Campus Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np  # linear algebra\n",
    "import pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "import gc\n",
    "import pylab \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import pearsonr, probplot, norm, shapiro\n",
    "%matplotlib inline\n",
    "import statsmodels.api as sm\n",
    "\n",
    "pal = sns.color_palette()\n",
    "pd.set_option(\"display.max_columns\", 50)\n",
    "\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.tools as tls\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, accuracy_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('0173eeb640e7-Challenge+Data+Set+-+Campus+Analytics+2020.xlsx')\n",
    "data_val = pd.read_excel('d59675225279-Evaluation+Data+Set+-+Campus+Analytics+2020.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>X11</th>\n",
       "      <th>X12</th>\n",
       "      <th>X13</th>\n",
       "      <th>X14</th>\n",
       "      <th>X15</th>\n",
       "      <th>X16</th>\n",
       "      <th>X17</th>\n",
       "      <th>X18</th>\n",
       "      <th>X19</th>\n",
       "      <th>X20</th>\n",
       "      <th>X21</th>\n",
       "      <th>X22</th>\n",
       "      <th>X23</th>\n",
       "      <th>X24</th>\n",
       "      <th>X25</th>\n",
       "      <th>X26</th>\n",
       "      <th>X27</th>\n",
       "      <th>X28</th>\n",
       "      <th>X29</th>\n",
       "      <th>XC</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.985703</td>\n",
       "      <td>0.899527</td>\n",
       "      <td>2.332283</td>\n",
       "      <td>1.044273</td>\n",
       "      <td>1.141718</td>\n",
       "      <td>-1.656647</td>\n",
       "      <td>-1.409265</td>\n",
       "      <td>1.043518</td>\n",
       "      <td>-0.931461</td>\n",
       "      <td>-1.726754</td>\n",
       "      <td>0.149757</td>\n",
       "      <td>0.723953</td>\n",
       "      <td>0.654222</td>\n",
       "      <td>-0.128981</td>\n",
       "      <td>-0.448738</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>1.068472</td>\n",
       "      <td>0.547043</td>\n",
       "      <td>-1.446777</td>\n",
       "      <td>0.846647</td>\n",
       "      <td>1.385378</td>\n",
       "      <td>0.217853</td>\n",
       "      <td>0.628801</td>\n",
       "      <td>0.511403</td>\n",
       "      <td>0.562767</td>\n",
       "      <td>-0.356514</td>\n",
       "      <td>0.867604</td>\n",
       "      <td>-0.218642</td>\n",
       "      <td>-0.336591</td>\n",
       "      <td>0.096781</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.539816</td>\n",
       "      <td>0.243602</td>\n",
       "      <td>1.636048</td>\n",
       "      <td>-0.562150</td>\n",
       "      <td>-0.235560</td>\n",
       "      <td>-1.316039</td>\n",
       "      <td>1.529069</td>\n",
       "      <td>-1.415871</td>\n",
       "      <td>-1.167266</td>\n",
       "      <td>-0.103195</td>\n",
       "      <td>1.264812</td>\n",
       "      <td>-0.046112</td>\n",
       "      <td>-0.052789</td>\n",
       "      <td>0.713823</td>\n",
       "      <td>0.489074</td>\n",
       "      <td>1.449449</td>\n",
       "      <td>-0.241008</td>\n",
       "      <td>-1.530879</td>\n",
       "      <td>-0.140254</td>\n",
       "      <td>-2.218684</td>\n",
       "      <td>0.129662</td>\n",
       "      <td>2.020398</td>\n",
       "      <td>0.734907</td>\n",
       "      <td>0.929244</td>\n",
       "      <td>-0.805046</td>\n",
       "      <td>0.268788</td>\n",
       "      <td>1.025799</td>\n",
       "      <td>1.112271</td>\n",
       "      <td>1.492585</td>\n",
       "      <td>-0.497020</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.424483</td>\n",
       "      <td>-0.470533</td>\n",
       "      <td>0.095100</td>\n",
       "      <td>0.131981</td>\n",
       "      <td>1.168902</td>\n",
       "      <td>-0.827530</td>\n",
       "      <td>-1.233111</td>\n",
       "      <td>0.556679</td>\n",
       "      <td>-1.384065</td>\n",
       "      <td>-1.197944</td>\n",
       "      <td>-0.401762</td>\n",
       "      <td>0.242434</td>\n",
       "      <td>-1.063001</td>\n",
       "      <td>-0.000186</td>\n",
       "      <td>-1.625695</td>\n",
       "      <td>0.549367</td>\n",
       "      <td>1.792426</td>\n",
       "      <td>-0.684977</td>\n",
       "      <td>0.380994</td>\n",
       "      <td>-0.007497</td>\n",
       "      <td>0.640268</td>\n",
       "      <td>1.470933</td>\n",
       "      <td>-1.398526</td>\n",
       "      <td>-2.139636</td>\n",
       "      <td>-1.238090</td>\n",
       "      <td>-0.139802</td>\n",
       "      <td>-2.317249</td>\n",
       "      <td>0.113483</td>\n",
       "      <td>0.521594</td>\n",
       "      <td>1.269208</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.034157</td>\n",
       "      <td>-1.054496</td>\n",
       "      <td>0.339467</td>\n",
       "      <td>-0.829151</td>\n",
       "      <td>0.012123</td>\n",
       "      <td>0.048887</td>\n",
       "      <td>0.573195</td>\n",
       "      <td>-0.189184</td>\n",
       "      <td>-0.674289</td>\n",
       "      <td>0.975835</td>\n",
       "      <td>-0.040081</td>\n",
       "      <td>-0.119562</td>\n",
       "      <td>0.765592</td>\n",
       "      <td>-0.589195</td>\n",
       "      <td>0.054909</td>\n",
       "      <td>0.670680</td>\n",
       "      <td>0.034362</td>\n",
       "      <td>-1.151614</td>\n",
       "      <td>-0.598841</td>\n",
       "      <td>0.278923</td>\n",
       "      <td>2.565787</td>\n",
       "      <td>0.881025</td>\n",
       "      <td>0.305298</td>\n",
       "      <td>-0.368107</td>\n",
       "      <td>-0.664859</td>\n",
       "      <td>0.225715</td>\n",
       "      <td>-0.865773</td>\n",
       "      <td>-0.026332</td>\n",
       "      <td>-0.209023</td>\n",
       "      <td>0.074814</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.177091</td>\n",
       "      <td>1.154216</td>\n",
       "      <td>1.004133</td>\n",
       "      <td>-0.724015</td>\n",
       "      <td>-0.508090</td>\n",
       "      <td>-0.478284</td>\n",
       "      <td>-0.524431</td>\n",
       "      <td>1.841560</td>\n",
       "      <td>-1.147140</td>\n",
       "      <td>0.435025</td>\n",
       "      <td>-0.385504</td>\n",
       "      <td>-1.895292</td>\n",
       "      <td>0.336079</td>\n",
       "      <td>-0.706587</td>\n",
       "      <td>0.058675</td>\n",
       "      <td>-0.318922</td>\n",
       "      <td>0.252725</td>\n",
       "      <td>-0.756372</td>\n",
       "      <td>-1.619190</td>\n",
       "      <td>-0.413788</td>\n",
       "      <td>0.909173</td>\n",
       "      <td>0.515647</td>\n",
       "      <td>1.095991</td>\n",
       "      <td>-0.993104</td>\n",
       "      <td>-0.775456</td>\n",
       "      <td>0.938553</td>\n",
       "      <td>0.662388</td>\n",
       "      <td>0.483111</td>\n",
       "      <td>-0.910481</td>\n",
       "      <td>1.544431</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>0.127214</td>\n",
       "      <td>-0.644439</td>\n",
       "      <td>-0.356251</td>\n",
       "      <td>-1.188522</td>\n",
       "      <td>0.646979</td>\n",
       "      <td>-1.018895</td>\n",
       "      <td>-0.153524</td>\n",
       "      <td>-0.102938</td>\n",
       "      <td>1.109257</td>\n",
       "      <td>0.173160</td>\n",
       "      <td>-0.188674</td>\n",
       "      <td>-0.088169</td>\n",
       "      <td>0.843558</td>\n",
       "      <td>1.075656</td>\n",
       "      <td>1.770062</td>\n",
       "      <td>-2.276914</td>\n",
       "      <td>0.136055</td>\n",
       "      <td>1.258887</td>\n",
       "      <td>0.311050</td>\n",
       "      <td>-0.468410</td>\n",
       "      <td>-1.267298</td>\n",
       "      <td>0.828288</td>\n",
       "      <td>-1.362069</td>\n",
       "      <td>-2.267345</td>\n",
       "      <td>0.329806</td>\n",
       "      <td>0.255234</td>\n",
       "      <td>-1.145799</td>\n",
       "      <td>0.673261</td>\n",
       "      <td>-0.072575</td>\n",
       "      <td>0.233888</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.427700</td>\n",
       "      <td>1.460896</td>\n",
       "      <td>-1.260326</td>\n",
       "      <td>2.206359</td>\n",
       "      <td>-0.981929</td>\n",
       "      <td>0.045186</td>\n",
       "      <td>0.406928</td>\n",
       "      <td>-0.409999</td>\n",
       "      <td>0.602068</td>\n",
       "      <td>-1.145479</td>\n",
       "      <td>0.556425</td>\n",
       "      <td>-0.157087</td>\n",
       "      <td>1.636547</td>\n",
       "      <td>0.860019</td>\n",
       "      <td>0.493489</td>\n",
       "      <td>-1.179582</td>\n",
       "      <td>-0.223230</td>\n",
       "      <td>-0.191313</td>\n",
       "      <td>-0.334412</td>\n",
       "      <td>-1.815481</td>\n",
       "      <td>2.045879</td>\n",
       "      <td>0.350893</td>\n",
       "      <td>0.558446</td>\n",
       "      <td>-1.430444</td>\n",
       "      <td>0.067506</td>\n",
       "      <td>-0.865400</td>\n",
       "      <td>0.571944</td>\n",
       "      <td>1.172911</td>\n",
       "      <td>-0.917354</td>\n",
       "      <td>0.126003</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.880540</td>\n",
       "      <td>0.678472</td>\n",
       "      <td>0.493897</td>\n",
       "      <td>-0.118869</td>\n",
       "      <td>0.005285</td>\n",
       "      <td>-0.201233</td>\n",
       "      <td>-0.601732</td>\n",
       "      <td>-0.747260</td>\n",
       "      <td>-0.413440</td>\n",
       "      <td>-1.144848</td>\n",
       "      <td>0.620897</td>\n",
       "      <td>-0.606322</td>\n",
       "      <td>-1.050082</td>\n",
       "      <td>-0.223199</td>\n",
       "      <td>-1.213268</td>\n",
       "      <td>0.094096</td>\n",
       "      <td>1.385225</td>\n",
       "      <td>1.131449</td>\n",
       "      <td>-2.645446</td>\n",
       "      <td>-2.195989</td>\n",
       "      <td>-0.615433</td>\n",
       "      <td>1.959871</td>\n",
       "      <td>0.924523</td>\n",
       "      <td>-0.304820</td>\n",
       "      <td>0.153800</td>\n",
       "      <td>0.316073</td>\n",
       "      <td>0.243841</td>\n",
       "      <td>-1.641217</td>\n",
       "      <td>0.287025</td>\n",
       "      <td>0.591607</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.151291</td>\n",
       "      <td>-0.949928</td>\n",
       "      <td>-1.960683</td>\n",
       "      <td>-1.352557</td>\n",
       "      <td>2.221015</td>\n",
       "      <td>-0.775556</td>\n",
       "      <td>0.932388</td>\n",
       "      <td>-0.963642</td>\n",
       "      <td>0.649496</td>\n",
       "      <td>-1.658190</td>\n",
       "      <td>1.491049</td>\n",
       "      <td>0.325477</td>\n",
       "      <td>0.077016</td>\n",
       "      <td>-0.563154</td>\n",
       "      <td>-0.598902</td>\n",
       "      <td>0.173198</td>\n",
       "      <td>1.100410</td>\n",
       "      <td>-0.191733</td>\n",
       "      <td>-1.457368</td>\n",
       "      <td>-0.085871</td>\n",
       "      <td>0.786170</td>\n",
       "      <td>-1.576338</td>\n",
       "      <td>0.896517</td>\n",
       "      <td>-0.586427</td>\n",
       "      <td>-0.314806</td>\n",
       "      <td>-0.048052</td>\n",
       "      <td>0.142921</td>\n",
       "      <td>1.057203</td>\n",
       "      <td>-1.335579</td>\n",
       "      <td>-0.108947</td>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>1.351919</td>\n",
       "      <td>-0.353120</td>\n",
       "      <td>-0.918519</td>\n",
       "      <td>-0.417465</td>\n",
       "      <td>-0.688730</td>\n",
       "      <td>-0.157284</td>\n",
       "      <td>-1.233306</td>\n",
       "      <td>-0.199669</td>\n",
       "      <td>-1.000622</td>\n",
       "      <td>-0.676667</td>\n",
       "      <td>-1.388037</td>\n",
       "      <td>-0.767243</td>\n",
       "      <td>0.033413</td>\n",
       "      <td>-1.506022</td>\n",
       "      <td>3.234820</td>\n",
       "      <td>-0.118135</td>\n",
       "      <td>1.340111</td>\n",
       "      <td>0.946641</td>\n",
       "      <td>0.399260</td>\n",
       "      <td>0.398864</td>\n",
       "      <td>0.218222</td>\n",
       "      <td>1.025231</td>\n",
       "      <td>-0.084711</td>\n",
       "      <td>1.105593</td>\n",
       "      <td>-1.728433</td>\n",
       "      <td>1.575538</td>\n",
       "      <td>-1.092105</td>\n",
       "      <td>0.863792</td>\n",
       "      <td>0.551103</td>\n",
       "      <td>-1.432635</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            X0        X1        X2        X3        X4        X5        X6  \\\n",
       "0     0.985703  0.899527  2.332283  1.044273  1.141718 -1.656647 -1.409265   \n",
       "1     0.539816  0.243602  1.636048 -0.562150 -0.235560 -1.316039  1.529069   \n",
       "2    -1.424483 -0.470533  0.095100  0.131981  1.168902 -0.827530 -1.233111   \n",
       "3    -1.034157 -1.054496  0.339467 -0.829151  0.012123  0.048887  0.573195   \n",
       "4     0.177091  1.154216  1.004133 -0.724015 -0.508090 -0.478284 -0.524431   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995  0.127214 -0.644439 -0.356251 -1.188522  0.646979 -1.018895 -0.153524   \n",
       "2996  0.427700  1.460896 -1.260326  2.206359 -0.981929  0.045186  0.406928   \n",
       "2997 -0.880540  0.678472  0.493897 -0.118869  0.005285 -0.201233 -0.601732   \n",
       "2998  0.151291 -0.949928 -1.960683 -1.352557  2.221015 -0.775556  0.932388   \n",
       "2999  1.351919 -0.353120 -0.918519 -0.417465 -0.688730 -0.157284 -1.233306   \n",
       "\n",
       "            X7        X8        X9       X10       X11       X12       X13  \\\n",
       "0     1.043518 -0.931461 -1.726754  0.149757  0.723953  0.654222 -0.128981   \n",
       "1    -1.415871 -1.167266 -0.103195  1.264812 -0.046112 -0.052789  0.713823   \n",
       "2     0.556679 -1.384065 -1.197944 -0.401762  0.242434 -1.063001 -0.000186   \n",
       "3    -0.189184 -0.674289  0.975835 -0.040081 -0.119562  0.765592 -0.589195   \n",
       "4     1.841560 -1.147140  0.435025 -0.385504 -1.895292  0.336079 -0.706587   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995 -0.102938  1.109257  0.173160 -0.188674 -0.088169  0.843558  1.075656   \n",
       "2996 -0.409999  0.602068 -1.145479  0.556425 -0.157087  1.636547  0.860019   \n",
       "2997 -0.747260 -0.413440 -1.144848  0.620897 -0.606322 -1.050082 -0.223199   \n",
       "2998 -0.963642  0.649496 -1.658190  1.491049  0.325477  0.077016 -0.563154   \n",
       "2999 -0.199669 -1.000622 -0.676667 -1.388037 -0.767243  0.033413 -1.506022   \n",
       "\n",
       "           X14       X15       X16       X17       X18       X19       X20  \\\n",
       "0    -0.448738  0.001453  1.068472  0.547043 -1.446777  0.846647  1.385378   \n",
       "1     0.489074  1.449449 -0.241008 -1.530879 -0.140254 -2.218684  0.129662   \n",
       "2    -1.625695  0.549367  1.792426 -0.684977  0.380994 -0.007497  0.640268   \n",
       "3     0.054909  0.670680  0.034362 -1.151614 -0.598841  0.278923  2.565787   \n",
       "4     0.058675 -0.318922  0.252725 -0.756372 -1.619190 -0.413788  0.909173   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995  1.770062 -2.276914  0.136055  1.258887  0.311050 -0.468410 -1.267298   \n",
       "2996  0.493489 -1.179582 -0.223230 -0.191313 -0.334412 -1.815481  2.045879   \n",
       "2997 -1.213268  0.094096  1.385225  1.131449 -2.645446 -2.195989 -0.615433   \n",
       "2998 -0.598902  0.173198  1.100410 -0.191733 -1.457368 -0.085871  0.786170   \n",
       "2999  3.234820 -0.118135  1.340111  0.946641  0.399260  0.398864  0.218222   \n",
       "\n",
       "           X21       X22       X23       X24       X25       X26       X27  \\\n",
       "0     0.217853  0.628801  0.511403  0.562767 -0.356514  0.867604 -0.218642   \n",
       "1     2.020398  0.734907  0.929244 -0.805046  0.268788  1.025799  1.112271   \n",
       "2     1.470933 -1.398526 -2.139636 -1.238090 -0.139802 -2.317249  0.113483   \n",
       "3     0.881025  0.305298 -0.368107 -0.664859  0.225715 -0.865773 -0.026332   \n",
       "4     0.515647  1.095991 -0.993104 -0.775456  0.938553  0.662388  0.483111   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995  0.828288 -1.362069 -2.267345  0.329806  0.255234 -1.145799  0.673261   \n",
       "2996  0.350893  0.558446 -1.430444  0.067506 -0.865400  0.571944  1.172911   \n",
       "2997  1.959871  0.924523 -0.304820  0.153800  0.316073  0.243841 -1.641217   \n",
       "2998 -1.576338  0.896517 -0.586427 -0.314806 -0.048052  0.142921  1.057203   \n",
       "2999  1.025231 -0.084711  1.105593 -1.728433  1.575538 -1.092105  0.863792   \n",
       "\n",
       "           X28       X29 XC  y  \n",
       "0    -0.336591  0.096781  C  0  \n",
       "1     1.492585 -0.497020  E  0  \n",
       "2     0.521594  1.269208  E  0  \n",
       "3    -0.209023  0.074814  E  0  \n",
       "4    -0.910481  1.544431  E  0  \n",
       "...        ...       ... .. ..  \n",
       "2995 -0.072575  0.233888  A  1  \n",
       "2996 -0.917354  0.126003  C  0  \n",
       "2997  0.287025  0.591607  B  1  \n",
       "2998 -1.335579 -0.108947  B  0  \n",
       "2999  0.551103 -1.432635  A  1  \n",
       "\n",
       "[3000 rows x 32 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=6, ncols=5, figsize=(15,20))\n",
    "ax= axes.flatten()\n",
    "for i, val in enumerate(data.columns.values[:30]):\n",
    "    sm.qqplot(data[val], fit = True, line='q', ax=ax[i])\n",
    "    ax[i].legend([val])\n",
    "plt.show() "
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  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X0 (P-value) -  0.188\n",
      "X1 (P-value) -  0.708\n",
      "X2 (P-value) -  0.902\n",
      "X3 (P-value) -  0.197\n",
      "X4 (P-value) -  0.157\n",
      "X5 (P-value) -  0.391\n",
      "X6 (P-value) -  0.402\n",
      "X7 (P-value) -  0.922\n",
      "X8 (P-value) -  0.823\n",
      "X9 (P-value) -  0.067\n",
      "X10 (P-value) -  0.803\n",
      "X11 (P-value) -  0.5\n",
      "X12 (P-value) -  0.091\n",
      "X13 (P-value) -  0.957\n",
      "X14 (P-value) -  0.775\n",
      "X15 (P-value) -  0.086\n",
      "X16 (P-value) -  0.438\n",
      "X17 (P-value) -  0.579\n",
      "X18 (P-value) -  0.502\n",
      "X19 (P-value) -  0.046\n",
      "X20 (P-value) -  0.797\n",
      "X21 (P-value) -  0.708\n",
      "X22 (P-value) -  0.199\n",
      "X23 (P-value) -  0.085\n",
      "X24 (P-value) -  0.852\n",
      "X25 (P-value) -  0.699\n",
      "X26 (P-value) -  0.082\n",
      "X27 (P-value) -  0.503\n",
      "X28 (P-value) -  0.877\n",
      "X29 (P-value) -  0.654\n"
     ]
    }
   ],
   "source": [
    "for i, val in enumerate(data.columns.values[:30]):\n",
    "    print(val, '(P-value) - ', round(shapiro(data[val])[1], 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
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       "  index   XC\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "XC_val = data[\"XC\"].value_counts().reset_index()\n",
    "fig = make_subplots(rows=1, cols=1)\n",
    "fig.add_trace(go.Bar(x=XC_val['index'], y=XC_val.XC),\n",
    "    row=1,\n",
    "    col=1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"mean_encode = data.groupby('XC')['y'].mean()\\ndata.loc[:, 'XC'] = data.XC.map(mean_encode)\\ndata\""
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''mean_encode = data.groupby('XC')['y'].mean()\n",
    "data.loc[:, 'XC'] = data.XC.map(mean_encode)\n",
    "data'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
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       "      <th>X5</th>\n",
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       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>X11</th>\n",
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       "      <th>X13</th>\n",
       "      <th>X14</th>\n",
       "      <th>X15</th>\n",
       "      <th>X16</th>\n",
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       "      <th>X18</th>\n",
       "      <th>X19</th>\n",
       "      <th>X20</th>\n",
       "      <th>X21</th>\n",
       "      <th>X22</th>\n",
       "      <th>X23</th>\n",
       "      <th>X24</th>\n",
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       "      <th>XC</th>\n",
       "      <th>y</th>\n",
       "      <th>XC_A</th>\n",
       "      <th>XC_B</th>\n",
       "      <th>XC_C</th>\n",
       "      <th>XC_D</th>\n",
       "      <th>XC_E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.985703</td>\n",
       "      <td>0.899527</td>\n",
       "      <td>2.332283</td>\n",
       "      <td>1.044273</td>\n",
       "      <td>1.141718</td>\n",
       "      <td>-1.656647</td>\n",
       "      <td>-1.409265</td>\n",
       "      <td>1.043518</td>\n",
       "      <td>-0.931461</td>\n",
       "      <td>-1.726754</td>\n",
       "      <td>0.149757</td>\n",
       "      <td>0.723953</td>\n",
       "      <td>0.654222</td>\n",
       "      <td>-0.128981</td>\n",
       "      <td>-0.448738</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>1.068472</td>\n",
       "      <td>0.547043</td>\n",
       "      <td>-1.446777</td>\n",
       "      <td>0.846647</td>\n",
       "      <td>1.385378</td>\n",
       "      <td>0.217853</td>\n",
       "      <td>0.628801</td>\n",
       "      <td>0.511403</td>\n",
       "      <td>0.562767</td>\n",
       "      <td>-0.356514</td>\n",
       "      <td>0.867604</td>\n",
       "      <td>-0.218642</td>\n",
       "      <td>-0.336591</td>\n",
       "      <td>0.096781</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.539816</td>\n",
       "      <td>0.243602</td>\n",
       "      <td>1.636048</td>\n",
       "      <td>-0.562150</td>\n",
       "      <td>-0.235560</td>\n",
       "      <td>-1.316039</td>\n",
       "      <td>1.529069</td>\n",
       "      <td>-1.415871</td>\n",
       "      <td>-1.167266</td>\n",
       "      <td>-0.103195</td>\n",
       "      <td>1.264812</td>\n",
       "      <td>-0.046112</td>\n",
       "      <td>-0.052789</td>\n",
       "      <td>0.713823</td>\n",
       "      <td>0.489074</td>\n",
       "      <td>1.449449</td>\n",
       "      <td>-0.241008</td>\n",
       "      <td>-1.530879</td>\n",
       "      <td>-0.140254</td>\n",
       "      <td>-2.218684</td>\n",
       "      <td>0.129662</td>\n",
       "      <td>2.020398</td>\n",
       "      <td>0.734907</td>\n",
       "      <td>0.929244</td>\n",
       "      <td>-0.805046</td>\n",
       "      <td>0.268788</td>\n",
       "      <td>1.025799</td>\n",
       "      <td>1.112271</td>\n",
       "      <td>1.492585</td>\n",
       "      <td>-0.497020</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.424483</td>\n",
       "      <td>-0.470533</td>\n",
       "      <td>0.095100</td>\n",
       "      <td>0.131981</td>\n",
       "      <td>1.168902</td>\n",
       "      <td>-0.827530</td>\n",
       "      <td>-1.233111</td>\n",
       "      <td>0.556679</td>\n",
       "      <td>-1.384065</td>\n",
       "      <td>-1.197944</td>\n",
       "      <td>-0.401762</td>\n",
       "      <td>0.242434</td>\n",
       "      <td>-1.063001</td>\n",
       "      <td>-0.000186</td>\n",
       "      <td>-1.625695</td>\n",
       "      <td>0.549367</td>\n",
       "      <td>1.792426</td>\n",
       "      <td>-0.684977</td>\n",
       "      <td>0.380994</td>\n",
       "      <td>-0.007497</td>\n",
       "      <td>0.640268</td>\n",
       "      <td>1.470933</td>\n",
       "      <td>-1.398526</td>\n",
       "      <td>-2.139636</td>\n",
       "      <td>-1.238090</td>\n",
       "      <td>-0.139802</td>\n",
       "      <td>-2.317249</td>\n",
       "      <td>0.113483</td>\n",
       "      <td>0.521594</td>\n",
       "      <td>1.269208</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.034157</td>\n",
       "      <td>-1.054496</td>\n",
       "      <td>0.339467</td>\n",
       "      <td>-0.829151</td>\n",
       "      <td>0.012123</td>\n",
       "      <td>0.048887</td>\n",
       "      <td>0.573195</td>\n",
       "      <td>-0.189184</td>\n",
       "      <td>-0.674289</td>\n",
       "      <td>0.975835</td>\n",
       "      <td>-0.040081</td>\n",
       "      <td>-0.119562</td>\n",
       "      <td>0.765592</td>\n",
       "      <td>-0.589195</td>\n",
       "      <td>0.054909</td>\n",
       "      <td>0.670680</td>\n",
       "      <td>0.034362</td>\n",
       "      <td>-1.151614</td>\n",
       "      <td>-0.598841</td>\n",
       "      <td>0.278923</td>\n",
       "      <td>2.565787</td>\n",
       "      <td>0.881025</td>\n",
       "      <td>0.305298</td>\n",
       "      <td>-0.368107</td>\n",
       "      <td>-0.664859</td>\n",
       "      <td>0.225715</td>\n",
       "      <td>-0.865773</td>\n",
       "      <td>-0.026332</td>\n",
       "      <td>-0.209023</td>\n",
       "      <td>0.074814</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.177091</td>\n",
       "      <td>1.154216</td>\n",
       "      <td>1.004133</td>\n",
       "      <td>-0.724015</td>\n",
       "      <td>-0.508090</td>\n",
       "      <td>-0.478284</td>\n",
       "      <td>-0.524431</td>\n",
       "      <td>1.841560</td>\n",
       "      <td>-1.147140</td>\n",
       "      <td>0.435025</td>\n",
       "      <td>-0.385504</td>\n",
       "      <td>-1.895292</td>\n",
       "      <td>0.336079</td>\n",
       "      <td>-0.706587</td>\n",
       "      <td>0.058675</td>\n",
       "      <td>-0.318922</td>\n",
       "      <td>0.252725</td>\n",
       "      <td>-0.756372</td>\n",
       "      <td>-1.619190</td>\n",
       "      <td>-0.413788</td>\n",
       "      <td>0.909173</td>\n",
       "      <td>0.515647</td>\n",
       "      <td>1.095991</td>\n",
       "      <td>-0.993104</td>\n",
       "      <td>-0.775456</td>\n",
       "      <td>0.938553</td>\n",
       "      <td>0.662388</td>\n",
       "      <td>0.483111</td>\n",
       "      <td>-0.910481</td>\n",
       "      <td>1.544431</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>0.127214</td>\n",
       "      <td>-0.644439</td>\n",
       "      <td>-0.356251</td>\n",
       "      <td>-1.188522</td>\n",
       "      <td>0.646979</td>\n",
       "      <td>-1.018895</td>\n",
       "      <td>-0.153524</td>\n",
       "      <td>-0.102938</td>\n",
       "      <td>1.109257</td>\n",
       "      <td>0.173160</td>\n",
       "      <td>-0.188674</td>\n",
       "      <td>-0.088169</td>\n",
       "      <td>0.843558</td>\n",
       "      <td>1.075656</td>\n",
       "      <td>1.770062</td>\n",
       "      <td>-2.276914</td>\n",
       "      <td>0.136055</td>\n",
       "      <td>1.258887</td>\n",
       "      <td>0.311050</td>\n",
       "      <td>-0.468410</td>\n",
       "      <td>-1.267298</td>\n",
       "      <td>0.828288</td>\n",
       "      <td>-1.362069</td>\n",
       "      <td>-2.267345</td>\n",
       "      <td>0.329806</td>\n",
       "      <td>0.255234</td>\n",
       "      <td>-1.145799</td>\n",
       "      <td>0.673261</td>\n",
       "      <td>-0.072575</td>\n",
       "      <td>0.233888</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.427700</td>\n",
       "      <td>1.460896</td>\n",
       "      <td>-1.260326</td>\n",
       "      <td>2.206359</td>\n",
       "      <td>-0.981929</td>\n",
       "      <td>0.045186</td>\n",
       "      <td>0.406928</td>\n",
       "      <td>-0.409999</td>\n",
       "      <td>0.602068</td>\n",
       "      <td>-1.145479</td>\n",
       "      <td>0.556425</td>\n",
       "      <td>-0.157087</td>\n",
       "      <td>1.636547</td>\n",
       "      <td>0.860019</td>\n",
       "      <td>0.493489</td>\n",
       "      <td>-1.179582</td>\n",
       "      <td>-0.223230</td>\n",
       "      <td>-0.191313</td>\n",
       "      <td>-0.334412</td>\n",
       "      <td>-1.815481</td>\n",
       "      <td>2.045879</td>\n",
       "      <td>0.350893</td>\n",
       "      <td>0.558446</td>\n",
       "      <td>-1.430444</td>\n",
       "      <td>0.067506</td>\n",
       "      <td>-0.865400</td>\n",
       "      <td>0.571944</td>\n",
       "      <td>1.172911</td>\n",
       "      <td>-0.917354</td>\n",
       "      <td>0.126003</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.880540</td>\n",
       "      <td>0.678472</td>\n",
       "      <td>0.493897</td>\n",
       "      <td>-0.118869</td>\n",
       "      <td>0.005285</td>\n",
       "      <td>-0.201233</td>\n",
       "      <td>-0.601732</td>\n",
       "      <td>-0.747260</td>\n",
       "      <td>-0.413440</td>\n",
       "      <td>-1.144848</td>\n",
       "      <td>0.620897</td>\n",
       "      <td>-0.606322</td>\n",
       "      <td>-1.050082</td>\n",
       "      <td>-0.223199</td>\n",
       "      <td>-1.213268</td>\n",
       "      <td>0.094096</td>\n",
       "      <td>1.385225</td>\n",
       "      <td>1.131449</td>\n",
       "      <td>-2.645446</td>\n",
       "      <td>-2.195989</td>\n",
       "      <td>-0.615433</td>\n",
       "      <td>1.959871</td>\n",
       "      <td>0.924523</td>\n",
       "      <td>-0.304820</td>\n",
       "      <td>0.153800</td>\n",
       "      <td>0.316073</td>\n",
       "      <td>0.243841</td>\n",
       "      <td>-1.641217</td>\n",
       "      <td>0.287025</td>\n",
       "      <td>0.591607</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.151291</td>\n",
       "      <td>-0.949928</td>\n",
       "      <td>-1.960683</td>\n",
       "      <td>-1.352557</td>\n",
       "      <td>2.221015</td>\n",
       "      <td>-0.775556</td>\n",
       "      <td>0.932388</td>\n",
       "      <td>-0.963642</td>\n",
       "      <td>0.649496</td>\n",
       "      <td>-1.658190</td>\n",
       "      <td>1.491049</td>\n",
       "      <td>0.325477</td>\n",
       "      <td>0.077016</td>\n",
       "      <td>-0.563154</td>\n",
       "      <td>-0.598902</td>\n",
       "      <td>0.173198</td>\n",
       "      <td>1.100410</td>\n",
       "      <td>-0.191733</td>\n",
       "      <td>-1.457368</td>\n",
       "      <td>-0.085871</td>\n",
       "      <td>0.786170</td>\n",
       "      <td>-1.576338</td>\n",
       "      <td>0.896517</td>\n",
       "      <td>-0.586427</td>\n",
       "      <td>-0.314806</td>\n",
       "      <td>-0.048052</td>\n",
       "      <td>0.142921</td>\n",
       "      <td>1.057203</td>\n",
       "      <td>-1.335579</td>\n",
       "      <td>-0.108947</td>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>1.351919</td>\n",
       "      <td>-0.353120</td>\n",
       "      <td>-0.918519</td>\n",
       "      <td>-0.417465</td>\n",
       "      <td>-0.688730</td>\n",
       "      <td>-0.157284</td>\n",
       "      <td>-1.233306</td>\n",
       "      <td>-0.199669</td>\n",
       "      <td>-1.000622</td>\n",
       "      <td>-0.676667</td>\n",
       "      <td>-1.388037</td>\n",
       "      <td>-0.767243</td>\n",
       "      <td>0.033413</td>\n",
       "      <td>-1.506022</td>\n",
       "      <td>3.234820</td>\n",
       "      <td>-0.118135</td>\n",
       "      <td>1.340111</td>\n",
       "      <td>0.946641</td>\n",
       "      <td>0.399260</td>\n",
       "      <td>0.398864</td>\n",
       "      <td>0.218222</td>\n",
       "      <td>1.025231</td>\n",
       "      <td>-0.084711</td>\n",
       "      <td>1.105593</td>\n",
       "      <td>-1.728433</td>\n",
       "      <td>1.575538</td>\n",
       "      <td>-1.092105</td>\n",
       "      <td>0.863792</td>\n",
       "      <td>0.551103</td>\n",
       "      <td>-1.432635</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            X0        X1        X2        X3        X4        X5        X6  \\\n",
       "0     0.985703  0.899527  2.332283  1.044273  1.141718 -1.656647 -1.409265   \n",
       "1     0.539816  0.243602  1.636048 -0.562150 -0.235560 -1.316039  1.529069   \n",
       "2    -1.424483 -0.470533  0.095100  0.131981  1.168902 -0.827530 -1.233111   \n",
       "3    -1.034157 -1.054496  0.339467 -0.829151  0.012123  0.048887  0.573195   \n",
       "4     0.177091  1.154216  1.004133 -0.724015 -0.508090 -0.478284 -0.524431   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995  0.127214 -0.644439 -0.356251 -1.188522  0.646979 -1.018895 -0.153524   \n",
       "2996  0.427700  1.460896 -1.260326  2.206359 -0.981929  0.045186  0.406928   \n",
       "2997 -0.880540  0.678472  0.493897 -0.118869  0.005285 -0.201233 -0.601732   \n",
       "2998  0.151291 -0.949928 -1.960683 -1.352557  2.221015 -0.775556  0.932388   \n",
       "2999  1.351919 -0.353120 -0.918519 -0.417465 -0.688730 -0.157284 -1.233306   \n",
       "\n",
       "            X7        X8        X9       X10       X11       X12       X13  \\\n",
       "0     1.043518 -0.931461 -1.726754  0.149757  0.723953  0.654222 -0.128981   \n",
       "1    -1.415871 -1.167266 -0.103195  1.264812 -0.046112 -0.052789  0.713823   \n",
       "2     0.556679 -1.384065 -1.197944 -0.401762  0.242434 -1.063001 -0.000186   \n",
       "3    -0.189184 -0.674289  0.975835 -0.040081 -0.119562  0.765592 -0.589195   \n",
       "4     1.841560 -1.147140  0.435025 -0.385504 -1.895292  0.336079 -0.706587   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995 -0.102938  1.109257  0.173160 -0.188674 -0.088169  0.843558  1.075656   \n",
       "2996 -0.409999  0.602068 -1.145479  0.556425 -0.157087  1.636547  0.860019   \n",
       "2997 -0.747260 -0.413440 -1.144848  0.620897 -0.606322 -1.050082 -0.223199   \n",
       "2998 -0.963642  0.649496 -1.658190  1.491049  0.325477  0.077016 -0.563154   \n",
       "2999 -0.199669 -1.000622 -0.676667 -1.388037 -0.767243  0.033413 -1.506022   \n",
       "\n",
       "           X14       X15       X16       X17       X18       X19       X20  \\\n",
       "0    -0.448738  0.001453  1.068472  0.547043 -1.446777  0.846647  1.385378   \n",
       "1     0.489074  1.449449 -0.241008 -1.530879 -0.140254 -2.218684  0.129662   \n",
       "2    -1.625695  0.549367  1.792426 -0.684977  0.380994 -0.007497  0.640268   \n",
       "3     0.054909  0.670680  0.034362 -1.151614 -0.598841  0.278923  2.565787   \n",
       "4     0.058675 -0.318922  0.252725 -0.756372 -1.619190 -0.413788  0.909173   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995  1.770062 -2.276914  0.136055  1.258887  0.311050 -0.468410 -1.267298   \n",
       "2996  0.493489 -1.179582 -0.223230 -0.191313 -0.334412 -1.815481  2.045879   \n",
       "2997 -1.213268  0.094096  1.385225  1.131449 -2.645446 -2.195989 -0.615433   \n",
       "2998 -0.598902  0.173198  1.100410 -0.191733 -1.457368 -0.085871  0.786170   \n",
       "2999  3.234820 -0.118135  1.340111  0.946641  0.399260  0.398864  0.218222   \n",
       "\n",
       "           X21       X22       X23       X24       X25       X26       X27  \\\n",
       "0     0.217853  0.628801  0.511403  0.562767 -0.356514  0.867604 -0.218642   \n",
       "1     2.020398  0.734907  0.929244 -0.805046  0.268788  1.025799  1.112271   \n",
       "2     1.470933 -1.398526 -2.139636 -1.238090 -0.139802 -2.317249  0.113483   \n",
       "3     0.881025  0.305298 -0.368107 -0.664859  0.225715 -0.865773 -0.026332   \n",
       "4     0.515647  1.095991 -0.993104 -0.775456  0.938553  0.662388  0.483111   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995  0.828288 -1.362069 -2.267345  0.329806  0.255234 -1.145799  0.673261   \n",
       "2996  0.350893  0.558446 -1.430444  0.067506 -0.865400  0.571944  1.172911   \n",
       "2997  1.959871  0.924523 -0.304820  0.153800  0.316073  0.243841 -1.641217   \n",
       "2998 -1.576338  0.896517 -0.586427 -0.314806 -0.048052  0.142921  1.057203   \n",
       "2999  1.025231 -0.084711  1.105593 -1.728433  1.575538 -1.092105  0.863792   \n",
       "\n",
       "           X28       X29 XC  y  XC_A  XC_B  XC_C  XC_D  XC_E  \n",
       "0    -0.336591  0.096781  C  0   0.0   0.0   1.0   0.0   0.0  \n",
       "1     1.492585 -0.497020  E  0   0.0   0.0   0.0   0.0   1.0  \n",
       "2     0.521594  1.269208  E  0   0.0   0.0   0.0   0.0   1.0  \n",
       "3    -0.209023  0.074814  E  0   0.0   0.0   0.0   0.0   1.0  \n",
       "4    -0.910481  1.544431  E  0   0.0   0.0   0.0   0.0   1.0  \n",
       "...        ...       ... .. ..   ...   ...   ...   ...   ...  \n",
       "2995 -0.072575  0.233888  A  1   1.0   0.0   0.0   0.0   0.0  \n",
       "2996 -0.917354  0.126003  C  0   0.0   0.0   1.0   0.0   0.0  \n",
       "2997  0.287025  0.591607  B  1   0.0   1.0   0.0   0.0   0.0  \n",
       "2998 -1.335579 -0.108947  B  0   0.0   1.0   0.0   0.0   0.0  \n",
       "2999  0.551103 -1.432635  A  1   1.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[3000 rows x 37 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohc = preprocessing.OneHotEncoder()\n",
    "ohe = ohc.fit_transform(data.XC.values.reshape(-1,1)).toarray()\n",
    "dfOneHot = pd.DataFrame(ohe, columns=[\"XC_\" + str(ohc.categories_[0][i]) for i in range(len(ohc.categories_[0]))])\n",
    "data = pd.concat([data, dfOneHot], axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>X0</th>\n",
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       "      <th>X17</th>\n",
       "      <th>X18</th>\n",
       "      <th>X19</th>\n",
       "      <th>X20</th>\n",
       "      <th>X21</th>\n",
       "      <th>X22</th>\n",
       "      <th>X23</th>\n",
       "      <th>X24</th>\n",
       "      <th>X25</th>\n",
       "      <th>X26</th>\n",
       "      <th>X27</th>\n",
       "      <th>X28</th>\n",
       "      <th>X29</th>\n",
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       "      <th>XC_D</th>\n",
       "      <th>XC_E</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.985703</td>\n",
       "      <td>0.899527</td>\n",
       "      <td>2.332283</td>\n",
       "      <td>1.044273</td>\n",
       "      <td>1.141718</td>\n",
       "      <td>-1.656647</td>\n",
       "      <td>-1.409265</td>\n",
       "      <td>1.043518</td>\n",
       "      <td>-0.931461</td>\n",
       "      <td>-1.726754</td>\n",
       "      <td>0.149757</td>\n",
       "      <td>0.723953</td>\n",
       "      <td>0.654222</td>\n",
       "      <td>-0.128981</td>\n",
       "      <td>-0.448738</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>1.068472</td>\n",
       "      <td>0.547043</td>\n",
       "      <td>-1.446777</td>\n",
       "      <td>0.846647</td>\n",
       "      <td>1.385378</td>\n",
       "      <td>0.217853</td>\n",
       "      <td>0.628801</td>\n",
       "      <td>0.511403</td>\n",
       "      <td>0.562767</td>\n",
       "      <td>-0.356514</td>\n",
       "      <td>0.867604</td>\n",
       "      <td>-0.218642</td>\n",
       "      <td>-0.336591</td>\n",
       "      <td>0.096781</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.539816</td>\n",
       "      <td>0.243602</td>\n",
       "      <td>1.636048</td>\n",
       "      <td>-0.562150</td>\n",
       "      <td>-0.235560</td>\n",
       "      <td>-1.316039</td>\n",
       "      <td>1.529069</td>\n",
       "      <td>-1.415871</td>\n",
       "      <td>-1.167266</td>\n",
       "      <td>-0.103195</td>\n",
       "      <td>1.264812</td>\n",
       "      <td>-0.046112</td>\n",
       "      <td>-0.052789</td>\n",
       "      <td>0.713823</td>\n",
       "      <td>0.489074</td>\n",
       "      <td>1.449449</td>\n",
       "      <td>-0.241008</td>\n",
       "      <td>-1.530879</td>\n",
       "      <td>-0.140254</td>\n",
       "      <td>-2.218684</td>\n",
       "      <td>0.129662</td>\n",
       "      <td>2.020398</td>\n",
       "      <td>0.734907</td>\n",
       "      <td>0.929244</td>\n",
       "      <td>-0.805046</td>\n",
       "      <td>0.268788</td>\n",
       "      <td>1.025799</td>\n",
       "      <td>1.112271</td>\n",
       "      <td>1.492585</td>\n",
       "      <td>-0.497020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.424483</td>\n",
       "      <td>-0.470533</td>\n",
       "      <td>0.095100</td>\n",
       "      <td>0.131981</td>\n",
       "      <td>1.168902</td>\n",
       "      <td>-0.827530</td>\n",
       "      <td>-1.233111</td>\n",
       "      <td>0.556679</td>\n",
       "      <td>-1.384065</td>\n",
       "      <td>-1.197944</td>\n",
       "      <td>-0.401762</td>\n",
       "      <td>0.242434</td>\n",
       "      <td>-1.063001</td>\n",
       "      <td>-0.000186</td>\n",
       "      <td>-1.625695</td>\n",
       "      <td>0.549367</td>\n",
       "      <td>1.792426</td>\n",
       "      <td>-0.684977</td>\n",
       "      <td>0.380994</td>\n",
       "      <td>-0.007497</td>\n",
       "      <td>0.640268</td>\n",
       "      <td>1.470933</td>\n",
       "      <td>-1.398526</td>\n",
       "      <td>-2.139636</td>\n",
       "      <td>-1.238090</td>\n",
       "      <td>-0.139802</td>\n",
       "      <td>-2.317249</td>\n",
       "      <td>0.113483</td>\n",
       "      <td>0.521594</td>\n",
       "      <td>1.269208</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.034157</td>\n",
       "      <td>-1.054496</td>\n",
       "      <td>0.339467</td>\n",
       "      <td>-0.829151</td>\n",
       "      <td>0.012123</td>\n",
       "      <td>0.048887</td>\n",
       "      <td>0.573195</td>\n",
       "      <td>-0.189184</td>\n",
       "      <td>-0.674289</td>\n",
       "      <td>0.975835</td>\n",
       "      <td>-0.040081</td>\n",
       "      <td>-0.119562</td>\n",
       "      <td>0.765592</td>\n",
       "      <td>-0.589195</td>\n",
       "      <td>0.054909</td>\n",
       "      <td>0.670680</td>\n",
       "      <td>0.034362</td>\n",
       "      <td>-1.151614</td>\n",
       "      <td>-0.598841</td>\n",
       "      <td>0.278923</td>\n",
       "      <td>2.565787</td>\n",
       "      <td>0.881025</td>\n",
       "      <td>0.305298</td>\n",
       "      <td>-0.368107</td>\n",
       "      <td>-0.664859</td>\n",
       "      <td>0.225715</td>\n",
       "      <td>-0.865773</td>\n",
       "      <td>-0.026332</td>\n",
       "      <td>-0.209023</td>\n",
       "      <td>0.074814</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.177091</td>\n",
       "      <td>1.154216</td>\n",
       "      <td>1.004133</td>\n",
       "      <td>-0.724015</td>\n",
       "      <td>-0.508090</td>\n",
       "      <td>-0.478284</td>\n",
       "      <td>-0.524431</td>\n",
       "      <td>1.841560</td>\n",
       "      <td>-1.147140</td>\n",
       "      <td>0.435025</td>\n",
       "      <td>-0.385504</td>\n",
       "      <td>-1.895292</td>\n",
       "      <td>0.336079</td>\n",
       "      <td>-0.706587</td>\n",
       "      <td>0.058675</td>\n",
       "      <td>-0.318922</td>\n",
       "      <td>0.252725</td>\n",
       "      <td>-0.756372</td>\n",
       "      <td>-1.619190</td>\n",
       "      <td>-0.413788</td>\n",
       "      <td>0.909173</td>\n",
       "      <td>0.515647</td>\n",
       "      <td>1.095991</td>\n",
       "      <td>-0.993104</td>\n",
       "      <td>-0.775456</td>\n",
       "      <td>0.938553</td>\n",
       "      <td>0.662388</td>\n",
       "      <td>0.483111</td>\n",
       "      <td>-0.910481</td>\n",
       "      <td>1.544431</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>0.127214</td>\n",
       "      <td>-0.644439</td>\n",
       "      <td>-0.356251</td>\n",
       "      <td>-1.188522</td>\n",
       "      <td>0.646979</td>\n",
       "      <td>-1.018895</td>\n",
       "      <td>-0.153524</td>\n",
       "      <td>-0.102938</td>\n",
       "      <td>1.109257</td>\n",
       "      <td>0.173160</td>\n",
       "      <td>-0.188674</td>\n",
       "      <td>-0.088169</td>\n",
       "      <td>0.843558</td>\n",
       "      <td>1.075656</td>\n",
       "      <td>1.770062</td>\n",
       "      <td>-2.276914</td>\n",
       "      <td>0.136055</td>\n",
       "      <td>1.258887</td>\n",
       "      <td>0.311050</td>\n",
       "      <td>-0.468410</td>\n",
       "      <td>-1.267298</td>\n",
       "      <td>0.828288</td>\n",
       "      <td>-1.362069</td>\n",
       "      <td>-2.267345</td>\n",
       "      <td>0.329806</td>\n",
       "      <td>0.255234</td>\n",
       "      <td>-1.145799</td>\n",
       "      <td>0.673261</td>\n",
       "      <td>-0.072575</td>\n",
       "      <td>0.233888</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.427700</td>\n",
       "      <td>1.460896</td>\n",
       "      <td>-1.260326</td>\n",
       "      <td>2.206359</td>\n",
       "      <td>-0.981929</td>\n",
       "      <td>0.045186</td>\n",
       "      <td>0.406928</td>\n",
       "      <td>-0.409999</td>\n",
       "      <td>0.602068</td>\n",
       "      <td>-1.145479</td>\n",
       "      <td>0.556425</td>\n",
       "      <td>-0.157087</td>\n",
       "      <td>1.636547</td>\n",
       "      <td>0.860019</td>\n",
       "      <td>0.493489</td>\n",
       "      <td>-1.179582</td>\n",
       "      <td>-0.223230</td>\n",
       "      <td>-0.191313</td>\n",
       "      <td>-0.334412</td>\n",
       "      <td>-1.815481</td>\n",
       "      <td>2.045879</td>\n",
       "      <td>0.350893</td>\n",
       "      <td>0.558446</td>\n",
       "      <td>-1.430444</td>\n",
       "      <td>0.067506</td>\n",
       "      <td>-0.865400</td>\n",
       "      <td>0.571944</td>\n",
       "      <td>1.172911</td>\n",
       "      <td>-0.917354</td>\n",
       "      <td>0.126003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.880540</td>\n",
       "      <td>0.678472</td>\n",
       "      <td>0.493897</td>\n",
       "      <td>-0.118869</td>\n",
       "      <td>0.005285</td>\n",
       "      <td>-0.201233</td>\n",
       "      <td>-0.601732</td>\n",
       "      <td>-0.747260</td>\n",
       "      <td>-0.413440</td>\n",
       "      <td>-1.144848</td>\n",
       "      <td>0.620897</td>\n",
       "      <td>-0.606322</td>\n",
       "      <td>-1.050082</td>\n",
       "      <td>-0.223199</td>\n",
       "      <td>-1.213268</td>\n",
       "      <td>0.094096</td>\n",
       "      <td>1.385225</td>\n",
       "      <td>1.131449</td>\n",
       "      <td>-2.645446</td>\n",
       "      <td>-2.195989</td>\n",
       "      <td>-0.615433</td>\n",
       "      <td>1.959871</td>\n",
       "      <td>0.924523</td>\n",
       "      <td>-0.304820</td>\n",
       "      <td>0.153800</td>\n",
       "      <td>0.316073</td>\n",
       "      <td>0.243841</td>\n",
       "      <td>-1.641217</td>\n",
       "      <td>0.287025</td>\n",
       "      <td>0.591607</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.151291</td>\n",
       "      <td>-0.949928</td>\n",
       "      <td>-1.960683</td>\n",
       "      <td>-1.352557</td>\n",
       "      <td>2.221015</td>\n",
       "      <td>-0.775556</td>\n",
       "      <td>0.932388</td>\n",
       "      <td>-0.963642</td>\n",
       "      <td>0.649496</td>\n",
       "      <td>-1.658190</td>\n",
       "      <td>1.491049</td>\n",
       "      <td>0.325477</td>\n",
       "      <td>0.077016</td>\n",
       "      <td>-0.563154</td>\n",
       "      <td>-0.598902</td>\n",
       "      <td>0.173198</td>\n",
       "      <td>1.100410</td>\n",
       "      <td>-0.191733</td>\n",
       "      <td>-1.457368</td>\n",
       "      <td>-0.085871</td>\n",
       "      <td>0.786170</td>\n",
       "      <td>-1.576338</td>\n",
       "      <td>0.896517</td>\n",
       "      <td>-0.586427</td>\n",
       "      <td>-0.314806</td>\n",
       "      <td>-0.048052</td>\n",
       "      <td>0.142921</td>\n",
       "      <td>1.057203</td>\n",
       "      <td>-1.335579</td>\n",
       "      <td>-0.108947</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>1.351919</td>\n",
       "      <td>-0.353120</td>\n",
       "      <td>-0.918519</td>\n",
       "      <td>-0.417465</td>\n",
       "      <td>-0.688730</td>\n",
       "      <td>-0.157284</td>\n",
       "      <td>-1.233306</td>\n",
       "      <td>-0.199669</td>\n",
       "      <td>-1.000622</td>\n",
       "      <td>-0.676667</td>\n",
       "      <td>-1.388037</td>\n",
       "      <td>-0.767243</td>\n",
       "      <td>0.033413</td>\n",
       "      <td>-1.506022</td>\n",
       "      <td>3.234820</td>\n",
       "      <td>-0.118135</td>\n",
       "      <td>1.340111</td>\n",
       "      <td>0.946641</td>\n",
       "      <td>0.399260</td>\n",
       "      <td>0.398864</td>\n",
       "      <td>0.218222</td>\n",
       "      <td>1.025231</td>\n",
       "      <td>-0.084711</td>\n",
       "      <td>1.105593</td>\n",
       "      <td>-1.728433</td>\n",
       "      <td>1.575538</td>\n",
       "      <td>-1.092105</td>\n",
       "      <td>0.863792</td>\n",
       "      <td>0.551103</td>\n",
       "      <td>-1.432635</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            X0        X1        X2        X3        X4        X5        X6  \\\n",
       "0     0.985703  0.899527  2.332283  1.044273  1.141718 -1.656647 -1.409265   \n",
       "1     0.539816  0.243602  1.636048 -0.562150 -0.235560 -1.316039  1.529069   \n",
       "2    -1.424483 -0.470533  0.095100  0.131981  1.168902 -0.827530 -1.233111   \n",
       "3    -1.034157 -1.054496  0.339467 -0.829151  0.012123  0.048887  0.573195   \n",
       "4     0.177091  1.154216  1.004133 -0.724015 -0.508090 -0.478284 -0.524431   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995  0.127214 -0.644439 -0.356251 -1.188522  0.646979 -1.018895 -0.153524   \n",
       "2996  0.427700  1.460896 -1.260326  2.206359 -0.981929  0.045186  0.406928   \n",
       "2997 -0.880540  0.678472  0.493897 -0.118869  0.005285 -0.201233 -0.601732   \n",
       "2998  0.151291 -0.949928 -1.960683 -1.352557  2.221015 -0.775556  0.932388   \n",
       "2999  1.351919 -0.353120 -0.918519 -0.417465 -0.688730 -0.157284 -1.233306   \n",
       "\n",
       "            X7        X8        X9       X10       X11       X12       X13  \\\n",
       "0     1.043518 -0.931461 -1.726754  0.149757  0.723953  0.654222 -0.128981   \n",
       "1    -1.415871 -1.167266 -0.103195  1.264812 -0.046112 -0.052789  0.713823   \n",
       "2     0.556679 -1.384065 -1.197944 -0.401762  0.242434 -1.063001 -0.000186   \n",
       "3    -0.189184 -0.674289  0.975835 -0.040081 -0.119562  0.765592 -0.589195   \n",
       "4     1.841560 -1.147140  0.435025 -0.385504 -1.895292  0.336079 -0.706587   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995 -0.102938  1.109257  0.173160 -0.188674 -0.088169  0.843558  1.075656   \n",
       "2996 -0.409999  0.602068 -1.145479  0.556425 -0.157087  1.636547  0.860019   \n",
       "2997 -0.747260 -0.413440 -1.144848  0.620897 -0.606322 -1.050082 -0.223199   \n",
       "2998 -0.963642  0.649496 -1.658190  1.491049  0.325477  0.077016 -0.563154   \n",
       "2999 -0.199669 -1.000622 -0.676667 -1.388037 -0.767243  0.033413 -1.506022   \n",
       "\n",
       "           X14       X15       X16       X17       X18       X19       X20  \\\n",
       "0    -0.448738  0.001453  1.068472  0.547043 -1.446777  0.846647  1.385378   \n",
       "1     0.489074  1.449449 -0.241008 -1.530879 -0.140254 -2.218684  0.129662   \n",
       "2    -1.625695  0.549367  1.792426 -0.684977  0.380994 -0.007497  0.640268   \n",
       "3     0.054909  0.670680  0.034362 -1.151614 -0.598841  0.278923  2.565787   \n",
       "4     0.058675 -0.318922  0.252725 -0.756372 -1.619190 -0.413788  0.909173   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995  1.770062 -2.276914  0.136055  1.258887  0.311050 -0.468410 -1.267298   \n",
       "2996  0.493489 -1.179582 -0.223230 -0.191313 -0.334412 -1.815481  2.045879   \n",
       "2997 -1.213268  0.094096  1.385225  1.131449 -2.645446 -2.195989 -0.615433   \n",
       "2998 -0.598902  0.173198  1.100410 -0.191733 -1.457368 -0.085871  0.786170   \n",
       "2999  3.234820 -0.118135  1.340111  0.946641  0.399260  0.398864  0.218222   \n",
       "\n",
       "           X21       X22       X23       X24       X25       X26       X27  \\\n",
       "0     0.217853  0.628801  0.511403  0.562767 -0.356514  0.867604 -0.218642   \n",
       "1     2.020398  0.734907  0.929244 -0.805046  0.268788  1.025799  1.112271   \n",
       "2     1.470933 -1.398526 -2.139636 -1.238090 -0.139802 -2.317249  0.113483   \n",
       "3     0.881025  0.305298 -0.368107 -0.664859  0.225715 -0.865773 -0.026332   \n",
       "4     0.515647  1.095991 -0.993104 -0.775456  0.938553  0.662388  0.483111   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2995  0.828288 -1.362069 -2.267345  0.329806  0.255234 -1.145799  0.673261   \n",
       "2996  0.350893  0.558446 -1.430444  0.067506 -0.865400  0.571944  1.172911   \n",
       "2997  1.959871  0.924523 -0.304820  0.153800  0.316073  0.243841 -1.641217   \n",
       "2998 -1.576338  0.896517 -0.586427 -0.314806 -0.048052  0.142921  1.057203   \n",
       "2999  1.025231 -0.084711  1.105593 -1.728433  1.575538 -1.092105  0.863792   \n",
       "\n",
       "           X28       X29  y  XC_A  XC_B  XC_C  XC_D  XC_E  \n",
       "0    -0.336591  0.096781  0   0.0   0.0   1.0   0.0   0.0  \n",
       "1     1.492585 -0.497020  0   0.0   0.0   0.0   0.0   1.0  \n",
       "2     0.521594  1.269208  0   0.0   0.0   0.0   0.0   1.0  \n",
       "3    -0.209023  0.074814  0   0.0   0.0   0.0   0.0   1.0  \n",
       "4    -0.910481  1.544431  0   0.0   0.0   0.0   0.0   1.0  \n",
       "...        ...       ... ..   ...   ...   ...   ...   ...  \n",
       "2995 -0.072575  0.233888  1   1.0   0.0   0.0   0.0   0.0  \n",
       "2996 -0.917354  0.126003  0   0.0   0.0   1.0   0.0   0.0  \n",
       "2997  0.287025  0.591607  1   0.0   1.0   0.0   0.0   0.0  \n",
       "2998 -1.335579 -0.108947  0   0.0   1.0   0.0   0.0   0.0  \n",
       "2999  0.551103 -1.432635  1   1.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[3000 rows x 36 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(columns=['XC'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.02</td>\n",
       "      <td>-0.04**</td>\n",
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       "      <td>-0.01</td>\n",
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       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
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       "      <th>X4</th>\n",
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       "      <td>-0.03</td>\n",
       "      <td>-0.05***</td>\n",
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       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
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       "      <td>-0.0</td>\n",
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       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
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       "    <tr>\n",
       "      <th>X6</th>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.17***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
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       "    <tr>\n",
       "      <th>X7</th>\n",
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       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
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       "      <td>-0.01</td>\n",
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       "      <td>0.01</td>\n",
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       "      <td>-0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
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       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.18***</td>\n",
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       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
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       "    <tr>\n",
       "      <th>X8</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.06***</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X9</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.04**</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05***</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X10</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X11</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.04**</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.18***</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X12</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.04**</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X13</th>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.04**</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X14</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X15</th>\n",
       "      <td>-0.03*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04**</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.19***</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X16</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04**</td>\n",
       "      <td>0.04**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.18***</td>\n",
       "      <td>0.04*</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X18</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X19</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.19***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X20</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.16***</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X21</th>\n",
       "      <td>-0.04**</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.18***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X22</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.21***</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X23</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.05***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X24</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.06***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X25</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.19***</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X26</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.21***</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X27</th>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.2***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X28</th>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X29</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.04**</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.05**</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.04**</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.2***</td>\n",
       "      <td>-0.15***</td>\n",
       "      <td>-0.19***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.17***</td>\n",
       "      <td>-0.18***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.18***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.19***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.18***</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.19***</td>\n",
       "      <td>-0.16***</td>\n",
       "      <td>-0.18***</td>\n",
       "      <td>-0.21***</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.19***</td>\n",
       "      <td>-0.21***</td>\n",
       "      <td>-0.2***</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.15***</td>\n",
       "      <td>0.1***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.1***</td>\n",
       "      <td>-0.14***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XC_A</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.04*</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05**</td>\n",
       "      <td>0.15***</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.24***</td>\n",
       "      <td>-0.27***</td>\n",
       "      <td>-0.25***</td>\n",
       "      <td>-0.25***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XC_B</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1***</td>\n",
       "      <td>-0.24***</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.25***</td>\n",
       "      <td>-0.23***</td>\n",
       "      <td>-0.23***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XC_C</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.27***</td>\n",
       "      <td>-0.25***</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.26***</td>\n",
       "      <td>-0.26***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XC_D</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.04**</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.1***</td>\n",
       "      <td>-0.25***</td>\n",
       "      <td>-0.23***</td>\n",
       "      <td>-0.26***</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.24***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XC_E</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04**</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.14***</td>\n",
       "      <td>-0.25***</td>\n",
       "      <td>-0.23***</td>\n",
       "      <td>-0.26***</td>\n",
       "      <td>-0.24***</td>\n",
       "      <td>1.0***</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            X0       X1       X2        X3        X4        X5        X6  \\\n",
       "X0      1.0***     0.01     0.01     -0.02  -0.06***      0.01      0.02   \n",
       "X1        0.01   1.0***   -0.03*       0.0      0.01      0.01     -0.02   \n",
       "X2        0.01   -0.03*   1.0***     -0.01     -0.02   -0.04**      -0.0   \n",
       "X3       -0.02      0.0    -0.01    1.0***       0.0     -0.03     -0.02   \n",
       "X4    -0.06***     0.01    -0.02       0.0    1.0***     -0.02     -0.02   \n",
       "X5        0.01     0.01  -0.04**     -0.03     -0.02    1.0***      0.02   \n",
       "X6        0.02    -0.02     -0.0     -0.02     -0.02      0.02    1.0***   \n",
       "X7      0.04**      0.0      0.0     -0.02      -0.0      0.02      0.01   \n",
       "X8       -0.01    -0.02     0.01     -0.01      -0.0      0.01      0.01   \n",
       "X9       -0.01  -0.04**   -0.03*      0.01      0.01      0.02     -0.01   \n",
       "X10       -0.0     0.01     0.01       0.0     -0.03      -0.0     0.03*   \n",
       "X11       0.01    -0.01    -0.01   -0.04**      0.01      0.03     -0.02   \n",
       "X12      -0.03     0.02      0.0     -0.02      0.01      0.01     -0.03   \n",
       "X13       0.03    -0.01     0.02     -0.01       0.0      0.03     -0.01   \n",
       "X14       0.01     0.02    -0.02      0.02      0.02      0.02      0.02   \n",
       "X15     -0.03*      0.0      0.0     -0.02      0.02     -0.01      0.01   \n",
       "X16      -0.03     0.02    -0.01       0.0      0.01     -0.01      0.02   \n",
       "X17        0.0    -0.01     -0.0     -0.03     -0.01     -0.01       0.0   \n",
       "X18       -0.0    0.03*    -0.01      0.02     -0.02       0.0     -0.03   \n",
       "X19       0.01      0.0     -0.0      -0.0      0.01      0.02      0.02   \n",
       "X20       0.01      0.0     0.01      -0.0      -0.0      0.01      -0.0   \n",
       "X21    -0.04**    -0.02     0.01     -0.02     -0.01     -0.03      0.01   \n",
       "X22      -0.03    -0.01     0.01      -0.0     -0.01     -0.03      0.01   \n",
       "X23      -0.01     0.03    -0.02       0.0      -0.0  -0.05***     -0.01   \n",
       "X24      -0.02   -0.03*    -0.02      0.01     -0.01      -0.0      -0.0   \n",
       "X25      -0.02     -0.0    -0.02     0.03*      0.02      -0.0      -0.0   \n",
       "X26      -0.01     0.02    -0.02     -0.01      0.01     -0.01      0.02   \n",
       "X27      0.03*      0.0     0.01     -0.02      -0.0      0.02     -0.01   \n",
       "X28       0.03     -0.0     0.02     -0.02      0.02     -0.02      0.01   \n",
       "X29      -0.02     -0.0  -0.04**      0.01      -0.0     0.03*      0.02   \n",
       "y       0.04**    0.03*  -0.2***  -0.15***  -0.19***      0.02  -0.17***   \n",
       "XC_A     -0.02     -0.0      0.0      0.02     -0.01      -0.0      0.01   \n",
       "XC_B      0.01    -0.01   -0.04*     -0.03      0.02    0.04**      0.01   \n",
       "XC_C      -0.0     0.02     0.02      -0.0       0.0      -0.0     -0.01   \n",
       "XC_D     -0.01    -0.02     0.01      0.01      0.01     -0.01     -0.02   \n",
       "XC_E      0.02     0.01      0.0       0.0     -0.02     -0.02      0.01   \n",
       "\n",
       "            X7       X8       X9     X10       X11      X12      X13      X14  \\\n",
       "X0      0.04**    -0.01    -0.01    -0.0      0.01    -0.03     0.03     0.01   \n",
       "X1         0.0    -0.02  -0.04**    0.01     -0.01     0.02    -0.01     0.02   \n",
       "X2         0.0     0.01   -0.03*    0.01     -0.01      0.0     0.02    -0.02   \n",
       "X3       -0.02    -0.01     0.01     0.0   -0.04**    -0.02    -0.01     0.02   \n",
       "X4        -0.0     -0.0     0.01   -0.03      0.01     0.01      0.0     0.02   \n",
       "X5        0.02     0.01     0.02    -0.0      0.03     0.01     0.03     0.02   \n",
       "X6        0.01     0.01    -0.01   0.03*     -0.02    -0.03    -0.01     0.02   \n",
       "X7      1.0***     0.02   -0.03*   -0.01       0.0     0.01    -0.02    0.03*   \n",
       "X8        0.02   1.0***    -0.03    -0.0      0.01     0.01     0.02     0.01   \n",
       "X9      -0.03*    -0.03   1.0***   -0.01      0.01     0.02     0.01  0.05***   \n",
       "X10      -0.01     -0.0    -0.01  1.0***     -0.01    -0.02     0.01     0.01   \n",
       "X11        0.0     0.01     0.01   -0.01    1.0***    -0.01     -0.0     0.03   \n",
       "X12       0.01     0.01     0.02   -0.02     -0.01   1.0***  -0.04**     -0.0   \n",
       "X13      -0.02     0.02     0.01    0.01      -0.0  -0.04**   1.0***     0.02   \n",
       "X14      0.03*     0.01  0.05***    0.01      0.03     -0.0     0.02   1.0***   \n",
       "X15    -0.04**    -0.01     -0.0    0.02      0.02    -0.02    -0.02    -0.03   \n",
       "X16       0.01    0.03*      0.0    0.02      0.02     -0.0     0.01    -0.01   \n",
       "X17      -0.03    -0.01     -0.0    0.01      0.02    0.03*    -0.01      0.0   \n",
       "X18       -0.0    -0.02      0.0    0.02      0.01     0.02    -0.01    -0.02   \n",
       "X19       0.01    -0.01      0.0   -0.01     -0.01      0.0      0.0    -0.02   \n",
       "X20      -0.01     -0.0     0.02    -0.0     -0.01    -0.02     0.01    -0.01   \n",
       "X21       0.01    -0.02     -0.0   -0.01     -0.02    -0.03    -0.01     0.01   \n",
       "X22       0.02     0.01    -0.02    0.02      0.03    -0.02    -0.02     0.01   \n",
       "X23      -0.01    -0.02    -0.01   -0.01      -0.0     0.01    -0.02     0.03   \n",
       "X24       0.01  0.06***     0.02   -0.02       0.0    0.03*     0.01    -0.03   \n",
       "X25      -0.02    -0.02    -0.03    0.01      0.02   -0.03*     0.02     0.02   \n",
       "X26        0.0    -0.02    -0.01    -0.0     -0.01     0.02     0.01     0.01   \n",
       "X27       0.02    -0.01     -0.0   -0.01     -0.01     0.01    -0.01     0.01   \n",
       "X28     0.04**    -0.01    -0.01   -0.01       0.0   -0.03*     0.01     0.03   \n",
       "X29       0.01    -0.01     0.02   -0.01      0.01    -0.02    -0.01     0.02   \n",
       "y     -0.18***     0.01     0.01   -0.01  -0.18***     0.02     0.02     -0.0   \n",
       "XC_A      0.01     0.01     0.02    0.02       0.0      0.0     0.02     -0.0   \n",
       "XC_B      -0.0     0.03     0.02   -0.02      0.01     0.01     0.01    -0.01   \n",
       "XC_C      -0.0     0.01      0.0   -0.02       0.0    -0.03    -0.01     0.01   \n",
       "XC_D     -0.01   -0.04*    -0.02     0.0      -0.0     0.03    -0.03      0.0   \n",
       "XC_E      -0.0    -0.02    -0.02    0.02     -0.02    -0.01     0.02      0.0   \n",
       "\n",
       "           X15      X16       X17     X18       X19       X20       X21  \\\n",
       "X0      -0.03*    -0.03       0.0    -0.0      0.01      0.01   -0.04**   \n",
       "X1         0.0     0.02     -0.01   0.03*       0.0       0.0     -0.02   \n",
       "X2         0.0    -0.01      -0.0   -0.01      -0.0      0.01      0.01   \n",
       "X3       -0.02      0.0     -0.03    0.02      -0.0      -0.0     -0.02   \n",
       "X4        0.02     0.01     -0.01   -0.02      0.01      -0.0     -0.01   \n",
       "X5       -0.01    -0.01     -0.01     0.0      0.02      0.01     -0.03   \n",
       "X6        0.01     0.02       0.0   -0.03      0.02      -0.0      0.01   \n",
       "X7     -0.04**     0.01     -0.03    -0.0      0.01     -0.01      0.01   \n",
       "X8       -0.01    0.03*     -0.01   -0.02     -0.01      -0.0     -0.02   \n",
       "X9        -0.0      0.0      -0.0     0.0       0.0      0.02      -0.0   \n",
       "X10       0.02     0.02      0.01    0.02     -0.01      -0.0     -0.01   \n",
       "X11       0.02     0.02      0.02    0.01     -0.01     -0.01     -0.02   \n",
       "X12      -0.02     -0.0     0.03*    0.02       0.0     -0.02     -0.03   \n",
       "X13      -0.02     0.01     -0.01   -0.01       0.0      0.01     -0.01   \n",
       "X14      -0.03    -0.01       0.0   -0.02     -0.02     -0.01      0.01   \n",
       "X15     1.0***    -0.01      0.02    0.02      0.01       0.0     -0.02   \n",
       "X16      -0.01   1.0***      0.02    -0.0      -0.0      0.01     -0.03   \n",
       "X17       0.02     0.02    1.0***   -0.01      -0.0     -0.01      0.02   \n",
       "X18       0.02     -0.0     -0.01  1.0***      0.02     -0.01     -0.01   \n",
       "X19       0.01     -0.0      -0.0    0.02    1.0***      -0.0      0.01   \n",
       "X20        0.0     0.01     -0.01   -0.01      -0.0    1.0***      0.02   \n",
       "X21      -0.02    -0.03      0.02   -0.01      0.01      0.02    1.0***   \n",
       "X22       0.02    -0.03      0.01     0.0    -0.03*     -0.03     -0.01   \n",
       "X23      -0.01     0.01      0.01    0.01     -0.01    -0.04*      -0.0   \n",
       "X24       0.02     0.01      -0.0   -0.01     -0.01      -0.0     -0.03   \n",
       "X25      0.03*     0.02      -0.0    0.01      0.02     -0.02      -0.0   \n",
       "X26       0.02     0.02      0.03   -0.01     -0.01     -0.01      0.02   \n",
       "X27       0.02     0.01      0.02    -0.0     -0.02      0.01      -0.0   \n",
       "X28       0.01     0.02      0.02   -0.01      -0.0     -0.01     -0.02   \n",
       "X29       -0.0     0.01     -0.02  -0.04*     -0.01      0.02       0.0   \n",
       "y     -0.19***     0.02  -0.18***    -0.0  -0.19***  -0.16***  -0.18***   \n",
       "XC_A     -0.02     -0.0     0.04*    -0.0      0.02       0.0      0.01   \n",
       "XC_B      0.01    -0.01     -0.02   -0.03     0.03*      -0.0     0.03*   \n",
       "XC_C     -0.03     0.01     -0.01    0.01     -0.01       0.0    -0.04*   \n",
       "XC_D      0.02  -0.04**      0.01    0.01      0.01       0.0      0.01   \n",
       "XC_E      0.02   0.04**     -0.02    0.01   -0.04**     -0.01     -0.02   \n",
       "\n",
       "           X22       X23      X24       X25       X26      X27     X28  \\\n",
       "X0       -0.03     -0.01    -0.02     -0.02     -0.01    0.03*    0.03   \n",
       "X1       -0.01      0.03   -0.03*      -0.0      0.02      0.0    -0.0   \n",
       "X2        0.01     -0.02    -0.02     -0.02     -0.02     0.01    0.02   \n",
       "X3        -0.0       0.0     0.01     0.03*     -0.01    -0.02   -0.02   \n",
       "X4       -0.01      -0.0    -0.01      0.02      0.01     -0.0    0.02   \n",
       "X5       -0.03  -0.05***     -0.0      -0.0     -0.01     0.02   -0.02   \n",
       "X6        0.01     -0.01     -0.0      -0.0      0.02    -0.01    0.01   \n",
       "X7        0.02     -0.01     0.01     -0.02       0.0     0.02  0.04**   \n",
       "X8        0.01     -0.02  0.06***     -0.02     -0.02    -0.01   -0.01   \n",
       "X9       -0.02     -0.01     0.02     -0.03     -0.01     -0.0   -0.01   \n",
       "X10       0.02     -0.01    -0.02      0.01      -0.0    -0.01   -0.01   \n",
       "X11       0.03      -0.0      0.0      0.02     -0.01    -0.01     0.0   \n",
       "X12      -0.02      0.01    0.03*    -0.03*      0.02     0.01  -0.03*   \n",
       "X13      -0.02     -0.02     0.01      0.02      0.01    -0.01    0.01   \n",
       "X14       0.01      0.03    -0.03      0.02      0.01     0.01    0.03   \n",
       "X15       0.02     -0.01     0.02     0.03*      0.02     0.02    0.01   \n",
       "X16      -0.03      0.01     0.01      0.02      0.02     0.01    0.02   \n",
       "X17       0.01      0.01     -0.0      -0.0      0.03     0.02    0.02   \n",
       "X18        0.0      0.01    -0.01      0.01     -0.01     -0.0   -0.01   \n",
       "X19     -0.03*     -0.01    -0.01      0.02     -0.01    -0.02    -0.0   \n",
       "X20      -0.03    -0.04*     -0.0     -0.02     -0.01     0.01   -0.01   \n",
       "X21      -0.01      -0.0    -0.03      -0.0      0.02     -0.0   -0.02   \n",
       "X22     1.0***      0.02     -0.0    0.04**      0.01    -0.01    0.02   \n",
       "X23       0.02    1.0***     0.03      0.01       0.0     -0.0    0.01   \n",
       "X24       -0.0      0.03   1.0***     -0.01      -0.0    -0.01    0.01   \n",
       "X25     0.04**      0.01    -0.01    1.0***      0.02     0.02    -0.0   \n",
       "X26       0.01       0.0     -0.0      0.02    1.0***    -0.02    0.01   \n",
       "X27      -0.01      -0.0    -0.01      0.02     -0.02   1.0***   -0.01   \n",
       "X28       0.02      0.01     0.01      -0.0      0.01    -0.01  1.0***   \n",
       "X29       -0.0      -0.0    -0.01     -0.01     -0.01     -0.0     0.0   \n",
       "y     -0.21***     0.03*     0.01  -0.19***  -0.21***  -0.2***   -0.02   \n",
       "XC_A      0.03       0.0     -0.0    0.04**     0.03*    -0.01     0.0   \n",
       "XC_B     -0.01     -0.01     -0.0     -0.02     -0.01     0.01   -0.02   \n",
       "XC_C     -0.01     -0.02     0.01     -0.02      -0.0     0.02    0.01   \n",
       "XC_D     -0.01      0.03      0.0       0.0     -0.03    -0.01    0.02   \n",
       "XC_E       0.0       0.0    -0.01     -0.01      0.01    -0.01   -0.01   \n",
       "\n",
       "          X29         y      XC_A      XC_B      XC_C      XC_D      XC_E  \n",
       "X0      -0.02    0.04**     -0.02      0.01      -0.0     -0.01      0.02  \n",
       "X1       -0.0     0.03*      -0.0     -0.01      0.02     -0.02      0.01  \n",
       "X2    -0.04**   -0.2***       0.0    -0.04*      0.02      0.01       0.0  \n",
       "X3       0.01  -0.15***      0.02     -0.03      -0.0      0.01       0.0  \n",
       "X4       -0.0  -0.19***     -0.01      0.02       0.0      0.01     -0.02  \n",
       "X5      0.03*      0.02      -0.0    0.04**      -0.0     -0.01     -0.02  \n",
       "X6       0.02  -0.17***      0.01      0.01     -0.01     -0.02      0.01  \n",
       "X7       0.01  -0.18***      0.01      -0.0      -0.0     -0.01      -0.0  \n",
       "X8      -0.01      0.01      0.01      0.03      0.01    -0.04*     -0.02  \n",
       "X9       0.02      0.01      0.02      0.02       0.0     -0.02     -0.02  \n",
       "X10     -0.01     -0.01      0.02     -0.02     -0.02       0.0      0.02  \n",
       "X11      0.01  -0.18***       0.0      0.01       0.0      -0.0     -0.02  \n",
       "X12     -0.02      0.02       0.0      0.01     -0.03      0.03     -0.01  \n",
       "X13     -0.01      0.02      0.02      0.01     -0.01     -0.03      0.02  \n",
       "X14      0.02      -0.0      -0.0     -0.01      0.01       0.0       0.0  \n",
       "X15      -0.0  -0.19***     -0.02      0.01     -0.03      0.02      0.02  \n",
       "X16      0.01      0.02      -0.0     -0.01      0.01   -0.04**    0.04**  \n",
       "X17     -0.02  -0.18***     0.04*     -0.02     -0.01      0.01     -0.02  \n",
       "X18    -0.04*      -0.0      -0.0     -0.03      0.01      0.01      0.01  \n",
       "X19     -0.01  -0.19***      0.02     0.03*     -0.01      0.01   -0.04**  \n",
       "X20      0.02  -0.16***       0.0      -0.0       0.0       0.0     -0.01  \n",
       "X21       0.0  -0.18***      0.01     0.03*    -0.04*      0.01     -0.02  \n",
       "X22      -0.0  -0.21***      0.03     -0.01     -0.01     -0.01       0.0  \n",
       "X23      -0.0     0.03*       0.0     -0.01     -0.02      0.03       0.0  \n",
       "X24     -0.01      0.01      -0.0      -0.0      0.01       0.0     -0.01  \n",
       "X25     -0.01  -0.19***    0.04**     -0.02     -0.02       0.0     -0.01  \n",
       "X26     -0.01  -0.21***     0.03*     -0.01      -0.0     -0.03      0.01  \n",
       "X27      -0.0   -0.2***     -0.01      0.01      0.02     -0.01     -0.01  \n",
       "X28       0.0     -0.02       0.0     -0.02      0.01      0.02     -0.01  \n",
       "X29    1.0***      0.01   -0.05**      0.02      0.02     -0.01      0.02  \n",
       "y        0.01    1.0***   0.15***    0.1***     -0.01   -0.1***  -0.14***  \n",
       "XC_A  -0.05**   0.15***    1.0***  -0.24***  -0.27***  -0.25***  -0.25***  \n",
       "XC_B     0.02    0.1***  -0.24***    1.0***  -0.25***  -0.23***  -0.23***  \n",
       "XC_C     0.02     -0.01  -0.27***  -0.25***    1.0***  -0.26***  -0.26***  \n",
       "XC_D    -0.01   -0.1***  -0.25***  -0.23***  -0.26***    1.0***  -0.24***  \n",
       "XC_E     0.02  -0.14***  -0.25***  -0.23***  -0.26***  -0.24***    1.0***  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rho = data.corr().round(2)\n",
    "pval = data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(*rho.shape)\n",
    "p = pval.applymap(lambda x: \"\".join([\"*\" for t in [0.01, 0.05, 0.1] if x <= t]))\n",
    "corr_sig = rho.astype(str) + p\n",
    "corr_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>Correlation &amp; Significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X0</td>\n",
       "      <td>0.04**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>X1</td>\n",
       "      <td>0.03*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>X2</td>\n",
       "      <td>-0.2***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>X3</td>\n",
       "      <td>-0.15***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>X4</td>\n",
       "      <td>-0.19***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>X5</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>X6</td>\n",
       "      <td>-0.17***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>X7</td>\n",
       "      <td>-0.18***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>X8</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>X9</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>X10</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>X11</td>\n",
       "      <td>-0.18***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>X12</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>X13</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>X14</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>X15</td>\n",
       "      <td>-0.19***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>X16</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>X17</td>\n",
       "      <td>-0.18***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>X18</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>X19</td>\n",
       "      <td>-0.19***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>X20</td>\n",
       "      <td>-0.16***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>X21</td>\n",
       "      <td>-0.18***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>X22</td>\n",
       "      <td>-0.21***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>X23</td>\n",
       "      <td>0.03*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>X24</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>X25</td>\n",
       "      <td>-0.19***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>X26</td>\n",
       "      <td>-0.21***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>X27</td>\n",
       "      <td>-0.2***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>X28</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>X29</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>y</td>\n",
       "      <td>1.0***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>XC_A</td>\n",
       "      <td>0.15***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>XC_B</td>\n",
       "      <td>0.1***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>XC_C</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>XC_D</td>\n",
       "      <td>-0.1***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>XC_E</td>\n",
       "      <td>-0.14***</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Variable Correlation & Significance\n",
       "0        X0                     0.04**\n",
       "1        X1                      0.03*\n",
       "2        X2                    -0.2***\n",
       "3        X3                   -0.15***\n",
       "4        X4                   -0.19***\n",
       "5        X5                       0.02\n",
       "6        X6                   -0.17***\n",
       "7        X7                   -0.18***\n",
       "8        X8                       0.01\n",
       "9        X9                       0.01\n",
       "10      X10                      -0.01\n",
       "11      X11                   -0.18***\n",
       "12      X12                       0.02\n",
       "13      X13                       0.02\n",
       "14      X14                       -0.0\n",
       "15      X15                   -0.19***\n",
       "16      X16                       0.02\n",
       "17      X17                   -0.18***\n",
       "18      X18                       -0.0\n",
       "19      X19                   -0.19***\n",
       "20      X20                   -0.16***\n",
       "21      X21                   -0.18***\n",
       "22      X22                   -0.21***\n",
       "23      X23                      0.03*\n",
       "24      X24                       0.01\n",
       "25      X25                   -0.19***\n",
       "26      X26                   -0.21***\n",
       "27      X27                    -0.2***\n",
       "28      X28                      -0.02\n",
       "29      X29                       0.01\n",
       "30        y                     1.0***\n",
       "31     XC_A                    0.15***\n",
       "32     XC_B                     0.1***\n",
       "33     XC_C                      -0.01\n",
       "34     XC_D                    -0.1***\n",
       "35     XC_E                   -0.14***"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_sig.iloc[:, 30].reset_index().rename(columns={'index': 'Variable', 'y': 'Correlation & Significance'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "num1 = (data[['X7', 'X11']].corr().iloc[0,1]*data[['y', 'X11']].corr().iloc[0,1]) - (data[['X7', 'y']].corr().iloc[0,1]*np.var(data['X11']))\n",
    "den1 = (data[['X7', 'X11']].corr().iloc[0,1]*data[['y', 'X7']].corr().iloc[0,1]) - (data[['X11', 'y']].corr().iloc[0,1]*np.var(data['X7']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['X7_11'] = (num1/den1*data['X7']) + data['X11']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "num2 = (data[['X17', 'X21']].corr().iloc[0,1]*data[['y', 'X21']].corr().iloc[0,1]) - (data[['X17', 'y']].corr().iloc[0,1]*np.var(data['X21']))\n",
    "den2 = (data[['X17', 'X21']].corr().iloc[0,1]*data[['y', 'X17']].corr().iloc[0,1]) - (data[['X21', 'y']].corr().iloc[0,1]*np.var(data['X17']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['X17_21'] = (num2/den2*data['X17']) + data['X21']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "num3 = (data[['X17_21', 'X7_11']].corr().iloc[0,1]*data[['y', 'X7_11']].corr().iloc[0,1]) - (data[['X17_21', 'y']].corr().iloc[0,1]*np.var(data['X7_11']))\n",
    "den3 = (data[['X17_21', 'X7_11']].corr().iloc[0,1]*data[['y', 'X17_21']].corr().iloc[0,1]) - (data[['X7_11', 'y']].corr().iloc[0,1]*np.var(data['X17_21']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['X7_11_17_21'] = (num3/den3*data['X17_21']) + data['X7_11']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "num4 = (data[['X1', 'X23']].corr().iloc[0,1]*data[['y', 'X23']].corr().iloc[0,1]) - (data[['X1', 'y']].corr().iloc[0,1]*np.var(data['X23']))\n",
    "den4 = (data[['X1', 'X23']].corr().iloc[0,1]*data[['y', 'X1']].corr().iloc[0,1]) - (data[['X23', 'y']].corr().iloc[0,1]*np.var(data['X1']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['X1_23'] = (num4/den4*data['X1']) + data['X23']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "num5 = (data[['X22', 'X26']].corr().iloc[0,1]*data[['y', 'X26']].corr().iloc[0,1]) - (data[['X22', 'y']].corr().iloc[0,1]*np.var(data['X26']))\n",
    "den5 = (data[['X22', 'X26']].corr().iloc[0,1]*data[['y', 'X22']].corr().iloc[0,1]) - (data[['X26', 'y']].corr().iloc[0,1]*np.var(data['X22']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['X22_26'] = (num5/den5*data['X22']) + data['X26']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "num6 = (data[['X15', 'X19']].corr().iloc[0,1]*data[['y', 'X19']].corr().iloc[0,1]) - (data[['X15', 'y']].corr().iloc[0,1]*np.var(data['X19']))\n",
    "den6 = (data[['X15', 'X19']].corr().iloc[0,1]*data[['y', 'X15']].corr().iloc[0,1]) - (data[['X19', 'y']].corr().iloc[0,1]*np.var(data['X15']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['X15_19'] = (num6/den6*data['X15']) + data['X19']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "num7 = (data[['X4', 'X25']].corr().iloc[0,1]*data[['y', 'X25']].corr().iloc[0,1]) - (data[['X4', 'y']].corr().iloc[0,1]*np.var(data['X25']))\n",
    "den7 = (data[['X4', 'X25']].corr().iloc[0,1]*data[['y', 'X4']].corr().iloc[0,1]) - (data[['X25', 'y']].corr().iloc[0,1]*np.var(data['X4']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['X4_25'] = (num7/den7*data['X4']) + data['X25']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "num8 = (data[['X4_25', 'X15_19']].corr().iloc[0,1]*data[['y', 'X15_19']].corr().iloc[0,1]) - (data[['X4_25', 'y']].corr().iloc[0,1]*np.var(data['X15_19']))\n",
    "den8 = (data[['X4_25', 'X15_19']].corr().iloc[0,1]*data[['y', 'X4_25']].corr().iloc[0,1]) - (data[['X15_19', 'y']].corr().iloc[0,1]*np.var(data['X4_25']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['X4_15_19_25'] = (num8/den8*data['X4_25']) + data['X15_19']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "num9 = (data[['XC_B', 'XC_D']].corr().iloc[0,1]*data[['y', 'XC_D']].corr().iloc[0,1]) + (data[['XC_B', 'y']].corr().iloc[0,1]*np.var(data['XC_D']))\n",
    "den9 = (data[['XC_B', 'XC_D']].corr().iloc[0,1]*data[['y', 'XC_B']].corr().iloc[0,1]) + (data[['XC_D', 'y']].corr().iloc[0,1]*np.var(data['XC_B']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data['XC_B_D'] = (-num9/den9*data['XC_B']) + data['XC_D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of statistically insignifant values which were found on analysis to 'y'\n",
    "data = data.drop(\n",
    "    [\n",
    "        \"X1\",\n",
    "        'X4',\n",
    "        'X4_25',\n",
    "        \"X5\",\n",
    "        \"X8\",\n",
    "        \"X7\",\n",
    "        \"X7_11\",\n",
    "        \"X9\",\n",
    "        \"X10\",\n",
    "        \"X11\",\n",
    "        \"X12\",\n",
    "        \"X13\",\n",
    "        \"X14\",\n",
    "        'X15',\n",
    "        'X15_19',\n",
    "        \"X16\",\n",
    "        \"X17\",\n",
    "        'X19',\n",
    "        \"X17_21\",\n",
    "        \"X18\",\n",
    "        \"X21\",\n",
    "        \"X22\",\n",
    "        \"X23\",\n",
    "        \"X24\",\n",
    "        'X25',\n",
    "        \"X26\",\n",
    "        \"X28\",\n",
    "        \"X29\",\n",
    "        \"XC_C\",\n",
    "    ],\n",
    "    axis=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X0</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X6</th>\n",
       "      <th>X20</th>\n",
       "      <th>X27</th>\n",
       "      <th>y</th>\n",
       "      <th>XC_A</th>\n",
       "      <th>XC_B</th>\n",
       "      <th>XC_D</th>\n",
       "      <th>XC_E</th>\n",
       "      <th>X7_11_17_21</th>\n",
       "      <th>X1_23</th>\n",
       "      <th>X22_26</th>\n",
       "      <th>X4_15_19_25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X0</th>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.05***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.2***</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X3</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.15***</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05***</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X6</th>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.17***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X20</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.16***</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X27</th>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.2***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.04**</td>\n",
       "      <td>-0.2***</td>\n",
       "      <td>-0.15***</td>\n",
       "      <td>-0.17***</td>\n",
       "      <td>-0.16***</td>\n",
       "      <td>-0.2***</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.15***</td>\n",
       "      <td>0.1***</td>\n",
       "      <td>-0.1***</td>\n",
       "      <td>-0.14***</td>\n",
       "      <td>-0.36***</td>\n",
       "      <td>0.05**</td>\n",
       "      <td>-0.3***</td>\n",
       "      <td>-0.37***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XC_A</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.15***</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.24***</td>\n",
       "      <td>-0.25***</td>\n",
       "      <td>-0.25***</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XC_B</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04*</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1***</td>\n",
       "      <td>-0.24***</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.23***</td>\n",
       "      <td>-0.23***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XC_D</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.1***</td>\n",
       "      <td>-0.25***</td>\n",
       "      <td>-0.23***</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.24***</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XC_E</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.14***</td>\n",
       "      <td>-0.25***</td>\n",
       "      <td>-0.23***</td>\n",
       "      <td>-0.24***</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X7_11_17_21</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05***</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.36***</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X1_23</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.03*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.05**</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X22_26</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.3***</td>\n",
       "      <td>0.04**</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03*</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0***</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X4_15_19_25</th>\n",
       "      <td>-0.05***</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.37***</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0***</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   X0       X2        X3        X6       X20      X27  \\\n",
       "X0             1.0***     0.01     -0.02      0.02      0.01    0.03*   \n",
       "X2               0.01   1.0***     -0.01      -0.0      0.01     0.01   \n",
       "X3              -0.02    -0.01    1.0***     -0.02      -0.0    -0.02   \n",
       "X6               0.02     -0.0     -0.02    1.0***      -0.0    -0.01   \n",
       "X20              0.01     0.01      -0.0      -0.0    1.0***     0.01   \n",
       "X27             0.03*     0.01     -0.02     -0.01      0.01   1.0***   \n",
       "y              0.04**  -0.2***  -0.15***  -0.17***  -0.16***  -0.2***   \n",
       "XC_A            -0.02      0.0      0.02      0.01       0.0    -0.01   \n",
       "XC_B             0.01   -0.04*     -0.03      0.01      -0.0     0.01   \n",
       "XC_D            -0.01     0.01      0.01     -0.02       0.0    -0.01   \n",
       "XC_E             0.02      0.0       0.0      0.01     -0.01    -0.01   \n",
       "X7_11_17_21      0.01      0.0  -0.05***       0.0     -0.01     0.01   \n",
       "X1_23            -0.0   -0.03*       0.0     -0.02     -0.03     -0.0   \n",
       "X22_26          -0.02    -0.01     -0.01      0.02     -0.03    -0.02   \n",
       "X4_15_19_25  -0.05***    -0.02       0.0      0.01     -0.01     0.01   \n",
       "\n",
       "                    y      XC_A      XC_B      XC_D      XC_E X7_11_17_21  \\\n",
       "X0             0.04**     -0.02      0.01     -0.01      0.02        0.01   \n",
       "X2            -0.2***       0.0    -0.04*      0.01       0.0         0.0   \n",
       "X3           -0.15***      0.02     -0.03      0.01       0.0    -0.05***   \n",
       "X6           -0.17***      0.01      0.01     -0.02      0.01         0.0   \n",
       "X20          -0.16***       0.0      -0.0       0.0     -0.01       -0.01   \n",
       "X27           -0.2***     -0.01      0.01     -0.01     -0.01        0.01   \n",
       "y              1.0***   0.15***    0.1***   -0.1***  -0.14***    -0.36***   \n",
       "XC_A          0.15***    1.0***  -0.24***  -0.25***  -0.25***       0.03*   \n",
       "XC_B           0.1***  -0.24***    1.0***  -0.23***  -0.23***        0.01   \n",
       "XC_D          -0.1***  -0.25***  -0.23***    1.0***  -0.24***         0.0   \n",
       "XC_E         -0.14***  -0.25***  -0.23***  -0.24***    1.0***       -0.03   \n",
       "X7_11_17_21  -0.36***     0.03*      0.01       0.0     -0.03      1.0***   \n",
       "X1_23          0.05**      -0.0     -0.01      0.01      0.01       -0.01   \n",
       "X22_26        -0.3***    0.04**     -0.02     -0.03      0.01       0.03*   \n",
       "X4_15_19_25  -0.37***      0.01      0.02      0.02     -0.02        -0.0   \n",
       "\n",
       "              X1_23   X22_26 X4_15_19_25  \n",
       "X0             -0.0    -0.02    -0.05***  \n",
       "X2           -0.03*    -0.01       -0.02  \n",
       "X3              0.0    -0.01         0.0  \n",
       "X6            -0.02     0.02        0.01  \n",
       "X20           -0.03    -0.03       -0.01  \n",
       "X27            -0.0    -0.02        0.01  \n",
       "y            0.05**  -0.3***    -0.37***  \n",
       "XC_A           -0.0   0.04**        0.01  \n",
       "XC_B          -0.01    -0.02        0.02  \n",
       "XC_D           0.01    -0.03        0.02  \n",
       "XC_E           0.01     0.01       -0.02  \n",
       "X7_11_17_21   -0.01    0.03*        -0.0  \n",
       "X1_23        1.0***     0.01         0.0  \n",
       "X22_26         0.01   1.0***        0.02  \n",
       "X4_15_19_25     0.0     0.02      1.0***  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rho = data.corr().round(2)\n",
    "pval = data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(*rho.shape)\n",
    "p = pval.applymap(lambda x: \"\".join([\"*\" for t in [0.01, 0.05, 0.1] if x <= t]))\n",
    "corr_sig_2 = rho.astype(str) + p\n",
    "corr_sig_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>Correlation &amp; Significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X0</td>\n",
       "      <td>0.04**</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>X2</td>\n",
       "      <td>-0.2***</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>X3</td>\n",
       "      <td>-0.15***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>X6</td>\n",
       "      <td>-0.17***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>X20</td>\n",
       "      <td>-0.16***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>X27</td>\n",
       "      <td>-0.2***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>y</td>\n",
       "      <td>1.0***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XC_A</td>\n",
       "      <td>0.15***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>XC_B</td>\n",
       "      <td>0.1***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>XC_D</td>\n",
       "      <td>-0.1***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XC_E</td>\n",
       "      <td>-0.14***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>X7_11_17_21</td>\n",
       "      <td>-0.36***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>X1_23</td>\n",
       "      <td>0.05**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>X22_26</td>\n",
       "      <td>-0.3***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>X4_15_19_25</td>\n",
       "      <td>-0.37***</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Variable Correlation & Significance\n",
       "0            X0                     0.04**\n",
       "1            X2                    -0.2***\n",
       "2            X3                   -0.15***\n",
       "3            X6                   -0.17***\n",
       "4           X20                   -0.16***\n",
       "5           X27                    -0.2***\n",
       "6             y                     1.0***\n",
       "7          XC_A                    0.15***\n",
       "8          XC_B                     0.1***\n",
       "9          XC_D                    -0.1***\n",
       "10         XC_E                   -0.14***\n",
       "11  X7_11_17_21                   -0.36***\n",
       "12        X1_23                     0.05**\n",
       "13       X22_26                    -0.3***\n",
       "14  X4_15_19_25                   -0.37***"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_sig_2.iloc[:, 6].reset_index().rename(columns={'index': 'Variable', 'y': 'Correlation & Significance'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>1.385378</td>\n",
       "      <td>-0.218642</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.506144</td>\n",
       "      <td>1.241760</td>\n",
       "      <td>1.470171</td>\n",
       "      <td>1.674117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.539816</td>\n",
       "      <td>1.636048</td>\n",
       "      <td>-0.562150</td>\n",
       "      <td>1.529069</td>\n",
       "      <td>0.129662</td>\n",
       "      <td>1.112271</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.049216</td>\n",
       "      <td>1.127033</td>\n",
       "      <td>1.730044</td>\n",
       "      <td>-0.696200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.424483</td>\n",
       "      <td>0.095100</td>\n",
       "      <td>0.131981</td>\n",
       "      <td>-1.233111</td>\n",
       "      <td>0.640268</td>\n",
       "      <td>0.113483</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.526011</td>\n",
       "      <td>-2.521678</td>\n",
       "      <td>-3.657426</td>\n",
       "      <td>1.630975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.034157</td>\n",
       "      <td>0.339467</td>\n",
       "      <td>-0.829151</td>\n",
       "      <td>0.573195</td>\n",
       "      <td>2.565787</td>\n",
       "      <td>-0.026332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.589643</td>\n",
       "      <td>-1.224289</td>\n",
       "      <td>-0.573213</td>\n",
       "      <td>1.210181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.177091</td>\n",
       "      <td>1.004133</td>\n",
       "      <td>-0.724015</td>\n",
       "      <td>-0.524431</td>\n",
       "      <td>0.909173</td>\n",
       "      <td>0.483111</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.279954</td>\n",
       "      <td>-0.055956</td>\n",
       "      <td>1.712653</td>\n",
       "      <td>-0.331024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>0.127214</td>\n",
       "      <td>-0.356251</td>\n",
       "      <td>-1.188522</td>\n",
       "      <td>-0.153524</td>\n",
       "      <td>-1.267298</td>\n",
       "      <td>0.673261</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.793292</td>\n",
       "      <td>-2.790587</td>\n",
       "      <td>-2.451040</td>\n",
       "      <td>-1.895780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.427700</td>\n",
       "      <td>-1.260326</td>\n",
       "      <td>2.206359</td>\n",
       "      <td>0.406928</td>\n",
       "      <td>2.045879</td>\n",
       "      <td>1.172911</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.425270</td>\n",
       "      <td>-0.244293</td>\n",
       "      <td>1.107090</td>\n",
       "      <td>-4.916658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-0.880540</td>\n",
       "      <td>0.493897</td>\n",
       "      <td>-0.118869</td>\n",
       "      <td>-0.601732</td>\n",
       "      <td>-0.615433</td>\n",
       "      <td>-1.641217</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.564062</td>\n",
       "      <td>0.246055</td>\n",
       "      <td>1.129791</td>\n",
       "      <td>-1.777225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.151291</td>\n",
       "      <td>-1.960683</td>\n",
       "      <td>-1.352557</td>\n",
       "      <td>0.932388</td>\n",
       "      <td>0.786170</td>\n",
       "      <td>1.057203</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.309121</td>\n",
       "      <td>-1.357706</td>\n",
       "      <td>1.002033</td>\n",
       "      <td>2.345330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>1.351919</td>\n",
       "      <td>-0.918519</td>\n",
       "      <td>-0.417465</td>\n",
       "      <td>-1.233306</td>\n",
       "      <td>0.218222</td>\n",
       "      <td>0.863792</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.901594</td>\n",
       "      <td>0.818883</td>\n",
       "      <td>-1.173282</td>\n",
       "      <td>1.138972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            X0        X2        X3        X6       X20       X27  y  XC_A  \\\n",
       "0     0.985703  2.332283  1.044273 -1.409265  1.385378 -0.218642  0   0.0   \n",
       "1     0.539816  1.636048 -0.562150  1.529069  0.129662  1.112271  0   0.0   \n",
       "2    -1.424483  0.095100  0.131981 -1.233111  0.640268  0.113483  0   0.0   \n",
       "3    -1.034157  0.339467 -0.829151  0.573195  2.565787 -0.026332  0   0.0   \n",
       "4     0.177091  1.004133 -0.724015 -0.524431  0.909173  0.483111  0   0.0   \n",
       "...        ...       ...       ...       ...       ...       ... ..   ...   \n",
       "2995  0.127214 -0.356251 -1.188522 -0.153524 -1.267298  0.673261  1   1.0   \n",
       "2996  0.427700 -1.260326  2.206359  0.406928  2.045879  1.172911  0   0.0   \n",
       "2997 -0.880540  0.493897 -0.118869 -0.601732 -0.615433 -1.641217  1   0.0   \n",
       "2998  0.151291 -1.960683 -1.352557  0.932388  0.786170  1.057203  0   0.0   \n",
       "2999  1.351919 -0.918519 -0.417465 -1.233306  0.218222  0.863792  1   1.0   \n",
       "\n",
       "      XC_B  XC_D  XC_E  X7_11_17_21     X1_23    X22_26  X4_15_19_25  \n",
       "0      0.0   0.0   0.0     2.506144  1.241760  1.470171     1.674117  \n",
       "1      0.0   0.0   1.0    -1.049216  1.127033  1.730044    -0.696200  \n",
       "2      0.0   0.0   1.0     1.526011 -2.521678 -3.657426     1.630975  \n",
       "3      0.0   0.0   1.0    -0.589643 -1.224289 -0.573213     1.210181  \n",
       "4      0.0   0.0   1.0    -0.279954 -0.055956  1.712653    -0.331024  \n",
       "...    ...   ...   ...          ...       ...       ...          ...  \n",
       "2995   0.0   0.0   0.0     1.793292 -2.790587 -2.451040    -1.895780  \n",
       "2996   0.0   0.0   0.0    -0.425270 -0.244293  1.107090    -4.916658  \n",
       "2997   1.0   0.0   0.0     1.564062  0.246055  1.129791    -1.777225  \n",
       "2998   1.0   0.0   0.0    -2.309121 -1.357706  1.002033     2.345330  \n",
       "2999   0.0   0.0   0.0     0.901594  0.818883 -1.173282     1.138972  \n",
       "\n",
       "[3000 rows x 15 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop([\"y\",], axis=1,), data[\"y\"], test_size=0.30, random_state=71,\n",
    ")\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train, y_train)\n",
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       626\n",
      "           1       0.99      1.00      0.99       274\n",
      "\n",
      "    accuracy                           1.00       900\n",
      "   macro avg       1.00      1.00      1.00       900\n",
      "weighted avg       1.00      1.00      1.00       900\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9966666666666667"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9945355191256832"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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