ml-charity/finding_donors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Engineer Nanodegree\n",
    "## Supervised Learning\n",
    "## Project: Finding Donors for *CharityML*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the second project of the Machine Learning Engineer Nanodegree! 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.\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": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "  </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",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male        2174.0           0.0   \n",
       "\n",
       "   hours-per-week  native-country income  \n",
       "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=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "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": [
    "# Total number of records\n",
    "n_records = len(data)\n",
    "\n",
    "# Number of records where individual's income is more than $50,000\n",
    "n_greater_50k = len(data[data.income == '>50K'])\n",
    "\n",
    "# Number of records where individual's income is at most $50,000\n",
    "n_at_most_50k = len(data[data.income == '<=50K'])\n",
    "\n",
    "# Percentage of individuals whose income is more than $50,000\n",
    "greater_percent = float(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": [
    "** 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": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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WUo5N8nnqleQPUVsubqbevPZE4A1l+ShPP6COgvIYljebQw0aXgX8rnTGv5/Bug+kjkDy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\n2gQEAADQJiAAAIA2AQEAALQJCAAAoO0Ga7UEP7pgqRcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107dd1f50>"
      ]
     },
     "metadata": {},
     "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": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CibbVdOff1H3BZGKXUM0CszA/9KOcZs48o/a134XSBWsx8JuIeEYtNv4J2I7S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\nlt95AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120a0450>"
      ]
     },
     "metadata": {},
     "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": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.301370</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.667492</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.452055</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.287671</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493151</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.150685</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age          workclass education_level  education-num  \\\n",
       "0  0.301370          State-gov       Bachelors       0.800000   \n",
       "1  0.452055   Self-emp-not-inc       Bachelors       0.800000   \n",
       "2  0.287671            Private         HS-grad       0.533333   \n",
       "3  0.493151            Private            11th       0.400000   \n",
       "4  0.150685            Private       Bachelors       0.800000   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  \n",
       "0      0.667492           0.0        0.397959   United-States  \n",
       "1      0.000000           0.0        0.122449   United-States  \n",
       "2      0.000000           0.0        0.397959   United-States  \n",
       "3      0.000000           0.0        0.397959   United-States  \n",
       "4      0.000000           0.0        0.397959            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler() # default=(0, 1)\n",
    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "\n",
    "features_log_minmax_transform = pd.DataFrame(data = features_log_transformed)\n",
    "features_log_minmax_transform[numerical] = scaler.fit_transform(features_log_transformed[numerical])\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_log_minmax_transform.head(n = 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing\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_raw'` 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": 7,
   "metadata": {
    "collapsed": false,
    "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"
     ]
    }
   ],
   "source": [
    "# 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",
    "# Encode the 'income_raw' data to numerical values\n",
    "income = pd.Series([1 if i == '>50K' else 0 for i in income_raw])\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": 8,
   "metadata": {
    "collapsed": false
   },
   "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",
    "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",
    "\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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.247843969749\n",
      "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",
    "Recall = [True Positives/(True Positives + False Negatives)]\n",
    "Precision = [True Positives/(True Positives + False Positives)]\n",
    "'''\n",
    "TP = float(np.sum(income))\n",
    "FP = income.count() - TP\n",
    "# Calculate accuracy, precision and recall\n",
    "accuracy = n_greater_50k/float(n_records)\n",
    "recall = TP / (TP + 0)\n",
    "precision = TP / (TP + FP)\n",
    "\n",
    "print recall\n",
    "print precision\n",
    "\n",
    "# Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n",
    "fscore = (1 + 0.5**2) * (precision * recall) / ((0.5**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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: **\n",
    "\n",
    "Decision Tree\n",
    "* Decision Trees have been used in many industries but the medical field is an excellent use case for them, particularly with the diagnosis of different diseases. For example, the features of the model could be the different symptoms of a particular disease. An example can be found <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2263124/\">here</a>\n",
    "* Some of the strengths of decisions trees is that they can handle both numerical and categorical data. Running a prediction on a trained model is also relatively low cost as it is logarithmic to the number of data points used to train the tree. It can perform well even if its assumptions are somewhat violated by the true model. <a href=\"http://scikit-learn.org/stable/modules/tree.html\">Reference</a>\n",
    "* Decision trees can be prone to overfitting when an overly complex tree is generated. They also struggle with XOR problems. <a href=\"http://scikit-learn.org/stable/modules/tree.html\">Reference</a>\n",
    "* Decision trees might be a good model for our data because it has both numerical and categorical data. Since a tree can be visualized it will also make it easy to see which features are most important to the model.\n",
    "\n",
    "Gradient Boosting\n",
    "* Gradient Boosting is used by several search engines to rank search results. The model attempts to rank new data similarly to how data was ranked in the training data. <a href=\"https://en.wikipedia.org/wiki/Gradient_boosting\">Reference</a>\n",
    "* Gradient Boosting works well with both numerical and categorical data. It also can handle outliers in the data well.<a href=\"http://scikit-learn.org/stable/modules/ensemble.html#gradient-boosting\">Reference</a>\n",
    "* Gradient Boosting does not scale well because it use multiple models that must be run in sequentially so you can't take advantage of parallelization. <a href=\"http://scikit-learn.org/stable/modules/ensemble.html#gradient-boosting\">Reference</a>\n",
    "* Gradient Boosting might be a good fit because we have both numerical and categorical data. It could also be a good fit based on the number of features we are using as well.\n",
    "\n",
    "Support Vector Machine\n",
    "* Support Vector Machine have also been used extensively in the medical industry for disease diagnosis. An example of a support vector machine for predicting diabetes can be found <a href=\"https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-10-16\">here</a>\n",
    "* Support Vector Machines support data with high dimensionality and due to the kernel trick can support non-linear transformations. <a href=\"http://scikit-learn.org/stable/modules/svm.html\">Reference</a>\n",
    "* SVM can't provide probability estimates easily and they are also somewhat of a blackbox so it can be difficult to determine how the model arrived at it's prediction. It can also be somewhat difficult to select the appropriate kernel function. <a href=\"http://scikit-learn.org/stable/modules/svm.html\">Reference</a>\n",
    "* I believe a SVM might be a good choice for this project because of the number of features and I think we might have some non-linear relationship among the features that a SVM might help model correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import two metrics from sklearn - fbeta_score and accuracy_score\n",
    "from sklearn.metrics import accuracy_score, fbeta_score\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",
    "    # 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",
    "    # Calculate the training time\n",
    "    results['train_time'] = end - start\n",
    "        \n",
    "    # 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",
    "    # Calculate the total prediction time\n",
    "    results['pred_time'] = end - start\n",
    "            \n",
    "    # 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",
    "    # Compute accuracy on test set using accuracy_score()\n",
    "    results['acc_test'] = accuracy_score(y_test, predictions_test)\n",
    "    \n",
    "    # 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",
    "    # 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",
    "        \n",
    "    # Return the results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier trained on 361 samples.\n",
      "DecisionTreeClassifier trained on 3617 samples.\n",
      "DecisionTreeClassifier trained on 36177 samples.\n",
      "GradientBoostingClassifier trained on 361 samples.\n",
      "GradientBoostingClassifier trained on 3617 samples.\n",
      "GradientBoostingClassifier trained on 36177 samples.\n",
      "SVC trained on 361 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/samuel.burden/anaconda/envs/ml-udacity/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: 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 3617 samples.\n",
      "SVC trained on 36177 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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LRB+rOCsCeFYI0UstcQ8GsImI7CBtb29AzoC1gBwczjY3MTUw6lJImDtE9D6A/xKRN+RL\n7LaSqzXkhsrVKngU5HGPWyBn3q4UsJfBFAXmARE9D7nRciPkzLsz5ElZSZAveLMpI3XoQfkKUmnd\nQUSfQm42tYOccX4BcsNmKoA5kKcE7SaiOZADT2fIwVUrIUR4AWksIqJykDP5xyA3nzeBPNHnHOQG\nawghrhLRLgBjiegG5KxvLwBVTMR7DfK7MRMhTVlGK5n+o+Iryvq+EvJEm0kA/qdmek1CRItwt05d\nB1AN0vRpm5LNrHYhhDhFRKsBTFaDzT8hZ6Y7mim3xrNEZLgn4bYQYruZeXQCUsGbSkQ5kIrEcAtl\nKAkiIevBBiIaD7nZ/U3IQywAOTA3GyHEaUgzrYLCHCOiNQAmqlXNvZD7HT4EsEYIoe3xWA55EtcG\nIhoHWS8GQu6F0cd3X+88IqoHYB7kxM5ZyHbWB3KF+IG/XcMwhcEKBFPmEEKMI6ITAAarn4DcbLgD\ncqB2v/EuIqJUyCXqTZCn92wGMEoIkfLAghfOu5AnhGizVpshjz08YPKOAhBCxBBRM8iNjdMgT3q5\nDPlsWpjNRPQEpN3yEsgZ/zjImbu19/cYhcr1JRFdhMznnpD9jHbcrn7T6juQXyX+AXKWdxKkPa8l\naRWWB2cgbbc/hLRJToIclLUT8mNglj5badehB0IIkUVEz0AObAZAngKUAjmw/wnKhEXtHWkBeSzp\naMiB7i1IRaKwvSOfQ5b7YEgbejvIfSqrAPxHCJGsC9sL8oShzyAHTcsgy3KxkXh3Qc7ufgy5dygK\nQAc16NOer6jq+3Z1X0XIPUuF8T9Ixew1AG6QKxerIBVcTTZz28VbkPXqPci8+1WF32OB/PONuB0H\nUMecPBJCZBJRZ8iyXAFplrMMco+EsbIpFZSc7SGfdyFkvq2GXMmZDqmkFQd9IE3M+kGevnQF8oju\nSQaytYPMwy8g29lqyHa20OA57uedFwdZHiMg20M65Cbz54UQhuaUDFPkkFz5YhiGYZiyCckP+e0R\nQvQqbVmYsg8R/QigphAipLRlYZhHFV6BYBiGYRjmoUTZ/SdDztS7AngJcm/M26UpF8M86rACwTAM\nwzDMw0oG5P6MQMh9AKcAvCmEMHZMLcMwRQSbMDEMwzAMwzAMYzb8JWqGYRiGYRiGYcyGFQiGYRiG\nYRiGYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiGYcyGFQiG\nYRiGYRiGYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiGYcyG\nFQiGYRiGYRiGYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiG\nYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiGYcyGFQiGYRiGYRiGYczGprQFYEqeQ4cOPWNjYzNBCOEH\nViIZhmEYhnn4ySWiuOzs7EkNGzbcWtrCPOqQEKK0ZWBKkEOHDj1jb2//eXBwcKajo2O6lZUVVwCG\nYRiGYR5qcnNzKS0tzSEmJsYuIyPjHVYiiheeff6XYWNjMyE4ODjT2dk5jZUHhmEYhmEeBaysrISz\ns3NacHBwpo2NzYTSludRhxWIfxlCCD9HR8f00paDYRiGYRimqHF0dExXJtpMMcIKxL8PK155YBiG\nYRjmUUSNcXh8W8xwBjMMwzAMwzAMYzasQDAM89BQsWLFuqNGjapg6vrfTrdu3YJbtGhRrbTS//HH\nH12JqNG5c+dsSyK9U6dO2RFRo61bt7pobmfPnrVt3rx5NUdHxwZE1AjgevIoYUlZGqsf/zaef/75\nKk8++WRoSaU3aNCgilWrVq2td1uwYIFHxYoV61pbWzfq2bNn4DfffFOOiBpdvXqVTwJ9iOHCY/C1\nl1dYRkJCidYFe0/P7B43bhyx5J7k5GQaP358hY0bN3pcu3bNzt7ePjcgICCjR48eCR988MH1vn37\nBvz000/uly9f/sfW9t7xS2hoaO3atWunbtq0KRoA4uLirCdOnFhhy5Yt5a9evWrn7OycU6VKlfQ+\nffrceOuttxKMxVHceH39dVhCRkaJlYWnvX32jR49LCoHjWvXrllPnjzZb8uWLeWvXLliZ2trK/z9\n/TPbtWt3e+jQoddDQ0OzilpeQ/78888TLi4uuUUdb2BgYJ0XX3zx5uzZs69obqdOnbKrUaNGXe3a\n2toaXl5eWU8//fSt+fPnX3J3dy9yOUzxxRdfeAwePLiyEOKg3n3RokUXc3OLT4wvvvjCY9myZd6n\nTp1yzM7OpkqVKmW2bdv29ujRo69Vrly52MvbkJCQkMzY2Ngjvr6+OZrbhAkTKiQkJNgcOHAgys3N\nLQcovnpS3Hh97RWWkFGyfbOnvWf2jR6W9c3dunUL3rBhgycAWFtbC1dX15wqVaqkd+jQ4daoUaPi\ny5UrV2R5b0lZGqsfD8qIESP858yZU6ACM2/evJghQ4YkFFWaprh8+bLNhAkT/H755ZfyV69etXNx\ncckJCQlJ79u3b3z//v1v2tiU/BBv2rRpVz/66KM47To1NZWGDx8ePGjQoLghQ4bElytXLsfJyUmo\ncskucQGZIoMVCAYlrTzcb5q9e/cO2rdvn+uMGTMuNmnSJDUxMdH6zz//dLpw4YIdAAwePDg+MjLS\nZ+3ateV79ep1S3/vtm3bnM+dO+cwf/78WEDOUj7xxBM1rK2txbhx4640bdo01c7OTuzcudPls88+\n823YsGFqixYt0ormac2nJJWHB0lPn39jxoy50rhx4zR3d/ec06dP269evdp96tSpfhERERcN70tP\nTycHB4ci24Pj7+9f4i+gVatWnW3VqlVKVlYWHT582HHQoEHBb731Fq1bty62pGUxxNPTs8gGSoa8\n/PLLQRs3bvQcOHBg3LRp0y4FBQVlnTx50j4iIsJz6tSpvkuWLLlUXGmbwsbGBoGBgfnqQHR0tEP9\n+vVT6tatm6G5FUU9Keq6aw4lrTw8SJqNGjVK3rBhw7nc3Fy6fv26zW+//eYyd+5cv1WrVnnv3r37\nZEBAQJG0VUvK0lj9eFAmTJgQN2zYsOvadXh4eGhAQEDG559/ntffeXh43NMOc3NzkZOTg6KamDp5\n8qRd69atazg6OuaOGzfucuPGjdNsbGzEr7/+6vLpp59WaNasWWqDBg1K/MAUNze3XDc3t7zr8+fP\n22VkZFB4ePit4ODgvEmGBy2Xos5PxnLYhIl5aNi2bVv5d955J+611167VaNGjczmzZunDRkyJGHW\nrFlXAaBx48bpDRs2TF66dKmX4b1ffvmld+XKldM7dOiQDAADBgwIyszMtPr7779PvP322zcbNWqU\nXrdu3Yx333034ejRoyfq1KmTYRgHc5cBAwYEZWVl0ZEjR6IGDx5887HHHkurVq1a5vPPP5+0evXq\nC0uXLr0IAE2bNq3+8ssvBw0dOtTf29u7XqVKleoBwMKFCz3q1atXw9XVtb67u3tYmzZtQv/55x97\nfRr79u1zbNCgQQ07O7uGQUFBdZYsWeJuKIehOUNGRgaNGDHCv2LFinXt7e0bhoaG1p45c2a++kBE\njaZPn+7duXPnys7Ozg18fX3rjR07Nu/EjqZNm1a/ePGi/Zw5cyoQUSMianTq1Ck7zd/LyysnMDAw\nOyQkJOvFF1+8Ex4efvPIkSPO+jTmz5/vGRISUtvW1rahr69vvSFDhvhnZd2doM/IyKBBgwZV9PHx\nqWdra9swJCSk9sKFCz30ccyePdurSpUqte3t7Ru6ubnVb9y4cfVz587Z/vjjj66DBw+urD0LETXq\n1q1bMHCvCZN2PWvWLC9/f/+6Li4uDZ566qnQixcv5hskTp482cfX17eeo6Njg9atW4cuWLDAQ2+K\nFBkZWX79+vVen3/+efRnn312pV27dinVqlXLfOGFF5K+++67mGnTpl01Vk9yc3PRo0ePoICAgDoO\nDg4NK1WqVPedd96pmJaWRlqYc+fO2T7zzDMh7u7uYfb29g0rVapU98MPP/TV/FetWlW+Zs2atRwd\nHRu4urrWr1u3bs3//e9/jsC9JipE1Gjfvn2u69ev99Lny/3WkylTpvh06tSpsqura/1u3bpVNvaM\njMTOzk4EBgZmBwcHZzVt2jRt9OjR8Xv37j2ZmJhoM3z48EpauKlTp/pUrly5tr29fcOgoKA6o0eP\n9tO3jaysLIwcObJCQEBAHTs7u4Y+Pj71evfuHaD5G5alJfUDAI4cOWLfpk2bUCcnpwZOTk4Nnnrq\nqdBjx47l9T2fffaZp42NTaNt27Y516pVq6ajo2OD2rVr19y1a5cTIAfIgYGB2drP1tZWODo65nNz\ncXERn3zyibeTk1ODb7/9tlz16tVr2dnZNdyyZYsrAKxdu9YtLCyshoODQ0NfX996PXr0CIqPj7fW\n5+d///tfj2rVqtWyt7dvWLFixboDBw6slJycnNdu+vXrF0REOHLkSNSAAQMSGzZsmF6vXr2MYcOG\nJRw9ejQqNDQ001g5/frrr84tW7as6u7uHubs7NygXr16NTZt2uSqDxMREeFevXr1Wo6Ojg3KlStX\nv379+jX+/PNPBwBIS0ujPn36BPj4+NTTyqdr167B2r16E6ZPPvnEu27dunUAoE2bNjWJqNGOHTuc\njZkwHT582KFt27Yhrq6u9d3c3Oq3atWq6sGDBx00/4LykykdWIFgHhq8vb2ztm/f7nbt2jVrU2H6\n9u17Y/fu3W56G+yEhATrzZs3u/fu3TsekKY3u3btcuvXr991YzO29vb2oiiX3B81tPx74403rnt4\neBjNJyuru13LTz/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nTsXVqFGj7o4dO1w0f0upX79+6ubNm8uHhoZmlvRJZ8z9wwoE89DQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11832a210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the three supervised learning models from sklearn\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Initialize the three models\n",
    "clf_A = DecisionTreeClassifier(random_state=13)\n",
    "clf_B = GradientBoostingClassifier(random_state=13)\n",
    "clf_C = SVC(random_state=13)\n",
    "\n",
    "# Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
    "samples_100 = len(y_train)\n",
    "samples_10 = samples_100//10\n",
    "samples_1 = samples_100//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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** I believe the best model is the Gradient Boosting classifer as it has the highest accuracy and F-score on the test data for all the different sample sizes used. The decision tree had the highest accuracy on the training data but dropped considerably for the test data which is a sign of overfitting. This is not too suprising as this can be one of the weaknesses of the decision tree model. The training time of the Gradient Boosting was also fairly low especially compared to the SVM which had a running time almost 7 times slower. The prediction time was so quick it didn't even really show on the graph above. The reason I believe Gradient Boosting did so well is that it takes a weaker models such a single decision tree and combines it with more decision tree to create a final model. We can see the decision tree did well on the training data so a decision tree was a good model choice in some way but the gradient boosting improves this by using regularization to prevent overfitting that the decision tree suffer from."
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** \n",
    "To help explain gradient boosting I will use the below visulization:\n",
    "\n",
    "![boosting](boosting.png)\n",
    "\n",
    "Gradient boosting is an iterative process, in other words it keeps repeating the same process over and over to eventually arrive at an optimal solution. The above image shows what the first 3 steps in this process might look like. We can think of the pluses and minuses above as points that have been graphed on a simple x-y plane. In our case we could think of the x-axis as represent the age of a person and the y-axis representing the capital-gain of a person. So after graphing this data we arrive with the points above in the image. For our example we can think of the pluses meaning that person earns more than \\$50K while a minus mean they earn at most \\$50K. So to start as you can see in the first image on the left a line is drawn to try to separate the points which creates a blue shaded area and a red shaded area. Any points that are in the blue we predict will make more than \\$50K and the opposite for the red area. As you can see we correctly predicted a majority of the points. We did incorrectly predict the three pluses at the top though so let's see if we can fix that. The main aspect of boosting is that at each step it mainly focuses on the prediction that it got wrong at previous steps, so we will focus more on 3 blue pluses we got wrong. To symbolize this we made those pluses larger in the middle image and we now draw a new line to separate the points. We can now see we got all the pluses in the blue region but we have now incorrectly predicted 3 minuses. So in the 3rd image we will focus on those 3 minuses signified by the larger minus signs and draw another line which correctly predicts these 3 minuses. You will notice this still incorrectly predicts some points. We keep repeating this process until our errors get smaller and smaller. At the end we will take an average of all the steps to produce a boundary between the blue and red area. Predicting new data is as simple as graphing the points based on the person's age and capital-gain. If the point falls in the blue area we predict they make more than \\$50K otherwise if in the red area they make at most \\$50K. Image above came from here [https://www.analyticsvidhya.com/blog/2016/02/complete-guide-parameter-tuning-gradient-boosting-gbm-python/](https://www.analyticsvidhya.com/blog/2016/02/complete-guide-parameter-tuning-gradient-boosting-gbm-python/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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: 0.8702\n",
      "Final F-score on the testing data: 0.7501\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "# Initialize the classifier\n",
    "clf = GradientBoostingClassifier(random_state=13)\n",
    "\n",
    "# Create the parameters list you wish to tune, using a dictionary if needed.\n",
    "parameters = {'n_estimators': [200, 500, 1000], 'max_depth': [5, 10, 25]}\n",
    "\n",
    "# Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(fbeta_score, beta=0.5)\n",
    "\n",
    "# Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "grid_obj = GridSearchCV(clf, parameters, scorer)\n",
    "\n",
    "# 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": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\n",
      "0      17.741702         0.051758         0.753532          0.794092   \n",
      "1      40.765947         0.113684         0.744398          0.825559   \n",
      "2      85.152015         0.235722         0.732735          0.862409   \n",
      "3      81.751266         0.110019         0.730234          0.911032   \n",
      "4     218.971363         0.251701         0.704170          0.960409   \n",
      "5     456.730888         0.511158         0.694734          0.961012   \n",
      "6     305.160333         0.255561         0.672260          0.961134   \n",
      "7     565.012721         0.529073         0.678342          0.960100   \n",
      "8    1001.360648         1.008193         0.683897          0.960008   \n",
      "\n",
      "  param_max_depth param_n_estimators  \\\n",
      "0               5                200   \n",
      "1               5                500   \n",
      "2               5               1000   \n",
      "3              10                200   \n",
      "4              10                500   \n",
      "5              10               1000   \n",
      "6              25                200   \n",
      "7              25                500   \n",
      "8              25               1000   \n",
      "\n",
      "                                      params  rank_test_score  \\\n",
      "0    {u'n_estimators': 200, u'max_depth': 5}                1   \n",
      "1    {u'n_estimators': 500, u'max_depth': 5}                2   \n",
      "2   {u'n_estimators': 1000, u'max_depth': 5}                3   \n",
      "3   {u'n_estimators': 200, u'max_depth': 10}                4   \n",
      "4   {u'n_estimators': 500, u'max_depth': 10}                5   \n",
      "5  {u'n_estimators': 1000, u'max_depth': 10}                6   \n",
      "6   {u'n_estimators': 200, u'max_depth': 25}                9   \n",
      "7   {u'n_estimators': 500, u'max_depth': 25}                8   \n",
      "8  {u'n_estimators': 1000, u'max_depth': 25}                7   \n",
      "\n",
      "   split0_test_score  split0_train_score  split1_test_score  \\\n",
      "0           0.752377            0.795771           0.750094   \n",
      "1           0.743611            0.825609           0.740019   \n",
      "2           0.731626            0.863679           0.725252   \n",
      "3           0.732601            0.911610           0.724226   \n",
      "4           0.707467            0.960581           0.699609   \n",
      "5           0.698617            0.961044           0.688541   \n",
      "6           0.678574            0.962506           0.669173   \n",
      "7           0.684730            0.960499           0.674667   \n",
      "8           0.690570            0.960953           0.679414   \n",
      "\n",
      "   split1_train_score  split2_test_score  split2_train_score  std_fit_time  \\\n",
      "0            0.792415           0.758126            0.794091      0.227061   \n",
      "1            0.825151           0.749564            0.825918      0.317423   \n",
      "2            0.862422           0.741328            0.861125      2.240983   \n",
      "3            0.909345           0.733876            0.912141      2.929136   \n",
      "4            0.960346           0.705434            0.960300      8.772918   \n",
      "5            0.960847           0.697044            0.961144      3.823475   \n",
      "6            0.960210           0.669031            0.960685      1.232633   \n",
      "7            0.959938           0.675628            0.959862     11.251222   \n",
      "8            0.960210           0.681706            0.958862     15.956294   \n",
      "\n",
      "   std_score_time  std_test_score  std_train_score  \n",
      "0        0.000324        0.003379         0.001370  \n",
      "1        0.003454        0.003936         0.000315  \n",
      "2        0.016033        0.006610         0.001043  \n",
      "3        0.003119        0.004280         0.001212  \n",
      "4        0.011473        0.003330         0.000123  \n",
      "5        0.002728        0.004426         0.000123  \n",
      "6        0.006630        0.004465         0.000990  \n",
      "7        0.003037        0.004534         0.000284  \n",
      "8        0.016481        0.004811         0.000865  \n"
     ]
    }
   ],
   "source": [
    "print pd.DataFrame(grid_fit.cv_results_)"
   ]
  },
  {
   "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     | Benchmark Predictor | Unoptimized Model | Optimized Model |\n",
    "| :------------: | :-----------------: | :---------------: | :-------------: | \n",
    "| Accuracy Score |           0.2478    |         0.8630    |       0.8702    |\n",
    "| F-score        |           0.2917    |         0.7395    |       0.7501    |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** The Gradient Boosting model has an accuracy and F-score that is more than 3 times better than the naive predictor from Question1. We can also see that just out of the box the Gradient Boosting model did well in its accuracy and F-score as the optimized model only improved each of these slightly. The parameter I chose to optimize where n_estimators and max_depth. The n_estimators determines the number of boosting stages to perform on the decision trees it creates while the max_depth ultimately limits the number of nodes in the tree. From the cv_results output above we can see the best combination was max_depth = 5 and n_estimators = 200."
   ]
  },
  {
   "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",
    "1. occupation - There can be a large discrepancy in pay between the different occupations even among people that have the same level of education which is why I feel this is the most important.\n",
    "2. age - Younger workers typically make less as they will most likely be occupying entry level positions.\n",
    "3. education-num - I believe it to be fairly common knowledge that, on average, as the higher a person goes academically the more money they will make. There are obviously exceptions to this.\n",
    "4. hours per week - I believe this will measure if the individual is fully employed. A person could have the education and a job that pays well but only be working part-time which will limit their income.\n",
    "5. sex - A hot topic in the world today and there are multiple studies to show that on average men earn more than women."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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VnB+D7zuBO81sb8KjRv5AuDG7JN9y5TDTKx6xmuvYWUKo3b0oxzwINaYQzgm5vv8VnRMy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c3e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier(random_state=13)\n",
    "# Train the supervised model on the training set using .fit(X_train, y_train)\n",
    "model = clf.fit(X_train, y_train)\n",
    "\n",
    "# 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:** I had picked 2 of the features in age and education-num as these were my 2nd and 3rd ranked respectively. I believe both of these are descent predictors of income even when use independently. Typically older workers make more money as well as people with higher educations. The one surprising feature to me was the marital-status. Specifically it appears that married people make more. In some ways this makes sense if a person has a family to provide for they might attempt to advance their career so they can provide better. \n",
    "\n",
    "To be honest in this dataset it is tough to comment on capital-gain and capital-loss which is why I didn't use it in my ranking. There is no clear explanation of what this data is measuring as I have looked at the website where the data came from and it has not details on these features. Is it measuring capital gain or loss for the current year or over a set time period?"
   ]
  },
  {
   "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": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8702\n",
      "F-score on testing data: 0.7501\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8583\n",
      "F-score on testing data: 0.7225\n",
      "Training time: 3.0874s\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",
    "start = time()\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "end = time()\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))\n",
    "print \"Training time: {:.4f}s\".format(end - start)"
   ]
  },
  {
   "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?"
   ]
  },
  {
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    "**Answer:** Both the accuracy and F-score dropped when using the reduced feature set but only slightly as we were using the most important features. This isn't suprising as the model has less decision points so it can't as accurately predict the data. The training time for the full model took about 5s to run where the reduced data model only took about 3s to run. For this dataset I would just train on the full model as 2s is negligible but it we have a lot more data and this ratio of 5s/3s holds or gets worse for adding more data it might be worth considering only using the reduced feature set."
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    "> **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."
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