ML2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "      <th>Q8</th>\n",
       "      <th>Q9</th>\n",
       "      <th>Q10</th>\n",
       "      <th>...</th>\n",
       "      <th>married</th>\n",
       "      <th>familysize</th>\n",
       "      <th>surveyaccurate</th>\n",
       "      <th>rwasTot</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>agreeableness</th>\n",
       "      <th>conscientiousness</th>\n",
       "      <th>emotionalstability</th>\n",
       "      <th>opennesstoexperience</th>\n",
       "      <th>vcheck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.0</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "      <td>9661.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.377635</td>\n",
       "      <td>2.086060</td>\n",
       "      <td>2.705890</td>\n",
       "      <td>7.581622</td>\n",
       "      <td>2.944682</td>\n",
       "      <td>7.465714</td>\n",
       "      <td>2.846556</td>\n",
       "      <td>6.700082</td>\n",
       "      <td>7.541436</td>\n",
       "      <td>3.190351</td>\n",
       "      <td>...</td>\n",
       "      <td>1.381845</td>\n",
       "      <td>2.632706</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102.271185</td>\n",
       "      <td>6.937490</td>\n",
       "      <td>8.630756</td>\n",
       "      <td>9.305078</td>\n",
       "      <td>8.989582</td>\n",
       "      <td>11.105564</td>\n",
       "      <td>0.275644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.171316</td>\n",
       "      <td>2.055279</td>\n",
       "      <td>2.522236</td>\n",
       "      <td>2.358335</td>\n",
       "      <td>2.099750</td>\n",
       "      <td>2.265610</td>\n",
       "      <td>2.515083</td>\n",
       "      <td>2.390200</td>\n",
       "      <td>1.977361</td>\n",
       "      <td>2.621313</td>\n",
       "      <td>...</td>\n",
       "      <td>0.608461</td>\n",
       "      <td>1.379121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.060758</td>\n",
       "      <td>3.431677</td>\n",
       "      <td>2.787375</td>\n",
       "      <td>3.034418</td>\n",
       "      <td>3.325870</td>\n",
       "      <td>2.432505</td>\n",
       "      <td>0.545731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Q1           Q2           Q3           Q4           Q5  \\\n",
       "count  9661.000000  9661.000000  9661.000000  9661.000000  9661.000000   \n",
       "mean      3.377635     2.086060     2.705890     7.581622     2.944682   \n",
       "std       2.171316     2.055279     2.522236     2.358335     2.099750   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%       2.000000     1.000000     1.000000     7.000000     1.000000   \n",
       "50%       3.000000     1.000000     1.000000     9.000000     2.000000   \n",
       "75%       5.000000     2.000000     3.000000     9.000000     4.000000   \n",
       "max       9.000000     9.000000     9.000000     9.000000     9.000000   \n",
       "\n",
       "                Q6           Q7           Q8           Q9          Q10  \\\n",
       "count  9661.000000  9661.000000  9661.000000  9661.000000  9661.000000   \n",
       "mean      7.465714     2.846556     6.700082     7.541436     3.190351   \n",
       "std       2.265610     2.515083     2.390200     1.977361     2.621313   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%       7.000000     1.000000     5.000000     7.000000     1.000000   \n",
       "50%       9.000000     2.000000     7.000000     8.000000     2.000000   \n",
       "75%       9.000000     4.000000     9.000000     9.000000     5.000000   \n",
       "max       9.000000     9.000000     9.000000     9.000000     9.000000   \n",
       "\n",
       "          ...           married   familysize  surveyaccurate      rwasTot  \\\n",
       "count     ...       9661.000000  9661.000000          9661.0  9661.000000   \n",
       "mean      ...          1.381845     2.632706             1.0   102.271185   \n",
       "std       ...          0.608461     1.379121             0.0    11.060758   \n",
       "min       ...          1.000000     1.000000             1.0    21.000000   \n",
       "25%       ...          1.000000     2.000000             1.0    96.000000   \n",
       "50%       ...          1.000000     2.000000             1.0   100.000000   \n",
       "75%       ...          2.000000     3.000000             1.0   108.000000   \n",
       "max       ...          3.000000    13.000000             1.0   179.000000   \n",
       "\n",
       "       extraversion  agreeableness  conscientiousness  emotionalstability  \\\n",
       "count   9661.000000    9661.000000        9661.000000         9661.000000   \n",
       "mean       6.937490       8.630756           9.305078            8.989582   \n",
       "std        3.431677       2.787375           3.034418            3.325870   \n",
       "min        2.000000       2.000000           2.000000            2.000000   \n",
       "25%        4.000000       7.000000           7.000000            6.000000   \n",
       "50%        7.000000       9.000000           9.000000            9.000000   \n",
       "75%       10.000000      11.000000          12.000000           12.000000   \n",
       "max       14.000000      14.000000          14.000000           14.000000   \n",
       "\n",
       "       opennesstoexperience       vcheck  \n",
       "count           9661.000000  9661.000000  \n",
       "mean              11.105564     0.275644  \n",
       "std                2.432505     0.545731  \n",
       "min                2.000000     0.000000  \n",
       "25%               10.000000     0.000000  \n",
       "50%               12.000000     0.000000  \n",
       "75%               13.000000     0.000000  \n",
       "max               14.000000     2.000000  \n",
       "\n",
       "[8 rows x 95 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#import dataset\n",
    "data = pd.read_csv('dataclean.csv')\n",
    "\n",
    "#describe our data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=9661, minmax=(21.0, 179.0), mean=102.2711845217077, variance=122.34036276116257, skewness=0.5957395533565029, kurtosis=2.560132878162708)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check distribution of our dependent variable\n",
    "stats.describe(data['rwasTot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1ca89390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's plot some variables and run some common tests\n",
    "#This should help us determine which predictors to include in our model\n",
    "\n",
    "#voted graph\n",
    "data.groupby('voted')['rwasTot'].mean()\n",
    "data.groupby('voted')['rwasTot'].std()\n",
    "objects = ['Yes', 'No']\n",
    "votemean = [101.58, 102.79]\n",
    "std = [10.67, 11.31]\n",
    "y_pos = [i for i, _ in enumerate(objects)]\n",
    "\n",
    "plt.bar(y_pos, votemean, yerr=std)\n",
    "plt.xlabel(\"Vote Status\")\n",
    "plt.ylabel('RWAS Total')\n",
    "plt.title('Voted in Last Election')\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.savefig('ML2_1.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-5.3593245684042055, pvalue=8.545794442564113e-08)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#t-test for 'voted' variable\n",
    "\n",
    "yesvote = data[data['voted'] == 1]['rwasTot']\n",
    "novote = data[data['voted'] == 2]['rwasTot']\n",
    "stats.ttest_ind(yesvote, novote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.11063592427830353\n"
     ]
    }
   ],
   "source": [
    "from statistics import mean, stdev\n",
    "from math import sqrt\n",
    "\n",
    "#Effect size for voted\n",
    "cohens_d = (mean(yesvote) - mean(novote)) / (sqrt((stdev(yesvote) ** 2 + stdev(novote) ** 2) / 2))\n",
    "\n",
    "print(cohens_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10f4fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Education graph\n",
    "data.groupby('education')['rwasTot'].mean()\n",
    "data.groupby('education')['rwasTot'].std()\n",
    "objects = ('< High School', 'High School', \n",
    "           'University', 'Graduate')\n",
    "educmean = [105.20,103.90,101.50,99.34]\n",
    "std = [11.75,11.46,10.68,9.71]\n",
    "y_pos = [i for i, _ in enumerate(objects)]\n",
    "\n",
    "plt.bar(y_pos, educmean, yerr=std)\n",
    "plt.xlabel(\"Education\")\n",
    "plt.ylabel('RWAS Total')\n",
    "plt.title('Respondent Level of Education') \n",
    "plt.xticks(y_pos, objects)\n",
    "plt.savefig('ML2_2.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10eb2a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's plot the same graph using Seaborn\n",
    "import seaborn as sns\n",
    "objects = ('< High School', 'High School', \n",
    "           'University', 'Graduate')\n",
    "y_pos = [i for i, _ in enumerate(objects)]\n",
    "boxpl = sns.boxplot(x=\"education\", y=\"rwasTot\", data=data)\n",
    "fig = boxpl.get_figure()\n",
    "plt.xlabel(\"Education\")\n",
    "plt.ylabel('RWAS Total')\n",
    "plt.title('Respondent Level of Education') \n",
    "plt.xticks(y_pos, objects)\n",
    "fig.tight_layout()\n",
    "fig.savefig('ML2_2b.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>sum_sq</th>\n",
       "      <th>df</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(education)</th>\n",
       "      <td>3.715373e+04</td>\n",
       "      <td>3.0</td>\n",
       "      <td>104.483834</td>\n",
       "      <td>1.447710e-66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>1.144654e+06</td>\n",
       "      <td>9657.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    sum_sq      df           F        PR(>F)\n",
       "C(education)  3.715373e+04     3.0  104.483834  1.447710e-66\n",
       "Residual      1.144654e+06  9657.0         NaN           NaN"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ANOVA: Education (using statsmodels gives you thorough information)\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "results = ols('rwasTot ~ C(education)', data=data).fit()\n",
    "results.summary()\n",
    "aov_table = sm.stats.anova_lm(results, typ=2)\n",
    "aov_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=30.476885199553795, pvalue=1.3323853229626246e-19)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Levene's test of homogeneity\n",
    "stats.levene(data['rwasTot'][data['education'] == 1],\n",
    "             data['rwasTot'][data['education'] == 2],\n",
    "             data['rwasTot'][data['education'] == 3],\n",
    "             data['rwasTot'][data['education'] == 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=315.53550686504457, pvalue=4.316873582808985e-68)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Kruskal-Wallis Test\n",
    "stats.kruskal(data['rwasTot'][data['education'] == 1],\n",
    "             data['rwasTot'][data['education'] == 2],\n",
    "             data['rwasTot'][data['education'] == 3],\n",
    "             data['rwasTot'][data['education'] == 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03143804488971962"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ANOVA: efffect size\n",
    "esq_sm = aov_table['sum_sq'][0]/(aov_table['sum_sq'][0]+aov_table['sum_sq'][1])\n",
    "esq_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple Comparison of Means - Tukey HSD,FWER=0.05\n",
      "=============================================\n",
      "group1 group2 meandiff  lower   upper  reject\n",
      "---------------------------------------------\n",
      " 1.0    2.0   -1.2934  -2.3094 -0.2773  True \n",
      " 1.0    3.0   -3.6959  -4.7232 -2.6687  True \n",
      " 1.0    4.0   -5.8505  -6.9387 -4.7623  True \n",
      " 2.0    3.0   -2.4026  -3.0912  -1.714  True \n",
      " 2.0    4.0   -4.5571  -5.3337 -3.7806  True \n",
      " 3.0    4.0   -2.1546  -2.9457 -1.3635  True \n",
      "---------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#ANOVA: post hoc\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "from statsmodels.stats.multicomp import MultiComparison\n",
    "\n",
    "mc = MultiComparison(data['rwasTot'], data['education'])\n",
    "mc_results = mc.tukeyhsd()\n",
    "print(mc_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=9661, minmax=(2.0, 14.0), mean=6.937490303056794, variance=11.776405898763315, skewness=0.3371478972618854, kurtosis=-0.9435374159123286)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check distribution of personality variables\n",
    "stats.describe(data['extraversion'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=9661, minmax=(2.0, 14.0), mean=8.630755539259372, variance=7.769458124760289, skewness=-0.07983867714823499, kurtosis=-0.45713034794226104)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.describe(data['agreeableness'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=9661, minmax=(2.0, 14.0), mean=9.305078050961276, variance=9.207692567187115, skewness=-0.3019078474520343, kurtosis=-0.6755958141665022)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.describe(data['conscientiousness'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=9661, minmax=(2.0, 14.0), mean=8.989582491155662, variance=11.06140933144433, skewness=-0.24758544033670835, kurtosis=-0.9356989533408107)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.describe(data['emotionalstability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=9661, minmax=(2.0, 14.0), mean=11.105564195064758, variance=5.917078981332699, skewness=-0.897968336709367, kurtosis=0.4558124486853319)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.describe(data['opennesstoexperience'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1cca54e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot and correlate personality variables with RWAS\n",
    "#correlation matrix heat map\n",
    "datasel = data[['rwasTot', 'extraversion', 'agreeableness', 'conscientiousness',\n",
    "                'emotionalstability', 'opennesstoexperience']]\n",
    "corr = datasel.corr()\n",
    "map = sns.heatmap(datasel.corr(), annot=True, fmt=\".2f\")\n",
    "fig3 = map.get_figure()\n",
    "plt.title('Correlations between Personality and RWAS') \n",
    "fig3.tight_layout()\n",
    "fig3.savefig('ML2_3.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.043839701713139646, 1.628593467711325e-05)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pvalues for previous correlations\n",
    "stats.pearsonr(data['rwasTot'], data['extraversion'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.05559314209380576, 4.5567385032037665e-08)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(data['rwasTot'], data['agreeableness'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.06450059030279585, 2.2159535206706746e-10)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(data['rwasTot'], data['conscientiousness'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.018452803383284963, 0.06973118224493942)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(data['rwasTot'], data['emotionalstability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.06761414851136918, 2.8801011924867488e-11)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(data['rwasTot'], data['opennesstoexperience'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10f49ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplot (with regression line) between conscientiousness and RWAS total\n",
    "plt.xlim(0,16)\n",
    "plt.ylim(0,200)\n",
    "reg = sns.regplot(x=data[\"conscientiousness\"], y=data[\"rwasTot\"])\n",
    "fig4 = reg.get_figure()\n",
    "plt.xlabel(\"Conscientiousness\")\n",
    "plt.ylabel('RWAS Total')\n",
    "plt.title('Correlation between Conscientiousness and RWAS') \n",
    "fig4.tight_layout()\n",
    "fig4.savefig('ML2_4.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>rwasTot</td>     <th>  R-squared:         </th>  <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>6.247e+31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 20 Jan 2019</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:44:22</td>     <th>  Log-Likelihood:    </th> <td>2.8106e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  9661</td>      <th>  AIC:               </th> <td>-5.621e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  9654</td>      <th>  BIC:               </th> <td>-5.621e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                <td>-2.487e-14</td> <td> 6.49e-15</td> <td>   -3.834</td> <td> 0.000</td> <td>-3.76e-14</td> <td>-1.22e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rwasTot</th>              <td>    1.0000</td> <td> 5.21e-17</td> <td> 1.92e+16</td> <td> 0.000</td> <td>    1.000</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extraversion</th>         <td> 1.096e-14</td> <td> 1.73e-16</td> <td>   63.379</td> <td> 0.000</td> <td> 1.06e-14</td> <td> 1.13e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>agreeableness</th>        <td> 3.025e-15</td> <td>  2.1e-16</td> <td>   14.386</td> <td> 0.000</td> <td> 2.61e-15</td> <td> 3.44e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>conscientiousness</th>    <td>-1.492e-15</td> <td> 1.99e-16</td> <td>   -7.498</td> <td> 0.000</td> <td>-1.88e-15</td> <td> -1.1e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>emotionalstability</th>   <td> 6.172e-15</td> <td> 1.82e-16</td> <td>   33.837</td> <td> 0.000</td> <td> 5.81e-15</td> <td> 6.53e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>opennesstoexperience</th> <td> 5.718e-15</td> <td> 2.44e-16</td> <td>   23.391</td> <td> 0.000</td> <td> 5.24e-15</td> <td>  6.2e-15</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13.160</td> <th>  Durbin-Watson:     </th> <td>   1.942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  11.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.034</td> <th>  Prob(JB):          </th> <td> 0.00295</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.844</td> <th>  Cond. No.          </th> <td>1.19e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                rwasTot   R-squared:                       1.000\n",
       "Model:                            OLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 6.247e+31\n",
       "Date:                Sun, 20 Jan 2019   Prob (F-statistic):               0.00\n",
       "Time:                        17:44:22   Log-Likelihood:             2.8106e+05\n",
       "No. Observations:                9661   AIC:                        -5.621e+05\n",
       "Df Residuals:                    9654   BIC:                        -5.621e+05\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                -2.487e-14   6.49e-15     -3.834      0.000   -3.76e-14   -1.22e-14\n",
       "rwasTot                  1.0000   5.21e-17   1.92e+16      0.000       1.000       1.000\n",
       "extraversion          1.096e-14   1.73e-16     63.379      0.000    1.06e-14    1.13e-14\n",
       "agreeableness         3.025e-15    2.1e-16     14.386      0.000    2.61e-15    3.44e-15\n",
       "conscientiousness    -1.492e-15   1.99e-16     -7.498      0.000   -1.88e-15    -1.1e-15\n",
       "emotionalstability    6.172e-15   1.82e-16     33.837      0.000    5.81e-15    6.53e-15\n",
       "opennesstoexperience  5.718e-15   2.44e-16     23.391      0.000    5.24e-15     6.2e-15\n",
       "==============================================================================\n",
       "Omnibus:                       13.160   Durbin-Watson:                   1.942\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               11.652\n",
       "Skew:                          -0.034   Prob(JB):                      0.00295\n",
       "Kurtosis:                       2.844   Cond. No.                     1.19e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.19e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Basic multiple linear regresson\n",
    "X = datasel\n",
    "y = data['rwasTot']\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(y, X).fit()\n",
    "predictions = model.predict(X)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's redo this including some categorical variables that we have to dummy code\n",
    "#create new predictor dataset with some categorical vars\n",
    "X = data[['extraversion', 'agreeableness', 'conscientiousness','emotionalstability', \n",
    "          'opennesstoexperience','education','gender','religion','voted','married']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
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       "      <td>6.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>5.000000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.109833</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</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",
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       "    <tr>\n",
       "      <th>9631</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9632</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9633</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9634</th>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9635</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9636</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9637</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9638</th>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9639</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9640</th>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9641</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9642</th>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9643</th>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9644</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9645</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9646</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9647</th>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9648</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9649</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9650</th>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9651</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9652</th>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9653</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9654</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9655</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9656</th>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9657</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9658</th>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9659</th>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9660</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9661 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      extraversion  agreeableness  conscientiousness  emotionalstability  \\\n",
       "0             12.0           12.0           6.000000                 8.0   \n",
       "1             11.0           12.0          10.000000                 8.0   \n",
       "2              2.0            3.0          11.000000                 3.0   \n",
       "3              6.0            6.0           8.000000                 5.0   \n",
       "4              2.0            2.0           5.000000                 2.0   \n",
       "5              5.0            6.0           6.000000                10.0   \n",
       "6              7.0            9.0           6.000000                 4.0   \n",
       "7              7.0            8.0          10.000000                12.0   \n",
       "8              2.0            8.0           6.000000                10.0   \n",
       "9              2.0            7.0           8.000000                 2.0   \n",
       "10             2.0           10.0          10.000000                 2.0   \n",
       "11            12.0            9.0          10.000000                 7.0   \n",
       "12             9.0            8.0           8.000000                12.0   \n",
       "13             7.0            6.0           5.000000                14.0   \n",
       "14             3.0            8.0           6.109833                 6.0   \n",
       "15            12.0            8.0          10.000000                 6.0   \n",
       "16             9.0           12.0          13.000000                 6.0   \n",
       "17             7.0           14.0           3.000000                 9.0   \n",
       "18            10.0            7.0           9.000000                11.0   \n",
       "19            12.0            8.0           4.000000                 4.0   \n",
       "20             2.0           12.0           4.000000                 8.0   \n",
       "21             5.0            9.0           5.000000                 4.0   \n",
       "22            14.0            9.0          10.000000                12.0   \n",
       "23             8.0            8.0          13.000000                 6.0   \n",
       "24             4.0           11.0          12.000000                 8.0   \n",
       "25             4.0           12.0          11.000000                 6.0   \n",
       "26            10.0            8.0           6.000000                10.0   \n",
       "27             2.0            5.0           9.000000                 7.0   \n",
       "28             3.0            9.0          12.000000                13.0   \n",
       "29             8.0           11.0          12.000000                12.0   \n",
       "...            ...            ...                ...                 ...   \n",
       "9631           2.0            8.0           9.000000                14.0   \n",
       "9632           4.0            6.0           3.000000                 2.0   \n",
       "9633           8.0            7.0           8.000000                 7.0   \n",
       "9634          13.0            7.0          12.000000                11.0   \n",
       "9635           5.0            5.0           9.000000                13.0   \n",
       "9636           4.0            6.0          14.000000                 7.0   \n",
       "9637          11.0           11.0           7.000000                 9.0   \n",
       "9638          11.0            4.0           7.000000                 9.0   \n",
       "9639          10.0            9.0           8.000000                 8.0   \n",
       "9640           7.0           14.0          11.000000                11.0   \n",
       "9641           5.0            9.0          10.000000                14.0   \n",
       "9642           4.0           10.0           6.000000                13.0   \n",
       "9643           6.0           11.0           9.000000                12.0   \n",
       "9644           2.0           10.0           4.000000                 3.0   \n",
       "9645           4.0            7.0          11.000000                10.0   \n",
       "9646           2.0            4.0           9.000000                 3.0   \n",
       "9647           6.0           13.0          12.000000                 5.0   \n",
       "9648           5.0            3.0          12.000000                13.0   \n",
       "9649           4.0            7.0           5.000000                 4.0   \n",
       "9650           5.0           14.0           8.000000                 6.0   \n",
       "9651          10.0            8.0          12.000000                13.0   \n",
       "9652          12.0            9.0          12.000000                 7.0   \n",
       "9653           3.0            5.0          11.000000                 9.0   \n",
       "9654           7.0            7.0           6.000000                 4.0   \n",
       "9655           8.0           10.0          12.000000                 6.0   \n",
       "9656          12.0           10.0          12.000000                12.0   \n",
       "9657           7.0            9.0          11.000000                14.0   \n",
       "9658          11.0           10.0           8.000000                12.0   \n",
       "9659          11.0            7.0          13.000000                14.0   \n",
       "9660           8.0            7.0          11.000000                12.0   \n",
       "\n",
       "      opennesstoexperience  education  gender  religion  voted  married  \n",
       "0                     12.0        3.0     1.0       4.0    1.0      1.0  \n",
       "1                     14.0        4.0     1.0       6.0    1.0      2.0  \n",
       "2                     13.0        1.0     1.0       2.0    2.0      1.0  \n",
       "3                     13.0        3.0     2.0      10.0    2.0      3.0  \n",
       "4                      4.0        2.0     2.0       2.0    1.0      1.0  \n",
       "5                     12.0        2.0     1.0      12.0    2.0      2.0  \n",
       "6                      9.0        2.0     2.0       2.0    2.0      2.0  \n",
       "7                     11.0        2.0     1.0       4.0    1.0      1.0  \n",
       "8                     13.0        2.0     1.0       2.0    2.0      1.0  \n",
       "9                      7.0        2.0     2.0       2.0    2.0      1.0  \n",
       "10                     4.0        2.0     2.0       2.0    2.0      1.0  \n",
       "11                    12.0        3.0     2.0       2.0    2.0      2.0  \n",
       "12                     9.0        2.0     1.0       2.0    1.0      3.0  \n",
       "13                    12.0        3.0     1.0       2.0    1.0      1.0  \n",
       "14                    11.0        2.0     2.0       6.0    2.0      1.0  \n",
       "15                    11.0        3.0     2.0       1.0    2.0      1.0  \n",
       "16                    14.0        2.0     2.0       1.0    2.0      1.0  \n",
       "17                    13.0        1.0     2.0       1.0    2.0      1.0  \n",
       "18                     7.0        3.0     1.0       4.0    1.0      1.0  \n",
       "19                    13.0        4.0     2.0       2.0    1.0      1.0  \n",
       "20                    11.0        3.0     1.0       2.0    2.0      2.0  \n",
       "21                    10.0        3.0     2.0       2.0    2.0      3.0  \n",
       "22                     8.0        3.0     1.0       1.0    2.0      1.0  \n",
       "23                    11.0        4.0     2.0       1.0    2.0      1.0  \n",
       "24                    14.0        4.0     2.0       2.0    2.0      1.0  \n",
       "25                    10.0        2.0     2.0       2.0    2.0      1.0  \n",
       "26                     7.0        2.0     1.0       1.0    1.0      1.0  \n",
       "27                     6.0        4.0     2.0      12.0    1.0      1.0  \n",
       "28                    14.0        4.0     1.0       2.0    1.0      2.0  \n",
       "29                    12.0        2.0     2.0       4.0    2.0      3.0  \n",
       "...                    ...        ...     ...       ...    ...      ...  \n",
       "9631                  10.0        3.0     1.0       1.0    1.0      1.0  \n",
       "9632                  13.0        4.0     1.0       2.0    2.0      1.0  \n",
       "9633                   6.0        3.0     1.0       4.0    2.0      1.0  \n",
       "9634                  11.0        3.0     2.0       6.0    2.0      1.0  \n",
       "9635                  13.0        1.0     1.0       7.0    2.0      1.0  \n",
       "9636                   9.0        1.0     1.0       7.0    2.0      1.0  \n",
       "9637                  14.0        2.0     2.0       1.0    2.0      1.0  \n",
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       "9642                   7.0        3.0     2.0       6.0    1.0      1.0  \n",
       "9643                  12.0        2.0     1.0       7.0    2.0      2.0  \n",
       "9644                  13.0        2.0     1.0      12.0    1.0      1.0  \n",
       "9645                  10.0        2.0     2.0       7.0    2.0      1.0  \n",
       "9646                  13.0        2.0     1.0       1.0    2.0      1.0  \n",
       "9647                  14.0        4.0     1.0       1.0    1.0      2.0  \n",
       "9648                  14.0        2.0     1.0       1.0    1.0      1.0  \n",
       "9649                   8.0        2.0     1.0       2.0    2.0      2.0  \n",
       "9650                  14.0        2.0     1.0       2.0    2.0      1.0  \n",
       "9651                  12.0        2.0     1.0       2.0    1.0      1.0  \n",
       "9652                  13.0        2.0     1.0       1.0    2.0      1.0  \n",
       "9653                  14.0        3.0     1.0       2.0    2.0      1.0  \n",
       "9654                  10.0        2.0     1.0       2.0    2.0      1.0  \n",
       "9655                   9.0        3.0     1.0       2.0    2.0      1.0  \n",
       "9656                  13.0        3.0     2.0       6.0    1.0      3.0  \n",
       "9657                  12.0        2.0     1.0       4.0    2.0      1.0  \n",
       "9658                  12.0        3.0     1.0       1.0    2.0      1.0  \n",
       "9659                  14.0        2.0     1.0       1.0    2.0      1.0  \n",
       "9660                  11.0        4.0     1.0       2.0    2.0      1.0  \n",
       "\n",
       "[9661 rows x 10 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X, columns=['education','gender','religion','voted','married'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
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       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9631</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9632</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9633</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9634</th>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9635</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9636</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9637</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9638</th>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9639</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9640</th>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9641</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9642</th>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9643</th>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9644</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9645</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9646</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9647</th>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9648</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9649</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9650</th>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9651</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9652</th>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9653</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9654</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9655</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9656</th>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9657</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9658</th>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9659</th>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9660</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9661 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      extraversion  agreeableness  conscientiousness  emotionalstability  \\\n",
       "0             12.0           12.0           6.000000                 8.0   \n",
       "1             11.0           12.0          10.000000                 8.0   \n",
       "2              2.0            3.0          11.000000                 3.0   \n",
       "3              6.0            6.0           8.000000                 5.0   \n",
       "4              2.0            2.0           5.000000                 2.0   \n",
       "5              5.0            6.0           6.000000                10.0   \n",
       "6              7.0            9.0           6.000000                 4.0   \n",
       "7              7.0            8.0          10.000000                12.0   \n",
       "8              2.0            8.0           6.000000                10.0   \n",
       "9              2.0            7.0           8.000000                 2.0   \n",
       "10             2.0           10.0          10.000000                 2.0   \n",
       "11            12.0            9.0          10.000000                 7.0   \n",
       "12             9.0            8.0           8.000000                12.0   \n",
       "13             7.0            6.0           5.000000                14.0   \n",
       "14             3.0            8.0           6.109833                 6.0   \n",
       "15            12.0            8.0          10.000000                 6.0   \n",
       "16             9.0           12.0          13.000000                 6.0   \n",
       "17             7.0           14.0           3.000000                 9.0   \n",
       "18            10.0            7.0           9.000000                11.0   \n",
       "19            12.0            8.0           4.000000                 4.0   \n",
       "20             2.0           12.0           4.000000                 8.0   \n",
       "21             5.0            9.0           5.000000                 4.0   \n",
       "22            14.0            9.0          10.000000                12.0   \n",
       "23             8.0            8.0          13.000000                 6.0   \n",
       "24             4.0           11.0          12.000000                 8.0   \n",
       "25             4.0           12.0          11.000000                 6.0   \n",
       "26            10.0            8.0           6.000000                10.0   \n",
       "27             2.0            5.0           9.000000                 7.0   \n",
       "28             3.0            9.0          12.000000                13.0   \n",
       "29             8.0           11.0          12.000000                12.0   \n",
       "...            ...            ...                ...                 ...   \n",
       "9631           2.0            8.0           9.000000                14.0   \n",
       "9632           4.0            6.0           3.000000                 2.0   \n",
       "9633           8.0            7.0           8.000000                 7.0   \n",
       "9634          13.0            7.0          12.000000                11.0   \n",
       "9635           5.0            5.0           9.000000                13.0   \n",
       "9636           4.0            6.0          14.000000                 7.0   \n",
       "9637          11.0           11.0           7.000000                 9.0   \n",
       "9638          11.0            4.0           7.000000                 9.0   \n",
       "9639          10.0            9.0           8.000000                 8.0   \n",
       "9640           7.0           14.0          11.000000                11.0   \n",
       "9641           5.0            9.0          10.000000                14.0   \n",
       "9642           4.0           10.0           6.000000                13.0   \n",
       "9643           6.0           11.0           9.000000                12.0   \n",
       "9644           2.0           10.0           4.000000                 3.0   \n",
       "9645           4.0            7.0          11.000000                10.0   \n",
       "9646           2.0            4.0           9.000000                 3.0   \n",
       "9647           6.0           13.0          12.000000                 5.0   \n",
       "9648           5.0            3.0          12.000000                13.0   \n",
       "9649           4.0            7.0           5.000000                 4.0   \n",
       "9650           5.0           14.0           8.000000                 6.0   \n",
       "9651          10.0            8.0          12.000000                13.0   \n",
       "9652          12.0            9.0          12.000000                 7.0   \n",
       "9653           3.0            5.0          11.000000                 9.0   \n",
       "9654           7.0            7.0           6.000000                 4.0   \n",
       "9655           8.0           10.0          12.000000                 6.0   \n",
       "9656          12.0           10.0          12.000000                12.0   \n",
       "9657           7.0            9.0          11.000000                14.0   \n",
       "9658          11.0           10.0           8.000000                12.0   \n",
       "9659          11.0            7.0          13.000000                14.0   \n",
       "9660           8.0            7.0          11.000000                12.0   \n",
       "\n",
       "      opennesstoexperience  education_2.0  education_3.0  education_4.0  \\\n",
       "0                     12.0              0              1              0   \n",
       "1                     14.0              0              0              1   \n",
       "2                     13.0              0              0              0   \n",
       "3                     13.0              0              1              0   \n",
       "4                      4.0              1              0              0   \n",
       "5                     12.0              1              0              0   \n",
       "6                      9.0              1              0              0   \n",
       "7                     11.0              1              0              0   \n",
       "8                     13.0              1              0              0   \n",
       "9                      7.0              1              0              0   \n",
       "10                     4.0              1              0              0   \n",
       "11                    12.0              0              1              0   \n",
       "12                     9.0              1              0              0   \n",
       "13                    12.0              0              1              0   \n",
       "14                    11.0              1              0              0   \n",
       "15                    11.0              0              1              0   \n",
       "16                    14.0              1              0              0   \n",
       "17                    13.0              0              0              0   \n",
       "18                     7.0              0              1              0   \n",
       "19                    13.0              0              0              1   \n",
       "20                    11.0              0              1              0   \n",
       "21                    10.0              0              1              0   \n",
       "22                     8.0              0              1              0   \n",
       "23                    11.0              0              0              1   \n",
       "24                    14.0              0              0              1   \n",
       "25                    10.0              1              0              0   \n",
       "26                     7.0              1              0              0   \n",
       "27                     6.0              0              0              1   \n",
       "28                    14.0              0              0              1   \n",
       "29                    12.0              1              0              0   \n",
       "...                    ...            ...            ...            ...   \n",
       "9631                  10.0              0              1              0   \n",
       "9632                  13.0              0              0              1   \n",
       "9633                   6.0              0              1              0   \n",
       "9634                  11.0              0              1              0   \n",
       "9635                  13.0              0              0              0   \n",
       "9636                   9.0              0              0              0   \n",
       "9637                  14.0              1              0              0   \n",
       "9638                  12.0              0              0              1   \n",
       "9639                  12.0              0              1              0   \n",
       "9640                  13.0              1              0              0   \n",
       "9641                  14.0              0              1              0   \n",
       "9642                   7.0              0              1              0   \n",
       "9643                  12.0              1              0              0   \n",
       "9644                  13.0              1              0              0   \n",
       "9645                  10.0              1              0              0   \n",
       "9646                  13.0              1              0              0   \n",
       "9647                  14.0              0              0              1   \n",
       "9648                  14.0              1              0              0   \n",
       "9649                   8.0              1              0              0   \n",
       "9650                  14.0              1              0              0   \n",
       "9651                  12.0              1              0              0   \n",
       "9652                  13.0              1              0              0   \n",
       "9653                  14.0              0              1              0   \n",
       "9654                  10.0              1              0              0   \n",
       "9655                   9.0              0              1              0   \n",
       "9656                  13.0              0              1              0   \n",
       "9657                  12.0              1              0              0   \n",
       "9658                  12.0              0              1              0   \n",
       "9659                  14.0              1              0              0   \n",
       "9660                  11.0              0              0              1   \n",
       "\n",
       "      gender_2.0  gender_3.0     ...       religion_6.0  religion_7.0  \\\n",
       "0              0           0     ...                  0             0   \n",
       "1              0           0     ...                  1             0   \n",
       "2              0           0     ...                  0             0   \n",
       "3              1           0     ...                  0             0   \n",
       "4              1           0     ...                  0             0   \n",
       "5              0           0     ...                  0             0   \n",
       "6              1           0     ...                  0             0   \n",
       "7              0           0     ...                  0             0   \n",
       "8              0           0     ...                  0             0   \n",
       "9              1           0     ...                  0             0   \n",
       "10             1           0     ...                  0             0   \n",
       "11             1           0     ...                  0             0   \n",
       "12             0           0     ...                  0             0   \n",
       "13             0           0     ...                  0             0   \n",
       "14             1           0     ...                  1             0   \n",
       "15             1           0     ...                  0             0   \n",
       "16             1           0     ...                  0             0   \n",
       "17             1           0     ...                  0             0   \n",
       "18             0           0     ...                  0             0   \n",
       "19             1           0     ...                  0             0   \n",
       "20             0           0     ...                  0             0   \n",
       "21             1           0     ...                  0             0   \n",
       "22             0           0     ...                  0             0   \n",
       "23             1           0     ...                  0             0   \n",
       "24             1           0     ...                  0             0   \n",
       "25             1           0     ...                  0             0   \n",
       "26             0           0     ...                  0             0   \n",
       "27             1           0     ...                  0             0   \n",
       "28             0           0     ...                  0             0   \n",
       "29             1           0     ...                  0             0   \n",
       "...          ...         ...     ...                ...           ...   \n",
       "9631           0           0     ...                  0             0   \n",
       "9632           0           0     ...                  0             0   \n",
       "9633           0           0     ...                  0             0   \n",
       "9634           1           0     ...                  1             0   \n",
       "9635           0           0     ...                  0             1   \n",
       "9636           0           0     ...                  0             1   \n",
       "9637           1           0     ...                  0             0   \n",
       "9638           0           0     ...                  1             0   \n",
       "9639           1           0     ...                  0             0   \n",
       "9640           0           0     ...                  0             0   \n",
       "9641           0           0     ...                  0             0   \n",
       "9642           1           0     ...                  1             0   \n",
       "9643           0           0     ...                  0             1   \n",
       "9644           0           0     ...                  0             0   \n",
       "9645           1           0     ...                  0             1   \n",
       "9646           0           0     ...                  0             0   \n",
       "9647           0           0     ...                  0             0   \n",
       "9648           0           0     ...                  0             0   \n",
       "9649           0           0     ...                  0             0   \n",
       "9650           0           0     ...                  0             0   \n",
       "9651           0           0     ...                  0             0   \n",
       "9652           0           0     ...                  0             0   \n",
       "9653           0           0     ...                  0             0   \n",
       "9654           0           0     ...                  0             0   \n",
       "9655           0           0     ...                  0             0   \n",
       "9656           1           0     ...                  1             0   \n",
       "9657           0           0     ...                  0             0   \n",
       "9658           0           0     ...                  0             0   \n",
       "9659           0           0     ...                  0             0   \n",
       "9660           0           0     ...                  0             0   \n",
       "\n",
       "      religion_8.0  religion_9.0  religion_10.0  religion_11.0  religion_12.0  \\\n",
       "0                0             0              0              0              0   \n",
       "1                0             0              0              0              0   \n",
       "2                0             0              0              0              0   \n",
       "3                0             0              1              0              0   \n",
       "4                0             0              0              0              0   \n",
       "5                0             0              0              0              1   \n",
       "6                0             0              0              0              0   \n",
       "7                0             0              0              0              0   \n",
       "8                0             0              0              0              0   \n",
       "9                0             0              0              0              0   \n",
       "10               0             0              0              0              0   \n",
       "11               0             0              0              0              0   \n",
       "12               0             0              0              0              0   \n",
       "13               0             0              0              0              0   \n",
       "14               0             0              0              0              0   \n",
       "15               0             0              0              0              0   \n",
       "16               0             0              0              0              0   \n",
       "17               0             0              0              0              0   \n",
       "18               0             0              0              0              0   \n",
       "19               0             0              0              0              0   \n",
       "20               0             0              0              0              0   \n",
       "21               0             0              0              0              0   \n",
       "22               0             0              0              0              0   \n",
       "23               0             0              0              0              0   \n",
       "24               0             0              0              0              0   \n",
       "25               0             0              0              0              0   \n",
       "26               0             0              0              0              0   \n",
       "27               0             0              0              0              1   \n",
       "28               0             0              0              0              0   \n",
       "29               0             0              0              0              0   \n",
       "...            ...           ...            ...            ...            ...   \n",
       "9631             0             0              0              0              0   \n",
       "9632             0             0              0              0              0   \n",
       "9633             0             0              0              0              0   \n",
       "9634             0             0              0              0              0   \n",
       "9635             0             0              0              0              0   \n",
       "9636             0             0              0              0              0   \n",
       "9637             0             0              0              0              0   \n",
       "9638             0             0              0              0              0   \n",
       "9639             0             0              0              0              0   \n",
       "9640             0             0              0              0              0   \n",
       "9641             0             0              0              0              0   \n",
       "9642             0             0              0              0              0   \n",
       "9643             0             0              0              0              0   \n",
       "9644             0             0              0              0              1   \n",
       "9645             0             0              0              0              0   \n",
       "9646             0             0              0              0              0   \n",
       "9647             0             0              0              0              0   \n",
       "9648             0             0              0              0              0   \n",
       "9649             0             0              0              0              0   \n",
       "9650             0             0              0              0              0   \n",
       "9651             0             0              0              0              0   \n",
       "9652             0             0              0              0              0   \n",
       "9653             0             0              0              0              0   \n",
       "9654             0             0              0              0              0   \n",
       "9655             0             0              0              0              0   \n",
       "9656             0             0              0              0              0   \n",
       "9657             0             0              0              0              0   \n",
       "9658             0             0              0              0              0   \n",
       "9659             0             0              0              0              0   \n",
       "9660             0             0              0              0              0   \n",
       "\n",
       "      voted_2.0  married_2.0  married_3.0  \n",
       "0             0            0            0  \n",
       "1             0            1            0  \n",
       "2             1            0            0  \n",
       "3             1            0            1  \n",
       "4             0            0            0  \n",
       "5             1            1            0  \n",
       "6             1            1            0  \n",
       "7             0            0            0  \n",
       "8             1            0            0  \n",
       "9             1            0            0  \n",
       "10            1            0            0  \n",
       "11            1            1            0  \n",
       "12            0            0            1  \n",
       "13            0            0            0  \n",
       "14            1            0            0  \n",
       "15            1            0            0  \n",
       "16            1            0            0  \n",
       "17            1            0            0  \n",
       "18            0            0            0  \n",
       "19            0            0            0  \n",
       "20            1            1            0  \n",
       "21            1            0            1  \n",
       "22            1            0            0  \n",
       "23            1            0            0  \n",
       "24            1            0            0  \n",
       "25            1            0            0  \n",
       "26            0            0            0  \n",
       "27            0            0            0  \n",
       "28            0            1            0  \n",
       "29            1            0            1  \n",
       "...         ...          ...          ...  \n",
       "9631          0            0            0  \n",
       "9632          1            0            0  \n",
       "9633          1            0            0  \n",
       "9634          1            0            0  \n",
       "9635          1            0            0  \n",
       "9636          1            0            0  \n",
       "9637          1            0            0  \n",
       "9638          1            1            0  \n",
       "9639          0            0            0  \n",
       "9640          1            0            0  \n",
       "9641          1            0            0  \n",
       "9642          0            0            0  \n",
       "9643          1            1            0  \n",
       "9644          0            0            0  \n",
       "9645          1            0            0  \n",
       "9646          1            0            0  \n",
       "9647          0            1            0  \n",
       "9648          0            0            0  \n",
       "9649          1            1            0  \n",
       "9650          1            0            0  \n",
       "9651          0            0            0  \n",
       "9652          1            0            0  \n",
       "9653          1            0            0  \n",
       "9654          1            0            0  \n",
       "9655          1            0            0  \n",
       "9656          0            0            1  \n",
       "9657          1            0            0  \n",
       "9658          1            0            0  \n",
       "9659          1            0            0  \n",
       "9660          1            0            0  \n",
       "\n",
       "[9661 rows x 24 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>rwasTot</td>     <th>  R-squared:         </th> <td>   0.066</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.063</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   28.25</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 20 Jan 2019</td> <th>  Prob (F-statistic):</th> <td>4.62e-123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:44:38</td>     <th>  Log-Likelihood:    </th> <td> -36599.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  9661</td>      <th>  AIC:               </th> <td>7.325e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  9636</td>      <th>  BIC:               </th> <td>7.343e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    24</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                <td>  104.1442</td> <td>    0.806</td> <td>  129.216</td> <td> 0.000</td> <td>  102.564</td> <td>  105.724</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extraversion</th>         <td>    0.2159</td> <td>    0.033</td> <td>    6.466</td> <td> 0.000</td> <td>    0.150</td> <td>    0.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>agreeableness</th>        <td>   -0.1271</td> <td>    0.042</td> <td>   -3.039</td> <td> 0.002</td> <td>   -0.209</td> <td>   -0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>conscientiousness</th>    <td>    0.3583</td> <td>    0.039</td> <td>    9.278</td> <td> 0.000</td> <td>    0.283</td> <td>    0.434</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>emotionalstability</th>   <td>   -0.0318</td> <td>    0.036</td> <td>   -0.881</td> <td> 0.378</td> <td>   -0.102</td> <td>    0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>opennesstoexperience</th> <td>   -0.2454</td> <td>    0.047</td> <td>   -5.181</td> <td> 0.000</td> <td>   -0.338</td> <td>   -0.153</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>education_2.0</th>        <td>   -1.0844</td> <td>    0.397</td> <td>   -2.732</td> <td> 0.006</td> <td>   -1.862</td> <td>   -0.306</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>education_3.0</th>        <td>   -3.1687</td> <td>    0.418</td> <td>   -7.583</td> <td> 0.000</td> <td>   -3.988</td> <td>   -2.350</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>education_4.0</th>        <td>   -5.0804</td> <td>    0.455</td> <td>  -11.153</td> <td> 0.000</td> <td>   -5.973</td> <td>   -4.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender_2.0</th>           <td>    0.4454</td> <td>    0.243</td> <td>    1.832</td> <td> 0.067</td> <td>   -0.031</td> <td>    0.922</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender_3.0</th>           <td>   -1.5781</td> <td>    0.810</td> <td>   -1.949</td> <td> 0.051</td> <td>   -3.165</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_2.0</th>         <td>    0.0107</td> <td>    0.291</td> <td>    0.037</td> <td> 0.971</td> <td>   -0.559</td> <td>    0.581</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_3.0</th>         <td>    2.7802</td> <td>    0.904</td> <td>    3.077</td> <td> 0.002</td> <td>    1.009</td> <td>    4.552</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_4.0</th>         <td>    2.1425</td> <td>    0.424</td> <td>    5.049</td> <td> 0.000</td> <td>    1.311</td> <td>    2.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_5.0</th>         <td>   -3.9481</td> <td>    1.213</td> <td>   -3.254</td> <td> 0.001</td> <td>   -6.327</td> <td>   -1.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_6.0</th>         <td>   -0.9375</td> <td>    0.413</td> <td>   -2.270</td> <td> 0.023</td> <td>   -1.747</td> <td>   -0.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_7.0</th>         <td>    1.5830</td> <td>    0.483</td> <td>    3.277</td> <td> 0.001</td> <td>    0.636</td> <td>    2.530</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_8.0</th>         <td>    6.9093</td> <td>    1.426</td> <td>    4.846</td> <td> 0.000</td> <td>    4.114</td> <td>    9.704</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_9.0</th>         <td>   -1.0117</td> <td>    0.962</td> <td>   -1.052</td> <td> 0.293</td> <td>   -2.897</td> <td>    0.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_10.0</th>        <td>    2.3437</td> <td>    0.923</td> <td>    2.539</td> <td> 0.011</td> <td>    0.535</td> <td>    4.153</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_11.0</th>        <td>    2.3568</td> <td>    4.378</td> <td>    0.538</td> <td> 0.590</td> <td>   -6.225</td> <td>   10.939</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>religion_12.0</th>        <td>    1.4402</td> <td>    0.443</td> <td>    3.250</td> <td> 0.001</td> <td>    0.572</td> <td>    2.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>voted_2.0</th>            <td>   -0.0219</td> <td>    0.234</td> <td>   -0.093</td> <td> 0.926</td> <td>   -0.480</td> <td>    0.437</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>married_2.0</th>          <td>   -2.0108</td> <td>    0.283</td> <td>   -7.102</td> <td> 0.000</td> <td>   -2.566</td> <td>   -1.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>married_3.0</th>          <td>   -1.5883</td> <td>    0.456</td> <td>   -3.483</td> <td> 0.000</td> <td>   -2.482</td> <td>   -0.694</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>898.496</td> <th>  Durbin-Watson:     </th> <td>   1.967</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2965.352</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.464</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.551</td>  <th>  Cond. No.          </th> <td>    832.</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                rwasTot   R-squared:                       0.066\n",
       "Model:                            OLS   Adj. R-squared:                  0.063\n",
       "Method:                 Least Squares   F-statistic:                     28.25\n",
       "Date:                Sun, 20 Jan 2019   Prob (F-statistic):          4.62e-123\n",
       "Time:                        17:44:38   Log-Likelihood:                -36599.\n",
       "No. Observations:                9661   AIC:                         7.325e+04\n",
       "Df Residuals:                    9636   BIC:                         7.343e+04\n",
       "Df Model:                          24                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                  104.1442      0.806    129.216      0.000     102.564     105.724\n",
       "extraversion             0.2159      0.033      6.466      0.000       0.150       0.281\n",
       "agreeableness           -0.1271      0.042     -3.039      0.002      -0.209      -0.045\n",
       "conscientiousness        0.3583      0.039      9.278      0.000       0.283       0.434\n",
       "emotionalstability      -0.0318      0.036     -0.881      0.378      -0.102       0.039\n",
       "opennesstoexperience    -0.2454      0.047     -5.181      0.000      -0.338      -0.153\n",
       "education_2.0           -1.0844      0.397     -2.732      0.006      -1.862      -0.306\n",
       "education_3.0           -3.1687      0.418     -7.583      0.000      -3.988      -2.350\n",
       "education_4.0           -5.0804      0.455    -11.153      0.000      -5.973      -4.188\n",
       "gender_2.0               0.4454      0.243      1.832      0.067      -0.031       0.922\n",
       "gender_3.0              -1.5781      0.810     -1.949      0.051      -3.165       0.009\n",
       "religion_2.0             0.0107      0.291      0.037      0.971      -0.559       0.581\n",
       "religion_3.0             2.7802      0.904      3.077      0.002       1.009       4.552\n",
       "religion_4.0             2.1425      0.424      5.049      0.000       1.311       2.974\n",
       "religion_5.0            -3.9481      1.213     -3.254      0.001      -6.327      -1.570\n",
       "religion_6.0            -0.9375      0.413     -2.270      0.023      -1.747      -0.128\n",
       "religion_7.0             1.5830      0.483      3.277      0.001       0.636       2.530\n",
       "religion_8.0             6.9093      1.426      4.846      0.000       4.114       9.704\n",
       "religion_9.0            -1.0117      0.962     -1.052      0.293      -2.897       0.874\n",
       "religion_10.0            2.3437      0.923      2.539      0.011       0.535       4.153\n",
       "religion_11.0            2.3568      4.378      0.538      0.590      -6.225      10.939\n",
       "religion_12.0            1.4402      0.443      3.250      0.001       0.572       2.309\n",
       "voted_2.0               -0.0219      0.234     -0.093      0.926      -0.480       0.437\n",
       "married_2.0             -2.0108      0.283     -7.102      0.000      -2.566      -1.456\n",
       "married_3.0             -1.5883      0.456     -3.483      0.000      -2.482      -0.694\n",
       "==============================================================================\n",
       "Omnibus:                      898.496   Durbin-Watson:                   1.967\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2965.352\n",
       "Skew:                           0.464   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.551   Cond. No.                         832.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Basic multiple linear regresson with categorical variables\n",
    "y = data['rwasTot']\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(y, X).fit()\n",
    "predictions = model.predict(X)\n",
    "model.summary()"
   ]
  }
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