1Miniproj_1_Machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning Mini Project 1\n",
    "## Jeba Akewak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Description\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem that we look at is a dataset of patients treated in a cardiology department.\n",
    "The dataset describes 6 properties of patients treated in a cardiology department.In this project we are asked to build a logistic regression model to predict wether a patient takes part in the rehabilitation program or not. As such, this is a regression predictive modeling problem. Input attributes include reason,Gender,age, mobility, distance and participation.\n",
    "\n",
    "We'll be trying to predict a classification- Participated or not participated. Let's begin our understanding of\n",
    "implementing Logistic Regression in Python for classification.\n",
    "We'll use a \"semi-cleaned\" version of the data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the classification problems, the response variable is categorical. The simplest case of classification is where the response variable is binary, meaning that it can only take one of two values, such as true or false. Logistic regression takes a linear combination of explanatory variables plus an intercept term just like linear regression, but then it takes the result and passes it through the logistic function. The logistic or sigmoid function is defined as:\n",
    "                S(t) = 1/1 + e-t\n",
    "where t is the same linear combination of variables used in linear regression. The logistic function looks like an elongated S when plotted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries\n",
    "Let's import some libraries to get started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "Then Import the data set given at the following link\n",
    "https://www.dropbox.com/s/gkqb7rk5dc6rdzs/Data.csv?dl=0\n",
    "\n",
    "After downloading the csv file I exported it to excel format so that it becomes easier for reading it.\n",
    "Let's start by reading in the Data.xls file into a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "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</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Mobility</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Participation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hospital readmission</td>\n",
       "      <td>M</td>\n",
       "      <td>61.3</td>\n",
       "      <td>No car</td>\n",
       "      <td>68.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hospital readmission</td>\n",
       "      <td>M</td>\n",
       "      <td>85.8</td>\n",
       "      <td>Car</td>\n",
       "      <td>86.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hospital readmission</td>\n",
       "      <td>F</td>\n",
       "      <td>65.0</td>\n",
       "      <td>No car</td>\n",
       "      <td>46.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hospital readmission</td>\n",
       "      <td>F</td>\n",
       "      <td>72.5</td>\n",
       "      <td>No car</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hospital readmission</td>\n",
       "      <td>M</td>\n",
       "      <td>93.0</td>\n",
       "      <td>No car</td>\n",
       "      <td>73.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Reason Gender   Age Mobility  Distance  Participation\n",
       "0  Hospital readmission      M  61.3   No car      68.7              0\n",
       "1  Hospital readmission      M  85.8      Car      86.3              0\n",
       "2  Hospital readmission      F  65.0   No car      46.2              1\n",
       "3  Hospital readmission      F  72.5   No car      39.7              0\n",
       "4  Hospital readmission      M  93.0   No car      73.3              0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('Data.xls')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reason            object\n",
       "Gender            object\n",
       "Age              float64\n",
       "Mobility          object\n",
       "Distance         float64\n",
       "Participation      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "Let's begin some exploratory data analysis! We'll start by checking out missing data!\n",
    "## Missing Data\n",
    "We can use seaborn to create a simple heatmap to see where we are missing data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1fb45690>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1fb45a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the Reason column, it looks like we are just missing too much of that data to do something useful with at a basic level. We'll probably drop this later.\n",
    "Let's continue on by visualizing some more of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1fb6f410>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SMAaSJIyBJAljIEnCGEiSMAaSJIyBJAljIEnCGEiSgLRkDwBQW1vLsmXLOHnyJOnp6Sxf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\nYyBJwhhIkoD/B5fUoqu3xxVPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1eb3a310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Participation',data=df,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1eced610>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1f6f3ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Participation',hue='Gender',data=df,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1fc3b890>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GO3fuJCsrS88pFhFpoay6mqhr16506NDB3rWIiIiTWNUz6NGjB7GxsQwZMgR3d3fL/OtP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\nAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1ebff810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Participation',hue='Mobility',data=df,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c20b470d0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c21f39fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Age'].dropna(),kde=False,color='darkred',bins=30)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c214aec10>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c214cd810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Distance'].hist(color='green',bins=40,figsize=(8,4))\n",
    "plt.xlabel('Distance')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1c20404f50>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r8UQcPdEiaL8UY5n5xtQaWvvw+F8PcB7PZCNyFeqN5B2pbf/g5u01rIV7AcpO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\nJisiIhLe/wHIvpHsVm/ksAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c20404e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Age',y='Distance',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pwv+EhcGkSeratEmTVCvNIBJoRjCi1aTHPiTE3OLRZ7JCqBkRbBYc7Sp0VKYM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\nQQNNREQsQQNNREQs4Rsz+KQdGmmG1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c20805fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Age',y='Participation',data=df,color='red',kind='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/oFIlNQrSBmTqKyfS8s3dgDDTGmSBhpe/9Ag7TeGmJbC0dg8GEmrS9RhezJj6\nSoujR9Uq1wcPqsEiDz2k/z78JF2O4nwBhJmWbrsdqak5N7Pk3meg+9bUJamlG9Lo7kchzFa2LEyY\nACVKQK9e8MUXVlckgRaWdJw9X2uQ2UWgAaep9WlVqMm5NGGV6tVh7FiIjIR77oEff7S0HAk0pzFi\nbsUAwiyQINvg500P/oSb5tZaMI9nk5aaCBWXXQbDh6uuzdtuU+upWUQCLdzkd6DUOcwKo0dA6R10\n/gRbwYXpd92eZtJKE1Zq0eLcbCLt2lk2m4gEmpPofVDzM8y0BIKeravC9hFoyGl5H4W21vQINWml\niVDSvj307Klm6L/jDkhLM70ECbRw4m/rLA8FhVl+9O4mDEQgNWgNtvx3anKoSStNWO2++1QLbd06\neOAB8HpN3b1chybylsfBMdAw82u3Gp4T7CD13PvQsq0dqakFXgKQsn17/sP7ZUYPEU5cLnjmGdXl\nOG+eOr82YoRpu5cWWrgL8lt9fmGmtTUUSNegnufOtL6+sNZawC016XoUThMdDSNHqguuR46E2bNN\n27UEmlMUdmAMsusqrwN2QWFW4OYJLoS0bDeYQSYFCTjUCtyxjp+EdDsKOyhZEsaMUYuEPvAArF1r\nym4l0ESh9Aozs8+jBRpuwYRa/hsN8p1LK02Emlq1YNgwNZw/KQlOnDB8lxJo4Syvg2wQB97CwsxK\ngXZr5ie/UDO061GIUHPVVXDvvbBzJzz6qOETGcugkHAQxLd7ra2z/A7H/h6mtVYazDS+2TVpGaqx\noYDn5TdYpMBBIkKEmwceUMegOXPg9tvVSEiDSAvNCfT6dq9hO3qHWfIFN60CfV1u/gxc0U0wrTSt\nX0yktSfsJDJSzcZftKiaof/oUcN2JYEWrgw+6BW29WCCqKDtBbLNYEItoK5HIcJNpUrw4IMqzAYM\nMGw3EmgiX1oOyoHEotHDGwIJt2AGrPg9SERaUCIcdeoEderA9Omwfr0hu5BAczodR8dpPXAXdLg2\ne6xeIMEWyGN5CaiVpuflF0LYidutlpkBGDrUkF1IoAklgFZDXq+wU5gFuu9AQs1OS+QIYVtXXqlm\nzvniC/jtN903L4Em8nRh6yLYA7Yd2hVW1JBvK83obkfp1hR25HJBly7qz+++q/vmJdDCkUEHu/y2\nGsi5rIJuwdDaBWnpNXUSRsLJmjeHcuXg449B554NCbRQZ9HBz6hpq/x5bjA1BBtqeZFuRyE0cLuh\nbVs4dQpH9wezAAAgAElEQVS++07XTUugCUMZERx6vVYIYZHERHX/7be6blYCTegimNnurdq3Hc7r\nBURGOopQ16gRREXBDz/oullLAm39+vV07979ot8vX76cTp06kZSUxNy5cwHwer0MGzaMpKQkunfv\nzu7du80u1/kccM7G7Hfgz/4sGxgihF1FR0PFirBrl66bNX0ux/fee4+FCxcSExNz3u8zMjIYN24c\n8+fPJyYmhq5du9KmTRt+/fVX0tPTmTNnDsnJyYwfP57JkyebXbYIUwXN5SiECEL58uDxwOnTcEEe\nBMr0Flr16tWZOHHiRb/fsWMH1atXp0SJEkRHR5OYmMjatWvxeDy0bNkSgISEBDZu3Gh2yUIIIfRW\nrJi613EwlekttFtuuYW9e/de9PuUlBSKFy+e83NsbCwpKSmkpKQQFxeX83u3201mZiaRkbJQgBBC\nFMTj8eDKyCB261arS7lIlWPHiAN+27IFrx8ttMTsASV5sE0qxMXFkZorqVNTUylevPhFv/d6vRJm\nQgihQWJiolpg88wZq0vJm8tFkxYt1FB+HdhmlGOdOnXYvXs3x48fJz09nXXr1tGkSROaNm3KypUr\nAUhOTiY+Pt7iSh2oceifJTLqHRj6yTjgcxciID4f7N4N9erpFmZggxbaokWLSEtLIykpieeff56e\nPXvi8/no1KkTFSpUoG3btqxevZouXbrg8/kYO3as1SWLPDQmsJGGgb7uwm0EIphFQv3Zpyz2KcQF\nDh+GlBS44gpdN2tJoFWtWjVnWP7tt9+e8/s2bdrQpk2b854bERHBqFGjTK1PmEuPUAsrCcFEsRA2\nsHatur/6al03a5suRxEgm3dbaT30+vsuGue6BUJLXf5uu05sbCClCBF+/j2NxF136bpZCbRwpEMI\n+rMFf0KtoJAKNsT8rSc/hn+FsPmXFCGCcuQI/Pqrmv6qZk1dN235OTQRGurExhY6+a6eXYdGHdKN\najHmJ9/zZxJaIlx98glkZUHv3rpvWlpoIk+BDmTI7zBt9VmfBD9qKChq8nvMdt2NEpjCjk6ehEWL\n1LRX3brpvnkJNKfTOoAggAOgv6+wItT8CTIIgWmuZECICGXTp0NaGgwcCEWK6L55CTShmdZWSEGh\nYNbh2N8gg8LDzN/WWUDdjdKyEk61axd8/jnUrQt9+xqyCwm0cKXhwKml27GgARz5CSRstAp024GG\nmRBCg6wsePXVc/fR0YbsRgJNnKMh5PJqjQQSanAufIIJtwSC246WUZMFPS6tMyE0mDcPNm6Ezp0h\n17XHepNRjk7QuLFha2vF1a2b/3peuUsg7xGOWkc+mn1mSGt0BBJmhjHwfKgQhtm+HT78ECpUgEmT\nwOUybFfSQgsHOg8k8PdAbqfDqz/XsQUaZjJUX4h/paTA8OGQkaFCrWxZQ3cngRbO8jrA5vG7vA7Q\n/nQ9Zj9m1eHc3wuyC3tuQGFW6E4LqU5GN4pQ4/PBhAmwfz8MGgS33mr4LqXLUQQsr4utsw/L+XUz\nFva4HoIJzsJeG3CYmXXuTFqBwi5mzIBVq+CGG8Ck+XilheYUgX7DD6KVVmA5Gh7XYyqrC7cT6La0\nvNaQMNNCWmci1CxfDtOmqamt5swBk9awlBaa0CyvASIFTYnlT2vMinaFP/s0LMykRSWcZvNm1dVY\nvDh88QWUK2farqWFFk6CbKVB/ufTCjrg2+mQ7W9LrrD3ZniY+dM6k3AUVvvrL3W+LCtLtcwaNjR1\n99JCcxK9h+/7uT0trTUwd+2zQA/xWkZyysKdQuRy9Cg895yar/HDD6F9e9NLkEATSn7hlcfvsw/k\neV2fpnVW/mx2m51f6yUJhYaZ2a0zIayUkqLC7OBBGDMGHnzQkjIk0MJNQgIkJ+uyqfwuus4OhcKC\nDezRHenPdXVBB5nW5/gbZtLdKKySng4vvAD/+x88/rjqcrSInENzmmAObPm9toBtFnSAL+z8k5Wy\na7NlmAkRKrKyYOxY9SW5Uyd4801DZwIpjLTQwlFBrTQ/uh6zFTY9Vu7Q0NJq01uwoarpXJmeYSat\nMxEKfD54+234/nto1Qo++gjcbktLkkBzIi2DOQwINcj7vFpuRoabnq1BvwZ8SJiJcPTxx/DZZ3D5\n5WpZmKJFra5IAk34qZCw1BpsYL9Vnv0etag1TIwKMyGssngxfPAB1KgBS5dCyZJWVwRIoDmXUa20\n7MdAt2CzUkBD7/UOMggszKR1JqywahW89pqaaPjrr6FyZasryiGBFu4CDTUtj3N+YFgdbkFfN+ZP\ngEiYCSf6/XcYPVp1Ly5eDPHxVld0Hgk0J9PjQmstoQaa9nNhoBgVcLpf8GxUkIF0M4rQsXMnDBmi\nRjZ+9hk0b251RReRQBOFX5umJbT8CLZstp5pI5AWkFlhJq0zYbZDh9SF06dOwcyZ0K6d1RXlSQLN\n6bS20rRccK1lWwEEm20EGhRmtsokzITZTp9WLbMjR+Dll6FbN6srypcEmjhHr1DLfl42u4ZbsOEQ\nyOslzEQo8Xph/HjYsQMefRT697e6ogJJoIUDf86laQ010L5NK8PNiBAIdJsSZiLUzJwJK1eqC6ff\nesvSWUC0kEALF3qHWvY2wb+Qyu/AHGjQmXmgtyLIgtmvEMH4/vtzi3TOnw/R0VZXVCgJtHBiRKhl\nbxeCa33Z9aAdTF16jGC06+cinG37dtXVGBurZgExcZHOYEigifz5OzN/KA8IyaZXgEiYiVCVlgYj\nRsCZM/DppyH171ACLdz4e21a9oE5kGAD+4eb3v9Z9bquLIQOIsJBfD54/XXYtw8GDoSOHa2uyC+m\nBprX62XEiBFs3bqV6OhoRo8eTY0aNQA4cuQI/fr1y3nuH3/8Qf/+/enatSsdO3YkLi4OgKpVqzJu\n3Dgzy3aeQC64DnQdtQsPzFYEnBnhoOcF0hJmwirffAPLlsHVV6uFOkOMqYG2bNky0tPTmTNnDsnJ\nyYwfP57JkycDUK5cOWbOnAnAb7/9xuuvv84999zD2bNn8fl8OY8JnQQaahDcAqEFHaz1GFxiNgky\n4RSHD8PEiRAXB//9L0RFWV2R30wNNI/HQ8uWLQFISEhg48aNFz3H5/Px4osv8sorr+B2u9m4cSOn\nT5/moYceIjMzk379+pEg0wXpI9CpsfQItvzqCQVG/PsLlfcunMnnUxdNp6TAe+9BrVpWVxQQUwMt\nJSUlp+sQwO12k5mZSWTkuTKWL19OvXr1qF27NgBFixalZ8+edO7cmV27dvHII4+wdOnS814jghDM\nfI9GBZsdGfUlSoJMGMjj8eDKyCB269YCn1f855+pvG4dJ665hu0JCeDxmFSh/xITE/N9zNRUiIuL\nIzXXoo5er/eiYFq4cCE9evTI+blWrVrUqFEDl8tFrVq1KFmyJEeOHKFSpUqm1e14wU5inPtg75Rw\nM6MXQMJMGCwxMRHS09WIxfykpKih+TExlPjoIxJDtHUGJgda06ZNWbFiBbfeeivJycnE57H0wMaN\nG2natGnOz/Pnz2fbtm2MGDGCQ4cOkZKSQrkQuSYipOgxMz+EZriZ3YUtQSbsZNo0OHZMDQIJ4TAD\nkwOtbdu2rF69mi5duuDz+Rg7diyLFi0iLS2NpKQkjh07RlxcHK5c06vcfffdDBo0iK5du+JyuRg7\ndqx0NxpF7+vILgwKqwPO6nOvEmTCbvbtU62z2rVtP0+jFi6fz+ezuggjeDweEqtVs7qM0GXF8Hp/\nA8/qgNJKgkzoqXx5TU/zeDznuhzXrMn7SSNHwnffwZw5cM89+tVoEWnqiLxZMetHqASUVhJkws52\n7lRh1qwZdO5sdTW6iLC6AGFzclD2X+PG8rkJ+/vvf9X9sGG2n0VfK2mhicI5YY5Go0mAiVBy8CAs\nXw6XXw4dOlhdjW4k0IR2EmznkxAToWrhQrV4Z//+EOGcjjoJNOG/UJp8WG8SYiLUpafDkiVQpgwk\nJVldja4k0ERwnB5uEmDCaX78EU6cgAEDoGhRq6vRlQSa0I8dZtYPlASXCBfLlqn7XDMyOYUEmjCO\nnQJOAksINc3VTz+pwSCXX251NbqTQBPmkVARwlq//AKZmY657uxCmgPtzz//5MSJE+SeWOTKK680\npCghhBAGyJ4x5P/+z9o6DKIp0EaOHMmKFSuolmsqKZfLxYwZMwwrTAghhI68XrUsTJUqju0t0RRo\nq1evZunSpRR12IgYIYQIG7t2wfHjcPvtjpkZ5EKarqirVq0aDp3DWAghwsP69er+hhusrcNAmlpo\nJUqUoEOHDjRp0oTo6Oic348bN86wwoQQQuho2zZ137y5tXUYSFOgtWzZkpYtWxpdixBCCKNs364u\npK5f3+pKDKOpy7Fjx440bNiQ1NRUTpw4QYMGDejYsaPRtQkhhNBDRoY6h9a4MTh4gWRNgfbZZ5/R\np08f9u7dy/79++nbty/z5883ujYhhBB62LVLXX/WpInVlRhKU1RPnTqVefPmUapUKQAee+wxevTo\nwd13321ocUIIIXSwY4e6d9oiuhfQ1ELzer05YQZQunRpXA4d9imEEI5z4IC6r1fP2joMpqmFVr9+\nfcaMGZPTIps/fz4NGjQwtDAhhBA6OXRI3deoYW0dBtPUQhs9ejRRUVEMHjyYQYMGERkZyfDhw42u\nTQghhB4OHlQXU+ea7cmJNLXQihYtyrPPPmt0LUIIIYxw6BBUqgRFilhdiaEKDLSOHTuyYMECGjRo\ncN45M5/Ph8vl4o8//jC8QCGEEEHweuHIEQiDyeQLDLQFCxYAsGXLloseS09PN6YiIYQQ+jlxArKy\noHx5qysxnKZzaElJSef97PV66dSpkyEFCSGE0NHff6v7MmWsrcMEBbbQevTowS+//AJw3qjGyMhI\n2rRpY2xlQgghgvfPP+o+3AMte72z0aNHM3ToUFMKEkIIoaPsFlrp0tbWYQJNoxwHDhzIN998Q2pq\nKgBZWVns3buXp556ytDihBBCBOn4cXUvgaY88cQTnD59mr/++otmzZqxdu1aEhw+hYoQQjhCWpq6\nL1bM2jpMoGlQyM6dO5kxYwZt27bl4YcfZt68eRw+fNjo2oQQQgTrzBl1HxNjbR0m0BRoZcqUweVy\nUatWLbZu3UqFChVk2L4QQoSC06fVfdGi1tZhAk1djvXq1ePFF1+ka9euDBgwgMOHD5ORkWF0bUII\nIYJ19qy6lxaaMmLECNq3b0/dunV58sknOXz4MK+++qrRtQkhhAhWdpejw6e9Ao0tNLfbzfHjxxk9\nejRut5sbbriB+Ph4o2sTQggRLJ9P3Udoar+ENE2BNn78eJKTk+nQoQNer5c333yTjRs38uijj/q1\nM6/Xy4gRI9i6dSvR0dGMHj2aGrmWM5g2bRrz5s2j9L/DS0eOHEnNmjULfI0QQogCZAdaGNAUaCtW\nrGDx4sVERqqnd+nShTvvvNPvQFu2bBnp6enMmTOH5ORkxo8fz+TJk3Me37hxIxMmTKBRo0Y5v/v6\n668LfI0QQggNwmBRZk2BVqZMGU6ePJnTcsrIyDhvBWutPB4PLVu2BCAhIYGNGzee9/imTZt49913\nOXLkCNdffz2PPvpooa8RQgiRN4/HQ5WDB6mImmQ+NTra6pKClpiYmO9jmgKtRIkS3HHHHbRp04bI\nyEhWrlxJmTJlGDRoEADjxo3TVEhKSgpxcXE5P7vdbjIzM3Nafh06dODee+8lLi6Ovn37smLFikJf\nI4QQIm+JiYlqHTSgQXw8FBAGTqApFW6++WZuvvnmnJ9zdwn6Iy4uLmf6LFDn1LKDyefzcf/991O8\neHEAWrduzebNmwt8jRBCiEJkj24Mg2uHC0yGI0eOUK5cOa666qo8H69cubJfO2vatCkrVqzg1ltv\nJTk5+byRkikpKdx2220sWbKEYsWK8fPPP9OpUyfOnDmT72uEEEIUIvuC6uzh+w5WYKANHTqUKVOm\n0K1bt5zfuVyunBWrv/32W7921rZtW1avXk2XLl3w+XyMHTuWRYsWkZaWRlJSEs888ww9evQgOjqa\nFi1a0Lp1a7xe70WvEUIIoVF2Cy0MAs3l82kb05mRkUFUVBQZGRmkp6cTGxtrdG1B8Xg8JFarZnUZ\nQgihL40rT3s8HnUO7T//gb594eOP4d57DS7OWpqutPvyyy+56667ADhw4AC33nory5YtM7QwIYQQ\nOsie8ip71n0H0xRokyZNYurUqQBUr16dTz/9lIkTJxpamBBCCB2ULKnus1eudjBNgZaRkUHZsmVz\nfi5TpgwaeyqFEEJYqUwZdZ+9crWDaRr/npiYSL9+/bj99tsB1QUpC3wKIUQIyJ4E49gxa+swgaZA\nGz58ODNnzmTOnDlERkbSrFkz7nX4yUUhhHCE7BZauAda9nVoR48epX379rRv3z7nsaNHj/p9HZoQ\nQgiTlSqlZto/fNjqSgyn+To0V66JLQO9Dk0IIYTJIiOhbFnYvdvqSgxXYKBNmTIFgE8//ZSS2SNl\n/rV3717jqhJCCKGfChVg0ybIyICoKKurMUyBoxwPHDjA/v376datW86f9+/fz549e3j44YfNqlEI\nIUQwKlYErxcc3hApsIX21ltv8fPPP3P48GHuu+++cy+KjOT66683ujYhhBB6qFhR3e/aBbVqWVqK\nkQoMtOxlYd5991169eplSkFCCCF09u8SMmzdCjfcYG0tBtJ0YfWCBQuMrkMIIYRR6tVT97/+am0d\nBtN0HVrdunV5++23ueKKKyiavRQBcOWVVxpWmBBCCJ3UrAnR0eDxWF2JoTQF2vHjx/n555/5+eef\nc37ncrmYMWOGYYUJIYTQSWQk1K4Nv/8OZ8+eW1LGYTQF2syZM42uQwghhJHq1YMtW9Tw/aZNra7G\nEJoCbd26dXzwwQekpaXh8/nwer3s37+f5cuXG12fEEIIPTRoAIsWwerVjg00TYNChg4dyk033URW\nVhb33XcfNWrU4KabbjK6NiGEEHrJDjEHr2WpKdCKFi1Kp06daN68OZdccgmjR49m7dq1RtcmhBBC\nLxUrQpUqsGIFpKdbXY0hNAVakSJFOH78OLVq1WL9+vW4XC7SwmD1UyGEcJSrr4ZTp8Chp4s0BdqD\nDz7IM888ww033MBnn31Ghw4daNSokdG1CSGE0FOrVup+/nxr6zBIgYNCDh06xIsvvsju3btp0qQJ\nXq+XTz/9lF27dtGgQQOzahRCCKGHhg3V+miffAITJ0JMjNUV6arAFtrgwYOpXbs2AwcOJCMjg3Hj\nxlGsWDEuu+wyIiI0Ne6EEELYhdsNt9wCx4+DA2eAKjCVDh06RL9+/WjVqhWjRo1iw4YNZtUlhBDC\nCNkLNf+7PJiTFBhoUbnWzYmKijrvZyGEECGoalVo1gxWroR166yuRleaLqzOlnvVaiFEPqzoyWjc\n2Px9itCVlKTC7OWXYc4cq6vRTYGB9ueff3LjjTfm/Hzo0CFuvPFGfD4fLpeLb7/91vAChbBUqHSz\nF1anBJ7ILTFRTYU1bx4MHQqXX251RbooMNC++uors+oQwhyhElB6y+t9S8iFL5cLevaE55+HIUNg\n4UKrK9JFgYFWpUoVs+oQQh/hGliBkJALb82bq7/vRYvgu+/g+uutrihofp1DE8JWJLz0JyEXPlwu\n6NMHevdW98nJas20ECaBJkKHBJg1LvzcJeCco359uP121eX46qswaJDVFQVFAk3Ym4SY/eT+O5Fw\nC30PPwyrVsGIEXDbbSE9QEQCTdiPhFjokNZb6CteHAYMgMGDoVs3+OWXkF3RWuavEvawYcO5mwhd\n8vcYmlq0UK2zDRvg2WetriZgprbQvF4vI0aMYOvWrURHRzN69Ghq1KiR8/gXX3zB9OnTcbvdxMfH\nM2LECCIiIujYsSNxcXEAVK1alXHjxplZtjBKOBz0kpON23ZCgnHb1kP236+02kJDnz6wcSO89ZZa\nZqZrV6sr8pupgbZs2TLS09OZM2cOycnJjB8/nsmTJwNw5swZ3njjDRYtWkRMTAz9+vVjxYoVXHfd\ndfh8PmbOnGlmqcIITgkwI0PKH4XVYZfAk3NuoSEmBkaOVKMeH35YzcwfYn9fpgaax+OhZcuWACQk\nJLBx48acx6Kjo5k9ezYx/y5nkJmZSZEiRdiyZQunT5/moYceIjMzk379+pFgl/+oomChFmB2CSq9\n5PV+rP6/I602U3k8HlwZGcRu3ar5NXHdu1NlyhTSb76ZP6ZPJ7NsWQMr9F9iYmK+j5kaaCkpKTld\nhwBut5vMzEwiIyOJiIig7L8f3MyZM0lLS+Paa69l27Zt9OzZk86dO7Nr1y4eeeQRli5dSmSkjGex\nJbuGmNPCKlB2CTkJNlMkJiZCejqcOaP9RfXrQ2Ym0R98wBVDhsD330NsrHFF6sjUVIiLiyM1NTXn\nZ6/Xe14web1eXn75ZXbu3MnEiRNxuVzUqlWLGjVq5Py5ZMmSHDlyhEqVKplZusiP3QJMgst/F35m\nZgacBJs93XcfHDgAS5ZAp07qOrUQuOja1EBr2rQpK1as4NZbbyU5OZn4+PjzHh82bBjR0dFMmjQp\nZwHR+fPns23bNkaMGMGhQ4dISUmhXLlyZpYtLmSnEJMA01/uz9SscJNgsxeXC555Bv7+G776SgXc\nrFlg854xl8/n85m1s+xRjtu2bcPn8zF27Fg2b95MWloajRo1olOnTjRr1ixnmZoePXrQunVrBg0a\nxP79+3G5XAwYMICmTZsWui+Px0NitWpGv6XwISGmr0A+T6sP9maFm9Xv0+7Kl9f0NI/Hc67Lcc2a\nwPZ19iw89xysXw/33w8ffKBWvbYpUwPNTBJoOrFLkIVCiFn9WZkZBGaEmwRb3swMNIDUVHXh9ZYt\n0KULzJgBNl3s2d7tR2Etqw/Qdgoxqz8LLfKr0YhgMKNbUroh7SE2Vi0EOmgQzJ4NaWlqUdCiRa2u\n7CISaOJiVh+8rQgyq9+zkYyenir778vIYJNQs1ZcHLz0Erzwghog8n//B599BsWKWV3ZeSTQxPms\nPLAbGWRODix/GXWhs5GtNgk168XEwNixahLjb76Bdu3giy/gkkusriyHBJpQnBRkEl7aGdV6M6LV\nJqFmvehoGDUKxoxRi4LeeCMsXqz5vJ7RJNCENQGgZ4jZNcDMqkvPg7zerTe9g01CzXqRkTB0qDqH\ntnSpmvdxyRJo0MDqyiTQwp7ZYeCkILN6/9mMWmVaz3BLTtY31ECCzUput5qVv3x5NerxmmtgwQJo\n3drSsiTQwplZB+RQDjG7hJa/9O5K1CNEpLXmLC4XPPggVKoEr7wCbdvC1KnqImyLSKCFKzMO1KES\nZKEaWv7Qq7WlV7BJqDlHu3ZQrpwaLNKtG+zcCUOGqMAzmQRaODLyAG7nAR42C66U7ds1Pzeubl39\ndqxHuAUbbHq21iTUrJeYqNZRGzRIDe3/3/9gyhTTL8CWQAs3RhzUjRhuH2ydNggvfwIrmG0FFXbB\nhpsewWb1kjZCH7VqwaRJKtSmToU9e2D+fChRwrQSZOqrcKL3Qd6o68YCrdOCENMztPQSdGsumNZO\noK/VI9TCpZVm9tRX/jp9GkaPVvts2FAN669Rw5RdSwtN+M9OQWZSiNkxuPKTu9aAwi2YVlugLTY9\nuiCl69EeYmLUtWr/+Y8a+Xj11eoC7AIW5tSLBFq40OvAb5fuRQODLJTCqzC6hVsgwRZIuATbBSmh\nZg9uNzz5JFSurLohW7VS8z/edpuhu5VACwd6HPzt0irTMcisCq4duRa59VedIFYODircAgk2q0JN\n2Mfdd0PFiqoL8o47VKvtsccM250EmiicA1plZodXMKHl73YDCbnszyOgYPM31CCwLshAQ01aafZy\n3XXw+usweDD07g2HDsGwYYYM65dAc7pgWzR2CLMA34NZIWZUeAWyf3/DLaBWm5mttUBJqNnLpZfC\nxIkwcKC6Xu3wYTXMX+fFQiN03ZqwF7uF2YYNhodZyvbtOTej7EhNPe9mJ8HU5fdnZvQXEzuthyeC\nV7UqvP021K6tzqvde69aEVtHEmgib3rP8mFwF6NRIXZheNktwAoSSM1+f47+/t2aGWo2uBZRXKBM\nGXjzTdV6njtXDRI5dUq3zUuXo1MF85/ZDlNWaXyd3iFmRmAFc5gNtBMt+31p7ZL0+xybP118/nYH\nyiARZ8leLPTFF2HZMrUEzddfQ8mSQW9aWmjCGIG2yrJfq4EeYWZ062tDHjc9t+evQFpsmhnZUguU\ntNLsqUgRGDlSzQO5dq26P3ky6M1KC82JrG6dBbN/g8PM6BaY2YfP3Pvzp/XmT4vNr9aaUS01aaU5\nj9utBol4vaqFduutan21uLiANymBJs4JNsxsGmRGhpidvv9n1+JvsPnTDal7qJnBbvWIcyIi1Lpq\nWVnw7beQlASff64WEQ1kczqXJ8KVCV07/oaZGd2IduRvbYZ0QWr99+DPvxsZ9ehMbrea0Lh5c7Xy\n9RNPQIBTDEugOU2gwWLlaDINr/cnzIwIMruHWF78qdefz8zSUAuUnEuzN7cbhg+HunXhnXfUSMgA\nSJejCI7NlnnRM8iMPAQGsu1AO802+PFarV2Qmrsf9Sbn0pyrWDEYO1ZNjfXss3DttXDllX5tQlpo\nInAmhZnWFoFeYWZES0yPkY7BvN6oLsjCd2yjVpqwv3Ll1BRZmZnqfNqJE369XAJNBNbdaLNlW4I9\nCBvVpWhUN2Ug29U71Jy0KoGwkcRENYvIzp1q9Ws/SKA5SSh9y9Wx1mDCzOjWmNH83Y/eNWkKNbss\nLBtK/z/C3f33q6myJk2CjRs1v0wCTfjPRl2NwYaZ3qw6ZBoRaqZP8yWBI7JFRUGfPmo4/8CBml8m\ngRbuwnAodCh1Lfpbg97P1a3rUcJK+Ovqq9X1g0uXwpYtml4igSb8E+KtMye1yvJiZfejELpyuaBj\nR/Xnd97R9BIJNBE2nB5mYSUMexbC0nXXQalSMHu2poutJdBESLLDMi52DTO96zJ1xKPRXZPS9Rla\nIhLg41sAABYqSURBVCNVt+OhQ/C//xX6dFMDzev1MmzYMJKSkujevTu7d+8+7/Hly5fTqVMnkpKS\nmDt3rqbXiH/Jf9QCGXHOzAlMfR/yb1QEolEjdb9mTaFPNTXQli1bRnp6OnPmzKF///6MHz8+57GM\njAzGjRvHhx9+yMyZM5kzZw5Hjx4t8DUixMgBzTTySQvHqFJF3R88WOhTTZ36yuPx0LJlSwASEhLY\nmOv6gh07dlC9enVKlCgBQGJiImvXriU5OTnf1wgh8iZzywvHKFpU3Wvo+jY10FJSUojLtdaN2+0m\nMzOTyMhIUlJSKF68eM5jsbGxpKSkFPgaIYQQ+fN4PLgyMojdutXqUgJW7K+/qAbs37ePAx4PiYmJ\n+T7X1FSIi4sjNVfKer3enGC68LHU1FSKFy9e4GuEEELkLzExEdLT4cwZq0sJ3J9/AlD5mmuoXECY\ngcnn0Jo2bcrKlSsBSE5OJj4+PuexOnXqsHv3bo4fP056ejrr1q2jSZMmBb5G5BIKCxiGQo0aOeWd\nOOV9CAf76y91X69eoU81tanTtm1bVq9eTZcuXfD5fIwdO5ZFixaRlpZGUlISzz//PD179sTn89Gp\nUycqVKiQ52uEqBMb69fQ/cboP1DCiG2GKk1LyWj5QmP0lx4HfakKCz4f/PQTxMRAkyaFPt3UQIuI\niGDUqFHn/a5OnTo5f27Tpg1t2rQp9DVCBCJcAkjvQ7aW9dFMJ2uihYf//Q/27IFOnUDDv0O5sFqY\nS+M3ZKMWj9T7YN/YgG0Gw59a7FS3EHlasEDdd+mi6ekSaOHO32+6NuqyCbTlYMQ7sMOnYkSYafmM\ndetuFCK3AwfUxMT168Odd2p6iQSaMJ+OB7dgQs1JrTWJC+E477+vlo8ZNkxNgaWBBJrwn0nfto3q\ndszNCcHm775Mb51pJa04kW3VKli+HK68EpKSNL9MAs1JzDwgBLsvHc+l6TFoIRSDLZDt6xlm2neq\n86cQ6IAQCczQcOIEvP46REfDtGngdmt+qQSasO4AYYOuxws1Rv8g0nObgW7Ln9do/SyldSZ0l5UF\nI0fCsWMwahRcdplfL5cpN4TtxdWtq2l5kuwDsV5Ly2QfYvUc6p/XYbug7esVglrpHmYSVMIf77wD\nv/0G//d/MHCg3y+XFprTBHoACaaVFsxBy4Bh/HpfN2VEqy2/7V9402O7WlkWZv78+5HuRuf6/HOY\nPx8uvRRmzoQI/+NJAk3ow4ahln3Tk9Hhphd/gywkwkw417ffwptvQvnysHAhXHJJQJuRQBPnBDv7\ngs1CLZsRwQb6tqb0YlSrDGzQzSizgzjTmjUwbpwKsa++giDOzco5NCdq3Ni6xTSzD2aB7F9j3VrP\nqV0o98Fbr/NsuV14GDfrbyCQ+PA34A0JMzO6Gv3djzDXDz+owR/R0fDFF0F/aZFAE+dLSIDk5OC3\nE2io+hFqQEDBBhcf0EMp4II9PBvSKgPjwkw40/LlMHasWsBz8WK47rqgNymB5lTBtNL0DDXwvw4/\nag822LLldZDXO+SsPIQH0uVqmzCT1pnzfPYZTJwIcXHw5ZdwzTW6bFYCTeRNr1CDwMLVzzAMtBuy\nIGa04oxmeJCBfcNM2I/XC+++C3PmqAEgixdDs2a6bV4CzcmCPZemd6iBKa01CL7FlhczWnF6CHQA\njKFBFsjzgyWtM3tJT1eDP777Tk04vGQJ1K6t6y4k0ETB9Aw1CCzYAghmo8Mtm11CLphRnAHN+GFG\nmElXo3OcOAFDh8LGjdCypepyLF1a991IoDmdHiMe9Q418L+uIEZPXnjANjLgwJyQ0+MyBFOCLNDX\nSFejc+zbB4MGqYU6u3SBqVPVQBADSKCFAzuHGpgWbNnyO5Cb3ZKzQsDzLwba4rEizKR1Zh+bN8OQ\nIXD8ODz3nBrVGMAMIFpJoAntsg80VrfWsl8Dul5vl9fB3ujWnFlCIshAwsxJVq2C0aMhIwMmT4bH\nHjN8lxJo4ULPi62NCLZgBo0E8jqNrGjN6SHomfBNmPXlItLN6ByffAL/+Q8UK6b+3KGDKbuVQAsn\nes8gYsdgC+S1AbBb0Om2lIsVQQb6hJm0zqyXlaVmzJ8/HypWVLN/JCaatnsJtHBjxLRYRgVboHVe\neGAzcRqwwoJFj8DTfSVvK9e106tVJmFmvbNnYcwYNZ3VZZepYfk1aphaggRaODJqrsfcBycrZxrJ\nbzsXsmC+S93DKFB6BECw25Awc47jx9Xgj82b4frrYcECKFnS9DIk0MKV0RMY69lqM+o8WUEHQqsm\ndzaSXgd+uwQZSJjZwd69agTj/v1w333wwQdQpIglpUighTMzZuXXs9Vm5nkyJ4Sdngd7vbYlYeYs\nmzfD4MHnLpweNQpcLsvKkUALd2YuNWNEuFkRLnYNOyMO8HYMMpAwswOPB154QU1p9d578PDDVlck\ngSawZv00vbokLRwAkictB9pgajTjQK7nPowYii9hZr2VK9U1ZhERalj+HXdYXREggSayWbUoqN4j\nJO0WcHmx4wFZ75qMuqbMjp9duFmyBF59VV1jtnAh3HCD1RXlkEAT51jZjWfkLCT5sWPYmcWoYJAg\nc7a5c9WsH6VLq3XMmje3uqLzSKCJi1nVWgP9h/4XpLCDpJMCz+hAMHKWDwkze5g2DaZPh8qV4Ztv\n1LVmNiOBJvJmZWstm1GtNq2MPh9mFLMCwIypqiTM7GHGDBVmtWvDsmVQq5bVFeVJAk0UzE7BBtaF\nW36sDD0rDvZmzbcoQWYfs2apJV9q1oQVK6B6dasrypcEmtDGDsEG9g63/IT6wdnMSYND/bNymoUL\n4d13oVo1WL7c1mEGJgfamTNnGDhwIH///TexsbFMmDCB0hesWjpt2jQWL14MQOvWrenbty8+n49W\nrVpRs2ZNABISEujfv7+ZpYtsJk8CXKBQDLdQYcXM9xJm9rJqFbz5JpQta+tuxtxMDbRZs2YRHx/P\nE088weLFi5k0aRJDhw7NeXzPnj0sXLiQefPmERERQdeuXbnpppuIiYmhYcOGvPPOO2aWKwpj13DL\nTYJOGyuXbpEgs59Nm+DFFyEmRg3Tj4+3uiJNTA00j8fDw/9eTd6qVSsmTZp03uMVK1bk/fffx+12\nA5CZmUmRIkXYtGkThw4donv37hQtWpRBgwZRu3ZtM0sXhbHr9V+FHajDNfDssPaYBJk9HT4Mw4ap\npWA+/xyuvNLqijQzLNDmzZvH9OnTz/tdmTJlKF68OACxsbGcOnXqvMejoqIoXbo0Pp+Pl156icsu\nu4xatWpx9OhRevXqRfv27Vm3bh0DBw7kk08+Map0oQe7BtyFtB7YQzn47BBeuUmQmcbj8eDKyCB2\n61ZNz3elp1P9lVcoeuwYe/r353C5cmqKKxtJLGB9NcMCrXPnznTu3Pm83/Xt25fU1FQAUlNTueSS\nSy563dmzZxk8eDCxsbEMHz4cgEaNGuW02po1a8bhw4fx+Xy4LJwEU/gpVAIuP6EQfHYLrtwkxCyR\nmJio5lo8c0bbC155Bf76Cx56iGovv0y1EDvGmtrl2LRpU77//nsaN27MypUrL0pan89Hnz59uOqq\nq+jVq1fO799++21KlizJI488wpYtW6hUqZKEWaiz0/k3Pdk5VKwgQRY6li+HxYuhSROYNMnSWfMD\nZWqgde3aleeee46uXbsSFRXFq6++CsDUqVOpXr06Xq+XX375hfT0dH744QcA+vXrR69evRg4cCDf\nf/89brebcePGmVm2MJpTwy1cSYiFnoMH1fyMsbEwe7Zl65kFy+Xz+XxWF2EEj8dDYrVqVpchgiHh\nFjokxMxTvrymp3k8nnNdjmvW5P9Enw+efRbWrYMPP4QHH9SpUPPJhdXCvqTlZl8SYM7x1VcqzNq1\ngwcesLqaoEigidAQ6oNKQp0EmDOlpMA770BcnLoPwfNmuUmgidAkAWcsCbDwMGMGnDgB48dDjRpW\nVxM0CTThDLLuWeAkvMLTvn2wYIGadPipp6yuRhcSaML5/DlgOz38JLxEto8/hsxMGDsWiha1uhpd\nSKAJkZu/B3w7BqCElijMwYPw9ddQvz7cc4/V1ehGAk2IYOgRHoWFogSU0Nsnn6i5GocMgX9nYXIC\nCTQhrCaBJcx0+jQsXQoVK0JSktXV6CrC6gKEEEKYaPlyNVy/Vy+Ijra6Gl1JoAkhRDj55ht137On\ntXUYQAJNCCHCxZEj6pxtq1ZQvbrV1ehOAk0IIcLFDz+ouRu7dLG6EkNIoAkhRLj4+Wd1f/vt1tZh\nEAk0IYQIB2fPqgVoGzeGqlWtrsYQEmhCCBEO/vhDLSVz001WV2IYCTQhhAgHv/+u7q+7zto6DCSB\nJoQQ4WDzZnV/zTXW1mEgCTQhhAgH27dDlSpQoYLVlRhGAk0IIZzuxAk4ehQSEqyuxFASaEII4XT7\n9qn7+vWtrcNgEmhCCOF0Bw+q+1q1rK3DYBJoQgjhdBJoQgghHCE70GrWtLQMo0mgCSGE02UHWo0a\n1tZhMAk0IYRwuhMnoFgxiIuzuhJDSaAJIYTTpaXBJZdYXYXhJNCEEMLpUlOhRAmrqzCcBJoQQjhd\naqq00IQQQoS4rCw1y37JklZXYrhIqwswkmfPHqtLEEIIfe3ZQ2Jiovbnx8SoVarDgMvnC5N3KoQQ\nwtGky1EIIYQjSKAJIYRwBAk0IYQQjiCBJoQQwhEk0IQQQjiCBJoQQghHcPR1aFbyer2MGDGCrVu3\nEh0dzejRo6lhw5muMzIyGDx4MPv27SM9PZ3evXtTt25dnn/+eVwuF/Xq1WP48OFERNjru8/ff//N\nXXfdxYcffkhkZKTt650yZQrLly8nIyODrl270rx5c1vXnJGRwfPPP8++ffuIiIjgxRdftO3nvH79\nel555RVmzpzJ7t2786xx7ty5zJ49m8jISHr37s0NN9xgm5r/+OMPXnzxRdxuN9HR0UyYMIGyZcva\nruaQ4BOG+Oqrr3zPPfecz+fz+X777TffY489ZnFFeZs/f75v9OjRPp/P5/vnn398rVu39j366KO+\nn376yefz+XwvvPCC7+uvv7ayxIukp6f7+vTp47v55pt927dvt329P/30k+/RRx/1ZWVl+VJSUnxv\nvfWW7Wv+5ptvfE8++aTP5/P5Vq1a5evbt68ta3733Xd9t912m69z584+n8+XZ42HDx/23Xbbbb6z\nZ8/6Tp48mfNnu9R83333+TZv3uzz+Xy+WbNm+caOHWu7mkOF9V+vHMrj8dCyZUsAEhIS2Lhxo8UV\n5a1du3Y89dRTAPh8PtxuN5s2baJ58+YAtGrVijVr1lhZ4kUmTJhAly5dKF++PIDt6121ahXx8fE8\n/vjjPPbYY1x//fW2r7lWrVpkZWXh9XpJSUkhMjLSljVXr16diRMn5vycV40bNmygSZMmREdHU7x4\ncapXr86WLVusKvmiml977TUuvfRSALKysihSpIjtag4VEmgGSUlJIS7X2kNut5vMzEwLK8pbbGws\ncXFxpKSk8OSTT/L0/7d3fyFN/X8cx5+nbPZXZUkm9AcWCeoss4Sk6MaLsTJnNigKtT9CF5F9K4fh\nn1IQo6ygAq0uKsolChuI9gfCC+1CJEojoUxKwZVf/5SFzj9Nt9+FP/fF79eg7xfM03o/7rZzDntt\nnO21zznb5/zxBx6PB0VRvMsHBgZmOeVf7HY7Wq3W+2UBUHVegP7+flpaWrhy5QoFBQVkZmaqPvPC\nhQv58OEDRqORvLw8UlJSVJnZYDDg5/fXmZPpMg4ODrJkyRLvOosWLWJwcPCnZ53098yTX8xevHhB\nWVkZBw4cUF3mX4WcQ5shixcvxul0em+73e4pO7GadHV1cfToUfbt28fOnTspLi72LnM6nQSoaJZu\nm82Goig0NDTw+vVrsrKy+Pz5s3e52vICBAUFodPp0Gg06HQ6/P39+XPyCsKoM/OdO3fYunUrp06d\noquri7S0NFwul3e5GjMDU87pTWb8+3vR6XROKQs1ePjwIaWlpdy8eROtVvtLZFYjGaHNkJiYGOrr\n6wFobm4mLCxslhNNr6+vj0OHDmGxWDCbzQBERETQ2NgIQH19PZs2bZrNiFNYrVbKysq4d+8e4eHh\nnD9/nm3btqk2L8DGjRt5+vQpHo+H7u5uhoeHiYuLU3XmgIAA7wdoYGAgY2Njqt4vJk2Xcd26dTx/\n/pzR0VEGBgZ49+6dqt6PVVVV3n165cqVAKrPrFYyOfEMmfyV49u3b/F4PBQVFbFmzZrZjvUPhYWF\nPHr0CJ1O570vJyeHwsJCXC4XOp2OwsJC5s6dO4spp5eSkkJ+fj5z5swhLy9P1XkvXLhAY2MjHo+H\nEydOsGLFClVndjqdZGdn09vbi8vlIjU1Fb1er8rMDoeDkydPUllZSXt7+7QZKysrqaiowOPxcOTI\nEQwGgyoyl5eXExcXR2hoqHfEGxsbS0ZGhuoy/wqk0IQQQvgEOeQohBDCJ0ihCSGE8AlSaEIIIXyC\nFJoQQgifIIUmhBDCJ0ihid+Ow+FAr9djMpkwmUwYDAYyMjLo6+vj1atX5OTkfHfbzs5OsrOzf2Ja\nIcSPUufUFULMsGXLllFVVQVMTJd0+fJlMjIyuH//PlFRUd/d7uPHj3R2dv6smEKIf0FGaOK3pygK\nx44do62tjbt375KSkgLA7du3SUxMJCkpiTNnzgATf0RvaWmhoKCAsbExcnNz2bNnD/Hx8aSnpzMy\nMoLD4SApKQmLxUJCQgJpaWl8+fIFgOrqarZv386OHTs4ffo0LpcLp9NJVlYWycnJmEwmampqZu21\nEOJXJoUmBKDRaFi9ejXBwcEAjI2NcePGDWw2G3a7HUVR6O7uJjc3F71ez9mzZ2lqamLevHlUVFTw\n5MkTRkdHqaurA+DNmzccPHiQmpoaAgICqK6upru7m3PnznHr1i0ePHjA+Pg4dXV1lJaWEhkZid1u\nx2q1cv36dRkFCvEfyCFHIf5PURTmz58PgJ+fHxs2bMBsNhMfH8/+/fsJCQmho6PDu35sbCxBQUFY\nrVbev39PR0cHQ0NDACxdupSIiAgA1q5dy9evX2lqaiImJobly5cDeCeBLikpYWRkBJvNBsDQ0BBt\nbW3eef2EED9GCk0I4Nu3b7S3t/Pp0yfvfSUlJTQ3N1NfX096ejoXL16csk1tbS1Xr14lNTWV5ORk\n+vv7mZxJzt/f37ueoih4PJ5/XG1h8ioBbreb4uJiIiMjgYkJowMDA2fkeQrhy+SQo/jtud1url27\nxvr161m1ahUwUTZGo5GwsDCOHz/Oli1baG1tnXJdu4aGBoxGI7t37yY4OJhnz54xPj7+3ceJiori\n5cuX9Pb2AlBUVERtbS2bN2+mvLwcgJ6eHhITE+nq6prhZy2E75ERmvgt9fT0YDKZgIlCCw8P59Kl\nS7S2tgKg1WrZu3cvZrOZBQsWEBoayq5du3C5XAwMDGCxWEhPTyczM5PHjx+j0WiIjo7G4XB89zFD\nQkLIycnh8OHDuN1uoqOjSU5OZnh4mPz8fBISEhgfH8disXiLVQjx42S2fSGEED5BDjkKIYTwCVJo\nQgghfIIUmhBCCJ8ghSaEEMInSKEJIYTwCVJoQgghfIIUmhBCCJ/wP/pjU9AvfttXAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c20f466d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Distance',y='Participation',data=df,color='red',kind='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6lMJK5M/CPajC/f2HoosuMsvXX7e3jgpQWIkcTztqQ59DaDn/fIiJgUWL7K6k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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c21310290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Age',y='Distance',data=df,color='red',kind='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1c214f37d0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eLz0eHKz06GoCFlQMU8Pkmprf2krdXdJvgnfcAUO1MGHrWbML8HoprSbLqQ4H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\nCAehCRVBEARBEISD/D+1i8WOGplPUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c214f3d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,hue='Participation',palette='bwr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Categorical Features\n",
    "We'll need to convert categorical features to dummy variables using pandas! Otherwise our machine\n",
    "learning algorithm won't be able to directly take in those features as inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gender = pd.get_dummies(df['Gender'],drop_first=True)\n",
    "mobility = pd.get_dummies(df['Mobility'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop(['Gender','Mobility','Reason'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df,gender,mobility],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": false
   },
   "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>Age</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Participation</th>\n",
       "      <th>M</th>\n",
       "      <th>No car</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61.3</td>\n",
       "      <td>68.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85.8</td>\n",
       "      <td>86.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>46.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72.5</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93.0</td>\n",
       "      <td>73.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Distance  Participation  M  No car\n",
       "0  61.3      68.7              0  1       1\n",
       "1  85.8      86.3              0  1       0\n",
       "2  65.0      46.2              1  0       1\n",
       "3  72.5      39.7              0  0       1\n",
       "4  93.0      73.3              0  1       1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 5 columns):\n",
      "Age              500 non-null float64\n",
      "Distance         500 non-null float64\n",
      "Participation    500 non-null int64\n",
      "M                500 non-null uint8\n",
      "No car           500 non-null uint8\n",
      "dtypes: float64(2), int64(1), uint8(2)\n",
      "memory usage: 12.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is ready for our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pearson's correlation coefficent is a measure for a linear relationship between two numerical attributes. The correlation coefficent value lies between -1 and 1. The larger the\n",
    "absolute value of the coefficent is the higher the linear relationship, if it is close to 0 then the lesser they are related to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Age</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Participation</th>\n",
       "      <th>M</th>\n",
       "      <th>No car</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.383263</td>\n",
       "      <td>-0.368875</td>\n",
       "      <td>0.043913</td>\n",
       "      <td>0.118585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>0.383263</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.463804</td>\n",
       "      <td>0.068119</td>\n",
       "      <td>-0.016538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Participation</th>\n",
       "      <td>-0.368875</td>\n",
       "      <td>-0.463804</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.040697</td>\n",
       "      <td>-0.358500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.043913</td>\n",
       "      <td>0.068119</td>\n",
       "      <td>-0.040697</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No car</th>\n",
       "      <td>0.118585</td>\n",
       "      <td>-0.016538</td>\n",
       "      <td>-0.358500</td>\n",
       "      <td>0.047370</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Age  Distance  Participation         M    No car\n",
       "Age            1.000000  0.383263      -0.368875  0.043913  0.118585\n",
       "Distance       0.383263  1.000000      -0.463804  0.068119 -0.016538\n",
       "Participation -0.368875 -0.463804       1.000000 -0.040697 -0.358500\n",
       "M              0.043913  0.068119      -0.040697  1.000000  0.047370\n",
       "No car         0.118585 -0.016538      -0.358500  0.047370  1.000000"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_pearson = df.corr('pearson')\n",
    "corr_pearson"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above correlation table we can see that distance has the highest correlation coefficient which is 0,463804.And this shows transportation assistance is likely to make the most impact."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a Logistic Regression model\n",
    "\n",
    "Let's start by splitting our data into a training set and test set. we divide the data set into X_train, X_test, Y_train, Y_test. Here we used the test size to be 20% while the train size to be 80%.\n",
    "\n",
    "In the logistic regression, the output is interpreted as a probability: the probability that an observation belongs to the second of the two categories being modeled. When the linear combination of variables produces positive numbers, the resulting probability is greater than 0.5 and when it produces negative numbers, the probability is less than 0.5.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = df.values\n",
    "# split into input (X) and output (Y) variables\n",
    "X = dataset[:,0:4]\n",
    "Y = dataset[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size\n",
    "= 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.02143639 -0.0287562  -1.80116715  0.14638249]]\n"
     ]
    }
   ],
   "source": [
    "print(logmodel.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute precision\n",
    "The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.\n",
    "The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.\n",
    "The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0.\n",
    "The F-beta score weights recall more than precision by a factor of beta. beta == 1.0 means recall and precision are equally important.\n",
    "The support is the number of occurrences of each class in Y_test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.70      0.70      0.70        47\n",
      "        1.0       0.74      0.74      0.74        53\n",
      "\n",
      "avg / total       0.72      0.72      0.72       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interpretation: \n",
    "\n",
    "Of the entire test set, 72% of the participated in the rehablitation. Of the entire test set, 72% of the customer's preferred to participate in the rehablitation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get the accuracy of a model using the scikit-learn model.score () function:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall Prediction accuracy is just one of many quantities you can use to evaluate a classification model. Oftentimes accuracy is not the best metric for assessing a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[33 14]\n",
      " [14 39]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(Y_test, predictions)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is telling us that we have 33+39 correct predictions and 14+14 incorrect predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.72\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logmodel.score(X_test,Y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curvefrom sklearn import metrics\n",
    "from ggplot import *\n",
    "\n",
    "prob = clf1.predict_proba(X_test)[:,1]\n",
    "fpr, sensitivity, _ = metrics.roc_curve(Y_test, prob)\n",
    "\n",
    "df = pd.DataFrame(dict(fpr=fpr, sensitivity=sensitivity))\n",
    "ggplot(df, aes(x='fpr', y='sensitivity')) +\\\n",
    "    geom_line() +\\\n",
    "    geom_abline(linetype='dashed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hITRp0gSAmjVrcu7cuSTHK1WqRFRUFPb29iilsLHSgSYiZzLnHEniORkMvDJuHFy5Aj4+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\n4sLMmTNZv349vr6+1KpViy1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tmjVrZumQzcZUWXh6euLt7Y2DgwOurq506tTJ0iFnqef9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\nIUQykhyEEEIkI8lBCCFEMpIchBBCJPN/e6wF5tb25MAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2169d7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(Y_test, logmodel.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(Y_test, logmodel.predict_proba(X_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Patients characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression is a common tool for generating class probabilities and predictions. Although logistic regression models are simple and often insufficient to fully Capture relationships between variables in many predictive modeling tasks, they are a good starting point because simple models tend to not overfit the data."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}