Supervised Learning - Logistic Regression - LiveCode.ipynb

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  {
   "cell_type": "code",
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   "source": [
    "# Data Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "# Math\n",
    "import math\n",
    "\n",
    "# Plot imports\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "# Machine Learning Imports\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# For evaluating our ML results\n",
    "from sklearn import metrics\n",
    "\n",
    "# Dataset Import\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = sm.datasets.fair.load_pandas().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
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       "  </thead>\n",
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       "      <td>32</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4.666666</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   rate_marriage  age  yrs_married  children  religious  educ  occupation  \\\n",
       "0              3   32          9.0         3          3    17           2   \n",
       "1              3   27         13.0         3          1    14           3   \n",
       "2              4   22          2.5         0          1    16           3   \n",
       "3              4   37         16.5         4          3    16           5   \n",
       "4              5   27          9.0         1          1    14           3   \n",
       "\n",
       "   occupation_husb   affairs  \n",
       "0                5  0.111111  \n",
       "1                4  3.230769  \n",
       "2                5  1.400000  \n",
       "3                5  0.727273  \n",
       "4                4  4.666666  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def affair_check(x):\n",
    "    if x != 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Had_Affair'] = df['affairs'].apply(affair_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.852174</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.826086</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
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       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4.799999</td>\n",
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       "      <th>9</th>\n",
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       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3.266665</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2.041666</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
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       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3.266665</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.361111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.826086</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.826086</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2.041666</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7.839996</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.545454</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.532609</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.799999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.187878</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>11.199999</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6336</th>\n",
       "      <td>5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6337</th>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6338</th>\n",
       "      <td>5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6339</th>\n",
       "      <td>4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6340</th>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6341</th>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6342</th>\n",
       "      <td>4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6343</th>\n",
       "      <td>5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6344</th>\n",
       "      <td>4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6345</th>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6346</th>\n",
       "      <td>4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6347</th>\n",
       "      <td>5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6348</th>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6349</th>\n",
       "      <td>5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6350</th>\n",
       "      <td>5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6351</th>\n",
       "      <td>4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6352</th>\n",
       "      <td>5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6353</th>\n",
       "      <td>5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6354</th>\n",
       "      <td>5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6355</th>\n",
       "      <td>4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6356</th>\n",
       "      <td>5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6357</th>\n",
       "      <td>5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6358</th>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6359</th>\n",
       "      <td>4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6360</th>\n",
       "      <td>5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6361</th>\n",
       "      <td>5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362</th>\n",
       "      <td>4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6363</th>\n",
       "      <td>5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6364</th>\n",
       "      <td>5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6365</th>\n",
       "      <td>4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6366 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
       "0                 3  32.0          9.0       3.0          3    17           2   \n",
       "1                 3  27.0         13.0       3.0          1    14           3   \n",
       "2                 4  22.0          2.5       0.0          1    16           3   \n",
       "3                 4  37.0         16.5       4.0          3    16           5   \n",
       "4                 5  27.0          9.0       1.0          1    14           3   \n",
       "5                 4  27.0          9.0       0.0          2    14           3   \n",
       "6                 5  37.0         23.0       5.5          2    12           5   \n",
       "7                 5  37.0         23.0       5.5          2    12           2   \n",
       "8                 3  22.0          2.5       0.0          2    12           3   \n",
       "9                 3  27.0          6.0       0.0          1    16           3   \n",
       "10                2  27.0          6.0       2.0          1    16           3   \n",
       "11                5  27.0          6.0       2.0          3    14           3   \n",
       "12                3  37.0         16.5       5.5          1    12           2   \n",
       "13                5  27.0          6.0       0.0          2    14           3   \n",
       "14                4  22.0          6.0       1.0          1    14           4   \n",
       "15                4  37.0          9.0       2.0          2    14           3   \n",
       "16                4  27.0          6.0       1.0          1    12           3   \n",
       "17                1  37.0         23.0       5.5          4    14           5   \n",
       "18                2  42.0         23.0       2.0          2    20           4   \n",
       "19                4  37.0          6.0       0.0          2    16           5   \n",
       "20                5  22.0          2.5       0.0          2    14           3   \n",
       "21                3  37.0         16.5       5.5          2     9           3   \n",
       "22                3  42.0         23.0       5.5          3    12           5   \n",
       "23                2  27.0          9.0       2.0          4    20           3   \n",
       "24                4  27.0          6.0       1.0          2    12           5   \n",
       "25                5  27.0          2.5       0.0          3    16           4   \n",
       "26                2  27.0          6.0       2.0          2    12           2   \n",
       "27                5  37.0         13.0       1.0          3    12           3   \n",
       "28                2  32.0         16.5       2.0          2    12           4   \n",
       "29                3  27.0          6.0       1.0          1    14           3   \n",
       "...             ...   ...          ...       ...        ...   ...         ...   \n",
       "6336              5  42.0         23.0       4.0          3    14           5   \n",
       "6337              5  27.0          6.0       0.0          4    14           4   \n",
       "6338              5  42.0         23.0       2.0          3    12           2   \n",
       "6339              4  32.0         13.0       3.0          3    16           4   \n",
       "6340              5  27.0         13.0       3.0          3    16           4   \n",
       "6341              5  27.0          9.0       1.0          2    14           4   \n",
       "6342              4  22.0          2.5       0.0          2    16           4   \n",
       "6343              5  17.5          2.5       0.0          4    12           3   \n",
       "6344              4  32.0         16.5       2.0          2    12           3   \n",
       "6345              5  27.0          9.0       1.0          3    12           3   \n",
       "6346              4  22.0          2.5       0.0          4    14           4   \n",
       "6347              5  22.0          2.5       1.0          2    12           3   \n",
       "6348              5  27.0          0.5       0.0          4    20           4   \n",
       "6349              5  37.0         16.5       3.0          3    14           5   \n",
       "6350              5  32.0         13.0       2.0          4    14           3   \n",
       "6351              4  22.0          0.5       0.0          2    16           3   \n",
       "6352              5  42.0         23.0       2.0          4    12           3   \n",
       "6353              5  22.0          2.5       2.0          2    14           3   \n",
       "6354              5  42.0         23.0       4.0          4    12           3   \n",
       "6355              4  27.0          6.0       0.0          3    12           3   \n",
       "6356              5  32.0         13.0       3.0          3    12           3   \n",
       "6357              5  32.0         13.0       4.0          2    14           4   \n",
       "6358              3  27.0          6.0       2.0          4    14           3   \n",
       "6359              4  22.0          2.5       0.0          3    16           5   \n",
       "6360              5  22.0          2.5       0.0          2    14           3   \n",
       "6361              5  32.0         13.0       2.0          3    17           4   \n",
       "6362              4  32.0         13.0       1.0          1    16           5   \n",
       "6363              5  22.0          2.5       0.0          2    14           3   \n",
       "6364              5  32.0          6.0       1.0          3    14           3   \n",
       "6365              4  22.0          2.5       0.0          2    16           2   \n",
       "\n",
       "      occupation_husb    affairs  Had_Affair  \n",
       "0                   5   0.111111           1  \n",
       "1                   4   3.230769           1  \n",
       "2                   5   1.400000           1  \n",
       "3                   5   0.727273           1  \n",
       "4                   4   4.666666           1  \n",
       "5                   4   4.666666           1  \n",
       "6                   4   0.852174           1  \n",
       "7                   3   1.826086           1  \n",
       "8                   3   4.799999           1  \n",
       "9                   5   1.333333           1  \n",
       "10                  5   3.266665           1  \n",
       "11                  5   2.041666           1  \n",
       "12                  3   0.484848           1  \n",
       "13                  2   2.000000           1  \n",
       "14                  4   3.266665           1  \n",
       "15                  6   1.361111           1  \n",
       "16                  5   2.000000           1  \n",
       "17                  2   1.826086           1  \n",
       "18                  4   1.826086           1  \n",
       "19                  4   2.041666           1  \n",
       "20                  4   7.839996           1  \n",
       "21                  2   2.545454           1  \n",
       "22                  4   0.532609           1  \n",
       "23                  4   0.622222           1  \n",
       "24                  4   0.583333           1  \n",
       "25                  1   4.799999           1  \n",
       "26                  5   0.166667           1  \n",
       "27                  4   0.615385           1  \n",
       "28                  2   1.187878           1  \n",
       "29                  6  11.199999           1  \n",
       "...               ...        ...         ...  \n",
       "6336                4   0.000000           0  \n",
       "6337                4   0.000000           0  \n",
       "6338                2   0.000000           0  \n",
       "6339                2   0.000000           0  \n",
       "6340                2   0.000000           0  \n",
       "6341                5   0.000000           0  \n",
       "6342                1   0.000000           0  \n",
       "6343                5   0.000000           0  \n",
       "6344                4   0.000000           0  \n",
       "6345                5   0.000000           0  \n",
       "6346                2   0.000000           0  \n",
       "6347                2   0.000000           0  \n",
       "6348                4   0.000000           0  \n",
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       "6350                6   0.000000           0  \n",
       "6351                1   0.000000           0  \n",
       "6352                2   0.000000           0  \n",
       "6353                5   0.000000           0  \n",
       "6354                5   0.000000           0  \n",
       "6355                4   0.000000           0  \n",
       "6356                5   0.000000           0  \n",
       "6357                4   0.000000           0  \n",
       "6358                1   0.000000           0  \n",
       "6359                5   0.000000           0  \n",
       "6360                3   0.000000           0  \n",
       "6361                3   0.000000           0  \n",
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       "6363                1   0.000000           0  \n",
       "6364                4   0.000000           0  \n",
       "6365                4   0.000000           0  \n",
       "\n",
       "[6366 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affairs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Had_Affair</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.329701</td>\n",
       "      <td>28.390679</td>\n",
       "      <td>7.989335</td>\n",
       "      <td>1.238813</td>\n",
       "      <td>2.504521</td>\n",
       "      <td>14.322977</td>\n",
       "      <td>3.405286</td>\n",
       "      <td>3.833758</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.647345</td>\n",
       "      <td>30.537019</td>\n",
       "      <td>11.152460</td>\n",
       "      <td>1.728933</td>\n",
       "      <td>2.261568</td>\n",
       "      <td>13.972236</td>\n",
       "      <td>3.463712</td>\n",
       "      <td>3.884559</td>\n",
       "      <td>2.187243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rate_marriage        age  yrs_married  children  religious  \\\n",
       "Had_Affair                                                               \n",
       "0                4.329701  28.390679     7.989335  1.238813   2.504521   \n",
       "1                3.647345  30.537019    11.152460  1.728933   2.261568   \n",
       "\n",
       "                 educ  occupation  occupation_husb   affairs  \n",
       "Had_Affair                                                    \n",
       "0           14.322977    3.405286         3.833758  0.000000  \n",
       "1           13.972236    3.463712         3.884559  2.187243  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Had_Affair').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x196a9d68>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "TCRJxQwTSVKx/w/C6mf2r2ZSOQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x196a9518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('age',data=df,hue='Had_Affair',palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19716e80>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9dbac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('yrs_married',data=df,hue='Had_Affair',palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1aafb438>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "mvTeAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ab07748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('children',data=df,hue='Had_Affair',palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1adbf518>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1adbfdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('educ',data=df,hue='Had_Affair',palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "occ_dummies = pd.get_dummies(df['occupation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hus_occ_dummies = pd.get_dummies(df['occupation_husb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "   1  2  3  4  5  6\n",
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   "source": [
    "occ_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "occ_dummies.columns = ['occ1','occ2','occ3','occ4','occ5','occ6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hus_occ_dummies.columns = ['hocc1','hocc2','hocc3','hocc4','hocc5','hocc6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df.drop(['occupation','occupation_husb','Had_Affair'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dummies = pd.concat([occ_dummies,hus_occ_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = pd.concat([X,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_marriage  age  yrs_married  children  religious  educ   affairs  occ1  \\\n",
       "0              3   32          9.0         3          3    17  0.111111     0   \n",
       "1              3   27         13.0         3          1    14  3.230769     0   \n",
       "2              4   22          2.5         0          1    16  1.400000     0   \n",
       "3              4   37         16.5         4          3    16  0.727273     0   \n",
       "4              5   27          9.0         1          1    14  4.666666     0   \n",
       "\n",
       "   occ2  occ3  occ4  occ5  occ6  hocc1  hocc2  hocc3  hocc4  hocc5  hocc6  \n",
       "0     1     0     0     0     0      0      0      0      0      1      0  \n",
       "1     0     1     0     0     0      0      0      0      1      0      0  \n",
       "2     0     1     0     0     0      0      0      0      0      1      0  \n",
       "3     0     0     0     1     0      0      0      0      0      1      0  \n",
       "4     0     1     0     0     0      0      0      0      1      0      0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6361    0\n",
       "6362    0\n",
       "6363    0\n",
       "6364    0\n",
       "6365    0\n",
       "Name: Had_Affair, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df.Had_Affair\n",
    "\n",
    "Y.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = X.drop('affairs',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <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>2</th>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>16</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_marriage  age  yrs_married  children  religious  educ  occ2  occ3  \\\n",
       "0              3   32          9.0         3          3    17     1     0   \n",
       "1              3   27         13.0         3          1    14     0     1   \n",
       "2              4   22          2.5         0          1    16     0     1   \n",
       "3              4   37         16.5         4          3    16     0     0   \n",
       "4              5   27          9.0         1          1    14     0     1   \n",
       "\n",
       "   occ4  occ5  occ6  hocc2  hocc3  hocc4  hocc5  hocc6  \n",
       "0     0     0     0      0      0      0      1      0  \n",
       "1     0     0     0      0      0      1      0      0  \n",
       "2     0     0     0      0      0      0      1      0  \n",
       "3     0     1     0      0      0      0      1      0  \n",
       "4     0     0     0      0      0      1      0      0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Had_Affair, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = np.ravel(Y)\n",
    "\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72588752748978946"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_model = LogisticRegression()\n",
    "\n",
    "log_model.fit(X,Y)\n",
    "\n",
    "log_model.score(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32249450204209867"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coeff_df = DataFrame(zip(X.columns,np.transpose(log_model.coef_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rate_marriage</td>\n",
       "      <td>[-0.69751024616]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>[-0.0561916647628]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yrs_married</td>\n",
       "      <td>[0.10377681236]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>children</td>\n",
       "      <td>[0.0182911397779]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>religious</td>\n",
       "      <td>[-0.36832290498]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>educ</td>\n",
       "      <td>[0.00890195753587]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>occ2</td>\n",
       "      <td>[0.296111837283]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>occ3</td>\n",
       "      <td>[0.606052332054]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>occ4</td>\n",
       "      <td>[0.343259482442]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>occ5</td>\n",
       "      <td>[0.94006396288]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>occ6</td>\n",
       "      <td>[0.919085673732]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hocc2</td>\n",
       "      <td>[0.219634563784]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hocc3</td>\n",
       "      <td>[0.323455986054]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hocc4</td>\n",
       "      <td>[0.189038509875]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>hocc5</td>\n",
       "      <td>[0.212503714395]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>hocc6</td>\n",
       "      <td>[0.212868942457]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0                   1\n",
       "0   rate_marriage    [-0.69751024616]\n",
       "1             age  [-0.0561916647628]\n",
       "2     yrs_married     [0.10377681236]\n",
       "3        children   [0.0182911397779]\n",
       "4       religious    [-0.36832290498]\n",
       "5            educ  [0.00890195753587]\n",
       "6            occ2    [0.296111837283]\n",
       "7            occ3    [0.606052332054]\n",
       "8            occ4    [0.343259482442]\n",
       "9            occ5     [0.94006396288]\n",
       "10           occ6    [0.919085673732]\n",
       "11          hocc2    [0.219634563784]\n",
       "12          hocc3    [0.323455986054]\n",
       "13          hocc4    [0.189038509875]\n",
       "14          hocc5    [0.212503714395]\n",
       "15          hocc6    [0.212868942457]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_model2 = LogisticRegression()\n",
    "\n",
    "log_model2.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_predict = log_model2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.722361809045\n"
     ]
    }
   ],
   "source": [
    "print metrics.accuracy_score(Y_test,class_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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