finding_donors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning\n",
    "## Finding Donors for *CharityML*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail. \n",
    "\n",
    "The mission of this project is to employ several supervised algorithms to accurately model individuals' income using data collected from the 1994 U.S. Census. Then I will choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data used\n",
    "\n",
    "`'finding_donors.ipynb'`: This is the main file where you will be performing your work on the project.\n",
    "\n",
    "`'census.csv'`: The project dataset.\n",
    "\n",
    "`'visuals.py'`: This Python script provides supplementary visualizations for the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries used\n",
    "Pandas\n",
    "\n",
    "Numpy\n",
    "\n",
    "SKlearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Import initial data/libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45219</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45220</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45221</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age      workclass education_level  education-num       marital-status  \\\n",
       "45219   38        Private       Bachelors           13.0   Married-civ-spouse   \n",
       "45220   44        Private       Bachelors           13.0             Divorced   \n",
       "45221   35   Self-emp-inc       Bachelors           13.0   Married-civ-spouse   \n",
       "\n",
       "             occupation relationship                 race    sex  \\\n",
       "45219    Prof-specialty      Husband                White   Male   \n",
       "45220      Adm-clerical    Own-child   Asian-Pac-Islander   Male   \n",
       "45221   Exec-managerial      Husband                White   Male   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week  native-country income  \n",
       "45219           0.0           0.0            50.0   United-States  <=50K  \n",
       "45220        5455.0           0.0            40.0   United-States  <=50K  \n",
       "45221           0.0           0.0            60.0   United-States   >50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from IPython.display import display\n",
    "\n",
    "#supplementary visualization\n",
    "import visuals as vs \n",
    "\n",
    "#load the Census dataset\n",
    "data = pd.read_csv(\"census.csv\")\n",
    "\n",
    "display(data.tail(n=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45222 entries, 0 to 45221\n",
      "Data columns (total 14 columns):\n",
      "age                45222 non-null int64\n",
      "workclass          45222 non-null object\n",
      "education_level    45222 non-null object\n",
      "education-num      45222 non-null float64\n",
      "marital-status     45222 non-null object\n",
      "occupation         45222 non-null object\n",
      "relationship       45222 non-null object\n",
      "race               45222 non-null object\n",
      "sex                45222 non-null object\n",
      "capital-gain       45222 non-null float64\n",
      "capital-loss       45222 non-null float64\n",
      "hours-per-week     45222 non-null float64\n",
      "native-country     45222 non-null object\n",
      "income             45222 non-null object\n",
      "dtypes: float64(4), int64(1), object(9)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration\n",
    "Create the following variables:\n",
    "- The total number of records, `\n",
    "- The number of individuals making more than \\$50,000 annually\n",
    "- The number of individuals making at most \\$50,000 annually\n",
    "- The percentage of individuals making more than \\$50,000 annually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 45222\n",
      "Individuals making more than $50,000: 11208\n",
      "Individuals making at most $50,000: 34014\n",
      "Percentage of individuals making more than $50,000: 24.78439697492371%\n"
     ]
    }
   ],
   "source": [
    "# TODO: Total number of records\n",
    "n_records = data.shape[0]\n",
    "\n",
    "# TODO: Number of records where individual's income is more than $50,000\n",
    "n_greater_50k = data[data['income'] == '>50K'].shape[0]\n",
    "\n",
    "# TODO: Number of records where individual's income is at most $50,000\n",
    "n_at_most_50k = data[data['income'] == '<=50K'].shape[0]\n",
    "\n",
    "# TODO: Percentage of individuals whose income is more than $50,000\n",
    "greater_percent = (n_greater_50k / n_records) * 100\n",
    "\n",
    "# Print the results\n",
    "print(\"Total number of records: {}\".format(n_records))\n",
    "print(\"Individuals making more than $50,000: {}\".format(n_greater_50k))\n",
    "print(\"Individuals making at most $50,000: {}\".format(n_at_most_50k))\n",
    "print(\"Percentage of individuals making more than $50,000: {}%\".format(greater_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Featureset Exploration **\n",
    "\n",
    "* **age**: continuous. \n",
    "* **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. \n",
    "* **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. \n",
    "* **education-num**: continuous. \n",
    "* **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. \n",
    "* **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. \n",
    "* **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. \n",
    "* **race**: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other. \n",
    "* **sex**: Female, Male. \n",
    "* **capital-gain**: continuous. \n",
    "* **capital-loss**: continuous. \n",
    "* **hours-per-week**: continuous. \n",
    "* **native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Preparing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming Skewed Continuous Features\n",
    "A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number.  Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: '`capital-gain'` and `'capital-loss'`. \n",
    "\n",
    "Plot a histogram of these two features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ba0af78c390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the data into features and target label\n",
    "income_raw = data['income']\n",
    "features_raw = data.drop('income', axis = 1)\n",
    "\n",
    "# Visualize skewed continuous features of original data\n",
    "vs.distribution(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zgEavtVjxlsK79xjFRNtV289P148pZ02um2AddQ5WrUVpttT0Csq1EE0T7Z8urH+7l++OtNc21jvMxLrV3OMZiPG1Q0R0t338Z2YeFxHvo7TB/ibllPBGlKNh51Pu+kNmnhcR3wI+UE/Fn0Z5MNVz67hubxHDuZRE/WNKQr0kMy+ZZPhfU9r0fj4i9qUktPdQblnY9wOtotzJaBvKnTouBu5FOaJyCeUCsZ4y85yIOBTYrx4l/jXl7Mh7gUO7Tl3PuMy8NiLeSVkO61Guo/gnZT09hXIR47ca62j/uo46d2HqJ5lAaUN/L8pRrfsCz6EUisdRlldPEfEcyt2Tvk85orU25faRS1n2o/7c+vftEfEj4LbMnO4R68sp94rfj2V3YVqbcieRjsNqTAdFxMGUH0Rvoyy/plZxZeZtbb4vbbVcZr18mnJG6rj63biWcveWf6MUUdPxEUpzwW/XZfUDyhH4jYEXUpp6rJuZN8zkMqjOBd4QES+lnOVbmv3dSauXtuv+A5Rt/PiI+BjlQMm7KT/u9u9nfFEetPdTyvUhnVtl7kRpCvWTKeJ9Rf3sKVGef/BLykGXzSnNRlYHjszMP9c4P1fPcJxIORK9CeX7/pV6RqGVlvvF79TldEhEfKoxzN+7xvUbSvOtsygXkz+Fsk0tahHKOhHRaUa0DqV55u6UIuUNmdk88/ljYGFEnEVpyrUzve9cNtF29WPg3RGxD6WpzlMpR9RnyiE19uMj4pOU2+3eCdiUciDm+fVAzI+B50fEpynXAG5B+f5330ms5/4pMy+NiBMpZwX+TmlivGudzozGupLrVvNBv1dd+5rbL5bdjaHX6+zGcJ17ut9MOaU52XMgrmbZMwaeTYs7KNXPP5FSeNxE465G1OdATPCZp1Kej3AjJQG8hXpHiq7hkq47PLHiHXQ6Twq9mGX3Z/8O8KDGZ3retYZlz4G4kPKj4EImfg7EayZZDw/s6n4CXXcJmWg8lELg55QfizdSEudBwEOmWEedu7Ps1ue2cmOdz+9RCojuuyN1L98HUdrz/rWu4yspP0oe1/jMqsDnKYnu9s56bLnsFjS6Lab8cH1N3S5urtvJU3t8/nWUH7Y3Uoq/LVjxzjlTxbVb1zjbfF8WA9/sEU9z259ymU2yvh5EKTz+WT+73HMgJtueJxln1Hn7OaXI/xflYv1DKU0JZ3wZ1Pf3rvO9tPY7Yap1P9U42677OtzjmOI5EG3GR7k268uUpobXUb6rp9C4O9QUy/8ulNtkdp4JczPljM9ngAd0DfuKus6vr9P6A/A5YOOuZbLS+8U63PMpBcWNdb0/kxXvwvSxGvs/a1xn0eKOVCz/zIbb6+fPoDx34KE9hr8XpaC7pr4OoTSdXe67Osl2tSZlP3ll7Xc0pSBcYRvqMe1t63BTPQfizpRc9ce6XK+u28J+1LspUc40fpBSrN1AKQYfTcv9U+23MaXY/wflup0PU/aLrb43fcQ6rXXra/68om4I0oyoR8Y/RtlRXTTV8JIkSZpbbMKkaavNLR5GOTJ0O+WuSO8ADrd4kCRJmp8sILQyllJOY+9Faav9N8qFbfsOMyhJkiQNjk2YJEmSJLXmbVwlSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgFBPEXFwRBw9A+PZLyLOnomYppjOgojIiNhy0NMadxGxW0RcN6BxnxARn2u8XxwR7xjQtAY2H9J8N5s5YqampcEZZK7vzgM1179oQNOald8s84EFxBxQd577zfJk9wR2bcSw3A+7EXQxsCHlqditRMS2EbF4imEW151V8/WPlYy1expDX7Z1WXTm7/aIuDYizoyIz0TE/bsG/zbwgJbj7bew2xnYu5/YW8bRK+G0ng9plJkjZk49sHDCFMN054SMiNa5p2UcAzt40kcMuzXm77aI+EdEnBoRH4qI9bsG/wTwlJbj7eSbe7UM5bHAF/qJvUUME+Wm1vMx7nwStXrKzH8OO4Z+ZOZtwGUDGv3+wBcb728f0HRWWkSsnpn/WolRPBS4GrgL8EjgrcBZEfHszDwRIDNvBG5c6WAbIuJOmXlLZl49k+OdzCDmQxoXcy1HDMBrgeZZkZXZ7w5MRKxCeWjwbdMcxQ3ApkAAd6X8mH838NqIeEpm/gEgM68DZvSMbiMvXDmT453MIOZjvvIMxBwUEXeKiA9HxIURcXNE/CUi3lL7rRoRX42Iv0bEjRFxfkS8q+5EOp8/OCKOjoj3RMTlEXFdRHwtItbsHqbzP6Uif2PjaMSCNtNqOT9rR8TXaxyXR8TeNb6DG8PsGhGnRMTSiLgiIr4TERs1+i93NKFxhONpEfHbiLihHjl5zDQW+dLMvKzxuqIx3btFxIE1pqURcWLziEZE3DMiDo2IJXUZnRMRuzf6T7RsVzhCM8k87hgRJ0fELcD2td9zI+K0iLiprp8PRcSdWszrFXUeL8jM/wW2BX4HHBQRq9ZxL9f0JyI2iYgjI+Lqupz/GBG71N5/rX9PqbGe0Jnvuo7fHRFLgCW1e6+jmHeJiG/W7eOy6DoqFz3OLkTj6F0sO8v0nTrs4l7zUbu9LiIuiIhb6t/X9pjWHnX7u75+93ZFGiExz3JEj/lbIyL+u8Z2U0ScFBFPavRfPSIOiIhL6vxfHBEfbfTfOcoZ1hvrfuvEiNigzzD+0ZUXrmqMf6OIOCwirqmvH0bEZo3+m9Z95mV1P3J6RDyn0f8E4H7AxzvLs3bvtc9aLld0hql54WzgFuDBtd/uEXFuXWZ/ioj/aLEuss7fpZl5XmZ+E3g88A/gS404lmv6ExEPj4jjo5zNXhoRv4+I7SJiAfDzOtiVNfaDO/MdEV+MiE9ExJXAr2r3Xmdj7l2X6w11O2+eDet5diGWzxUT5abu+VglIt5bt6GbI+KsiNipx7ReGBHH1XjOjYhnTLFc5zwLiLlpEfBK4G2UHcOrKV9mKOv0b8BLar//BPYBdu8ax1MoR5ifBrwQeCbwsQmmtyfwG+BrlGZCG1KaDLWd1lQ+WeN5AfDUGteTu4a5E7Bv7fcc4F7AoS3G/RFgL+AxwFXAIRERfcbXUx3PD4GNakyPBn4B/CwiNqyD3Rk4vfZ/KPAZ4MsR8bTaf6Jl24+PAe8BNgd+GxHbA4cAn6vTfBXwIuDD/c5jPWr1aUpTn0dPMNgXgLWA7er03sqy7XGr+ncHyrzt3PjcU4BH1H5PY2JvA/5AWYf7Ah+OiJ0nGb7bY+vf19YYHttroIh4AWWZ/TfwMMq6+kJEPLdr0PcBR1K2xW9Tiqv79RGPNGjzLUd0+y/gpZR926OBs4AfN/a7b6Hkk12Azeqw5wFExL2BwyjL6MHANsA3VjKeO0TEWpQfyDdRluHjgUuBn9Z+UM7w/gh4BmUZ/y/w3YjYvPbfmXJQZX+WLc9+3JmSE14HPAS4MMrBkA9T9l8PBt5OOZPwhn7nsR6l/xKwTUSsN8Fg36LM91aUdbQfZZlcTNmeoOSLDSnbT8eulLMdT6ZswxN5P3AU8CjgQODr3QXDFCbLTU17Au+kLKuHA9+jrKtHdQ33IeAAyvo8BTgsIu7SRzxzT2b6mkMvys4wgR36+MxHgZ823h9MSSZ3aXTbFbgZWLsxzNGN/icAn5vGtPYDzp5k+LtQjpDs0ui2NnANcPAkn9u8LoeN6/sF9f2W9f229f32jc88sfmZlstucV0u1zVe+9R+T63v1+z6zBnAuyYZ52HAVyZbto3479XoNtE8vrDrs78A3tvV7fk11pggphWm12NZv6S+3w24rtH/TGDfCca7XMxd2+CVwBpd3ZdbFnX5H9c1zFeAXzbeJ/CiHuvtHVMM0z0fvwIO6hFn97Q+0ni/GuUU/65ttylfvgb5Yp7liO5pUfLDLcArG/1XBf4MfLC+PwA4vtf+jnIgIoH7rcQyTkrzx2ZeeHnt9yrg/Oa0a3xXdfahE4zzJOA9jffL7cNqt+X2WbXbtjT23XWYBLboGu4i4BVd3d4KnDtJTCtMr9FvhzqdrXqtR+BaYOEEn10u5q5t6Mwewy+3LOpn/6drmJ8C36z/L6B33rkjD0wyTPd8/A14X484u6f1ukb/jWq3J013G5sLL6+BmHseTWmD//OJBoiI1wOvoZwCXRNYHbiwa7AzsxxF6PgN5Sj/ppQfhK20nFZn2CdTjrp0vA44u37m5E7HzLw+uu6CEKXp0b6Uow33oByhALgvtfnLBJrzckn9u/4Un+n2KeCrjfeddvpbUI68X9l1UuPOlOVIlGY/e1GOgG0ErEFZzif0Mf2pnNr1fgtgq4h4d6PbKpT1c2/KUaF+dGYuJ+j/GeBLEbEDJWl/LzNPazHeszPz5hbD/abH+37OQLT1YOCgrm6/BJ7X1e2ObSozb62n2rsvKJSGZV7liMw8pGuwTes4ftXpkJm3RcRvKEfboRQcxwF/ioifAMcAP8rM24HfU35snl37/RQ4IvtvZ/9O4MeN95fXv1sA9weWduWFtViWF9am5LPnUI5+r07JG62X6xRupXFDkXqWYBPK2e/m9XyrsWz/3q+p8sKngK9ExEJKXvjfzPxji/G2yR3QOy88u+VnW4mIuwL3obGtVb8EduzqNtFvjXnLAmLumfTLHhEvpTTBeAfwa8pRgDdSTufObCD9T+tUSgHQcTl1h8rEO6HOzvZYyo7+FcAVlCZM/0dJaJNpXtjWmUa/TfeuyswLenRfhTIP3c2toCwLKMvm7ZTToGdRjlR9mKl3LJ0LtZvre/UJhr2+R1zvB77TY9jpXIzWScp/6dUzM78aEcdSdqhPB34dER/JzP2mGG933NOVrPi9mGhZtRnXVN26L5ZMbA6q0THfcsQKo61/J/yuZubpta39DpQzxYuA30fEM2qx8Uxga0qzrFcDH4lyQfDv288dl02SF86gNJ/q1jn49Ika2zsoZytuAL7O1Pnsdtrt627O5S+a7uyfXk9ZDzPhIZTlvbhXz8zcLyIOAZ5FuTZv34h4fWZ2H6TpNhN5YYX8GRHTzQnQZ17IzKzF47zOCxYQc8/plI1yO5Y/+tHxJOC3mdm8l/6mPYZ7eESsnZmdL+vWlNPCf55gurdQTsNOZ1rAHXe9WW6HGxEXUL54W1EvaqrtRB/WiGVzSsGwT2Z2hhnEEeh+nQ5sANyemT1/XFOW0Q8y8xtwx3UT/8ay9sjQe9l2fuhv2Pi/u83lZHFtPkFy60s9g/JWyrqY8DaFmbmE0g71wHrmY0/KqeBb6iDd89ePrXu8/0Pj/ZU02ghHuRiyu83wv1rE8AfK+momuCcB5/YTrDRk8ypH9HBBndaTqAc16n7q8ZR2951xLaUcRPlOvUj3JOCBwJ+ytDP5DfCbiNgfOIdylrifAmIipwMvA/6emRPd8vtJwNez3KiCiOictf5TY5iJ8sJaEXHXzOwcpJoyL2Tm5RHxN2DTzPx6+1nprbbtfz1w4mRnbjLzfEqBdEA98/Eayv51pvLCQV3vO3mhmT87upfTlDFk5rURcQllff2s0cu8gAXEnJOZ50fE4ZRTg3tSdlYbAwvqj9Q/AbtFxLMoO9pdKBdyXdM1qtUoF3/uTzlF91FKm8KJqv/FlGYxCyhH0a/uY1qTzc91EXEQ8LGI+Dulec17KAmwU+FfRGl7+6aI+DylqckH2k5jgH5KObV5ZES8C/gjpYnQDpQ2vv9HWUYvjXKHkL8Db6ac3v5dYzyLWXHZXkC52Gy/iNiL0s7yPS3j2h84OiIuBA6nnM5+GKWt6rum+Oz6EbEa5dqURwD/QWkSsWNOcBvAiPgMpdnBnyi3+duBZTvXKyhthbePcvejm7L/2z9uHRF7A0dQ2s6+Enh5o//PKHd/+TVwG+UMz01d41gMPC0iTqQcneu1jX6c8mPjNOAndT5ezmCaS0kDMd9yRI/5u77+GP1ozRl/peynNqA+KyAi3kbJJWdQDh78O+Xsx5KI2JpypvRYyhmOR1Oa98zUD8JDKGcWjoyI91Hy1ybATsCX6o/qPwEviIgja3z7UpowNS0GnhwR36Tss/4O/JZyhP4jEfFpygW7bS+C3g/4bJTnGB1DOXPxGGCjzPzIJJ+LeuE5wN1YdhvXu7Fi887OB9aknGX5Tp2PDajFZB1H6oWfAAAfGUlEQVTkQkp+f3ZE/AC4sau5XBs7R8QplObAL6Jc7P84KIVoRJwEvDsi/lxj7Z7Htrnp48D+EXE+pXnVrpRWB1v0Ge+8M69Pr8xjr6QcaTmA8qP1YMoXBODLlB+N36LcCWAB5S5H3U6kHHX5OeWuAj8DJvtx+QlKxX4upbq/bx/Tmso7KM2RjqrxnEk5lX0TQD3CsZByIfC5lJ3t26YxnRlVj2LtSFl2/0O5y8fhwINY1gbyg5TrO35Eubj5ekqCaVph2WZ5lsMulLsf/Z7SJGmflnEdS2kLul2d9smU6zAuavHxcyiJ93eUQuR3wCMy8xeTfGYV4LM1/uMoSXlhjeVWyh1RXkNZJke2mYcun6IUM7+jLM/3ZeYRjf5vpxyJPIFSZHyFkhzoGmY7SlH2O3rIzO9TCrz/qPOyJ/CGzPzBNGKWhmm+5Yhu767j/RqlSHgE5aLxzvVdSynXKJxMKaAeBTwrM28A/km5ocbRlKPjnwQ+kOX2pCutTmMbyj7pO5TlvwhYl2WF09so+6j/o+SGk+r/Te+jFB5/ph5Rz/KcnJdT7t50FrAH8N6WcX2FcoH3Kyg55f/q5/86xUfXouSESyjL823AD4CHZX0GRA+3UeZ3ESUvfo9yxudtNZa/UfL4hyj5YjoPINyPcjenM4H/B+yemac0+r+q/j2Fsh0udwCuj9x0AKWI+C/KNZsvoNy4ZEYfHDgXRfkNpHFST+feKzOfM9WwwxARa1COUHw8M2ci2UiSWhr1HCFp+GzCpKGLiEdTmiWdDKxDObq0DuUe+5IkSRohQ2vCFBGHRMR5EXF2RBzUuUI+igOiPAX2zGg8OTgiFkZ5kuX59dZgne5bRHk64AX1szPyoDDNqrdRmpb8jNJecpt6Ya6kMWJukKTRN7AmTBGx7gQXKnb678iy+z1/C/hFZn6xdn8zpW3544DPZObjIuIelHbxW1IuvjmN8qCUayLiZEp75ZMoFwcdkJk/QpI0UswNkjT3DfIMxKkR8a2IeGqvoz6ZeUxWlKYrG9deO1Fub5aZeRJw9yiPp9+e8kTaq2vyOQ7Yofa7a2b+po7r65SLbSVJo8fcIElz3CCvgfg3ygNE3gR8PiK+ARycmZc0B6qnp19BOUoE5Wm9FzcGWVK7TdZ9SY/uK4iIPSh3HWDttdfeYvPNN+97pk676qq+ht/invfsexqSNGinnXba3zNzvSFM2tyAuUHSaGqbGwZWQNR7xh9NuR/9epR78F4UEU/IzJMbg36Bcoq6cwuzXm1Uez1pdqruvWI6kPKwK7bccss89dRTW81LUyxa1Nfwpy5cOPVAkjTL6nNCZp25oTA3SBpFbXPDQC+ijoi71SM7R1GOOr2acs/eTv99gfVY/p7+Syj3Pu7YmHKP3sm6b9yjuyRpBJkbJGluG1gBUZ+eeDrlQVivzMxtMnNRZt5U+7+G0nb1ZZl5e+OjRwGvrHfc2Br4Z304zLHAMyNi3YhYF3gmcGzttzQitq7taV/J9B5WJUkaMHODJM19g7wG4nBgt/q0v16+RHlY2G/qdXTfzcz9KXfK2JHy2PsbgN2hPIExIj5AeaogwP71qYxQnkJ4MLAm5e4d3mVDkkaTuUGS5rhBXgNx1BT9e0673i3jjRP0Owg4qEf3U4GHTSNMSdIsMjdI0tw3tAfJSZIkSZp7LCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzQJCkiRJUmsWEJIkSZJas4CQJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzQJCkiRJUmsWEJIkSZJas4CQJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWhtaARERB0XEFRFxdqPbfhHxt4g4o752bPTbOyIuiIjzImL7RvcdarcLImKv2Z4PSdLMMj9I0mgb5hmIg4EdenT/dGY+qr6OAYiIhwC7AA+tn/lCRKwaEasCnweeBTwEeFkdVpI0dx2M+UGSRtZqw5pwZv4iIha0HHwn4LDMvBn4a0RcAGxV+12QmX8BiIjD6rDnznC4kqRZYn6QNNfEokV9DZ8LFw4oktkxitdAvCkizqynsNet3TYCLm4Ms6R2m6i7JGn+MT9I0ggYtQLii8CmwKOAS4FP1u7RY9icpHtPEbFHRJwaEadeeeWVKxurJGn2DCw/mBskqT8jVUBk5uWZeVtm3g78D8tOQy8BNmkMujFwySTdJxr/gZm5ZWZuud56681s8JKkgRlkfjA3SFJ/RqqAiIgNG29fAHTuwHEUsEtErBER9wc2A04GTgE2i4j7R8SdKBfSHTWbMUuSBs/8IEmjY2gXUUfEocC2wL0iYgmwL7BtRDyKcpp5MfA6gMw8JyIOp1z8divwxsy8rY7nTcCxwKrAQZl5zizPiiRpBpkfJGm0DfMuTC/r0fmrkwz/IeBDPbofAxwzg6FJkobI/CBJo22kmjBJkiRJGm0WEJIkSZJas4CQJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLU2ZQEREU+MiLXr/7tGxKci4n6DD02SNKrMDZI0vtqcgfgicENEPBJ4F3Ah8PWBRiVJGnXmBkkaU20KiFszM4GdgM9k5meAdQYbliRpxJkbJGlMrdZimKURsTewK7BNRKwKrD7YsCRJI87cIEljqs0ZiJcCNwOvzszLgI2Ajw80KknSqDM3SNKYmvIMRE0Mn2q8vwjbuUrSWDM3SNL4mrCAiIilQE7UPzPvOpCIJEkjy9wgSZqwgMjMdQAiYn/gMuAbQAAvxwvlJGksmRskSW2ugdg+M7+QmUsz89rM/CLwwkEHJkkaaeYGSRpTbQqI2yLi5RGxakSsEhEvB24bdGCSpJFmbpCkMdWmgPh34CXA5fX14tpNkjS+zA2SNKYmvQtTva/3CzJzp1mKR5I04swNkjTeJj0DkZm3UZ4yKkkSYG6QpHHX5knUv4qIzwHfBq7vdMzM0wcWlSRp1JkbJGlMtSkgnlD/7t/olsBTZz4cSdIcYW6QpDHV5knU281GIJKkucPcIEnja8q7MEXE3SLiUxFxan19MiLuNhvBSZJGk7lBksZXm9u4HgQspdyu7yXAtcDXBhmUJGnkmRskaUy1uQZi08xsPl30/RFxxqACkiTNCeYGSRpTbc5A3BgRT+q8iYgnAjcOLiRJ0hxgbpCkMdXmDMT/AxY12rZeA+w2sIgkSXOBuUGSxlSbuzCdATwyIu5a31878KgkSSPN3CBJ46vNXZg+HBF3z8xrM/PaiFg3Ij44G8FJkkaTuUGSxlebayCelZn/6LzJzGuAHQcXkiRpDjA3SNKYalNArBoRa3TeRMSawBqTDC9Jmv/MDZI0ptpcRP1N4PiI+BqQwKuARQONSpI06swNkjSm2lxE/V8RcSbwdCCAD2TmsQOPTJI0sswNkjS+2pyBAPgDcGtm/jQi1oqIdTJz6SADkySNPHODJI2hNndhei1wBPDl2mkj4PuDDEqSNNrMDZI0vtpcRP1G4InAtQCZeT6w/iCDkiSNPHODJI2pNgXEzZl5S+dNRKxGuWBOkjS+zA2SNKbaFBAnRsQ+wJoR8QzgO8APBhuWJGnEmRskaUy1KSD2Aq4EzgJeBxwDvGeQQUmSRp65QZLGVJvbuN4O/E99ARARTwR+NcC4JEkjzNwgSeNrwgIiIlYFXkK5s8aPM/PsiHgOsA+wJvDo2QlRkjQqzA2SpMnOQHwV2AQ4GTggIi4EHg/slZneqk+SxpO5QZLG3GQFxJbAIzLz9oi4M/B34IGZednshCZJGkHmBkkac5NdRH1LbeNKZt4E/MkEIUljz9wgSWNusjMQm0fEmfX/ADat7wPIzHzEwKOTJI0ac4MkjbnJCogHz1oUkqS5wtwgSWNuwgIiMy+czUAkSaPP3CBJavMgOUmSJEkCLCAkSZIk9WHCAiIijq9/PzaoiUfEQRFxRUSc3eh2j4g4LiLOr3/Xrd0jIg6IiAsi4syIeEzjMwvr8OdHxMJBxStJ487cIEma7AzEhhHxFOB5EfHoiHhM8zVD0z8Y2KGr217A8Zm5GXB8fQ/wLGCz+toD+CKUpALsCzwO2ArYt5NYJEkzztwgSWNusrswvY+yg94Y+FRXvwSeurITz8xfRMSCrs47AdvW/xcBJwDvrt2/npkJnBQRd4+IDeuwx2Xm1QARcRwl8Ry6svFJklZgbpCkMTfZXZiOAI6IiPdm5gdmMaYNMvPSGsOlEbF+7b4RcHFjuCW120TdJUkzzNwgSZrsDAQAmfmBiHgesE3tdEJmHj3YsHqKHt1yku4rjiBiD8opbu573/vOXGSSNGbMDZI0vqa8C1NEfATYEzi3vvas3Qbl8nr6mfr3itp9CbBJY7iNgUsm6b6CzDwwM7fMzC3XW2+9GQ9cksaFuUGSxleb27g+G3hGZh6UmQdR2pA+e4AxHQV07paxEDiy0f2V9Y4bWwP/rKezjwWeGRHr1gvknlm7SZIGx9wgSWNqyiZM1d2Bq+v/d5upiUfEoZQL3e4VEUsod8z4KHB4RLwauAh4cR38GGBH4ALgBmB3gMy8OiI+AJxSh9u/c9GcJGmgzA2SNIbaFBAfAX4XET+ntCndBth7JiaemS+boNfTegybwBsnGM9BwEEzEZMkqRVzgySNqTYXUR8aEScAj6UkiXdn5mWDDkySOmLRor6Gz4U+M2zQzA2SNL5aNWGq7UmPGnAskqQ5xNwgSeOpzUXUkiRJkgRYQEiSJEnqw6QFRESsEhFnz1YwkqTRZ26QpPE2aQGRmbcDv48IH80pSQLMDZI07tpcRL0hcE5EnAxc3+mYmc8bWFSSpFFnbpCkMdWmgHj/wKOQJM015gZJGlNtngNxYkTcD9gsM38aEWsBqw4+NEnSqDI3SNL4mvIuTBHxWuAI4Mu100bA9wcZlCRptJkbJGl8tbmN6xuBJwLXAmTm+cD6gwxKkjTyzA2SNKbaFBA3Z+YtnTcRsRqQgwtJkjQHmBskaUy1KSBOjIh9gDUj4hnAd4AfDDYsSdKIMzdI0phqU0DsBVwJnAW8DjgGeM8gg5IkjTxzgySNqTZ3Ybo9IhYBv6Wcnj4vMz1NLUljzNwgSeNrygIiIp4NfAn4MxDA/SPidZn5o0EHJ0kaTeYGSRpfbR4k90lgu8y8ACAiNgV+CJgkJGl8mRskaUy1uQbiik6CqP4CXDGgeCRJc4O5QZLG1IRnICJi5/rvORFxDHA4pZ3ri4FTZiE2SdKIMTdIkiZrwvTcxv+XA0+p/18JrDuwiCRJo8zcIEljbsICIjN3n81AJEmjz9wgSWpzF6b7A28GFjSHz8znDS4sSdIoMzdI0vhqcxem7wNfpTxh9PbBhiNJmiPMDZI0ptoUEDdl5gEDj0SSNJeYGyRpTLUpID4TEfsCPwFu7nTMzNMHFpUkadSZGyRpTLUpIB4OvAJ4KstOU2d9L0kaT+YGSRpTbQqIFwAPyMxbBh2MJGnOMDdI0phq8yTq3wN3H3QgkqQ5xdwgSWOqzRmIDYA/RsQpLN/O1Vv1SdL4MjdI0phqU0DsO/AoJElzjblBksbUlAVEZp44G4FIkuYOc4Mkja82T6JeSrmzBsCdgNWB6zPzroMMTJI0uswNkjS+2pyBWKf5PiKeD2w1sIgkSSPP3CBJ46vNXZiWk5nfx/t8S5IazA2SND7aNGHaufF2FWBLlp22lqSRE4sW9TV8Llw4oEjmL3ODJI2vNndhem7j/1uBxcBOA4lGkjRXmBskaUy1uQZi99kIRJI0d5gbJGl8TVhARMT7JvlcZuYHBhCPJGmEmRskSZOdgbi+R7e1gVcD9wRMEpI0fswNkjTmJiwgMvOTnf8jYh1gT2B34DDgkxN9TpI0f5kbJEmTXgMREfcA3ga8HFgEPCYzr5mNwCRJo8ncIEnjbbJrID4O7AwcCDw8M6+btagkSSPJ3CBJmuxBcm8H7gO8B7gkIq6tr6URce3shCdJGjHmBkkac5NdA9H3U6olSfObuUGS1OZBcpI0JZ/+LEnSeLCAkDQU/RYckiRpNHgqWpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzQJCkiRJUmsWEJIkSZJas4CQJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklob2QIiIhZHxFkRcUZEnFq73SMijouI8+vfdWv3iIgDIuKCiDgzIh4z3OglSYNgbpCk4RvZAqLaLjMflZlb1vd7Acdn5mbA8fU9wLOAzeprD+CLsx6pJGm2mBskaYhGvYDothOwqP6/CHh+o/vXszgJuHtEbDiMACVJs87cIEmzaJQLiAR+EhGnRcQetdsGmXkpQP27fu2+EXBx47NLarflRMQeEXFqRJx65ZVXDjB0SdKAmBskachWG3YAk3hiZl4SEesDx0XEHycZNnp0yxU6ZB4IHAiw5ZZbrtBfkjTyzA2SNGQjewYiMy+pf68AvgdsBVzeOf1c/15RB18CbNL4+MbAJbMXrSRpNpgbJGn4RrKAiIi1I2Kdzv/AM4GzgaOAhXWwhcCR9f+jgFfWO25sDfyzczpbkjQ/mBskaTSMahOmDYDvRQSUGL+VmT+OiFOAwyPi1cBFwIvr8McAOwIXADcAu89+yJKkATM3SNIIGMkCIjP/AjyyR/ergKf16J7AG2chNEnSkJgbJGk0jGQTJkmSJEmjyQJCkiRJUmsWEJIkSZJas4CQJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa2tNuwAJEmSpFETixYNO4SR5RkISZIkSa1ZQEiSJElqzQJCkiRJUmteAyGpJ9t+SpKkXiwgJEmSpFnU70G6XLhwQJFMj02YJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzYuoJWmE9HNh3ahdVCdJGg+egZAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzQJCkiRJUmsWEJIkSZJas4CQJEmS1JoFhCRJkqTWVht2AJJmTyxaNOwQJEnSHOcZCEmSJEmteQZCkvrQ71mcXLhwQJFIkjQcnoGQJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1L6KWRogX6A6Ht7eVJKk9z0BIkiRJas0CQpIkSVJr86YJU0TsAHwGWBX4SmZ+dMghSZKGzNwgzV82Px2eeXEGIiJWBT4PPAt4CPCyiHjIcKOSJA2TuUGSBmO+nIHYCrggM/8CEBGHATsB5w41KmnAPPoiTcrcIGle6Cffz8YNVuZLAbERcHHj/RLgcUOKRXOMdz7SIFnkDZW5QZphg96nmWPnhsjMYcew0iLixcD2mfma+v4VwFaZ+eau4fYA9qhvHwScN43J3Qv4+0qEO0qcl9EzX+YDnJdR1ZmX+2XmesMOZpDMDSPB5dKby6U3l8uKZnuZtMoN8+UMxBJgk8b7jYFLugfKzAOBA1dmQhFxamZuuTLjGBXOy+iZL/MBzsuomk/z0oK5YchcLr25XHpzuaxoVJfJvLiIGjgF2Cwi7h8RdwJ2AY4ackySpOEyN0jSAMyLMxCZeWtEvAk4lnKrvoMy85whhyVJGiJzgyQNxrwoIAAy8xjgmFmY1Eqd5h4xzsvomS/zAc7LqJpP8zIlc8PQuVx6c7n05nJZ0Uguk3lxEbUkSZKk2TFfroGQJEmSNAssIPoQETtExHkRcUFE7DXseKYrIjaJiJ9HxB8i4pyI2HPYMa2MiFg1In4XEUcPO5aVERF3j4gjIuKPdd08ftgxTVdE/Efdts6OiEMj4s7DjqmtiDgoIq6IiLMb3e4REcdFxPn177rDjLGtCebl43UbOzMivhcRdx9mjPPBfMkNM2m+5ZmZNF9y1kyaT/lvJo1yLrWAaCkiVgU+DzwLeAjwsoh4yHCjmrZbgbdn5oOBrYE3zuF5AdgT+MOwg5gBnwF+nJmbA49kjs5TRGwEvAXYMjMfRrl4dZfhRtWXg4EdurrtBRyfmZsBx9f3c8HBrDgvxwEPy8xHAH8C9p7toOaTeZYbZtJ8yzMzab7krJk0L/LfTBr1XGoB0d5WwAWZ+ZfMvAU4DNhpyDFNS2Zempmn1/+XUr6oGw03qumJiI2BZwNfGXYsKyMi7gpsA3wVIDNvycx/DDeqlbIasGZErAasRY9774+qzPwFcHVX552AzuNXFwHPn9WgpqnXvGTmTzLz1vr2JMqzETR98yY3zKT5lGdm0nzJWTNpHua/mTSyudQCor2NgIsb75cwD3aGEbEAeDTw2+FGMm3/DbwLuH3YgaykBwBXAl+rp7a/EhFrDzuo6cjMvwGfAC4CLgX+mZk/GW5UK22DzLwUyg8jYP0hxzNTXgX8aNhBzHHzMjfMpHmQZ2bSfMlZM2ne5L+ZNOq51AKivejRbU7fwioi7gL8L/DWzLx22PH0KyKeA1yRmacNO5YZsBrwGOCLmflo4HrmTjOZ5dTrA3YC7g/cB1g7InYdblTqFhH/SWlmcsiwY5nj5l1umElzPc/MpHmWs2bSvMl/M2nUc6kFRHtLgE0a7zdmhE4l9SsiVqfs1A/JzO8OO55peiLwvIhYTGk28NSI+OZwQ5q2JcCSzOwcoTuCskOdi54O/DUzr8zMfwHfBZ4w5JhW1uURsSFA/XvFkONZKRGxEHgO8PL0Xt4ra17lhpk0T/LMTJpPOWsmzaf8N5NGOpdaQLR3CrBZRNw/Iu5EuZDlqCHHNC0REZS2hn/IzE8NO57pysy9M3PjzFxAWR8/y8yRqc77kZmXARdHxINqp6cB5w4xpJVxEbB1RKxVt7WnMfcviDsKWFj/XwgcOcRYVkpE7AC8G3heZt4w7HjmgXmTG2bSfMkzM2k+5ayZNM/y30wa6Vw6b55EPWiZeWtEvAk4lnIl/EGZec6Qw5quJwKvAM6KiDNqt33qE1s1PG8GDqk/Qv4C7D7keKYlM38bEUcAp1OayPyOEX2SZi8RcSiwLXCviFgC7At8FDg8Il5N2am/eHgRtjfBvOwNrAEcV3ISJ2Xm64cW5Bw3z3LDTDLPqB/zIv/NpFHPpT6JWpIkSVJrNmGSJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASCshIk6IiO27ur01Ir4wyWeuG3xkkqRhMTdovrOAkFbOoZQHAjXtUrtLksaTuUHzmgWEtHKOAJ4TEWsARMQC4D7AGRFxfEScHhFnRcRO3R+MiG0j4ujG+89FxG71/y0i4sSIOC0ijo2IDWdjZiRJM8LcoHnNAkJaCZl5FXAysEPttAvwbeBG4AWZ+RhgO+CT9VH0U4qI1YHPAi/KzC2Ag4APzXTskqTBMDdovltt2AFI80DnVPWR9e+rgAA+HBHbALcDGwEbAJe1GN+DgIcBx9W8sipw6cyHLUkaIHOD5i0LCGnlfR/4VEQ8BlgzM0+vp5vXA7bIzH9FxGLgzl2fu5XlzwJ2+gdwTmY+frBhS5IGyNygecsmTNJKyszrgBMop5M7F8jdDbiiJojtgPv1+OiFwEMiYo2IuBvwtNr9PGC9iHg8lNPWEfHQQc6DJGlmmRs0n3kGQpoZhwLfZdldNw4BfhARpwJnAH/s/kBmXhwRhwNnAucDv6vdb4mIFwEH1OSxGvDfwDkDnwtJ0kwyN2heiswcdgySJEmS5gibMEmSJElqzQJCkiRJUmsWEJIkSZJas4CQJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrf1/J3CpYK7WAygAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ba0ad5c4198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log-transform the skewed features\n",
    "skewed = ['capital-gain', 'capital-loss']\n",
    "features_log_transformed = pd.DataFrame(data = features_raw)\n",
    "features_log_transformed[skewed] = features_raw[skewed].apply(lambda x: np.log(x + 1))\n",
    "\n",
    "# Visualize the new log distributions\n",
    "vs.distribution(features_log_transformed, transformed = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.301370</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.667492</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.452055</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.287671</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493151</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.150685</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age          workclass education_level  education-num  \\\n",
       "0  0.301370          State-gov       Bachelors       0.800000   \n",
       "1  0.452055   Self-emp-not-inc       Bachelors       0.800000   \n",
       "2  0.287671            Private         HS-grad       0.533333   \n",
       "3  0.493151            Private            11th       0.400000   \n",
       "4  0.150685            Private       Bachelors       0.800000   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  \n",
       "0      0.667492           0.0        0.397959   United-States  \n",
       "1      0.000000           0.0        0.122449   United-States  \n",
       "2      0.000000           0.0        0.397959   United-States  \n",
       "3      0.000000           0.0        0.397959   United-States  \n",
       "4      0.000000           0.0        0.397959            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler() # default=(0, 1)\n",
    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "\n",
    "features_log_minmax_transform = pd.DataFrame(data = features_log_transformed)\n",
    "features_log_minmax_transform[numerical] = scaler.fit_transform(features_log_transformed[numerical])\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_log_minmax_transform.head(n = 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 total features after one-hot encoding.\n",
      "['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_level_ 10th', 'education_level_ 11th', 'education_level_ 12th', 'education_level_ 1st-4th', 'education_level_ 5th-6th', 'education_level_ 7th-8th', 'education_level_ 9th', 'education_level_ Assoc-acdm', 'education_level_ Assoc-voc', 'education_level_ Bachelors', 'education_level_ Doctorate', 'education_level_ HS-grad', 'education_level_ Masters', 'education_level_ Preschool', 'education_level_ Prof-school', 'education_level_ Some-college', 'marital-status_ Divorced', 'marital-status_ Married-AF-spouse', 'marital-status_ Married-civ-spouse', 'marital-status_ Married-spouse-absent', 'marital-status_ Never-married', 'marital-status_ Separated', 'marital-status_ Widowed', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'relationship_ Husband', 'relationship_ Not-in-family', 'relationship_ Other-relative', 'relationship_ Own-child', 'relationship_ Unmarried', 'relationship_ Wife', 'race_ Amer-Indian-Eskimo', 'race_ Asian-Pac-Islander', 'race_ Black', 'race_ Other', 'race_ White', 'sex_ Female', 'sex_ Male', 'native-country_ Cambodia', 'native-country_ Canada', 'native-country_ China', 'native-country_ Columbia', 'native-country_ Cuba', 'native-country_ Dominican-Republic', 'native-country_ Ecuador', 'native-country_ El-Salvador', 'native-country_ England', 'native-country_ France', 'native-country_ Germany', 'native-country_ Greece', 'native-country_ Guatemala', 'native-country_ Haiti', 'native-country_ Holand-Netherlands', 'native-country_ Honduras', 'native-country_ Hong', 'native-country_ Hungary', 'native-country_ India', 'native-country_ Iran', 'native-country_ Ireland', 'native-country_ Italy', 'native-country_ Jamaica', 'native-country_ Japan', 'native-country_ Laos', 'native-country_ Mexico', 'native-country_ Nicaragua', 'native-country_ Outlying-US(Guam-USVI-etc)', 'native-country_ Peru', 'native-country_ Philippines', 'native-country_ Poland', 'native-country_ Portugal', 'native-country_ Puerto-Rico', 'native-country_ Scotland', 'native-country_ South', 'native-country_ Taiwan', 'native-country_ Thailand', 'native-country_ Trinadad&Tobago', 'native-country_ United-States', 'native-country_ Vietnam', 'native-country_ Yugoslavia']\n"
     ]
    }
   ],
   "source": [
    "# TODO: One-hot encode the 'features_log_minmax_transform' data using pandas.get_dummies()\n",
    "features_final = pd.get_dummies(features_log_minmax_transform)\n",
    "\n",
    "# TODO: Encode the 'income_raw' data to numerical values\n",
    "income = income_raw.apply(lambda x: 1 if x == '>50K' else 0)\n",
    "\n",
    "# Print the number of features after one-hot encoding\n",
    "encoded = list(features_final.columns)\n",
    "print(\"{} total features after one-hot encoding.\".format(len(encoded)))\n",
    "\n",
    "print(encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffle and Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 36177 samples.\n",
      "Testing set has 9045 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Import train_test_split\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features_final, \n",
    "                                                    income, \n",
    "                                                    test_size = 0.2, \n",
    "                                                    random_state = 0)\n",
    "\n",
    "# Show the results of the split\n",
    "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print(\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Evaluating Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics and the Naive Predictor\n",
    "*CharityML*, equipped with their research, knows individuals that make more than \\$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \\$50,000 accurately. It would seem that using **accuracy** as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that *does not* make more than \\$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \\$50,000 is *more important* than the model's ability to **recall** those individuals. We can use **F-beta score** as a metric that considers both precision and recall:\n",
    "\n",
    "$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{precision \\cdot recall}{\\left( \\beta^2 \\cdot precision \\right) + recall} $$\n",
    "\n",
    "In particular, when $\\beta = 0.5$, more emphasis is placed on precision. This is called the **F$_{0.5}$ score** (or F-score for simplicity).\n",
    "\n",
    "Looking at the distribution of classes (those who make at most \\$50,000, and those who make more), it's clear most individuals do not make more than \\$50,000. This can greatly affect **accuracy**, since we could simply say *\"this person does not make more than \\$50,000\"* and generally be right, without ever looking at the data! Making such a statement would be called **naive**, since we have not considered any information to substantiate the claim. It is always important to consider the *naive prediction* for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \\$50,000, *CharityML* would identify no one as donors. \n",
    "\n",
    "\n",
    "#### Note: Recap of accuracy, precision, recall\n",
    "\n",
    "** Accuracy ** measures how often the classifier makes the correct prediction. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points).\n",
    "\n",
    "** Precision ** tells us what proportion of messages we classified as spam, actually were spam.\n",
    "It is a ratio of true positives(words classified as spam, and which are actually spam) to all positives(all words classified as spam, irrespective of whether that was the correct classificatio), in other words it is the ratio of\n",
    "\n",
    "`[True Positives/(True Positives + False Positives)]`\n",
    "\n",
    "** Recall(sensitivity)** tells us what proportion of messages that actually were spam were classified by us as spam.\n",
    "It is a ratio of true positives(words classified as spam, and which are actually spam) to all the words that were actually spam, in other words it is the ratio of\n",
    "\n",
    "`[True Positives/(True Positives + False Negatives)]`\n",
    "\n",
    "For classification problems that are skewed in their classification distributions like in our case, for example if we had a 100 text messages and only 2 were spam and the rest 98 weren't, accuracy by itself is not a very good metric. We could classify 90 messages as not spam(including the 2 that were spam but we classify them as not spam, hence they would be false negatives) and 10 as spam(all 10 false positives) and still get a reasonably good accuracy score. For such cases, precision and recall come in very handy. These two metrics can be combined to get the F1 score, which is weighted average(harmonic mean) of the precision and recall scores. This score can range from 0 to 1, with 1 being the best possible F1 score(we take the harmonic mean as we are dealing with ratios)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 - Naive Predictor Performace\n",
    "* If we chose a model that always predicted an individual made more than $50,000, what would  that model's accuracy and F-score be on this dataset? You must use the code cell below and assign your results to `'accuracy'` and `'fscore'` to be used later.\n",
    "\n",
    "** Please note ** that the the purpose of generating a naive predictor is simply to show what a base model without any intelligence would look like. In the real world, ideally your base model would be either the results of a previous model or could be based on a research paper upon which you are looking to improve. When there is no benchmark model set, getting a result better than random choice is a place you could start from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n"
     ]
    }
   ],
   "source": [
    "TP = np.sum(income) # Counting the ones as this is the naive case. Note that 'income' is the 'income_raw' data \n",
    "#encoded to numerical values done in the data preprocessing step.\n",
    "FP = income.count() - TP # Specific to the naive case\n",
    "\n",
    "TN = 0 # No predicted negatives in the naive case\n",
    "FN = 0 # No predicted negatives in the naive case\n",
    "\n",
    "# TODO: Calculate accuracy, precision and recall\n",
    "accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
    "recall = TP / (TP + FN)\n",
    "precision = TP / (TP + FP)\n",
    "\n",
    "# TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n",
    "beta = 0.5\n",
    "fscore = (1 + beta**2) * (precision * recall) / (beta**2 * precision + recall)\n",
    "\n",
    "# Print the results \n",
    "print(\"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Supervised Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 - Model Application\n",
    "List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen\n",
    "\n",
    "- Describe one real-world application in industry where the model can be applied. \n",
    "- What are the strengths of the model; when does it perform well?\n",
    "- What are the weaknesses of the model; when does it perform poorly?\n",
    "- What makes this model a good candidate for the problem, given what you know about the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: **\n",
    ">Model 1: Random Forest Classifier\n",
    "\n",
    ">    Real World Application:\n",
    ">>        Random Forest can be used in financial industries for fraud detection. It can analyze large datasets and identify fradulent transactions\n",
    "\n",
    ">Strengths\n",
    ">> * Can handle non-linear relationships and capture complex interactions\n",
    ">> * Can handle both numeric and categorical data\n",
    "\n",
    ">Weaknesses\n",
    ">> * Can be computationally taxing for larger datasets\n",
    "\n",
    ">Suitability for problem:\n",
    ">>Random Forest is suitable for this problem because it can model complex relationships in the census data, such as interactions between age, education level, and work class, which might affect income. It also handles missing data well.\n",
    "\n",
    ">Model 2: Logistic Regression\n",
    "\n",
    ">    Real World Application:\n",
    ">>Logistic regression can be used in the healthcare industy for predicting the probablity of a disease based on patient data, such as determining heart disease and/or cancer.\n",
    "\n",
    ">Strengths\n",
    ">> * Provides probabilities for each class\n",
    ">> * Computationally efficient\n",
    "\n",
    ">Weaknesses\n",
    ">> * Struggles with complex, non-linear relationships\n",
    "\n",
    ">Suitability for problem:\n",
    ">>Given that the census data involves a binary classification, Logistic Regression is a strong candidate to solve this problem due to its simplicity and ability to handle binary outcomes efficiently.\n",
    "\n",
    ">Model 3: Support Vector Machines\n",
    "\n",
    ">    Real World Application:\n",
    ">>SVMs are commonly used in image classification tasks, such as facial recognition where they excel at distinguishing between distinct categories based on pixelation.\n",
    "\n",
    ">Strengths\n",
    ">> * Supports both linear and non-linear decision boundaries\n",
    ">> * Works well with clear margins of seperation between classes\n",
    "\n",
    ">Weaknesses\n",
    ">> * Can be computationally taxing for large datasets\n",
    ">> * Struggles with noisy data\n",
    "\n",
    ">Suitability for problem:\n",
    ">>SVM is a good candidate because it can create non-linear decision boundaries if relationships in the census data are complex. It is particulatly useful if the data has clear patterns that seperate high income individuals from others, but it requires tuning when handling larger datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Training and Predicting Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score\n",
    "from sklearn.metrics import fbeta_score, accuracy_score\n",
    "\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    # TODO: Fit the learner to the training data using slicing with 'sample_size' using .fit(training_features[:], training_labels[:])\n",
    "    start = time() # Get start time\n",
    "    learner = learner.fit(X_train[:sample_size], y_train[:sample_size])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # TODO: Calculate the training time\n",
    "    results['train_time'] = end - start\n",
    "        \n",
    "    # TODO: Get the predictions on the test set(X_test),\n",
    "    #       then get predictions on the first 300 training samples(X_train) using .predict()\n",
    "    start = time() # Get start time\n",
    "    predictions_test = learner.predict(X_test)\n",
    "    predictions_train = learner.predict(X_train[:300])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # TODO: Calculate the total prediction time\n",
    "    results['pred_time'] = end - start\n",
    "            \n",
    "    # TODO: Compute accuracy on the first 300 training samples which is y_train[:300]\n",
    "    results['acc_train'] = accuracy_score(y_train[:300], predictions_train)\n",
    "        \n",
    "    # TODO: Compute accuracy on test set using accuracy_score()\n",
    "    results['acc_test'] = accuracy_score(y_test, predictions_test)\n",
    "    \n",
    "    # TODO: Compute F-score on the the first 300 training samples using fbeta_score()\n",
    "    results['f_train'] = fbeta_score(y_train[:300], predictions_train, beta=1.0)\n",
    "        \n",
    "    # TODO: Compute F-score on the test set which is y_test\n",
    "    results['f_test'] = fbeta_score(y_test, predictions_test, beta=1.0)\n",
    "       \n",
    "    # Success\n",
    "    print(\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))\n",
    "        \n",
    "    # Return the results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier trained on 361 samples.\n",
      "RandomForestClassifier trained on 3617 samples.\n",
      "RandomForestClassifier trained on 36177 samples.\n",
      "LogisticRegression trained on 361 samples.\n",
      "LogisticRegression trained on 3617 samples.\n",
      "LogisticRegression trained on 36177 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC trained on 361 samples.\n",
      "SVC trained on 3617 samples.\n",
      "SVC trained on 36177 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ba0d798f2e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Import the three supervised learning models from sklearn\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# TODO: Initialize the three models\n",
    "clf_A = RandomForestClassifier(random_state=42)\n",
    "clf_B = LogisticRegression(random_state=42)\n",
    "clf_C = SVC(random_state=42)\n",
    "\n",
    "# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
    "# HINT: samples_100 is the entire training set i.e. len(y_train)\n",
    "# HINT: samples_10 is 10% of samples_100 (ensure to set the count of the values to be `int` and not `float`)\n",
    "# HINT: samples_1 is 1% of samples_100 (ensure to set the count of the values to be `int` and not `float`)\n",
    "samples_100 =len(y_train)\n",
    "samples_10 = int(0.1 * samples_100)\n",
    "samples_1 = int(0.01 * samples_100)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1, samples_10, samples_100]):\n",
    "        results[clf_name][i] = \\\n",
    "        train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "\n",
    "# Run metrics visualization for the three supervised learning models chosen\n",
    "vs.evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Improving Results\n",
    "In this final section, you will choose from the three supervised learning models the *best* model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (`X_train` and `y_train`) by tuning at least one parameter to improve upon the untuned model's F-score. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3 - Choosing the Best Model\n",
    "\n",
    "* Based on the evaluation you performed earlier, in one to two paragraphs, explain to *CharityML* which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \\$50,000. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After evaluating the performance of the three models (Random Forest, Logistic Regression and SVM) on the task of identifying individuals who make more than $50,000, I believe the Logistic Regression classifier is the most appropriate. This model denonstrates the highest F-Score on the testing set consistently. This suggests that is has the best balance between precision and recall. This model shows consistent performance across the different sample sizes, providing reliable predictions without a significant drop in the accuracy or F-score. The prediction and training times for Logistic Regression are minimal, making it efficient in terms of resources and time. Compared to the other two models, Logistic Regression is well-suited for this data because it works well with the linear relationships that are often present in binary classification tasks like this one.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4 - Describing the Model in Layman's Terms\n",
    "\n",
    "* In one to two paragraphs, explain to *CharityML*, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical jargon, such as describing equations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: \n",
    "Logistic regression is a model that helps make predictions by analyzing the relationship between the data and an outcome. In this case, predicting whether an individual makes more than $50,000 based on data points such as age, education, ocupation and other characteristics. The model then learns to identify these patterns to link these features to a target outcome. It is then trained by looking at historical data where the outcome is already known and then uses this knowledge to create rules for predicting future outcomes. Once trained, the model can make predictions by applying the rules it has learned during the training.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Model Tuning\n",
    "Fine tune the chosen model. Use grid search (`GridSearchCV`) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:\n",
    "- Import [`sklearn.grid_search.GridSearchCV`](http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html) and [`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).\n",
    "- Initialize the classifier you've chosen and store it in `clf`.\n",
    " - Set a `random_state` if one is available to the same state you set before.\n",
    "- Create a dictionary of parameters you wish to tune for the chosen model.\n",
    " - Example: `parameters = {'parameter' : [list of values]}`.\n",
    " - **Note:** Avoid tuning the `max_features` parameter of your learner if that parameter is available!\n",
    "- Use `make_scorer` to create an `fbeta_score` scoring object (with $\\beta = 0.5$).\n",
    "- Perform grid search on the classifier `clf` using the `'scorer'`, and store it in `grid_obj`.\n",
    "- Fit the grid search object to the training data (`X_train`, `y_train`), and store it in `grid_fit`.\n",
    "\n",
    "**Note:** Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8419\n",
      "F-score on testing data: 0.6832\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8420\n",
      "Final F-score on the testing data: 0.6842\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "# TODO: Initialize the classifier\n",
    "clf = LogisticRegression(random_state=42)\n",
    "\n",
    "# TODO: Create the parameters list you wish to tune, using a dictionary if needed.\n",
    "# HINT: parameters = {'parameter_1': [value1, value2], 'parameter_2': [value1, value2]}\n",
    "parameters = {\n",
    "    'C': [0.1, 1, 10],\n",
    "    'max_iter': [100, 200, 300]\n",
    "}\n",
    "\n",
    "# TODO: Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(fbeta_score, beta=0.5)\n",
    "\n",
    "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "grid_obj = GridSearchCV(estimator=clf, param_grid=parameters, scoring=scorer)\n",
    "\n",
    "# TODO: Fit the grid search object to the training data and find the optimal parameters using fit()\n",
    "grid_fit = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Get the estimator\n",
    "best_clf = grid_fit.best_estimator_\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "\n",
    "# Report the before-and-afterscores\n",
    "print(\"Unoptimized model\\n------\")\n",
    "print(\"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5)))\n",
    "print(\"\\nOptimized Model\\n------\")\n",
    "print(\"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print(\"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5 - Final Model Evaluation\n",
    "\n",
    "* What is your optimized model's accuracy and F-score on the testing data? \n",
    "* Are these scores better or worse than the unoptimized model? \n",
    "* How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in **Question 1**?_  \n",
    "\n",
    "**Note:** Fill in the table below with your results, and then provide discussion in the **Answer** box."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results:\n",
    "\n",
    "|     Metric     | Unoptimized Model | Optimized Model |\n",
    "| :------------: | :---------------: | :-------------: | \n",
    "| Accuracy Score |      0.8419       |     0.8420      |\n",
    "| F-score        |      0.6832       |     0.6842      |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The optimized model shows a marginal improvement over the unoptimized model with a slight increase in both accuracy and F-score. While these changes are very small, they demonstrate that fine-tuning the model's performance can lead to a slight enhancement in performance.**\n",
    "\n",
    "**When comparing these results to the naieve predictor benchmarks from question 1, which had an accuracy of 0.2479 and an F-score of 0.2917, the optimized model shows a significant improvement. The naive predictors performance is far lower indicating that the chosen model, even before oprimization is much better at identifying individuals making more than $50,000.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Feature Importance\n",
    "\n",
    "An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \\$50,000.\n",
    "\n",
    "Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a `feature_importance_` attribute, which is a function that ranks the importance of features according to the chosen classifier.  In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6 - Feature Relevance Observation\n",
    "When **Exploring the Data**, it was shown there are thirteen available features for each individual on record in the census data. Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of the thirteen features in this census dataset, I believe the most importartant for prediction are as follows:\n",
    "> 1. Education Level\n",
    ">>  Higher education levels are typically associated with higher-paying jobs and better career oopportunities.\n",
    "> 2. Occupation\n",
    ">> Higher income is often tied to specific professions as some fields offer higher salaries compared to others.\n",
    "> 3. Marital Status\n",
    ">> Marital status often correlates with financial stability and earning potential. Also, a married person will often seek higher paying jobs because they have a spouse and/or family to support.\n",
    "> 4. Work Class\n",
    ">> Work class provides contextual information about the employment environment which can influence income levels. For example, government jobs often have capped salaries, while private sector jobs may have higher potential for earnings.\n",
    "> 5. Age\n",
    ">> Age often correlates to work experience and seniority which typically results in higher earnings over time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation - Extracting Feature Importance\n",
    "Choose a `scikit-learn` supervised learning algorithm that has a `feature_importance_` attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.\n",
    "\n",
    "In the code cell below, you will need to implement the following:\n",
    " - Import a supervised learning model from sklearn if it is different from the three used earlier.\n",
    " - Train the supervised model on the entire training set.\n",
    " - Extract the feature importances using `'.feature_importances_'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ba0d7f70978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Train the supervised model on the training set using .fit(X_train, y_train)\n",
    "model = RandomForestClassifier(random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# TODO: Extract the feature importances using .feature_importances_ \n",
    "importances = model.feature_importances_\n",
    "\n",
    "# Plot\n",
    "vs.feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7 - Extracting Feature Importance\n",
    "\n",
    "Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \\$50,000.  \n",
    "* How do these five features compare to the five features you discussed in **Question 6**?\n",
    "* If you were close to the same answer, how does this visualization confirm your thoughts? \n",
    "* If you were not close, why do you think these features are more relevant?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "> Similarities\n",
    "> * **Education num vs education level**: These features are conceptually similar. Education num is a numerical representation of education, likely covering nuances missed by the categorical representation of *Education Level*\n",
    "> * **Marital Status**: I also included Marital status in my list highlighting that being married, especially in a dual-income, household, correlates with higher earning potential\n",
    "> * **Age**: Both lists included age, highlighting its importance in capturing work experience and earning trends\n",
    "\n",
    ">Differences\n",
    "> * **Capital Gain**: I did not include capital gain on my list. In hindsight, I do believe that capital gain may be more relevant because high capital gains often indicate high investments which is strongly correlated with high income.\n",
    "> * **Hours per week**: I did not include hours per week on my list because I wrongly predicted that most people that earn a higher pay would be paid by salary so I did not think the hours would be a strong factor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection\n",
    "How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of **all** features present in the data. This hints that we can attempt to *reduce the feature space* and simplify the information required for the model to learn. The code cell below will use the same optimized model you found earlier, and train it on the same training set *with only the top five important features*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8420\n",
      "F-score on testing data: 0.6842\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8271\n",
      "F-score on testing data: 0.6499\n"
     ]
    }
   ],
   "source": [
    "# Import functionality for cloning a model\n",
    "from sklearn.base import clone\n",
    "\n",
    "# Reduce the feature space\n",
    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "\n",
    "# Train on the \"best\" model found from grid search earlier\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "\n",
    "# Make new predictions\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# Report scores from the final model using both versions of data\n",
    "print(\"Final Model trained on full data\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))\n",
    "print(\"\\nFinal Model trained on reduced data\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8 - Effects of Feature Selection\n",
    "\n",
    "* How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?\n",
    "* If training time was a factor, would you consider using the reduced data as your training set?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "The final model's F-score and accuracy on the reduced data a re slightly lower compared to using all features with a drop of 0.015 in accuracy and 0.034 in F-score.\n",
    "\n",
    "If training was a factor, I would not use the reduced data as my training set because you should not train you data with a testing set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \n",
    "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before You Submit\n",
    "You will also need run the following in order to convert the Jupyter notebook into HTML, so that your submission will include both files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!!jupyter nbconvert *.ipynb"
   ]
  }
 ],
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