finding_donors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Scientist Nanodegree\n",
    "## Supervised Learning\n",
    "## Project: Finding Donors for *CharityML*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the first project of the Data Scientist 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. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.  \n",
    "\n",
    ">**Note:** Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. \n",
    "\n",
    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income). The datset was donated by Ron Kohavi and Barry Becker, after being published in the article _\"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid\"_. You can find the article by Ron Kohavi [online](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf). The data we investigate here consists of small changes to the original dataset, such as removing the `'fnlwgt'` feature and records with missing or ill-formatted entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Exploring the Data\n",
    "Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, `'income'`, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass education_level  education-num       marital-status  \\\n",
       "0   39          State-gov       Bachelors           13.0        Never-married   \n",
       "1   50   Self-emp-not-inc       Bachelors           13.0   Married-civ-spouse   \n",
       "2   38            Private         HS-grad            9.0             Divorced   \n",
       "3   53            Private            11th            7.0   Married-civ-spouse   \n",
       "4   28            Private       Bachelors           13.0   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male        2174.0   \n",
       "1     Exec-managerial         Husband   White     Male           0.0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male           0.0   \n",
       "3   Handlers-cleaners         Husband   Black     Male           0.0   \n",
       "4      Prof-specialty            Wife   Black   Female           0.0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country income  \n",
       "0           0.0            40.0   United-States  <=50K  \n",
       "1           0.0            13.0   United-States  <=50K  \n",
       "2           0.0            40.0   United-States  <=50K  \n",
       "3           0.0            40.0   United-States  <=50K  \n",
       "4           0.0            40.0            Cuba  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.547941</td>\n",
       "      <td>10.118460</td>\n",
       "      <td>1101.430344</td>\n",
       "      <td>88.595418</td>\n",
       "      <td>40.938017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.217870</td>\n",
       "      <td>2.552881</td>\n",
       "      <td>7506.430084</td>\n",
       "      <td>404.956092</td>\n",
       "      <td>12.007508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age  education-num  capital-gain  capital-loss  hours-per-week\n",
       "count  45222.000000   45222.000000  45222.000000  45222.000000    45222.000000\n",
       "mean      38.547941      10.118460   1101.430344     88.595418       40.938017\n",
       "std       13.217870       2.552881   7506.430084    404.956092       12.007508\n",
       "min       17.000000       1.000000      0.000000      0.000000        1.000000\n",
       "25%       28.000000       9.000000      0.000000      0.000000       40.000000\n",
       "50%       37.000000      10.000000      0.000000      0.000000       40.000000\n",
       "75%       47.000000      13.000000      0.000000      0.000000       45.000000\n",
       "max       90.000000      16.000000  99999.000000   4356.000000       99.000000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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=5))\n",
    "#data.head()\n",
    "data.describe()"
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 45222\n",
      "Individuals making more than $50,000: 11208\n",
      "Individuals making at most $50,000: 34014\n",
      "Percentage of individuals making more than $50,000: 24.78439697492371%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bishebanm\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Total number of records\n",
    "n_records = len(data.index)# the slower: data[0].count(), or data.shape[0]\n",
    "\n",
    "# TODO: Number of records where individual's income is more than $50,000\n",
    "#print(data['income'].dtypes)#https://pbpython.com/pandas_dtypes.html\n",
    "n_greater_50k = sum(data['income'] == '>50K')\n",
    "\n",
    "# TODO: Number of records where individual's income is at most $50,000\n",
    "n_at_most_50k = sum(data['income'] == '<=50K')\n",
    "\n",
    "# or use the vlaue count:\n",
    "n_at_most_50k, n_greater_50k = data.income.value_counts()\n",
    "\n",
    "# TODO: Percentage of individuals whose income is more than $50,000\n",
    "greater_percent = n_greater_50k/(n_greater_50k+n_at_most_50k)*100\n",
    "\n",
    "# Print the results\n",
    "print(\"Total number of records: {}\".format(n_records))\n",
    "print(\"Individuals making more than $50,000: {}\".format(n_greater_50k))\n",
    "print(\"Individuals making at most $50,000: {}\".format(n_at_most_50k))\n",
    "print(\"Percentage of individuals making more than $50,000: {}%\".format(greater_percent))\n",
    "\n",
    "#Distribution of target variable (income) below shows that the data is imbalanced.\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.countplot(data['income'] )\n",
    "\n",
    "# Add labels\n",
    "plt.title('Countplot of income')\n",
    "plt.xlabel('income (low (0) and high (1))')\n",
    "plt.ylabel('people ')\n",
    "plt.show()"
   ]
  },
  {
   "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": [
    "## Removing redundant column\n",
    "\"education-num\" represnets \"education_level\", so \"education\" can be deleted. This results in fewer categories; 87 vs 103 total features after one-hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#features_raw = data.drop('education_level', axis = 1)"
   ]
  },
  {
   "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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education-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>39</td>\n",
       "      <td>State-gov</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  education-num       marital-status  \\\n",
       "0   39          State-gov           13.0        Never-married   \n",
       "1   50   Self-emp-not-inc           13.0   Married-civ-spouse   \n",
       "2   38            Private            9.0             Divorced   \n",
       "3   53            Private            7.0   Married-civ-spouse   \n",
       "4   28            Private           13.0   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male        2174.0   \n",
       "1     Exec-managerial         Husband   White     Male           0.0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male           0.0   \n",
       "3   Handlers-cleaners         Husband   Black     Male           0.0   \n",
       "4      Prof-specialty            Wife   Black   Female           0.0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country  \n",
       "0           0.0            40.0   United-States  \n",
       "1           0.0            13.0   United-States  \n",
       "2           0.0            40.0   United-States  \n",
       "3           0.0            40.0   United-States  \n",
       "4           0.0            40.0            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bishebanm\\Documents\\CTE\\Machine-Learning-with-TensorFlow-intro-Udacity\\projects\\p1_charityml\\visuals.py:48: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the data into features and target label\n",
    "#\"education-num\" represnets \"education_level\", so \"education\" can be deleted.\n",
    "income_raw = data['income']\n",
    "features_raw = data.drop(['income','education_level'], axis = 1)\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_raw.head(n = 5))\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": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log-transform the skewed features\n",
    "skewed = ['capital-gain', 'capital-loss']\n",
    "features_log_transformed = pd.DataFrame(data = features_raw)\n",
    "features_log_transformed[skewed] = features_raw[skewed].apply(lambda x: np.log(x + 1))\n",
    "\n",
    "# Visualize the new log distributions\n",
    "vs.distribution(features_log_transformed, transformed = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing Numerical Features\n",
    "In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as `'capital-gain'` or `'capital-loss'` above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.\n",
    "\n",
    "Run the code cell below to normalize each numerical feature. We will use [`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education-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>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>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>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>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>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-num       marital-status  \\\n",
       "0  0.301370          State-gov       0.800000        Never-married   \n",
       "1  0.452055   Self-emp-not-inc       0.800000   Married-civ-spouse   \n",
       "2  0.287671            Private       0.533333             Divorced   \n",
       "3  0.493151            Private       0.400000   Married-civ-spouse   \n",
       "4  0.150685            Private       0.800000   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male      0.667492   \n",
       "1     Exec-managerial         Husband   White     Male      0.000000   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male      0.000000   \n",
       "3   Handlers-cleaners         Husband   Black     Male      0.000000   \n",
       "4      Prof-specialty            Wife   Black   Female      0.000000   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country  \n",
       "0           0.0        0.397959   United-States  \n",
       "1           0.0        0.122449   United-States  \n",
       "2           0.0        0.397959   United-States  \n",
       "3           0.0        0.397959   United-States  \n",
       "4           0.0        0.397959            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler() # default=(0, 1)\n",
    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "\n",
    "features_log_minmax_transform = pd.DataFrame(data = features_log_transformed)\n",
    "features_log_minmax_transform[numerical] = scaler.fit_transform(features_log_transformed[numerical])\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_log_minmax_transform.head(n = 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Data Preprocessing\n",
    "\n",
    "From the table in **Exploring the Data** above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called *categorical variables*) be converted. One popular way to convert categorical variables is by using the **one-hot encoding** scheme. One-hot encoding creates a _\"dummy\"_ variable for each possible category of each non-numeric feature. For example, assume `someFeature` has three possible entries: `A`, `B`, or `C`. We then encode this feature into `someFeature_A`, `someFeature_B` and `someFeature_C`.\n",
    "\n",
    "|   | someFeature |                    | someFeature_A | someFeature_B | someFeature_C |\n",
    "| :-: | :-: |                            | :-: | :-: | :-: |\n",
    "| 0 |  B  |  | 0 | 1 | 0 |\n",
    "| 1 |  C  | ----> one-hot encode ----> | 0 | 0 | 1 |\n",
    "| 2 |  A  |  | 1 | 0 | 0 |\n",
    "\n",
    "Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `'income'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label (\"<=50K\" and \">50K\"), we can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively. In code cell below, you will need to implement the following:\n",
    " - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_log_minmax_transform'` data.\n",
    " - Convert the target label `'income_raw'` to numerical entries.\n",
    "   - Set records with \"<=50K\" to `0` and records with \">50K\" to `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87 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', '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: Encode the 'income_raw' data to numerical values\n",
    "income = income_raw.apply(lambda x: 0 if x == '<=50K' else 1)\n",
    "\n",
    "# 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",
    "# 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": [
    "### Visualize Preprocced Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is ther any null data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_final.isnull().sum()\n",
    "income.isnull().sum()"
   ]
  },
  {
   "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": 9,
   "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.cross_validation 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 = 42)\n",
    "#TODO: change random_state\n",
    "\n",
    "# Show the results of the split\n",
    "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print(\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Evaluating Model Performance\n",
    "In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a *naive predictor*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics and the Naive Predictor\n",
    "*CharityML*, equipped with their research, knows individuals that make more than \\$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \\$50,000 accurately. It would seem that using **accuracy** as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that *does not* make more than \\$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \\$50,000 is *more important* than the model's ability to **recall** those individuals. We can use **F-beta score** as a metric that considers both precision and recall:\n",
    "\n",
    "$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{precision \\cdot recall}{\\left( \\beta^2 \\cdot precision \\right) + recall} $$\n",
    "\n",
    "In particular, when $\\beta = 0.5$, more emphasis is placed on precision. This is called the **F$_{0.5}$ score** (or F-score for simplicity).\n",
    "\n",
    "Looking at the distribution of classes (those who make at most \\$50,000, and those who make more), it's clear most individuals do not make more than \\$50,000. This can greatly affect **accuracy**, since we could simply say *\"this person does not make more than \\$50,000\"* and generally be right, without ever looking at the data! Making such a statement would be called **naive**, since we have not considered any information to substantiate the claim. It is always important to consider the *naive prediction* for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \\$50,000, *CharityML* would identify no one as donors. \n",
    "\n",
    "\n",
    "#### Note: Recap of accuracy, precision, recall\n",
    "\n",
    "** Accuracy ** measures how often the classifier makes the correct prediction. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points).\n",
    "\n",
    "** Precision ** tells us what proportion of messages we classified as spam, actually were spam.\n",
    "It is a ratio of true positives(words classified as spam, and which are actually spam) to all positives(all words classified as spam, irrespective of whether that was the correct classificatio), in other words it is the ratio of\n",
    "\n",
    "`[True Positives/(True Positives + False Positives)]`\n",
    "\n",
    "** Recall(sensitivity)** tells us what proportion of messages that actually were spam were classified by us as spam.\n",
    "It is a ratio of true positives(words classified as spam, and which are actually spam) to all the words that were actually spam, in other words it is the ratio of\n",
    "\n",
    "`[True Positives/(True Positives + False Negatives)]`\n",
    "\n",
    "For classification problems that are skewed in their classification distributions like in our case, for example if we had a 100 text messages and only 2 were spam and the rest 98 weren't, accuracy by itself is not a very good metric. We could classify 90 messages as not spam(including the 2 that were spam but we classify them as not spam, hence they would be false negatives) and 10 as spam(all 10 false positives) and still get a reasonably good accuracy score. For such cases, precision and recall come in very handy. These two metrics can be combined to get the F1 score, which is weighted average(harmonic mean) of the precision and recall scores. This score can range from 0 to 1, with 1 being the best possible F1 score(we take the harmonic mean as we are dealing with ratios)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 - Naive Predictor Performace\n",
    "* If we chose a model that always predicted an individual made more than $50,000, what would  that model's accuracy and F-score be on this dataset? You must use the code cell below and assign your results to `'accuracy'` and `'fscore'` to be used later.\n",
    "\n",
    "** Please note ** that the the purpose of generating a naive predictor is simply to show what a base model without any intelligence would look like. In the real world, ideally your base model would be either the results of a previous model or could be based on a research paper upon which you are looking to improve. When there is no benchmark model set, getting a result better than random choice is a place you could start from.\n",
    "\n",
    "** HINT: ** \n",
    "\n",
    "* When we have a model that always predicts '1' (i.e. the individual makes more than 50k) then our model will have no True Negatives(TN) or False Negatives(FN) as we are not making any negative('0' value) predictions. Therefore our Accuracy in this case becomes the same as our Precision(True Positives/(True Positives + False Positives)) as every prediction that we have made with value '1' that should have '0' becomes a False Positive; therefore our denominator in this case is the total number of records we have in total. \n",
    "* Our Recall score(True Positives/(True Positives + False Negatives)) in this setting becomes 1 as we have no False Negatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [recall score: 1.0000, precision: 0.2478]\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",
    "'''\n",
    "TP = np.sum(income)\n",
    "FP = income.count() - TP\n",
    "FN = 0\n",
    "TN = 0\n",
    "# TODO: Calculate accuracy, precision and recall\n",
    "accuracy_NP = (TP+TN)/(TP+FP+FN+TN)\n",
    "recall_NP = 1\n",
    "precision_NP = (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",
    "def compute_fscore(beta_value, precision , recall):\n",
    "    beta = beta_value\n",
    "    fscore = (1+beta**2)*(precision*recall)/((beta**2*precision)+recall)\n",
    "    return fscore\n",
    "fscore_NP = compute_fscore(0.5, precision_NP , recall_NP)\n",
    "# Print the results \n",
    "print(\"Naive Predictor: [recall score: {:.4f}, precision: {:.4f}]\".format(recall_NP, precision_NP))\n",
    "print(\"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy_NP, fscore_NP))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Supervised Learning Models\n",
    "**The following are some of the supervised learning models that are currently available in** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) **that you may choose from:**\n",
    "- Gaussian Naive Bayes (GaussianNB)\n",
    "- Decision Trees\n",
    "- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)\n",
    "- K-Nearest Neighbors (KNeighbors)\n",
    "- Stochastic Gradient Descent Classifier (SGDC)\n",
    "- Support Vector Machines (SVM)\n",
    "- Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 - Model Application\n",
    "List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen\n",
    "\n",
    "- Describe one real-world application in industry where the model can be applied. \n",
    "- What are the strengths of the model; when does it perform well?\n",
    "- What are the weaknesses of the model; when does it perform poorly?\n",
    "- What makes this model a good candidate for the problem, given what you know about the data?\n",
    "\n",
    "** HINT: **\n",
    "\n",
    "Structure your answer in the same format as above^, with 4 parts for each of the three models you pick. Please include references with your answer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: **\n",
    "\n",
    "**Decision Trees**(source:http://csci.viu.ca/~barskym/teaching/DM2012/lectures/Lecture2.DTApplications.pdf)\n",
    "\n",
    "Applictaion:\n",
    "- Selecting the most promising eggs for invitro fertilization – England, 2000\n",
    "- Soybean disease classification – 1979, 97% accuracy vs. 72% by human expert\n",
    "- Classification system for serial criminal patterns (CSSCP) - using three years' worth of data on armed robbery, the system was able to spot 10 times as many patterns as a team of experienced detectives with access to the same data.\n",
    "- Screening potential terrorists and drug smugglers at border crossings\n",
    "\n",
    "strengths:\n",
    "- The factors of decision are not less important than the classification accuracy\n",
    "- The goal is to assign each record to one of a few broad categories (Categorical attributes with low cardinality*), cardinality - the number of possible distinct values\n",
    "- You suspect that there is a set of objective rules underlying the data\n",
    "\n",
    "weekness: not good for:\n",
    "- Continuous numeric attributes, ordinal attributes\n",
    "- Hierarchical relationships between classes\n",
    "- High-cardinality attributes\n",
    "- Numeric value prediction\n",
    "\n",
    "why good on this data?\n",
    "\n",
    "- Because Precision is important and the data is imbalanced.\n",
    "\n",
    "\n",
    "**Ensemble Methods (Random Forest)**\n",
    "(source: https://www.toptal.com/machine-learning/ensemble-methods-machine-learning)\n",
    "\n",
    "Applications:\n",
    "- In the popular Netflix Competition, the winner used an ensemble method to implement a powerful collaborative filtering algorithm. \n",
    "- Another example is KDD 2009 where the winner also used ensemble methods. \n",
    "- You can also find winners who used these methods in Kaggle competitions, for example here is the interview with the winner of CrowdFlower competition.\n",
    "\n",
    "strengths:\n",
    "- produces more accurate solutions than a single model would\n",
    "\n",
    "weekness & strengths:\n",
    "\n",
    "Although ensemble methods can help you win machine learning competitions by devising sophisticated algorithms and producing results with high accuracy, it is often not preferred in the industries where interpretability is more important. Nonetheless, the effectiveness of these methods are undeniable, and their benefits in appropriate applications can be tremendous. In fields such as healthcare, even the smallest amount of improvement in the accuracy of machine learning algorithms can be something truly valuable.\n",
    "\n",
    "why good on this data?\n",
    "- Because Precision is important and the data is imbalanced.\n",
    "\n",
    "**Support Vector Machines (SVM)**\n",
    "Applications: source: https://medium.com/@rinu.gour123/8-unique-real-life-applications-of-svm-8a96ca43313\n",
    "- Face detection — SVMc classify parts of the image as a face and non-face and create a square boundary around the face.\n",
    "- Text and hypertext categorization — SVMs allow Text and hypertext categorization for both inductive and transductive models. They use training data to classify documents into different categories. It categorizes on the basis of the score generated and then compares with the threshold value.\n",
    "- Classification of images — Use of SVMs provides better search accuracy for image classification. It provides better accuracy in comparison to the traditional query-based searching techniques.\n",
    "- Bioinformatics — It includes protein classification and cancer classification. We use SVM for identifying the classification of genes, patients on the basis of genes and other biological problems.\n",
    "- Protein fold and remote homology detection — Apply SVM algorithms for protein remote homology detection.\n",
    "- Handwriting recognition — We use SVMs to recognize handwritten characters used widely.\n",
    "- Generalized predictive control(GPC) — Use SVM based GPC to control chaotic dynamics with useful parameters.\n",
    "\n",
    "strengths: source:https://dhirajkumarblog.medium.com/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107#:~:text=SVM%20algorithm%20is%20not%20suitable,samples%2C%20the%20SVM%20will%20underperform.)\n",
    "- SVM works relatively well when there is a clear margin of separation between classes.\n",
    "- SVM is more effective in high dimensional spaces.\n",
    "- SVM is effective in cases where the number of dimensions is greater than the number of samples.\n",
    "- SVM is relatively memory efficient\n",
    "\n",
    "weekness: source:https://dhirajkumarblog.medium.com/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107#:~:text=SVM%20algorithm%20is%20not%20suitable,samples%2C%20the%20SVM%20will%20underperform.)\n",
    "- SVM algorithm is not suitable for large data sets.\n",
    "- SVM does not perform very well when the data set has more noise i.e. target classes are overlapping.\n",
    "- In cases where the number of features for each data point exceeds the number of training data samples, the SVM will - underperform.\n",
    "- As the support vector classifier works by putting data points, above and below the classifying hyperplane there is no probabilistic explanation for the classification.\n",
    "\n",
    "why good on this data?\n",
    "- Because Precision is important and the data is imbalanced."
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_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",
    "    # 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",
    "    predictions_train = learner.predict(X_train[:300])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # TODO: Calculate the total prediction time\n",
    "    results['pred_time'] = end - start\n",
    "            \n",
    "    # TODO: Compute accuracy on the first 300 training samples which is y_train[:300]\n",
    "    results['acc_train'] = accuracy_score(y_train[:300],predictions_train)\n",
    "        \n",
    "    # TODO: Compute accuracy on test set using accuracy_score()\n",
    "    results['acc_test'] = accuracy_score(y_test,predictions_test)\n",
    "    \n",
    "    # TODO: Compute F-score on the the first 300 training samples using fbeta_score()\n",
    "    results['f_train'] = fbeta_score(y_train[:300],predictions_train, beta=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",
    "    \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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361 3617 36177\n",
      "DecisionTreeClassifier trained on 361 samples.\n",
      "DecisionTreeClassifier trained on 3617 samples.\n",
      "DecisionTreeClassifier trained on 36177 samples.\n",
      "RandomForestClassifier trained on 361 samples.\n",
      "RandomForestClassifier trained on 3617 samples.\n",
      "RandomForestClassifier trained on 36177 samples.\n",
      "SVC trained on 361 samples.\n",
      "SVC trained on 3617 samples.\n",
      "SVC trained on 36177 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bishebanm\\Documents\\CTE\\Machine-Learning-with-TensorFlow-intro-Udacity\\projects\\p1_charityml\\visuals.py:118: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n",
      "  pl.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\nclf_A = GaussianNB()## good for recall\\nclf_B = DecisionTreeClassifier(random_state=random_state, max_depth=10 ,min_samples_leaf=15 )\\nclf_C = BaggingClassifier(random_state=random_state)\\nclf_d = RandomForestClassifier(random_state=random_state, max_depth=10 ,min_samples_leaf=15 )##\\nclf_e = AdaBoostClassifier(random_state=random_state)\\nclf_f = GradientBoostingClassifier(random_state=random_state)\\nclf_g = KNeighborsClassifier()#\\nclf_h = SGDClassifier(random_state=random_state)\\nclf_i = SVC(random_state=random_state)\\n# Collect results on the learners\\nresults = {}\\nfor 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] =         train_predict(clf, samples, X_train, y_train, X_test, y_test)\\n\\n# Run metrics visualization for the three supervised learning models chosen\\nvs.evaluate(results, accuracy_NP, fscore_NP)\\n\\n# Collect results on the learners\\nresults = {}\\nfor clf in [clf_d, clf_e, clf_f]:\\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] =         train_predict(clf, samples, X_train, y_train, X_test, y_test)\\n\\n# Run metrics visualization for the three supervised learning models chosen\\nvs.evaluate(results, accuracy, fscore)\\n\\n# Collect results on the learners\\nresults = {}\\nfor clf in [clf_g, clf_h, clf_i]:\\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] =         train_predict(clf, samples, X_train, y_train, X_test, y_test)\\n\\n# Run metrics visualization for the three supervised learning models chosen\\nvs.evaluate(results, accuracy_NP, fscore_NP)\\n'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Import the three supervised learning models from sklearn\n",
    "'''\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",
    "'''\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# TODO: Initialize the three models\n",
    "random_state=100\n",
    "\n",
    "clf_B = DecisionTreeClassifier(random_state=random_state, max_depth=10 ,min_samples_leaf=15 )\n",
    "clf_d = RandomForestClassifier(random_state=random_state, max_depth=10 ,min_samples_leaf=15 )\n",
    "clf_i = SVC(random_state=random_state)\n",
    "\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 = len(y_train)//10\n",
    "samples_1 = len(y_train)//100\n",
    "print(samples_1, samples_10,samples_100)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_B, clf_d, clf_i]:\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_NP, fscore_NP)\n",
    "\n",
    "'''\n",
    "clf_A = GaussianNB()## good for recall\n",
    "clf_B = DecisionTreeClassifier(random_state=random_state, max_depth=10 ,min_samples_leaf=15 )\n",
    "clf_C = BaggingClassifier(random_state=random_state)\n",
    "clf_d = RandomForestClassifier(random_state=random_state, max_depth=10 ,min_samples_leaf=15 )##\n",
    "clf_e = AdaBoostClassifier(random_state=random_state)\n",
    "clf_f = GradientBoostingClassifier(random_state=random_state)\n",
    "clf_g = KNeighborsClassifier()#\n",
    "clf_h = SGDClassifier(random_state=random_state)\n",
    "clf_i = SVC(random_state=random_state)\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_NP, fscore_NP)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_d, clf_e, clf_f]:\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)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_g, clf_h, clf_i]:\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_NP, fscore_NP)\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Improving Results\n",
    "In this final section, you will choose from the three supervised learning models the *best* model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (`X_train` and `y_train`) by tuning at least one parameter to improve upon the untuned model's F-score. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3 - Choosing the Best Model\n",
    "\n",
    "* Based on the evaluation you performed earlier, in one to two paragraphs, explain to *CharityML* which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \\$50,000. \n",
    "\n",
    "** HINT: ** \n",
    "Look at the graph at the bottom left from the cell above(the visualization created by `vs.evaluate(results, accuracy, fscore)`) and check the F score for the testing set when 100% of the training set is used. Which model has the highest score? Your answer should include discussion of the:\n",
    "* metrics - F score on the testing when 100% of the training data is used, \n",
    "* prediction/training time\n",
    "* the algorithm's suitability for the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: **\n",
    "\n",
    "The Decision Tree is chosen.\n",
    "\n",
    "Decision Tree is very faster than RandomForest and SVC. Methods on 1% of data seem to overfit the data, but with all the data, this problem is resolved.\n",
    "All the methods on testing data (predictions) have very similar F1Score; RandomForest have slightly better Fscore. However, due to the high computational cost, Decision Tree is chosen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4 - Describing the Model in Layman's Terms\n",
    "\n",
    "* In one to two paragraphs, explain to *CharityML*, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical jargon, such as describing equations.\n",
    "\n",
    "** HINT: **\n",
    "\n",
    "When explaining your model, if using external resources please include all citations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** \n",
    "\n",
    "CharityML wants to predict which people have an income of larger than 50 K based on their level of education, race, marital status,.... It highly depends on their capital-loss, age and education level, and capital gain. See the table below:\n",
    "\n",
    "For CharityML, a DecsioinTree is used. It is a non-parametric supervised learning method used for classification and regression.\n",
    "\n",
    "In in Layman's Terms: \n",
    "\n",
    "Decision tree spreads data on tree leaves. While the branches act as a guide to the correct classification. For example, it starts from the thickest part of the tree, by categorizing data into some large classes, then for each class again it categorizes the data into some smaller classes, and it repeats this process till we reach the leaves.\n",
    "\n",
    "\"Decision tree builds classification or regression models in the form of a tree structure. It breaks down a data set into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision node has two or more branches. Leaf node represents a classification or decision. The topmost decision node in a tree which corresponds to the best predictor called root node. Decision trees can handle both categorical and numerical data.\" [Source: https://chiragsehra42.medium.com/decision-trees-explained-easily-28f23241248]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Relative Importance')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "clf_A = GaussianNB()## good for recall\n",
    "clf_B = DecisionTreeClassifier()\n",
    "clf_C = BaggingClassifier()\n",
    "clf_d = RandomForestClassifier()##\n",
    "clf_e = AdaBoostClassifier()\n",
    "clf_f = GradientBoostingClassifier()\n",
    "clf_g = KNeighborsClassifier()#\n",
    "clf_h = SGDClassifier()\n",
    "clf_i = SVC()\n",
    "'''\n",
    "\n",
    "learner = AdaBoostClassifier(random_state=100)\n",
    "learner.fit(X_train, y_train)\n",
    "predictions_test = learner.predict(X_test)\n",
    "\n",
    "features = features_final.columns[:features_final.shape[1]]\n",
    "importances = learner.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices[-5:])), importances[indices[-5:]], color='b', align='center')\n",
    "plt.yticks(range(len(indices[-5:])), features[indices[-5:]])\n",
    "plt.xlabel('Relative Importance')"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "#### Use Grid Serach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pred_time: 167.6437\n",
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8226\n",
      "F-score on testing data: 0.6517\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8577\n",
      "Final F-score on the testing data: 0.7442\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6391</td>\n",
       "      <td>354</td>\n",
       "      <td>6745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>933</td>\n",
       "      <td>1367</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>7324</td>\n",
       "      <td>1721</td>\n",
       "      <td>9045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted     0     1   All\n",
       "Actual                     \n",
       "0          6391   354  6745\n",
       "1           933  1367  2300\n",
       "All        7324  1721  9045"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "start = time() # Get start time      \n",
    "# TODO: Initialize the classifier\n",
    "clf_1 = RandomForestClassifier(random_state=100)\n",
    "clf_2 = AdaBoostClassifier(random_state=100)\n",
    "\n",
    "# The chosen model:\n",
    "clf_3 = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# TODO: Create the parameters list you wish to tune, using a dictionary if needed.\n",
    "# HINT: parameters = {'parameter_1': [value1, value2], 'parameter_2': [value1, value2]}\n",
    "parameters_1 = {\"max_depth\": [3, 10],\n",
    "              \"n_estimators\": list(range(10, 200)),\n",
    "              \"max_features\": list(range(1, X_test.shape[1]+1)),\n",
    "              \"min_samples_split\": list(range(2, 11)),\n",
    "              \"min_samples_leaf\": list(range(1, 15)),\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "parameters_2 = {\"n_estimators\": [10, 100, 200, 400],\n",
    "              \"learning_rate\": [0.001, 0.005, .01, 0.05, 0.1, 0.2]}\n",
    "parameters_3 = {'max_depth':[2,4,6,8,10,15],'min_samples_leaf':[2,4,6,8,10,15], 'min_samples_split':[2,4,6,8,10,15]}\n",
    "\n",
    "clf = clf_3\n",
    "parameters = parameters_3\n",
    "\n",
    "# TODO: Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(fbeta_score,beta=0.5)\n",
    "\n",
    "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "grid_obj = GridSearchCV(clf, param_grid=parameters, scoring = scorer)\n",
    "\n",
    "# TODO: Fit the grid search object to the training data and find the optimal parameters using fit()\n",
    "grid_fit = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Get the estimator\n",
    "best_clf = grid_fit.best_estimator_\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "\n",
    "end = time() # Get end time\n",
    "print(\"pred_time: {:.4f}\".format(end - start))\n",
    "\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)))\n",
    "\n",
    "#Confusion_matrix: source: https://www.analyticsvidhya.com/blog/2020/09/precision-recall-machine-learning/\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test,best_predictions)\n",
    "pd.crosstab(y_test, best_predictions, rownames = ['Actual'], colnames =['Predicted'], margins = True)\n",
    "\n",
    "#from sklearn.metrics import classification_report\n",
    "#print(classification_report(y_test, best_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use Randomized Serach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nstart = time() # Get start time  \\n# build a classifier for support vector machines\\nclf_svc = SVC()\\n\\n# Set up the hyperparameter search\\n# look at setting up your search for C (recommend 0-10 range), \\nparam_dist = {\"C\": [0.1, 0.5, 1, 3, 5],\\n              \"kernel\": [\\'linear\\',\\'rbf\\']\\n             }\\n             \\nscorer = make_scorer(fbeta_score,beta=0.5)\\n\\n\\n# Run a randomized search over the hyperparameters\\nsvc_search = RandomizedSearchCV(clf_svc, param_distributions=param_dist, scoring = scorer)\\n\\n# Fit the model on the training data\\nsvc_search.fit(X_train, y_train)\\n\\n# Make predictions on the test data\\nsvc_preds = svc_search.best_estimator_.predict(X_test)\\n\\nprint_metrics(y_test, svc_preds, \\'svc\\')\\nend = time() # Get end time\\nprint(\"pred_time: {:.4f}\".format(end - start))\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "def print_metrics(y_true, preds, model_name=None):\n",
    "\n",
    "    #INPUT:\n",
    "    #y_true - the y values that are actually true in the dataset (numpy array or pandas series)\n",
    "    #preds - the predictions for those values from some model (numpy array or pandas series)\n",
    "    #model_name - (str - optional) a name associated with the model if you would like to add it to the print statements \n",
    "\n",
    "    #OUTPUT:\n",
    "    #None - prints the accuracy, precision, recall, and F1 score\n",
    "\n",
    "    if model_name == None:\n",
    "        print('Accuracy score: ', format(accuracy_score(y_true, preds)))\n",
    "        print('Precision score: ', format(precision_score(y_true, preds)))\n",
    "        print('Recall score: ', format(recall_score(y_true, preds)))\n",
    "        print('F1 score: ', format(f1_score(y_true, preds)))\n",
    "        print('\\n\\n')\n",
    "    \n",
    "    else:\n",
    "        print('Accuracy score for ' + model_name + ' :' , format(accuracy_score(y_true, preds)))\n",
    "        print('Precision score ' + model_name + ' :', format(precision_score(y_true, preds)))\n",
    "        print('Recall score ' + model_name + ' :', format(recall_score(y_true, preds)))\n",
    "        print('F1 score ' + model_name + ' :', format(f1_score(y_true, preds)))\n",
    "        print('\\n\\n')\n",
    "        \n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "start = time() # Get start time        \n",
    "clf_rf = RandomForestClassifier()\n",
    "\n",
    "# Set up the hyperparameter search\n",
    "param_dist = {\"max_depth\": [3, None],\n",
    "              \"n_estimators\": list(range(10, 200)),\n",
    "              \"max_features\": list(range(1, X_test.shape[1]+1)),\n",
    "              \"min_samples_split\": list(range(2, 11)),\n",
    "              \"min_samples_leaf\": list(range(1, 11)),\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "\n",
    "scorer = make_scorer(fbeta_score,beta=0.5)\n",
    "\n",
    "# Run a randomized search over the hyperparameters\n",
    "random_search = RandomizedSearchCV(clf_rf, param_distributions=param_dist, scoring = scorer)\n",
    "\n",
    "# Fit the model on the training data\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "rf_preds = random_search.best_estimator_.predict(X_test)\n",
    "\n",
    "print_metrics(y_test, rf_preds, 'random forest')\n",
    "end = time() # Get end time\n",
    "print(\"pred_time: {:.4f}\".format(end - start))\n",
    "\n",
    "'''\n",
    "\n",
    "'''\n",
    "start = time() # Get start time  \n",
    "# build a classifier for ada boost\n",
    "clf_ada = AdaBoostClassifier()\n",
    "\n",
    "# Set up the hyperparameter search\n",
    "param_dist = {\"n_estimators\": [10, 100, 200, 400],\n",
    "              \"learning_rate\": [0.001, 0.005, .01, 0.05, 0.1, 0.2, 0.3]}#, 0.4, 0.5, 1, 2, 10, 20]}\n",
    "\n",
    "scorer = make_scorer(fbeta_score,beta=0.5)\n",
    "\n",
    "# Run a randomized search over the hyperparameters\n",
    "ada_search = RandomizedSearchCV(clf_ada, param_distributions=param_dist, scoring = scorer)\n",
    "\n",
    "# Fit the model on the training data\n",
    "ada_search.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "ada_preds = ada_search.best_estimator_.predict(X_test)\n",
    "\n",
    "print_metrics(y_test, ada_preds, 'adaboost')\n",
    "end = time() # Get end time\n",
    "print(\"pred_time: {:.4f}\".format(end - start))\n",
    "'''\n",
    "\n",
    "'''\n",
    "start = time() # Get start time  \n",
    "# build a classifier for support vector machines\n",
    "clf_svc = SVC()\n",
    "\n",
    "# Set up the hyperparameter search\n",
    "# look at setting up your search for C (recommend 0-10 range), \n",
    "param_dist = {\"C\": [0.1, 0.5, 1, 3, 5],\n",
    "              \"kernel\": ['linear','rbf']\n",
    "             }\n",
    "             \n",
    "scorer = make_scorer(fbeta_score,beta=0.5)\n",
    "\n",
    "\n",
    "# Run a randomized search over the hyperparameters\n",
    "svc_search = RandomizedSearchCV(clf_svc, param_distributions=param_dist, scoring = scorer)\n",
    "\n",
    "# Fit the model on the training data\n",
    "svc_search.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "svc_preds = svc_search.best_estimator_.predict(X_test)\n",
    "\n",
    "print_metrics(y_test, svc_preds, 'svc')\n",
    "end = time() # Get end time\n",
    "print(\"pred_time: {:.4f}\".format(end - start))\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code does not give good results! It is worst than the grid search! It gives F1score of 0.68 for the optimized adaboost method which is pretty low!"
   ]
  },
  {
   "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",
    "For the Decision Tree\n",
    "\n",
    "|     Metric     | Unoptimized Model | Optimized Model | Naive Predictor |\n",
    "| :------------: | :---------------: | :-------------: | :--------------:|\n",
    "| Accuracy Score |0.8226             |  0.8583         |0.2478           |\n",
    "| F-score        |0.6517             |  0.7463         |0.2917           |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: **\n",
    "\n",
    "The optimized model's accuracy and F-score on the testing data are presented in the above table.\n",
    "\n",
    "The results of the optimized model are better than the unoptimized better.\n",
    "\n",
    "For the Naive Predictor: the recall score is 1.0000, and the precision is 0.2478. So, its accuracy is low, because the F-score is low too. However, for the chosen model F-score is much higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Feature Importance\n",
    "\n",
    "An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \\$50,000.\n",
    "\n",
    "Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a `feature_importance_` attribute, which is a function that ranks the importance of features according to the chosen classifier.  In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6 - Feature Relevance Observation\n",
    "When **Exploring the Data**, it was shown there are thirteen available features for each individual on record in the census data. Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "By intuition, the first 5 features, are age, education_num, capital_gain, capital_loss, and hours-per-week. \n",
    "\n",
    "There are 87 different parameters in the dataset after On-Hot Encoding, so the heatmap will be very busy. So, I  only considered the following featured:\n",
    "`features_final['Capital-loss','age','capital-gain','hours-per-week','education-num']` \n",
    "and plot the following.\n",
    "\n",
    "We can see that the \"education-num\" is the most important factor, and then capital-gain, and then age, and then, hours perweek, and then capital loss are important. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 947.425x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "features_final.describe()\n",
    "\n",
    "selected_columns = features_final[['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']]\n",
    "new_df1 = selected_columns.copy()\n",
    "new_df2 = pd.concat([new_df1, income], axis=1, sort=False)\n",
    "\n",
    "sns.heatmap(new_df2.corr(), annot=True, cmap=\"YlGnBu\");\n",
    "\n",
    "sns.pairplot(new_df2, hue = 'income');\n",
    "\n",
    "new_df2.hist();"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score on testing data: 0.8595\n",
      "F-score on testing data: 0.7455\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import a supervised learning model that has 'feature_importances_'\n",
    "'''\n",
    "clf_A = GaussianNB()## good for recall\n",
    "clf_B = DecisionTreeClassifier()\n",
    "clf_C = BaggingClassifier()\n",
    "clf_d = RandomForestClassifier()##\n",
    "clf_e = AdaBoostClassifier()\n",
    "clf_f = GradientBoostingClassifier()\n",
    "clf_g = KNeighborsClassifier()#\n",
    "clf_h = SGDClassifier()\n",
    "clf_i = SVC()\n",
    "'''\n",
    "\n",
    "learner = AdaBoostClassifier()\n",
    "\n",
    "# TODO: Train the supervised model on the training set using .fit(X_train, y_train)\n",
    "learner.fit(X_train, y_train)\n",
    "predictions_test = learner.predict(X_test)\n",
    "\n",
    "features = features_final.columns[:features_final.shape[1]]\n",
    "\n",
    "# TODO: Extract the feature importances using .feature_importances_ \n",
    "importances = learner.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices[-5:])), importances[indices[-5:]], color='b', align='center')\n",
    "plt.yticks(range(len(indices[-5:])), features[indices[-5:]])\n",
    "plt.xlabel('Relative Importance')\n",
    "\n",
    "# Plot\n",
    "vs.feature_plot(importances, X_train, y_train)\n",
    "\n",
    "print(\"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions_test)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions_test, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7 - Extracting Feature Importance\n",
    "\n",
    "Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \\$50,000.  \n",
    "* How do these five features compare to the five features you discussed in **Question 6**?\n",
    "* If you were close to the same answer, how does this visualization confirm your thoughts? \n",
    "* If you were not close, why do you think these features are more relevant?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "In Question 6, the \"education-num\" is the most important factor, and then capital-gain, and then age, and then, hours perweek, and then capital loss are important.\n",
    "\n",
    "But, adaBoost classifier, gives a different order for the important features. This should be a better order, because an ML algorithm with FScore of 0.7455 gives this order."
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8577\n",
      "F-score on testing data: 0.7442\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8299\n",
      "F-score on testing data: 0.6870\n"
     ]
    }
   ],
   "source": [
    "# Import functionality for cloning a model\n",
    "from sklearn.base import clone\n",
    "\n",
    "# Reduce the feature space\n",
    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "\n",
    "# Train on the \"best\" model found from grid search earlier\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "\n",
    "# Make new predictions\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# Report scores from the final model using both versions of data\n",
    "print(\"Final Model trained on full data\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))\n",
    "print(\"\\nFinal Model trained on reduced data\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the best number of important features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the first elements from feature_importances_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8577\n",
      "F-score on testing data: 0.7442\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8556\n",
      "F-score on testing data: 0.7408\n"
     ]
    }
   ],
   "source": [
    "# Import functionality for cloning a model\n",
    "from sklearn.base import clone\n",
    "\n",
    "n_imp =14\n",
    "# Reduce the feature space\n",
    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:n_imp]]]\n",
    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:n_imp]]]\n",
    "\n",
    "# Train on the \"best\" model found from grid search earlier\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "\n",
    "# Make new predictions\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# Report scores from the final model using both versions of data\n",
    "print(\"Final Model trained on full data\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))\n",
    "print(\"\\nFinal Model trained on reduced data\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use SelectPercentile and SelectKBest to find the best faetures:\n",
    "\n",
    "SelectPercentile select features according to a percentile of the highest scores.\n",
    "\n",
    "For SelectBest see [https://stats.stackexchange.com/questions/253086/selectkbest-feature-selection-python-scikit-learn#:~:text=SelectKBest%20then%20simply%20retains%20the,feature%20is%20independent%20of%20y.]\n",
    "\"if chi2 is passed for SelectBest as a score function, SelectKBest will compute the chi2 statistic between each feature of X and y (assumed to be class labels). A small value will mean the feature is independent of y. A large value will mean the feature is non-randomly related to y, and so likely to provide important information. Only k features will be retained.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Final Model trained on reduced data using SelectPercentile\n",
      "------\n",
      "Accuracy on testing data: 0.8023\n",
      "F-score on testing data: 0.6042\n",
      "\n",
      "Final Model trained on reduced data using SelectKBest\n",
      "------\n",
      "Accuracy on testing data: 0.8503\n",
      "F-score on testing data: 0.7301\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectPercentile, chi2\n",
    "X_train_reduced = SelectPercentile(chi2, percentile=10).fit_transform(X_train, y_train)\n",
    "X_test_reduced = SelectPercentile(chi2, percentile=10).fit_transform(X_test, y_test)\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# Report scores from the final model using both versions of data\n",
    "print(\"\\nFinal Model trained on reduced data using SelectPercentile\\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",
    "\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "X_train_reduced = SelectKBest(chi2,  k=20).fit_transform(X_train, y_train)\n",
    "X_test_reduced = SelectKBest(chi2,  k=20).fit_transform(X_test, y_test)\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# Report scores from the final model using both versions of data\n",
    "print(\"\\nFinal Model trained on reduced data using SelectKBest\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the specefic methods that train a model using only the most important features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Feature ranking with Recursive Feature Elimination (RFE):\n",
    "\n",
    "\"Technically, RFE is a wrapper-style feature selection algorithm that also uses filter-based feature selection internally. RFE works by searching for a subset of features by starting with all features in the training dataset and successfully removing features until the desired number remains.\" Source: https://machinelearningmastery.com/rfe-feature-selection-in-python/#:~:text=Technically%2C%20RFE%20is%20a%20wrapper,until%20the%20desired%20number%20remains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Final Model trained with RFE\n",
      "------\n",
      "Accuracy on testing data: 0.8521\n",
      "F-score on testing data: 0.7379\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "estimator = clone(best_clf)\n",
    "selector = RFE(estimator, n_features_to_select=5, step=1)\n",
    "selector = selector.fit(X_train, y_train)\n",
    "#print(selector.support_)\n",
    "#print(selector.ranking_)\n",
    "\n",
    "# Make new predictions\n",
    "RFE_predictions = selector.predict(X_test)\n",
    "\n",
    "# Report scores from the final model using both versions of data\n",
    "print(\"\\nFinal Model trained with RFE\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, RFE_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, RFE_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8 - Effects of Feature Selection\n",
    "\n",
    "* How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?\n",
    "* If training time was a factor, would you consider using the reduced data as your training set?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "Some features are ignored, and the FScore becomes lower. It means that although the 5 most features are the most important features, there are some other features that still have an effect on the model, and should not be ignored if we desire to have a high FScore.\n",
    "\n",
    "I have increased the number of important features, and I noticed that the first 14 features give very similar Fscore to the model trained on the whole data. So, instead of considering all 87 features, one can only consider the first 14 features to reduce the computation time."
   ]
  },
  {
   "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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}