ABSENTEEISM_Logistic_Regression_Exercise_custom_scaler.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Reason_1</th>\n",
       "      <th>Reason_2</th>\n",
       "      <th>Reason_3</th>\n",
       "      <th>Reason_4</th>\n",
       "      <th>Month Value</th>\n",
       "      <th>Day of the Week</th>\n",
       "      <th>Transportation Expense</th>\n",
       "      <th>Distance to Work</th>\n",
       "      <th>Age</th>\n",
       "      <th>Daily Work Load Average</th>\n",
       "      <th>Body Mass Index</th>\n",
       "      <th>Education</th>\n",
       "      <th>Children</th>\n",
       "      <th>Pet</th>\n",
       "      <th>Absenteeism Time in Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>289</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>239.554</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>13</td>\n",
       "      <td>50</td>\n",
       "      <td>239.554</td>\n",
       "      <td>31</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>179</td>\n",
       "      <td>51</td>\n",
       "      <td>38</td>\n",
       "      <td>239.554</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>279</td>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>239.554</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>289</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>239.554</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Reason_1  Reason_2  Reason_3  Reason_4  Month Value  Day of the Week  \\\n",
       "0         0         0         0         1            7                1   \n",
       "1         0         0         0         0            7                1   \n",
       "2         0         0         0         1            7                2   \n",
       "3         1         0         0         0            7                3   \n",
       "4         0         0         0         1            7                3   \n",
       "\n",
       "   Transportation Expense  Distance to Work  Age  Daily Work Load Average  \\\n",
       "0                     289                36   33                  239.554   \n",
       "1                     118                13   50                  239.554   \n",
       "2                     179                51   38                  239.554   \n",
       "3                     279                 5   39                  239.554   \n",
       "4                     289                36   33                  239.554   \n",
       "\n",
       "   Body Mass Index  Education  Children  Pet  Absenteeism Time in Hours  \n",
       "0               30          0         2    1                          4  \n",
       "1               31          0         1    0                          0  \n",
       "2               31          0         0    0                          2  \n",
       "3               24          0         2    0                          4  \n",
       "4               30          0         2    1                          2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_preprocessed = pd.read_csv('data/Absenteeism-preprocessed.csv')\n",
    "data_preprocessed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use a logistic regression which will take the reason for absence month of the year day of the week transportation expense distance to work age daily work load average education children and pets of a given employee and we'll predict their absenteeism we expect that half of those predictors won't have merit.\n",
    "To me it seems that the reason for absence will be the most indicative maybe workload will have something to do with it as well since the busier a person is the less he or she will want to skip work. Finally children and pets together with distance from work should also have something to do with absenteeism. If your child or pet is sick at home you'll have to go home take them to the doctor and get them back which will be much more time consuming than a simple visit to the doctor OK. We have a good idea what to expect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Target\n",
    "\n",
    "We've got a bit more pre processing to do but it is of a different kind. We said we will use a logistic regression to predict absenteeism logistic regression is a type of classification so we will be basically classifying people into classes. What are these classes then.That's what we must settle first and then pre process our data to reflect this decision the approach we will use here is to create two classes one representing people who have been excessively absent and another which represents people that haven't for this exercise. We will use a methodology which is a tiny bit naive but quite numerically stable.We will take the median value of the absenteeism time into our cell everything below the median would be considered normal. Everything above the median would be excessive OK let's find that median there is the panda's method called median which can help us with this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_preprocessed['Absenteeism Time in Hours'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good **three hours** is our cutoff line. If an observation has been absent for less than three hours we will assign it the value of zero. Otherwise the value of 1 in supervised machine learning we call these zeros and ones targets. These are the values we are aiming for. Our task will be to predict whether we will obtain a zero or a one. So let's create a new variable called targets which will measure if a person has been absent for more than three hours. The `np.where` function in this method is constructed with three arguments a condition, value of true and value if false. Our condition is that the series absenteeism time and hours from data pre processed is bigger than 3 if it is true. We want the function to return 1 otherwise zero."
   ]
  },
  {
   "attachments": {
    "where_function.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![where_function.png](attachment:where_function.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = np.where(data_preprocessed['Absenteeism Time in Hours'] > data_preprocessed['Absenteeism Time in Hours'].median(),1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I told you that using the median as a cutoff line is numerically stable and rigid. That's because by using the median we have implicitly balance the dataset roughly half of the targets are zeros while the other half ones as you may remember this will prevent our model from learning to output one of the two classes exclusively thinking it did very well in order to prove that. Let's divide the number of targets that are ones by the total number of targets the number of targets that are ones can be found by summing up all values of targets while the total number of targets is simply the shape on axis 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45571428571428574"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets.sum()/targets.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is around zero point four six. So around forty six percent of the targets are ones thus around 54 percent of the targets are zeros.So our result will do for this exercise let's proceed noting that our two groups have been distributed roughly equally finally let's drop the absenteeism time and hours from the data frame since we won't be needing it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reason_1</th>\n",
       "      <th>Reason_2</th>\n",
       "      <th>Reason_3</th>\n",
       "      <th>Reason_4</th>\n",
       "      <th>Month Value</th>\n",
       "      <th>Day of the Week</th>\n",
       "      <th>Transportation Expense</th>\n",
       "      <th>Distance to Work</th>\n",
       "      <th>Age</th>\n",
       "      <th>Daily Work Load Average</th>\n",
       "      <th>Body Mass Index</th>\n",
       "      <th>Education</th>\n",
       "      <th>Children</th>\n",
       "      <th>Pet</th>\n",
       "      <th>Absenteeism Time in Hours</th>\n",
       "      <th>Excessive Absenteeism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>289</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>239.554</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>13</td>\n",
       "      <td>50</td>\n",
       "      <td>239.554</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>289</td>\n",
       "      <td>36</td>\n",
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       "      <td>239.554</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Reason_1  Reason_2  Reason_3  Reason_4  Month Value  Day of the Week  \\\n",
       "0         0         0         0         1            7                1   \n",
       "1         0         0         0         0            7                1   \n",
       "2         0         0         0         1            7                2   \n",
       "3         1         0         0         0            7                3   \n",
       "4         0         0         0         1            7                3   \n",
       "\n",
       "   Transportation Expense  Distance to Work  Age  Daily Work Load Average  \\\n",
       "0                     289                36   33                  239.554   \n",
       "1                     118                13   50                  239.554   \n",
       "2                     179                51   38                  239.554   \n",
       "3                     279                 5   39                  239.554   \n",
       "4                     289                36   33                  239.554   \n",
       "\n",
       "   Body Mass Index  Education  Children  Pet  Absenteeism Time in Hours  \\\n",
       "0               30          0         2    1                          4   \n",
       "1               31          0         1    0                          0   \n",
       "2               31          0         0    0                          2   \n",
       "3               24          0         2    0                          4   \n",
       "4               30          0         2    1                          2   \n",
       "\n",
       "   Excessive Absenteeism  \n",
       "0                      1  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      1  \n",
       "4                      0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's add the targets to the data frame data pre processed.\n",
    "data_preprocessed['Excessive Absenteeism'] = targets\n",
    "data_preprocessed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Reason_1  Reason_2  Reason_3  Reason_4  Month Value  Day of the Week  \\\n",
       "0         0         0         0         1            7                1   \n",
       "1         0         0         0         0            7                1   \n",
       "2         0         0         0         1            7                2   \n",
       "3         1         0         0         0            7                3   \n",
       "4         0         0         0         1            7                3   \n",
       "\n",
       "   Transportation Expense  Distance to Work  Age  Daily Work Load Average  \\\n",
       "0                     289                36   33                  239.554   \n",
       "1                     118                13   50                  239.554   \n",
       "2                     179                51   38                  239.554   \n",
       "3                     279                 5   39                  239.554   \n",
       "4                     289                36   33                  239.554   \n",
       "\n",
       "   Body Mass Index  Education  Children  Pet  Excessive Absenteeism  \n",
       "0               30          0         2    1                      1  \n",
       "1               31          0         1    0                      0  \n",
       "2               31          0         0    0                      0  \n",
       "3               24          0         2    0                      1  \n",
       "4               30          0         2    1                      0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's drop the 'Absenteeism Time in Hours' column and create new data and continiue with it \n",
    "\n",
    "data_with_target = data_preprocessed.drop(['Absenteeism Time in Hours'], axis=1)\n",
    "data_with_target.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the inputs for our regression.\n",
    "We will use the panda's method iloc, since it is extremely useful and common. There are two arguments.\n",
    "The first one refers to the row indices and the second one two column indices.\n",
    "```python\n",
    "#select (slices)data by position where given rows and columns wanted\n",
    "DataFrame.iloc[row indices, column incides]\n",
    "```\n",
    "I would actually prefer to select all rows so I can simply leave colons in the brackets as the first argument to select the inputs for our regression. We must select all rows and all columns except for `excessive absenteeism`. \n",
    "```python\n",
    "data_with_target.iloc[:,:14]\n",
    "#OR \n",
    "data_with_target.iloc[:,:-1]\n",
    "```\n",
    "\n",
    "Both method is fine, Let's use last one and assign the results `unscaled_inputs` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700, 15)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Reason_1  Reason_2  Reason_3  Reason_4  Month Value  Day of the Week  \\\n",
       "0         0         0         0         1            7                1   \n",
       "1         0         0         0         0            7                1   \n",
       "2         0         0         0         1            7                2   \n",
       "3         1         0         0         0            7                3   \n",
       "4         0         0         0         1            7                3   \n",
       "\n",
       "   Transportation Expense  Distance to Work  Age  Daily Work Load Average  \\\n",
       "0                     289                36   33                  239.554   \n",
       "1                     118                13   50                  239.554   \n",
       "2                     179                51   38                  239.554   \n",
       "3                     279                 5   39                  239.554   \n",
       "4                     289                36   33                  239.554   \n",
       "\n",
       "   Body Mass Index  Education  Children  Pet  \n",
       "0               30          0         2    1  \n",
       "1               31          0         1    0  \n",
       "2               31          0         0    0  \n",
       "3               24          0         2    0  \n",
       "4               30          0         2    1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unscaled_inputs = data_with_target.iloc[:,:-1]\n",
    "unscaled_inputs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize the data\n",
    "\n",
    "It is time to standardize our data. Let's import the relevant module from S.K. learn. First we must declare a standard scalar object.  Note that we have created an empty standard scalar object. There is no information in it for those of you that haven't seen scaling with standard scalar.\n",
    "**Standard scalar is subtract the mean and divide by the standard deviation from each point variable**\n",
    "The next step is to fit our input data. We write absenteeism scalar.fit unskilled inputs this line will calculate the mean and standard deviation of each feature from unskilled inputs. This information will be stored in the absenteeism scale object so it won't be an empty object anymore. It will contain information about the mean and standard deviation. This is extremely important whenever you get new data you will know that the standardization information is contained in the absenteeism scalar. Thus you'll be able to standardize the new data in the same way. Remember this as it will be very useful later on. OK so we have the information but the inputs are still on scaled. We have just prepare the scaling mechanism in order to apply it. We must use another method called transform so let's scale inputs be equal to absenteeism scalar dot transform unskilled inputs this operation transforms the unskilled inputs using the information contained in absenteeism scalar in simple words we subtract the mean and divide by the standard deviation whenever we get new data we will just apply absenteeism scalar dot transform new data to reach the same transformation as we just did. This is the most common and useful way to transform New data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "class CustomScaler(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self,columns,copy=True,with_mean=True,with_std=True):\n",
    "        self.scaler = StandardScaler(copy, with_mean,with_std)\n",
    "        self.columns = columns\n",
    "        self.mean_ = None\n",
    "        self.var_ = None\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        self.scaler.fit(X[self.columns], y)\n",
    "        self.mean_ = np.mean(X[self.columns])\n",
    "        self.var_ = np.var(X[self.columns])\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None, copy=None):\n",
    "        init_col_order =X.columns\n",
    "        X_scaled = pd.DataFrame(self.scaler.transform(X[self.columns]), columns=self.columns)\n",
    "        X_not_sclaed = X.loc[:,~X.columns.isin(self.columns)]\n",
    "        return pd.concat([X_not_sclaed, X_scaled], axis=1)[init_col_order]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Reason_1', 'Reason_2', 'Reason_3', 'Reason_4', 'Month Value',\n",
       "       'Day of the Week', 'Transportation Expense', 'Distance to Work',\n",
       "       'Age', 'Daily Work Load Average', 'Body Mass Index', 'Education',\n",
       "       'Children', 'Pet'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we must choose the columns to be scaled to see what columns we've got.\n",
    "unscaled_inputs.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_sclae = ['Month Value','Day of the Week', 'Transportation Expense', 'Distance to Work',\n",
    "       'Age', 'Daily Work Load Average', 'Body Mass Index', 'Children', 'Pet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's created an empty standard scalar object\n",
    "#absenteeism_scaler = StandardScaler()\n",
    "\n",
    "absenteeism_scaler =CustomScaler(columns_to_sclae)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomScaler(columns=['Month Value', 'Day of the Week', 'Transportation Expense', 'Distance to Work', 'Age', 'Daily Work Load Average', 'Body Mass Index', 'Children', 'Pet'],\n",
       "       copy=None, with_mean=None, with_std=None)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit our input data, this will calculate the mean and standard deviation of each feature from unskilled inputs\n",
    "absenteeism_scaler.fit(unscaled_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also standardize the dummies.This is bad practice because when we standardize we lose the whole interpret ability of a dummy if we had left them as zeros and ones we could have said for a unit change it is seven point two times more likely that a person will be excessively absent a unit change in the dummy variable universe means a change from disregarding this dummy to taking only this dummy into account so if the reason is reason one we would have said it is around set points seven times more likely that a person will be absent compared to no reason given however we standardize the reasons and now a unit change is completely on interpretable the predictive power of the model is still valid and it is a good classifier but we don't know how the different reasons compare. This is a problem since those are the most important features. This brings us to a correction of our code. Good thing we had all those checkpoints. Maybe they'll help us do that effortlessly. I'll go back to the part where we standardize the data and put all the code in comments since we won't be needing it now. I'll copy paste some code that I prepared prior to this lecture. The idea is that this is a custom scalar based on the standard scalar from SDK learn. However when we declare the scalar object there's an extra argument columns to scale so our custom scalar will not standardize all inputs but only the ones we choose. In this way we will be able to preserve the dummies untouched."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Until we transform the data inputs still on non-scaled. For using this method we always transform new data\n",
    "scaled_inputs = absenteeism_scaler.transform(unscaled_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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>Reason_1</th>\n",
       "      <th>Reason_2</th>\n",
       "      <th>Reason_3</th>\n",
       "      <th>Reason_4</th>\n",
       "      <th>Month Value</th>\n",
       "      <th>Day of the Week</th>\n",
       "      <th>Transportation Expense</th>\n",
       "      <th>Distance to Work</th>\n",
       "      <th>Age</th>\n",
       "      <th>Daily Work Load Average</th>\n",
       "      <th>Body Mass Index</th>\n",
       "      <th>Education</th>\n",
       "      <th>Children</th>\n",
       "      <th>Pet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
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       "      <td>0.030796</td>\n",
       "      <td>-0.800950</td>\n",
       "      <td>1.005844</td>\n",
       "      <td>0.412816</td>\n",
       "      <td>-0.536062</td>\n",
       "      <td>-0.806331</td>\n",
       "      <td>0.767431</td>\n",
       "      <td>0</td>\n",
       "      <td>0.880469</td>\n",
       "      <td>0.268487</td>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>0.030796</td>\n",
       "      <td>-0.800950</td>\n",
       "      <td>-1.574681</td>\n",
       "      <td>-1.141882</td>\n",
       "      <td>2.130803</td>\n",
       "      <td>-0.806331</td>\n",
       "      <td>1.002633</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.019280</td>\n",
       "      <td>-0.589690</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>0.030796</td>\n",
       "      <td>-0.232900</td>\n",
       "      <td>-0.654143</td>\n",
       "      <td>1.426749</td>\n",
       "      <td>0.248310</td>\n",
       "      <td>-0.806331</td>\n",
       "      <td>1.002633</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.919030</td>\n",
       "      <td>-0.589690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030796</td>\n",
       "      <td>0.335149</td>\n",
       "      <td>0.854936</td>\n",
       "      <td>-1.682647</td>\n",
       "      <td>0.405184</td>\n",
       "      <td>-0.806331</td>\n",
       "      <td>-0.643782</td>\n",
       "      <td>0</td>\n",
       "      <td>0.880469</td>\n",
       "      <td>-0.589690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.030796</td>\n",
       "      <td>0.335149</td>\n",
       "      <td>1.005844</td>\n",
       "      <td>0.412816</td>\n",
       "      <td>-0.536062</td>\n",
       "      <td>-0.806331</td>\n",
       "      <td>0.767431</td>\n",
       "      <td>0</td>\n",
       "      <td>0.880469</td>\n",
       "      <td>0.268487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Reason_1  Reason_2  Reason_3  Reason_4  Month Value  Day of the Week  \\\n",
       "0         0         0         0         1     0.030796        -0.800950   \n",
       "1         0         0         0         0     0.030796        -0.800950   \n",
       "2         0         0         0         1     0.030796        -0.232900   \n",
       "3         1         0         0         0     0.030796         0.335149   \n",
       "4         0         0         0         1     0.030796         0.335149   \n",
       "\n",
       "   Transportation Expense  Distance to Work       Age  \\\n",
       "0                1.005844          0.412816 -0.536062   \n",
       "1               -1.574681         -1.141882  2.130803   \n",
       "2               -0.654143          1.426749  0.248310   \n",
       "3                0.854936         -1.682647  0.405184   \n",
       "4                1.005844          0.412816 -0.536062   \n",
       "\n",
       "   Daily Work Load Average  Body Mass Index  Education  Children       Pet  \n",
       "0                -0.806331         0.767431          0  0.880469  0.268487  \n",
       "1                -0.806331         1.002633          0 -0.019280 -0.589690  \n",
       "2                -0.806331         1.002633          0 -0.919030 -0.589690  \n",
       "3                -0.806331        -0.643782          0  0.880469 -0.589690  \n",
       "4                -0.806331         0.767431          0  0.880469  0.268487  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can see all our dummies have remained untouched and that was the wholo point\n",
    "scaled_inputs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700, 14)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_inputs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split to the data into train & test \n",
    "We will split the dataset into train and test so we can assess the model's accuracy on data.Sklearn has a pretty neat method of splitting the data into train and test in order to use it. Split the train test split method is very powerful and has many arguments.The method to split the data the output we obtain consists of for arrays a\n",
    "training dataset with inputs then a test data set with inputs a trained data set with targets and a test data set with targets. We will call them X_train X_test y_train and y_test.\n",
    " \n",
    "We can see the shapes of these variables. This will be very indicative of what the train test split has actually achieved as you can see that 75% of the observations will help us with training and 25% will serve for testing as default.We can specify the train_size(or test_size) argument  equal to a certain value between 0 and 1 .\n",
    "\n",
    "The train test split method has a shuffle parameter shuffle is a boolean so it can be either true or false by default. Shuffling is set to true. It may be a small issue for us when we rerun our code. We get a different shuffle and this means a different split. This causes the final model to defer every time due to the shuffling.\n",
    "So the shuffle can make things difficult for us. There is a simple solution though all S.K. learn functions that include some randomness like the random shuffle here contain a random state parameter random state takes integer values. What we can do is set the random state to a number say 20. This will make the shuffle pseudo random.\n",
    "It will always shuffle observations in the same random way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(scaled_inputs, targets, test_size =0.2, random_state=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(560, 14) (560,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(140, 14) (140,)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LOGISTIC REGRESSION FOR SKLEARN\n",
    "\n",
    "And now it's finally time for the modeling part as usual.Let's import the relevant modules since we are building a logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import  metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we must declare a new variable which will be a logistic regression object\n",
    "reg = LogisticRegression()\n",
    "\n",
    "#We must fit the regression.\n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`As an output we get all the parameters of the logistic regression object we specified above obviously they are all the default ones since we didn't specify anything. Each of those parameters has a special meaning which can improve our model one way or the other.`\n",
    "\n",
    "For now let's focus on the results of this regression. It is easy to evaluate the model accuracy.\n",
    "\n",
    "```python\n",
    "sklearn.linear_model.LogisticRegression.score(inputs, targets) # HERE (X_train, y_train)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7660714285714286"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`The result is around zero point eighty we conclude that our model has an accuracy of zero point eight or 80 percent. In other words based on the data we used our model learned to classify 80 percent of the observations`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manually check the accuracy\n",
    "Accuracy means that 80 percent of the model outputs match the targets so if we want to find the accuracy of a model manually we should find the outputs and compare them with the targets let's do that in order to find the model outputs. We will use a simple as K learned method. It is called predict.This method will find the predicted outputs of the regression the model itself is contained in the variable reg and we are choosing to predict the outputs associated with the training inputs and contained in X train. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
       "       1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_outputs = reg.predict(X_train)\n",
    "model_outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see an array with zeros and ones. These are the predictions of our model. If we look at the difrences between using (model_outputs == targets) we can see that If there is a match the result is true. Otherwise it is false. Now we can clearly see which elements have been guessed correctly and which haven't but how many are there. if we  sum this array using np.sum. The result will be the total number of true entries and if we divide the number of matches by the total number of elements. We will get the accuracy.\n",
    "let's divide the number of true entries by that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7660714285714286"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(model_outputs == y_train)/ model_outputs.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`What we get the exact same result as using the SKlearn methods score. However this time we have a much better idea of what that result means.`"
   ]
  },
  {
   "attachments": {
    "Screen%20Shot%202019-01-27%20at%209.33.44%20AM.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intercept & Coefficient\n",
    "\n",
    "Regression analysis, no matter if linear or nonlinear it's about determining certain coefficients or weights which we apply to the inputs to obtain a final result. So to use this logistic regression model outside of Python we must get our hands on the coefficients and the intercept.\n",
    "![Screen%20Shot%202019-01-27%20at%209.33.44%20AM.png](attachment:Screen%20Shot%202019-01-27%20at%209.33.44%20AM.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.43101781])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.61893423,  0.83461948,  2.95258195,  0.64428488,  0.01123706,\n",
       "        -0.0748093 ,  0.62180009, -0.02934223, -0.17585164, -0.02583315,\n",
       "         0.27705024, -0.29385863,  0.3549178 , -0.27486307]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to know what variable those coefficients refer to we can get the coefficients from the names of our inputs column values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Reason_1', 'Reason_2', 'Reason_3', 'Reason_4', 'Month Value',\n",
       "       'Day of the Week', 'Transportation Expense', 'Distance to Work',\n",
       "       'Age', 'Daily Work Load Average', 'Body Mass Index', 'Education',\n",
       "       'Children', 'Pet'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_names = unscaled_inputs.columns.values\n",
    "features_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to create a data frame that will contain the intercept the feature names and the corresponding coefficients we can call this data frame summary table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "Summary_of_Table = pd.DataFrame(columns=['Feature Name'], data=features_names)\n",
    "Summary_of_Table['Coefficient'] = np.transpose(reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature Name</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Reason_1</td>\n",
       "      <td>2.618934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Reason_2</td>\n",
       "      <td>0.834619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Reason_3</td>\n",
       "      <td>2.952582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reason_4</td>\n",
       "      <td>0.644285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Month Value</td>\n",
       "      <td>0.011237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Day of the Week</td>\n",
       "      <td>-0.074809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Transportation Expense</td>\n",
       "      <td>0.621800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Distance to Work</td>\n",
       "      <td>-0.029342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.175852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Daily Work Load Average</td>\n",
       "      <td>-0.025833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Body Mass Index</td>\n",
       "      <td>0.277050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Education</td>\n",
       "      <td>-0.293859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Children</td>\n",
       "      <td>0.354918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pet</td>\n",
       "      <td>-0.274863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Feature Name  Coefficient\n",
       "0                  Reason_1     2.618934\n",
       "1                  Reason_2     0.834619\n",
       "2                  Reason_3     2.952582\n",
       "3                  Reason_4     0.644285\n",
       "4               Month Value     0.011237\n",
       "5           Day of the Week    -0.074809\n",
       "6    Transportation Expense     0.621800\n",
       "7          Distance to Work    -0.029342\n",
       "8                       Age    -0.175852\n",
       "9   Daily Work Load Average    -0.025833\n",
       "10          Body Mass Index     0.277050\n",
       "11                Education    -0.293859\n",
       "12                 Children     0.354918\n",
       "13                      Pet    -0.274863"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summary_of_Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To add the intercept to the beginning of table we will follow the method showed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Summary_of_Table.index = Summary_of_Table.index +1\n",
    "Summary_of_Table.loc[0] = ['intercept', reg.intercept_[0]]\n",
    "Summary_of_Table =Summary_of_Table.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature Name</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>intercept</td>\n",
       "      <td>-1.431018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Reason_1</td>\n",
       "      <td>2.618934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Reason_2</td>\n",
       "      <td>0.834619</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>Reason_3</td>\n",
       "      <td>2.952582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Reason_4</td>\n",
       "      <td>0.644285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Month Value</td>\n",
       "      <td>0.011237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Day of the Week</td>\n",
       "      <td>-0.074809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Transportation Expense</td>\n",
       "      <td>0.621800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Distance to Work</td>\n",
       "      <td>-0.029342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.175852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Daily Work Load Average</td>\n",
       "      <td>-0.025833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Body Mass Index</td>\n",
       "      <td>0.277050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Education</td>\n",
       "      <td>-0.293859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Children</td>\n",
       "      <td>0.354918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pet</td>\n",
       "      <td>-0.274863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Feature Name  Coefficient\n",
       "0                 intercept    -1.431018\n",
       "1                  Reason_1     2.618934\n",
       "2                  Reason_2     0.834619\n",
       "3                  Reason_3     2.952582\n",
       "4                  Reason_4     0.644285\n",
       "5               Month Value     0.011237\n",
       "6           Day of the Week    -0.074809\n",
       "7    Transportation Expense     0.621800\n",
       "8          Distance to Work    -0.029342\n",
       "9                       Age    -0.175852\n",
       "10  Daily Work Load Average    -0.025833\n",
       "11          Body Mass Index     0.277050\n",
       "12                Education    -0.293859\n",
       "13                 Children     0.354918\n",
       "14                      Pet    -0.274863"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summary_of_Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**interprete the coefficients** Coefficients are also called weights while the intercept bias. These notions are useful because the weights show how we weigh a certain input. The closer they are to zero the smaller the weight and alternatively the further away from zero no matter if positive or negative the bigger the weight of this feature. Note that this is something which is true for our model but it's not universally true. It holds only four models where all variables are of the same scale such as the one we just built there are coefficient values and standardised coefficient values. These standardized coefficients are basically the coefficient values of a regression where all variables have been standardized other packages and software include the standardized coefficients because they allow for a simple and easy to understand comparison between the variables since. In such cases the features are standardized they all have a variance of one or the same scale. And whenever the scale is standard or the same that is we can simply say whichever way it is bigger its corresponding feature is more important for machine learning purposes and prediction in general we usually standardize the variables like we did now.  Another notion we must emphasize is that whenever we are dealing with a `logistic regression`  the coefficients we are predicting are the so-called `log odds`. This is a consequence of the choice of model logistic regression by default are nothing but a linear function. Predicting log odds these log odds are later transformed into zeros and ones let's make this clearer. Here's the logistic regression equation...\n",
    "\n",
    "```python\n",
    "log(odds) = intercept + b1x1 +b2x2+ ..... b13x13 + b14x14\n",
    "\n",
    "-0.21 + 2.07*Reason_1 + 0.33*Reason_2 + 1.31*Reason_4 + 0.02*Month Value + (-0.09)*Day of the Week + 0.72*Transportation Expense + (-0.06)*Distance to Work + (-0.21)*Age +  (-0.03)*Daily Work Load Average+ \n",
    "0.33*Body Mass Index + (-0.16)*Education + 0.38*Children + (-0.32)*Pet\n",
    "```\n",
    "\n",
    "So to make them more interpretable let's find the exponential of these coefficients. I'll create a new series in our data frame called Odds Ratio "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "Summary_of_Table['Odds Ratio'] = np.exp(Summary_of_Table.Coefficient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature Name</th>\n",
       "      <th>Coefficient</th>\n",
       "      <th>Odds Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>intercept</td>\n",
       "      <td>-1.431018</td>\n",
       "      <td>0.239065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Reason_1</td>\n",
       "      <td>2.618934</td>\n",
       "      <td>13.721092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Reason_2</td>\n",
       "      <td>0.834619</td>\n",
       "      <td>2.303937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reason_3</td>\n",
       "      <td>2.952582</td>\n",
       "      <td>19.155348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Reason_4</td>\n",
       "      <td>0.644285</td>\n",
       "      <td>1.904625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Month Value</td>\n",
       "      <td>0.011237</td>\n",
       "      <td>1.011300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Day of the Week</td>\n",
       "      <td>-0.074809</td>\n",
       "      <td>0.927920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Transportation Expense</td>\n",
       "      <td>0.621800</td>\n",
       "      <td>1.862277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Distance to Work</td>\n",
       "      <td>-0.029342</td>\n",
       "      <td>0.971084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.175852</td>\n",
       "      <td>0.838742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Daily Work Load Average</td>\n",
       "      <td>-0.025833</td>\n",
       "      <td>0.974498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Body Mass Index</td>\n",
       "      <td>0.277050</td>\n",
       "      <td>1.319233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Education</td>\n",
       "      <td>-0.293859</td>\n",
       "      <td>0.745382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Children</td>\n",
       "      <td>0.354918</td>\n",
       "      <td>1.426063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pet</td>\n",
       "      <td>-0.274863</td>\n",
       "      <td>0.759676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Feature Name  Coefficient  Odds Ratio\n",
       "0                 intercept    -1.431018    0.239065\n",
       "1                  Reason_1     2.618934   13.721092\n",
       "2                  Reason_2     0.834619    2.303937\n",
       "3                  Reason_3     2.952582   19.155348\n",
       "4                  Reason_4     0.644285    1.904625\n",
       "5               Month Value     0.011237    1.011300\n",
       "6           Day of the Week    -0.074809    0.927920\n",
       "7    Transportation Expense     0.621800    1.862277\n",
       "8          Distance to Work    -0.029342    0.971084\n",
       "9                       Age    -0.175852    0.838742\n",
       "10  Daily Work Load Average    -0.025833    0.974498\n",
       "11          Body Mass Index     0.277050    1.319233\n",
       "12                Education    -0.293859    0.745382\n",
       "13                 Children     0.354918    1.426063\n",
       "14                      Pet    -0.274863    0.759676"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summary_of_Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll sort the data frame using the sort values method. Sort values requires us to choose the appropriate\n",
    "column by which we want to sort the whole data frame logically for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature Name</th>\n",
       "      <th>Coefficient</th>\n",
       "      <th>Odds Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reason_3</td>\n",
       "      <td>2.952582</td>\n",
       "      <td>19.155348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Reason_1</td>\n",
       "      <td>2.618934</td>\n",
       "      <td>13.721092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Reason_2</td>\n",
       "      <td>0.834619</td>\n",
       "      <td>2.303937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Reason_4</td>\n",
       "      <td>0.644285</td>\n",
       "      <td>1.904625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Transportation Expense</td>\n",
       "      <td>0.621800</td>\n",
       "      <td>1.862277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Children</td>\n",
       "      <td>0.354918</td>\n",
       "      <td>1.426063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Body Mass Index</td>\n",
       "      <td>0.277050</td>\n",
       "      <td>1.319233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Month Value</td>\n",
       "      <td>0.011237</td>\n",
       "      <td>1.011300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Daily Work Load Average</td>\n",
       "      <td>-0.025833</td>\n",
       "      <td>0.974498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Distance to Work</td>\n",
       "      <td>-0.029342</td>\n",
       "      <td>0.971084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Day of the Week</td>\n",
       "      <td>-0.074809</td>\n",
       "      <td>0.927920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.175852</td>\n",
       "      <td>0.838742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pet</td>\n",
       "      <td>-0.274863</td>\n",
       "      <td>0.759676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Education</td>\n",
       "      <td>-0.293859</td>\n",
       "      <td>0.745382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>intercept</td>\n",
       "      <td>-1.431018</td>\n",
       "      <td>0.239065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Feature Name  Coefficient  Odds Ratio\n",
       "3                  Reason_3     2.952582   19.155348\n",
       "1                  Reason_1     2.618934   13.721092\n",
       "2                  Reason_2     0.834619    2.303937\n",
       "4                  Reason_4     0.644285    1.904625\n",
       "7    Transportation Expense     0.621800    1.862277\n",
       "13                 Children     0.354918    1.426063\n",
       "11          Body Mass Index     0.277050    1.319233\n",
       "5               Month Value     0.011237    1.011300\n",
       "10  Daily Work Load Average    -0.025833    0.974498\n",
       "8          Distance to Work    -0.029342    0.971084\n",
       "6           Day of the Week    -0.074809    0.927920\n",
       "9                       Age    -0.175852    0.838742\n",
       "14                      Pet    -0.274863    0.759676\n",
       "12                Education    -0.293859    0.745382\n",
       "0                 intercept    -1.431018    0.239065"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summary_of_Table.sort_values('Odds Ratio', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So how can we interpret them. If a coefficient is around zero or its odds ratio is close to one. This means that the corresponding feature is not particularly important the reasoning in terms of weights is that a weight of 0 implies that no matter the feature value we will multiply it by 0 and the whole result will be 0 the meaning in terms of odds ratios as the following for one unit change in the standardised feature. The odds increase by a multiple equal to the odds ratio so if the odds ratio was 1 then the odds don't change at all. We have weights like that. The daily work load average is almost 1. So this feature is almost useless for our model and with or without it the result would likely be the same. We have other variables that can fall into this category. Day of the week and distance to work. given all features these seem to be the ones that make no difference. We will keep that features for now but consider dropping them later on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting the important features\n",
    "\n",
    "We will focus on the most important features I'll just remind you that the further away from zero a coefficient is the bigger its importance. So by looking at the coefficients table we will notice that the most strongly pronounced features seem to be the four reasons for absence. The transportation expense and whether a person has children pets and education. Note that pet and education are at the bottom of the table but their weights are still far away from zero. They are indeed important we can carry on in this way. Finishing with the daily work load average distance to work and day of the week would seem to have the smallest impact. The weight is almost zero. So regardless of the particular values they will barely affect our model. What about the reasons we said that the base model includes no reason. But what is the impact of the various reasons I'll quickly recap what the five reason variables stand for reason zero or no reason which is the baseline model. Reason 1 which comprises of various diseases. Reason to relating to pregnancy and giving birth. Reason 3 regarding poisoning and peculiar reasons not categorized elsewhere and reason for which relates to light diseases in the light of this clarification. We can easily understand our coefficients the most crucial reason for excess of absence is poisoning not much of a surprise there.  The weight means the odds of someone being excessively absent after being poisoned are 20 times higher than when no reason was reported OK. Clear another very important reason seems to be no one or various diseases. I'd call this the normal absenteeism case you got sick you skipped work no drama. A person who was reported this is 14 times more likely to be excessively absent than a person who didn't specify a reason then we have pregnancy and giving birth.  All right after that we've got transportation expense. This is the most important non dummy feature in the model. But here's the problem. It is one of our standardized variables.We don't have direct interpret ability of it. Its odds ratio implies that for one standardized unit or for one standard deviation increase in transportation expense it is close to twice as likely to be excessively absent. This is the main drawback of standardization. Standardized models almost always yield higher accuracy because the optimization algorithms work better in this way. `Machine learning engineers` prefer models with higher accuracy so they normally go for standardization `economy patricians and statisticians` however prefer less accurate but more interpretable models because they care about the underlying reasons behind different phenomena.\n",
    "`Data scientists` may be in either position sometimes they need higher accuracy.Other times they must find the main drivers of a problem so it makes sense to create two different models one with standardized features and one without them and then draw insights from both.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Model\n",
    "It is time to use the test data. This will also be the end of the machine learning part.That's because testing is done only once and at the very end of the machine learning process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of zero and one we can get the probability of an output being zero or 1.There is an SDK learn method called `predict probe`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140, 2)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#instead of 0 and 1 we can get the Probability of an output beging 0 or 1\n",
    "predicted_proba =reg.predict_proba(X_test)\n",
    "predicted_proba.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first column shows the probability our model assigned to the observation being 0 and the second. The probability of the model assigned to the observation being 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.24317661, 0.37463683, 0.54177647, 0.22900075, 0.92457372,\n",
       "       0.71721095, 0.70198458, 0.88951845, 0.24828394, 0.24476587,\n",
       "       0.50124778, 0.8166621 , 0.93109413, 0.31720501, 0.72480078,\n",
       "       0.49830375, 0.48334522, 0.44729395, 0.61858827, 0.94389387,\n",
       "       0.26739396, 0.23658997, 0.56099766, 0.54331094, 0.78128297,\n",
       "       0.2472754 , 0.50532304, 0.12529269, 0.76947642, 0.24435098,\n",
       "       0.40027784, 0.72282213, 0.68309208, 0.50183789, 0.23658997,\n",
       "       0.57490362, 0.26244825, 0.76322809, 0.45070188, 0.58941464,\n",
       "       0.2545466 , 0.45376473, 0.2442691 , 0.43454262, 0.80228922,\n",
       "       0.64667768, 0.74190084, 0.25131584, 0.26873282, 0.21760326,\n",
       "       0.5187978 , 0.33929554, 0.69210482, 0.26325808, 0.84108495,\n",
       "       0.41846317, 0.91617148, 0.29643862, 0.35033133, 0.34573211,\n",
       "       0.73164572, 0.66843639, 0.29005712, 0.79176288, 0.24924481,\n",
       "       0.24578167, 0.08973456, 0.25026941, 0.77127351, 0.32080676,\n",
       "       0.25384078, 0.34924202, 0.89412946, 0.44546805, 0.61064223,\n",
       "       0.24435098, 0.78442702, 0.73968465, 0.7298458 , 0.65170162,\n",
       "       0.2710819 , 0.0964559 , 0.28013576, 0.70718961, 0.4593773 ,\n",
       "       0.12529666, 0.7001314 , 0.53524232, 0.25125332, 0.71422809,\n",
       "       0.18485496, 0.12453233, 0.26408979, 0.23683612, 0.22939748,\n",
       "       0.88462779, 0.23272196, 0.75543942, 0.24082042, 0.20969141,\n",
       "       0.60153782, 0.73125498, 0.70560126, 0.67575955, 0.43521068,\n",
       "       0.43340125, 0.22827616, 0.87096491, 0.74673204, 0.14849485,\n",
       "       0.09597737, 0.91115517, 0.67341481, 0.40587287, 0.56808575,\n",
       "       0.61089756, 0.77178522, 0.83218538, 0.57010053, 0.28963318,\n",
       "       0.23386317, 0.13341992, 0.82033086, 0.51484764, 0.25482954,\n",
       "       0.29268225, 0.23112053, 0.14500338, 0.68569376, 0.28070721,\n",
       "       0.62908087, 0.24022072, 0.23612179, 0.29758151, 0.2841349 ,\n",
       "       0.57529595, 0.45248698, 0.29891686, 0.26489094, 0.47232453])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This will give us the probabilities of excessive absenteeism and this result his much cooler than simply 1 or 0\n",
    "#probability of beging 1 \n",
    "predicted_proba[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save The Model\n",
    "Saving a model is the process of creating a file that will contain all the information regarding the machine learning.We will use the python module pickle that is the process of converting a python object into a character stream. The main idea is that this character stream will contain sufficient information then later when we would like to convert the character stream into a Python object add another notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'model' is the namse of the file and 'wb' meaning write bytes\n",
    "with open('model', 'wb') as file:\n",
    "    pickle.dump(reg, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#picle the sclaer file\n",
    "with open('scaler', 'wb') as file:\n",
    "    pickle.dump(absenteeism_scaler, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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