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": "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>9th</td>\n",
       "      <td>5.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</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>45.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14.0</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>14084.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</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>5178.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age          workclass education_level  education-num  \\\n",
       "0   39          State-gov       Bachelors           13.0   \n",
       "1   50   Self-emp-not-inc       Bachelors           13.0   \n",
       "2   38            Private         HS-grad            9.0   \n",
       "3   53            Private            11th            7.0   \n",
       "4   28            Private       Bachelors           13.0   \n",
       "5   37            Private         Masters           14.0   \n",
       "6   49            Private             9th            5.0   \n",
       "7   52   Self-emp-not-inc         HS-grad            9.0   \n",
       "8   31            Private         Masters           14.0   \n",
       "9   42            Private       Bachelors           13.0   \n",
       "\n",
       "           marital-status          occupation    relationship    race  \\\n",
       "0           Never-married        Adm-clerical   Not-in-family   White   \n",
       "1      Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "2                Divorced   Handlers-cleaners   Not-in-family   White   \n",
       "3      Married-civ-spouse   Handlers-cleaners         Husband   Black   \n",
       "4      Married-civ-spouse      Prof-specialty            Wife   Black   \n",
       "5      Married-civ-spouse     Exec-managerial            Wife   White   \n",
       "6   Married-spouse-absent       Other-service   Not-in-family   Black   \n",
       "7      Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "8           Never-married      Prof-specialty   Not-in-family   White   \n",
       "9      Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "\n",
       "       sex  capital-gain  capital-loss  hours-per-week  native-country income  \n",
       "0     Male        2174.0           0.0            40.0   United-States  <=50K  \n",
       "1     Male           0.0           0.0            13.0   United-States  <=50K  \n",
       "2     Male           0.0           0.0            40.0   United-States  <=50K  \n",
       "3     Male           0.0           0.0            40.0   United-States  <=50K  \n",
       "4   Female           0.0           0.0            40.0            Cuba  <=50K  \n",
       "5   Female           0.0           0.0            40.0   United-States  <=50K  \n",
       "6   Female           0.0           0.0            16.0         Jamaica  <=50K  \n",
       "7     Male           0.0           0.0            45.0   United-States   >50K  \n",
       "8   Female       14084.0           0.0            50.0   United-States   >50K  \n",
       "9     Male        5178.0           0.0            40.0   United-States   >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(n=10))"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 45222\n",
      "Individuals making more than $50,000: 11208\n",
      "Individuals making at most $50,000: 34014\n",
      "Percentage of individuals making more than $50,000: 24.78439697492371%\n"
     ]
    }
   ],
   "source": [
    "# TODO: Total number of records\n",
    "n_records = data.shape[0]\n",
    "\n",
    "# TODO: Number of records where individual's income is more than $50,000\n",
    "n_greater_50k = 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 = np.sum(data['income'] == '>50K')*100/n_records\n",
    "\n",
    "# Print the results\n",
    "print(\"Total number of records: {}\".format(n_records))\n",
    "print(\"Individuals making more than $50,000: {}\".format(n_greater_50k))\n",
    "print(\"Individuals making at most $50,000: {}\".format(n_at_most_50k))\n",
    "print(\"Percentage of individuals making more than $50,000: {}%\".format(greater_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Featureset Exploration **\n",
    "\n",
    "* **age**: continuous. \n",
    "* **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. \n",
    "* **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. \n",
    "* **education-num**: continuous. \n",
    "* **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. \n",
    "* **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. \n",
    "* **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. \n",
    "* **race**: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other. \n",
    "* **sex**: Female, Male. \n",
    "* **capital-gain**: continuous. \n",
    "* **capital-loss**: continuous. \n",
    "* **hours-per-week**: continuous. \n",
    "* **native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Preparing the Data\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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9902b0b898>"
      ]
     },
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f990293aa20>"
      ]
     },
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.301370</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.667492</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.452055</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.287671</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493151</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.150685</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age          workclass education_level  education-num  \\\n",
       "0  0.301370          State-gov       Bachelors       0.800000   \n",
       "1  0.452055   Self-emp-not-inc       Bachelors       0.800000   \n",
       "2  0.287671            Private         HS-grad       0.533333   \n",
       "3  0.493151            Private            11th       0.400000   \n",
       "4  0.150685            Private       Bachelors       0.800000   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  \n",
       "0      0.667492           0.0        0.397959   United-States  \n",
       "1      0.000000           0.0        0.122449   United-States  \n",
       "2      0.000000           0.0        0.397959   United-States  \n",
       "3      0.000000           0.0        0.397959   United-States  \n",
       "4      0.000000           0.0        0.397959            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler() # default=(0, 1)\n",
    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "\n",
    "features_log_minmax_transform = pd.DataFrame(data = features_log_transformed)\n",
    "features_log_minmax_transform[numerical] = scaler.fit_transform(features_log_transformed[numerical])\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_log_minmax_transform.head(n = 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 9,
   "metadata": {
    "scrolled": true
   },
   "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",
      "        age  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
      "0  0.301370       0.800000      0.667492           0.0        0.397959   \n",
      "1  0.452055       0.800000      0.000000           0.0        0.122449   \n",
      "2  0.287671       0.533333      0.000000           0.0        0.397959   \n",
      "3  0.493151       0.400000      0.000000           0.0        0.397959   \n",
      "4  0.150685       0.800000      0.000000           0.0        0.397959   \n",
      "\n",
      "   workclass_ Federal-gov  workclass_ Local-gov  workclass_ Private  \\\n",
      "0                       0                     0                   0   \n",
      "1                       0                     0                   0   \n",
      "2                       0                     0                   1   \n",
      "3                       0                     0                   1   \n",
      "4                       0                     0                   1   \n",
      "\n",
      "   workclass_ Self-emp-inc  workclass_ Self-emp-not-inc  \\\n",
      "0                        0                            0   \n",
      "1                        0                            1   \n",
      "2                        0                            0   \n",
      "3                        0                            0   \n",
      "4                        0                            0   \n",
      "\n",
      "              ...              native-country_ Portugal  \\\n",
      "0             ...                                     0   \n",
      "1             ...                                     0   \n",
      "2             ...                                     0   \n",
      "3             ...                                     0   \n",
      "4             ...                                     0   \n",
      "\n",
      "   native-country_ Puerto-Rico  native-country_ Scotland  \\\n",
      "0                            0                         0   \n",
      "1                            0                         0   \n",
      "2                            0                         0   \n",
      "3                            0                         0   \n",
      "4                            0                         0   \n",
      "\n",
      "   native-country_ South  native-country_ Taiwan  native-country_ Thailand  \\\n",
      "0                      0                       0                         0   \n",
      "1                      0                       0                         0   \n",
      "2                      0                       0                         0   \n",
      "3                      0                       0                         0   \n",
      "4                      0                       0                         0   \n",
      "\n",
      "   native-country_ Trinadad&Tobago  native-country_ United-States  \\\n",
      "0                                0                              1   \n",
      "1                                0                              1   \n",
      "2                                0                              1   \n",
      "3                                0                              1   \n",
      "4                                0                              0   \n",
      "\n",
      "   native-country_ Vietnam  native-country_ Yugoslavia  \n",
      "0                        0                           0  \n",
      "1                        0                           0  \n",
      "2                        0                           0  \n",
      "3                        0                           0  \n",
      "4                        0                           0  \n",
      "\n",
      "[5 rows x 103 columns] 0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "Name: income, dtype: int64\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.replace({'<=50K':0,'>50K':1})\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)\n",
    "print(features_final.head(),income.head())"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 36177 samples.\n",
      "Testing set has 9045 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Import train_test_split\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features_final, \n",
    "                                                    income, \n",
    "                                                    test_size = 0.2, \n",
    "                                                    random_state = 0)\n",
    "\n",
    "# Show the results of the split\n",
    "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print(\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Evaluating Model Performance\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": 11,
   "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",
    "# TODO: Calculate accuracy, precision and recall\n",
    "TP = np.sum(income)\n",
    "FP = income.shape[0] - TP\n",
    "TN = 0\n",
    "FN = 0\n",
    "beta = 0.5\n",
    "#print(TP)\n",
    "\n",
    "accuracy = (TP + TN)/income.shape[0]\n",
    "recall = TP/(TP + FN)\n",
    "precision = TP/(TP + FP)\n",
    "\n",
    "# TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n",
    "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",
    "1. Logistic Regression:\n",
    "\n",
    "    1. Calculate the probability that someone will vote for one of two parties.\n",
    "    2. It is a widely used technique because it is very efficient, does not require too many computational resources, it’s highly interpretable, it doesn’t require input features to be scaled, it doesn’t require any tuning, it’s easy to regularize, and it outputs well-calibrated predicted probabilities. Another advantage of Logistic Regression is that it is incredibly easy to implement and very efficient to train.\n",
    "    Source(https://machinelearning-blog.com/2018/04/23/logistic-regression-101/)\n",
    "    3. A disadvantage of it is that we can’t solve non-linear problems with logistic regression since it’s decision surface is linear.Logistic Regression is also not one of the most powerful algorithms out there and can be easily outperformed by more complex ones. Another disadvantage is its high reliance on a proper presentation of your data. This means that logistic regression is not a useful tool unless you have already identified all the important independent variables. It is also an Algorithm that is known for its vulnerability to overfitting.\n",
    "    Source(https://machinelearning-blog.com/2018/04/23/logistic-regression-101/)\n",
    "    4. Logistic Regression is the most basic classification algorithms. It can serve as a good starting point or benchmark for the outperformance of more complex models.\n",
    "\n",
    "\n",
    "2. AdaBoost:\n",
    "\n",
    "    1.  One of the biggest applications of AdaBoost is the Viola-Jones face detector, which seems to be the standard algorithm for detecting faces in an image.\n",
    "    2. AdaBoost is one of the best out of the box classification algorithms. The combination of weak learners can also help identify non-linear relationships. It usually has high precision.\n",
    "    3. The most obvious drawback of AdaBoost is the computational complexity.Another major criticism for Adaboost comes from its sensitivity to noisy data and hence its tendency to overfit.\n",
    "    Source(https://codesachin.wordpress.com/tag/adaboost/)\n",
    "    4. AdaBoost was chosen for this problem in order for the 'weak models' to look at patterns between individual features and the target feature as there could be correlations in individual features.\n",
    "\n",
    "\n",
    "3. SVM:\n",
    "\n",
    "    1. Text and hypertext categorization\n",
    "    2. SVM works relatively well when there is clear margin of separation between classes. SVM is more effective in high dimensional spaces. SVM is effective in cases where number of dimensions is greater than the number of samples. SVM is relatively memory efficient\n",
    "    Source(https://medium.com/@dhiraj8899/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107)\n",
    "    3. SVM algorithm is not suitable for large data sets. SVM does not perform very well, when the data set has more noise i.e. target classes are overlapping. In cases where number of features for each data point exceeds the number of training data sample, the SVM will under perform.\n",
    "    4. SVM is being used as the dataset is high-dimensional. SVM also is more memory efficient."
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import 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 = 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'] = np.sum(y_train[:300] == predictions_train)/y_train[:300].shape[0]\n",
    "        \n",
    "    # TODO: Compute accuracy on test set using accuracy_score()\n",
    "    results['acc_test'] = accuracy_score(y_test, predictions_test)\n",
    "    \n",
    "    # TODO: Compute F-score on the the first 300 training samples using fbeta_score()\n",
    "    results['f_train'] = fbeta_score(y_train[:300], predictions_train, beta = 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, beta = 0.5)\n",
    "       \n",
    "    # Success\n",
    "    print(\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))   \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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression trained on 361 samples.\n",
      "LogisticRegression trained on 3617 samples.\n",
      "LogisticRegression trained on 36177 samples.\n",
      "AdaBoostClassifier trained on 361 samples.\n",
      "AdaBoostClassifier trained on 3617 samples.\n",
      "AdaBoostClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC trained on 361 samples.\n",
      "SVC trained on 3617 samples.\n",
      "SVC trained on 36177 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f98ff9ba828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Import the three supervised learning models from sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# TODO: Initialize the three models\n",
    "clf_A = LogisticRegression()\n",
    "clf_B = AdaBoostClassifier()\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 = y_train.shape[0]\n",
    "samples_10 = y_train.shape[0]//10\n",
    "samples_1 = y_train.shape[0]//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",
    "\n",
    "# Run metrics visualization for the three supervised learning models chosen\n",
    "vs.evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>LogisticRegression</th>\n",
       "      <th>AdaBoost</th>\n",
       "      <th>SVC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>acc_test</th>\n",
       "      <td>0.841902</td>\n",
       "      <td>0.857601</td>\n",
       "      <td>0.837148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_train</th>\n",
       "      <td>0.846667</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.853333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_test</th>\n",
       "      <td>0.683165</td>\n",
       "      <td>0.724551</td>\n",
       "      <td>0.674477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_train</th>\n",
       "      <td>0.698529</td>\n",
       "      <td>0.711538</td>\n",
       "      <td>0.719697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.002663</td>\n",
       "      <td>0.076523</td>\n",
       "      <td>23.528144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>0.365732</td>\n",
       "      <td>2.136495</td>\n",
       "      <td>141.231073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LogisticRegression  AdaBoost         SVC\n",
       "acc_test              0.841902  0.857601    0.837148\n",
       "acc_train             0.846667  0.850000    0.853333\n",
       "f_test                0.683165  0.724551    0.674477\n",
       "f_train               0.698529  0.711538    0.719697\n",
       "pred_time             0.002663  0.076523   23.528144\n",
       "train_time            0.365732  2.136495  141.231073"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'LogisticRegression': results['LogisticRegression'][2],'AdaBoost': results['AdaBoostClassifier'][2], 'SVC': results['SVC'][2]})"
   ]
  },
  {
   "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",
    "AdaBoost received the highest F-score and accuracy for our testing set after sampling 100% of our training dataset. It achieved an accuracy of 85.76% and an F-Score of 72.45%. Compared to our baseline accuracy of 24.78%, F-score of 29.17% from our naive predictor AdaBoost does significantly better prior to tuning. \n",
    "\n",
    "AdaBoost was also relatively time efficient, going through the entire training dataset in 2.03 seconds, performing much better than the SVM model at 112.88 seconds but slightly worse compared logistic model at 0.35 seconds. The simpler logistic model did remarkably well and can be favoured when computing time is an important factor. \n",
    "\n",
    "Having said that, considering the performance and time efficiency, AdaBoost is the best model for us to identify potential donors.\n",
    "\n",
    "I think AdaBoost is a good model for our data as our data doesn't have too many outliers and the features seem to have low variance, which are the strengths of AdaBoost. If we choose the correct weak learners for this Ensemble, our model could benefit from the best of both worlds (as an ensemble) and have low variance (low overfitting) while also preventing over-generalization (lower bias). Lastly, the relative higher-dimensionality of our data indicates that AdaBoost would be a good model since it deals with that issue well."
   ]
  },
  {
   "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."
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** \n",
    "\n",
    "AdaBoost is a machine learning model that combines weak learners (simple algorithms) to form a strong rule to classify our dataset.\n",
    "\n",
    "Here the simple algorithms or weak learners are decision trees, which are tree-like models that classify the data based on those features that best split the data. In our dataset this could be any feature of the person such as age, sex or race.\n",
    "\n",
    "The AdaBoost model combines the predictive power of these weak learners. Consider the simple example below, where our weak learners are D1, D2 and D3.\n",
    "![image.png](attachment:image.png)\n",
    "Source (https://towardsdatascience.com/understanding-adaboost-for-decision-tree-ff8f07d2851)\n",
    "\n",
    "\n",
    "1: The first learner (D1) takes all the data points and assigns equal weight or attention to each observation. It then uses the features provided to predict the outcome of the data points. For example D1 could try to predict whether the person's income is >50k based on their age and sex.\n",
    "\n",
    "2: If there are any data points that the first learner L1 predicted incorrectly, then we pay higher attention to these observations by penalizing the next learner L2 for inaccurately classifying them again. Then, we apply D2. For example it could decide whether the person's income is >50k based on their marital status and education level.\n",
    "\n",
    "3: We keep repeating the above process until we have reached a specific number of weak learners L(n).\n",
    "\n",
    "Finally, we combines the outputs from all the weak learners by asking the question 'Which class do you think this data point belongs?' for all the data points. In our case all the weak learners vote on whether they think the data points have an income >50k or <=50k. The aggregate decision is the decision of our AdaBoost model. This process creates a strong learner which eventually has good predictive power as shown by the evalutaion metrics above.\n",
    "\n",
    "Using this single strong prediction rule, the model can now look at new data and provide a good prediction of whether the person has an income greater or less than $50,000."
   ]
  },
  {
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8576\n",
      "F-score on testing data: 0.7246\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8663\n",
      "Final F-score on the testing data: 0.7425\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
    "from  sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "# TODO: Initialize the classifier\n",
    "clf = AdaBoostClassifier(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_estimators':[10,50,100,300],\n",
    "              'learning_rate':[0.1,1,10]}\n",
    "              \n",
    "# TODO: Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(fbeta_score, beta=0.5)\n",
    "\n",
    "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "grid_obj = GridSearchCV(clf, parameters, scoring=scorer)\n",
    "\n",
    "# TODO: Fit the grid search object to the training data and find the optimal parameters using fit()\n",
    "grid_fit = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Get the estimator\n",
    "best_clf = grid_fit.best_estimator_\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "\n",
    "# Report the before-and-afterscores\n",
    "print(\"Unoptimized model\\n------\")\n",
    "print(\"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5)))\n",
    "print(\"\\nOptimized Model\\n------\")\n",
    "print(\"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print(\"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5 - Final Model Evaluation\n",
    "\n",
    "* What is your optimized model's accuracy and F-score on the testing data? \n",
    "* Are these scores better or worse than the unoptimized model? \n",
    "* How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in **Question 1**?_  \n",
    "\n",
    "**Note:** Fill in the table below with your results, and then provide discussion in the **Answer** box."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results:\n",
    "\n",
    "|     Metric     | Unoptimized Model | Optimized Model |\n",
    "| :------------: | :---------------: | :-------------: | \n",
    "| Accuracy Score |  0.8576           |  0.8663         |\n",
    "| F-score        |  0.7246           |  0.7425         |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: **\n",
    "\n",
    "The accuracy score and F-score for the optimized model are better than the unoptimized model. The optimized model's accuracy score is 86.63% whereas the f-score is 74.25%.\n",
    "\n",
    "In comparison to the naive predictor benchmark, that had an accuracy score and f-score of 24.78% an 29.17% respectively, the final model is significantly better."
   ]
  },
  {
   "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",
    "1. race: Race has been a major cause of income disparity in the US and is correlated with other factors which predict income.\n",
    "\n",
    "\n",
    "2. education/education_num: Highly qualified workers tend to have a higher income.\n",
    "\n",
    "\n",
    "3. workclass: The salaries of those who are out of work or never worked is likley to be very low and should have good predictive power.\n",
    "\n",
    "\n",
    "4. age: Older people in the workforce tend to be more experienced and earn more.\n",
    "\n",
    "\n",
    "5. native-country: Immigrants and converted citizens tend to be selected by the authorities to have a minimum criteria of being able to support themselves. They may have a higher income compared to native citizens."
   ]
  },
  {
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c4a790ac8>"
      ]
     },
     "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 = clf_B.fit(X_train, y_train)\n",
    "\n",
    "# TODO: Extract the feature importances using .feature_importances_ \n",
    "importances = model.feature_importances_\n",
    "\n",
    "# Plot\n",
    "vs.feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7 - Extracting Feature Importance\n",
    "\n",
    "Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \\$50,000.  \n",
    "* How do these five features compare to the five features you discussed in **Question 6**?\n",
    "* If you were close to the same answer, how does this visualization confirm your thoughts? \n",
    "* If you were not close, why do you think these features are more relevant?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "1. Two of my hypothesis were correct while three were wrong.\n",
    "\n",
    "2. 1,3 - Capital loss and capital gain are strong factors. This may indicate that only those who have a high income are even capable of making investments and reporting capital gains and losses.\n",
    "\n",
    "   2 - Age was expected but the weight is stronger than expected.\n",
    "   \n",
    "   4 - Hours-per-week is slightly unexpected. I had initially hypothesized that workclass would be a better predictor of low income levels. But it seems that hours-per-week may be better. \n",
    "   \n",
    "   5 - Education was predicted but the weight is weaker than expected."
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8663\n",
      "F-score on testing data: 0.7425\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8404\n",
      "F-score on testing data: 0.6973\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",
    "The final model's F-score on the selected features was 0.6973 in comparison to 0.7425 using the complete data. The final model's accuracy score on the reduced data was 0.8663 compared to 0.8404 using the full data. The F-score has reduced significantly (~5%) while the fall in accuracy is relatively small (~2.5%).\n",
    "\n",
    "Training time is not much of a factor because AdaBoost is one of the faster ensemble methods. However, if I was training a SVM or using the same model that had much higher number of features or/and data points, which showed very high training and testing times, I would consider using the most important features."
   ]
  },
  {
   "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."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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