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": [
    "### Python version -- Python 3.6.6 :: Anaconda custom (64-bit)"
   ]
  },
  {
   "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": {},
   "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",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\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": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    34014\n",
       ">50K     11208\n",
       "Name: income, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#understanding how 'income' entries are formatted\n",
    "#display(data['income'])\n",
    "display(data['income'].value_counts())"
   ]
  },
  {
   "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": 4,
   "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: 24.784%\n"
     ]
    }
   ],
   "source": [
    "# TODO: Total number of records\n",
    "n_records = data['income'].shape[0]\n",
    "\n",
    "# TODO: Number of records where individual's income is more than $50,000\n",
    "n_greater_50k = data['income'].loc[data['income'] == '>50K'].count()\n",
    "\n",
    "# TODO: Number of records where individual's income is at most $50,000\n",
    "n_at_most_50k = data['income'].loc[data['income'] == '<=50K'].count()\n",
    "\n",
    "# TODO: Percentage of individuals whose income is more than $50,000\n",
    "greater_percent = ((n_greater_50k / n_records)*100).round(3)\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_level**: 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": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45222 entries, 0 to 45221\n",
      "Data columns (total 14 columns):\n",
      "age                45222 non-null int64\n",
      "workclass          45222 non-null object\n",
      "education_level    45222 non-null object\n",
      "education-num      45222 non-null float64\n",
      "marital-status     45222 non-null object\n",
      "occupation         45222 non-null object\n",
      "relationship       45222 non-null object\n",
      "race               45222 non-null object\n",
      "sex                45222 non-null object\n",
      "capital-gain       45222 non-null float64\n",
      "capital-loss       45222 non-null float64\n",
      "hours-per-week     45222 non-null float64\n",
      "native-country     45222 non-null object\n",
      "income             45222 non-null object\n",
      "dtypes: float64(4), int64(1), object(9)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\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 0x7f5496ff06a0>"
      ]
     },
     "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 0x7f54948c36a0>"
      ]
     },
     "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>\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": [
    "### 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 ----> |one-hot encode ----> | someFeature_A | someFeature_B | someFeature_C \n",
    "| :---: | :---: |     :---: |                        :---: | :---: \n",
    "| 0 |  B   | 0 | 1 | 0 \n",
    "| 1 |  C  |  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"
     ]
    }
   ],
   "source": [
    "# TODO: One-hot encode the 'features_log_minmax_transform' data using pandas.get_dummies()\n",
    "features_final = pd.get_dummies(features_log_minmax_transform)\n",
    "\n",
    "# TODO: Encode the 'income_raw' data to numerical values\n",
    "income = income_raw.apply(lambda x: 0 if x=='<=50K' else 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)"
   ]
  },
  {
   "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"
     ]
    }
   ],
   "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 USD and those who make more), it is 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": [
    "#True Positives\n",
    "TP = np.sum(income) # Counting the ones as this is the naive case. \n",
    "#Note that 'income' is the 'income_raw' data encoded to numerical values done in the data preprocessing step.\n",
    "\n",
    "#False Positives\n",
    "FP = income.count() - TP # Specific to the naive case\n",
    "\n",
    "#True Negatives\n",
    "TN = 0 # No predicted negatives in the naive case\n",
    "\n",
    "#False Negatives\n",
    "FN = 0 # No predicted negatives in the naive case\n",
    "\n",
    "# TODO: Calculate accuracy, precision and recall\n",
    "accuracy = ((TP + TN) / (TP + TN + FP + FN))\n",
    "recall = ((TP) / (TP + FN))\n",
    "precision = ((TP) / (TP + FP))\n",
    "\n",
    "# TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n",
    "beta = 0.5\n",
    "fscore = ((1 + (beta**2)) * ((precision * recall) / (((beta**2) * precision ) + recall)))\n",
    "\n",
    "# Print the results \n",
    "print(\"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Supervised Learning Models\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": [
    "-----\n",
    "**ANSWER:**<br>\n",
    "\n",
    "## Model I ) RANDOM FOREST - \n",
    "\n",
    "**INDUSTRY APPLICATION** :- <br>\n",
    "**Application of Random Forest to Aircraft Engine Fault Diagnosis**\n",
    ": Aircraft engine fault diagnosis plays a critical role in modern, cost-effective condition-based maintenance strategy in aircraft industry. Due to several inherent characteristics associated with aircraft engines, accurately diagnosing aircraft engine faults is a challenging classification problem. The Random Forest Algorithm was applied to aircraft engine fault diagnosis in an attempt to achieve more accurate and reliable classification performance. Their Primary Objective was to evaluate effectiveness of random forest classifier on aircraft engine fault diagnosis. Also efforts were made by the Engineer on investigating strategies for improving random forest performance specifically for aircraft engine fault diagnosis problem.<br>[IEEE Paper](https://ieeexplore.ieee.org/document/4281698) <br><br>\n",
    "**STRENGTHS** :-\n",
    "- Powerful and accurate.\n",
    "- Good performance on many problems including non linear.\n",
    "- There is no need for feature normalization.\n",
    "- Individual decision trees can be trained in parallel.<br>\n",
    "[StrengthsRef1](https://www.quora.com/What-are-the-advantages-and-disadvantages-for-a-random-forest-algorithm)\n",
    "[StrengthsRef2](http://www.dataversity.net/machine-learning-algorithms-introduction-random-forests/)<br>\n",
    "\n",
    "**WEAKNESSES** :-\n",
    "- Random Forests are not easily interpretable.\n",
    "- They’re not a state-of-the-art algorithm i.e. no interpretability. \n",
    "- Need to choose the number of trees manually/using calculations/assumptions.<br>\n",
    "[WeaknessesRef1](https://www.quora.com/What-are-the-advantages-and-disadvantages-for-a-random-forest-algorithm)\n",
    "[WeaknessesRef2](http://www.dataversity.net/machine-learning-algorithms-introduction-random-forests/)<br>\n",
    "\n",
    "**WHY THIS MODEL FOR GIVEN DATA?**\n",
    ": __Random Forest is an ensemble of decision trees__. It can solve both regression and classification problems with large data sets. Random Forest is highly scalable to any number of dimensions and has generally quite acceptable performances. Since the given dataset is large, and we want to send letters to only the probable people who will donate, we need high accuracy to predict such people from the dataset. Hence I think this model can be used for the given data.<br>\n",
    "[REFERENCE](https://hackernoon.com/choosing-the-right-machine-learning-algorithm-68126944ce1f)<br>\n",
    "\n",
    "## Model II ) Support Vector Machines - \n",
    "\n",
    "**INDUSTRY APPLICATION** :- <br>\n",
    "**Application of Support Vector Machines for Recognition of Handwritten Arabic/Persian Digits**\n",
    ": Abstract - A new method for recognition of isolated Handwritten Arabic/Persian digits is presented. This method is based on Support Vector Machines (SVMs), and a new approach of feature extraction. Each digit is considered from four different views, and from each view 16 features are extracted and combined to obtain 64 features. Using these features, multiple SVM classifiers are trained to separate different classes of digits. CENP ARMI Indian (Arabic/Persian) handwritten digit database is used for training and testing of SVM classifiers. Based on this database, differences between Arabic and Persian digits in digit\n",
    "recognition are shown. This database provides 7390 samples for training and 3035 samples for testing from the real life samples. Experiments show that the proposed features can provide a very good recognition result using Support Vector Machines at a recognition rate 94.14%, compared with 91.25% obtained by MLP neural network classifier using\n",
    "the same features and test set.<br>\n",
    "[The above presented abstract is taken from this paper.](https://pdfs.semanticscholar.org/d370/8948a7efb2ef007c4a6e80948bc7a0d164b7.pdf)<br>\n",
    "\n",
    "**STRENGTHS** :-<br>\n",
    "- Very fast at classifying new data. There is no need to go through training data for new classifications.\n",
    "- SVMs are “Robust to high dimensionality”, meaning they can work well even with a large number of features.\n",
    "- Highly accurate.\n",
    "- Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.\n",
    "\n",
    "**WEAKNESSES** :- <br>\n",
    "- Black box technique. Unlike Bayesian Classifiers and Decision Trees, SVMs don’t offer easily digestible data “under the hood”. A SVM can be a highly effective classifier, but you may not be able to figure out why it makes those classifications.\n",
    "- Typically requires a very large dataset. Other methods, like decision trees, can still give interesting output for small data sets; this may not be the case with a SVM.\n",
    "- SVMs are not online. They will need to be updated every time new training data is to be incorporated.<br>\n",
    "\n",
    "[RefForStrengths&Weaknesses1_SVM](https://emerj.com/ai-sector-overviews/machine-learning-algorithms-for-business-applications-complete-guide/)<br>\n",
    "[RefForStrengths&Weaknesses2_OfficialDocumentation](https://scikit-learn.org/stable/modules/svm.html)\n",
    "\n",
    "**WHY THIS MODEL FOR GIVEN DATA?**\n",
    ": __Support Vector Machines (SVMs) are a complex but powerful classification technique.__ Working on numerical data, SVMs classify by finding the dividing line (formally called a maximum-margin hyperplane) that separates data most cleanly. Classification is based on which side of the dividing line new data falls. Since our data is numeric and high dimensions(more number of features), also we want high accuracy to find probable donors, I think Support vector Machines can be used and so the ouput will be compared with Random Forests too.<br>\n",
    "[REFERENCE](https://emerj.com/ai-sector-overviews/machine-learning-algorithms-for-business-applications-complete-guide/) <br>\n",
    "\n",
    "## Model III ) SGDC - scikit cheat sheet\n",
    "\n",
    "\n",
    "**INDUSTRY APPLICATION** :- <br>\n",
    "**Application of Stochastic Gradient Descent Algorithm in\n",
    "Evaluating the Performance Contribution of Employees**\n",
    ": In the attached paper the applicability of stochastic gradient descent algorithm as an approach for\n",
    "evaluation of performance contribution has been presented. Under that, performance evaluation is established in corporate, department and team levels with the intention of creating an employee incentive scheme. Effective evaluation of performance contribution is challenging in various situations across the industry. There are no identifiable researches done on application of data driven approach like machine learning to overcome the above issues. Thus the adaptability of stochastic gradient descent algorithm in evaluating the performance contribution has been implemented.<br>\n",
    "[The above presented Application can be found in this paper.](http://iosrjournals.org/iosr-jbm/papers/Vol16-issue6/Version-3/I016637780.pdf)\n",
    "\n",
    "**STRENGTHS** :-\n",
    "- Efficiency.\n",
    "- Ease of implementation (lots of opportunities for code tuning).<br>\n",
    "\n",
    "**WEAKNESSES** :-\n",
    "- SGD requires a number of hyperparameters such as the regularization parameter and the number of iterations.\n",
    "- SGD is sensitive to feature scaling.<br>\n",
    "[Strengths&WeaknessesRef](https://scikit-learn.org/stable/modules/sgd.html)\n",
    "\n",
    "**WHY THIS MODEL FOR GIVEN DATA?**\n",
    ": Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to discriminative learning of linear classifiers under convex loss functions such as (linear) Support Vector Machines and Logistic Regression. Advantage of the stochastic algorithm over batch gradient descent is, it updates the parameters with\n",
    "respect to each training example and identifies the gradient of error for each training example. Therefore speed\n",
    "of the algorithm is faster and more priority is given to the final training example processes. Thus because of this benefit it prompted me to use SGDC. Also, using the Scikit Cheat Sheet, the path shows to use SGD Classifier.\n",
    "<br>[REFERENCE_OfficialDocumentation](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html)<br>"
   ]
  },
  {
   "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 fbeta_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    # TODO: Fit the learner to the training data using slicing with 'sample_size' \n",
    "    # using .fit(training_features[:], training_labels[:])\n",
    "    start = time() # Get start time\n",
    "    learner.fit(X_train[:sample_size], y_train[:sample_size])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # TODO: Calculate the training time\n",
    "    results['train_time'] = (end - start)\n",
    "        \n",
    "    # TODO: Get the predictions on the test set(X_test),\n",
    "    #       then get predictions on the first 300 training samples(X_train) using .predict()\n",
    "    start = time() # Get start time\n",
    "    predictions_test = learner.predict(X_test)\n",
    "    # now, since we have get predictionns on the first 300 samples\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",
    "    # accuracy_score(y_true_values, y_predicted_values_by_classifiers)\n",
    "    results['acc_train'] = accuracy_score(y_train[:300], predictions_train)\n",
    "        \n",
    "    # TODO: Compute accuracy on test set using accuracy_score()\n",
    "    results['acc_test'] = accuracy_score(y_test, predictions_test)\n",
    "    \n",
    "    # TODO: Compute F-score on the the first 300 training samples using fbeta_score()\n",
    "    # fbeta_score(y_true_values, y_predicted_values_by_classifiers, beta_value)\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",
    "        \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": [
      "RandomForestClassifier\n",
      "RandomForestClassifier trained on 361 samples.\n",
      "RandomForestClassifier trained on 3617 samples.\n",
      "RandomForestClassifier trained on 36177 samples.\n",
      "SVC\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",
      "SGDClassifier\n",
      "SGDClassifier trained on 361 samples.\n",
      "SGDClassifier trained on 3617 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGDClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54941d8cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Import the three supervised learning models from sklearn\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import SGDClassifier \n",
    "\n",
    "# TODO: Initialize the three models\n",
    "# I am initializing random_state to arbitrary number 5 because the seed will be set common for all clfs\n",
    "clf_A = RandomForestClassifier(random_state = 5)\n",
    "clf_B = SVC(random_state = 5)\n",
    "clf_C = SGDClassifier(random_state = 5)\n",
    "\n",
    "# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
    "# HINT: samples_100 is the entire training set i.e. len(y_train)\n",
    "# HINT: samples_10 is 10% of samples_100 (ensure to set the count of the values to be `int` and not `float`)\n",
    "# HINT: samples_1 is 1% of samples_100 (ensure to set the count of the values to be `int` and not `float`)\n",
    "samples_100 = len(y_train)\n",
    "samples_10 = int((len(y_train)) / 10)\n",
    "samples_1 = int((len(y_train)) / 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",
    "    print(clf_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": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier\n"
     ]
    },
    {
     "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>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>100%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>acc_test</th>\n",
       "      <td>0.806855</td>\n",
       "      <td>0.831509</td>\n",
       "      <td>0.838695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_train</th>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.986667</td>\n",
       "      <td>0.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_test</th>\n",
       "      <td>0.596889</td>\n",
       "      <td>0.660408</td>\n",
       "      <td>0.673585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_train</th>\n",
       "      <td>0.967262</td>\n",
       "      <td>0.988372</td>\n",
       "      <td>0.933544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.028345</td>\n",
       "      <td>0.033000</td>\n",
       "      <td>0.049708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>0.022421</td>\n",
       "      <td>0.064438</td>\n",
       "      <td>0.965426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1%       10%      100%\n",
       "acc_test    0.806855  0.831509  0.838695\n",
       "acc_train   0.973333  0.986667  0.950000\n",
       "f_test      0.596889  0.660408  0.673585\n",
       "f_train     0.967262  0.988372  0.933544\n",
       "pred_time   0.028345  0.033000  0.049708\n",
       "train_time  0.022421  0.064438  0.965426"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>100%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>acc_test</th>\n",
       "      <td>0.756219</td>\n",
       "      <td>0.832615</td>\n",
       "      <td>0.837148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_train</th>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.853333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_test</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.671256</td>\n",
       "      <td>0.674477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_train</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.671296</td>\n",
       "      <td>0.719697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.659047</td>\n",
       "      <td>2.822516</td>\n",
       "      <td>27.042902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>0.015310</td>\n",
       "      <td>1.305978</td>\n",
       "      <td>167.090359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1%       10%        100%\n",
       "acc_test    0.756219  0.832615    0.837148\n",
       "acc_train   0.760000  0.833333    0.853333\n",
       "f_test      0.000000  0.671256    0.674477\n",
       "f_train     0.000000  0.671296    0.719697\n",
       "pred_time   0.659047  2.822516   27.042902\n",
       "train_time  0.015310  1.305978  167.090359"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGDClassifier\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\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>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>100%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>acc_test</th>\n",
       "      <td>0.777114</td>\n",
       "      <td>0.798121</td>\n",
       "      <td>0.784743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_train</th>\n",
       "      <td>0.773333</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.763333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_test</th>\n",
       "      <td>0.358718</td>\n",
       "      <td>0.594120</td>\n",
       "      <td>0.579056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_train</th>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.542636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.004371</td>\n",
       "      <td>0.003421</td>\n",
       "      <td>0.007035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>0.009364</td>\n",
       "      <td>0.009666</td>\n",
       "      <td>0.154595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1%       10%      100%\n",
       "acc_test    0.777114  0.798121  0.784743\n",
       "acc_train   0.773333  0.790000  0.763333\n",
       "f_test      0.358718  0.594120  0.579056\n",
       "f_train     0.288462  0.576923  0.542636\n",
       "pred_time   0.004371  0.003421  0.007035\n",
       "train_time  0.009364  0.009666  0.154595"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in results.items():\n",
    "    print (i[0])\n",
    "    display(pd.DataFrame(i[1]).rename(columns={0:'1%', 1:'10%', 2:'100%'}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the predictions above, I am still confused, \"whether to use Random Forest Classsifier or the Support Vector Machines\", Hence printing confusion matrix for these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5491159cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f549110e4e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "for i,model in enumerate([clf_A,clf_B]):\n",
    "    cm = confusion_matrix(y_test, model.predict(X_test))\n",
    "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] # normalize the data\n",
    "\n",
    "    # view with a heatmap\n",
    "    plt.figure(i)\n",
    "    sns.heatmap(cm, annot=True, annot_kws={\"size\":30}, \n",
    "            cmap='Blues', square=True, fmt='.3f')\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.title('Confusion matrix for:\\n{}'.format(model.__class__.__name__));"
   ]
  },
  {
   "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",
    " Random Forest.<br>\n",
    "From the above graphs and predictions, it can be seen that Random Forest Classifier and the SVM Classifier give very close accuracy(0.838695(RandomForests) & 0.837148(SVC)) and f_score(0.673585(RandomForests) & 0.674477(SVC)). But the time taken for prediction `pred_time` of the Random Forest Classifier is very less(0.04097) as compared to the SVC(26.256914), hence __Random Forest seems to be the best__, among the chosen three classifiers.<br>\n",
    "Since Random Forest is a group of decision trees, also we want high accuracy because sending letters to the non-donors will cause loss in time and resources and the given data is large, it can be concluded that Random Forest Classifier is suitable for the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4 - Describing the Model in Layman's Terms\n",
    "\n",
    "* In one to two paragraphs, explain to *CharityML*, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical jargon, such as describing equations.\n",
    "\n",
    "**HINT:**\n",
    "\n",
    "When explaining your model, if using external resources please include all citations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANSWER:** <br> Model Explanation : <br>\n",
    "- The final model I have chosen is Random Forest model. Random Forests is an ensemble i.e. a group of decision trees.\n",
    "- A Decision tree is a tree-like structure consisting of nodes and branches where each node represents a feature, each link(branch) represents a decision and each leaf node(i.e. the last node of the tree) represents an outcome(the required prediction).\n",
    "- Thus, using such group decision trees, we get higher accuracy in our prediction model so that the letters can be sent to only the probable donors without wasting any resources.\n",
    "- This(Random Forest Classifier) is a part of Classification technique in Supervised Machine Learning Process where we use labeled data to train the classifier and testing is done further using the trained model. This data classification is a two step process: Learning(Training using Labeled Data) and Classification(Prediction on unlabeled data)\n",
    "    1. __Model Construction__ -- Model Construction is used for describing a set of predetermined classes. Each sample is assumed to belonng to a predefined class, as determined by the class label attribute. Training set is a set of samples. Here, the model is represented as Random Forests. I will explain this step using an __hypothetical example__: Suppose there is company known as \"Starcents\" which has Cafes worldwide hence the quality of the coffee beans used is an important factor to consider. Their team has to decide whether to `select` the current batch of coffee beans or to `do_not_select`. The selection criteria will depend upon various factors like `avg_size_of_bean`, `avg_weight_of_bean`, `colour`, `age`, etc. So a random forest classifier can be built where decisions are taken at each level of the tree and using historical data of either `select`ing or `do_not_select`ing will be used to train the random forest model.\n",
    "    2. __Model Usage__ -- Model Usage is used for classifying future or unknown objects. It estimates an accuracy of the constructed model. The known label of test samples is compared with the classified result from the model trained inn the above step.__Back to our example__: After training the model, when a new batch of coffee beans arrives, the data can be entered as input to the model and checked if the quality matches or not. Thus with high accuracy of Random Forests, it will be beneficial for CharityML to use Random Forest model.<br><br>\n",
    "(To explain the above working I have referred a local textbook I used in my engineering and the example is intuitive. Also the major explanation part is by myself.)"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "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",
      "/opt/conda/lib/python3.6/site-packages/sklearn/grid_search.py:42: 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. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8387\n",
      "F-score on testing data: 0.6736\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8618\n",
      "Final F-score on the testing data: 0.7356\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
    "from sklearn import grid_search\n",
    "from sklearn.metrics import make_scorer, r2_score\n",
    "\n",
    "# TODO: Initialize the classifier\n",
    "clf = RandomForestClassifier(random_state = 5)\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' : [5, 10, 14, 18, 50],\n",
    "              'max_depth' : [5, 10, 15, 25],\n",
    "              'min_samples_split' : [4, 8, 16, 25],\n",
    "              'min_samples_leaf' : [1, 10, 25,40]\n",
    "              }\n",
    "\n",
    "# TODO: Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(fbeta_score, beta = 0.5)\n",
    "\n",
    "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "# n_jobs: number of jobs to run in parellel\n",
    "grid_obj = grid_search.GridSearchCV(clf, parameters, scoring = scorer, n_jobs = 5)\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 | Benchmarked Predictor|\n",
    "| :------------: | :---------------: | :-------------: | :-------------------:|\n",
    "| Accuracy Score |     0.8387        |     0.8618      |         0.2478       |\n",
    "| F-score        |     0.6736        |     0.7356      |         0.2917       |\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** : \n",
    "- The optimized model's accuracy is 0.8618 and the F-score is 0.7356 on the testing data.\n",
    "- The Optimized model's scores are comparatively better than the Unoptimized model's scores which is obvious. :)<br> The __Accuracy score__ increased by __2.754%__ from Unoptimized model to Optimized model whereas the the __f-score__ increased by __9.204%__ from Unoptimized model to Optimized model.\n",
    "- The Optimized model's both the scores are __much better__ than the Naive Predictor Benchmarks which is expected because the benchmarked values were calculated without applying any 'Training and Tuning Techniques'.<br> The __Accuracy score__ increased by __247.781%__ from the Benchmarked predictor to Optimized model whereas the the __f-score__ increased by __152.177%__ from the Benchmarked predictor to Optimized model."
   ]
  },
  {
   "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": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'workclass', 'education_level', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income']\n"
     ]
    }
   ],
   "source": [
    "print(list(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Married-civ-spouse       21055\n",
      " Never-married            14598\n",
      " Divorced                  6297\n",
      " Separated                 1411\n",
      " Widowed                   1277\n",
      " Married-spouse-absent      552\n",
      " Married-AF-spouse           32\n",
      "Name: marital-status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data['marital-status'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**<br> According to me, I will the top 5 important features for prediction as follows along with the reasons stated - \n",
    "\n",
    "|     RANK       |        FEATURE         |     REASON      | \n",
    "| :------------: | :-------------------:  | :----------------------: | \n",
    "|      I         |        __age__         | The income generally tends to be higher of more experienced people, also they are most likely to donate. |   \n",
    "|      II        |  __education_level__   | The income generally tends to be higher of people who hold a professional degree(Masters/Doctorate).       |  \n",
    "|      III        |     __occupation__    | The income is higher of the people working at Higher levels(like Executives) compared to others(like Handlers-cleaners). |    \n",
    "|      IV         |    __capital-gain__   | The more a person is in profit, he/she is more likely to make donations. |\n",
    "|      V          |  __hours-per-week__   | The more the number of hours a person works, the income tends to be higher and thus more likely to donate in charity. |   "
   ]
  },
  {
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54910b7eb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Import a supervised learning model that has 'feature_importances_'\n",
    "\n",
    "# The Random Forest Classifier has the feature_importances_ parameter hence no need to import again.\n",
    "\n",
    "# TODO: Train the supervised model on the training set using .fit(X_train, y_train)\n",
    "start = time()\n",
    "model = RandomForestClassifier().fit(X_train, y_train)\n",
    "end = time()\n",
    "all_features_dataset_time = (end - start)\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",
    "- Of the five features discussed in the previous answer, I got three features out of five correct.\n",
    "- As explained before, `age` is an important feature since older a person is, his/her income will be higher and hence more likely to donate. Similar thing is with the `capital-gain` feature, since the person is debt-free and gaining profits, he/she is more likely to donate. The third correct feature was `hours-per-week` showing higher chances of making donations because of higher income.\n",
    "- The two features being seen in the plot and I misinterpreted are `marital-status` and `education-num`. In `education-num` feature, \"Number of educational years completed\" can be seen which shows they have a higher degree of education thus higher income leading to more probability to donate. And it is also seen that people who are married and having families(i.e. people who are well-settled), it seem they are more likely to make donations probably to contribute towards the society.\n",
    "- The other two features I chose are `occupation` and `education-level`. These seem to be quite obvious and common since higher the level in occupation and more a person has been educated, he/she will have higher income and more likely to donate."
   ]
  },
  {
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8618\n",
      "F-score on testing data: 0.7356\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8407\n",
      "F-score on testing data: 0.6849\n",
      "\n",
      "------\n",
      "Time required for training datset of all features:  0.8900423049926758\n",
      "Time required for training datset of Top 5 features:  1.5714225769042969\n",
      "Delta(Time): 0.6813802719116211\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",
    "# Since training time is to be compared, I will add the start and end timings. Here as well as in all_features_dataset\n",
    "start = time()\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "end = time()\n",
    "reduced_dataset_time = (end - start)\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(\"\\n------\")\n",
    "print(\"Time required for training datset of all features: \", all_features_dataset_time)\n",
    "print(\"Time required for training datset of Top 5 features: \", reduced_dataset_time)\n",
    "print(\"Delta(Time):\", (abs(all_features_dataset_time - reduced_dataset_time)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8 - Effects of Feature Selection\n",
    "\n",
    "* How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?\n",
    "* If training time was a factor, would you consider using the reduced data as your training set?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "- The Accuracy score decreased by 2.448% from testing on dataset with all features to testing on dataset with top 5 features whereas the the f-score decreased by 6.892% from testing on dataset with all features to testing on dataset with top 5 features.\n",
    "- If training time was a factor, I __wouldn't consider using the reduced data__ since the time required for training datset of all features is less than the time required for training datset of Top 5 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "START\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "print('START')\n",
    "!!jupyter nbconvert *.ipynb\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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