finding_donors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning\n",
    "## Project: Finding Donors for *CharityML*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.  \n",
    "\n",
    ">**Note:** Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version:  3.7.4\n",
      "Sklean version:  0.21.2\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "print('Python version: ', platform.python_version())\n",
    "import sklearn\n",
    "print('Sklean version: ', sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn off the futurn waring\n",
    "# Source: https://machinelearningmastery.com/how-to-fix-futurewarning-messages-in-scikit-learn/\n",
    "# import warnings filter\n",
    "from warnings import simplefilter\n",
    "# ignore all future warnings\n",
    "simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. \n",
    "\n",
    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income). The datset was donated by Ron Kohavi and Barry Becker, after being published in the article _\"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid\"_. You can find the article by Ron Kohavi [online](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf). The data we investigate here consists of small changes to the original dataset, such as removing the `'fnlwgt'` feature and records with missing or ill-formatted entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Exploring the Data\n",
    "Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, `'income'`, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</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>2174.0</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",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-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>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</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>0.0</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",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</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>0.0</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",
       "      <td>4</td>\n",
       "      <td>28</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>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass education_level  education-num       marital-status  \\\n",
       "0   39          State-gov       Bachelors           13.0        Never-married   \n",
       "1   50   Self-emp-not-inc       Bachelors           13.0   Married-civ-spouse   \n",
       "2   38            Private         HS-grad            9.0             Divorced   \n",
       "3   53            Private            11th            7.0   Married-civ-spouse   \n",
       "4   28            Private       Bachelors           13.0   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male        2174.0   \n",
       "1     Exec-managerial         Husband   White     Male           0.0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male           0.0   \n",
       "3   Handlers-cleaners         Husband   Black     Male           0.0   \n",
       "4      Prof-specialty            Wife   Black   Female           0.0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country income  \n",
       "0           0.0            40.0   United-States  <=50K  \n",
       "1           0.0            13.0   United-States  <=50K  \n",
       "2           0.0            40.0   United-States  <=50K  \n",
       "3           0.0            40.0   United-States  <=50K  \n",
       "4           0.0            40.0            Cuba  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Import supplementary visualization code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the Census dataset\n",
    "data = pd.read_csv(\"census.csv\")\n",
    "\n",
    "# Success - Display the first record\n",
    "display(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>38.547941</td>\n",
       "      <td>10.118460</td>\n",
       "      <td>1101.430344</td>\n",
       "      <td>88.595418</td>\n",
       "      <td>40.938017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>13.217870</td>\n",
       "      <td>2.552881</td>\n",
       "      <td>7506.430084</td>\n",
       "      <td>404.956092</td>\n",
       "      <td>12.007508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age  education-num  capital-gain  capital-loss  hours-per-week\n",
       "count  45222.000000   45222.000000  45222.000000  45222.000000    45222.000000\n",
       "mean      38.547941      10.118460   1101.430344     88.595418       40.938017\n",
       "std       13.217870       2.552881   7506.430084    404.956092       12.007508\n",
       "min       17.000000       1.000000      0.000000      0.000000        1.000000\n",
       "25%       28.000000       9.000000      0.000000      0.000000       40.000000\n",
       "50%       37.000000      10.000000      0.000000      0.000000       40.000000\n",
       "75%       47.000000      13.000000      0.000000      0.000000       45.000000\n",
       "max       90.000000      16.000000  99999.000000   4356.000000       99.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A brife look of data. (This cell is updated after reviewer suggestion.)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f9670a92650>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 971.75x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A depper look of data. (This cell is updated after reviewer suggestion.)\n",
    "import seaborn as sns\n",
    "sns.pairplot(data,palette='Set1', hue='income')\n",
    "\n",
    "# Below graph give out a nice look that how x and y features relate to income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Data Exploration\n",
    "A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \\$50,000. In the code cell below, you will need to compute the following:\n",
    "- The total number of records, `'n_records'`\n",
    "- The number of individuals making more than \\$50,000 annually, `'n_greater_50k'`.\n",
    "- The number of individuals making at most \\$50,000 annually, `'n_at_most_50k'`.\n",
    "- The percentage of individuals making more than \\$50,000 annually, `'greater_percent'`.\n",
    "\n",
    "** HINT: ** You may need to look at the table above to understand how the `'income'` entries are formatted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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: 24.78%\n"
     ]
    }
   ],
   "source": [
    "# TODO: Total number of records\n",
    "n_records = len(data)\n",
    "\n",
    "# TODO: Number of records where individual's income is more than $50,000\n",
    "n_greater_50k = np.sum(data['income'] == '>50K')\n",
    "\n",
    "# TODO: Number of records where individual's income is at most $50,000\n",
    "n_at_most_50k = np.sum(data['income'] == '<=50K')\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: {:.2f}%\".format(greater_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**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\n",
    "Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as **preprocessing**. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms."
   ]
  },
  {
   "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",
    "Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mhyip/Udacity/DSND_Term1/projects/Udacity-Project-Finding-Donors-for-CharityML/visuals.py:48: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "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": [
    "For highly-skewed feature distributions such as `'capital-gain'` and `'capital-loss'`, it is common practice to apply a <a href=\"https://en.wikipedia.org/wiki/Data_transformation_(statistics)\">logarithmic transformation</a> on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of `0` is undefined, so we must translate the values by a small amount above `0` to apply the the logarithm successfully.\n",
    "\n",
    "Run the code cell below to 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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BzwMavdZixVsK795jFBNtV20/P1M/ppw1uW6CddQ5WLUWpdlS0yso10I0TbR/urD+7V6+O9Je21jv0I91q7nHMxDja4eI6G77+M/MPC4i3kdpg/1NyinhjShHw86n3PWHzDwvIr4FfKCeij+N8mCq59Zx3d4ihnMpifrHlIR6SWZeMsnwv6a06f18ROxLSWjvodyycNoPtIpyJ6NtKHfquBi4F+WIyiWUC8R6ysxzIuJQYL96lPjXlLMj7wUO7Tp13XeZeW1EvJOyHNajXEfxT8p6egrlIsZvNdbR/nUdde7CNJ1kAqUN/b0oR7XuCzyHUigeR1lePUXEcyh3T/o+5YjW2pTbRy5l2Y/6c+vft0fEj4DbMnOmR6wvp9wrfj+W3YVpbcqdRDoOqzEdFBEHU34QvY2y/JpaxZWZt7X5vrTVcpn18mnKGanj6nfjWsrdW/6NUkTNxEcozQW/XZfVDyhH4DcGXkhp6rFuZt7Qz2VQnQu8ISJeSjnLtzSndyetXtqu+w9QtvHjI+JjlAMl76b8uNt/OuOL8qC9n1KuD+ncKnMnSlOon0wR7yvqZ0+J8vyDX1IOumxOaTayOnBkZv65xvm5eobjRMqR6E0o3/ev1DMKrbTcL36nLqdDIuJTjWH+3jWu31Cab51FuZj8KZRtalGLUNaJiE4zonUozTN3pxQpb8jM5pnPHwMLI+IsSlOunel957KJtqsfA++OiH0oTXWeSjmi3i+H1NiPj4hPUm63eydgU8qBmOfXAzE/Bp4fEZ+mXAO4BeX7330nsZ77p8y8NCJOpJwV+DulifGudTp9jXUl163mg+lede1rbr9YdjeGXq+zG8N17ul+M+WU5mTPgbiaZc8YeDYt7qBUP/9ESuFxE427GlGfAzHBZ55KeT7CjZQE8BbqHSm6hku67vDEinfQ6Twp9GKW3Z/9O8CDGp/pedcalj0H4kLKj4ILmfg5EK+ZZD08sKv7CXTdJWSi8VAKgZ9TfizeSEmcBwEPmWIdde7Osts0t5Ub63x+j1JAdN8dqXv5PojSnvevdR1fSflR8rjGZ1YFPk9JdLd31mPLZbeg0W0x5Yfra+p2cXPdTp7a4/Ovo/ywvZFS/G3BinfOmSqu3brG2eb7shj4Zo94mtv+lMtskvX1IErh8c/62eWeAzHZ9jzJOKPO288pRf6/KBfrH0ppStj3ZVDf37vO99La74Sp1v1U42y77utwj2OK50C0GR/l2qwvU5oaXkf5rp5C4+5QUyz/u1Buk9l5JszNlDM+nwEe0DXsK+o6v75O6w/A54CNu5bJSu8X63DPpxQUN9b1/kxWvAvTx2rs/6xxnUWLO1Kx/DMbbq+fP4Py3IGH9hj+XpSC7pr6OoTSdHa57+ok29WalP3klbXf0ZSCcIVtqMe0t63DTfUciDtTctUf63K9um4L+1HvpkQ50/hBSrF2A6UYfDQt90+138aUYv8flOt2PkzZL7b63kwj1hmtW1/z5xV1Q5D6oh4Z/xhlR3XRVMNLkiRpbrEJk2asNrd4GOXI0O2UuyK9Azjc4kGSJGl+soDQylhKOY29F6Wt9t8oF7btO8ygJEmSNDg2YZIkSZLUmrdxlSRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzQJCE4qIgyPi6D6MZ7+IOLsfMU0xnQURkRGx5aCnNe4iYreIuG5A4z4hIj7XeL84It4xoGkNbD6kcTGbuaJf09LgDDLnd+eDmvNfNKBpzcpvl7nKAmKOqDvN/WZ5snsCuzZiWO6H3Qi6GNiQ8mTsViJi24hYPMUwi+tOqvn6x0rG2j2NoS/buiw683d7RFwbEWdGxGci4v5dg38beEDL8U63sNsZ2Hs6sbeMo1eiaT0f0lxgruifeoDhhCmG6c4NGRGtc1DLOAZ2EGUaMezWmL/bIuIfEXFqRHwoItbvGvwTwFNajreTd+7VMpTHAl+YTuwtYpgoR7Wej3Hkk6g1ocz857BjmI7MvA24bECj3x/4YuP97QOazkqLiNUz818rMYqHAlcDdwEeCbwVOCsinp2ZJwJk5o3AjSsdbENE3Ckzb8nMq/s53skMYj6kcTPXcsUAvBZonhVZmf3vwETEKpQHCN82w1HcAGwKBHBXyo/5dwOvjYinZOYfADLzOqCvZ3Yb+eHKfo53MoOYj/nEMxBzVETcKSI+HBEXRsTNEfGXiHhL7bdqRHw1Iv4aETdGxPkR8a668+h8/uCIODoi3hMRl0fEdRHxtYhYs3uYzv+USvyNjaMQC9pMq+X8rB0RX69xXB4Re9f4Dm4Ms2tEnBIRSyPiioj4TkRs1Oi/3FGExpGNp0XEbyPihnrE5DEzWORLM/OyxuuKxnTvFhEH1piWRsSJzSMZEXHPiDg0IpbUZXROROze6D/Rsl3hyMwk87hjRJwcEbcA29d+z42I0yLiprp+PhQRd2oxr1fUebwgM/8X2Bb4HXBQRKxax71c05+I2CQijoyIq+ty/mNE7FJ7/7X+PaXGekJnvus6fndELAGW1O69jl7eJSK+WbePy6LraFz0OLsQjaN2sews03fqsIt7zUft9rqIuCAibql/X9tjWnvU7e/6+t3bFWkExTzLFT3mb42I+O8a200RcVJEPKnRf/WIOCAiLqnzf3FEfLTRf+coZ1pvrPuvEyNig2mG8Y+u/HBVY/wbRcRhEXFNff0wIjZr9N+07jsvq/uT0yPiOY3+JwD3Az7eWZ61e69913I5ozNMzQ9nA7cAD679do+Ic+sy+1NE/EeLdZF1/i7NzPMy85vA44F/AF9qxLFc05+IeHhEHB/lrPbSiPh9RGwXEQuAn9fBrqyxH9yZ74j4YkR8IiKuBH5Vu/c6G3PvulxvqNt582xYz7MLsXzOmChHdc/HKhHx3roN3RwRZ0XETj2m9cKIOK7Gc25EPGOK5TonWUDMXYuAVwJvo+wQXk35EkNZr38DXlL7/SewD7B71zieQjnC/DTghcAzgY9NML09gd8AX6M0E9qQ0mSo7bSm8skazwuAp9a4ntw1zJ2AfWu/5wD3Ag5tMe6PAHsBjwGuAg6JiJhmfD3V8fwQ2KjG9GjgF8DPImLDOtidgdNr/4cCnwG+HBFPq/0nWrbT8THgPcDmwG8jYnvgEOBzdZqvAl4EfHi681iPVn2a0tTn0RMM9gVgLWC7Or23smx73Kr+3YEybzs3PvcU4BG139OY2NuAP1DW4b7AhyNi50mG7/bY+ve1NYbH9hooIl5AWWb/DTyMsq6+EBHP7Rr0fcCRlG3x25Ti6n7TiEeaLfMtV3T7L+CllH3co4GzgB839r9voeSVXYDN6rDnAUTEvYHDKMvowcA2wDdWMp47RMRalB/IN1GW4eOBS4Gf1n5QzvT+CHgGZRn/L/DdiNi89t+ZcnBlf5Ytz+m4MyU3vA54CHBhlIMiH6bsxx4MvJ1yJuEN053HepT+S8A2EbHeBIN9izLfW1HW0X6UZXIxZXuCkjc2pGw/HbtSznY8mbINT+T9wFHAo4ADga93FwxTmCxHNe0JvJOyrB4OfI+yrh7VNdyHgAMo6/MU4LCIuMs04pkbMtPXHHtRdoIJ7DCNz3wU+Gnj/cGUJHKXRrddgZuBtRvDHN3ofwLwuRlMaz/g7EmGvwvlyMgujW5rA9cAB0/yuc3rcti4vl9Q329Z329b32/f+MwTm59puewW1+VyXeO1T+331Pp+za7PnAG8a5JxHgZ8ZbJl24j/Xo1uE83jC7s++wvgvV3dnl9jjQliWmF6PZb1S+r73YDrGv3PBPadYLzLxdy1DV4JrNHVfbllUZf/cV3DfAX4ZeN9Ai/qsd7eMcUw3fPxK+CgHnF2T+sjjferUU7t79p2m/LlazZezLNc0T0tSp64BXhlo/+qwJ+BD9b3BwDH99rvUQ5IJHC/lVjGSWkG2cwPL6/9XgWc35x2je+qzr50gnGeBLyn8X65fVnttty+q3bblsY+vA6TwBZdw10EvKKr21uBcyeJaYXpNfrtUKezVa/1CFwLLJzgs8vF3LUNndlj+OWWRf3s/3QN81Pgm/X/BfTOP3fkg0mG6Z6PvwHv6xFn97Re1+i/Ue32pJluY6P68hqIuenRlDb4P59ogIh4PfAayqnPNYHVgQu7Bjszy9GDjt9QjvJvSvlB2ErLaXWGfTLlaEvH64Cz62dO7nTMzOuj6+4HUZoe7Us5ynAPypEJgPtSm79MoDkvl9S/60/xmW6fAr7aeN9pp78F5cj7lV0nNe5MWY5EafazF+XI10bAGpTlfMI0pj+VU7vebwFsFRHvbnRbhbJ+7k05GjQdnZnLCfp/BvhSROxASdbfy8zTWoz37My8ucVwv+nxfjpnINp6MHBQV7dfAs/r6nbHNpWZt9ZT7N0XEkrDNq9yRWYe0jXYpnUcv+p0yMzbIuI3lKPtUAqO44A/RcRPgGOAH2Xm7cDvKT82z679fgockdNvZ/9O4MeN95fXv1sA9weWduWHtViWH9am5LXnUI5+r07JH62X6xRupXFjkXqWYBPKWfDmdX2rsWw/P11T5YdPAV+JiIWU/PC/mfnHFuNtk0Ogd354dsvPthIRdwXuQ2Nbq34J7NjVbaLfHPOKBcTcNOmXPCJeSmmC8Q7g15Tq/42U07j9DWT60zqVUgB0XE7dkTLxzqezkz2WsoN/BXAFpQnT/1ES2WSaF7R1pjHd5ntXZeYFPbqvQpmH7uZWUJYFlGXzdsrpz7MoR6g+zNQ7lM6F2s31vfoEw17fI673A9/pMexMLkLrJOO/9OqZmV+NiGMpO9KnA7+OiI9k5n5TjLc77plKVvxeTLSs2oxrqm7dF0kmNgnV6JlvuWKF0da/E35nM/P02tZ+B8oZ40XA7yPiGbXYeCawNaVZ1quBj0S5IPj37eeOyybJD2dQmk916xyE+kSN7R2UsxU3AF9n6rx2O+32eTfn8hdNd/ZTr6esh354CGV5L+7VMzP3i4hDgGdRrtHbNyJen5ndB2u69SM/rJBHI2KmuQGmmR8yM2vxOO/ygwXE3HQ6ZWPcjuWPenQ8CfhtZjbvpb9pj+EeHhFrZ2bnS7o15XTwnyeY7i2U068zmRZwx11vltvRRsQFlC/cVtSLmWr70Ic1YtmcUjDsk5mdYQZxBHq6Tgc2AG7PzJ4/rinL6AeZ+Q2447qJf2NZO2TovWw7P/Q3bPzf3dZysrg2nyCpTUs9g/JWyrqY8PaEmbmE0v70wHrmY0/KKeBb6iDd8zcdW/d4/4fG+ytptA2OchFkd1vhf7WI4Q+U9dVMbE8Czp1OsNKImFe5oocL6rSeRD24UfdXj6e0u++MaynlYMp36kW6JwEPBP6UpZ3Jb4DfRMT+wDmUs8XTKSAmcjrwMuDvmTnRrb+fBHw9yw0riIjO2es/NYaZKD+sFRF3zczOwaop80NmXh4RfwM2zcyvt5+V3mrb/tcDJ0525iYzz6cUSAfUMx+voexn+5UfDup638kPzTza0b2cpowhM6+NiEso6+tnjV5jmx8sIOagzDw/Ig6nnBLck7KT2hhYUH+k/gnYLSKeRdnB7kK5gOuarlGtRrn4c3/KqbmPUtoSTlT1L6Y0i1lAOYp+9TSmNdn8XBcRBwEfi4i/U5rXvIeS+DqV/UWUNrdviojPU5qafKDtNAbop5RTmkdGxLuAP1KaCO1Aadv7f5Rl9NIodwb5O/Bmymnt3zXGs5gVl+0FlIvM9ouIvSjtK9/TMq79gaMj4kLgcMpp7IdR2qi+a4rPrh8Rq1GuTXkE8B+UphA75gS3/4uIz1CaG/yJcnu/HVi2U72C0kZ4+yh3P7opp3/bx60jYm/gCEqb2VcCL2/0/xnlri+/Bm6jnOG5qWsci4GnRcSJlKNyvbbRj1N+ZJwG/KTOx8sZTHMpaaDmW67oMX/X1x+jH62546+U/dUG1GcFRMTbKDnlDMpBhH+nnP1YEhFbU86YHks5w/FoSvOefv0gPIRyZuHIiHgfJY9tAuwEfKn+qP4T8IKIOLLGty+lCVPTYuDJEfFNyr7r78BvKUfoPxIRn6ZcsNv2Iuj9gM9GeZ7RMZQzF48BNsrMj0zyuagXngPcjWW3cb0bKzbz7HxgTcpZlu/U+RIVVPgAAB9WSURBVNiAWkzWQS6k5PlnR8QPgBu7msu1sXNEnEJpFvwiysX+j4NSiEbEScC7I+LPNdbueWyboz4O7B8R51OaV+1KaX2wxTTjnRfm3SmVMfJKyhGWAyg/Wg+mfDEAvkz50fgtyh0AFlDuctTtRMrRlp9T7ibwM2CyH5efoFTq51Kq+vtOY1pTeQelOdJRNZ4zKaewbwKoRzYWUi4EPpeyk33bDKbTV/Xo1Y6UZfc/lLt7HA48iGVtHz9Iub7jR5SLm6+nJJamFZZtlmc57EK5+9HvKU2S9mkZ17GUNqDb1WmfTLkO46IWHz+HknB/RylEfgc8IjN/MclnVgE+W+M/jpKMF9ZYbqXcCeU1lGVyZJt56PIpSjHzO8ryfF9mHtHo/3bKEcgTKEXGVyhJga5htqMUZb+jh8z8PqXA+486L3sCb8jMH8wgZmkUzLdc0e3ddbxfoxQJj6BcNN65zmsp5RqFkykF1KOAZ2XmDcA/KTfWOJpydPyTwAey3J50pdVpbEPZN32HsvwXAeuyrHB6G2Vf9X+UHHFS/b/pfZTC48/UI+pZnpfzcsrdm84C9gDe2zKur1Au8H4FJbf8X/38X6f46FqU3HAJZXm+DfgB8LCsz4Do4TbK/C6i5MfvUc74vK3G8jdKPv8QJW/M5AGE+1Hu5nQm8P+A3TPzlEb/V9W/p1C2w+UOxE0jRx1AKSL+i3Lt5gsoNzDp64MD54oov380bupp3Htl5nOmGnYYImINypGJj2dmP5KMJGmaRj1XSBoOmzBpJETEoynNkk4G1qEcVVqHco99SZIkjYihNWGKiEMi4ryIODsiDupcFR/FAVGeAHtmNJ4aHBELozy98vx6O7BO9y2iPBHwgvrZvjwkTLPubZSmJT+jtJPcpl6YK2nMmCMkaXQNrAlTRKw7wUWKnf47suwez98CfpGZX6zd30xpV/444DOZ+biIuAelTfyWlAtuTqM8HOWaiDiZ0lb5JMoFQQdk5o+QJI0kc4QkzV2DPANxakR8KyKe2utoT2YekxWl2crGtddOlFuaZWaeBNw9yiPpt6c8jfbqmnSOA3ao/e6amb+p4/o65UJbSdLoMkdI0hw1yGsg/o3y0JA3AZ+PiG8AB2fmJc2B6mnpV1CODkF5Uu/FjUGW1G6TdV/So/sKImIPyp0GWHvttbfYfPPNpz1Tp1111bSG3+Ke95z2NCRptpx22ml/z8z1hjDpkcoR/cgPYI6QNL9MlCMGVkDU+8UfTbkX/XqU++5eFBFPyMyTG4N+gXJqunPbsl5tU3s9ZXaq7r1iOpDyoCu23HLLPPXUU1vNS1MsWjSt4U9duHDqgSRpSOqzQmbdqOWIfuQHMEdIml8myhEDvYg6Iu5Wj+ocRTna9GrKfXo7/fcF1mP5+/kvodzvuGNjyn15J+u+cY/ukqQRZo6QpLlpYAVEfWLi6ZSHYL0yM7fJzEWZeVPt/xpKm9WXZebtjY8eBbyy3mlja+Cf9YEwxwLPjIh1I2Jd4JnAsbXf0ojYurajfSUze1CVJGmWmCMkae4a5DUQhwO71Sf89fIlyoPCflOvn/tuZu5PuUPGjpRH3d8A7A7lqYsR8QHKkwQB9q9PYoTy5MGDgTUpd+3w7hqSNNrMEZI0Rw3yGoijpujfc9r1LhlvnKDfQcBBPbqfCjxsBmFKkobAHCFJc9fQHiQnSZIkae6xgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzQJCkiRJUmsWEJIkSZJas4CQJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzQJCkiRJUmsWEJIkSZJas4CQJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqbWgFREQcFBFXRMTZjW77RcTfIuKM+tqx0W/viLggIs6LiO0b3Xeo3S6IiL1mez4kSf1njpCk0TXMMxAHAzv06P7pzHxUfR0DEBEPAXYBHlo/84WIWDUiVgU+DzwLeAjwsjqsJGluOxhzhCSNpNWGNeHM/EVELGg5+E7AYZl5M/DXiLgA2Kr2uyAz/wIQEYfVYc/tc7iSpFlkjpA0l8WiRa2HzYULBxjJYIziNRBviogz6+nrdWu3jYCLG8Msqd0m6i5Jmp/MEZI0ZKNWQHwR2BR4FHAp8MnaPXoMm5N07yki9oiIUyPi1CuvvHJlY5Ukza6B5QjzgyS1N1IFRGZenpm3ZebtwP+w7BT0EmCTxqAbA5dM0n2i8R+YmVtm5pbrrbdef4OXJA3UIHOE+UGS2hupAiIiNmy8fQHQufvGUcAuEbFGRNwf2Aw4GTgF2Cwi7h8Rd6JcRHfUbMYsSZod5ghJGg1Du4g6Ig4FtgXuFRFLgH2BbSPiUZRTzIuB1wFk5jkRcTjlwrdbgTdm5m11PG8CjgVWBQ7KzHNmeVYkSX1mjpCk0TXMuzC9rEfnr04y/IeAD/XofgxwTB9DkyQNmTlCkkbXSDVhkiRJkjTaLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqbcoCIiKeGBFr1/93jYhPRcT9Bh+aJGnUmSMkafy0OQPxReCGiHgk8C7gQuDrA41KkjRXmCMkacy0KSBuzcwEdgI+k5mfAdYZbFiSpDnCHCFJY2a1FsMsjYi9gV2BbSJiVWD1wYYlSZojzBGSNGbanIF4KXAz8OrMvAzYCPj4QKOSJM0V5ghJGjNTnoGoCeFTjfcXYftWSRLmCEkaRxMWEBGxFMiJ+mfmXQcSkSRp5JkjJGl8TVhAZOY6ABGxP3AZ8A0ggJfjBXKSNNbMEZI0vtpcA7F9Zn4hM5dm5rWZ+UXghYMOTJI0J5gjJGnMtCkgbouIl0fEqhGxSkS8HLht0IFJkuYEc4QkjZk2BcS/Ay8BLq+vF9dukiSZIyRpzEx6F6Z6P+8XZOZOsxSPJGmOMEdI0nia9AxEZt5GebqoJEnLMUdI0nhq8yTqX0XE54BvA9d3Ombm6QOLSpI0V5gjJGnMtCkgnlD/7t/olsBT+x+OJGmOMUdI0php8yTq7WYjEEnS3GOOkKTxM+VdmCLibhHxqYg4tb4+GRF3m43gJEmjzRwhSeOnzW1cDwKWUm7T9xLgWuBrgwxKkjRnmCMkacy0uQZi08xsPlX0/RFxxqACkiTNKeYISRozbc5A3BgRT+q8iYgnAjcOLiRJ0hxijpCkMdPmDMT/AxY12rReA+w2sIgkSXOJOUKSxkybuzCdATwyIu5a31878KgkSXOCOUKSxk+buzB9OCLunpnXZua1EbFuRHxwNoKTJI02c4QkjZ8210A8KzP/0XmTmdcAOw4uJEnSHGKOkKQx06aAWDUi1ui8iYg1gTUmGV6SND7MEZI0ZtpcRP1N4PiI+BqQwKuARQONSpI0V5gjJGnMtLmI+r8i4kzg6UAAH8jMYwcemSRp5JkjJGn8tDkDAfAH4NbM/GlErBUR62Tm0kEGJkmaM8wRkjRG2tyF6bXAEcCXa6eNgO8PMihJ0txgjpCk8dPmIuo3Ak8ErgXIzPOB9QcZlCRpzjBHSNKYaVNA3JyZt3TeRMRqlAvlJEkyR0jSmGlTQJwYEfsAa0bEM4DvAD8YbFiSpDnCHCFJY6ZNAbEXcCVwFvA64BjgPYMMSpI0Z5gjJGnMtLmN6+3A/9QXABHxROBXA4xLkjQHmCMkafxMWEBExKrASyh31PhxZp4dEc8B9gHWBB49OyFKkkaNOUKSxtdkZyC+CmwCnAwcEBEXAo8H9spMb9EnSePNHCFJY2qyAmJL4BGZeXtE3Bn4O/DAzLxsdkKTJI0wc4QkjanJLqK+pbZtJTNvAv5kYpAkVeYISRpTk52B2Dwizqz/B7BpfR9AZuYjBh6dJGlUmSMkaUxNVkA8eNaikCTNNeYISRpTExYQmXnhbAYiSZo7zBGSNL7aPEhOkiRJkgALCEmSJEnTMGEBERHH178fG9TEI+KgiLgiIs5udLtHRBwXEefXv+vW7hERB0TEBRFxZkQ8pvGZhXX48yNi4aDilSQVg84R5gdJGl2TnYHYMCKeAjwvIh4dEY9pvvo0/YOBHbq67QUcn5mbAcfX9wDPAjarrz2AL0JJKMC+wOOArYB9O0lFkjQwg84RB2N+kKSRNNldmN5H2TlvDHyqq18CT13ZiWfmLyJiQVfnnYBt6/+LgBOAd9fuX8/MBE6KiLtHxIZ12OMy82qAiDiOknQOXdn4JEkTGmiOMD9I0uia7C5MRwBHRMR7M/MDsxjTBpl5aY3h0ohYv3bfCLi4MdyS2m2i7pKkARlSjjA/SNIImOwMBACZ+YGIeB6wTe10QmYePdiweooe3XKS7iuOIGIPyult7nvf+/YvMkkaUyOSI8wPkjSLprwLU0R8BNgTOLe+9qzdBuXyeuqZ+veK2n0JsEljuI2BSybpvoLMPDAzt8zMLddbb72+By5J42aWc4T5QZJGQJvbuD4beEZmHpSZB1Hajz57gDEdBXTulLEQOLLR/ZX1bhtbA/+sp7KPBZ4ZEevWi+OeWbtJkgZvNnOE+UGSRsCUTZiquwNX1//v1q+JR8ShlIvc7hURSyh3y/gocHhEvBq4CHhxHfwYYEfgAuAGYHeAzLw6Ij4AnFKH279zwZwkaVb0PUeYHyRpdLUpID4C/C4ifk5pT7oNsHc/Jp6ZL5ug19N6DJvAGycYz0HAQf2ISZI0LQPJEeYHSRpdbS6iPjQiTgAeS0kO787MywYdmCR1i0WLWg+bC31m2GwwR0jS+GnVhKm2JT1qwLFIkuYgc4QkjZc2F1FLkiRJEmABIUmSJGkaJi0gImKViDh7toKRJM0d5ghJGk+TFhCZeTvw+4jwsZySpOWYIyRpPLW5iHpD4JyIOBm4vtMxM583sKgkSXOFOUKSxkybAuL9A49CkjRXmSMkacy0eQ7EiRFxP2CzzPxpRKwFrDr40CRJo84cIUnjZ8q7MEXEa4EjgC/XThsB3x9kUJKkucEcIUnjp81tXN8IPBG4FiAzzwfWH2RQkqQ5wxwhSWOmTQFxc2be0nkTEasBObiQJElziDlCksZMmwLixIjYB1gzIp4BfAf4wWDDkiTNEeYISRozbQqIvYArgbOA1wHHAO8ZZFCSpDnDHCFJY6bNXZhuj4hFwG8pp6XPy0xPT0uSzBGSNIamLCAi4tnAl4A/AwHcPyJel5k/GnRwkqTRZo6QpPHT5kFynwS2y8wLACJiU+CHgMlBkmSOkKQx0+YaiCs6iaH6C3DFgOKRJM0t5ghJGjMTnoGIiJ3rv+dExDHA4ZT2rS8GTpmF2CRJI8ocIUnja7ImTM9t/H858JT6/5XAugOLSJI0F5gjJGlMTVhAZObusxmIJGnuMEdI0vhqcxem+wNvBhY0h8/M5w0uLEnSXGCOkKTx0+YuTN8Hvkp5sujtgw1HkjTHmCMkacy0KSBuyswDBh6JJGkuMkdI0phpU0B8JiL2BX4C3NzpmJmnDywqSdJcYY6QpDHTpoB4OPAK4KksOz2d9b0kabyZIyRpzLQpIF4APCAzbxl0MJKkOcccIUljps2TqH8P3H3QgUiS5iRzhCSNmTZnIDYA/hgRp7B8+1Zv0SdJMkdI0phpU0DsO/AoJElzlTlCksbMlAVEZp44G4FIkuYec4QkjZ82T6JeSrmjBsCdgNWB6zPzroMMTJI0+swRkjR+2pyBWKf5PiKeD2w1sIgkSXOGOUKSxk+buzAtJzO/j/f3liT1YI6QpPmvTROmnRtvVwG2ZNnpakkaSbFo0bSGz4ULBxTJ/GaOkKTx0+YuTM9t/H8rsBjYaSDRSJLmGnOEJI2ZNtdA7D4bgUiS5h5zhCSNnwkLiIh43ySfy8z8wADikSTNAeYISRpfk52BuL5Ht7WBVwP3BEwOkjS+zBGSNKYmLCAy85Od/yNiHWBPYHfgMOCTE31OkjT/mSMkaXxNeg1ERNwDeBvwcmAR8JjMvGY2ApMkjTZzhCSNp8mugfg4sDNwIPDwzLxu1qKSJI00c4Qkja/JHiT3duA+wHuASyLi2vpaGhHXzk54kqQRZY6QpDE12TUQ035KtSRpPJgjJGl8tXmQnCS14tOfJUma/ywgJA3NdAsOSZI0fJ6CliRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzQJCkiRJUmsWEJIkSZJas4CQJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNZGtoCIiMURcVZEnBERp9Zu94iI4yLi/Pp33do9IuKAiLggIs6MiMcMN3pJ0qCYHyRpuEa2gKi2y8xHZeaW9f1ewPGZuRlwfH0P8Cxgs/raA/jirEcqSZpN5gdJGpJRLyC67QQsqv8vAp7f6P71LE4C7h4RGw4jQEnSUJgfJGmWjHIBkcBPIuK0iNijdtsgMy8FqH/Xr903Ai5ufHZJ7baciNgjIk6NiFOvvPLKAYYuSRog84MkDdFqww5gEk/MzEsiYn3guIj44yTDRo9uuUKHzAOBAwG23HLLFfpLkuYE84MkDdHInoHIzEvq3yuA7wFbAZd3Tj3Xv1fUwZcAmzQ+vjFwyexFK0maLeYHSRqukSwgImLtiFin8z/wTOBs4ChgYR1sIXBk/f8o4JX1bhtbA//snMqWJM0f5gdJGr5RbcK0AfC9iIAS47cy88cRcQpweES8GrgIeHEd/hhgR+AC4AZg99kPWZI0C8wPkjRkI1lAZOZfgEf26H4V8LQe3RN44yyEJkkaIvODJA3fSDZhkiRJkjSaLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzQJCkiRJUmsWEJIkSZJas4CQJEmS1Npqww5AkiRJGmWxaNGwQxgpnoGQJEmS1JoFhCRJkqTWLCAkSZIkteY1EJImZJtPSZLUzQJCkiRJGpLpHqzLhQsHFEl7NmGSJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1L6KWpBEzFy+okySND89ASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS1ZgEhSZIkqTULCEmSJEmtWUBIkiRJas0CQpIkSVJrFhCSJEmSWrOAkCRJktSaBYQkSZKk1iwgJEmSJLVmASFJkiSpNQsISZIkSa1ZQEiSJElqzQJCkiRJUmurDTsASbMrFi0adgiSJGkO8wyEJEmSpNY8AyFJMzDdMzm5cOGAIpEkaXZ5BkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNa8iFoaMV6cOxze3laSpHY8AyFJkiSpNQsISZIkSa3NmyZMEbED8BlgVeArmfnRIYckSRoR5ghpfrMZ6uyaF2cgImJV4PPAs4CHAC+LiIcMNypJ0igwR0hSf82XMxBbARdk5l8AIuIwYCfg3KFGJc0Cj7pIUzJHSJo3RuFmK/OlgNgIuLjxfgnwuCHFojloFL6Mmt8s9IbKHCENwKD3a+ba0RWZOewYVlpEvBjYPjNfU9+/AtgqM9/cNdwewB717YOA82YwuXsBf1+JcEeJ8zKanJfRNJ/mBZbNz/0yc71hBzNIbXJEn/IDzL/tpF9cLr25XHpzuUxstpdNzxwxX85ALAE2abzfGLike6DMPBA4cGUmFBGnZuaWKzOOUeG8jCbnZTTNp3mB+Tc/U5gyR/QjP8DYLdfWXC69uVx6c7lMbFSWzby4iBo4BdgsIu4fEXcCdgGOGnJMkqTRYI6QpD6aF2cgMvPWiHgTcCzlFn0HZeY5Qw5LkjQCzBGS1F/zooAAyMxjgGNmYVIrfYp7hDgvo8l5GU3zaV5g/s3PpMwRQ+dy6c3l0pvLZWIjsWzmxUXUkiRJkmbHfLkGQpIkSdIssICYhojYISLOi4gLImKvYcczUxGxSUT8PCL+EBHnRMSew45pZUXEqhHxu4g4etixrIyIuHtEHBERf6zr5/HDjmmmIuI/6vZ1dkQcGhF3HnZMbUXEQRFxRUSc3eh2j4g4LiLOr3/XHWaMbU0wLx+v29iZEfG9iLj7MGOcL+ZLjuin+Zhv+mm+5K5+mk95sJ9GLadaQLQUEasCnweeBTwEeFlEPGS4Uc3YrcDbM/PBwNbAG+fwvHTsCfxh2EH0wWeAH2fm5sAjmaPzFBEbAW8BtszMh1EuXN1luFFNy8HADl3d9gKOz8zNgOPr+7ngYFacl+OAh2XmI4A/AXvPdlDzzTzLEf00H/NNP82X3NVP8yIP9tMo5lQLiPa2Ai7IzL9k5i3AYcBOQ45pRjLz0sw8vf6/lPLl3Gi4Uc1cRGwMPBv4yrBjWRkRcVdgG+CrAJl5S2b+Y7hRrZTVgDUjYjVgLXo8m2VUZeYvgKu7Ou8EdB67ugh4/qwGNUO95iUzf5KZt9a3J1Gei6CVM29yRD/Nt3zTT/Mld/XTPMyD/TRSOdUCor2NgIsb75cwD3aCEbEAeDTw2+FGslL+G3gXcPuwA1lJDwCuBL5WT2l/JSLWHnZQM5GZfwM+AVwEXAr8MzN/MtyoVtoGmXkplB9FwPpDjqdfXgX8aNhBzAPzMkf00zzJN/00X3JXP82bPNhPo5hTLSDaix7d5vQtrCLiLsD/Am/NzGuHHc9MRMRzgCsy87Rhx9IHqwGPAb6YmY8GrmfuNJNZTr0+YCfg/sB9gLUjYtfhRqVuEfGflCYmhww7lnlg3uWIfpoP+aaf5lnu6qd5kwf7aRRzqgVEe0uATRrvN2YONcnoFhGrU3bmh2Tmd4cdz0p4IvC8iFhMaTLw1Ij45nBDmrElwJLM7BydO4KyI52Lng78NTOvzMx/Ad8FnjDkmFbW5RGxIUD9e8WQ41kpEbEQeA7w8vR+3v0wr3JEP82jfNNP8yl39dN8yoP9NHI51QKivVOAzSLi/hFxJ8rFK0cNOaYZiYigtC/8Q2Z+atjxrIzM3DszN87MBZR18rPMnJNHujPzMuDiiHhQ7fQ04NwhhrQyLgK2joi16vb2NOb+hXBHAQvr/wuBI4cYy0qJiB2AdwPPy8wbhh3PPDFvckQ/zad800/zKXf10zzLg/00cjl13jyJetAy89aIeBNwLOXq94My85whhzVTTwReAZwVEWfUbvvUJ7VquN4MHFJ/gPwF2H3I8cxIZv42Io4ATqc0kfkdI/L0zDYi4lBgW+BeEbEE2Bf4KHB4RLyasjN/8fAibG+CedkbWAM4ruQiTsrM1w8tyHlgnuWIfjLfaLrmRR7sp1HMqT6JWpIkSVJrNmGSJEmS1JoFhCRJkqTWLCAkSZIktWYBIUmSJKk1CwhJkiRJrVlASCspIk6IiO27ur01Ir4wyWeuG3xkkqRhM0doPrKAkFbeoZQHATXtUrtLksabOULzjgWEtPKOAJ4TEWsARMQC4D7AGRFxfEScHhFnRcRO3R+MiG0j4ujG+89FxG71/y0i4sSIOC0ijo2IDWdjZiRJfWWO0LxjASGtpMy8CjgZ2KF22gX4NnAj8ILMfAywHfDJ+gj6KUXE6sBngRdl5hbAQcCH+h27JGmwzBGaj1YbdgDSPNE5RX1k/fsqIIAPR8Q2wO3ARsAGwGUtxvcg4GHAcTWfrApc2v+wJUmzwByhecUCQuqP7wOfiojHAGtm5un1NPN6wBaZ+a+IWAzcuetzt7L8mcBO/wDOyczHDzZsSdIsMEdoXrEJk9QHmXkdcALlNHLnwri7AVfUxLAdcL8eH70QeEhErBERdwOeVrufB6wXEY+Hcro6Ih46yHmQJA2GOULzjWcgpP45FPguy+62cQjwg4g4FTgD+GP3BzLz4og4HDgTOB/4Xe1+S0S8CDigJo3VgP8Gzhn4XEiSBsEcoXkjMnPYMUiSJEmaI2zCJEmSJKk1CwhJkiRJrVlASJIkSWrNAkKSJElSaxYQkiRJklqzgJAkSZLUmgWEJEmSpNYsICRJkiS19v8B/YKpYHUXMGwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "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\n",
    "In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as `'capital-gain'` or `'capital-loss'` above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.\n",
    "\n",
    "Run the code cell below to normalize each numerical feature. We will use [`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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",
       "      <td>0</td>\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>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>0.000000</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.273973</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.438356</td>\n",
       "      <td>Private</td>\n",
       "      <td>9th</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153061</td>\n",
       "      <td>Jamaica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.479452</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</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>0.000000</td>\n",
       "      <td>0.448980</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.191781</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.829751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.342466</td>\n",
       "      <td>Private</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>0.742849</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.273973</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.806122</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.178082</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.082192</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.295918</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.205479</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.232877</td>\n",
       "      <td>Private</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.448980</td>\n",
       "      <td>Mexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.109589</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.205479</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.287671</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.356164</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Masters</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.448980</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.315068</td>\n",
       "      <td>Private</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>1.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.602041</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.506849</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.193878</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>9th</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.356164</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.909617</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.575342</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.534247</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.301370</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.806122</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.438356</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.082192</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.520408</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.041096</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.438776</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.383562</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.865279</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.178082</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.068493</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.424658</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>Puerto-Rico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.054795</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Married-AF-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.244898</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.424658</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>0.733333</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.191781</td>\n",
       "      <td>Private</td>\n",
       "      <td>9th</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.493151</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>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.438356</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.109589</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Never-married</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>0.000000</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.547945</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.493151</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.377551</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.369863</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.328767</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.164384</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.109589</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.410959</td>\n",
       "      <td>Private</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.901146</td>\n",
       "      <td>0.602041</td>\n",
       "      <td>Honduras</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.452055</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.551020</td>\n",
       "      <td>United-States</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",
       "5   0.273973            Private         Masters       0.866667   \n",
       "6   0.438356            Private             9th       0.266667   \n",
       "7   0.479452   Self-emp-not-inc         HS-grad       0.533333   \n",
       "8   0.191781            Private         Masters       0.866667   \n",
       "9   0.342466            Private       Bachelors       0.800000   \n",
       "10  0.273973            Private    Some-college       0.600000   \n",
       "11  0.178082          State-gov       Bachelors       0.800000   \n",
       "12  0.082192            Private       Bachelors       0.800000   \n",
       "13  0.205479            Private      Assoc-acdm       0.733333   \n",
       "14  0.232877            Private         7th-8th       0.200000   \n",
       "15  0.109589   Self-emp-not-inc         HS-grad       0.533333   \n",
       "16  0.205479            Private         HS-grad       0.533333   \n",
       "17  0.287671            Private            11th       0.400000   \n",
       "18  0.356164   Self-emp-not-inc         Masters       0.866667   \n",
       "19  0.315068            Private       Doctorate       1.000000   \n",
       "20  0.506849            Private         HS-grad       0.533333   \n",
       "21  0.246575        Federal-gov             9th       0.266667   \n",
       "22  0.356164            Private            11th       0.400000   \n",
       "23  0.575342            Private         HS-grad       0.533333   \n",
       "24  0.534247          Local-gov       Bachelors       0.800000   \n",
       "25  0.027397            Private         HS-grad       0.533333   \n",
       "26  0.301370            Private         HS-grad       0.533333   \n",
       "27  0.438356            Private         HS-grad       0.533333   \n",
       "28  0.082192          Local-gov      Assoc-acdm       0.733333   \n",
       "29  0.041096            Private    Some-college       0.600000   \n",
       "30  0.383562            Private       Bachelors       0.800000   \n",
       "31  0.178082        Federal-gov    Some-college       0.600000   \n",
       "32  0.068493          State-gov    Some-college       0.600000   \n",
       "33  0.424658            Private            11th       0.400000   \n",
       "34  0.054795            Private    Some-college       0.600000   \n",
       "35  0.027397            Private         HS-grad       0.533333   \n",
       "36  0.424658   Self-emp-not-inc      Assoc-acdm       0.733333   \n",
       "37  0.191781            Private             9th       0.266667   \n",
       "38  0.493151   Self-emp-not-inc       Bachelors       0.800000   \n",
       "39  0.095890            Private       Bachelors       0.800000   \n",
       "40  0.438356            Private         HS-grad       0.533333   \n",
       "41  0.109589            Private         HS-grad       0.533333   \n",
       "42  0.547945        Federal-gov       Bachelors       0.800000   \n",
       "43  0.493151            Private         HS-grad       0.533333   \n",
       "44  0.369863            Private         Masters       0.866667   \n",
       "45  0.328767          State-gov       Assoc-voc       0.666667   \n",
       "46  0.164384            Private       Assoc-voc       0.666667   \n",
       "47  0.109589            Private    Some-college       0.600000   \n",
       "48  0.410959            Private     Prof-school       0.933333   \n",
       "49  0.452055        Federal-gov       Bachelors       0.800000   \n",
       "\n",
       "            marital-status          occupation    relationship  \\\n",
       "0            Never-married        Adm-clerical   Not-in-family   \n",
       "1       Married-civ-spouse     Exec-managerial         Husband   \n",
       "2                 Divorced   Handlers-cleaners   Not-in-family   \n",
       "3       Married-civ-spouse   Handlers-cleaners         Husband   \n",
       "4       Married-civ-spouse      Prof-specialty            Wife   \n",
       "5       Married-civ-spouse     Exec-managerial            Wife   \n",
       "6    Married-spouse-absent       Other-service   Not-in-family   \n",
       "7       Married-civ-spouse     Exec-managerial         Husband   \n",
       "8            Never-married      Prof-specialty   Not-in-family   \n",
       "9       Married-civ-spouse     Exec-managerial         Husband   \n",
       "10      Married-civ-spouse     Exec-managerial         Husband   \n",
       "11      Married-civ-spouse      Prof-specialty         Husband   \n",
       "12           Never-married        Adm-clerical       Own-child   \n",
       "13           Never-married               Sales   Not-in-family   \n",
       "14      Married-civ-spouse    Transport-moving         Husband   \n",
       "15           Never-married     Farming-fishing       Own-child   \n",
       "16           Never-married   Machine-op-inspct       Unmarried   \n",
       "17      Married-civ-spouse               Sales         Husband   \n",
       "18                Divorced     Exec-managerial       Unmarried   \n",
       "19      Married-civ-spouse      Prof-specialty         Husband   \n",
       "20               Separated       Other-service       Unmarried   \n",
       "21      Married-civ-spouse     Farming-fishing         Husband   \n",
       "22      Married-civ-spouse    Transport-moving         Husband   \n",
       "23                Divorced        Tech-support       Unmarried   \n",
       "24      Married-civ-spouse        Tech-support         Husband   \n",
       "25           Never-married        Craft-repair       Own-child   \n",
       "26                Divorced     Exec-managerial   Not-in-family   \n",
       "27      Married-civ-spouse        Craft-repair         Husband   \n",
       "28           Never-married     Protective-serv   Not-in-family   \n",
       "29           Never-married               Sales       Own-child   \n",
       "30                Divorced     Exec-managerial       Own-child   \n",
       "31      Married-civ-spouse        Adm-clerical       Own-child   \n",
       "32      Married-civ-spouse       Other-service         Husband   \n",
       "33           Never-married   Machine-op-inspct       Unmarried   \n",
       "34           Never-married   Machine-op-inspct       Own-child   \n",
       "35       Married-AF-spouse        Adm-clerical            Wife   \n",
       "36      Married-civ-spouse      Prof-specialty         Husband   \n",
       "37      Married-civ-spouse   Machine-op-inspct         Husband   \n",
       "38      Married-civ-spouse      Prof-specialty         Husband   \n",
       "39      Married-civ-spouse        Tech-support         Husband   \n",
       "40               Separated        Adm-clerical       Unmarried   \n",
       "41           Never-married   Handlers-cleaners   Not-in-family   \n",
       "42      Married-civ-spouse      Prof-specialty         Husband   \n",
       "43      Married-civ-spouse   Machine-op-inspct         Husband   \n",
       "44                Divorced     Exec-managerial       Unmarried   \n",
       "45      Married-civ-spouse        Craft-repair         Husband   \n",
       "46           Never-married      Prof-specialty   Not-in-family   \n",
       "47      Married-civ-spouse     Exec-managerial            Wife   \n",
       "48      Married-civ-spouse      Prof-specialty            Wife   \n",
       "49                Divorced     Exec-managerial   Not-in-family   \n",
       "\n",
       "                   race      sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0                 White     Male      0.667492      0.000000        0.397959   \n",
       "1                 White     Male      0.000000      0.000000        0.122449   \n",
       "2                 White     Male      0.000000      0.000000        0.397959   \n",
       "3                 Black     Male      0.000000      0.000000        0.397959   \n",
       "4                 Black   Female      0.000000      0.000000        0.397959   \n",
       "5                 White   Female      0.000000      0.000000        0.397959   \n",
       "6                 Black   Female      0.000000      0.000000        0.153061   \n",
       "7                 White     Male      0.000000      0.000000        0.448980   \n",
       "8                 White   Female      0.829751      0.000000        0.500000   \n",
       "9                 White     Male      0.742849      0.000000        0.397959   \n",
       "10                Black     Male      0.000000      0.000000        0.806122   \n",
       "11   Asian-Pac-Islander     Male      0.000000      0.000000        0.397959   \n",
       "12                White   Female      0.000000      0.000000        0.295918   \n",
       "13                Black     Male      0.000000      0.000000        0.500000   \n",
       "14   Amer-Indian-Eskimo     Male      0.000000      0.000000        0.448980   \n",
       "15                White     Male      0.000000      0.000000        0.346939   \n",
       "16                White     Male      0.000000      0.000000        0.397959   \n",
       "17                White     Male      0.000000      0.000000        0.500000   \n",
       "18                White   Female      0.000000      0.000000        0.448980   \n",
       "19                White     Male      0.000000      0.000000        0.602041   \n",
       "20                Black   Female      0.000000      0.000000        0.193878   \n",
       "21                Black     Male      0.000000      0.000000        0.397959   \n",
       "22                White     Male      0.000000      0.909617        0.397959   \n",
       "23                White   Female      0.000000      0.000000        0.397959   \n",
       "24                White     Male      0.000000      0.000000        0.397959   \n",
       "25                White     Male      0.000000      0.000000        0.397959   \n",
       "26                White     Male      0.000000      0.000000        0.806122   \n",
       "27                White     Male      0.000000      0.000000        0.397959   \n",
       "28                White     Male      0.000000      0.000000        0.520408   \n",
       "29                Black     Male      0.000000      0.000000        0.438776   \n",
       "30                White     Male      0.000000      0.865279        0.397959   \n",
       "31                White     Male      0.000000      0.000000        0.397959   \n",
       "32                Black     Male      0.000000      0.000000        0.142857   \n",
       "33                White     Male      0.000000      0.000000        0.397959   \n",
       "34                White     Male      0.000000      0.000000        0.397959   \n",
       "35                White   Female      0.000000      0.000000        0.244898   \n",
       "36                White     Male      0.000000      0.000000        0.397959   \n",
       "37                White     Male      0.000000      0.000000        0.428571   \n",
       "38                White     Male      0.000000      0.000000        0.397959   \n",
       "39                White     Male      0.000000      0.000000        0.500000   \n",
       "40                White   Female      0.000000      0.000000        0.397959   \n",
       "41                White     Male      0.000000      0.000000        0.346939   \n",
       "42                Black     Male      0.000000      0.000000        0.397959   \n",
       "43                White     Male      0.000000      0.000000        0.377551   \n",
       "44                White   Female      0.000000      0.000000        0.397959   \n",
       "45                White     Male      0.000000      0.000000        0.397959   \n",
       "46                White     Male      0.000000      0.000000        0.428571   \n",
       "47                Other   Female      0.000000      0.000000        0.397959   \n",
       "48                White   Female      0.000000      0.901146        0.602041   \n",
       "49                White     Male      0.000000      0.000000        0.551020   \n",
       "\n",
       "    native-country  \n",
       "0    United-States  \n",
       "1    United-States  \n",
       "2    United-States  \n",
       "3    United-States  \n",
       "4             Cuba  \n",
       "5    United-States  \n",
       "6          Jamaica  \n",
       "7    United-States  \n",
       "8    United-States  \n",
       "9    United-States  \n",
       "10   United-States  \n",
       "11           India  \n",
       "12   United-States  \n",
       "13   United-States  \n",
       "14          Mexico  \n",
       "15   United-States  \n",
       "16   United-States  \n",
       "17   United-States  \n",
       "18   United-States  \n",
       "19   United-States  \n",
       "20   United-States  \n",
       "21   United-States  \n",
       "22   United-States  \n",
       "23   United-States  \n",
       "24   United-States  \n",
       "25   United-States  \n",
       "26   United-States  \n",
       "27   United-States  \n",
       "28   United-States  \n",
       "29   United-States  \n",
       "30   United-States  \n",
       "31   United-States  \n",
       "32   United-States  \n",
       "33     Puerto-Rico  \n",
       "34   United-States  \n",
       "35   United-States  \n",
       "36   United-States  \n",
       "37   United-States  \n",
       "38   United-States  \n",
       "39   United-States  \n",
       "40   United-States  \n",
       "41   United-States  \n",
       "42   United-States  \n",
       "43   United-States  \n",
       "44   United-States  \n",
       "45   United-States  \n",
       "46   United-States  \n",
       "47   United-States  \n",
       "48        Honduras  \n",
       "49   United-States  "
      ]
     },
     "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 = 50))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Data Preprocessing\n",
    "\n",
    "From the table in **Exploring the Data** above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called *categorical variables*) be converted. One popular way to convert categorical variables is by using the **one-hot encoding** scheme. One-hot encoding creates a _\"dummy\"_ variable for each possible category of each non-numeric feature. For example, assume `someFeature` has three possible entries: `A`, `B`, or `C`. We then encode this feature into `someFeature_A`, `someFeature_B` and `someFeature_C`.\n",
    "\n",
    "|   | someFeature |                    | someFeature_A | someFeature_B | someFeature_C |\n",
    "| :-: | :-: |                            | :-: | :-: | :-: |\n",
    "| 0 |  B  |  | 0 | 1 | 0 |\n",
    "| 1 |  C  | ----> one-hot encode ----> | 0 | 0 | 1 |\n",
    "| 2 |  A  |  | 1 | 0 | 0 |\n",
    "\n",
    "Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `'income'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label (\"<=50K\" and \">50K\"), we can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively. In code cell below, you will need to implement the following:\n",
    " - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_log_minmax_transform'` data.\n",
    " - Convert the target label `'income_raw'` to numerical entries.\n",
    "   - Set records with \"<=50K\" to `0` and records with \">50K\" to `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
    "# Uncomment the following line to see the encoded feature names\n",
    "print(encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffle and Split Data\n",
    "Now all _categorical variables_ have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing.\n",
    "\n",
    "Run the code cell below to perform this split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 36177 samples.\n",
      "Testing set has 9045 samples.\n"
     ]
    }
   ],
   "source": [
    "# Import train_test_split\n",
    "import numpy as np\n",
    "from sklearn.model_selection 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\n",
    "In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a *naive predictor*."
   ]
  },
  {
   "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.\n",
    "\n",
    "**HINT:** \n",
    "\n",
    "* When we have a model that always predicts '1' (i.e. the individual makes more than 50k) then our model will have no True Negatives(TN) or False Negatives(FN) as we are not making any negative('0' value) predictions. Therefore our Accuracy in this case becomes the same as our Precision(True Positives/(True Positives + False Positives)) as every prediction that we have made with value '1' that should have '0' becomes a False Positive; therefore our denominator in this case is the total number of records we have in total. \n",
    "* Our Recall score(True Positives/(True Positives + False Negatives)) in this setting becomes 1 as we have no False Negatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "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",
    "TP = np.sum(income)\n",
    "FP = income.count() - TP\n",
    "tot = TP + FP\n",
    "\n",
    "# TODO: Calculate accuracy, precision and recall\n",
    "accuracy = TP / tot\n",
    "recall = TP / TP\n",
    "precision = TP / tot # It just like accuracy in this case.\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\n",
    "**The following are some of the supervised learning models that are currently available in** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) **that you may choose from:**\n",
    "- Gaussian Naive Bayes (GaussianNB)\n",
    "- Decision Trees\n",
    "- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)\n",
    "- K-Nearest Neighbors (KNeighbors)\n",
    "- Stochastic Gradient Descent Classifier (SGDC)\n",
    "- Support Vector Machines (SVM)\n",
    "- Logistic Regression"
   ]
  },
  {
   "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?\n",
    "\n",
    "**HINT:**\n",
    "\n",
    "Structure your answer in the same format as above^, with 4 parts for each of the three models you pick. Please include references with your answer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "> **Propoties of our data set**:  \n",
    "Since we have dummified all the categorical features. It creates 98 binary features. This mean it can be either 0 or 1. Another side, we have log-tranform and normanlized all 5 nummerical features. These continuous features will have number between 0 and 1. \n",
    "\n",
    "---\n",
    "1. **RandomForestClassifier**\n",
    "    * **Application**  \n",
    "        * Mining data with random forests: Current options for real-world applications [Article](https://www.researchgate.net/publication/259543750_Mining_data_with_random_forests_Current_options_for_real-world_applications)\n",
    "    * **Pros:**  \n",
    "        * Tent to less overfit than Decision tree.\n",
    "        * Have less variance. It works better than Decision tree for large test data.\n",
    "        * Fast training time.\n",
    "    * **Cons:** \n",
    "        * Tent to overfit data when the data is noisy.\n",
    "        * when used for regression they cannot predict beyond the range in the training data. [Ref](https://blog.citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics)\n",
    "    * **Candidacy:**  \n",
    "        * It is fast even we have 103 features. It works well when a lot features is binary values. It is good idea to use as benchmark.\n",
    "    \n",
    "    \n",
    "2. **Gradient Boosting**\n",
    "    * **Application**  \n",
    "        * Higgs Boson Discovery with Boosted Trees. [Article](http://proceedings.mlr.press/v42/chen14.pdf)\n",
    "    * **Pros:**  \n",
    "        * Often provides predictive accuracy that cannot be beat.\n",
    "        * Lots of flexibility - can optimize on different loss functions and provides several hyperparameter tuning options that make the function fit very flexible.\n",
    "        * No data pre-processing required - often works great with categorical and numerical values as is.\n",
    "        * See. [Ref](http://uc-r.github.io/gbm_regression) \n",
    "        * GB tent to create weaker learner than than Random forest did. It did not suffer overfitting in the same degree as Random forest. \n",
    "    * **Cons:**  \n",
    "        * Computationally expensive - GBMs often require many trees (>1000) which can be time and memory exhaustive.\n",
    "        * The high flexibility results in many parameters that interact and influence heavily the behavior of the approach (number of iterations, tree depth, regularization parameters, etc.). This requires a large grid search during tuning.\n",
    "        * See. [Ref](http://uc-r.github.io/gbm_regression)\n",
    "    * **Candidacy:**  \n",
    "        * Our data set is not too small that GB will give bad predition model and it is not too large that it GM become very computational expansive.\n",
    "\n",
    "\n",
    "3. **SVC**  \n",
    "    * **Application:** \n",
    "        * Image classification [Harvard lecture](https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=92e3adbf-2212-4cff-b1a9-b1bfe72d93bf).\n",
    "    * **Pros:** \n",
    "        * Powerful kernel `poly` and `rbf` to translate data in higher dimension and seperate them that it seems hard to seperate in original dimension.\n",
    "        * Margin error and Classification error were combined together. By changing the parameter `C/gamma` for `poly/rbf` configure bias vs variance.  \n",
    "    * **Cons:**  \n",
    "        * Less effective in noise or large data set. \n",
    "        * Computational expansive in training on large data set.\n",
    "        * Need to some tuning for small data set.\n",
    "    * **Candidacy:**  \n",
    "        * We have 103 features in total after one-hot encoding. 98 of them is binary value and 5 of them lay between 0 and 1. SVC should be able to deal with these high dimensionality. On anothor hand, our data set is not too large. \n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation - Creating a Training and Predicting Pipeline\n",
    "To properly evaluate the performance of each model you've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. Your implementation here will be used in the following section.\n",
    "In the code block below, you will need to implement the following:\n",
    " - Import `fbeta_score` and `accuracy_score` from [`sklearn.metrics`](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics).\n",
    " - Fit the learner to the sampled training data and record the training time.\n",
    " - Perform predictions on the test data `X_test`, and also on the first 300 training points `X_train[:300]`.\n",
    "   - Record the total prediction time.\n",
    " - Calculate the accuracy score for both the training subset and testing set.\n",
    " - Calculate the F-score for both the training subset and testing set.\n",
    "   - Make sure that you set the `beta` parameter!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score\n",
    "from sklearn.metrics import accuracy_score, fbeta_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.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, 0.5)\n",
    "        \n",
    "    # TODO: Compute F-score on the test set which is y_test\n",
    "    results['f_test'] = fbeta_score(y_test, predictions_test, 0.5)\n",
    "       \n",
    "    # Success\n",
    "    print(\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))\n",
    "    print('sum of pred_train is {} and sum of pred_test {} \\n ------'.format(np.sum(predictions_train),\n",
    "                                                                np.sum(predictions_test)))\n",
    "\n",
    "    # Return the results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Initial Model Evaluation\n",
    "In the code cell, you will need to implement the following:\n",
    "- Import the three supervised learning models you've discussed in the previous section.\n",
    "- Initialize the three models and store them in `'clf_A'`, `'clf_B'`, and `'clf_C'`.\n",
    "  - Use a `'random_state'` for each model you use, if provided.\n",
    "  - **Note:** Use the default settings for each model — you will tune one specific model in a later section.\n",
    "- Calculate the number of records equal to 1%, 10%, and 100% of the training data.\n",
    "  - Store those values in `'samples_1'`, `'samples_10'`, and `'samples_100'` respectively.\n",
    "\n",
    "**Note:** Depending on which algorithms you chose, the following implementation may take some time to run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier trained on 361 samples.\n",
      "sum of pred_train is 70 and sum of pred_test 1690 \n",
      " ------\n",
      "RandomForestClassifier trained on 3617 samples.\n",
      "sum of pred_train is 69 and sum of pred_test 1792 \n",
      " ------\n",
      "RandomForestClassifier trained on 36177 samples.\n",
      "sum of pred_train is 65 and sum of pred_test 1917 \n",
      " ------\n",
      "GradientBoostingClassifier trained on 361 samples.\n",
      "sum of pred_train is 54 and sum of pred_test 1794 \n",
      " ------\n",
      "GradientBoostingClassifier trained on 3617 samples.\n",
      "sum of pred_train is 45 and sum of pred_test 1718 \n",
      " ------\n",
      "GradientBoostingClassifier trained on 36177 samples.\n",
      "sum of pred_train is 45 and sum of pred_test 1712 \n",
      " ------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mhyip/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: 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",
      "sum of pred_train is 0 and sum of pred_test 0 \n",
      " ------\n",
      "SVC trained on 3617 samples.\n",
      "sum of pred_train is 36 and sum of pred_test 1337 \n",
      " ------\n",
      "SVC trained on 36177 samples.\n",
      "sum of pred_train is 48 and sum of pred_test 1732 \n",
      " ------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mhyip/Udacity/DSND_Term1/projects/Udacity-Project-Finding-Donors-for-CharityML/visuals.py:119: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n",
      "  pl.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Import the three supervised learning models from sklearn\n",
    "import visuals as vs\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier, GradientBoostingClassifier\n",
    "\n",
    "# Others model to try:\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# TODO: Initialize the three models\n",
    "clf_A = RandomForestClassifier(random_state=1)\n",
    "clf_B = GradientBoostingClassifier(random_state=1)\n",
    "clf_C = SVC()\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. \n",
    "\n",
    "**HINT:**  \n",
    "Look at the graph at the bottom left from the cell above(the visualization created by `vs.evaluate(results, accuracy, fscore)`) and check the F score for the testing set when 100% of the training set is used. Which model has the highest score? Your answer should include discussion of the:\n",
    "* metrics - F score on the testing when 100% of the training data is used, \n",
    "* prediction/training time\n",
    "* the algorithm's suitability for the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "In this project ```low recall``` means that we potensially loss a lot of donor. ```low precision``` means could waste a lot of money by send post to people will not donate. What we want is ```high recall```, ```high precision``` and ```high accuracy```.\n",
    "\n",
    "The best choice will be Gradient boost classifier. It has the highest ```F-score``` and ```accuracy```. The training time is acceptable, and it has also fast prediction time. The algorithm is suitable with our data set since it works much better thant Naive predictor and the low trainging. With this, grid search can be a good idea to find a better hyper-parameter."
   ]
  },
  {
   "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",
    "\n",
    "**HINT:**\n",
    "\n",
    "When explaining your model, if using external resources please include all citations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**  \n",
    "\n",
    "We found that Gradient boosting classifier could be a good choice for us.\n",
    "\n",
    "* **Decision tree**:\n",
    "    * By answering binary questions. We can obtain the answer we want. Try [https://en.akinator.com/](https://en.akinator.com/)\n",
    "    * We can just use this tree to be our model. But this is too naive since single decision tree does not generalize good to the real world data. It creates it's own image for the training data set. The same situation for human is that we just memorize the old exams. It does not work well for new exam usually. To tackle this problem, we need the concept of weak/strong learner.\n",
    "\n",
    "* **Weak learner**:  \n",
    "    * A weak learner is any ML algorithm that give better result than random guessing. In Gradient boosting classifier, it obtain a lot weak learner and each of them is a decision tree to do specifitly good job to identify just few or one feature(s) in our case. OK. That is great, but which should we listen to? Now, we need the concept of boosting.\n",
    "  \n",
    "* **Boosting and strong learner**: \n",
    "    * We give each weak learner a weight, some weak learner are more accurate that anothers. In this case, we should listen to them more.\n",
    "    * During the training, GB is constantly making new tree to compensate the old tree's error. The weight of the tree is created during the training process.\n",
    "    * Lastly, combine all the tree with weights, we have our strong learner. To the end, we will obtain is a probabilty and compare to 0.5 as threshold. We otain our answer."
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  36 out of  36 | elapsed:  6.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8630\n",
      "F-score on testing data: 0.7395\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8713\n",
      "Final F-score on the testing data: 0.7528\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, r2_score, fbeta_score\n",
    "\n",
    "# TODO: Initialize the classifier\n",
    "clf = GradientBoostingClassifier(random_state=1)\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",
    "              'max_depth' : [5, 6, 7],\n",
    "              'n_estimators' :[150, 200],\n",
    "              'min_samples_split' : [3,4]\n",
    "              }\n",
    "\n",
    "# TODO: Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(fbeta_score, beta=0.5)\n",
    "\n",
    "import multiprocessing\n",
    "tot_cpu = int(multiprocessing.cpu_count()/2) # Intel i7 hyper-threading\n",
    "\n",
    "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "grid_obj = GridSearchCV(clf, parameters, scoring=scorer, cv = 3, verbose=True, n_jobs=tot_cpu)\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": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
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       "      <td>0.756723</td>\n",
       "      <td>0.753074</td>\n",
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       "      <td>0.900579</td>\n",
       "      <td>0.087654</td>\n",
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       "      <td>0.754166</td>\n",
       "      <td>0.003680</td>\n",
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       "      <td>0.205067</td>\n",
       "      <td>0.089730</td>\n",
       "      <td>0.016875</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 5, 'min_samples_split': 4, 'n_es...</td>\n",
       "      <td>0.753971</td>\n",
       "      <td>0.750244</td>\n",
       "      <td>0.758677</td>\n",
       "      <td>0.754297</td>\n",
       "      <td>0.003450</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>32.265612</td>\n",
       "      <td>0.265844</td>\n",
       "      <td>0.082466</td>\n",
       "      <td>0.007437</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "      <td>{'max_depth': 6, 'min_samples_split': 3, 'n_es...</td>\n",
       "      <td>0.751902</td>\n",
       "      <td>0.746441</td>\n",
       "      <td>0.757797</td>\n",
       "      <td>0.752047</td>\n",
       "      <td>0.004637</td>\n",
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       "      <td>40.342610</td>\n",
       "      <td>1.307035</td>\n",
       "      <td>0.094634</td>\n",
       "      <td>0.006564</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "      <td>{'max_depth': 6, 'min_samples_split': 3, 'n_es...</td>\n",
       "      <td>0.751744</td>\n",
       "      <td>0.743550</td>\n",
       "      <td>0.755596</td>\n",
       "      <td>0.750297</td>\n",
       "      <td>0.005023</td>\n",
       "      <td>8</td>\n",
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       "      <td>6</td>\n",
       "      <td>32.055108</td>\n",
       "      <td>0.359471</td>\n",
       "      <td>0.077579</td>\n",
       "      <td>0.002298</td>\n",
       "      <td>6</td>\n",
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       "      <td>0.758472</td>\n",
       "      <td>0.752690</td>\n",
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       "      <td>0.750243</td>\n",
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       "      <td>9</td>\n",
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       "      <td>4.388005</td>\n",
       "      <td>0.127186</td>\n",
       "      <td>0.017190</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 7, 'min_samples_split': 3, 'n_es...</td>\n",
       "      <td>0.751359</td>\n",
       "      <td>0.743188</td>\n",
       "      <td>0.748199</td>\n",
       "      <td>0.747582</td>\n",
       "      <td>0.003364</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>53.772238</td>\n",
       "      <td>2.133872</td>\n",
       "      <td>0.118578</td>\n",
       "      <td>0.012119</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>150</td>\n",
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       "      <td>0.750991</td>\n",
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       "      <td>0.749561</td>\n",
       "      <td>0.747364</td>\n",
       "      <td>0.004160</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>56.702065</td>\n",
       "      <td>2.938344</td>\n",
       "      <td>0.084155</td>\n",
       "      <td>0.026171</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
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       "      <td>0.748979</td>\n",
       "      <td>0.739726</td>\n",
       "      <td>0.747966</td>\n",
       "      <td>0.745557</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       24.454504      0.201202         0.068477        0.003257   \n",
       "1       31.682708      0.868242         0.108947        0.025312   \n",
       "2       24.752736      0.900579         0.087654        0.026038   \n",
       "3       29.065701      0.205067         0.089730        0.016875   \n",
       "4       32.265612      0.265844         0.082466        0.007437   \n",
       "5       40.342610      1.307035         0.094634        0.006564   \n",
       "6       32.055108      0.359471         0.077579        0.002298   \n",
       "7       40.268619      0.070650         0.119758        0.022146   \n",
       "8       42.574108      0.893950         0.085910        0.001191   \n",
       "9       61.483633      4.388005         0.127186        0.017190   \n",
       "10      53.772238      2.133872         0.118578        0.012119   \n",
       "11      56.702065      2.938344         0.084155        0.026171   \n",
       "\n",
       "   param_max_depth param_min_samples_split param_n_estimators  \\\n",
       "0                5                       3                150   \n",
       "1                5                       3                200   \n",
       "2                5                       4                150   \n",
       "3                5                       4                200   \n",
       "4                6                       3                150   \n",
       "5                6                       3                200   \n",
       "6                6                       4                150   \n",
       "7                6                       4                200   \n",
       "8                7                       3                150   \n",
       "9                7                       3                200   \n",
       "10               7                       4                150   \n",
       "11               7                       4                200   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'max_depth': 5, 'min_samples_split': 3, 'n_es...           0.754782   \n",
       "1   {'max_depth': 5, 'min_samples_split': 3, 'n_es...           0.754273   \n",
       "2   {'max_depth': 5, 'min_samples_split': 4, 'n_es...           0.756581   \n",
       "3   {'max_depth': 5, 'min_samples_split': 4, 'n_es...           0.753971   \n",
       "4   {'max_depth': 6, 'min_samples_split': 3, 'n_es...           0.751902   \n",
       "5   {'max_depth': 6, 'min_samples_split': 3, 'n_es...           0.751744   \n",
       "6   {'max_depth': 6, 'min_samples_split': 4, 'n_es...           0.750771   \n",
       "7   {'max_depth': 6, 'min_samples_split': 4, 'n_es...           0.750694   \n",
       "8   {'max_depth': 7, 'min_samples_split': 3, 'n_es...           0.751742   \n",
       "9   {'max_depth': 7, 'min_samples_split': 3, 'n_es...           0.751359   \n",
       "10  {'max_depth': 7, 'min_samples_split': 4, 'n_es...           0.750991   \n",
       "11  {'max_depth': 7, 'min_samples_split': 4, 'n_es...           0.748979   \n",
       "\n",
       "    split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
       "0            0.747548           0.756683         0.753005        0.003936   \n",
       "1            0.748226           0.756723         0.753074        0.003571   \n",
       "2            0.748967           0.756950         0.754166        0.003680   \n",
       "3            0.750244           0.758677         0.754297        0.003450   \n",
       "4            0.746441           0.757797         0.752047        0.004637   \n",
       "5            0.743550           0.755596         0.750297        0.005023   \n",
       "6            0.748826           0.758472         0.752690        0.004165   \n",
       "7            0.748977           0.755470         0.751714        0.002747   \n",
       "8            0.746818           0.752168         0.750243        0.002428   \n",
       "9            0.743188           0.748199         0.747582        0.003364   \n",
       "10           0.741540           0.749561         0.747364        0.004160   \n",
       "11           0.739726           0.747966         0.745557        0.004144   \n",
       "\n",
       "    rank_test_score  \n",
       "0                 4  \n",
       "1                 3  \n",
       "2                 2  \n",
       "3                 1  \n",
       "4                 6  \n",
       "5                 8  \n",
       "6                 5  \n",
       "7                 7  \n",
       "8                 9  \n",
       "9                10  \n",
       "10               11  \n",
       "11               12  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid_fit.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 24.0, 'Predicted')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A Confusion Matrix for the best classifier (This cell is updated after reviewer suggestion.)\n",
    "import seaborn as sns # Install using 'pip install seaborn'\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "cm_test = confusion_matrix(y_test, best_clf.predict(X_test))\n",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "sns.heatmap(cm_test, annot=True, cmap='Greys', xticklabels=['No', 'Yes'], yticklabels=['No', 'Yes'])\n",
    "plt.title('Confusion Matrix for the Test Set')\n",
    "plt.ylabel('True')\n",
    "plt.xlabel('Predicted')"
   ]
  },
  {
   "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     | Naive predictor |Unoptimized Model | Optimized Model |\n",
    "| :------------: |:---------------: |:---------------: | :-------------: | \n",
    "| Accuracy Score | 0.25 |          0.86        |      0.87           |\n",
    "| F-score        | 0.2917|           0.7395       |   0.7528       |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**  \n",
    "* **What is your optimized model's accuracy and F-score on the testing data?**  \n",
    "    * Optimized model's has accuracy 0.87 and F-score 0.7528  \n",
    "\n",
    "* **Are these scores better or worse than the unoptimized model?**\n",
    "    * Better\n",
    "    \n",
    "* **How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in Question 1?**\n",
    "    * It has more than triple accuracy score and double F-score than the naive predictor."
   ]
  },
  {
   "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": [
    "**Answer:**  \n",
    "\n",
    "age - Elderly people tent to donate more in my personal experience.\n",
    "\n",
    "capital-gain - profit for an indiviual income.\n",
    "\n",
    "capital-loss - money loss by an individual.\n",
    "\n",
    "education-num - Usually, the higher the education one has, the higher the income one has.\n",
    "\n",
    "occupation - income depends on the type of occupation.\n"
   ]
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Import a supervised learning model that has 'feature_importances_'\n",
    "\n",
    "\n",
    "# TODO: Train the supervised model on the training set using .fit(X_train, y_train)\n",
    "model = GradientBoostingClassifier().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": [
    "#### some hint form reviewer\n",
    "An alternative feature selection approach consists in leveraging the power of Recursive Feature Selection to automate the selection process and find a good indication of the number of relevant features (it is not suitable for this problem because that is not what is required by the project rubric, though it is generally a very good approach)."
   ]
  },
  {
   "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",
    "\n",
    "Supringly, it did not depends on occupation, but marital status. I guess people who are married are feel more secure and they may not much money, derfore they are more willing to donate. However, this is just my guess. Capital-loss is more important than capital-gain. Age is the most important, however. It dose not mean my hypothese is right. It could be that we have too many young or tenage in our data set. It can be easily verify. But we do not do here."
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8713\n",
      "F-score on testing data: 0.7528\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8589\n",
      "F-score on testing data: 0.7246\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",
    "\n",
    "No bad. It is quite decent model. Only has tree precentage different in F-score. Imagine that we have a super large data set, training time would be a factor. This this case, We could only keep these most important features and train the model must faster. Keep it in mind that more data means better model usually."
   ]
  },
  {
   "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": []
  }
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