F3      0     0.0\n",
      "F4      0     0.0\n",
      "F5   1268  1353.0\n",
      "F6      0     0.0\n",
      "F7      0     0.0\n",
      "F8      0     0.0\n",
      "F9      0     0.0\n",
      "Y       0     NaN\n"
     ]
    }
   ],
   "source": [
    "print(pd.concat([train.isnull().sum(), test.isnull().sum()], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F5: [0.0, 1.0, 2.0, 3.0, 5.0, nan, 4.0, 6.0, 7.0, 8.0, 9.0, 10.0]\n"
     ]
    }
   ],
   "source": [
    "print_unique_values(train, 'F5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How we're handling missing values:\n",
    "We have a good number of missing values in F5 and F19. Looks like we'll need to impute them. Since F5 looks like an ordinal categorical feature, its probably safer to impute via the median. F19 looks like a continuous feature, so it's safe to just take the mean of the column. We may want to discard F19 later, since it could lead to issues down the line.\n",
    "\n",
    "Further resources for imputation:\n",
    "- [Impute categorical missing values in scikit-learn](https://stackoverflow.com/questions/25239958/impute-categorical-missing-values-in-scikit-learn)\n",
    "- [Imputation of missing values for categories in pandas](https://stackoverflow.com/questions/32617811/imputation-of-missing-values-for-categories-in-pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F5's mean: 0.7534783500923455, median:0.0\n",
      "0.0     29110\n",
      "1.0      8682\n",
      "2.0      6516\n",
      "3.0      3129\n",
      "4.0       977\n",
      "5.0       232\n",
      "6.0        55\n",
      "7.0        18\n",
      "8.0         8\n",
      "9.0         2\n",
      "10.0        1\n",
      "Name: F5, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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y6UrBMZBO3UdjD+R4DvYxFuza+YkQgcAhrO1CWwAIstCFiKB96AQDl7pHuaa6\nC9bTGUGqC/yQrb+x1zOE4HoVuhCB8kZIiIYQnDv7FOJNKynaNZgIEQXgAlcstHQqr25pFPJ0RqCE\n7epRhN0Q3D0dtp1ZfEFrQUIEAi3z9lTQDqBICu4VKidudRambENnCUT5s0uECAROuR+QwfrsgQ9C\nvnmIG0g/jkoX6cmlUc6noQsR/KXNcAvK3o3yScMLHKbRVMrjJvThvkRCFyJ8HbLkzFd0seT/EtgF\nzop+EmVfBUKQbuMEpybwSuhCBMof155dwtoWAbquhVRYew6CxrcQYYy52RjT7Vf5ffHl3MQJr3Qy\n2z4g+yJsg1LF3pxCdmO5DUMH6Z3/QPjywUqPmyYsbV3OfAkRxpiTJX1NgTnlo6yE7AI9EPEDyJsG\nKewPcHn7F7g42e9C9w6fsL2pKITnIcIYUyHpLknjvS671CLcT4ono5F7XXrYCUBBShHf+gqN5Taq\nhP75MRLxXUltkib6UDYiINc75qA8ux62N9Yh25zIC8I7Y8+PETppIFV4OcxojDlA0iJJ50jaIel9\na21lvq8//8eTy7qb7PWB3bWtuaPU1UCKPYfvpu2tnaWuBlA0/3vBGN0zuarU1SiK3XcbpI7O/D56\nVzmoQl3dZX2J6dfU8RcUPT56PRIxXtIEa631uNyyQIAIHgIEoiYqAUJS3gFCUugDRKkM9qogY8y5\nks6S9O3EpAAMqAEAAL94ORJxuaT9Ja0zxtRKmiepwhhTY4z5kofrAQAAAeDZSISkH0n6Zcrvh0ma\nJekkSQ0ergcAAASAZyHCWrtN0rae340xu0mKWWurvVoHAAAIDi9HItJYa9dKyvvJDAAAUF742xkA\nAMAJIQIAADghRAAAACeECAAA4IQQAQAAnBAiAACAE0IEAABwQogAAABOCBEAAMAJIQIAADghRAAA\nACeECAAA4IQQAQAAnBAiAACAE0IEAABwQogAAABOCBEAAMAJIQIAADghRAAAACeECAAA4IQQAQAA\nnBAiAACAE0IEAABwQogAAABOCBEAAMAJIQIAADghRAAAACeECAAA4IQQAQAAnBAiAACAE0IEAABw\nQogAAABOCBEAAMAJIQIAADghRAAAACeECAAA4IQQAQAAnBAiAACAE0IEAABwQogAAABOCBEAAMAJ\nIQIAADghRAAAACeECAAA4IQQAQAAnBAiAACAE0IEAABwQogAAABOCBEAAMAJIQIAADghRAAAACeD\nvS7QGHPyzhKbAAAIrElEQVS4pFsknSOpU9K/JP3QWtvk9boAAEDp+DESMVXSVkmHSTpV0hhJN/mw\nHgAAUEKehghjzF6S3pb0c2ttm7V2k6SHFB+VAAAAIeLp7Qxr7TZJ38qYfLikjV6uBwAAlJ7nn4lI\nZYw5TdJVkr7g53oAAEDx+fZ0hjHmbEkvSvqZtXaGX+sBAACl4ctIhDHmfEkPS7rSWvuIH+sAAACl\n5ccjnmdJelDSxdbaV7wuHwAABIPXT2dUSrpX0rUECAAAws3rkYiPSjpW0q3GmNskxSRVJP411tr1\nHq8PAACUiNePeM6UVOllmQAAIJj42xkAAMAJIQIAADghRAAAACeECAAA4IQQAQAAnBAiAACAE0IE\nAABwQogAAABOCBEAAMAJIQIAADghRAAAACeECAAA4IQQAQAAnBAiAACAE0IEAABwQogAAABOCBEA\nAMAJIQIAADghRAAAACeECAAA4IQQAQAAnBAiAACAE0IEAABwQogAAABOCBEAAMAJIQIAADghRAAA\nACeECAAA4IQQAQAAnBAiAACAE0IEAABwQogAAABOCBEAAMAJIQIAADghRAAAACeECAAA4IQQAQAA\nnBAiAACAE0IEAABwQogAAABOCBEAAMAJIQIAADghRAAAACeECAAA4IQQAQAAnBAiAACAE0IEAABw\nQogAAABOCBEAAMAJIQIAADghRAAAACeDvSzMGHO4pDslnSlpu6THrbXXebkOAAAQDF6PRDwjab2k\n0ZLOk3ShMeZqj9cBAAACwLMQYYw5TdKJkq611jZba1dJ+quk73i1DgAAEBxejkScImmNtbYpZdp8\nScYYs4eH6wEAAAHgZYgYJakhY9rWxL/7ergeAAAQAJ5+sFJSxUBePHX8BQN6PQAAKB4vRyJqFR+N\nSDVKUiwxDwAAhIiXIeIdSYcbY0amTPuIpKXW2lYP1wMAAAKgIhaLeVaYMeYtSUsk/VjSIZKmSfqL\ntfZuz1YCAAACwevvibhE8fCwWdJ0SQ8SIAAACCdPRyIAAEB08LczAACAE0IEAABwQogAAABOCBEA\nAMAJIQIAADghRAAAACde/+0MJ8aYwyXdKelMSdslPW6tva60tQo+Y8ynJT0kabq19rKMeeMk3SDp\nWEnrJN1grZ2YMv8Hkq6QdKCkRZKuttbOT8wbIulvkj4vaYikVyV911q7NTE/svsrse23SDpHUqek\nf0n6obW2iTb3njHmJEnjJZ0mqU3Sa5J+YK2tob39Z4y5WfH+PSjxO23uA2NMt6R2xf9MREXi33ut\ntT8MepsHZSTiGUnrJY2WdJ6kC40xV5e0RgFnjPmp4hezFVnmHShpsuKdYz9JV0u61xhzSmL++ZJ+\nI+mrkg6Q9Jyk54wxwxJF/FHSWElnSDpG8X7yQMoqory/pir+12kPk3SqpDGSbqLNvWeM2V3Si4p/\ncd1+ko5XvO3uor39Z4w5WdLXFL+gyRhzkGhzv8QkHWOtHW6tHZb494fl0M9L/mVTxpjTJL0laV9r\nbVNi2v8qnn6PK2nlAswYc5XioxC3ShqSOhJhjPmxpK9Ya09LmfaopAZr7RXGmKmSrLX2J4l5FZI2\nSPqRpKcl1Un6qrV2WmK+kbRU8W8jPVQR3V/GmL0Uf1f8c2ttbWLalZK+L+le0eaeMsbsLekixb/5\ntjsx7fuSrpL0d9Hevkm011uSpki63lpbaYz5iaRLaXPvJUYiRltr12VMD/y5PAgjEadIWtOzEQnz\nFd/ePUpUp8Cz1t5urd3ex+xTFW/DVPMlnZ5tvrU2JundxPyjJO0laUHKfKv4UPKpivD+stZus9Z+\nqydAJBwmaaNoc89ZaxuttfenBAgj6euSHhft7bfvKt4eE1OmnSLa3E9/MsasNcY0GGPuTmx34Pt5\nEELEKEkNGdO2Jv7dt8h1CYu+2nTfPOb3/Pn2zPkNKfPZX0qOol0l6Q+izX1jjDncGNMuqUrSHEm/\nFe3tG2PMAYq38fcyZtHm/pkl6SVJRyv++YQzFb+FEfg2D0KIkOIfJIG3+mvTgcyP/P4yxpyt+P36\na6210xOTaXMfWGvXWWuHSDKJ/x5OzKK9/TFe0oTEu9ZMtLkPrLVnW2sfsNZ2Jtr9OkmXKf7wQ6Db\nPAgholbxRJSqJ0HV9l4ceeirTWvymF+reMfKnD8yZX6k91fiw0zTFH9K4I7EZNrcZ9baVZJ+Iekr\nkjpEe3vOGHOupLMk/T4xKfUiQx8vnjWSKiV1K+BtHoQQ8Y6kw40xI1OmfUTSUmtta4nqVO7eUfye\nV6rTFR8K7jXfGDNI8ftjsyW9r/gQV+r84yXtnnhdpPeXMeYsSQ9Kutha+0jKLNrcY8aYTxpjlmdM\njiX+m6v4Y5+paO+Bu1zS/pLWGWNqJc2TVGGMqZG0WLS554wxJxtjbsqYfJykHZKeV8DbvORPZ0iS\nMeYtSUsk/VjxT41Ok/QXa+3dJa1YGTDGPKDeT2fsJ+k9SddIekTSuZKekHSGtbbKxL9f4lFJn1X8\nueKfSvqmJGOtbTfG3KDE4z6KfwjnAUmt1tpLE+VHcn8ZYyoVb6+brbX3ZcyjzT1mjBkhabnity9+\nK+kDij+RNEzSf0taKdrbU4knkFI/VHeY4vfrD1F8aH2xaHNPGWMOVryfX6/4Y/ujFX/08mXFH9EM\n9HklCCMRknSJ4huwWfFnwh8Me8cZKGNMmzGmVfHng/875Xclnh74guKPHjYqfo/zcmttVWL+i5J+\nrnhnrFe8Y37OWtueKP7XiifZhZJWSdom6dspq4/q/vqo4l/4cmtPe6e0+1DR5p5KfGr8PxV/d1Sr\n+AWsUdJl1to60d6eSzyBtKnnP8W3P2atrbbWrhdt7rlEO39O0gWKP5I5U/ERiGvL4VweiJEIAABQ\nfoIyEgEAAMoMIQIAADghRAAAACeECAAA4IQQAQAAnBAiAACAE0IEAABwQogAAABOCBEAAMAJIQIA\nADghRAAAACf/H9lVaD7w4TbpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25bb85d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(train.ix[:, 'F5'])\n",
    "feat5 = train.ix[:, 'F5']\n",
    "print('F5\\'s mean: {}, median:{}'.format(feat5.mean(), feat5.median()))\n",
    "print(feat5.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F19's mean: 6665.120064724919, median:5362.0\n",
      "5000.0     908\n",
      "4000.0     704\n",
      "6000.0     669\n",
      "3000.0     608\n",
      "0.0        557\n",
      "2500.0     525\n",
      "10000.0    493\n",
      "3500.0     476\n",
      "7000.0     408\n",
      "4500.0     396\n",
      "8000.0     356\n",
      "2000.0     349\n",
      "7500.0     341\n",
      "10500.0    324\n",
      "5500.0     318\n",
      "6500.0     277\n",
      "9000.0     256\n",
      "1.0        212\n",
      "3600.0     203\n",
      "4200.0     198\n",
      "6250.0     193\n",
      "8333.0     191\n",
      "12000.0    190\n",
      "3750.0     190\n",
      "3200.0     183\n",
      "1500.0     180\n",
      "4166.0     176\n",
      "3333.0     175\n",
      "12500.0    172\n",
      "2800.0     170\n",
      "          ... \n",
      "74872.0      1\n",
      "8569.0       1\n",
      "34920.0      1\n",
      "10078.0      1\n",
      "10832.0      1\n",
      "10925.0      1\n",
      "14383.0      1\n",
      "5915.0       1\n",
      "1634.0       1\n",
      "82000.0      1\n",
      "17860.0      1\n",
      "2537.0       1\n",
      "1387.0       1\n",
      "4744.0       1\n",
      "9772.0       1\n",
      "58000.0      1\n",
      "9172.0       1\n",
      "1460.0       1\n",
      "12444.0      1\n",
      "14854.0      1\n",
      "10615.0      1\n",
      "1107.0       1\n",
      "5856.0       1\n",
      "13425.0      1\n",
      "6005.0       1\n",
      "12387.0      1\n",
      "3831.0       1\n",
      "8784.0       1\n",
      "4024.0       1\n",
      "7687.0       1\n",
      "Name: F19, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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vrnm3T/TJls6TExERkeAo4IiIiEhwFHBEREQkOAo4IiIiEhwFHBEREQmOAo6IiIgERwFH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\n9/D6B2v2SePZaEwkWba2nE8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25bab162b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "feat19 = train.ix[:, 'F19']\n",
    "ax.plot(feat19)\n",
    "print('F19\\'s mean: {}, median:{}'.format(feat19.mean(), feat19.median()))\n",
    "print(feat19.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop necessary labels before imputation.\n",
    "y = train.Y\n",
    "X = train.drop([\"Y\"], axis=1)\n",
    "X_test = test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>F10</th>\n",
       "      <th>...</th>\n",
       "      <th>F18</th>\n",
       "      <th>F19</th>\n",
       "      <th>F20</th>\n",
       "      <th>F21</th>\n",
       "      <th>F22</th>\n",
       "      <th>F23</th>\n",
       "      <th>F24</th>\n",
       "      <th>F25</th>\n",
       "      <th>F26</th>\n",
       "      <th>F27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.107576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>104.0</td>\n",
       "      <td>22902.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.042295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.028250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>144.0</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.021417</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.253574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.492318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4205.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>112.0</td>\n",
       "      <td>4833.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.502212</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.373397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.053028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>127.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.674254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.730797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>148.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.787592</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.371157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    F1   F2        F3   F4   F5      F6   F7   F8   F9  F10    ...       F18  \\\n",
       "0  1.0  0.0  0.107576  0.0  0.0     1.0  1.0  1.0  1.0  0.0    ...     104.0   \n",
       "1  1.0  0.0  0.142357  0.0  0.0     7.0  1.0  1.0  1.0  1.0    ...     144.0   \n",
       "2  1.0  0.0  0.492318  0.0  3.0  4205.0  1.0  1.0  3.0  1.0    ...     112.0   \n",
       "3  1.0  0.0 -0.053028  0.0  2.0     2.0  1.0  1.0  5.0  2.0    ...     127.0   \n",
       "4  1.0  0.0  0.730797  0.0  0.0    11.0  1.0  1.0  1.0  1.0    ...     148.0   \n",
       "\n",
       "       F19  F20  F21   F22       F23  F24  F25   F26       F27  \n",
       "0  22902.0  1.0  0.0  18.0  0.042295  1.0  0.0  27.0  0.028250  \n",
       "1  11400.0  1.0  0.0   8.0  0.021417  1.0  0.0  67.0  0.253574  \n",
       "2   4833.0  1.0  0.0  13.0  0.502212  1.0  1.0  35.0  0.373397  \n",
       "3   3250.0  1.0  1.0   8.0  0.000000  1.0  0.0  50.0  0.674254  \n",
       "4   4000.0  1.0  1.0   5.0  0.787592  1.0  0.0  71.0  0.371157  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "# train = train.apply(lambda x: x.fillna(x.value_counts().index[0]))\n",
    "imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
    "train_xform = imp.fit_transform(X)\n",
    "# TODO: Impute dataframe so that F5 uses median\n",
    "# and F19 uses mean. For now, we'll impute via mean for both.\n",
    "\n",
    "X = pd.DataFrame(train_xform, columns=X.columns)\n",
    "test_xform = imp.transform(X_test)\n",
    "X_test = pd.DataFrame(test_xform, columns=X_test.columns)\n",
    "\n",
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>F10</th>\n",
       "      <th>...</th>\n",
       "      <th>F18</th>\n",
       "      <th>F19</th>\n",
       "      <th>F20</th>\n",
       "      <th>F21</th>\n",
       "      <th>F22</th>\n",
       "      <th>F23</th>\n",
       "      <th>F24</th>\n",
       "      <th>F25</th>\n",
       "      <th>F26</th>\n",
       "      <th>F27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.460297</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>127.0</td>\n",
       "      <td>5016.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.405964</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.943392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.112573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>137.0</td>\n",
       "      <td>8300.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.100761</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.161306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.061461</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>142.0</td>\n",
       "      <td>6665.120065</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.063096</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>705.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.523372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.534274</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.358657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.586772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44188.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>106.0</td>\n",
       "      <td>7472.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.759767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.244748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    F1   F2        F3   F4   F5       F6   F7   F8   F9  F10     ...      \\\n",
       "0  1.0  0.0  0.460297  0.0  0.0      1.0  1.0  1.0  1.0  3.0     ...       \n",
       "1  1.0  0.0  0.112573  0.0  3.0      2.0  1.0  1.0  1.0  1.0     ...       \n",
       "2  1.0  0.0  0.061461  1.0  0.0     29.0  1.0  1.0  3.0  1.0     ...       \n",
       "3  1.0  0.0  0.523372  0.0  0.0      1.0  1.0  1.0  1.0  0.0     ...       \n",
       "4  1.0  0.0  0.586772  0.0  2.0  44188.0  1.0  1.0  7.0  1.0     ...       \n",
       "\n",
       "     F18          F19  F20  F21   F22       F23  F24  F25   F26         F27  \n",
       "0  127.0  5016.000000  1.0  0.0   8.0  0.405964  1.0  2.0  50.0    0.943392  \n",
       "1  137.0  8300.000000  1.0  5.0   5.0  0.100761  1.0  0.0  60.0    0.161306  \n",
       "2  142.0  6665.120065  1.0  5.0   4.0  0.063096  1.0  0.0  65.0  705.000000  \n",
       "3  160.0  2500.000000  1.0  1.0   7.0  0.534274  1.0  0.0  83.0    0.358657  \n",
       "4  106.0  7472.000000  1.0  4.0  12.0  0.759767  1.0  0.0  29.0    0.244748  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's get down to business and start building models!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.10, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False),\n",
       "          fit_params={}, iid=True, n_iter=30, n_jobs=-1,\n",
       "          param_distributions={'max_depth': [None, 5, 8, 15, 25], 'n_estimators': [120, 300, 500, 800, 1200], 'min_samples_split': [2, 5, 10, 15, 100], 'max_features': ['sqrt', 'log2', None], 'min_samples_leaf': [1, 2, 5, 10]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "param_grid = {\n",
    "              'n_estimators': [120, 300, 500, 800, 1200],\n",
    "              'max_features': ['sqrt', 'log2', None], \n",
    "              'max_depth': [None, 5, 8, 15, 25], \n",
    "              'min_samples_split': [2, 5, 10, 15, 100], \n",
    "              'min_samples_leaf':[1, 2, 5, 10], \n",
    "             }\n",
    "\n",
    "clf = RandomizedSearchCV(RandomForestClassifier(), param_grid, n_iter=30, cv=10, scoring='roc_auc', n_jobs=-1)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter set found on development set with cv=10:\n",
      "\n",
      "{'min_samples_leaf': 10, 'n_estimators': 500, 'max_features': 'sqrt', 'min_samples_split': 15, 'max_depth': 8}\n",
      "Best holdout score found on development set with cv=10:\n",
      "\n",
      "0.859748413386\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameter set found on development set with cv={}:\\n\"\n",
    "      .format(10))\n",
    "print(clf.best_params_)\n",
    "print(\"Best holdout score found on development set with cv={}:\\n\"\n",
    "      .format(10))\n",
    "print(clf.best_score_)\n",
    "print()\n",
    "score = clf.score(X_val, y_val)\n",
    "# print(\"Test score for these parameters: {}\".format(score))\n",
    "# for params, mean_score, scores in clf.grid_scores_:\n",
    "#     print(\"{0:.3f} (+/-{1:.03f}) for {2}\".format(\n",
    "#           mean_score, scores.std() * 2, params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.86473373  0.95928276  0.95183945  0.923868    0.8908181   0.96333435\n",
      "  0.88413278  0.94328041  0.91384638  0.88777455  0.99205791  0.93592429\n",
      "  0.95592948  0.9825018   0.96133698  0.86449871  0.99074495  0.88724079\n",
      "  0.8845861   0.86125324  0.86459293  0.88533655  0.86441594  0.98213464\n",
      "  0.99993941  0.97622046  0.94303856  0.95503566  0.97157248  0.86008199]\n",
      "[ 0.85502309  0.85790791  0.85363026  0.85903901  0.85875259  0.85376526\n",
      "  0.85924699  0.85894807  0.85960304  0.8586607   0.85098529  0.85780587\n",
      "  0.85345011  0.85539866  0.85620105  0.85504218  0.84361216  0.85914986\n",
      "  0.85804865  0.85448195  0.8546846   0.85974841  0.85494374  0.85390707\n",
      "  0.83281743  0.85671466  0.85930093  0.85893367  0.84872776  0.85273896]\n",
      "0.85420814445\n"
     ]
    }
   ],
   "source": [
    "print(clf.cv_results_['mean_train_score'])\n",
    "print(clf.cv_results_['mean_test_score'])\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = clf.predict_proba(X_test)[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def write_results(outfile, predictions):\n",
    "    ID = range(49999, 99998 + 1)\n",
    "    with open(outfile, 'w') as f:\n",
    "        f.write('id,Y\\n')\n",
    "        for instance, prediction in zip(ID, predictions):\n",
    "            f.write('{},{}\\n'.format(instance, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "outfile = 'output/rf_depth15_estimators300_featureslog2_split2_leaf10.csv'\n",
    "write_results(outfile, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's explore the hyperparameters some more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'max_depth': None,\n",
       "  'max_features': None,\n",
       "  'min_samples_leaf': 1,\n",
       "  'min_samples_split': 100,\n",
       "  'n_estimators': 500},\n",
       " {'max_depth': 5,\n",
       "  'max_features': 'log2',\n",
       "  'min_samples_leaf': 5,\n",
       "  'min_samples_split': 100,\n",
       "  'n_estimators': 120},\n",
       " {'max_depth': 5,\n",
       "  'max_features': 'log2',\n",
       "  'min_samples_leaf': 2,\n",
       "  'min_samples_split': 15,\n",
       "  'n_estimators': 800},\n",
       " {'max_depth': None,\n",
       "  'max_features': None,\n",
       "  'min_samples_leaf': 1,\n",
       "  'min_samples_split': 5,\n",
       "  'n_estimators': 300},\n",
       " {'max_depth': 5,\n",
       "  'max_features': 'log2',\n",
       "  'min_samples_leaf': 10,\n",
       "  'min_samples_split': 100,\n",
       "  'n_estimators': 120},\n",
       " {'max_depth': 15,\n",
       "  'max_features': 'sqrt',\n",
       "  'min_samples_leaf': 10,\n",
       "  'min_samples_split': 2,\n",
       "  'n_estimators': 300},\n",
       " {'max_depth': 5,\n",
       "  'max_features': None,\n",
       "  'min_samples_leaf': 2,\n",
       "  'min_samples_split': 2,\n",
       "  'n_estimators': 1200},\n",
       " {'max_depth': 5,\n",
       "  'max_features': 'log2',\n",
       "  'min_samples_leaf': 2,\n",
       "  'min_samples_split': 2,\n",
       "  'n_estimators': 300},\n",
       " {'max_depth': 15,\n",
       "  'max_features': 'log2',\n",
       "  'min_samples_leaf': 2,\n",
       "  'min_samples_split': 2,\n",
       "  'n_estimators': 500},\n",
       " {'max_depth': 8,\n",
       "  'max_features': 'sqrt',\n",
       "  'min_samples_leaf': 2,\n",
       "  'min_samples_split': 100,\n",
       "  'n_estimators': 800})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.cv_results_['params']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('F23', 0.11948052),\n",
       " ('F27', 0.1140123),\n",
       " ('F3', 0.10102529),\n",
       " ('F18', 0.098974712),\n",
       " ('F19', 0.08475735),\n",
       " ('F22', 0.073957622),\n",
       " ('F25', 0.07272727),\n",
       " ('F2', 0.067805879),\n",
       " ('F14', 0.043198906),\n",
       " ('F10', 0.041285031),\n",
       " ('F11', 0.035133287),\n",
       " ('F6', 0.032672592),\n",
       " ('F5', 0.030758714),\n",
       " ('F16', 0.024606971),\n",
       " ('F9', 0.02378674),\n",
       " ('F21', 0.017634997),\n",
       " ('F26', 0.013943951),\n",
       " ('F1', 0.0010936433),\n",
       " ('F20', 0.00095693779),\n",
       " ('F4', 0.00082023238),\n",
       " ('F12', 0.00054682163),\n",
       " ('F17', 0.00054682163),\n",
       " ('F7', 0.00027341081),\n",
       " ('F8', 0.0),\n",
       " ('F13', 0.0),\n",
       " ('F15', 0.0),\n",
       " ('F24', 0.0)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances = clf.best_estimator_.feature_importances_\n",
    "zipped_importances = zip(X.columns, feat_importances)\n",
    "sorted(zipped_importances, key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise',\n",
       "          estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=1),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=6,\n",
       "          param_distributions={'max_depth': [3, 7, 12, 17, 25], 'reg_lambda': [0.01, 0.1, 1.0], 'reg_alpha': [0, 0.1, 0.5, 1.0], 'min_child_weight': [1, 3, 5, 7], 'colsample_bytree': [0.6, 0.8, 1.0], 'learning_rate': [0.01, 0.025, 0.05, 0.1], 'gamma': [0.1, 0.3, 0.5, 0.7, 0.9], 'subsample': [0.6, 0.8, 1.0]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "\n",
    "\n",
    "param_grid = {\n",
    "              'learning_rate': [0.01, 0.025, 0.05, 0.1],\n",
    "              'gamma': [0.1, 0.3, 0.5, 0.7, 0.9], \n",
    "              'max_depth': [3, 7, 12, 17, 25], \n",
    "              'min_child_weight': [1, 3, 5, 7],\n",
    "              'subsample': [0.6, 0.8, 1.0], \n",
    "              'colsample_bytree': [0.6, 0.8, 1.0], \n",
    "              'reg_lambda': [0.01, 0.1, 1.0],\n",
    "              'reg_alpha': [0, 0.1, 0.5, 1.0],\n",
    "             }\n",
    "\n",
    "clf = RandomizedSearchCV(xgb.XGBClassifier(), param_grid, cv=10, scoring='roc_auc', n_jobs=6)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter set found on development set with cv=10:\n",
      "\n",
      "{'min_child_weight': 7, 'gamma': 0.5, 'reg_alpha': 0.5, 'max_depth': 7, 'colsample_bytree': 0.8, 'learning_rate': 0.01, 'reg_lambda': 0.1, 'subsample': 1.0}\n",
      "Best holdout score found on development set with cv=10:\n",
      "\n",
      "0.858686706849\n",
      "\n",
      "[ 0.97596245  0.91785885  0.96575021  0.95216593  0.96235367  0.99999977\n",
      "  0.87802163  0.8585545   0.92305593  0.85858715]\n",
      "[ 0.84243356  0.85676173  0.85045427  0.85164255  0.85422049  0.83496424\n",
      "  0.85868671  0.85556953  0.85562792  0.85587362]\n",
      "0.851234825513\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameter set found on development set with cv={}:\\n\"\n",
    "      .format(10))\n",
    "print(clf.best_params_)\n",
    "print(\"Best holdout score found on development set with cv={}:\\n\"\n",
    "      .format(10))\n",
    "print(clf.best_score_)\n",
    "print()\n",
    "score = clf.score(X_val, y_val)\n",
    "\n",
    "print(clf.cv_results_['mean_train_score'])\n",
    "print(clf.cv_results_['mean_test_score'])\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "outfile = 'output/xgb_lambda0.1_alpha0.5_subsample1.0_gamma0.9_lr0.01_colsample0.8_minchildw7_maxdepth7.csv'\n",
    "predictions = clf.predict_proba(X_test)[:, 0]\n",
    "write_results(outfile, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# param_grid = {\n",
    "#               'n_estimators': [120, 300, 500, 800, 1200],\n",
    "#               'max_features': ['sqrt', 'log2', None], \n",
    "#               'max_depth': [5, 8, 15, 25], \n",
    "#               'min_samples_split': [2, 5, 10, 15, 100], \n",
    "#               'min_samples_leaf':[1, 2, 5, 10], \n",
    "#              }\n",
    "\n",
    "# clf = RandomizedSearchCV(RandomForestClassifier(), param_grid, cv=10, scoring='roc_auc', n_jobs=-1)\n",
    "# clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# print(clf.predict_proba(X_val)[4950:, :])\n",
    "# print(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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