Supervised Learning - Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Supervised Learning: Logistic Regression\n",
    "\n",
    "#### Overview\n",
    "\n",
    "In this section we will be learning about logistic regression. In this section we will be using Logistic Regression to perform classification of a data set. In this lecture, we will focus on binary classification, so that we have two outputs, a positive and negative, (1 or 0). To give some examples of what we mean by binary: we could classify email as either spam or not spam, or tumors as either malignant or benign. In both these cases we have a set of data and features, but only two possible outputs. It is possible to have more than just two classes, but for now we will focus on binary classification.\n",
    "\n",
    "In order to perform this classification we will be using the logistic function to perform logistic regression.\n",
    "\n",
    "Here is an overview of what we will do throughout this lecture:\n",
    "\n",
    "    1. Basic Mathematical Overview of the Logistic Function\n",
    "    2. Extra Math Resources\n",
    "    3. DataSet Analysis\n",
    "    4. Data Visualization\n",
    "    5. Data Preparation\n",
    "    6. Multicollinearity Consideration\n",
    "    7. Logistic Regression with SciKit Learn\n",
    "    8. Testing and Training Data Sets\n",
    "    9. Conclusion and More Resources\n",
    "    \n",
    "Now we'll start with our imports before continuing the lecture (this is because we want to plot some things in our explanation)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------\n",
    "#### IMPORTS\n",
    "\n",
    "##### Module Install Notice!\n",
    "You'll need to install a new module we haven't used before: [Statsmodels](http://statsmodels.sourceforge.net/).\n",
    "\n",
    "You can install it with 'pip install statsmodels' or 'conda install statsmodels' depending on your Python installation. In this lecture we will only be using a dataset from it, but it can do quite a bit, including many statistical computations that SciKit Learn does.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bp/opt/miniconda3/envs/udemy/lib/python3.9/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "# Data Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "# Math\n",
    "import math\n",
    "\n",
    "# Plot imports\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "# Machine Learning Imports\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# For evaluating our ML results\n",
    "from sklearn import metrics\n",
    "\n",
    "# Dataset Import\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1: Basic Mathematical Overview\n",
    "\n",
    "First, let's take a look at the [Logistic Function](http://en.wikipedia.org/wiki/Logistic_function). The logistic function can take an input from negative to positive infinity and it has always has an output between 0 and 1. The logistic function is defined as:\n",
    "$$ \\sigma (t)= \\frac{1}{1+e^{-t}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A graph of the logistic function looks like this (following code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, ' Logistic Function ')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiEAAAGxCAYAAAC0mWZZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABHfElEQVR4nO3dd3hUZf738fdkJsmkQgqEXkMJIUAIAmJA18KCFXFRdlVYG6i7uLusj4qFshYE0bUg0sxvUVksu2BF7FhAQUEglAQIvSckISFlJlOePwJxYygzkORMZj6v68qVzJkz93zzzWTyySn3MbndbjciIiIi9SzI6AJEREQkMCmEiIiIiCEUQkRERMQQCiEiIiJiCIUQERERMYRCiIiIiBhCIUREREQMoRAiIiIihlAIERGPBMq8hoHyfYr4AoUQEYPt27ePLl26sHjx4np5vpdeeokuXbp4vP6hQ4cYM2YM+/fvr1p26aWX8tBDD51XHV26dDntR2pq6nmNfS6Kiop44IEH+Omnn6qW3Xrrrdx66631XotIoLAYXYCI1K8RI0YwcOBAj9dfuXIlX3/9dbVlM2fOJDIy8rxr+d3vfseIESNqLA8Kqv//j7Zs2cJ7773HDTfcULVs0qRJ9V6HSCBRCBEJMM2aNaNZs2bnNUa3bt1qrZZevXrVylh1ITEx0egSRPyadseINBA2m42XX36ZIUOGkJKSwuDBg5k7dy4ul6vaeq+++iqXXXYZPXr0YOTIkXz55Zd06dKFVatWATV3x+zZs4e7776bfv360bNnT2666aaqLR+LFy9mwoQJAFx22WVVu2B+vTvm+PHjPP744wwcOJBevXpxww03sHz58vP+nletWlWt9pN+vZvk0ksv5cUXX2TatGkMGDCAHj16cMcdd7Br165qj/v6668ZOXIkvXr1Ij09nYkTJ1JUVMSqVasYNWoUAKNGjaoa+9fP48nP4NZbb+WRRx5h7ty5XHLJJaSkpDBy5Eg2bNhw3v0Q8TcKISINgNvt5u6772b+/PmMGDGC2bNnM2TIEJ5//vlquwxmzpzJjBkzGDp0KLNmzaJnz5789a9/Pe24LpeLsWPHUlZWxvTp05k1axaNGzfmnnvuYffu3VxyySXcc889VWPfe++9NcZwOp3cfvvtfPDBB4wdO5ZZs2bRoUMH/vSnP1U7vuJ0z+9wOGp8nIvXXnuNHTt2MHXqVJ544gk2btzIgw8+WHX/V199xdixY4mLi+P555/n/vvv5/PPP+dvf/sbycnJTJw4EYCJEyeecjeMpz8DgE8++YQvvviCRx99lOeee468vDzGjRuH0+k8p+9NxF9pd4xIA/DNN9+wcuVKnnvuOa666ioALrroIqxWKy+88AKjRo2iZcuWzJs3j5tvvpn7778fgPT0dMrKynjrrbdOOe7Ro0fZsWMH9957LxdffDEAPXr0YObMmdjtdmJjY2nTpg0ASUlJtGrV6pS1rV+/npdffpnLL78cgP79+7N3715++OEH+vTpc9rva9asWcyaNavG8u+++44mTZp40SGIjo5m1qxZmM1moHILz0svvURBQQExMTG89NJLJCUlMXPmTEwmEwAhISG88MILlJeXV+16SUxMPOVuGE9+Bp06dQLA4XDw6quvVh03U1JSwoMPPsiWLVvo3r27V9+XiD9TCBFpAFavXo3FYmHIkCHVll977bW88MILrF69mvbt21NeXl5jnauvvvq0ISQ+Pp7ExEQee+wxvvvuO9LT0xk0aFDVLhhPrFmzhuDgYC699NKqZUFBQbz55ptnfeyNN97IjTfeWGN5TEyMx89/UkpKSlUAAaqOeykrKyMsLIzNmzczbty4qgACcOWVV3LllVcCkJOTc8bxPfkZnAwhiYmJ1Q7cTUhIqKpFRH6hECLSABw7doyYmJhqf2SBqq0FxcXF5OfnAxAbG1ttnbi4uNOOazKZyMjI4JVXXuGzzz7j3XffJTg4mMsvv5wpU6bQqFGjs9ZWWFhI48aNz+mMlqZNm5KSkuL1404lLCys2u2T9bhcLo4dO4bb7T5jL87Gk5+BJ7WIyC90TIhIA9CoUSMKCgpqHFNw5MgRoHLLwcn//I8ePVptnZPh5HQSEhKYPHky3333He+++y533HEHn376Kc8//7xHtUVFRVFYWFhjkq/NmzezadMmj8Y4nZNbLX79x7ukpMSrcSIjIzGZTDV6YbPZ+PrrryksLDzrGJ78DETEOwohIg1A3759cTgcLFu2rNry999/H4C0tDS6du1KVFQUn332WbV1Pv3009OO+/PPPzNgwAA2bNiAyWQiKSmJv/3tb3Tu3JkDBw4AZ5+zo0+fPlRUVPDNN99ULXO73UyYMIE5c+Z49X3+2sldGocOHapaduzYsbPuOvm1iIgIkpKS+Oqrr6ot/+abbxgzZgxHjhypsYXj1zz5GYiId7Q7RsRHrFixgqKiohrLhw4dyqBBg+jXrx+PPvoohw8fpmvXrqxevZp58+Zx/fXXVx1Ieeedd/Liiy8SFhZG3759Wb16NYsWLQJOHSa6deuG1WrlgQceYNy4ccTHx7Ny5Uq2bNlSdcpqdHQ0AJ999hmDBg2iY8eO1ca45JJLSE1N5aGHHuKvf/0rrVu35r333iMnJ4fHH3/8vHrSpUsXmjdvzssvv1y1NWPOnDk1dnd44r777uOee+5h/PjxDBs2jLy8PJ577jkuv/xyOnfuTHZ2NgDLly+nUaNGdO3atdrjPf0ZiIjnFEJEfMSHH37Ihx9+WGN59+7dSUhIYM6cObz44ov861//Ij8/n1atWjF+/Hhuu+22qnXHjh2L2+3mrbfe4tVXX6Vnz57cf//9TJ06lfDw8Bpjh4aGkpGRwbPPPsuTTz5JUVER7dq14x//+AfDhw8HoF+/fgwYMIBnn32W77//nrlz51Ybw2w2M2/ePGbMmMELL7xAWVkZXbp0ISMjgx49epxXT8xmMy+++CJPPfUU48ePJz4+ntGjR7Njxw527tzp1Vi/+c1vmD17NjNnzuRPf/oTsbGxXHPNNYwbNw6ATp06cfXVV7Nw4UK+/fbbGj+LkwHobD8DEfGcya2rNYn4BYfDwYcffki/fv1o3rx51fKFCxfyxBNPsGrVqqqtGiIivkAhRMSPXHXVVYSEhHDPPfcQExPD1q1bef7557n88suZOnWq0eWJiFSjECLiR/bu3ctzzz3HqlWrKCoqokWLFlx77bWMHTuW4OBgo8sTEalGIUREREQMoVN0RURExBAKISIiImIIhRARERExhM/OE3LyEt9BQUHVLjglIiIivsvtduNyubBYLGedcdlnQ4jD4SAzM9PoMkREROQcpKSkEBIScsZ1fDaEnExPv748d21wOp1kZmbWydj+Rr3ynHrlOfXKc+qV59Qr79RVv06O68mVtX02hJzcBWM2m+vsxVSXY/sb9cpz6pXn1CvPqVeeU6+8U1f98uRQCh2YKiIiIoZQCBERERFDKISIiIiIIRRCRERExBAKISIiImIIhRARERExhEKIiIiIGEIhRERERAxxziHEbrdz9dVXs2rVqtOus3nzZkaMGEHPnj254YYb2Lhx47k+nYiIiPiZcwohNpuN8ePHs23bttOuU1paypgxY+jTpw+LFy8mNTWVsWPHUlpaes7FioiIiP/wOoRs376dG2+8kT179pxxvaVLlxIaGsoDDzxAx44deeSRR4iIiGDZsmXnXKyIiIj4D69DyOrVq+nXrx9vvfXWGddbv349aWlpVXPHm0wmevfuzbp1686pUBEREfEvXl/A7g9/+INH6+Xm5pKYmFhtWVxc3Bl34ZyK0+n0an1vxqyLsf2NeuU59cpz6pXn1CvPBUKv3G43NoeLsgonZXYn5RVOyitc2J0u7I7/+Xzy62q33dXuM5ugd5Sz1vvlzXh1dhXdsrIyQkJCqi0LCQnBbrd7NU5mZmZtllVvY/sb9cpz6pXn1CvPqVee85Veud1ubE43ZRVuSh2Vn0sqXCduuyitOHFfhYsyhxubo3L9k5/t//O1zQn2E1+7a7HGm5IjiQs3rl91FkJCQ0NrBA673Y7VavVqnJSUlFq/xLDT6SQzM7NOxvY36pXn1CvPqVeeU688V5e9crnc5JfaOVpip6DETmFpBQWlFRSW2SkoqaCg9MSysgoKSyuXFdscOF21GRmqCzGbsAabCbWYCQ0OIthsIsQcRIjlxIf5V59/9XVkiJkeEUW13q+TPwdP1FkISUhIIC8vr9qyvLw8mjZt6tU4ZrO5zn7x6nJsf6NeeU698px65Tn1ynPe9MrtdnO0xM7+gjIOFZVzpNhGblE5ucdtHCmycaTYxpHicvKO2885UASZIDLUQpQ1+MRnC5FWS9XXUdZgIkIsRISasQabCQs2ExZS+dn6P1+Hh/xy22oJwmI+v6m+nE4n69atM/S1VWchpGfPnsybNw+3243JZMLtdrN27VruvvvuunpKERGRatxuN/kldnbmlbC3oJT9BWXsLyxj34nPBwrLKK9weTSWyQQx4SE0Dg8mNjyExuEhxIQHExNx6mWNwipDR3iIueokDamuVkNIbm4uUVFRWK1WhgwZwrPPPsuTTz7JyJEjefPNNykrK2Po0KG1+ZQiIiKUVzjJPniM7/aU8W3+dnYdLWXn0VJ25h6nqNxxxseaTNA0KpRmjcJoGhV64sNK0+jqX8dFhJz31geprlZDSHp6OlOnTmX48OFERkYyZ84cJk2axNtvv02XLl2YO3cu4eHhtfmUIiISQFwuN/sKythyqIjsQ8VkHypmy6EiduWV8MvekmPVHmMyQYtGYbSJDadlTBgtG4fRMiaMVic+N2tkJdSiXV1GOK8Qkp2dfcbbPXr0YMmSJefzFCIiEqDcbjf7C8tYv/cY6/cVsm5vIZv2H6PEfupTQGPCg2kWDt3bNqVDkyjax0fQPj6CtnHhWIMVMnxRnR0TIiIi4g27w0Xm/kJ+2JHPz3sKWLf3GHnHbTXWCzEHkdg0kq7No+jaLIquzaLp2iyK2HAL69evp1cvnUnUUCiEiIiIIWwOJxv2HeOHnKOs2pnPmt0FlFVU38phCTLRtXkUPVs1pmfrxvRs1ZgOTSIIPsWxGf48SZm/UggREZF6sze/lOXZR1iencvKnKM1QkdMeDD92sdxQftYerVuTHKLaO1K8WMKISIiUmecLjc/7crn082HWZ59hJzckmr3x0WE0K9DLP07xNGvfRydmkYSFKTTWQOFQoiIiNQqh9PFqp35LM08yCebDpF3/JfZs81BJtLaxHBJ1yZc0rkpSc2jNIdGAFMIERGR8+Z2u1m7p4D/rNnPso0HKSitqLov2mrh8m4JXJ6UwEWJ8TQKCzawUvElCiEiInLODhSWseTn/fxnzT525v2yqyUmPJjfJjdjaEpzLuwQR4hFk3xJTQohIiLiFYfTxWebD/Pv1Xv4bnse7hOThIWHmBnavTnXp7akf4dYzS4qZ6UQIiIiHsk7buPN1XtYuGoPB4+VVy3v3yGW36W1Zmj3ZkSE6s+KeE6vFhEROaPsQ8XM+SaHD9cfxO6svNhbfGQIIy9ow00XtKZ1rC7HIedGIURERE5pze58Xlmew+dbjlQtS23TmNEXtmNoSjNdb0XOm0KIiIhUs3J7Hs9/vo3Vu/KBygvADe3ejLGDOtKzdWNjixO/ohAiIiIArN1TwIxPslmZcxSAYLOJ4amtGHtxBzo0iTS4OvFHCiEiIgFuy8Einv00u2q3S7DZxB/6tuGeSxJp1shqcHXizxRCREQC1NHjNmZ8upU3f9yD2w1BJvhdWivuu6wTrWJ0sKnUPYUQEZEAU+F08dr3u3n+860UlzsAuDKlGeOv6EJiU+12kfqjECIiEkBW5uQx8b1NbD9yHIBuzaOZfG0yfdvHGlyZBCKFEBGRAHCstIKnlm7hrZ/2AhAbEcL/+20XbuzTGrOuWisGUQgREfFzH2ceZOL7m8gttgFwS/82/L/fdtWF5MRwCiEiIn7q6HEbjyzZyLJNhwDo0CSCaTf04IJ22vUivkEhRETED329NZf731lPbrENS5CJey7pyJ9+k4g1WLOciu9QCBER8SPlFU6mL8smY8VOABKbRvLCyF4kt2hkcGUiNSmEiIj4iW2Hixm36GeyDhUDMPrCtky4MklbP8RnKYSIiPiBD9Yf4IH/bKCswkl8ZAjTf9eDS7smGF2WyBkphIiINGAVThdPf5zFq99V7n4Z0DGOF0am0iQq1ODKRM5OIUREpIHKLbbx53+vZdXOyqvd3n1xR+4f3BmLOcjgykQ8oxAiItIAbdx/jDsX/MShonIiQsw8e2NPhnRvbnRZIl5RCBERaWC+zDrMn//9M6V2Jx2bRDDn1j665os0SAohIiINyGvf72Ly+5twuSE9MZ6Xb+6tmU+lwVIIERFpAJwuN099vLnqANQb+7TiyetTCNbxH9KAKYSIiPi4Cqebv7y1jo83Hgbg//22C/de0hGTSReek4ZNIURExIeV2h1MXVHA+sN2QsxBzLixJ9f2bGF0WSK1QiFERMRHHSur4PZ//cT6w3bCgs3MHZXGwE5NjC5LpNYohIiI+KD8Eju3zF/F5oNFRASb+NftfbigfbzRZYnUKoUQEREfk19i5w/zfiDrUDFxESE8PCCK3m1ijC5LpNbpsGoRER9ScGILSNahYppEhbLorr60a6xTcMU/KYSIiPiIwlI7t7xauQsmPjKURXf1o2MTTUIm/kshRETEBxSXVzAqYzWbDhQRHxnCorv6kdg0yuiyROqUQoiIiMHKK5yMeW0NG/YdIzYihH/f1Z9OCQog4v8UQkREDORwurhv0c98v+MokaEWFtzWl84KIBIgFEJERAzidruZsDiTTzcfJsQSxLxRfUhp1cjoskTqjUKIiIhBnvkkm3fW7MMcZGLm71O5sGOc0SWJ1CuFEBERAyxavYdZy3MAmDo8hcHJzQyuSKT+KYSIiNSzb7bm8ui7GwH4y2WduLFPa4MrEjGGQoiISD3KOlTEvQvX4nS5GZ7akr9e3snokkQMoxAiIlJPcott3P5/P3Lc5qBf+1im3pCCyWQyuiwRwyiEiIjUA7vDxT1vrOHAsXI6xEcw59Y0Qi1mo8sSMZRCiIhIPZj8wSZ+2l1AVKiFeaP70Dg8xOiSRAynECIiUscWrtrNv1ftwWSCF3+fquvBiJygECIiUod+3JXP5Pc3AfD/ftuF33RtanBFIr5DIUREpI7kFtu4d+FaKpxuru7RnHsu7mh0SSI+RSFERKQOOF1u/vbWOnKLbXRqGsn03/XQmTAiv6IQIiJSB2Z+uZ3vtucRFmxm1s29CQ+xGF2SiM9RCBERqWUrt+fx/BdbAXjy+u500lVxRU5JIUREpBYdKS7nvjfX4XbDTX1aM7x3K6NLEvFZXocQm83Gww8/TJ8+fUhPTycjI+O063722WcMHTqU1NRUfv/737Np06bzKlZExJc5XW7+smgdecdtdG0WxZTrko0uScSneR1Cpk+fzsaNG1mwYAGTJk1i5syZLFu2rMZ627Zt4+9//ztjx47lvffeIykpibFjx1JWVlYrhYuI+JrZX+fw/Y6jhIeYefnm3liDNSOqyJl4FUJKS0t55513eOSRR0hOTuaKK67gzjvvZOHChTXWXbFiBYmJiQwbNow2bdowfvx4cnNz2b59e60VLyLiKzL3HeOfn1UeB/KP67prQjIRD3gVQrKysnA4HKSmplYtS0tLY/369bhcrmrrNm7cmO3bt7NmzRpcLheLFy8mMjKSNm3a1E7lIiI+oszu5K9v/YzD5WZo92bc0Lul0SWJNAhenTOWm5tLTEwMISG/XPMgPj4em81GYWEhsbGxVcuvvPJKvvzyS/7whz9gNpsJCgpizpw5NGrUyKsCnU6nV+t7M2ZdjO1v1CvPqVee87dePbV0Mzm5JTSNCuXx67rV+KfsfPhbr+qSeuWduuqXN+N5FULKysqqBRCg6rbdbq+2vKCggNzcXCZOnEjPnj1ZtGgREyZMYMmSJcTFxXn8nJmZmd6U6JW6HNvfqFeeU6885w+9+vmQjdd/KABgTK9wdm/dzO46eB5/6FV9Ua+8Y2S/vAohoaGhNcLGydtWq7Xa8hkzZtC5c2duvvlmAB5//HGGDh3Kf//7X8aMGePxc6akpGA21+7BXU6nk8zMzDoZ29+oV55TrzznL70qKLVzz7IVANzavw23DelW68/hL72qD+qVd+qqXyfH9YRXISQhIYGCggIcDgcWS+VDc3NzsVqtREdHV1t306ZN3HrrrVW3g4KC6Nq1KwcOHPDmKTGbzXX2YqrLsf2NeuU59cpzDb1Xkz7YwpFiGx2bRPDwld3q9Htp6L2qT+qVd4zsl1cHpiYlJWGxWFi3bl3VsjVr1pCSkkJQUPWhmjZtSk5OTrVlO3fupFUrTdwjIg3fso2H+GjDQcxBJv55Uy/CQvRHT8RbXoWQsLAwhg0bxuTJk9mwYQOff/45GRkZjBo1CqjcKlJeXg7AjTfeyNtvv827777L7t27mTFjBgcOHOD666+v/e9CRKQeHSut4LH3NgIwdlAHerRqbGxBIg2U11dUmjBhApMnT2b06NFERkYybtw4Bg8eDEB6ejpTp05l+PDhXHnllZSUlDBnzhwOHTpEUlISCxYs8OqgVBERX/T4R5vJPbEb5r7LOhldjkiD5XUICQsLY9q0aUybNq3GfdnZ2dVujxgxghEjRpx7dSIiPubrrbn8Z80+TCaY/rsemhVV5DzoAnYiIh46bnPw8OLKo/5HX9iOtLaxZ3mEiJyJQoiIiIemL8tif2EZrWLC+H+/7WJ0OSINnkKIiIgHftqVz2vfV05D9vTwHkSEer03W0R+RSFEROQsKpwuHn238myYEWmtSO8Ub3BFIv5BIURE5CwWrNxF1qFiGocHM+HKJKPLEfEbCiEiImdw8FgZ//xsKwAThnYlNiLkLI8QEU8phIiInMHjH26mxO6kd5vGjEhrbXQ5In5FIURE5DSWZx9haeYhzEEmnhiWQlCQyeiSRPyKQoiIyCmUVziZ9P4mAP44oB3dWkSf5REi4i2FEBGRU5j9dQ67j5aSEB3KXy/X1OwidUEhRETkV/YVlPLK8sqrgD96VTeirMEGVyTinxRCRER+ZerHWdgcLvp3iOXqHs2NLkfEbymEiIj8j9U78/low0GCTDDx6mRMJh2MKlJXFEJERE5wutxM+aDyYNSbLmijg1FF6phCiIjICf9Zs5dNB4qIslq4f3Bno8sR8XsKISIiQHF5Bc98kg3AXy7rRFxkqMEVifg/hRAREWDmV9vJO26nQ3wEoy5sZ3Q5IgFBIUREAt7uoyX833e7AHjkqiRCLHprFKkP+k0TkYA3/ZNs7E4XAzvFc2nXpkaXIxIwFEJEJKCt21vIRxsOYjLBw1cm6ZRckXqkECIiAcvtdjN16RYAhqe2Iqm5TskVqU8KISISsL7MOsKqnfmEWIL4u07JFal3CiEiEpAcThdPf5wFwG0XtaNF4zCDKxIJPAohIhKQ/rt2H9uOHKdRWDD3XpxodDkiAUkhREQCTpndyXOfbQVg3KWJNArXVXJFjKAQIiIBJ2PFTg4X2WjZOIxbL2xrdDkiAUshREQCSmGpndnLcwC4/7edCbWYDa5IJHAphIhIQJn7zQ6KbQ66Noviup4tjS5HJKAphIhIwMg7buP/VuwCYPwVnQkK0sRkIkZSCBGRgPHK8hzKKpz0bNWIK7olGF2OSMBTCBGRgHDwWBmv/7AbgL8P7qLp2UV8gEKIiASEmV9ux+5w0bddLAM7xRtdjoigECIiAWBvfilv/bgXgL8P7qytICI+QiFERPzeC19sw+FyM7BTPP06xBldjoicoBAiIn5t+5HjLF67D6g8FkREfIdCiIj4tec/34rLDZcnJdCrdWOjyxGR/6EQIiJ+K+tQER9uOAhUHgsiIr5FIURE/NZLX2wH4KoezUlqHm1wNSLyawohIuKXth4uZunGyq0g913ayeBqRORUFEJExC/N/HI7bjcM7d6MLs2ijC5HRE5BIURE/M72I8f5YMMBAP58aaLB1YjI6SiEiIjfmfVV5VaQK7olkNyikdHliMhpKISIiF/ZlVfCu+v2AzoWRMTXKYSIiF+ZtXw7Ljdc2rUpKa20FUTElymEiIjf2JtfyuK1lVtBxulYEBGfpxAiIn5j1vIcHC43gzo3IbVNjNHliMhZKISIiF/YX1jGf9ZUXin3Pm0FEWkQFEJExC/MXp5DhdPNgI5x9GkXa3Q5IuIBhRARafByi2289VPlVpBxOiNGpMFQCBGRBi9jxU7sDhepbRrTv4O2gog0FAohItKgFZVX8Mb3uwG495JETCaTwRWJiKcUQkSkQXv9+90U2xx0Tojksq5NjS5HRLygECIiDVZ5hZP/W7ETgHsu6UhQkLaCiDQkCiEi0mC989Ne8o7badk4jKt7tDC6HBHxkkKIiDRIDqeLOd/sAGDsxR0INuvtTKSh0W+tiDRIH2w4wL6CMuIiQrixT2ujyxGRc+B1CLHZbDz88MP06dOH9PR0MjIyTrtudnY2v//97+nRowfXXHMNP/zww3kVKyIC4HK5eWV5DgC3p7fHGmw2uCIRORdeh5Dp06ezceNGFixYwKRJk5g5cybLli2rsV5xcTG33347iYmJfPDBB1xxxRX8+c9/5ujRo7VSuIgEri+zjrD18HEiQy3c0r+t0eWIyDnyKoSUlpbyzjvv8Mgjj5CcnMwVV1zBnXfeycKFC2usu2TJEsLDw5k8eTJt27blvvvuo23btmzcuLHWiheRwON2u5m1fDsAt/RvS6OwYIMrEpFzZfFm5aysLBwOB6mpqVXL0tLSmD17Ni6Xi6CgXzLN6tWrueyyyzCbf9lM+t///rcWShaRQLZqZz5r9xQSYgni9vR2RpcjIufBqxCSm5tLTEwMISEhVcvi4+Ox2WwUFhYSG/vLdMl79+6lR48ePPbYY3z55Ze0bNmSBx98kLS0NK8KdDqdXq3vzZh1Mba/Ua88p1557nx6Neuryq0gv+vdkrjwYL/vt15XnlOvvFNX/fJmPK9CSFlZWbUAAlTdttvt1ZaXlpYyd+5cRo0axbx58/joo4+44447+Pjjj2nevLnHz5mZmelNiV6py7H9jXrlOfXKc972aldhBd9sO0oQcFFcGevWrauTunyRXleeU6+8Y2S/vAohoaGhNcLGydtWq7XacrPZTFJSEvfddx8A3bp1Y8WKFbz33nvcfffdHj9nSkpKtV06tcHpdJKZmVknY/sb9cpz6pXnzrVXC/+zAYAhKc0Ykt6rjqrzLXpdeU698k5d9evkuJ7wKoQkJCRQUFCAw+HAYql8aG5uLlarlejo6GrrNmnShA4dOlRb1q5dOw4ePOjNU2I2m+vsxVSXY/sb9cpz6pXnvOnV4aJyPthQ+f4xZlDHgOuxXleeU6+8Y2S/vDo7JikpCYvFUm0T6Jo1a0hJSal2UCpAr169yM7OrrZsx44dtGzZ8tyrFZGAtWDlLiqcbi5oF0Ov1o2NLkdEaoFXISQsLIxhw4YxefJkNmzYwOeff05GRgajRo0CKreKlJeXAzBy5Eiys7N56aWX2L17Ny+88AJ79+7luuuuq/3vQkT8WonNwRs/7AbgzoEdzrK2iDQUXk9WNmHCBJKTkxk9ejRTpkxh3LhxDB48GID09HSWLl0KQMuWLZk/fz5fffUVV199NV999RVz584lISGhdr8DEfF77/y0l6JyB+3iwrk8Se8hIv7Cq2NCoHJryLRp05g2bVqN+369+yUtLY3Fixefe3UiEvCcLjcZK3YBcEd6e8xBJmMLEpFaowvYiYhP+3TTIfbkl9I4PJjfpelCdSL+RCFERHzavG93AHBLv7aEheiMBxF/ohAiIj5rze6CyinazUGMGqAL1Yn4G4UQEfFZ809sBRmW2oKmUdazrC0iDY1CiIj4pD1HS/lk0yFAp+WK+CuFEBHxSRkrduJyw8Wdm9A5IcrockSkDiiEiIjPOVZawds/7QXgLm0FEfFbCiEi4nMWrt5Nqd1J12ZRXJQYZ3Q5IlJHFEJExKfYHS7+dWJysrsGdsBk0uRkIv5KIUREfMr76w9wpNhGQnQo1/RsYXQ5IlKHFEJExGe43e6q03JHD2hHiEVvUSL+TL/hIuIzvtueR9ahYsJDzNzcV5OTifg7hRAR8Rnzvt0JwI19WtMoPNjgakSkrimEiIhPyD5UzDdbcwkywe0XtTe6HBGpBwohIuITTh4LMqR7M9rEhRtcjYjUB4UQETHckeJy3lt3ANAU7SKBRCFERAz32srd2J0u0trG0LtNjNHliEg9UQgREUOV2h28sWo3AHcN1LEgIoFEIUREDPXfNfsoLK2gbVw4V3RrZnQ5IlKPFEJExDBOl5v531Welnv7Re0xB2mKdpFAohAiIob5fMsRdh8tpVFYMCP6tDK6HBGpZwohImKYV1dUbgW5uV8bwkMsBlcjIvVNIUREDLH1qJ01uwsJNpsYPaCd0eWIiAEUQkTEEO9vLQXg2p4tSYi2GlyNiBhBIURE6t3e/FJW7SsH4K5BOi1XJFAphIhIvfvX97txAQMT4+jaLNrockTEIAohIlKvjpVV8M5P+wC4I11bQUQCmUKIiNSrRav3UGJ30qaRhfTEOKPLEREDKYSISL2xO1z8a8UuAK7pHI7JpMnJRAKZQoiI1JuPMg9wqKicJpGhDGwdZnQ5ImIwhRARqRdut5u531ROTjbqwjYEm7UVRCTQKYSISL1YmXOULQeLCAs284e+rY0uR0R8gEKIiNSLed/uAGBEn1Y0Dg8xuBoR8QUKISJS57YdLmZ5di4mU+XVckVEQCFEROrB/G8rjwUZ3C2BdvERBlcjIr5CIURE6lRusY0lP+8HYMygDgZXIyK+RCFEROrU69/vwu50kdqmMWltY40uR0R8iEKIiNSZMruT13/YDcBdA7UVRESqUwgRkTrz37X7KCitoHVsGL9NbmZ0OSLiYxRCRKROuFxuXv2u8oDU2y9qjzlIk5OJSHUKISJSJz7fcpideSVEWy3c2EeTk4lITQohIlInTk5Odkv/tkSEWgyuRkR8kUKIiNS6tXsK+HFXAcFmE38c0M7ockTERymEiEitm39iK8iwXi1pGm01uBoR8VUKISJSq3YfLWHZxkMA3KXJyUTkDBRCRKRWvfrdTlxuuKRLEzonRBldjoj4MIUQEak1BSV23v5pLwBjNDmZiJyFQoiI1Jo3fthNeYWL5BbRXNgxzuhyRMTHKYSISK0or3Cy4PtdQOWF6kwmTU4mImemECIiteLdn/eTd9xOy8ZhXJnS3OhyRKQBUAgRkfPmcrmrJie77aJ2BJv11iIiZ6d3ChE5b19lHyEnt4Qoq4WRfdsYXY6INBAKISJy3uZ+U7kV5A/92hCpKdpFxEMKISJyXtbvLWTVznwsQSZuG9De6HJEpAFRCBGR83LyWJBre7WgWSNN0S4invM6hNhsNh5++GH69OlDeno6GRkZZ33Mvn37SE1NZdWqVedUpIj4pr35pSzNPAjAXZqcTES85PXO2+nTp7Nx40YWLFjAgQMHePDBB2nRogVDhgw57WMmT55MaWnpeRUqIr4nY0XlFO0DO8WT1Dza6HJEpIHxKoSUlpbyzjvvMG/ePJKTk0lOTmbbtm0sXLjwtCHk/fffp6SkpFaKFRHfUVBi560fT0zRrgvVicg58Gp3TFZWFg6Hg9TU1KplaWlprF+/HpfLVWP9goICnnnmGf7xj3+cf6Ui4lNe+343pXYn3ZpHk54Yb3Q5ItIAebUlJDc3l5iYGEJCQqqWxcfHY7PZKCwsJDY2ttr6Tz/9NNdffz2dOnU65wKdTuc5P/ZsY9bF2P5GvfJcIPWq1O7gXyt3AjB2UPtT/hNyJoHUq/OlXnlOvfJOXfXLm/G8CiFlZWXVAghQddtut1dbvnLlStasWcOHH37ozVPUkJmZeV6PN2psf6NeeS4QevXRthIKSitoFmGmueMQ69YdPqdxAqFXtUW98px65R0j++VVCAkNDa0RNk7etlp/OTWvvLyciRMnMmnSpGrLz0VKSgpms/m8xvg1p9NJZmZmnYztb9QrzwVKryqcLv786TcA/OnyLqT19n6G1EDpVW1QrzynXnmnrvp1clxPeBVCEhISKCgowOFwYLFUPjQ3Nxer1Up09C9Hxm/YsIG9e/dy3333VXv8XXfdxbBhw7w6RsRsNtfZi6kux/Y36pXn/L1X7647yMFj5cRHhjKiT5vz+l79vVe1Sb3ynHrlHSP75VUISUpKwmKxsG7dOvr06QPAmjVrSElJISjol2Nce/TowaefflrtsYMHD+aJJ57goosuqoWyRcQILpeb2V/nAHBHenuswXqjF5Fz51UICQsLY9iwYUyePJmnnnqKI0eOkJGRwdSpU4HKrSJRUVFYrVbatm1b4/EJCQnExcXVTuUiUu++yDrCtiPHiQq1cHN/XahORM6P1zOmTpgwgeTkZEaPHs2UKVMYN24cgwcPBiA9PZ2lS5fWepEiYjy3282s5dsBuOXCtkRbgw2uSEQaOq9nTA0LC2PatGlMmzatxn3Z2dmnfdyZ7hMR37dqZz4/7ykkxBLEbRe1M7ocEfEDuoCdiHjkleWVx4KMSGtF0yhdqE5Ezp9CiIic1aYDx/h6ay5BJk3RLiK1RyFERM5q9tc7ALiqRwvaxkUYXI2I+AuFEBE5o91HS/howwEA7r5YW0FEpPYohIjIGc36KgeXGy7u3ITkFo2MLkdE/IhCiIic1r6CUv67dh8A4y5NNLgaEfE3CiEiclqzv87B4XJzYYc4+rSLPfsDRES8oBAiIqd06Fg5b/94YivIZdoKIiK1TyFERE5pzjc52J0u+rSN4cIOutyCiNQ+hRARqSG32Mai1XsAGHdZJ0wmk8EViYg/UggRkRrmf7eD8goXPVs3ZlCneKPLERE/pRAiItUUlNh5/fvdANx3aaK2gohInVEIEZFqMlbspNTuJLlFNJd2bWp0OSLixxRCRKTKsbIK/rViF1A5L4i2gohIXVIIEZEqC1buotjmoEtCFIO7NTO6HBHxcwohIgJUbgWZ/23lher+dGkiQUHaCiIidUshREQAePXbHRSVO+icEMnVKc2NLkdEAoBCiIhQUGIn48SxIH+7vLO2gohIvVAIERHmfLOD4zYHyS2i+W2yjgURkfqhECIS4HKLbSxYuQuA8VdoK4iI1B+FEJEA98ryHMoqnPRq3VjzgohIvVIIEQlgh46V88aqytlRx1/RWfOCiEi9UggRCWAvf7Udu8PFBe1iGKhrxIhIPVMIEQlQ+wpKefPHyivljr+ii7aCiEi9UwgRCVAzv9xOhdPNRYlxXNgxzuhyRCQAKYSIBKCdeSW8s2YfULkVRETECAohIgFoxifZOF1uLu3alLS2MUaXIyIBSiFEJMCs21vIR5kHMZnggSHaCiIixlEIEQkgbrebpz/eAsDw1FZ0bRZtcEUiEsgUQkQCyNdbc/lhRz4hliDGD+5sdDkiEuAUQkQChMvl5umPswAYfWFbWjYOM7giEQl0CiEiAeLddfvJOlRMlNXCvZckGl2OiIhCiEggKK9w8uynWwG455KOxESEGFyRiIhCiEhAeOOH3ewvLCMhOpTbBrQ3uhwREUAhRMTvFZTYefGLbQD87fLOhIWYDa5IRKSSQoiIn3v+860UlTvo2iyKEX1aG12OiEgVhRARP7b9SDFvrKq8SN3Eq7thDtJF6kTEdyiEiPixJz7agtPl5opuCQxIjDe6HBGRahRCRPzU8uwjLM/OJdhs4uErk4wuR0SkBoUQET/kcLp48qPK6dlHX9iO9vERBlckIlKTQoiIH1q0eg/bjhwnJjyYcZd1MrocEZFTUggR8TPHSit47rPKicnGX9GZRmHBBlckInJqCiEifuaZT7MoKK2gc0Ikv+/bxuhyREROSyFExI9k7jvGwhOn5E65tjsWs37FRcR36R1KxE+4XG4efW8jbjcM69WCCzvGGV2SiMgZKYSI+Im3ftrL+r2FRIZadEquiDQICiEifiC/xM60ZVkA/O2KzjSNthpckYjI2SmEiPiBZz7JorC0gq7Nohh9YVujyxER8YhCiEgD9/OeAt78cS8A/7hOB6OKSMOhdyuRBqzC6eLhJZUHow7v3ZK+7WONLklExGMKISIN2Lxvd7DlYBGNw4N1MKqINDgKISIN1I7c4zz/+TYAHruqG/GRoQZXJCLiHYUQkQbI5XLz0OJM7A4XAzvFM7x3S6NLEhHxmkKISAP05o97Wb0zn7BgM09dn4LJZDK6JBERrymEiDQwh46VM3XpFgDu/20XWseGG1yRiMi5UQgRaUDcbjePvbeRYpuDnq0b88cB7YwuSUTknHkdQmw2Gw8//DB9+vQhPT2djIyM0667fPlyrrvuOlJTU7nmmmv44osvzqtYkUD3/voDfLb5MJYgE9NuSMEcpN0wItJweR1Cpk+fzsaNG1mwYAGTJk1i5syZLFu2rMZ6WVlZ/PnPf+aGG27g3XffZeTIkfzlL38hKyurVgoXCTQHj5Xx2LsbAfjzpYl0bRZtcEUiIufH4s3KpaWlvPPOO8ybN4/k5GSSk5PZtm0bCxcuZMiQIdXW/fDDD+nfvz+jRo0CoG3btnz55Zd8/PHHdO3atfa+A5EA4Ha7eeA/Gygqd9CzVSP+9JtEo0sSETlvXoWQrKwsHA4HqampVcvS0tKYPXs2LpeLoKBfNqxcf/31VFRU1BijuLj4PMoVCUyv/7Cbb7flEWoJ4rmbehGsqdlFxA94FUJyc3OJiYkhJCSkall8fDw2m43CwkJiY3+ZMrpjx47VHrtt2za+//57Ro4c6VWBTqfTq/W9GbMuxvY36pXn6qpXO/NKeOrE2TAPDulCu9iwBv/z0OvKc+qV59Qr79RVv7wZz6sQUlZWVi2AAFW37Xb7aR+Xn5/PuHHj6N27N5dddpk3T0lmZqZX6/vK2P5GvfJcbfbK6XLzyFf5lFe46NE0hBRrPuvWFdTa+EbT68pz6pXn1CvvGNkvr0JIaGhojbBx8rbVaj3lY/Ly8rjttttwu928+OKL1XbZeCIlJQWz2ezVY87G6XSSmZlZJ2P7G/XKc3XRq5e+3M62/MNEWS28PPpCWjQOq5VxjabXlefUK8+pV96pq36dHNcTXoWQhIQECgoKcDgcWCyVD83NzcVqtRIdXfNI/cOHD1cdmPraa69V213jKbPZXGcvproc29+oV56rrV79uCufF7/cDsDj13WndVzkeY/pa/S68px65Tn1yjtG9surzRJJSUlYLBbWrVtXtWzNmjWkpKTU2MJRWlrKnXfeSVBQEG+88QYJCQm1UrBIICgstXPfop9xuWF4akuGperaMCLif7wKIWFhYQwbNozJkyezYcMGPv/8czIyMqq2duTm5lJeXg7AnDlz2LNnD9OmTau6Lzc3V2fHiJyF2+3m/nc2cPBYOe3jI/jHsO5GlyQiUie82h0DMGHCBCZPnszo0aOJjIxk3LhxDB48GID09HSmTp3K8OHD+eSTTygvL2fEiBHVHn/99dfz9NNP1071In7oXyt38fmWw4SYg3jp96lEhnr9ayoi0iB4/e4WFhbGtGnTqrZw/K/s7Oyqr081i6qInFnmvmNMXVo5q/DDV3ale8tGBlckIlJ3NOORiI/IL7Fz9xtrsDtdXNEtgdG6OJ2I+DmFEBEf4HS5+cubP7O/sIx2ceHMGNETk0kXpxMR/6YQIuID/vnZVr7dloc1OIjZt6bRKCzY6JJEROqcQoiIwT7ddIiZX1XOBzLthh66Oq6IBAyFEBEDbT1czPi31wPwxwHtuK6X5gMRkcChECJikPwSO3cs+JHjNgd928fy8JVJRpckIlKvFEJEDGB3uLj79TXszS+jTWw4s29JI8SiX0cRCSx61xOpZ263m0eWZLJ6Vz5RoRZeHd2H2IiQsz9QRMTPKISI1LPZX+/gnTX7CDLBi39IpVNClNEliYgYQiFEpB4tXruPacsqZ0R99Kpu/KZLU4MrEhExjkKISD35dlsuD/xnAwB3prfn9vT2BlckImIshRCRerBx/zHufn0NDpeba3q20JkwIiIohIjUuZ15Jfzx/36kxO7kwg5xzBjRg6AgTckuIqIQIlKH9hWUcvO8H8g7biOpeTRzRqURajEbXZaIiE9QCBGpI4eLyrl5/ioOHCunQ5MIXr+jL9FWXRNGROQkhRCROnD0uI2b569i99FSWseG8e87+xMfGWp0WSIiPsVidAEi/ibvuI3R//cT248cp3kjK/++sz/NGlmNLktExOdoS4hILSooc3Lz/NVkHSqmSVQob9zZj9ax4UaXJSLik7QlRKSWHDxWzmPL8zl43EmzaCv/vqsfHZpEGl2WiIjPUggRqQV780u5ef4qDh530rKxlUV3XUibOG0BERE5E4UQkfO0+UARo/9vNbnFNppFmFl0Zz8FEBERDyiEiJyH73OOMua1nyi2OeiSEMn9F4TRMibM6LJERBoEHZgqco6WZh5kdMZqim0O+raP5c27+hEbponIREQ8pS0hIl5yu9288nUO05dlAzAkuRnPj+xFsCK9iIhXFEJEvGBzOJmwOJPFa/cDMOrCtky6JhlzkAmn02lwdSIiDYtCiIiH8o7buPv1Nfy0uwBzkIlJ13Rj1IXtjC5LRKTBUggR8cCa3fn8aeHPHCoqJ8pqYdbNvRnYqYnRZYmINGgKISJn4Ha7yVixi6lLt+BwuenQJIK5t/YhsakmIRMROV8KISKnUVxewYP/3cDSzEMAXN2jOU/f0IPIUP3aiIjUBr2bipzChn2F/PXNdezIKyHYbOKRK5MYPaAdJpPJ6NJERPyGQojI/3A4XcxansOLX2zD4XLTvJGVl2/uTe82MUaXJiLidxRCRE7YlVfC395ex897CgG4KqU5T17fncbhIcYWJiLipxRCJOA5XW4WrtrN1KVZlFU4ibJaePy67lzXq4V2v4iI1CGFEAloWw4WMWFxJuv2FgJwYYc4ZtzYk5aNdf0XEZG6phAiAanM7uSFL7Yx/9sdOFxuIkMtPDCkC7f0a0tQkLZ+iIjUB4UQCShut5tPNh3iyaVb2JtfBlRe+2Xytck0a2Q1uDoRkcCiECIBY+P+Yzz+4WZW7cwHoHkjK/+4rjtXdEswuDIRkcCkECJ+73BROTM+yeY/a/fhdkOoJYixgzow9uKORGjiMRERw+gdWPxWbrGNV5bn8Maq3dgdLgCG9WrBA0O60kIHnoqIGE4hRPxOfomdOd/k8NrK3ZRVOAG4oF0ME65M0qRjIiI+RCFE/Mb+wjJe/XYnb/64h1J7Zfjo2boxf7+iMwM7xWvODxERH6MQIg3eloNFzP1mB++vP4DT5QYguUU046/ozKVdmyp8iIj4KIUQaZAqnC4+23yYN37Yzcqco1XLB3SMY+zFHRmkLR8iIj5PIUQalP2FZby5eg9v/riX3GIbAEEmGJrSnLGDOtCjVWNjCxQREY8phIjPK69w8vmWwyxZu5+vso9wYo8L8ZGhjLygNSP7tqZVTLixRYqIiNcUQsQnOV1ufthxlCU/72fZxkMctzmq7ruwQxy39G/LFd0SCLEEGViliIicD4UQ8RkVTherd+bz6aZDLNt0iMNFtqr7WjYOY1hqC4b3bkXHJpEGVikiIrVFIUQMVWJz8O22XD7ddJgvso5wrKyi6r5GYcFc1aM516e2JK1NjC4sJyLiZxRCpF45XW42HTjGt9vy+HZbLmt2F1DhdFfdHxcRwuVJCQxOTiC9UzyhFrOB1YqISF1SCJE65Xa7yckt4cdd+Xy3PY8V2/MoLK2otk6b2HAGd0tgcHIz0trGYNYWDxGRgKAQIrWqvMJJ5v5j/LSrgDW781mzu4CCX4WOqFALF3aMY2CneAZ2akLbuHDN6SEiEoAUQuSclVc4yT5UzMYDx9i4v4hNB46RdbAYu9NVbb1QSxA9Wzfmwg5xDOocT89WjbGYdVaLiEigUwiRs3K53ewvKGPH0VK2HzlO1qFiNu4/xrYjx6umSf9f8ZEhpLWN4YJ2saS1jSG5RSOdSisiIjUohEiV4zYHe46Wsie/lJzc4+QcOc7Ww8VsP1xMufPwKR8TGxFC95aNSG4RTfcWjejeMpo2sdq9IiIiZ6cQEkBKbA4OFZVz+Fg5ewsqw8ae/DL25JeyN7+U/BL7aR8bbDbRPj6CxKaRdGoaRfeWlYGjWbRVgUNERM6JQkgD53a7KbY5yD9u52iJnaPHbRwutnH4WHll4Cgq59Cxyo/i/5l19HRiI0JoExtOu7hwOiVE0T4uHEf+XgYP6I01JLgeviMREQkUXocQm83GlClT+PTTT7Fardx+++3cfvvtp1x38+bNTJo0ia1bt5KYmMiUKVPo3r37eRftj9xuN+UVLorKKygur+BYmYOi8gqKyiooLndQUFIZMvJPfFR+bSO/xF5tno2ziQy1kBAdSquYcNrEVn60rvocRpS1etBwOp2sW3eQYB1IKiIitczrEDJ9+nQ2btzIggULOHDgAA8++CAtWrRgyJAh1dYrLS1lzJgxXHPNNTz99NMsWrSIsWPH8tlnnxEe3nAvNuZ2u3G43NgdLmwOF2UVTsrsDkrtTkpsTsoqKr8utTspq/rsoOR/vi61OymxOygud1BUVkFRuYPi8gqvwsSvhYeYiY0IIS4ihCZRVpo3stKskZWE6MqvE6Irb0eGauOXiIj4Bq/+IpWWlvLOO+8wb948kpOTSU5OZtu2bSxcuLBGCFm6dCmhoaE88MADmEwmHnnkEb755huWLVvG8OHDa/Wb8Eap3cG8b3awaccxonIyqXCeDBRO7E4XdoerKmCc/Fz5deX9NocL97lnhbMKMkF0WDBRVgvR1mCirZVfx4SHEBtZGTJiT3zERYRWLbMGa2ZRERFpWLwKIVlZWTgcDlJTU6uWpaWlMXv2bFwuF0FBv2yyX79+PWlpaVUHLZpMJnr37s26deu8CiFOp9ObEs9qxbZc/vn5thO39p/3eCFmE2EhFsJDzISHmAkLPvG52u3/uT/ETHiwmfBQC9EngkZUmKUqbESEmM/pQM/a7tOvx62r8f2JeuU59cpz6pXn1Cvv1FW/vBnPqxCSm5tLTEwMISEhVcvi4+Ox2WwUFhYSGxtbbd3ExMRqj4+Li2Pbtm14IzMz06v1zybS6ea2XlGU2t1YzBASZMJiNhEcVBkoLEGVXwebTQQHmQg2c+LzieVVX5uwmMF81sDgAk5z1kl55UdZIZQBpz4J1jfU9s/Bn6lXnlOvPKdeeU698o6R/fIqhJSVlVULIEDVbbvd7tG6v17vbFJSUjCba3dXQ1ovJ5mZmXUytr9xOtUrT6lXnlOvPKdeeU698k5d9evkuJ7wKoSEhobWCBEnb1utVo/W/fV6Z2M2m+vsxVSXY/sb9cpz6pXn1CvPqVeeU6+8Y2S/vDrvMiEhgYKCAhyOX+abyM3NxWq1Eh0dXWPdvLy8asvy8vJo2rTpeZQrIiIi/sKrEJKUlITFYmHdunVVy9asWUNKSkq1g1IBevbsyc8//4z7xKkkbrebtWvX0rNnz/OvWkRERBo8r0JIWFgYw4YNY/LkyWzYsIHPP/+cjIwMRo0aBVRuFSkvLwdgyJAhFBUV8eSTT7J9+3aefPJJysrKGDp0aO1/FyIiItLgeD0N5oQJE0hOTmb06NFMmTKFcePGMXjwYADS09NZunQpAJGRkcyZM4c1a9YwfPhw1q9fz9y5cxv0RGUiIiJSe7yePjMsLIxp06Yxbdq0GvdlZ2dXu92jRw+WLFly7tWJiIiI39IFQURERMQQCiEiIiJiCIUQERERMYRCiIiIiBhCIUREREQMoRAiIiIihlAIEREREUN4PU9IfTk53bvT6az1sU+OWRdj+xv1ynPqlefUK8+pV55Tr7xTV/06Od7Jv+NnYnJ7spYB7Ha7x5cCFhEREd+SkpJCSEjIGdfx2RDicrlwOBwEBQVhMpmMLkdEREQ84Ha7cblcWCyWGhe3/TWfDSEiIiLi33RgqoiIiBhCIUREREQMoRAiIiIihlAIEREREUMohIiIiIghFEJERETEEAohIiIiYgiFEBERETFEQIcQt9vNiy++yIABA+jbty+PPfYYNpvN6LJ83pQpU7j11luNLsNnFRUV8cgjjzBgwAD69+/PQw89RFFRkdFl+QybzcbDDz9Mnz59SE9PJyMjw+iSfNbhw4e577776Nu3LwMHDmTq1Kl6j/LAmDFjeOihh4wuw2fZ7XamTJnCBRdcwIABA3juuec8us5LXQjoEDJv3jz+/e9/8+yzzzJ//nx++OEHZs6caXRZPm3t2rUsWrTI6DJ82qRJk8jKymLu3Lm8+uqr5OTk8Oijjxpdls+YPn06GzduZMGCBUyaNImZM2eybNkyo8vyOW63m/vuu4+ysjIWLlzIP//5T7766iuef/55o0vzaR999BFff/210WX4tCeeeIKVK1fy6quv8uyzz/L222/z1ltvGVOMO0A5HA53//793YsXL65a9t5777lvu+02A6vybTabzX3VVVe5b7rpJvctt9xidDk+qaSkxJ2UlORet25d1bK1a9e6k5KS3OXl5QZW5htKSkrcKSkp7h9++KFq2csvv6zX0yls377d3blzZ3dubm7Vsg8++MCdnp5uYFW+raCgwD1o0CD3DTfc4H7wwQeNLscnFRQUuLt16+ZetWpV1bI5c+a4H3roIUPqsRgTfYy3bds2CgoKuPzyy6uWXXvttVx77bUGVuXb5s6dS5cuXWjXrh2rV682uhyfFBQUxOzZs0lKSqq23Ol0UlJSQmhoqEGV+YasrCwcDgepqalVy9LS0pg9ezYul+usF7sKJE2aNGH+/PnEx8dXW378+HGDKvJ906ZN47rrruPIkSNGl+Kz1qxZQ2RkJH379q1aNmbMGMPqCdjf+H379tGoUSPWrl3LsGHDuPjii3nyySex2+1Gl+aTcnJyWLRoERMmTDC6FJ9mtVoZNGhQtctXv/baa3Tp0oXY2FgDK/MNubm5xMTEVOtPfHw8NpuNwsJC4wrzQdHR0QwcOLDqtsvl4o033qB///4GVuW7vv/+e3766Sfuvfdeo0vxaXv37qVly5a8++67DBkyhMsuu4yXX34Zl8tlSD1+vSWkvLycw4cPn/K+4uJiysvLefbZZ5kwYQIul4tJkybhcrl47LHH6rlS452pV02aNGHixImMGzeuxn9lgehsvQoPD6+6/cYbb/Dxxx8zf/78+irPp5WVlVULIEDVbf0DcGbPPPMMmzdv5j//+Y/Rpfgcm83GpEmTmDhxIlar1ehyfFppaSm7d+/mzTffZOrUqeTm5jJx4kTCwsK4/fbb670evw4h69evZ9SoUae877nnnqO8vJxHH320arPUQw89xPjx43nkkUcCbrPwmXr197//HafTyU033VTPVfmmM/Xq5ZdfrtrFt3DhQp544gkmTJhAenp6fZbos0JDQ2uEjZO39cfj9J555hkWLFjAP//5Tzp37mx0OT5n5syZdO/evdqWIzk1i8XC8ePHefbZZ2nZsiUABw4cYNGiRQohta1fv35kZ2ef8r6TxzR06NChaln79u2x2Wzk5+cH3H/8Z+rVrbfeysaNG+nduzcAFRUVOJ1OUlNT+eijj2jRokV9lmq4M/XqpFdffZXp06fzwAMPMHr06HqqzPclJCRQUFCAw+HAYql8+8nNzcVqtRIdHW1wdb7p8ccfZ9GiRTzzzDP89re/Nbocn/TRRx+Rl5dXdazRyWD7ySef8PPPPxtZms9p0qQJoaGhVQEEKv/2HTx40JB6/DqEnEm3bt0IDg4mKyur6r/UnJwcIiIiaNy4sbHF+ZgZM2ZQXl5edfv1119n/fr1zJgxg6ZNmxpYmW9asmQJ06dPZ8KECfzxj380uhyfkpSUhMViYd26dfTp0weoPFAuJSUl4LY+emLmzJm8+eabPPfccwwZMsTocnzW66+/jsPhqLo9Y8YMAO6//36jSvJZPXv2xGazsXPnTtq3bw/Ajh07qoWS+hSwISQyMpIbb7yRxx9/nGnTpuF2u5kxYwYjRoyo+g9NKiUkJFS73ahRI6xWK23btjWoIt9VWFjIP/7xD66//nquuuoqcnNzq+6LjY3FbDYbWJ3xwsLCGDZsGJMnT+app57iyJEjZGRkMHXqVKNL8zk5OTnMmjWLMWPGkJaWVu211KRJEwMr8z2//gMaEREBoPeoU+jQoQOXXHIJEyZMYPLkyeTm5jJ37lzuueceQ+oJ6L+2Dz30EM888wxjxozB7XZz7bXX8ve//93osqQBW7FiBaWlpSxZsoQlS5ZUu++LL76gVatWBlXmO06++Y0ePZrIyEjGjRvH4MGDjS7L53zxxRc4nU5eeeUVXnnllWr3nW13oMiZzJgxg8cff5zf//73hIWFcfPNNxs2C7bJ7TZorlYREREJaNoJKyIiIoZQCBERERFDKISIiIiIIRRCRERExBAKISIiImIIhRARERExhEKIiIiIGEIhRERERAyhECIiIiKGUAgRERERQyiEiIiIiCH+P0QAKgGawiG4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Function\n",
    "def logistic(t):\n",
    "    return 1.0 / (1 + math.exp((-1.0)*t) )\n",
    "\n",
    "# Set t from -6 to 6 ( 500 elements, linearly spaced)\n",
    "t = np.linspace(-6,6,500)\n",
    "\n",
    "# Set up y values (using list comprehension)\n",
    "y = np.array([logistic(ele) for ele in t])\n",
    "\n",
    "# Plot\n",
    "plt.plot(t,y)\n",
    "plt.title(' Logistic Function ')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we remember back to the Linear Regression Lectures, we could describe a [Linear Regression](http://en.wikipedia.org/wiki/Linear_regression) Function model as:\n",
    "$$ y_i = \\beta _1 x_{i1} + ... + \\beta _i x_{ip}$$\n",
    "\n",
    "Which was basically an expanded linear equation (y=mx+b) for various x data features. In the case of the above equation, we presume a data set of 'n' number of units, so that the data set would have the form:\n",
    "$$ [ y_i, x_{i1},...,x_{ip}]^{n}_{i=1}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our logistic function, if we view *t* as a linear function with a variable *x* we could express t as:\n",
    "$$ t = \\beta _0 + \\beta _1 x $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we've basically just substituted a linear function (form similar to y=mx+b) for t. We could then rewrite our logistic function equation as:\n",
    "$$ F(x)= \\frac{1}{1+e^{-(\\beta _0 + \\beta _1 x)}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can interpret F(x) as the probability that the dependent variable is a \"success\" case, this is a similar style of thinking as in the Binomial Distribution, in which we had successes and failures. So the formula for F(x) that we have here states that the probability of the dependent variable equaling a \"success\" case is equal to the value of the logistic function of the linear regression expression (the linear equation we used to replace *t* ).\n",
    "\n",
    "Inputting the linear regression expression into the logistic function allows us to have a linear regression expression value that can vary from positive to negative infinity, but after the transformation due to the logistic expression we will have an output of F(x) that ranges from 0 to 1.\n",
    "\n",
    "We can now perform a binary classification based on where F(x) lies, either from 0 to 0.5, or 0.5 to 1. \n",
    "\n",
    "### Part 2: Extra Math Resources\n",
    "\n",
    "This is a very basic overview of binary classification using Logistic Regression, if you're still interested in a deeper dive into the mathematics, check out these sources:\n",
    "\n",
    "1.) [Andrew Ng's class notes](http://cs229.stanford.edu/notes/cs229-notes1.pdf) on Logistic Regression (Note: Scroll down) \n",
    "NB - original link is broken, try [this](https://docs.google.com/spreadsheets/d/1sEu4ygD5HWxaqjvbR2nsjvG6NBoW5tRW/edit#gid=348511794)\n",
    "\n",
    "2.) [CMU notes](http://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch12.pdf) Note: Advanced math notation.\n",
    "\n",
    "3.) [Wikipedia](http://en.wikipedia.org/wiki/Logistic_regression) has a very extensive look at logistic regression.\n",
    "\n",
    "Scroll down to the bottom for more resources similar to this lecture!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------\n",
    "### Part 3: Dataset Analysis\n",
    "Let us go ahead and take a look at the [dataset](https://www.statsmodels.org/stable/examples/notebooks/generated/discrete_choice_example.html#Fair's-Affair-data)\n",
    "\n",
    "The dataset is packaged within Statsmodels. It is a data set from a 1974 survey of women by Redbook magazine. Married women were asked if they have had extramarital affairs. The published work on the data set can be found in:\n",
    "\n",
    "[Fair, Ray. 1978. “A Theory of Extramarital Affairs,” `Journal of Political Economy`, February, 45-61.](http://fairmodel.econ.yale.edu/rayfair/pdf/1978a200.pdf)\n",
    "\n",
    "It is important to note that this data comes from a self-reported survey, which can have many issues as far as the accuracy of the data. Also this analysis isn't trying to promote any agenda concerning women or marriage, the data is just interesting but its accuracy should be met with a healthy dose of skepticism.\n",
    "\n",
    "We'll ignore those issues concerning the data and just worry about the logistic regression aspects to the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case we will approach this as a classification problem by asking the question:\n",
    "\n",
    "*Given certain variables for each woman, can we classify them as either having particpated in an affair, or not participated in an affair?*\n",
    "\n",
    "\n",
    "#### DataSet Description\n",
    "\n",
    "From the [Statsmodels website](https://www.statsmodels.org/stable/index.html) we have the following information about the data:\n",
    "\n",
    "Number of observations: 6366\n",
    "Number of variables: 9\n",
    "Variable name definitions:\n",
    "\n",
    "    rate_marriage   : How rate marriage, 1 = very poor, 2 = poor, 3 = fair,\n",
    "                    4 = good, 5 = very good\n",
    "    age             : Age\n",
    "    yrs_married     : No. years married. Interval approximations. See\n",
    "                    original paper for detailed explanation.\n",
    "    children        : No. children\n",
    "    religious       : How relgious, 1 = not, 2 = mildly, 3 = fairly,\n",
    "                    4 = strongly\n",
    "    educ            : Level of education, 9 = grade school, 12 = high\n",
    "                    school, 14 = some college, 16 = college graduate,\n",
    "                    17 = some graduate school, 20 = advanced degree\n",
    "    occupation      : 1 = student, 2 = farming, agriculture; semi-skilled,\n",
    "                    or unskilled worker; 3 = white-colloar; 4 = teacher\n",
    "                    counselor social worker, nurse; artist, writers;\n",
    "                    technician, skilled worker, 5 = managerial,\n",
    "                    administrative, business, 6 = professional with\n",
    "                    advanced degree\n",
    "    occupation_husb : Husband's occupation. Same as occupation.\n",
    "    affairs         : measure of time spent in extramarital affairs\n",
    "\n",
    "See the original paper for more details.\n",
    "\n",
    "*Why a Statsmodels data set?* So you can have the option of working through additional example datasets included in SciKit Learn and their own tutorials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------\n",
    "### Part 4: Data Visualization\n",
    "\n",
    "Now that we've done a quick overview of some math and the data we will be working with, let's go ahead and dive into the code!\n",
    "\n",
    "We will start with loading the data and visualizing it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Standard method of loading Statsmodels datasets into a pandas DataFrame. Note the name fair stands for 'affair' dataset.\n",
    "df = sm.datasets.fair.load_pandas().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": 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>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affairs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.666666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
       "0            3.0  32.0          9.0       3.0        3.0  17.0         2.0   \n",
       "1            3.0  27.0         13.0       3.0        1.0  14.0         3.0   \n",
       "2            4.0  22.0          2.5       0.0        1.0  16.0         3.0   \n",
       "3            4.0  37.0         16.5       4.0        3.0  16.0         5.0   \n",
       "4            5.0  27.0          9.0       1.0        1.0  14.0         3.0   \n",
       "\n",
       "   occupation_husb   affairs  \n",
       "0              5.0  0.111111  \n",
       "1              4.0  3.230769  \n",
       "2              5.0  1.400000  \n",
       "3              5.0  0.727273  \n",
       "4              4.0  4.666666  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Let's go ahead and start our classfication by creating a new column called 'Had_Affair'. We will set this column equal to 0 if the affairs column is 0 (meaning no time spent in affairs) otherwise the 'Had_Affair' value will be set as 1, indicating that the woman had an affair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create check function\n",
    "def affair_check(x):\n",
    "    if x != 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "# Apply to DataFrame\n",
    "df['Had_Affair'] = df['affairs'].apply(affair_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go ahead and see the result!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": 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>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affairs</th>\n",
       "      <th>Had_Affair</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.230769</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.666666</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6361</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6363</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6364</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6365</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6366 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
       "0               3.0  32.0          9.0       3.0        3.0  17.0         2.0   \n",
       "1               3.0  27.0         13.0       3.0        1.0  14.0         3.0   \n",
       "2               4.0  22.0          2.5       0.0        1.0  16.0         3.0   \n",
       "3               4.0  37.0         16.5       4.0        3.0  16.0         5.0   \n",
       "4               5.0  27.0          9.0       1.0        1.0  14.0         3.0   \n",
       "...             ...   ...          ...       ...        ...   ...         ...   \n",
       "6361            5.0  32.0         13.0       2.0        3.0  17.0         4.0   \n",
       "6362            4.0  32.0         13.0       1.0        1.0  16.0         5.0   \n",
       "6363            5.0  22.0          2.5       0.0        2.0  14.0         3.0   \n",
       "6364            5.0  32.0          6.0       1.0        3.0  14.0         3.0   \n",
       "6365            4.0  22.0          2.5       0.0        2.0  16.0         2.0   \n",
       "\n",
       "      occupation_husb   affairs  Had_Affair  \n",
       "0                 5.0  0.111111           1  \n",
       "1                 4.0  3.230769           1  \n",
       "2                 5.0  1.400000           1  \n",
       "3                 5.0  0.727273           1  \n",
       "4                 4.0  4.666666           1  \n",
       "...               ...       ...         ...  \n",
       "6361              3.0  0.000000           0  \n",
       "6362              5.0  0.000000           0  \n",
       "6363              1.0  0.000000           0  \n",
       "6364              4.0  0.000000           0  \n",
       "6365              4.0  0.000000           0  \n",
       "\n",
       "[6366 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame Check\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's go ahead and groupby the newly created 'Had_Affair' column. We'll do this by grouping by the column and then calling the mean aggregate function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": 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>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affairs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Had_Affair</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.329701</td>\n",
       "      <td>28.390679</td>\n",
       "      <td>7.989335</td>\n",
       "      <td>1.238813</td>\n",
       "      <td>2.504521</td>\n",
       "      <td>14.322977</td>\n",
       "      <td>3.405286</td>\n",
       "      <td>3.833758</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.647345</td>\n",
       "      <td>30.537019</td>\n",
       "      <td>11.152460</td>\n",
       "      <td>1.728933</td>\n",
       "      <td>2.261568</td>\n",
       "      <td>13.972236</td>\n",
       "      <td>3.463712</td>\n",
       "      <td>3.884559</td>\n",
       "      <td>2.187243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rate_marriage        age  yrs_married  children  religious  \\\n",
       "Had_Affair                                                               \n",
       "0                4.329701  28.390679     7.989335  1.238813   2.504521   \n",
       "1                3.647345  30.537019    11.152460  1.728933   2.261568   \n",
       "\n",
       "                 educ  occupation  occupation_husb   affairs  \n",
       "Had_Affair                                                    \n",
       "0           14.322977    3.405286         3.833758  0.000000  \n",
       "1           13.972236    3.463712         3.884559  2.187243  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby Had Affair column\n",
    "df.groupby('Had_Affair').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at this brief glance of the data, it seems that the women who had affairs were slightly older,married longer, and slightly less religious and less educated. However, the mean values of both classes are very close for all variables.\n",
    "\n",
    "Let's go ahead and try to visualize some of this data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First some histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29b43f640>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAHpCAYAAAB3H70WAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA64ElEQVR4nO3deVyVdf7//+fxIIsZOSqSW+a4YCIeENIWncyczHLS0CwsGrPSSdRpzDK0zHQYRtHq45JGlmuluU4u02JZUxOpYaDmMmiLJC6gERLL0cP1+8Nv59cJVCDOcsHjfrtxq/N+X9f1fl3vc4lPr+s617EYhmEIAADApOp5uwAAAIDfgjADAABMjTADAABMjTADAABMjTADAABMjTADAABMjTADAABMjTBTAcMw5HA4xCN4AADwfYSZCpSVlSkjI0NlZWXeLgUAAFwCYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJiaT4QZu92uAQMGaPv27eX6zpw5o169emndunUu7Zs2bVLfvn1ls9mUkJCg06dPO/sMw9CsWbN03XXXqXv37po5cybfgA0AQC3l9TBTWlqq8ePHKysrq8L+lJQUnTx50qVt9+7dmjx5ssaMGaNVq1apoKBAiYmJzv7Fixdr06ZNmjdvnubMmaONGzdq8eLFbt0PAADgHV4NM4cOHdLQoUN15MiRCvu/+OILff755woJCXFpX7Fihfr3769BgwapU6dOmjlzpj7++GNlZ2dLkpYtW6Zx48YpJiZG1113nSZMmKDXX3/d7fsDAAA8z6thZseOHerRo4dWrVpVrs9ut+uZZ57RlClT5O/v79KXmZmpmJgY5+vmzZurRYsWyszM1IkTJ3Ts2DFde+21zv7o6GgdPXq03BkeAABgfn7eHHzYsGEX7Fu4cKE6d+6snj17lus7efKkmjVr5tLWpEkTHT9+XLm5uZLk0t+0aVNJ0vHjx8utdzEOh6PSywIAUBlWq9XbJdQ6Xg0zF3Lo0CGtXLlSb7/9doX9JSUl5c7W+Pv7y263q6SkxPn6l33S+bM9VbFnz54qLV9X1a9fX35+njmUzp07p7Nnz3pkLABwh+joaG+XUOv4XJgxDENPP/20xo0b5zyj8msBAQHlgondbldQUJBLcAkICHD+vyQFBQVVqZaIiAgSdCXUq1dPFovFI2MZhsEn0wAALnwuzOTk5OjLL7/UwYMHNWPGDElScXGxnn32WW3ZskWLFi1SaGio8vLyXNbLy8tTSEiIQkNDJUm5ublq1aqV8/8llbuR+FKsVithppIyDpeosMS9IaNhYD1FtgvkPQEAuPC5MBMaGqr33nvPpS0+Pl7x8fG68847JUk2m03p6emKjY2VJB07dkzHjh2TzWZTaGioWrRoofT0dGeYSU9PV4sWLap0vwyqprCkTAVFnDEBAHiez4UZPz8/tWnTplxbkyZNnGdd4uLiFB8fr8jISEVERCgpKUm9e/dW69atnf2zZs3SlVdeKUmaPXu2RowY4dkdAQAAHuFzYaYyoqKiNG3aNM2ZM0c//vijbrzxRk2fPt3Z/9BDD+nUqVMaM2aMrFarhgwZouHDh3uvYAAA4DYWwzAMbxfhaxwOhzIyMhQZGcn9GZX06VdFbr/MFNygnnqGN3DrGAAA8/H61xkAAAD8FoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZoIYZhlErxwIAX+Xn7QKA2sZisSjjcIkKS8rcOk7DwHqKbBfo1jEAwAwIM4AbFJaUqaDIvWEGAHAel5kAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICp+USYsdvtGjBggLZv3+5sy8jI0L333quoqCj169dPq1evdlnns88+04ABA2Sz2fTAAw8oOzvbpX/JkiXq1auXoqKiNGnSJBUXF3tkXwAAgGd5PcyUlpZq/PjxysrKcrbl5ubqkUceUffu3bV+/XqNGzdO06dP10cffSRJysnJUUJCgmJjY7VmzRo1btxYo0ePlmEYkqR3331X8+bN07Rp07R06VJlZmYqJSXFG7sHAADczKth5tChQxo6dKiOHDni0r5161Y1bdpU48eP19VXX6077rhDgwYN0saNGyVJq1evVpcuXTRixAh16NBBycnJOnr0qHbs2CFJWrZsmf785z/r5ptvVteuXfXcc89p7dq1nJ0BAKAW8mqY2bFjh3r06KFVq1a5tPfq1UvJycnlli8sLJQkZWZmKiYmxtkeFBSk8PBwZWRkyOFwaM+ePS79kZGROnv2rA4cOOCmPQEAAN7i583Bhw0bVmF7q1at1KpVK+frU6dOafPmzRo7dqyk85ehmjVr5rJOkyZNdPz4cRUUFKi0tNSl38/PT40aNdLx48erVJ/D4ajS8nWV1Wr16Hi+/r4wHwAuxtO/I+oCr4aZyigpKdHYsWPVtGlT3XPPPZKk4uJi+fv7uyzn7+8vu92ukpIS5+uK+qtiz549v6HyuiEoKEidO3f26JgHDx702UuGzAeAS4mOjvZ2CbWOT4eZn376SaNHj9a3336rN954Q0FBQZKkgICAcsHEbrcrODhYAQEBzte/7v95/cqKiIggQfugsLAwb5fgU5gPAHWdz4aZwsJCPfzwwzpy5IiWLl2qq6++2tkXGhqqvLw8l+Xz8vJ0zTXXqFGjRgoICFBeXp7atWsnSTp37pzy8/MVEhJSpRqsVithxgfxnrhiPgDUdV7/aHZFysrKNGbMGH3//fdavny5OnTo4NJvs9mUnp7ufF1cXKx9+/bJZrOpXr16ioiIcOnPyMiQn5+fOnXq5LF9AAAAnuGTYWbNmjXavn27/v73vys4OFi5ubnKzc1Vfn6+JGnw4MHatWuXUlNTlZWVpcTERLVq1Uo9evSQdP7G4ldffVVbt27V7t27NXXqVA0dOrTKl5kAAIDv88nLTO+++67Kyso0atQol/bu3btr+fLlatWqlebOnat//OMfmj9/vqKiojR//nxZLBZJ0h133KGjR49qypQpstvtuvXWW/XEE094Y1cAAICbWYyfH5sLJ4fDoYyMDEVGRnI/QiV9+lWRCorK3DpGcIN66hnewK1j1BTmAwA8xycvMwEAAFQWYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJiaT4QZu92uAQMGaPv27c627OxsDR8+XJGRkbr99tv16aefuqzz2WefacCAAbLZbHrggQeUnZ3t0r9kyRL16tVLUVFRmjRpkoqLiz2yLwAAwLO8HmZKS0s1fvx4ZWVlOdsMw1BCQoKaNm2qtWvXauDAgRozZoxycnIkSTk5OUpISFBsbKzWrFmjxo0ba/To0TIMQ5L07rvvat68eZo2bZqWLl2qzMxMpaSkeGX/AACAe3k1zBw6dEhDhw7VkSNHXNo///xzZWdna9q0aWrXrp1GjRqlyMhIrV27VpK0evVqdenSRSNGjFCHDh2UnJyso0ePaseOHZKkZcuW6c9//rNuvvlmde3aVc8995zWrl3L2RkAAGohP28OvmPHDvXo0UN/+9vfFBkZ6WzPzMxU586d1aBBA2dbdHS0MjIynP0xMTHOvqCgIIWHhysjI0MxMTHas2ePxowZ4+yPjIzU2bNndeDAAUVFRVW6PofDUf2dq0OsVqtHx/P194X5AHAxnv4dURd4NcwMGzaswvbc3Fw1a9bMpa1JkyY6fvz4JfsLCgpUWlrq0u/n56dGjRo516+sPXv2VGn5uigoKEidO3f26JgHDx702bNszAeAS4mOjvZ2CbWOV8PMhRQXF8vf39+lzd/fX3a7/ZL9JSUlztcXWr+yIiIiSNA+KCwszNsl+BTmA0Bd55NhJiAgQPn5+S5tdrtdgYGBzv5fBxO73a7g4GAFBAQ4X/+6PygoqEp1WK1WwowP4j1xxXwAqOu8/mmmioSGhiovL8+lLS8vz3np6EL9ISEhatSokQICAlz6z507p/z8fIWEhLi/eAAA4FE+GWZsNpu++uor5yUjSUpPT5fNZnP2p6enO/uKi4u1b98+2Ww21atXTxERES79GRkZ8vPzU6dOnTy3EwAAwCN8Msx0795dzZs3V2JiorKyspSamqrdu3dryJAhkqTBgwdr165dSk1NVVZWlhITE9WqVSv16NFD0vkbi1999VVt3bpVu3fv1tSpUzV06NAqX2YCAAC+zyfDjNVq1UsvvaTc3FzFxsbq7bff1vz589WiRQtJUqtWrTR37lytXbtWQ4YMUX5+vubPny+LxSJJuuOOOzRq1ChNmTJFI0aMUNeuXfXEE094c5cAAICbWIyfH5sLJ4fDoYyMDEVGRnJzZSV9+lWRCorK3DpGcIN66hne4NIL+gDmAwA8xyfPzAAAAFQWYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJiaT4eZY8eOadSoUerWrZv69OmjJUuWOPv27dunu+++WzabTYMHD9bevXtd1t20aZP69u0rm82mhIQEnT592sPVAwAAT/DpMPPYY4+pQYMGWrdunSZNmqQXX3xR77//voqKijRy5EjFxMRo3bp1ioqK0qhRo1RUVCRJ2r17tyZPnqwxY8Zo1apVKigoUGJiopf3BgAAuIPPhpkff/xRGRkZevTRR3X11Verb9++6tWrl9LS0rRlyxYFBAToySefVLt27TR58mRddtlleueddyRJK1asUP/+/TVo0CB16tRJM2fO1Mcff6zs7Gwv7xUAAKhpft4u4EICAwMVFBSkdevW6fHHH1d2drZ27dqlxx57TJmZmYqOjpbFYpEkWSwWdevWTRkZGYqNjVVmZqYeeeQR57aaN2+uFi1aKDMzU61bt650DQ6Ho8b3qzayWq0eHc/X3xfmA8DFePp3RF3gs2EmICBAU6ZM0fTp07Vs2TI5HA7Fxsbq7rvv1gcffKD27du7LN+kSRNlZWVJkk6ePKlmzZqV6z9+/HiVatizZ89v24k6ICgoSJ07d/bomAcPHlRxcbFHx6ws5gPApURHR3u7hFrHZ8OMJB0+fFg333yzHnzwQWVlZWn69Om6/vrrVVxcLH9/f5dl/f39ZbfbJUklJSUX7a+siIgIErQPCgsL83YJPoX5AFDX+WyYSUtL05o1a/Txxx8rMDBQEREROnHihBYsWKDWrVuXCyZ2u12BgYGSzp/Vqag/KCioSjVYrVbCjA/iPXHFfACo63z2BuC9e/eqTZs2zoAiSZ07d1ZOTo5CQ0OVl5fnsnxeXp7z0tKF+kNCQtxfOAAA8CifDTPNmjXTd99953KG5euvv1arVq1ks9n05ZdfyjAMSZJhGNq1a5dsNpskyWazKT093bnesWPHdOzYMWc/AACoPXw2zPTp00f169fX008/rW+++UYffvihFi5cqPj4eN12220qKChQUlKSDh06pKSkJBUXF6t///6SpLi4OP3rX//S6tWrdeDAAT355JPq3bt3lT7JBAAAzKFaYeaBBx5QQUFBufbTp08rNjb2NxclSZdffrmWLFmi3NxcDRkyRMnJyXr00Ud1zz33qGHDhnr55ZeVnp7u/Ch2amqqGjRoIEmKiorStGnTNH/+fMXFxemKK65QcnJyjdQFAAB8S6VvAP7Pf/6j3bt3S5J27typhQsXOsPDz7777jsdPXq0xopr3769Fi9eXGFf165dtX79+guuGxsbW2PBCgAA+K5Kh5m2bdtq0aJFMgzDeY9K/fr1nf0Wi0UNGjRQUlKSWwoFAACoSKXDTOvWrbVs2TJJUmJioiZPnqyGDRu6rTAAAIDKqNZzZn6+/yQ3N1fnzp1zfqroZy1atPjtlQEAAFRCtcLMf//7Xz3zzDM6duyYpPMfjbZYLM7/7t+/v0aLBAAAuJBqhZlp06apa9euWrBgAZeaAACAV1UrzBw/flyLFi3iuS0AAMDrqvWcmZiYGJcn7AIAAHhLtcLMtddeq+eee06PPfaYXnjhBc2bN8/lBwAAVE+fPn20bt26cu3r1q1Tnz59qrXN+Ph4zZ07t0rrZGdnKywsTE888US5vtLSUo0ePVpdu3ZVfHy8vvvuOw0cOFARERF68cUXL7rd77//XmFhYfr++++rVM/FVPsG4C5duujUqVM6deqUS5/FYqmRwgAAgPds2bJFV111lbZu3aqffvpJl112mbPvk08+0SeffKI333xTzZo10yuvvCJJ2rx5s6644oqLbrd58+b69NNP1bhx4xqrtVphZvny5TVWAAAA8D2bNm3S/fffr/nz5+vdd991ear+mTNn1LRpU3Xp0kWSVFhYqE6dOumqq6665HatVqtCQkJqtNZqhZkNGzZctH/QoEHV2SwAAKiE9PR0zZo1S/v27ZPFYtG1116rpKQkNWvWTJL0/vvva9asWTpx4oRiY2PlcDiqtP1Dhw7pf//7n3r06KHdu3dr/fr1zjCzbt06JSYmSpLCwsLUsmVL51cZbdiwQR988IHq16+vpKQkpaWlqbi4WB06dNDTTz+t6Ohoff/997rlllv0wQcfqFWrVgoLC9Po0aP1xhtvKCoqSgsXLqzyfFQrzMyZM8fltcPh0KlTp+Tn56euXbsSZgAAcJMzZ85o1KhRGj58uGbOnKmTJ09q0qRJSk1N1dNPP61Dhw7pscce0xNPPKFevXpp6dKlSk9P1/XXX1/pMTZt2qSWLVuqU6dOuuWWWzR+/HgdPXpULVu21O23364zZ87otdde05o1axQYGKhnnnlGkjR58mQ1btxYw4cPV3BwsFauXCnDMDRr1ixNnTpVGzdurHC8bdu26c0331RZWVm15qRaYebDDz8s1/bTTz9pypQpCgsLq1YhAADgvGeffVbTp093aTt37pxCQkJUUlKi0aNH68EHH5TFYlHr1q116623Or8Meu3atYqJidHw4cMlSc8884y2bdtWpfG3bNnivNn4pptukr+/vzZs2KCEhAQFBgbq8ssvd7lcFBgYKEkKCQmRYRjq27ev+vXrpyuvvFKSdN9992nkyJEXHO+ee+7R73//+yrV+EvVCjMVueyyyzR27FjFxcVdtGAAAHBx48aN06233urS9t577+nNN99USEiIBg0apCVLlmj//v06dOiQDh48qG7dukmSDh8+rGuuuca5Xv369V1eX8ru3bv13XffqW/fvpLO//1+ww036F//+pcSEhIuub7FYlFcXJy2bNmiXbt26ZtvvtHevXsvetalZcuWla6vIjUWZiTpwIED1T5FBAAAzmvSpInatGlTrk2STpw4ocGDBys8PFw33HCDhg4dqo8++kiZmZnOZX/9nYn169ev9NibN2+WJI0YMcLZVlZWJsMwlJ6erujo6IuuX1ZWphEjRqigoEC33367+vTpo7Nnz2rMmDEXXCcgIKDS9VWkWmEmPj6+3Eewf/rpJx08eNB5WgsAANS8999/X1dccYVefvllZ9vy5cudAaZDhw768ssvnX1lZWU6cOCAOnXqdMltl5WV6d///rcGDhyohx9+2NnucDh0//33a8OGDZcMM4cOHdLOnTuVlpbm/Pj166+/Lql8yKop1QozPXr0KNfm7++vCRMmVOkGIwAAUDWNGjVSTk6O0tLS1KpVK/373//We++9p4iICEnS0KFDtXz5ci1YsED9+vXTqlWrlJOTU6ltf/HFFzpx4oTi4+PVsWNHl74777xTGzdu1NNPP33RbQQHB6tevXravHmz+vTpoz179jgf2Ge326uxx5dWrTDzy1NFhYWFcjgcl3xIDgAA+O369++vnTt3aty4cbJYLIqIiNDEiRM1d+5c2e12tWnTRgsWLFBycrIWLFigvn376qabbqrUtjdt2qSwsDBnMPqluLg4vfHGG9q6detFt3HllVdq6tSpmj9/vp5//nm1bdtWTz/9tCZOnKh9+/bV+DNmJMliVPOcz9KlS7Vo0SLl5eVJkho3bqy4uLiLXhMzC4fDoYyMDEVGRspqtXq7HFP49KsiFRS5936p4Ab11DO8gVvHqCnMBwB4TrXOzMyfP18rVqzQX//6V0VFRamsrEy7du3SvHnz5O/vz6eZAACAx1QrzLz11ltKSkpy+cKra665RqGhoUpKSiLMAADggxISEvTZZ59dsP+5557TnXfe6cGKaka1wkxhYaGuvvrqcu1t27bV6dOnf2tNAADADZ599lkVFxdfsP/nj3+bTbXCTFRUlF577TVNmzZN9erVk3T+PpNXX31VXbt2rdECAQBAzfj5u5tqm2qFmcTERN1333367LPPFB4eLkn66quvZLfbtWjRohotEAAA4GKqFWbatWunSZMmKT8/X19//bUCAgK0bds2zZkzp1IP5QEAAKgp9aqz0vLlyzV16lRdfvnlmjp1qhITExUfH68JEyborbfequkaAQAALqhaYWbx4sWaPXu27rrrLmfbxIkTlZKSotTU1BorDgAA4FKqFWZ++OEHXXXVVeXa27Zt63yIHgAAgCdUK8xER0dr7ty5Lh/vKi0t1cKFCxUVFVVjxQEAgPLc9YWNNTVWaWmpJk2apJiYGPXs2VOvvfaaGyr7/1XrBuApU6ZoxIgR6tmzp/N5M0eOHFHTpk310ksv1WR9AADgVywWizIOl6iwxL1fm9IwsJ4i2wVWeb2ZM2dq7969Wrp0qXJycjRx4kS1aNFCt912mxuqrGaYueqqq7RlyxZ98skn+vbbb+Xn56err75aPXv25LuMAADwgMKSMrd/B1x1FBUVafXq1XrllVcUHh6u8PBwZWVl6fXXX/etMCNJ/v7+uuWWW2qyFgAAYHIHDhzQuXPnXG47iY6O1sKFC1VWVuZ82G5NqvktAgCAOis3N1e/+93v5O/v72xr2rSpSktLlZ+f75YxCTMAAKDGFBcXuwQZSc7XdrvdLWMSZgAAQI0JCAgoF1p+fh0YWPWbiSuDMAMAAGpMaGiofvjhB507d87Zlpubq8DAQAUHB7tlTMIMAACoMddcc438/PyUkZHhbEtPT1dERIRbbv6VfsOnmQAAgPc0DHT/+YjqjBEUFKRBgwZp6tSp+sc//qGTJ0/qtddeU3JyshsqPI8wA8CtDMOQxWKpdWMB3mQYRrUeZlfdsar65yoxMVFTp07Vn//8ZzVs2FBjx47Vrbfe6qYKCTMA3MzXn1QKmJEnQ3t1xgoKCtKMGTM0Y8YMN1RUHmEGgNv56pNKAdQO3AAMmFRAfYvPf9kcAHgCZ2YAk/Kznj/9e2ZvmhxFBW4dy9ogWJd3ud6tYwBAdRFmAJNzFBXIceYHb5cBAF7DZSYAAGBqhBkAAGBqPh1m7Ha7nnvuOV177bW64YYb9PzzzztvQty3b5/uvvtu2Ww2DR48WHv37nVZd9OmTerbt69sNpsSEhJ0+vRpb+wCAABwM58OM3//+9/12Wef6dVXX9Xs2bP11ltvadWqVSoqKtLIkSMVExOjdevWKSoqSqNGjVJRUZEkaffu3Zo8ebLGjBmjVatWqaCgQImJiV7eGwAA4A4+ewNwfn6+1q5dq8WLF6tr166SpBEjRigzM1N+fn4KCAjQk08+KYvFosmTJ+s///mP3nnnHcXGxmrFihXq37+/Bg0aJEmaOXOmbr75ZmVnZ6t169Ze3CsAAFDTfPbMTHp6uho2bKju3bs720aOHKnk5GRlZmYqOjra+VRCi8Wibt26Ob/UKjMzUzExMc71mjdvrhYtWigzM9Oj+wAAgDuY5RlTdrtdAwYM0Pbt22uwovJ89sxMdna2WrZsqQ0bNmjhwoU6e/asYmNj9eijjyo3N1ft27d3Wb5JkybKysqSJJ08eVLNmjUr13/8+PEq1eBwOH7bTtQRVqvVo+P5+vvi6fnwpOrMPccH4Kom/kyY4RlTpaWlevzxx51/N7uTz4aZoqIifffdd1q5cqWSk5OVm5urKVOmKCgoSMXFxfL393dZ3t/fX3a7XZJUUlJy0f7K2rNnz2/biTogKChInTt39uiYBw8eVHFxsUfHrCxvzIcnVXXuOT6A8qKjo2tkO778jKlDhw7p8ccf99gZJJ8NM35+fiosLNTs2bPVsmVLSVJOTo7efPNNtWnTplwwsdvtCgw8/yVzAQEBFfYHBQVVqYaIiIha/a9sswoLC/N2CXWWGebeDDUCtd2OHTvUo0cP/e1vf1NkZKTbx/PZMBMSEqKAgABnkJGktm3b6tixY+revbvy8vJcls/Ly3NeWgoNDa2wPyQkpEo1WK1WwowP4j3xHjPMvRlqBGq7YcOGeXQ8n70B2GazqbS0VN98842z7euvv1bLli1ls9n05ZdfOk9fGYahXbt2yWazOddNT093rnfs2DEdO3bM2Q8AAGoPnw0zv//979W7d28lJibqwIED+uSTT5Samqq4uDjddtttKigoUFJSkg4dOqSkpCQVFxerf//+kqS4uDj961//0urVq3XgwAE9+eST6t27Nx/LBgCgFvLZMCNJs2bN0lVXXaW4uDhNnDhR9913n+Lj49WwYUO9/PLLSk9PV2xsrDIzM5WamqoGDRpIkqKiojRt2jTNnz9fcXFxuuKKK5ScnOzlvQEAAO7gs/fMSNLll1+umTNnVtjXtWtXrV+//oLrxsbGKjY21l2lAQAAH+HTYQYAAFTM2iC4VoxREwgzAACYjGEY1X6YXXXG+vmJ+76KMAMAgMl4Mlz81rEOHjxYQ5VcmE/fAAwAAHAphBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAgNcYhlErx4Jn+Xm7AABA3WWxWJRxuESFJWVuHadhYD1Ftgt06xjwHsIMAMCrCkvKVFDk3jCD2o3LTAAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwBqhYD6FhmG4bHxPDkWgIvz83YBAFAT/KySxWLRmb1pchQVuHUsa4NgXd7lereOAaDyCDMAahVHUYEcZ37wdhkAPMg0l5lGjhypp556yvl63759uvvuu2Wz2TR48GDt3bvXZflNmzapb9++stlsSkhI0OnTpz1dMgAA8ABThJnNmzfr448/dr4uKirSyJEjFRMTo3Xr1ikqKkqjRo1SUVGRJGn37t2aPHmyxowZo1WrVqmgoECJiYneKh8AALiRz4eZ/Px8zZw5UxEREc62LVu2KCAgQE8++aTatWunyZMn67LLLtM777wjSVqxYoX69++vQYMGqVOnTpo5c6Y+/vhjZWdne2s3UAO4wRMAUBGfv2dmxowZGjhwoE6ePOlsy8zMVHR0tCwWi6TzN/1169ZNGRkZio2NVWZmph555BHn8s2bN1eLFi2UmZmp1q1bV3psh8NRcztSi1mtVo+M440bPKtzDHhqPryB+XDF74jfztPHhy+8Z7X5z4S3+HSYSUtL0xdffKGNGzdq6tSpzvbc3Fy1b9/eZdkmTZooKytLknTy5Ek1a9asXP/x48erNP6ePXuqV3gdEhQUpM6dO3t0TE/e4Hnw4EEVFxdXenlvzIcnMR+uqjofcOWN48MX3rPo6Givjl8b+WyYKS0t1bPPPqspU6YoMDDQpa+4uFj+/v4ubf7+/rLb7ZKkkpKSi/ZXVkREBAm6jgsLC/N2CT6F+XDFfJgP71nt5LNhZt68eerSpYt69epVri8gIKBcMLHb7c7Qc6H+oKCgKtVgtVoJM3Uc778r5sMV82E+vGe1k8+Gmc2bNysvL09RUVGS5Awn7777rgYMGKC8vDyX5fPy8pyXlkJDQyvsDwkJ8UDlAADAk3w2zCxfvlznzp1zvp41a5YkacKECdq5c6deeeUVGYYhi+X8J1x27dqlv/zlL5Ikm82m9PR0xcbGSpKOHTumY8eOyWazeX5HAACAW/lsmGnZsqXL68suu0yS1KZNGzVp0kSzZ89WUlKS7r33Xq1cuVLFxcXq37+/JCkuLk7x8fGKjIxURESEkpKS1Lt37yp9kgkAAJiDzz9npiINGzbUyy+/7Dz7kpmZqdTUVDVo0ECSFBUVpWnTpmn+/PmKi4vTFVdcoeTkZC9XDQAA3MFnz8z82j//+U+X1127dtX69esvuHxsbKzzMhMAAKi9THlmBgAA4GeEGQAAYGqEGQAAYGqEGQDwIL4sFah5prkBGABqA4vFoozDJSosKXPrOA0D6ymyXeClFwRqAcIMAHhYYUmZCorcG2aAuoTLTAAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwCAWi+gvkWGYXhsPE+OBcnP2wUAAOBuflbJYrHozN40OYoK3DqWtUGwLu9yvVvHgCvCDACgznAUFchx5gdvl4EaxmUmAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAKiFeOIt6hIemgcAtRBPvEVdQpgBgFqMJ96iLuAyEwAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDWfDjMnTpzQuHHj1L17d/Xq1UvJyckqLS2VJGVnZ2v48OGKjIzU7bffrk8//dRl3c8++0wDBgyQzWbTAw88oOzsbG/sAgAAcDOfDTOGYWjcuHEqLi7W66+/rhdeeEHbtm3Tiy++KMMwlJCQoKZNm2rt2rUaOHCgxowZo5ycHElSTk6OEhISFBsbqzVr1qhx48YaPXq0DMPw8l4BAICa5uftAi7k66+/VkZGhv773/+qadOmkqRx48ZpxowZ+sMf/qDs7GytXLlSDRo0ULt27ZSWlqa1a9dq7NixWr16tbp06aIRI0ZIkpKTk3XjjTdqx44d6tGjhzd3CwAA1DCfDTMhISFatGiRM8j8rLCwUJmZmercubMaNGjgbI+OjlZGRoYkKTMzUzExMc6+oKAghYeHKyMjo0phxuFw/LadqCOsVqu3S3Cb6hwDzIcr5sMV8+GqLs5Hbd5nb/HZMBMcHKxevXo5X5eVlWnFihW67rrrlJubq2bNmrks36RJEx0/flySLtlfWXv27Klm9XVHUFCQOnfu7O0y3ObgwYMqLi6u9PLMhyvmwxXz4aquzkd0dLQXqqndfDbM/FpKSor27dunNWvWaMmSJfL393fp9/f3l91ulyQVFxdftL+yIiIiSNB1XFhYmLdL8CnMhyvmwxXz4Yr58BxThJmUlBQtXbpUL7zwgjp27KiAgADl5+e7LGO32xUYGChJCggIKBdc7Ha7goODqzSu1WolzNRxvP+umA9XzIcr5sMV8+E5Pvtppp9Nnz5dixcvVkpKivr16ydJCg0NVV5enstyeXl5zktLF+oPCQnxTNEAAMBjfDrMzJs3TytXrtTzzz+vO+64w9lus9n01VdfqaSkxNmWnp4um83m7E9PT3f2FRcXa9++fc5+AABQe/hsmDl8+LBeeuklPfLII4qOjlZubq7zp3v37mrevLkSExOVlZWl1NRU7d69W0OGDJEkDR48WLt27VJqaqqysrKUmJioVq1a8bFsAABqIZ8NMx988IEcDocWLFignj17uvxYrVa99NJLys3NVWxsrN5++23Nnz9fLVq0kCS1atVKc+fO1dq1azVkyBDl5+dr/vz5slgsXt4rAABQ03z2BuCRI0dq5MiRF+xv06aNVqxYccH+m266STfddJM7SgMAAD7EZ8/MAAAAVAZhBgAAmBphBgAAmBphBgAAmBphBgAAmBphBgAAmBphBgAAmBphBgAAmBphBgAAmBphBgAAmBphBgAAmBphBgAAmBphBgAAmBphBgAAmBphphoMw6iVYwEAYEZ+3i7AjCwWizIOl6iwpMyt4zQMrKfIdoFuHQMAALMjzFRTYUmZCorcG2YAAMClcZkJAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYWq0NM6WlpZo0aZJiYmLUs2dPvfbaa94uCQAAuIGftwtwl5kzZ2rv3r1aunSpcnJyNHHiRLVo0UK33Xabt0urtID6FhmGIYvF4pHxPDkWAAA1pVaGmaKiIq1evVqvvPKKwsPDFR4erqysLL3++uumCjN+VslisejM3jQ5igrcOpa1QbAu73K9W8cAAMAdamWYOXDggM6dO6eoqChnW3R0tBYuXKiysjLVq2euq2uOogI5zvzg7TIAAPBJtTLM5Obm6ne/+538/f2dbU2bNlVpaany8/PVuHHji65vGIYkyW63y2q1luu3Wq26LMCQ/t9y7hLkZ8jhcEhBwbLIzZd/gi6Xw+E4P14VMR+umA9XzIcr5sNVXZwPq9WqevXqcVm/BlkMw81HkBds2LBB//d//6dt27Y527Kzs9W3b199/PHHuvLKKy+6vt1u1549e9xdJgCgjoqMjKzwH8uonlp5ZiYgIEB2u92l7efXgYGBl1zfz89PERERJGcAgFuY7XYHX1crw0xoaKh++OEHnTt3Tn5+53cxNzdXgYGBCg4OvuT69erVc7lEBQAAfFetjIbXXHON/Pz8lJGR4WxLT093nm0BAAC1R638mz0oKEiDBg3S1KlTtXv3bm3dulWvvfaaHnjgAW+XBgAAalitvAFYkoqLizV16lS99957atiwoR566CENHz7c22UBAIAaVmvDDAAAqBtq5WUmAABQdxBmAACAqRFmAACAqRFmvMRut2vAgAHavn27JOmpp55SWFhYuZ8LfQLrxx9/LLdsjx49PLkLNeLEiRMaN26cunfvrl69eik5OVmlpaWSpIyMDN17772KiopSv379tHr16otua9OmTerbt69sNpsSEhJ0+vRpT+xCjbrQfFT1+JCkJUuWqFevXoqKitKkSZNUXFzswT2pGd99950eeughRUVFqXfv3lq0aJGzry4eHxeaj7p6fPxs5MiReuqpp5yvP/roIw0cOFBRUVH605/+pA8++OCi69emuaizDHhcSUmJkZCQYHTs2NH4/PPPDcMwjIKCAuPkyZPOny+//NLo0qWL8f7771e4jS+++MLo3r27yzp5eXme3I3frKyszBg6dKjx8MMPG//73/+MnTt3Gn/84x+Nf/7zn8bJkyeNmJgYY/bs2cY333xjbNq0yYiIiDC2bdtW4bYyMzONrl27GuvXrzf2799v3H///cbIkSM9u0O/0cXmo6rHxzvvvGNER0cbH374oZGZmWncfvvtxnPPPefhPfptHA6HceuttxqPP/648c033xgfffSR0a1bN+Ptt9+uk8fHxeajLh4fP9u0aZPRsWNHY+LEiYZhGMb+/fuN8PBwY+nSpca3335rrFixwggPDzf2799f4fq1aS7qMsKMh2VlZRl33nmn8ac//cklzPzaiBEjjAkTJlxwO2+99ZZxzz33uKtMjzh06JDRsWNHIzc319m2ceNGo2fPnsYbb7xh3HbbbS7LP/PMM8b48eMr3NYTTzzh/GVmGIaRk5NjhIWFGUeOHHFP8W5wsfn4tUsdH8OGDTPmzJnjfL1z506ja9euRlFRUc0W7UYnTpww/vrXvxpnzpxxtiUkJBjPPvtsnTw+LjYfv1YXjg/DMIwffvjB+MMf/mAMHjzY+f6mpKQYDz30kMtyI0aMMJ5//vkKt1Fb5qKu4zKTh+3YsUM9evTQqlWrLrhMWlqadu7cqfHjx19wmUOHDunqq692Q4WeExISokWLFqlp06Yu7YWFhc5LLL9WWFhY4bYyMzMVExPjfN28eXO1aNFCmZmZNVu0G11sPn7pUseHw+HQnj17XOYjMjJSZ8+e1YEDB2q+cDdp1qyZXnzxRTVs2FCGYSg9PV07d+50uQT3a7X5+LjYfPxSXTk+JGnGjBkaOHCg2rdv72y76667NGHChHLLnjlzplxbbZqLuq5WfjeTLxs2bNgll0lNTdVdd92l5s2bX3CZw4cP69y5cxoyZIhOnDihmJgYJSYmqlmzZjVZrlsFBwerV69eztdlZWVasWKFrrvuOrVq1UqtWrVy9p06dUqbN2/W2LFjK9zWyZMny+17kyZNdPz4cfcU7wYXm49futTxUVBQoNLSUpf58PPzU6NGjUw1H7/Up08f5eTk6Oabb1a/fv1ktVrr3PHxS7+ej1+qK8dHWlqavvjiC23cuFFTp051trdr185luaysLKWlpenee+8tt43aMhfgBmCfk52drc8//1zx8fEXXe7rr79WYWGhEhMT9cILL+jkyZP6y1/+IofD4aFKa15KSor27dunv/3tby7tJSUlGjt2rJo2bap77rmnwnVLSkrKfTmov79/uW9PN5OK5qMyx0dJSYkk1ar5mDNnjhYuXKj9+/eXOyNTF4+PC81HXTk+SktL9eyzz2rKlCkKDAy84HKnT5/W2LFj1a1bN91yyy3l+mvDXOA8zsz4mHfffVfXXHONy2nTimzevFkWi8X5B3nOnDnq2bOnMjMz1a1bN0+UWqNSUlK0dOlSvfDCC+rYsaOz/aefftLo0aP17bff6o033lBQUFCF6wcEBJT75WO32y+4vK+70HxU5vgICAiQpFo1HxEREZLO/yU2YcIEPfnkk/L396+zx8eF5qOuHB/z5s1Tly5dXM5k/lpeXp4efPBBGYahOXPmVPglw7VhLnAeZ2Z8zCeffFLhvyB+LSgoyOVfJE2aNFGjRo104sQJd5bnFtOnT9fixYuVkpLicsq8sLBQDz30kLKysrR06dKL3iMUGhqqvLw8l7a8vDyFhIS4q2y3udB8SJU7Pho1aqSAgACX+Th37pzy8/NNNR95eXnaunWrS1v79u119uxZFRYW1rnj41LzIdWd42Pz5s3aunWroqKiFBUVpY0bN2rjxo2KioqSdP4RB/fdd5/sdruWLVumxo0bV7id2jAXOI8w40MMw9CePXsueWalsLBQ1157rT7//HNn24kTJ/TDDz/o97//vbvLrFHz5s3TypUr9fzzz+uOO+5wtpeVlWnMmDH6/vvvtXz5cnXo0OGi27HZbEpPT3e+PnbsmI4dOyabzea22t3hQvMhVf74qFevniIiIlzmIyMjQ35+furUqZNb6naH77//XmPGjHEJ6Hv37lXjxo3VqFGjOnd8XGw+GjduXKeOj+XLl2vjxo3asGGDNmzYoD59+qhPnz7asGGDioqK9PDDD6tevXpasWKFQkNDL7id2jAXOI/LTD7k6NGj+umnnyo8RVxSUqIzZ84oJCREDRs2VHR0tJKTkzV9+nRZrVYlJSWpV69eCgsL80Ll1XP48GG99NJLGjlypKKjo5Wbm+vs27Ztm7Zv364FCxYoODjY2Ve/fn01atRIdrtdP/74oxo3biyr1aq4uDjFx8crMjJSERERSkpKUu/evdW6dWtv7V6VXWw+QkJCKn18SOdvNJ8yZYo6duyoZs2aaerUqRo6dKipTp1HREQoPDxckyZNUmJioo4ePaqUlBT95S9/0Zo1a+rc8XGx+ZAq//tDMv/x0bJlS5fXl112mSSpTZs2euGFF3TkyBEtX75ckpzHRmBgoC6//PJaNxf4f7z5ufC67tfPmcnIyDA6duxolJaWllt27dq1RseOHZ2v8/Pzjaeeesro0aOHERUVZUyYMMHIz8/3SN015eWXXzY6duxY4c+IESMqbL///vsNwzCMzz//3OjYsaORnZ3t3N7atWuNm266yYiMjDQSEhKM06dPe2vXquVi82EYVTs+ft7e9ddfb0RHRxuJiYlGSUmJR/ajJh0/ftxISEgwunXrZtx4443GggULjLKysjp5fBjGhefDMOrm8fGziRMnOp8z069fvwqPjZ/7a/tc1FUWwzAMbwcqAACA6uKeGQAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQAXlJ6erri4ONlsNkVGRuqRRx7RyZMnJUmffvqp/vSnP6lr1656+OGHNX36dD311FPOdVeuXKk+ffooKipK8fHxOnjwoLd2A0AtR5gBUKEzZ85o1KhRuvHGG7Vp0ya9+uqrOnLkiFJTU5Wdna1HH31U/fv314YNGxQREaHXX3/due6HH36oefPm6ZlnntH69esVHR2tBx54QD/++KMX9whAbcW3ZgOoUG5urjZu3KgHH3xQFotFkjR79mzt3r1bNptN6enpLgHmnnvuUdu2bfXPf/5Tw4YNU//+/RUfH+/sj42N1V133eXSBgA1wc/bBQDwTSEhIRo0aJCWLFmi/fv369ChQzp48KC6deumgwcPKiIiwmX5yMhI55mXw4cPKyUlRc8//7yzv7S0VN9++60ndwFAHUGYAVChEydOaPDgwQoPD9cNN9ygoUOH6qOPPlJmZqasVqt+fVL3l68dDocmTZqk66+/3mWZhg0beqR2AHULYQZAhd5//31dccUVevnll51ty5cvl2EY6tChg9LT012W/+qrr9S6dWtJUtu2bXX8+HG1adPG2Z+YmKi+ffvqlltu8cwOAKgzuAEYQIUaNWqknJwcpaWlKTs7W6mpqXrvvfdkt9s1dOhQZWRkKDU1Vd98840WLlyoL774wnlvzYMPPqilS5dqw4YNOnLkiFJSUvTvf/9b7dq18/JeAaiNuAEYQIUcDoemTZumLVu2yGKxKCIiQr169dLcuXOVlpamTz/9VDNmzNDRo0d14403ymq1qmnTppo2bZokadmyZVqyZIny8vLUvn17PfHEE+UuOwFATSDMAKiy//3vfzp37pw6d+7sbBs5cqQiIiI0duxYL1YGoC7iMhOAKjty5IgefPBB/fe//9XRo0e1evVqpaWl6Y9//KO3SwNQB3FmBkC1LFiwQKtWrdKpU6fUtm1bjRs3Tn379vV2WQDqIMIMAAAwNS4zAQAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAU/v/AE2iozQS5sOWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Factorplot for age with Had Affair hue\n",
    "sns.catplot(x='age',data=df,hue='Had_Affair',palette='coolwarm',kind='count')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This suggests a higher probability of an affair as age increases. Let's check the number of years married."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29b593730>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Factorplot for years married with Had Affair hue\n",
    "sns.catplot(x='yrs_married',data=df,hue='Had_Affair',palette='coolwarm',kind='count')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like probability of having an affair increases with the number of years married. Let's check the number of children."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29b83d280>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAHqCAYAAADxi8+4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA/F0lEQVR4nO3deVxV1eL///fhIIMSkoLklFOKSggEiaV+UjPT8pZpw9VyuFr6+TjQp3AILcXUvFdsMDXN1ByzMr12s8Gyj9ebZWYYiJokmKapBA4pMRw9Z//+8Of5dkJUCDhnw+v5eJzH9ay19l5rr4vybu+197YYhmEIAADApLzcPQAAAIA/gzADAABMjTADAABMjTADAABMjTADAABMjTADAABMjTADAABMjTADAABMjTADAABMza1hJjs7W/Hx8Wrfvr06d+6smTNnqqioSJJ05MgRDRkyRFFRUbrnnnu0bds2l22/+uor9e7dW5GRkRo0aJCOHDniUr9s2TJ17txZ0dHRmjhxogoKCirtuAAAQOVxW5gxDEPx8fEqKCjQ6tWr9fLLL2vLli165ZVXZBiGRo0apeDgYK1bt07333+/Ro8erWPHjkmSjh07plGjRqlv37567733VKdOHY0cOVKX3sywadMmzZs3T88//7yWL1+utLQ0JScnu+tQAQBABbK4691MWVlZuueee/Tll18qODhYkrRx40b94x//0KxZszRy5Eh9+eWXqlmzpiRpyJAhiomJ0ZgxYzRnzhx9++23WrlypSSpoKBAHTt21IIFCxQXF6dHH31UHTp00JgxYyRJ3377rYYNG6avv/5a/v7+7jhcAABQQdx2ZiYkJESLFy92BplL8vLylJaWprZt2zqDjCTFxMQoNTVVkpSWlqbY2Fhnnb+/v8LDw5Wamiq73a709HSX+qioKJ0/f1779++/prEZhiG73S7ewQkAgOfzdlfHgYGB6ty5s/O7w+HQqlWr1KFDB+Xk5KhevXou7evWrasTJ05I0hXrz549q6KiIpd6b29vBQUFObe/GofDodTUVEVERMhqtZb1EAEAKIbfK+XPbWHmj5KTk7Vv3z699957WrZsmXx8fFzqfXx8ZLPZJF28rFRSfWFhofN7Sdtfq/T09NIeBgAAVxQTE+PuIVQ5HhFmkpOTtXz5cr388stq1aqVfH19debMGZc2NptNfn5+kiRfX99iwcRmsykwMFC+vr7O73+sL+16Gc7MAADg+dweZqZNm6Y1a9YoOTlZd999tyQpNDRUmZmZLu1yc3Odl45CQ0OVm5tbrL5NmzYKCgqSr6+vcnNz1aJFC0nShQsXdObMGYWEhJRqbFarlTADAICHc+tzZubNm6e3335bL730ku69915neWRkpPbu3eu8ZCRJKSkpioyMdNanpKQ46woKCrRv3z5FRkbKy8tLERERLvWpqany9vZW69atK+GoAABAZXJbmMnKytJrr72mJ554QjExMcrJyXF+2rdvr/r16ysxMVEHDhzQokWLtHv3bj344IOSpH79+mnXrl1atGiRDhw4oMTERDVq1EhxcXGSpAEDBmjJkiXavHmzdu/eraSkJD388MPclg0AQBXktufMLFq0SC+++OJl6zIyMnT48GFNmjRJaWlpatKkiSZOnKjbb7/d2Wbr1q164YUXdOLECUVHR2vatGlq3Lixy/6XLVsmm82mHj16aMqUKc71NFdjt9uVmpqqqKgoLjMBAODh3BZmPBlhBgAA8+BFkwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIM2VQme/m5D2gAABcmbe7B2BGFotFqVmFyit0VGg/AX5eimrhV6F9AABgdoSZMsordOhsfsWGGQAAcHVcZgIAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKbmEWHGZrOpd+/e2rFjhyTpmWeeUVhYWLHPoEGDnNvExsYWq//tt98kSUVFRZo4caJiY2PVqVMnLV261C3HBQAAKp63uwdQVFSkhIQEHThwwFk2adIkJSQkOL///PPPGjhwoDPMZGdn69y5c9q8ebP8/Pyc7WrWrClJmjVrlvbs2aPly5fr2LFjmjBhgho0aKCePXtW0lEBAIDK4tYwk5mZqYSEBBmG4VJ+3XXX6brrrnN+f+aZZ9SzZ091795dkpSVlaWQkBA1bty42D7z8/O1du1avfHGGwoPD1d4eLgOHDig1atXE2YAAKiC3HqZ6ZtvvlFcXJzeeeedEtts375dO3fu1NNPP+0sy8zMVLNmzS7bfv/+/bpw4YKio6OdZTExMUpLS5PD4Si/wQMAAI/g1jMzAwYMuGqbRYsW6YEHHlD9+vWdZVlZWSooKNDAgQP1448/qk2bNpo4caKaNWumnJwcXX/99fLx8XG2Dw4OVlFRkc6cOaM6depc8/jsdvtly61W6zXvozyUNA4AgPlU9u+Q6sDta2au5MiRI/r66681adIkl/KDBw/q119/1dNPP62AgAC98cYbGjJkiD788EMVFBS4BBlJzu82m61U/aenpxcr8/f3V9u2bUt5JH9ORkaGCgoKKrVPAEDFiImJcfcQqhyPDjObNm1SmzZtdNNNN7mUL1myROfPn1etWrUkSbNnz9Ydd9yhLVu2yNfXt1houfT994uFr0VERIRHJOiwsDB3DwEAAI/l0WHmiy++0J133lms3MfHx+Xsi6+vrxo1aqTs7GzdcsstOn36tC5cuCBv74uHl5OTIz8/PwUGBpaqf6vV6hFhxhPGAACAp/KI58xcjmEYSk9P1y233FKsvHv37lq/fr2zLD8/X4cPH1bz5s3Vpk0beXt7KzU11VmfkpKiiIgIeXl57OECAIAy8tgzMz///LN+++23YpeYLBaLunTporlz56phw4aqU6eO5syZoxtuuEF33HGHrFar+vTpo6SkJL3wwgv65ZdftHTpUs2cOdNNRwIAACqSx4aZkydPSpJq165drG7cuHHy9vZWQkKC8vLy1KFDBy1atMh5OSYxMVFJSUkaPHiwAgICNGbMGPXo0aNSxw8AACqHxfjjE+sgu92u1NRURUVFlbheZdvefJ3Nr9jn1gTW9FKn8JoV2gcAAGbHIhIAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqHhFmbDabevfurR07djjLpk+frrCwMJfPqlWrnPUbN25U9+7dFRkZqVGjRunUqVPOOsMwNHv2bHXo0EHt27fXrFmz5HA4KvWYAABA5fB29wCKioqUkJCgAwcOuJRnZWUpISFBDzzwgLMsICBAkrR7925NmjRJU6dOVevWrTVjxgwlJibq9ddflyS9+eab2rhxo+bNm6cLFy5o3Lhxqlu3roYNG1Z5BwYAACqFW8/MZGZm6uGHH9ZPP/1UrC4rK0tt27ZVSEiI8+Pv7y9JWrVqlXr16qU+ffqodevWmjVrlrZu3aojR45IklasWKH4+HjFxsaqQ4cOGjt2rFavXl2pxwYAACqHW8PMN998o7i4OL3zzjsu5Xl5ecrOzlbTpk0vu11aWppiY2Od3+vXr68GDRooLS1N2dnZOn78uG699VZnfUxMjH7++Wf98ssvFXIcAADAfdx6mWnAgAGXLc/KypLFYtHChQv1n//8R0FBQfrb3/7mvOT0yy+/qF69ei7b1K1bVydOnFBOTo4kudQHBwdLkk6cOFFsOwAAYG5uXzNzOQcPHpTFYlHz5s312GOPaefOnXruuecUEBCgu+66S4WFhfLx8XHZxsfHRzabTYWFhc7vv6+TLi40Lg273X7ZcqvVWqr9/FkljQMAYD6V/TukOvDIMNOnTx917dpVQUFBkqTWrVvr0KFDWrNmje666y75+voWCyY2m03+/v4uwcXX19f5Z0nONTfXKj09vViZv7+/2rZtW9pD+lMyMjJUUFBQqX0CACpGTEyMu4dQ5XhkmLFYLM4gc0nz5s319ddfS5JCQ0OVm5vrUp+bm6uQkBCFhoZKknJyctSoUSPnnyUpJCSkVOOIiIjwiAQdFhbm7iEAAOCxPDLMzJkzR999952WLVvmLNu/f7+aN28uSYqMjFRKSor69u0rSTp+/LiOHz+uyMhIhYaGqkGDBkpJSXGGmZSUFDVo0KDU62WsVqtHhBlPGAMAAJ7KI8NM165dtWjRIi1ZskR33XWXtm3bpg0bNmjFihWSpP79+2vgwIGKiopSRESEZsyYoS5duqhx48bO+tmzZ+uGG26QJL344osaOnSo244HAABUHI8MM+3atdOcOXP06quvas6cOWrYsKFefPFFRUdHS5Kio6P1/PPP69VXX9Wvv/6qjh07atq0ac7thw0bppMnT2r06NGyWq168MEHNWTIEDcdDQAAqEgWwzAMdw/C09jtdqWmpioqKqrESzzb9ubrbH7FviIhsKaXOoXXrNA+AAAwO494NxMAAEBZEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpeUSYsdls6t27t3bs2OEsS01N1V//+ldFR0fr7rvv1tq1a122ue+++xQWFuby+eGHHyRJhmFo9uzZ6tChg9q3b69Zs2bJ4XBU6jEBAIDK4e3uARQVFSkhIUEHDhxwluXk5OiJJ55Q//799fe//1179+5VYmKiQkJC1KVLF9ntdh06dEirVq1S06ZNndtdf/31kqQ333xTGzdu1Lx583ThwgWNGzdOdevW1bBhwyr78AAAQAVza5jJzMxUQkKCDMNwKd+8ebOCg4P19NNPS5KaNm2qHTt26IMPPlCXLl109OhRnT9/Xu3atZOvr2+x/a5YsULx8fGKjY2VJI0dO1Zz5swhzAAAUAW59TLTN998o7i4OL3zzjsu5Z07d9bMmTOLtc/Ly5N0MQTVr1//skEmOztbx48f16233uosi4mJ0c8//6xffvmlnI8AAAC4m1vPzAwYMOCy5Y0aNVKjRo2c30+ePKkPP/xQY8aMkSRlZWWpRo0aGjFihPbs2aNmzZpp/PjxateunXJyciRJ9erVc24fHBwsSTpx4oRL+dXY7fbLllut1mveR3koaRwAAPOp7N8h1YHb18xcTWFhocaMGaPg4GA98sgjkqQff/xRv/76qx566CHFx8fr3Xff1eDBg/XRRx+psLBQkuTj4+Pcx6U/22y2UvWdnp5erMzf319t27Yt6+GUSUZGhgoKCiq1TwBAxYiJiXH3EKocjw4zv/32m0aOHKlDhw7prbfekr+/vyRp2rRpKiwsVEBAgCQpKSlJu3bt0vvvv6/bb79d0sXgcuky1KUQc2n7axUREeERCTosLMzdQwAAwGN5bJjJy8vT448/rp9++knLly93uWvJ29vbGWQkyWKxqHnz5srOzlZoaKiki3dEXbpUdenSU0hISKnGYLVaPSLMeMIYAADwVB7xnJk/cjgcGj16tI4ePaqVK1eqZcuWLvUDBw7UvHnzXNpnZGSoefPmCg0NVYMGDZSSkuKsT0lJUYMGDUq1XgYAAJiDR56Zee+997Rjxw4tWLBAgYGBzjMrNWrUUFBQkLp166b58+erTZs2atasmVasWKFz587pgQcekCT1799fs2fP1g033CBJevHFFzV06FC3HQ8AAKg4HhlmNm3aJIfDoREjRriUt2/fXitXrtSQIUNUVFSk6dOnKzc3V5GRkXrzzTedl56GDRumkydPavTo0bJarXrwwQc1ZMgQNxwJAACoaBbjj0+sg+x2u1JTUxUVFVXiepVte/N1Nr9iX5EQWNNLncJrVmgfAACYnUeumQEAALhWhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqhBkAAGBqZQozgwYN0tmzZ4uVnzp1Sn379v3TgwIAALhW3tfa8D//+Y92794tSdq5c6cWLlyomjVrurQ5fPiwfv755/IdIQAAwBVcc5hp1qyZFi9eLMMwZBiGdu3apRo1ajjrLRaLatasqRkzZlTIQAEAAC7nmsNM48aNtWLFCklSYmKiJk2apICAgAobGAAAwLW45jDzezNnzpQk5eTk6MKFCzIMw6W+QYMGf35kAAAA16BMYebLL7/Uc889p+PHj0uSDMOQxWJx/u/3339froMEAAAoSZnCzPPPP6927dppwYIFXGoCAABuVaZbs0+cOKGEhASFhYWpYcOGxT4AAKBsunXrpvXr1xcrX79+vbp161amfQ4cOFBz584t1TZHjhxRWFiYxo0bV6yuqKhII0eOVLt27TRw4EAdPnxY999/vyIiIvTKK69ccb9Hjx5VWFiYjh49WqrxXEmZzszExsYqJSVFjRs3LreBAAAAz/HRRx/pxhtv1ObNm/Xbb7+pVq1azrovvvhCX3zxhdasWaN69erpjTfekCR9+OGHql279hX3W79+fW3btk116tQpt7GWKczceuutmjp1qv7973+rSZMmLrdoS9Lo0aPLZXAAAMA9Nm7cqMcee0zz58/Xpk2bXB6Ke+7cOQUHB+vmm2+WJOXl5al169a68cYbr7pfq9WqkJCQch1rmRcA33zzzTp58qROnjzpUmexWMplYAAA4PJSUlI0e/Zs7du3TxaLRbfeeqtmzJihevXqSZI+++wzzZ49W9nZ2erbt6/sdnup9p+ZmakffvhBcXFx2r17t/75z386w8z69euVmJgoSc7lJpcemLthwwZ9/vnnqlGjhmbMmKHt27eroKBALVu21LPPPquYmBgdPXpUd955pz7//HM1atRIYWFhGjlypN566y1FR0dr4cKFpZ6PMq2ZWblyZYmfS8+iKQ2bzabevXtrx44dzrIjR45oyJAhioqK0j333KNt27a5bPPVV1+pd+/eioyM1KBBg3TkyBGX+mXLlqlz586Kjo7WxIkTVVBQUJZDBQDAo5w7d04jRoxQx44dtXHjRi1ZskQ//fSTFi1aJOliEPnf//1f9e/fX+vWrdOFCxeUkpJSqj42btyohg0bqnXr1rrzzju1c+dOZ2C55557NHHiRN1www3atm2b3n//ffXq1Uu9evXStm3bVL9+fY0dO1Z2u11vv/22NmzYoNDQUCUlJZXY35YtW7RmzRqNHTu2THNSpjMzGzZsuGJ9nz59rnlfRUVFSkhI0IEDB5xlhmFo1KhRatWqldatW6fNmzdr9OjR+uijj9SgQQMdO3ZMo0aN0pgxY9S5c2fNnz9fI0eO1L/+9S9ZLBZt2rRJ8+bNU3JysurWravExEQlJydr8uTJZTlcAAAq1ZQpUzRt2jSXsgsXLigkJESFhYUaOXKk/va3v8lisahx48bq0aOH85VD69atU2xsrIYMGSJJeu6557Rly5ZS9f/RRx85Fxvfcccd8vHx0YYNGzRq1Cj5+fnpuuuuc7lc5OfnJ0kKCQmRYRjq3r277r77bt1www2SpEcffVTDhw8vsb9HHnlEzZs3L9UYf69MYebVV191+W6323Xy5El5e3urXbt21xxmMjMzlZCQUOyhe19//bWOHDmit99+WzVr1lSLFi20fft2rVu3TmPGjNHatWt18803a+jQoZIuPsSvY8eO+uabbxQXF6cVK1Zo8ODB6tq1qyRp6tSpGjZsmMaNGyd/f/+yHDIAAJUmPj5ePXr0cCn79NNPtWbNGoWEhKhPnz5atmyZvv/+e2VmZiojI0O33HKLJCkrK0tt2rRxblejRg2X71eze/duHT58WN27d5ck1apVS7fffrvef/99jRo16qrbWywW9e/fXx999JF27dqlH3/8UXv27JHD4Shxmz97J3SZwsz//d//FSv77bffNHnyZIWFhV3zfi6Fj6eeekpRUVHO8rS0NLVt29blRZYxMTFKTU111sfGxjrr/P39FR4ertTUVMXGxio9Pd1lEXJUVJTOnz+v/fv3Kzo6uhRHCgBA5atbt66aNGlSrEySsrOz1a9fP4WHh+v222/Xww8/rH//+99KS0tztv3jSYI/3qhzJR9++KEkOU8YSJLD4ZBhGEpJSVFMTMwVt3c4HBo6dKjOnj2re+65R926ddP58+eveHOQr6/vNY/vcsoUZi6nVq1aGjNmjPr373/FU0m/N2DAgMuW5+TkOBcxXVK3bl2dOHHiqvVnz55VUVGRS723t7eCgoKc21+rkhZMWa3WUu3nzyrtwi0AgOf6s79DPvvsM9WuXVuvv/66s2zlypXOANOyZUt99913zjqHw6H9+/erdevWV923w+HQxx9/rPvvv1+PP/64s9xut+uxxx7Thg0brhpmMjMztXPnTm3fvt15+/Xq1aslFQ9Z5aXcwowk7d+//4qnka5VQUGBfHx8XMp8fHxks9muWl9YWOj8XtL21yo9Pb1Ymb+/v9q2bVuq/fxZGRkZLGAGgCriamHgaoKCgnTs2DFt375djRo10scff6xPP/1UERERkqSHH35YK1eu1IIFC3T33XfrnXfe0bFjx65p399++62ys7M1cOBAtWrVyqXuvvvu0wcffKBnn332ivsIDAyUl5eXPvzwQ3Xr1k3p6enOB/aV9vfwtSpTmBk4cGCxW7B/++03ZWRkOBcc/Rm+vr46c+aMS5nNZnMuMPL19S02ITabTYGBgc5TVZerL+16mYiIiEo/C3M5pbl0BwCo2nr16qWdO3cqPj5eFotFERERmjBhgubOnSubzaYmTZpowYIFmjlzphYsWKDu3bvrjjvuuKZ9b9y4UWFhYc5g9Hv9+/fXW2+9pc2bN19xHzfccIOSkpI0f/58vfTSS2rWrJmeffZZTZgwQfv27Sv3Z8xIZQwzcXFxxcp8fHw0duxY3XbbbX96UKGhocrMzHQpy83NdV46Cg0NVW5ubrH6Nm3aKCgoSL6+vsrNzVWLFi0kXVwBfubMmVJPoNVq9Ygw4wljAABUjsutS5Wkvn37Op/1MnXqVE2dOtWl/vcnEy7dtl1azz//fIl1rVq1UkZGhst4Lvn73//u0vaRRx7RI4884lLWu3dv559/v5/f/7msyhRmfr+IJy8vT3a7/aqPLy6NyMhILVq0SIWFhc6zMb9fdBQZGelyz3xBQYH27dun0aNHy8vLSxEREUpJSXGGrtTUVHl7e1/T9UIAAGAuZV4zs3z5ci1evNh5hqROnTrq379/ubzKoH379qpfv74SExM1cuRIbdmyRbt379bMmTMlSf369dOSJUu0aNEide3aVfPnz1ejRo2c4WXAgAGaPHmyWrVqpXr16ikpKUkPP/wwt2UDAKq1UaNG6auvviqxfurUqbrvvvsqcUTlo0xhZv78+Vq1apWefPJJRUdHy+FwaNeuXZo3b558fHyu+W6mklitVr322muaNGmS+vbtqyZNmmj+/Plq0KCBJKlRo0aaO3euXnjhBc2fP1/R0dGaP3++cx3Pvffeq59//lmTJ0+WzWZTjx49LvvWTwAAqpMpU6Zc8YaSS7d/m43FKMN9UnfccYemTJlS7FXkmzdv1owZM0r9pEFPY7fblZqaqqioqBLXq2zbm6+z+X/+zq0rCazppU7hNa/eEACAaqxM72bKy8tT06ZNi5U3a9ZMp06d+rNjAgAAuGZlCjPR0dFaunSpyzNl7Ha7lixZonbt2pXb4AAAAK6mTGtmEhMT9eijj+qrr75SeHi4JGnv3r2y2WxavHhxuQ4QAADgSsoUZlq0aKGJEyfqzJkzOnjwoHx9fbVlyxa9+uqr3P4MAAAqVZkuM61cuVJJSUm67rrrlJSUpMTERA0cOFBjx47Vu+++W95jBAAAKFGZwsybb76pF198UQ888ICzbMKECUpOTtaiRYvKbXAAAMB8ioqKNHHiRMXGxqpTp05aunRphfZXpstMp0+f1o033lisvFmzZsVeMwAAAMqXYRjF3pHoSX3NmjVLe/bs0fLly3Xs2DFNmDBBDRo0UM+ePStkjGUKMzExMZo7d65mzpzpfKpuUVGRFi5cqOjo6HIdIAAAcGWxWJSaVai8wop93lmAn5eiWviVapv8/HytXbtWb7zxhsLDwxUeHq4DBw5o9erVnhVmJk+erKFDh6pTp07O58389NNPCg4O1muvvVae4wMAAJeRV+io8Ie3lsX+/ft14cIFl5MbMTExWrhwoRwOh7y8yrTC5YrKFGZuvPFGffTRR/riiy906NAheXt7q2nTpurUqRNveAYAoBrLycnR9ddfLx8fH2dZcHCwioqKdObMGdWpU6fc+yzziyZ9fHx05513ludYAACAyRUUFLgEGUnO7zabrUL6LP9zPQAAoNry9fUtFloufffzK936m2tFmAEAAOUmNDRUp0+f1oULF5xlOTk58vPzU2BgYIX0SZgBAADlpk2bNvL29lZqaqqzLCUlRRERERWy+FcizAAAgHLk7++vPn36KCkpSbt379bmzZu1dOlSDRo0qML6LPMCYAAA4D4BfhV/PqKsfSQmJiopKUmDBw9WQECAxowZox49epTz6P4fi2EYRoXt3aTsdrtSU1MVFRVV4q3m2/bmV/j9/YE1vdQpvGaF9gEAMB9PfwJwZeMyEwAAJlOZ4cLTg4xEmAEAACZHmAEAAKZGmAHKWWUuQ2PJGwBwNxNQ7jz5bbYAUBURZoAK4KlvswWAqojLTAAAwNQIMwAAwNQIMwAAwNQIMwAAoELYbDb17t1bO3bsqNB+CDMAAJiMGR4BUVRUpKeffloHDhwo5xEVx91MAACYjMVi0bk922XPP1uh/VhrBuq6m28r9XaZmZlKSEiotNBFmAEAwITs+WdlP3fa3cO4rG+++UZxcXF66qmnFBUVVeH9EWYAAEC5GjBgQKX2x5oZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgatzNBACACVlrBlaJPsoDYQYAAJMxDKNMD7Mra18Wi6XM22dkZJTjaC6Py0wAAJjMnwkXntxXWRFmAACAqXlsmFm/fr3CwsKKfVq3bi1J+p//+Z9idVu2bHFuv2zZMnXu3FnR0dGaOHGiCgoK3HUoAACgAnnsmpl77rlHnTt3dn6/cOGCBg8erC5dukiSsrKylJycrNtu+3/XDGvXri1J2rRpk+bNm6fk5GTVrVtXiYmJSk5O1uTJkyv1GAAAQMXz2DMzfn5+CgkJcX7+9a9/yTAMjR07VjabTUePHlVERIRLGx8fH0nSihUrNHjwYHXt2lXt2rXT1KlTtW7dOs7OAABQBXlsmPm9M2fO6I033lBCQoJ8fHx08OBBWSwWNW7cuFhbu92u9PR0xcbGOsuioqJ0/vx57d+/vzKHDQAAKoEpwsyaNWtUr1499ezZU5J08OBBBQQEaPz48erUqZMefPBBbd26VZJ09uxZFRUVqV69es7tvb29FRQUpBMnTrhl/AAAoOJ47JqZSwzD0Nq1a/X44487yw4ePKjCwkJ16tRJw4cP12effab/+Z//0TvvvKPg4GBJcl5yusTHx0c2m61Ufdvt9suWW63WUh7Fn1PSOOCZ+PkAcCWV/W9EdeDxYSY9PV3Z2dm69957nWUjR47UwIEDnQt+W7durb179+rdd9/VU089JUnFgovNZpO/v3+p+/4jf39/tW3btrSH8adkZGR47HqfGjVqKDz8ZlmtlXOSz253aO/ePTp//nyl9Fda/HwAuJqYmBh3D6HK8fgw88UXXyg2NtYZXCTJy8vL5bskNW/eXJmZmQoKCpKvr69yc3PVokULSRfvhDpz5oxCQkJK1XdERIRHJOiwsDB3D+GKrFYvpWYVKq/QUaH9BPh5KaqFn8LDwyu0H7Px9J8PAKhoHh9mdu/erVtuucWl7JlnnpHFYtHMmTOdZfv371erVq3k5eWliIgIpaSkKC4uTpKUmpoqb29v5zNqrpXVavWIMOMJY7iavEKHzuZXbJi5xAzzUZmYDwDVnccvAD5w4IBuuukml7Ju3brpgw8+0IYNG3T48GHNmzdPKSkpeuyxxyRJAwYM0JIlS7R582bt3r1bSUlJevjhh0t9mQkAAHg+jz8zk5ubq8BA17d29ujRQ1OmTNGCBQt07NgxtWzZUosXL1ajRo0kSffee69+/vlnTZ48WTabTT169NC4cePcMXwAAFDBLIZhGO4ehKex2+1KTU1VVFRUiafwt+3Nr/DLKoE1vdQpvGaF9lFemA9XzAcAVB6Pv8wEAABwJYQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZwKR8a1hkGEal9VeZfQFAaXi7ewAAysbbKlksFp3bs132/LMV2pe1ZqCuu/m2Cu0DAMqKMAOYnD3/rOznTrt7GADgNlxmAgAApkaYAQAApkaYAQAApkaYAQAApkaYAQAApkaYAQAApkaYAQAApkaYAQAApkaYAQAApkaYAQAApkaYAQAApkaYAQAApkaYAQAApkaYAQAApubRYeazzz5TWFiYyyc+Pl6StG/fPj300EOKjIxUv379tGfPHpdtN27cqO7duysyMlKjRo3SqVOn3HEIAACggnl0mMnMzFTXrl21bds252f69OnKz8/X8OHDFRsbq/Xr1ys6OlojRoxQfn6+JGn37t2aNGmSRo8erXfeeUdnz55VYmKim48GAABUBI8OM1lZWWrVqpVCQkKcn8DAQH300Ufy9fXV+PHj1aJFC02aNEm1atXSJ598IklatWqVevXqpT59+qh169aaNWuWtm7dqiNHjrj5iAAAQHnz+DDTtGnTYuVpaWmKiYmRxWKRJFksFt1yyy1KTU111sfGxjrb169fXw0aNFBaWlplDBsAAFQib3cPoCSGYejHH3/Utm3b9Prrr8tut6tnz56Kj49XTk6ObrrpJpf2devW1YEDByRJv/zyi+rVq1es/sSJE6Uag91uv2y51Wot1X7+rJLG4SmYD1eVPR+VydPnHjCDqvxvhLt4bJg5duyYCgoK5OPjo1deeUVHjx7V9OnTVVhY6Cz/PR8fH9lsNklSYWHhFeuvVXp6erEyf39/tW3btpRHUza+NSwyDKPSfvAddrv27N2r8+fPX/M2lTkfl2RkZKigoKBS+7xW7piPyuTJcw+YRUxMjLuHUOV4bJhp2LChduzYodq1a8tisahNmzZyOBwaN26c2rdvXyyY2Gw2+fn5SZJ8fX0vW+/v71+qMURERLg1QXtbL15CO7dnu+z5Zyu0L2vNQF13820KDw+v0H7KQ1hYmLuHUG0x9wA8kceGGUkKCgpy+d6iRQsVFRUpJCREubm5LnW5ubnOS0uhoaGXrQ8JCSlV/1ar1SNOB9rzz8p+7nSl9OUJx3s1ZhhjVcXcA/BEHrsA+IsvvlBcXJzLKe3vv/9eQUFBiomJ0XfffSfDMCRdXF+za9cuRUZGSpIiIyOVkpLi3O748eM6fvy4sx4AAFQdHhtmoqOj5evrq2effVYHDx7U1q1bNWvWLD3++OPq2bOnzp49qxkzZigzM1MzZsxQQUGBevXqJUnq37+/3n//fa1du1b79+/X+PHj1aVLFzVu3NjNRwUAAMqbx4aZgIAALVmyRKdOnVK/fv00adIkPfLII3r88ccVEBCg119/XSkpKerbt6/S0tK0aNEi1axZU9LFIPT8889r/vz56t+/v2rXrq2ZM2e6+YgAAEBF8Og1My1bttSbb7552bp27drpn//8Z4nb9u3bV3379q2ooQEAAA/hsWdmAAAArgVhBgAAmBphBkCFunTXYVXrC4Dn8Og1MwDMz2KxKDWrUHmFjgrtJ8DPS1Et/Cq0DwCeiTADoMLlFTp0Nr9iwwyA6ovLTAAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMwAAwNQIMzAN3xoWniYLACiGh+bBNLytF58me27Pdtnzz1ZoX9aagbru5tsqtA8AQPkgzMB07PlnZT932t3DAAB4CC4zAQAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAAAAUyPMAKgSfGtYZBhGpfVXmX0BuDJvdw8AAMqDt1WyWCw6t2e77PlnK7Qva81AXXfzbRXaB4BrR5gBUKXY88/Kfu60u4cBoBJxmQkAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJiaR4eZ7OxsxcfHq3379urcubNmzpypoqIiSdL06dMVFhbm8lm1apVz240bN6p79+6KjIzUqFGjdOrUKXcdBgAAqEAe+9A8wzAUHx+vwMBArV69Wr/++qsmTpwoLy8vTZgwQVlZWUpISNADDzzg3CYgIECStHv3bk2aNElTp05V69atNWPGDCUmJur111931+EAAIAK4rFnZg4ePKjU1FTNnDlTLVu2VGxsrOLj47Vx40ZJUlZWltq2bauQkBDnx9/fX5K0atUq9erVS3369FHr1q01a9Ysbd26VUeOHHHnIQEAgArgsWEmJCREixcvVnBwsEt5Xl6e8vLylJ2draZNm15227S0NMXGxjq/169fXw0aNFBaWlpFDhkAALiBx15mCgwMVOfOnZ3fHQ6HVq1apQ4dOigrK0sWi0ULFy7Uf/7zHwUFBelvf/ub85LTL7/8onr16rnsr27dujpx4kSpxmC32y9bbrVaS3k05lHSMV8J8+GK+XDFfACuqvLfCXfx2DDzR8nJydq3b5/ee+897d27VxaLRc2bN9djjz2mnTt36rnnnlNAQIDuuusuFRYWysfHx2V7Hx8f2Wy2UvWZnp5erMzf319t27b9U8fiyTIyMlRQUHDN7ZkPV8yHK+YDKC4mJsbdQ6hyTBFmkpOTtXz5cr388stq1aqVWrZsqa5duyooKEiS1Lp1ax06dEhr1qzRXXfdJV9f32LBxWazOdfUXKuIiIhql6DDwsLcPQSPwny4Yj5cMR+AZ/D4MDNt2jStWbNGycnJuvvuuyVJFovFGWQuad68ub7++mtJUmhoqHJzc13qc3NzFRISUqq+rVZrtQsz1e14r4b5cMV8uGI+AM/gsQuAJWnevHl6++239dJLL+nee+91ls+ZM0dDhgxxabt//341b95ckhQZGamUlBRn3fHjx3X8+HFFRkZWyrgBAEDl8dgwk5WVpddee01PPPGEYmJilJOT4/x07dpVO3fu1JIlS/TTTz/prbfe0oYNGzR06FBJUv/+/fX+++9r7dq12r9/v8aPH68uXbqocePGbj4qAABQ3jz2MtPnn38uu92uBQsWaMGCBS51GRkZmjNnjl599VXNmTNHDRs21Isvvqjo6GhJUnR0tJ5//nm9+uqr+vXXX9WxY0dNmzbNHYcBAAAqmMeGmeHDh2v48OEl1nfv3l3du3cvsb5v377q27dvRQwNAAB4EI+9zAQAAHAtCDMAAMDUCDMAAMDUCDMAAMDUCDMAUIkMw6iSfQHu5LF3MwFAVWSxWJSaVai8QkeF9hPg56WoFn4V2gfgKQgzAFDJ8godOptfsWEGqE64zAQAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAVZBvDQtv6Ea1wYsmAaAK8rZefEP3uT3bZc8/W6F9WWsG6rqbb6vQPoArIcwAQBVmzz8r+7nT7h5GiQzDkMViqXJ9oXIRZgAAbmOxWJSaVai8QkeF9hPg56WoFn4V2gfchzADAHCrvEKHzuZXbJhB1cYCYAAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQAAYGqEGQBAlcfrHao2njMDAKjyeL1D1UaYAQBUG57+egeUDZeZAACAqRFmAACAqRFmAACAqRFmAACAqRFmAACAqRFmAACAqRFmAACAqRFmAACAqVXZMFNUVKSJEycqNjZWnTp10tKlS909JAAAUAGq7BOAZ82apT179mj58uU6duyYJkyYoAYNGqhnz57uHhoAAChHVTLM5Ofna+3atXrjjTcUHh6u8PBwHThwQKtXrybMAABQxVTJy0z79+/XhQsXFB0d7SyLiYlRWlqaHA6HG0cGAADKW5UMMzk5Obr++uvl4+PjLAsODlZRUZHOnDnjvoEBAIByVyUvMxUUFLgEGUnO7zab7arbG4bhbGu1WovVW61W1fI1pP+/XUXx9zZkt9sl/0BZZKnQvuR/nex2+8X+Son5cMV8uGI+XDEfrqrjfFitVnl5ecliqeBxVCMWw6jgnyA3+PjjjzV9+nR9+eWXzrKsrCzdc8892rFjh4KCgq64vc1mU3p6egWPEgBQXUVFRV32P5ZRNlXyzExoaKhOnz6tCxcuyNv74iHm5OTIz89PgYGBV93e29tbERERJGcAQIXw8qqSqzzcpkqGmTZt2sjb21upqamKjY2VJKWkpDgDytV4eXkVu0wFAAA8U5WMhv7+/urTp4+SkpK0e/dubd68WUuXLtWgQYPcPTQAAFDOquSaGeniIuCkpCR9+umnCggI0LBhwzRkyBB3DwsAAJSzKhtmAABA9VAlLzMBAIDqgzADAABMjTADAABMjTADAABMjTDjZkVFRZo4caJiY2PVqVMnLV26tMS2+/bt00MPPaTIyEj169dPe/bsqcSRVi6bzabevXtrx44dJbapDvORnZ2t+Ph4tW/fXp07d9bMmTNVVFR02bbVYT4OHz6sYcOGKTo6Wl26dNHixYtLbFsd5uOS4cOH65lnnimx/quvvlLv3r0VGRmpQYMG6ciRI5U4usrz2WefKSwszOUTHx9/2bb33XdfsbY//PBDJY8Y5YUw42azZs3Snj17tHz5ck2ZMkXz5s3TJ598Uqxdfn6+hg8frtjYWK1fv17R0dEaMWKE8vPz3TDqilVUVKSnn35aBw4cKLFNdZgPwzAUHx+vgoICrV69Wi+//LK2bNmiV155pVjb6jAfDodDw4cP1/XXX69//vOfmjp1qhYsWKAPPvigWNvqMB+XfPjhh9q6dWuJ9ceOHdOoUaPUt29fvffee6pTp45Gjhypqngja2Zmprp27apt27Y5P9OnTy/Wzm6369ChQ1q1apVL2+bNm7th1CgXBtzmt99+MyIiIoyvv/7aWTZ//nzjscceK9Z27dq1Rrdu3QyHw2EYhmE4HA7jrrvuMtatW1dp460MBw4cMO677z7jL3/5i9GqVSuXufm96jAfmZmZRqtWrYycnBxn2QcffGB06tSpWNvqMB/Z2dnGk08+aZw7d85ZNmrUKGPKlCnF2laH+TAMwzh9+rTxX//1X0a/fv2MCRMmXLbNK6+84vJvSn5+vhEdHV3i3y0zS0hIMF588cWrtjt06JDRunVro7CwsBJGhcrAmRk32r9/vy5cuKDo6GhnWUxMjNLS0uRwOFzapqWlKSYmxvmuKIvFoltuuUWpqamVOeQK98033yguLk7vvPPOFdtVh/kICQnR4sWLFRwc7FKel5dXrG11mI969erplVdeUUBAgAzDUEpKinbu3Kn27dsXa1sd5kOS/vGPf+j+++/XTTfdVGKbtLQ052tdpItPSA8PD69ycyFdfKFw06ZNr9ouMzNT9evXl6+vb8UPCpWCMONGOTk5uv76613eAxUcHKyioiKdOXOmWNt69eq5lNWtW1cnTpyojKFWmgEDBmjixIny9/e/YrvqMB+BgYHq3Lmz87vD4dCqVavUoUOHYm2rw3z8Xrdu3TRgwABFR0fr7rvvLlZfHeZj+/bt+vbbbzVy5MgrtqsOcyFdvCz7448/atu2bbr77rvVvXt3zZ49WzabrVjbrKws1ahRQyNGjFDHjh312GOPaffu3W4YNcoLYcaNCgoKir3Q8tL3P/4FLKnt5f6iVgfVcT6Sk5O1b98+PfXUU8Xqqtt8vPrqq1q4cKG+//57zZw5s1h9VZ+PoqIiTZkyRZMnT5afn98V21b1ubjk2LFjzmN95ZVXNGHCBH3wwQeaNWtWsbY//vijfv31Vz300ENatGiRWrRoocGDB+v48eNuGDnKQ5V8a7ZZ+Pr6FvsH5dL3P/4DVVLbq/1DVlVVt/lITk7W8uXL9fLLL6tVq1bF6qvbfEREREi6+Et97NixGj9+vMsv7Ko+H/PmzdPNN9/scuauJCXNRWBgYEUNzy0aNmyoHTt2qHbt2rJYLGrTpo0cDofGjRunxMREWa1WZ9tp06apsLBQAQEBkqSkpCTt2rVL77//vv77v//bXYeAP4Ew40ahoaE6ffq0Lly4IG/vi/9X5OTkyM/Pr9g/NKGhocrNzXUpy83NLXb6uLqoTvMxbdo0rVmzRsnJyZe9pCJVj/nIzc1Vamqqunfv7iy76aabdP78eeXl5alOnTrO8qo+Hx9++KFyc3Od6+0uhZVNmzbpu+++c2lb0ly0adOmcgZbiYKCgly+t2jRQkVFRfr1119dfj68vb2dQUa6uKaqefPmys7OrqyhopxxmcmN2rRpI29vb5eFeCkpKYqIiJCXl+v/NZGRkfruu++ct1MahqFdu3YpMjKyMofsMarLfMybN09vv/22XnrpJd17770ltqsO83H06FGNHj3a5RfOnj17VKdOHZdfVFLVn4+VK1fqgw8+0IYNG7RhwwZ169ZN3bp104YNG4q1jYyMVEpKivN7QUGB9u3bV2Xm4pIvvvhCcXFxKigocJZ9//33CgoKKvbzMXDgQM2bN8/53eFwKCMjg1uzTYww40b+/v7q06ePkpKStHv3bm3evFlLly7VoEGDJF08S1NYWChJ6tmzp86ePasZM2YoMzNTM2bMUEFBgXr16uXOQ6hU1W0+srKy9Nprr+mJJ55QTEyMcnJynB+p+s1HRESEwsPDNXHiRGVmZmrr1q1KTk52XhaoTvPRsGFDNWnSxPmpVauWatWqpSZNmshutysnJ8d5tqZfv37atWuXFi1apAMHDigxMVGNGjVSXFycm4+ifEVHR8vX11fPPvusDh48qK1bt2rWrFl6/PHHi81Jt27dtGzZMn3++ec6ePCgnn/+eZ07d04PPPCAm48CZeaue8JxUX5+vjF+/HgjKirK6NSpk/Hmm28661q1auXyXIy0tDSjT58+RkREhPHggw8ae/fudcOIK88fnzNT3ebj9ddfN1q1anXZj2FUv/kwDMM4ceKEMWrUKOOWW24xOnbsaCxYsMD5LJnqOB+XTJgwwfmcmSNHjhT7u/Pvf//b6NGjh9GuXTtj8ODBxk8//eSuoVaoH374wRgyZIgRFRVldOzY0Zg7d67hcDiKzYnD4TAWLFhgdOnSxbj55puNRx991MjIyHDz6PFnWAyjCj4GEgAAVBtcZgIAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAGqsR07digsLKzE+rlz52rgwIGSpPXr16tbt24ltn3mmWf0zDPPlPsYAeBqeNEkgBINHTrUGWYAwFMRZgCUqFatWu4eAgBcFZeZgGri8OHDGjZsmKKjo9WlSxetWLHCWbdmzRp17txZ0dHRSkxMdL6Q7/eXmf7o22+/VZ8+fdSuXTs9+eSTLm8rnjt3rkaOHKlHH31U7du31zfffCObzabp06crLi5OcXFxGjt2rM6cOSPp4huxw8LC9Omnn6p79+6KiIjQiBEjnPUAcCWEGaAaKCoq0tChQ1WrVi29++67mjx5sl5++WXl5+dLkjZt2qQlS5Zo3rx5+uSTT7Ru3bor7u/UqVMaMWKEbr/9dm3YsEE33XSTPvnkE5c2n3/+uXr37q3ly5erXbt2eumll7Rnzx698cYbWrFihfLy8vTkk0+6bLNw4UK99NJLWrVqldLT0/Xmm2+W70QAqJK4zARUA9u2bdOpU6f0wgsvKCAgQC1bttSzzz4rL6+L/z0zZcoUNWvWTK1atdLtt9+u/fv3X3F/H3/8serUqaNx48bJYrFozJgx2rp1q0ub4OBg9e/fX5JUUFCgVatWad26dc4Fx7NmzVJcXJwyMjKcl7Pi4+PVrl07SdJf/vIXpaenl+s8AKiaCDNANfDjjz+qWbNmCggIcJb169dPO3bskCTdeOONzvLrrrvOeZmpJJmZmWrdurUsFouzLCIiwuVSU8OGDZ1/PnLkiM6fP6+//vWvLvtxOBw6dOiQwsPDJUlNmjRx1gUEBOj8+fOlOUwA1RRhBqgGvL2v/FfdarW6fDcM46r7/GObGjVquIQZX19f55/tdrsk6a233lLNmjVdtqtbt65zbUyNGjWu2i8A/BFrZoBqoGnTpjp8+LBL2PjHP/6h6dOnl2l/LVu21L59+5whRZK+//77Ets3btxYVqtVZ86cUZMmTdSkSRMFBARo5syZOnnyZJnGAACXEGaAaqBTp04KDg7W5MmTlZWVpc8//1xvv/22EhISyrS/e++9VwUFBZoxY4YOHjyoxYsXKyUlpcT2AQEBeuihh5SUlKQdO3YoMzNT48eP1+HDh9WoUaOyHhYASCLMANWCt7e3XnvtNf3yyy964IEHNGPGDI0fP17+/v5l2l/t2rW1ePFipaen6/7779dXX32l+++//4rbPPPMM7rtttsUHx+vhx9+WN7e3lq0aFGxS1wAUFoW41oujgMAAHgozswAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABT+/8AvhW0wRKekdgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Factorplot for number of children with Had Affair hue\n",
    "sns.catplot(x='children',data=df,hue='Had_Affair',palette='coolwarm',kind='count')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty strong evidence suggesting that less children results in a lower probability of an affair. Finally let's check the education level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29b81c5b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Factorplot for number of children with Had Affair hue\n",
    "sns.catplot(x='educ',data=df,hue='Had_Affair',palette='coolwarm',kind='count')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! Go ahead and finish the remaining columns if you wish. For now, we will go ahead and begin diving into the Logistic Regression Analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 5:  Data Preparation\n",
    "\n",
    "If we look at the data, we'll notice that two columns are unlike the others. Occupation and Husband's Occupation. These columns are in a format know as *Categorical Variables*. Basically they are in set quantity/category, so that 1.0 and 2.0 are seperate variables, not values along a spectrum that goes from 1-2 (e.g. There is no 1.5 for the occupation column). Pandas has a built-in method of getting [dummy variables](http://en.wikipedia.org/wiki/Dummy_variable_%28statistics%29) and creating new columns from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": 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>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     1.0    2.0    3.0    4.0    5.0    6.0\n",
       "0  False   True  False  False  False  False\n",
       "1  False  False   True  False  False  False\n",
       "2  False  False   True  False  False  False\n",
       "3  False  False  False  False   True  False\n",
       "4  False  False   True  False  False  False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new DataFrames for the Categorical Variables\n",
    "occ_dummies = pd.get_dummies(df['occupation'])\n",
    "hus_occ_dummies = pd.get_dummies(df['occupation_husb'])\n",
    "\n",
    "# Let's take a quick look at the results\n",
    "occ_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now let's name the columns something a little more readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create column names for the new DataFrames\n",
    "occ_dummies.columns = ['occ1','occ2','occ3','occ4','occ5','occ6']\n",
    "hus_occ_dummies.columns = ['hocc1','hocc2','hocc3','hocc4','hocc5','hocc6']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will create the X and Y data sets for out logistic regression!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Set X as new DataFrame without the occupation columns or the Y target\n",
    "X = df.drop(['occupation','occupation_husb','Had_Affair'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Concat the dummy DataFrames Together\n",
    "dummies = pd.concat([occ_dummies,hus_occ_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will concatenate all the DataFrames together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": 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>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>affairs</th>\n",
       "      <th>occ1</th>\n",
       "      <th>occ2</th>\n",
       "      <th>occ3</th>\n",
       "      <th>occ4</th>\n",
       "      <th>occ5</th>\n",
       "      <th>occ6</th>\n",
       "      <th>hocc1</th>\n",
       "      <th>hocc2</th>\n",
       "      <th>hocc3</th>\n",
       "      <th>hocc4</th>\n",
       "      <th>hocc5</th>\n",
       "      <th>hocc6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.230769</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.666666</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_marriage   age  yrs_married  children  religious  educ   affairs  \\\n",
       "0            3.0  32.0          9.0       3.0        3.0  17.0  0.111111   \n",
       "1            3.0  27.0         13.0       3.0        1.0  14.0  3.230769   \n",
       "2            4.0  22.0          2.5       0.0        1.0  16.0  1.400000   \n",
       "3            4.0  37.0         16.5       4.0        3.0  16.0  0.727273   \n",
       "4            5.0  27.0          9.0       1.0        1.0  14.0  4.666666   \n",
       "\n",
       "    occ1   occ2   occ3   occ4   occ5   occ6  hocc1  hocc2  hocc3  hocc4  \\\n",
       "0  False   True  False  False  False  False  False  False  False  False   \n",
       "1  False  False   True  False  False  False  False  False  False   True   \n",
       "2  False  False   True  False  False  False  False  False  False  False   \n",
       "3  False  False  False  False   True  False  False  False  False  False   \n",
       "4  False  False   True  False  False  False  False  False  False   True   \n",
       "\n",
       "   hocc5  hocc6  \n",
       "0   True  False  \n",
       "1  False  False  \n",
       "2   True  False  \n",
       "3   True  False  \n",
       "4  False  False  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now Concat the X DataFrame with the dummy variables\n",
    "X = pd.concat([X,dummies],axis=1)\n",
    "\n",
    "# Preview of Result\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's go ahead and set up the Y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Had_Affair, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set Y as Target class, Had Affair\n",
    "Y = df.Had_Affair\n",
    "\n",
    "# Preview\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 6: Multicollinearity Consideration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to get rid of a few columns. We will be dropping the occ1 and hocc1 columns to avoid [multicollinearity](http://en.wikipedia.org/wiki/Multicollinearity#Remedies_for_multicollinearity). Multicollinearity occurs due to the [dummy variables](http://en.wikipedia.org/wiki/Dummy_variable_(statistics)) we created. This is because the dummy variables are highly correlated, our model begins to get distorted because one of the dummy variables can be linearly predicted from the others. We take care of this problem by dropping one of the dummy variables from each set, we do this at the cost of losing a data set point.\n",
    "\n",
    "The other column we will drop is the affairs column. This is because it is basically a repeat of what will be our Y target, instead of 0 and 1 it just has 0 or a number, so we'll need to drop it for our target to make sense.\n",
    "\n",
    "Nice article [here](https://www.researchgate.net/publication/375756030_Understanding_the_Dummy_Variable_Trap_in_Regression_Models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": 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>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occ2</th>\n",
       "      <th>occ3</th>\n",
       "      <th>occ4</th>\n",
       "      <th>occ5</th>\n",
       "      <th>occ6</th>\n",
       "      <th>hocc2</th>\n",
       "      <th>hocc3</th>\n",
       "      <th>hocc4</th>\n",
       "      <th>hocc5</th>\n",
       "      <th>hocc6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_marriage   age  yrs_married  children  religious  educ   occ2   occ3  \\\n",
       "0            3.0  32.0          9.0       3.0        3.0  17.0   True  False   \n",
       "1            3.0  27.0         13.0       3.0        1.0  14.0  False   True   \n",
       "2            4.0  22.0          2.5       0.0        1.0  16.0  False   True   \n",
       "3            4.0  37.0         16.5       4.0        3.0  16.0  False  False   \n",
       "4            5.0  27.0          9.0       1.0        1.0  14.0  False   True   \n",
       "\n",
       "    occ4   occ5   occ6  hocc2  hocc3  hocc4  hocc5  hocc6  \n",
       "0  False  False  False  False  False  False   True  False  \n",
       "1  False  False  False  False  False   True  False  False  \n",
       "2  False  False  False  False  False  False   True  False  \n",
       "3  False   True  False  False  False  False   True  False  \n",
       "4  False  False  False  False  False   True  False  False  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping one column of each dummy variable set to avoid multicollinearity\n",
    "# Explaination (from wiki): if you have 3 dummy variable for a category, and you know the value of 2 of them, the 3rd value is automatically\n",
    "# known. So we need to drop one column for each category\n",
    "# NB - the columns that we drop are then interpreted as the \"reference class\" to the other categorical variable coefficients will\n",
    "# so this means that if occ4 has a positive coefficient it means that being in occ4 class increases the probability of a positive result (i.e affair)\n",
    "X = X.drop('occ1',axis=1)\n",
    "X = X.drop('hocc1',axis=1)\n",
    "\n",
    "# Drop affairs column so Y target makes sense\n",
    "X = X.drop('affairs',axis=1)\n",
    "\n",
    "# PReview\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to use the Y with SciKit Learn, we need to set it as a 1-D array. This means we need to \"flatten\" the array. Numpy has a built in method for this called [ravel](http://docs.scipy.org/doc/numpy/reference/generated/numpy.ravel.html). Let's use it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Flatten array\n",
    "Y = np.ravel(Y)\n",
    "\n",
    "# Check result\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 7: Logistic Regression with SciKit Learn\n",
    "\n",
    "Awesome! Now let's go ahead and run the logistic regression. This is a very similar process to the Linear Regression from the previous lecture. We'll create the model, the fit the data into the model, and check our accuracy score. Then we'll split the data into testing and training sets and see if our results improve.\n",
    "\n",
    "Let's start by initiating the model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7266729500471254"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create LogisticRegression model\n",
    "log_model = LogisticRegression(max_iter=500)\n",
    "\n",
    "# Fit our data\n",
    "log_model.fit(X,Y)\n",
    "\n",
    "# Check our accuracy\n",
    "log_model.score(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like we got a 73% accuracy rating. Let's go ahead and compare this to the original Y data. We can do this by simply taking the mean of the Y data, since it is in the format 1 or 0, we can use the mean to calulate the percentage of women who reported having affairs. This is known as checking the [null error rate](http://en.wikipedia.org/wiki/Type_I_and_type_II_errors)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3224945020420987"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check percentage of women that had affairs\n",
    "Y.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that if our model just simply guessed \"no affair\" we would have had 1-0.32=0.68 accuracy (or 68%) accuracy. So while we are doing better than the null error rate, we aren't doing that much better.\n",
    "\n",
    "Let's go ahead and check the coefficients of our model to check what seemed to be the stronger predictors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": 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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rate_marriage</td>\n",
       "      <td>[-0.7101187048258819]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>[-0.061123077283230194]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yrs_married</td>\n",
       "      <td>[0.1080203455030433]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>children</td>\n",
       "      <td>[0.015117929816137288]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>religious</td>\n",
       "      <td>[-0.3747369293056142]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>educ</td>\n",
       "      <td>[-0.0022302286267933727]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>occ2</td>\n",
       "      <td>[0.0645905701650003]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>occ3</td>\n",
       "      <td>[0.3749889486435682]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>occ4</td>\n",
       "      <td>[0.1448341230688381]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>occ5</td>\n",
       "      <td>[0.7208092195656967]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>occ6</td>\n",
       "      <td>[0.742314379886476]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hocc2</td>\n",
       "      <td>[0.15206043764371172]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hocc3</td>\n",
       "      <td>[0.2630354599870578]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hocc4</td>\n",
       "      <td>[0.12552295748271705]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>hocc5</td>\n",
       "      <td>[0.1548654601093527]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>hocc6</td>\n",
       "      <td>[0.17011012053892619]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0                         1\n",
       "0   rate_marriage     [-0.7101187048258819]\n",
       "1             age   [-0.061123077283230194]\n",
       "2     yrs_married      [0.1080203455030433]\n",
       "3        children    [0.015117929816137288]\n",
       "4       religious     [-0.3747369293056142]\n",
       "5            educ  [-0.0022302286267933727]\n",
       "6            occ2      [0.0645905701650003]\n",
       "7            occ3      [0.3749889486435682]\n",
       "8            occ4      [0.1448341230688381]\n",
       "9            occ5      [0.7208092195656967]\n",
       "10           occ6       [0.742314379886476]\n",
       "11          hocc2     [0.15206043764371172]\n",
       "12          hocc3      [0.2630354599870578]\n",
       "13          hocc4     [0.12552295748271705]\n",
       "14          hocc5      [0.1548654601093527]\n",
       "15          hocc6     [0.17011012053892619]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use zip to bring the column names and the np.transpose function to bring together the coefficients from the model\n",
    "coeff_df = DataFrame(zip(X.columns, np.transpose(log_model.coef_)))\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the coefficients we can see that a positive coeffecient corresponds to increasing the likelihood of having an affair while a negative coefficient means it corresponds to a decreased likelihood of having an affair as the actual data value point increases.\n",
    "\n",
    "As you might expect, an increased marriage rating corresponded to a decrease in the likelihood of having an affair. Increased religiousness also seems to correspond to a decrease in the likelihood of having an affair. \n",
    "\n",
    "Since all the dummy variables (the wife and husband occupations) are positive that means the lowest likelihood of having an affair corresponds to the baseline occupation we dropped (1-Student)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 8: Testing and Training Data Sets\n",
    "\n",
    "Just like we did in the Linear Regression Lecture, we should be splitting our data into training and testing data sets. We'll follow a very similar procedure to the Linear Regression Lecture by using SciKit Learn's built-in train_test_split method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=600)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=600)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=600)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y)\n",
    "\n",
    "# Make a new log_model\n",
    "log_model2 = LogisticRegression(max_iter=600)\n",
    "\n",
    "# Now fit the new model\n",
    "log_model2.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use predict to predict classification labels for the next test set, then we will reevaluate our accuracy score!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test dataset: 0.7361809045226131 %\n"
     ]
    }
   ],
   "source": [
    "# Predict the classes of the testing data set\n",
    "class_predict = log_model2.predict(X_test)\n",
    "\n",
    "# Compare the predicted classes to the actual test classes\n",
    "print(f\"Accuracy on test dataset: {metrics.accuracy_score(Y_test,class_predict)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a 73.35% accuracy score, which is basically the same as our previous accuracy score, 72.58%.\n",
    "\n",
    "### Part 9:  Conclusion and more Resources\n",
    "\n",
    "So what could we do to try to further improve our Logistic Regression model? We could try some [regularization techniques](http://en.wikipedia.org/wiki/Regularization_%28mathematics%29#Regularization_in_statistics_and_machine_learning) or using a non-linear model.\n",
    "\n",
    "I'll leave the Logistic Regression topic here for you to explore more possibilites on your own. Here are several more resources and tutorials with other data sets to explore:\n",
    "\n",
    "1.) Here's another great post on how to do logistic regression analysis using Statsmodels from [yhat](http://blog.yhathq.com/posts/logistic-regression-and-python.html)! BROKEN LINK\n",
    "\n",
    "2.) The SciKit learn Documentation includes several [examples](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) at the bottom of the page.\n",
    "\n",
    "3.) DataRobot has a great overview of [Logistic Regression](http://www.datarobot.com/blog/classification-with-scikit-learn/)\n",
    "\n",
    "4.) Fantastic resource from [aimotion.blogspot](http://aimotion.blogspot.com/2011/11/machine-learning-with-python-logistic.html) on the Logistic Regression and the Mathmatics of how it relates to the cost function and gradient!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "udemy",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}