capstone.ipynb

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    "\n",
    "# Capstone Project: \n",
    "## *Machine learning approaches to the classification problem for autism spectrum disorder*\n",
    "\n",
    "### Project designed and executed by Dr. Kanad Basu"
   ]
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    "We'll be using the \"Autistic Spectrum Disorder Screening Data for Adult\" public dataset from the \n",
    "[`UCI repository`](http://archive.ics.uci.edu/ml/datasets/Autism+Screening+Adult#). The datset was donated by Prof. Fadi Thabtah, after being published in the article \"Autism Spectrum Disorder Screening: Machine Learning\n",
    "Adaptation and DSM-5 Fulfillment\". You can find the article by Fadi Thabtah [`online`](https://dl.acm.org/citation.cfm?id=3107515). The data we investigate here consists of small changes to the original dataset, such as removing the 'age_desc' feature and records with missing or ill-formatted entries. Very few published articles are available online as the dataset has not been out for long and as a result refernce to a benchmark model is very rare.\n",
    "\n",
    "\n",
    "**Abstract:** \n",
    "Autistic Spectrum Disorder (ASD) is a neurodevelopment  condition associated with significant healthcare costs, and early diagnosis can significantly reduce these. Unfortunately, waiting times for an ASD diagnosis are lengthy and procedures are not cost effective. The economic impact of autism and the increase in the number of ASD cases across the world reveals an urgent need for the development of easily implemented and effective screening methods. Therefore, a time-efficient and accessible ASD screening is imminent to help health professionals and inform individuals whether they should pursue formal clinical diagnosis.  \n",
    "\n",
    "The rapid growth in the number of ASD cases worldwide necessitates datasets related to behaviour traits. However, such datasets are rare making it difficult to perform thorough analyses to improve the efficiency, sensitivity, specificity and predictive accuracy of the ASD screening process. Presently, very limited autism datasets associated with clinical or screening are available and most of them are genetic in nature. Hence, we propose a new dataset related to autism screening of adults that contained 20 features to be utilised for further analysis especially in determining influential autistic traits and improving the classification of ASD cases. In this dataset, we record ten behavioural features (AQ-10-Adult) plus ten individuals characteristics that have proved to be effective in detecting the ASD cases from controls in behaviour science. "
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    "## Road Map:\n",
    "\n",
    "* [Step 0](#step0): Import Datasets.\n",
    "\n",
    "* [Step 1](#step1): Clean Datasets (The data needs to be cleaned; many rows contain missing data, and there may be erroneous data identifiable as outliers).\n",
    "\n",
    "* [Step 2](#step2): A quick visualization with *Seaborn*.\n",
    "\n",
    "* [Step 3](#step3): At First, I applied several Supervised Machine Learning (SML) techniques on the data for classification purpose.\n",
    "\n",
    "* [Step 4](#step4): Next, I experimented with different topologies, optimizers, and hyperparameters for different models.\n",
    "\n",
    "* [Step 5](#step5): Model tuning.\n",
    "\n",
    "* [Step 6](#step6): Feature Selection.\n",
    "\n",
    "* [Step 7](#step7): Then I built a Multi-Layer Perceptron and train it to classify indivisual with ASD based on its features.\n",
    "\n",
    "* [Step 8](#step8): Conclusion."
   ]
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   "source": [
    "## Let's begin with preparing our data set."
   ]
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   "source": [
    "---\n",
    "<a id='step0'></a>\n",
    "## Step 0: Import Datasets\n",
    "\n",
    " I start by importing the 'asv.csv' file into a Pandas dataframe and take a look at it."
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
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       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>A9_Score</th>\n",
       "      <th>...</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>jundice</th>\n",
       "      <th>austim</th>\n",
       "      <th>contry_of_res</th>\n",
       "      <th>used_app_before</th>\n",
       "      <th>result</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>relation</th>\n",
       "      <th>Class/ASD</th>\n",
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       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>United States</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Self</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>Latino</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Self</td>\n",
       "      <td>NO</td>\n",
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       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>Latino</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spain</td>\n",
       "      <td>no</td>\n",
       "      <td>8</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Parent</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Self</td>\n",
       "      <td>NO</td>\n",
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       "      <td>?</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>?</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
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       "   id  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n",
       "0   1         1         1         1         1         0         0         1   \n",
       "1   2         1         1         0         1         0         0         0   \n",
       "2   3         1         1         0         1         1         0         1   \n",
       "3   4         1         1         0         1         0         0         1   \n",
       "4   5         1         0         0         0         0         0         0   \n",
       "\n",
       "   A8_Score  A9_Score    ...     gender       ethnicity jundice austim  \\\n",
       "0         1         0    ...          f  White-European      no     no   \n",
       "1         1         0    ...          m          Latino      no    yes   \n",
       "2         1         1    ...          m          Latino     yes    yes   \n",
       "3         1         0    ...          f  White-European      no    yes   \n",
       "4         1         0    ...          f               ?      no     no   \n",
       "\n",
       "   contry_of_res used_app_before result     age_desc  relation Class/ASD  \n",
       "0  United States              no      6  18 and more      Self        NO  \n",
       "1         Brazil              no      5  18 and more      Self        NO  \n",
       "2          Spain              no      8  18 and more    Parent       YES  \n",
       "3  United States              no      6  18 and more      Self        NO  \n",
       "4          Egypt              no      2  18 and more         ?        NO  \n",
       "\n",
       "[5 rows x 22 columns]"
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   "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",
    "\n",
    "data = pd.read_csv('ASD.csv')\n",
    "display(data.head(n=5))"
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     "text": [
      "Total number of records: 704\n",
      "Individuals diagonised with ASD: 189\n",
      "Individuals not diagonised with ASD: 515\n",
      "Percentage of individuals diagonised with ASD: 26.85%\n"
     ]
    }
   ],
   "source": [
    "# Total number of records\n",
    "n_records = len(data.index)\n",
    "\n",
    "# TODO: Number of records where individual's with ASD\n",
    "n_asd_yes = len(data[data['Class/ASD'] == 'YES'])\n",
    "\n",
    "# TODO: Number of records where individual's with no ASD\n",
    "n_asd_no = len(data[data['Class/ASD'] == 'NO'])\n",
    "\n",
    "# TODO: Percentage of individuals whose are with ASD\n",
    "yes_percent = float(n_asd_yes) / n_records *100\n",
    "\n",
    "# Print the results\n",
    "print \"Total number of records: {}\".format(n_records)\n",
    "print \"Individuals diagonised with ASD: {}\".format(n_asd_yes)\n",
    "print \"Individuals not diagonised with ASD: {}\".format(n_asd_no)\n",
    "print \"Percentage of individuals diagonised with ASD: {:.2f}%\".format(yes_percent)"
   ]
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    "** Featureset Exploration **\n",
    "\n",
    "This data contains 704 instances, and contains the following attributes:\n",
    "* **age**: *number* (Age in years). \n",
    "* **gendar**: *String* [Male/Female]. \n",
    "* **ethnicity**: *String* (List of common ethnicities in text format). \n",
    "* **Born with jaundice**: *Boolean* [yes or no]. \n",
    "* **Family member with PDD**: *Boolean* [yes or no]. \n",
    "* **Who is completing the test**: *String* [Parent, self, caregiver, medical staff, clinician ,etc.].\n",
    "* **Country of residence **: *String* (List of countries in text format).\n",
    "* **Used the screening app before **: *Boolean* [yes or no] (Whether the user has used a screening app) \n",
    "* **Screening Method Type**: *Integer* [0,1,2,3] (The type of screening methods chosen based on age category (0=toddler, 1=child, 2= adolescent, 3= adult).\n",
    "* **Question 1-10 Answer**: *Binary* [0, 1] (The answer code of the question based on the screening method used).\n",
    "* **Screening Score**: *Integer* (The final score obtained based on the scoring algorithm of the screening method used. This was computed in an automated manner)."
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   "source": [
    "----\n",
    "## Preparing the Data\n",
    "Before data can be used as input for machine learning algorithms, it must be cleaned, formatted, and maybe even restructured — this is typically known as **preprocessing**. Unfortunately, for this dataset, there are many invalid or missing entries(?) we must deal with, moreover, 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."
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   "source": [
    "I use the optional parmaters in read_csv to convert missing data (indicated by a ?) into NaN, and to add the appropriate column names ():"
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       "      <td>Latino</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Self</td>\n",
       "      <td>NO</td>\n",
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       "      <td>m</td>\n",
       "      <td>Latino</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spain</td>\n",
       "      <td>no</td>\n",
       "      <td>8</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Parent</td>\n",
       "      <td>YES</td>\n",
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       "      <th>3</th>\n",
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       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Self</td>\n",
       "      <td>NO</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   id  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n",
       "0   1         1         1         1         1         0         0         1   \n",
       "1   2         1         1         0         1         0         0         0   \n",
       "2   3         1         1         0         1         1         0         1   \n",
       "3   4         1         1         0         1         0         0         1   \n",
       "4   5         1         0         0         0         0         0         0   \n",
       "\n",
       "   A8_Score  A9_Score    ...     gender       ethnicity jundice austim  \\\n",
       "0         1         0    ...          f  White-European      no     no   \n",
       "1         1         0    ...          m          Latino      no    yes   \n",
       "2         1         1    ...          m          Latino     yes    yes   \n",
       "3         1         0    ...          f  White-European      no    yes   \n",
       "4         1         0    ...          f             NaN      no     no   \n",
       "\n",
       "   contry_of_res used_app_before result     age_desc  relation Class/ASD  \n",
       "0  United States              no      6  18 and more      Self        NO  \n",
       "1         Brazil              no      5  18 and more      Self        NO  \n",
       "2          Spain              no      8  18 and more    Parent       YES  \n",
       "3  United States              no      6  18 and more      Self        NO  \n",
       "4          Egypt              no      2  18 and more       NaN        NO  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asd_data = pd.read_csv('ASD.csv', na_values=['?'])\n",
    "asd_data.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here I evaluate whether the data needs cleaning; your model is only as good as the data it's given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
       "      <th>A6_Score</th>\n",
       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>A9_Score</th>\n",
       "      <th>A10_Score</th>\n",
       "      <th>age</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>704.000000</td>\n",
       "      <td>704.000000</td>\n",
       "      <td>704.000000</td>\n",
       "      <td>704.000000</td>\n",
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       "      <td>702.000000</td>\n",
       "      <td>704.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>352.500000</td>\n",
       "      <td>0.721591</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.457386</td>\n",
       "      <td>0.495739</td>\n",
       "      <td>0.498580</td>\n",
       "      <td>0.284091</td>\n",
       "      <td>0.417614</td>\n",
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       "      <td>0.323864</td>\n",
       "      <td>0.573864</td>\n",
       "      <td>29.698006</td>\n",
       "      <td>4.875000</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>203.371581</td>\n",
       "      <td>0.448535</td>\n",
       "      <td>0.498152</td>\n",
       "      <td>0.498535</td>\n",
       "      <td>0.500337</td>\n",
       "      <td>0.500353</td>\n",
       "      <td>0.451301</td>\n",
       "      <td>0.493516</td>\n",
       "      <td>0.477576</td>\n",
       "      <td>0.468281</td>\n",
       "      <td>0.494866</td>\n",
       "      <td>16.507465</td>\n",
       "      <td>2.501493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>176.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>352.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>4.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>528.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>7.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>10.000000</td>\n",
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      ],
      "text/plain": [
       "               id    A1_Score    A2_Score    A3_Score    A4_Score    A5_Score  \\\n",
       "count  704.000000  704.000000  704.000000  704.000000  704.000000  704.000000   \n",
       "mean   352.500000    0.721591    0.453125    0.457386    0.495739    0.498580   \n",
       "std    203.371581    0.448535    0.498152    0.498535    0.500337    0.500353   \n",
       "min      1.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%    176.750000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%    352.500000    1.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%    528.250000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "max    704.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         A6_Score    A7_Score    A8_Score    A9_Score   A10_Score         age  \\\n",
       "count  704.000000  704.000000  704.000000  704.000000  704.000000  702.000000   \n",
       "mean     0.284091    0.417614    0.649148    0.323864    0.573864   29.698006   \n",
       "std      0.451301    0.493516    0.477576    0.468281    0.494866   16.507465   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000   17.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000   21.000000   \n",
       "50%      0.000000    0.000000    1.000000    0.000000    1.000000   27.000000   \n",
       "75%      1.000000    1.000000    1.000000    1.000000    1.000000   35.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000  383.000000   \n",
       "\n",
       "           result  \n",
       "count  704.000000  \n",
       "mean     4.875000  \n",
       "std      2.501493  \n",
       "min      0.000000  \n",
       "25%      3.000000  \n",
       "50%      4.000000  \n",
       "75%      7.000000  \n",
       "max     10.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asd_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "There are quite a few missing values in the data set. Before I just drop every row that's missing data, I make sure not to bias the data in any way. In other words we need to make sure that there does not appear to be any sort of correlation to what sort of data has missing fields. If there were, I'd have to try and go back and fill that data in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='step1'></a>\n",
    "## Step 1: Clean Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>A1_Score</th>\n",
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       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Russia</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>258</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>271</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Russia</td>\n",
       "      <td>no</td>\n",
       "      <td>7</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>277</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>278</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>no</td>\n",
       "      <td>7</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>316</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>338</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>339</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>340</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>341</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>424</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>428</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>429</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>430</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>433</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>439</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>454</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>519</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>528</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Iran</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>535</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Iran</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>536</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Iran</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>537</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Iran</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>538</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Iran</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>557</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>565</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>yes</td>\n",
       "      <td>4</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>572</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>573</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>589</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>no</td>\n",
       "      <td>9</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>594</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Russia</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>637</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Russia</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>643</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>646</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>no</td>\n",
       "      <td>7</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>653</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>United States</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>659</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>660</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>no</td>\n",
       "      <td>8</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>702</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Russia</td>\n",
       "      <td>no</td>\n",
       "      <td>7</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  \\\n",
       "4      5         1         0         0         0         0         0   \n",
       "12    13         0         1         1         1         1         1   \n",
       "13    14         1         0         0         0         0         0   \n",
       "14    15         1         0         0         0         0         0   \n",
       "19    20         0         0         0         0         0         0   \n",
       "20    21         0         1         1         1         0         0   \n",
       "24    25         1         1         1         1         0         0   \n",
       "25    26         0         1         1         0         0         0   \n",
       "62    63         0         0         0         0         0         0   \n",
       "79    80         1         1         0         0         0         0   \n",
       "80    81         0         0         1         1         0         0   \n",
       "81    82         1         0         0         0         0         0   \n",
       "91    92         0         1         0         0         1         0   \n",
       "216  217         1         0         0         0         0         0   \n",
       "221  222         0         0         1         0         0         0   \n",
       "238  239         0         1         0         0         0         0   \n",
       "257  258         0         1         1         0         1         0   \n",
       "270  271         1         0         1         1         1         1   \n",
       "276  277         1         0         0         0         1         0   \n",
       "277  278         1         1         0         0         0         0   \n",
       "285  286         0         0         0         1         1         1   \n",
       "306  307         1         0         0         1         1         0   \n",
       "315  316         1         1         0         0         0         0   \n",
       "324  325         1         0         0         0         0         0   \n",
       "337  338         1         1         0         0         0         0   \n",
       "338  339         1         0         1         0         0         0   \n",
       "339  340         1         0         0         0         0         0   \n",
       "340  341         1         0         1         1         0         0   \n",
       "341  342         1         1         1         1         1         0   \n",
       "342  343         1         0         1         1         0         0   \n",
       "..   ...       ...       ...       ...       ...       ...       ...   \n",
       "423  424         0         1         0         1         0         0   \n",
       "427  428         1         0         0         1         0         0   \n",
       "428  429         0         0         0         0         1         0   \n",
       "429  430         1         0         1         0         1         0   \n",
       "432  433         1         0         0         1         1         0   \n",
       "438  439         1         1         0         0         0         0   \n",
       "453  454         0         0         1         1         1         0   \n",
       "485  486         0         1         0         0         0         0   \n",
       "505  506         1         0         1         0         1         0   \n",
       "518  519         0         1         0         0         0         0   \n",
       "527  528         0         0         0         0         1         0   \n",
       "534  535         0         0         0         1         1         0   \n",
       "535  536         0         1         0         0         1         0   \n",
       "536  537         0         1         0         0         1         0   \n",
       "537  538         0         1         0         0         1         1   \n",
       "556  557         1         1         0         0         0         0   \n",
       "564  565         0         1         0         0         1         1   \n",
       "571  572         1         0         0         0         0         0   \n",
       "572  573         1         0         0         1         0         0   \n",
       "588  589         1         0         1         1         1         1   \n",
       "593  594         1         0         0         0         0         0   \n",
       "636  637         0         0         0         0         0         0   \n",
       "642  643         0         0         0         0         1         0   \n",
       "645  646         1         0         1         1         0         0   \n",
       "651  652         1         0         0         0         0         0   \n",
       "652  653         0         0         0         0         0         0   \n",
       "658  659         0         0         1         1         0         0   \n",
       "659  660         1         1         1         1         1         1   \n",
       "666  667         0         0         0         0         0         0   \n",
       "701  702         1         0         1         1         1         0   \n",
       "\n",
       "     A7_Score  A8_Score  A9_Score    ...     gender  ethnicity jundice austim  \\\n",
       "4           0         1         0    ...          f        NaN      no     no   \n",
       "12          0         0         1    ...          f        NaN      no     no   \n",
       "13          1         1         0    ...          m        NaN      no     no   \n",
       "14          1         1         0    ...          f        NaN      no     no   \n",
       "19          1         1         0    ...          m        NaN     yes     no   \n",
       "20          0         0         0    ...          m        NaN      no     no   \n",
       "24          0         1         0    ...          m        NaN      no     no   \n",
       "25          0         1         0    ...          f        NaN     yes     no   \n",
       "62          0         0         0    ...          m        NaN      no     no   \n",
       "79          0         0         0    ...          f        NaN     yes     no   \n",
       "80          1         1         0    ...          m        NaN     yes    yes   \n",
       "81          1         0         0    ...          m        NaN      no     no   \n",
       "91          1         0         0    ...          f        NaN      no     no   \n",
       "216         1         1         0    ...          f        NaN     yes     no   \n",
       "221         0         0         0    ...          f        NaN      no     no   \n",
       "238         0         1         1    ...          m        NaN      no     no   \n",
       "257         0         1         0    ...          m        NaN      no     no   \n",
       "270         0         0         1    ...          f        NaN      no    yes   \n",
       "276         0         0         1    ...          m        NaN      no     no   \n",
       "277         0         0         0    ...          f        NaN      no    yes   \n",
       "285         1         0         1    ...          f        NaN      no     no   \n",
       "306         1         1         1    ...          f        NaN      no     no   \n",
       "315         0         1         0    ...          m        NaN      no     no   \n",
       "324         0         0         0    ...          f        NaN      no     no   \n",
       "337         0         0         0    ...          m        NaN      no     no   \n",
       "338         1         1         0    ...          f        NaN      no     no   \n",
       "339         1         0         0    ...          f        NaN      no     no   \n",
       "340         1         1         1    ...          f        NaN      no     no   \n",
       "341         0         1         0    ...          m        NaN      no     no   \n",
       "342         0         0         0    ...          f        NaN      no     no   \n",
       "..        ...       ...       ...    ...        ...        ...     ...    ...   \n",
       "423         0         0         0    ...          f        NaN      no     no   \n",
       "427         1         1         0    ...          m        NaN      no     no   \n",
       "428         1         0         0    ...          f        NaN      no     no   \n",
       "429         1         1         1    ...          m        NaN      no     no   \n",
       "432         0         0         0    ...          m        NaN      no     no   \n",
       "438         0         1         0    ...          f        NaN      no     no   \n",
       "453         0         0         0    ...          f        NaN     yes     no   \n",
       "485         0         1         1    ...          m        NaN      no     no   \n",
       "505         1         1         0    ...          m        NaN     yes    yes   \n",
       "518         0         0         1    ...          f        NaN      no     no   \n",
       "527         0         1         0    ...          f        NaN      no     no   \n",
       "534         1         0         0    ...          f        NaN      no     no   \n",
       "535         0         0         0    ...          m        NaN      no     no   \n",
       "536         0         0         0    ...          f        NaN      no     no   \n",
       "537         0         0         1    ...          m        NaN      no     no   \n",
       "556         0         1         0    ...          m        NaN      no     no   \n",
       "564         0         0         1    ...          m        NaN      no     no   \n",
       "571         0         1         0    ...          m        NaN      no     no   \n",
       "572         0         1         0    ...          m        NaN      no     no   \n",
       "588         1         1         1    ...          f        NaN      no     no   \n",
       "593         0         0         0    ...          m        NaN      no     no   \n",
       "636         0         0         0    ...          f        NaN      no     no   \n",
       "642         0         1         0    ...          f        NaN      no     no   \n",
       "645         1         1         1    ...          m        NaN      no    yes   \n",
       "651         1         0         0    ...          m        NaN     yes     no   \n",
       "652         0         0         0    ...          f        NaN      no     no   \n",
       "658         1         0         0    ...          m        NaN      no     no   \n",
       "659         0         0         1    ...          m        NaN      no     no   \n",
       "666         0         1         0    ...          m        NaN      no     no   \n",
       "701         1         1         0    ...          f        NaN      no     no   \n",
       "\n",
       "            contry_of_res used_app_before result     age_desc  relation  \\\n",
       "4                   Egypt              no      2  18 and more       NaN   \n",
       "12                Bahamas              no      6  18 and more       NaN   \n",
       "13                Austria              no      4  18 and more       NaN   \n",
       "14              Argentina              no      4  18 and more       NaN   \n",
       "19   United Arab Emirates              no      3  18 and more       NaN   \n",
       "20   United Arab Emirates              no      3  18 and more       NaN   \n",
       "24                Lebanon              no      5  18 and more       NaN   \n",
       "25            Afghanistan              no      3  18 and more       NaN   \n",
       "62           Saudi Arabia              no      0  18 and more       NaN   \n",
       "79                 Jordan              no      2  18 and more       NaN   \n",
       "80            Afghanistan              no      5  18 and more       NaN   \n",
       "81                 Jordan              no      3  18 and more       NaN   \n",
       "91                 Jordan              no      4  18 and more       NaN   \n",
       "216  United Arab Emirates              no      4  18 and more       NaN   \n",
       "221           Afghanistan              no      1  18 and more       NaN   \n",
       "238                Russia              no      3  18 and more       NaN   \n",
       "257           New Zealand              no      4  18 and more       NaN   \n",
       "270                Russia              no      7  18 and more       NaN   \n",
       "276                Jordan              no      3  18 and more       NaN   \n",
       "277                Jordan              no      2  18 and more       NaN   \n",
       "285             Hong Kong              no      6  18 and more       NaN   \n",
       "306          Saudi Arabia              no      7  18 and more       NaN   \n",
       "315  United Arab Emirates              no      3  18 and more       NaN   \n",
       "324  United Arab Emirates              no      2  18 and more       NaN   \n",
       "337                Jordan              no      3  18 and more       NaN   \n",
       "338                Jordan              no      4  18 and more       NaN   \n",
       "339                Jordan              no      2  18 and more       NaN   \n",
       "340                Jordan              no      6  18 and more       NaN   \n",
       "341                Jordan              no      6  18 and more       NaN   \n",
       "342                Jordan              no      3  18 and more       NaN   \n",
       "..                    ...             ...    ...          ...       ...   \n",
       "423  United Arab Emirates              no      2  18 and more       NaN   \n",
       "427                Jordan              no      4  18 and more       NaN   \n",
       "428                Brazil             yes      2  18 and more       NaN   \n",
       "429                Jordan              no      6  18 and more       NaN   \n",
       "432                Jordan              no      3  18 and more       NaN   \n",
       "438                Jordan              no      4  18 and more       NaN   \n",
       "453                Jordan              no      4  18 and more       NaN   \n",
       "485                Jordan              no      3  18 and more       NaN   \n",
       "505        United Kingdom              no      6  18 and more       NaN   \n",
       "518           New Zealand              no      3  18 and more       NaN   \n",
       "527                  Iran              no      3  18 and more       NaN   \n",
       "534                  Iran              no      4  18 and more       NaN   \n",
       "535                  Iran              no      3  18 and more       NaN   \n",
       "536                  Iran              no      2  18 and more       NaN   \n",
       "537                  Iran              no      5  18 and more       NaN   \n",
       "556                Jordan              no      3  18 and more       NaN   \n",
       "564            Kazakhstan             yes      4  18 and more       NaN   \n",
       "571                Jordan              no      3  18 and more       NaN   \n",
       "572                Jordan              no      3  18 and more       NaN   \n",
       "588           New Zealand              no      9  18 and more       NaN   \n",
       "593                Russia              no      1  18 and more       NaN   \n",
       "636                Russia              no      0  18 and more       NaN   \n",
       "642           New Zealand              no      2  18 and more       NaN   \n",
       "645           New Zealand              no      7  18 and more       NaN   \n",
       "651                Jordan              no      3  18 and more       NaN   \n",
       "652         United States              no      1  18 and more       NaN   \n",
       "658            Azerbaijan              no      3  18 and more       NaN   \n",
       "659              Pakistan              no      8  18 and more       NaN   \n",
       "666                  Iraq              no      1  18 and more       NaN   \n",
       "701                Russia              no      7  18 and more       NaN   \n",
       "\n",
       "    Class/ASD  \n",
       "4          NO  \n",
       "12         NO  \n",
       "13         NO  \n",
       "14         NO  \n",
       "19         NO  \n",
       "20         NO  \n",
       "24         NO  \n",
       "25         NO  \n",
       "62         NO  \n",
       "79         NO  \n",
       "80         NO  \n",
       "81         NO  \n",
       "91         NO  \n",
       "216        NO  \n",
       "221        NO  \n",
       "238        NO  \n",
       "257        NO  \n",
       "270       YES  \n",
       "276        NO  \n",
       "277        NO  \n",
       "285        NO  \n",
       "306       YES  \n",
       "315        NO  \n",
       "324        NO  \n",
       "337        NO  \n",
       "338        NO  \n",
       "339        NO  \n",
       "340        NO  \n",
       "341        NO  \n",
       "342        NO  \n",
       "..        ...  \n",
       "423        NO  \n",
       "427        NO  \n",
       "428        NO  \n",
       "429        NO  \n",
       "432        NO  \n",
       "438        NO  \n",
       "453        NO  \n",
       "485        NO  \n",
       "505        NO  \n",
       "518        NO  \n",
       "527        NO  \n",
       "534        NO  \n",
       "535        NO  \n",
       "536        NO  \n",
       "537        NO  \n",
       "556        NO  \n",
       "564        NO  \n",
       "571        NO  \n",
       "572        NO  \n",
       "588       YES  \n",
       "593        NO  \n",
       "636        NO  \n",
       "642        NO  \n",
       "645       YES  \n",
       "651        NO  \n",
       "652        NO  \n",
       "658        NO  \n",
       "659       YES  \n",
       "666        NO  \n",
       "701       YES  \n",
       "\n",
       "[95 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asd_data.loc[(asd_data['age'].isnull()) |(asd_data['gender'].isnull()) |(asd_data['ethnicity'].isnull()) \n",
    "|(asd_data['jundice'].isnull())|(asd_data['austim'].isnull()) |(asd_data['contry_of_res'].isnull())\n",
    "            |(asd_data['used_app_before'].isnull())|(asd_data['result'].isnull())|(asd_data['age_desc'].isnull())\n",
    "            |(asd_data['relation'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Since the missing data seems randomly distributed, I go ahead and drop rows with missing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
       "      <th>A6_Score</th>\n",
       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>A9_Score</th>\n",
       "      <th>A10_Score</th>\n",
       "      <th>age</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>349.725780</td>\n",
       "      <td>0.740558</td>\n",
       "      <td>0.469622</td>\n",
       "      <td>0.481117</td>\n",
       "      <td>0.520525</td>\n",
       "      <td>0.525452</td>\n",
       "      <td>0.307061</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.665025</td>\n",
       "      <td>0.341544</td>\n",
       "      <td>0.597701</td>\n",
       "      <td>30.215107</td>\n",
       "      <td>5.077176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>207.856238</td>\n",
       "      <td>0.438689</td>\n",
       "      <td>0.499487</td>\n",
       "      <td>0.500054</td>\n",
       "      <td>0.499989</td>\n",
       "      <td>0.499762</td>\n",
       "      <td>0.461654</td>\n",
       "      <td>0.495278</td>\n",
       "      <td>0.472370</td>\n",
       "      <td>0.474617</td>\n",
       "      <td>0.490765</td>\n",
       "      <td>17.287470</td>\n",
       "      <td>2.522717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>166.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>329.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>533.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>704.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id    A1_Score    A2_Score    A3_Score    A4_Score    A5_Score  \\\n",
       "count  609.000000  609.000000  609.000000  609.000000  609.000000  609.000000   \n",
       "mean   349.725780    0.740558    0.469622    0.481117    0.520525    0.525452   \n",
       "std    207.856238    0.438689    0.499487    0.500054    0.499989    0.499762   \n",
       "min      1.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%    166.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%    329.000000    1.000000    0.000000    0.000000    1.000000    1.000000   \n",
       "75%    533.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "max    704.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         A6_Score    A7_Score    A8_Score    A9_Score   A10_Score         age  \\\n",
       "count  609.000000  609.000000  609.000000  609.000000  609.000000  609.000000   \n",
       "mean     0.307061    0.428571    0.665025    0.341544    0.597701   30.215107   \n",
       "std      0.461654    0.495278    0.472370    0.474617    0.490765   17.287470   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000   17.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000   22.000000   \n",
       "50%      0.000000    0.000000    1.000000    0.000000    1.000000   27.000000   \n",
       "75%      1.000000    1.000000    1.000000    1.000000    1.000000   35.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000  383.000000   \n",
       "\n",
       "           result  \n",
       "count  609.000000  \n",
       "mean     5.077176  \n",
       "std      2.522717  \n",
       "min      0.000000  \n",
       "25%      3.000000  \n",
       "50%      5.000000  \n",
       "75%      7.000000  \n",
       "max     10.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asd_data.dropna(inplace=True)\n",
    "asd_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### If we could have fill with median values for 'NaN' instead of dropping them, but in this situation that is little complicated as I have lot of categorical colums with 'NaN'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#asd_data.median()\n",
    "#numerical = ['age', 'result']\n",
    "#asd_data.fillna(asd_data[numerical].median())\n",
    "#asd_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's check out the data types of all our features including the target feature. Moreover, lets count the total number of instances and the target-class distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                   int64\n",
      "A1_Score             int64\n",
      "A2_Score             int64\n",
      "A3_Score             int64\n",
      "A4_Score             int64\n",
      "A5_Score             int64\n",
      "A6_Score             int64\n",
      "A7_Score             int64\n",
      "A8_Score             int64\n",
      "A9_Score             int64\n",
      "A10_Score            int64\n",
      "age                float64\n",
      "gender              object\n",
      "ethnicity           object\n",
      "jundice             object\n",
      "austim              object\n",
      "contry_of_res       object\n",
      "used_app_before     object\n",
      "result               int64\n",
      "age_desc            object\n",
      "relation            object\n",
      "Class/ASD           object\n",
      "dtype: object\n",
      "Total number of records: 609\n",
      "Individuals diagonised with ASD: 180\n",
      "Individuals not diagonised with ASD: 429\n"
     ]
    }
   ],
   "source": [
    "# Reminder of the features:\n",
    "print(asd_data.dtypes)\n",
    "\n",
    "\n",
    "# Total number of records in clean dataset\n",
    "n_records = len(asd_data.index)\n",
    "\n",
    "# TODO: Number of records where individual's with ASD in the clean dataset\n",
    "n_asd_yes = len(asd_data[asd_data['Class/ASD'] == 'YES'])\n",
    "\n",
    "# TODO: Number of records where individual's with no ASD in the clean dataset\n",
    "n_asd_no = len(asd_data[asd_data['Class/ASD'] == 'NO'])\n",
    "\n",
    "# Print the results\n",
    "print \"Total number of records: {}\".format(n_records)\n",
    "print \"Individuals diagonised with ASD: {}\".format(n_asd_yes)\n",
    "print \"Individuals not diagonised with ASD: {}\".format(n_asd_no)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='step2'></a>\n",
    "\n",
    "## Step 2: A quick visualization with *Seaborn*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"whitegrid\", color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Qe6H39i6/oQxYtBXGYrHg82W7IN/is1QZcfcPg5rL8tqMJEnw8uTfJ4njE10b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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a nested violinplot and split the violins for easier comparison\n",
    "sns.violinplot(x=\"jundice\", y=\"result\", hue=\"austim\", data=asd_data, split=True,\n",
    "                inner=\"quart\", palette={'yes': \"r\", 'no': \"b\"})\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qNfx69UTIgP7w6RLPOfFbwL8danDy3BWs2XwEh0/mi44iTJNnzEHB0Lq7sZip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\nFjYREZEMsLCJiIhkgIVNREQkA/8Hx3JQYHWwY6QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a nested violinplot and split the violins for easier comparison\n",
    "sns.violinplot(x=\"jundice\", y=\"result\", hue=\"Class/ASD\", data=asd_data, split=True,\n",
    "                inner=\"quart\", palette={'YES': \"r\", 'NO': \"b\"})\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6g7IeqqV2d0Ng27YIbN8OgW3byHqmInIsRqMJq7cdw9L1B1GlN4iOY3MuXl4I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\nwMImIiKSARY2ERGRDPw/XrJFd5v2kT0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a nested violinplot and split the violins for easier comparison\n",
    "sns.violinplot(x=\"gender\", y=\"result\", hue=\"Class/ASD\", data=asd_data, split=True,\n",
    "                inner=\"quart\", palette={'YES': \"r\", 'NO': \"b\"})\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ce+idExanlX11h4gLj2FI0kCg/Uz0wYbDtDotjEkeRljHPWz1bY0cbiohJ6G/7wZ0p9vJ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\n",
      "text/plain": [
       "<Figure size 631.875x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"jundice\", y=\"result\", hue=\"Class/ASD\", col=\"gender\", data=asd_data, kind=\"swarm\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QoPr162vRokW64oorXP9ddtllOnXqVBmHDQAAAFRemzdvVpcuXVxf1OvUqaPV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\nAAAAYCEUAAAAAICF/D/lA9wjG2MaEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 775.875x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"gender\", y=\"result\", hue=\"Class/ASD\",\n",
    "               col=\"relation\", data=asd_data, kind=\"box\", size=4, aspect=.5, palette={'YES': \"r\", 'NO': \"b\"});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YcpVYp7K2HfiOA98uISiJonCiWqzIuOoXi/BtuxY/dkuRNQ2xrlmIdXfD7JoFs7MTRkcH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\n",
      "text/plain": [
       "<Figure size 548.375x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x=\"result\", y=\"jundice\",\n",
    "                   hue=\"gender\", row=\"relation\",\n",
    "                   data=asd_data,\n",
    "                    orient=\"h\", size=2, aspect=3.5, palette={'f': \"r\", 'm': \"b\"},\n",
    "                  kind=\"violin\", dodge=True, cut=0, bw=.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In the two figures above, I have used two different depiction techniques to have a quick peek on the ASD dataset we are dealing with. Both of the occasions I have used `factorplot` module from `seaborn` visualization software. In the first case, I used '`swamp`' kind of graph expressing the relationship between several different features present in the data whereas in the second case, '`box`' method was used to present the visual way of showing how different features were associated with each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "Next I'll need to convert the Pandas dataframes into numpy arrays that can be used by scikit_learn. Let's create an array that extracts only the feature data we want to work with and another array that contains the classes (class/ASD). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Split the data into features and target label\n",
    "asd_raw = asd_data['Class/ASD']\n",
    "features_raw = asd_data[['age', 'gender', 'ethnicity', 'jundice', 'austim', 'contry_of_res', 'result',\n",
    "                      'relation','A1_Score','A2_Score','A3_Score','A4_Score','A5_Score','A6_Score','A7_Score','A8_Score',\n",
    "                      'A9_Score','A10_Score']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Some of our models require the input data to be normalized, so I proceed to normalize the attribute data. Here, I use preprocessing.MinMaxScaler()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>jundice</th>\n",
       "      <th>austim</th>\n",
       "      <th>contry_of_res</th>\n",
       "      <th>result</th>\n",
       "      <th>relation</th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
       "      <th>A6_Score</th>\n",
       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>A9_Score</th>\n",
       "      <th>A10_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.024590</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>United States</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Self</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.019126</td>\n",
       "      <td>m</td>\n",
       "      <td>Latino</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Self</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.027322</td>\n",
       "      <td>m</td>\n",
       "      <td>Latino</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Parent</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049180</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Self</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.051913</td>\n",
       "      <td>m</td>\n",
       "      <td>Others</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>United States</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Self</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age gender       ethnicity jundice austim  contry_of_res  result  \\\n",
       "0  0.024590      f  White-European      no     no  United States     0.6   \n",
       "1  0.019126      m          Latino      no    yes         Brazil     0.5   \n",
       "2  0.027322      m          Latino     yes    yes          Spain     0.8   \n",
       "3  0.049180      f  White-European      no    yes  United States     0.6   \n",
       "5  0.051913      m          Others     yes     no  United States     0.9   \n",
       "\n",
       "  relation  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  \\\n",
       "0     Self         1         1         1         1         0         0   \n",
       "1     Self         1         1         0         1         0         0   \n",
       "2   Parent         1         1         0         1         1         0   \n",
       "3     Self         1         1         0         1         0         0   \n",
       "5     Self         1         1         1         1         1         0   \n",
       "\n",
       "   A7_Score  A8_Score  A9_Score  A10_Score  \n",
       "0         1         1         0          0  \n",
       "1         0         1         0          1  \n",
       "2         1         1         1          1  \n",
       "3         1         1         0          1  \n",
       "5         1         1         1          1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "numerical = ['age', 'result']\n",
    "\n",
    "features_minmax_transform = pd.DataFrame(data = features_raw)\n",
    "features_minmax_transform[numerical] = scaler.fit_transform(features_raw[numerical])\n",
    "features_minmax_transform\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_minmax_transform.head(n = 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### One-Hot-Coding\n",
    "\n",
    "From the table in **Clean Data Sets** above, we can see there are several features for each record that are non-numeric such as `Country_of_residence`, `ethnicity` etc. 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, I need to convert the non-numeric target label, `'Class/ASD'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label (\"YES\" and \"NO\" to Class/ASD), I can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively. In code cell below, I will 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_minmax_transform'` data.\n",
    " - Convert the target label `'asd_raw'` to numerical entries.\n",
    "   - Set records with \"NO\" to `0` and records with \"YES\" to `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>result</th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
       "      <th>A6_Score</th>\n",
       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>...</th>\n",
       "      <th>contry_of_res_United Arab Emirates</th>\n",
       "      <th>contry_of_res_United Kingdom</th>\n",
       "      <th>contry_of_res_United States</th>\n",
       "      <th>contry_of_res_Uruguay</th>\n",
       "      <th>contry_of_res_Viet Nam</th>\n",
       "      <th>relation_Health care professional</th>\n",
       "      <th>relation_Others</th>\n",
       "      <th>relation_Parent</th>\n",
       "      <th>relation_Relative</th>\n",
       "      <th>relation_Self</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049180</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.051913</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  result  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  \\\n",
       "0  0.024590     0.6         1         1         1         1         0   \n",
       "1  0.019126     0.5         1         1         0         1         0   \n",
       "2  0.027322     0.8         1         1         0         1         1   \n",
       "3  0.049180     0.6         1         1         0         1         0   \n",
       "5  0.051913     0.9         1         1         1         1         1   \n",
       "\n",
       "   A6_Score  A7_Score  A8_Score      ...        \\\n",
       "0         0         1         1      ...         \n",
       "1         0         0         1      ...         \n",
       "2         0         1         1      ...         \n",
       "3         0         1         1      ...         \n",
       "5         0         1         1      ...         \n",
       "\n",
       "   contry_of_res_United Arab Emirates  contry_of_res_United Kingdom  \\\n",
       "0                                   0                             0   \n",
       "1                                   0                             0   \n",
       "2                                   0                             0   \n",
       "3                                   0                             0   \n",
       "5                                   0                             0   \n",
       "\n",
       "   contry_of_res_United States  contry_of_res_Uruguay  contry_of_res_Viet Nam  \\\n",
       "0                            1                      0                       0   \n",
       "1                            0                      0                       0   \n",
       "2                            0                      0                       0   \n",
       "3                            1                      0                       0   \n",
       "5                            1                      0                       0   \n",
       "\n",
       "   relation_Health care professional  relation_Others  relation_Parent  \\\n",
       "0                                  0                0                0   \n",
       "1                                  0                0                0   \n",
       "2                                  0                0                1   \n",
       "3                                  0                0                0   \n",
       "5                                  0                0                0   \n",
       "\n",
       "   relation_Relative  relation_Self  \n",
       "0                  0              1  \n",
       "1                  0              1  \n",
       "2                  0              0  \n",
       "3                  0              1  \n",
       "5                  0              1  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94 total features after one-hot encoding.\n",
      "['age', 'result', 'A1_Score', 'A2_Score', 'A3_Score', 'A4_Score', 'A5_Score', 'A6_Score', 'A7_Score', 'A8_Score', 'A9_Score', 'A10_Score', 'gender_f', 'gender_m', 'ethnicity_Asian', 'ethnicity_Black', 'ethnicity_Hispanic', 'ethnicity_Latino', 'ethnicity_Middle Eastern ', 'ethnicity_Others', 'ethnicity_Pasifika', 'ethnicity_South Asian', 'ethnicity_Turkish', 'ethnicity_White-European', 'ethnicity_others', 'jundice_no', 'jundice_yes', 'austim_no', 'austim_yes', 'contry_of_res_Afghanistan', 'contry_of_res_AmericanSamoa', 'contry_of_res_Angola', 'contry_of_res_Armenia', 'contry_of_res_Aruba', 'contry_of_res_Australia', 'contry_of_res_Austria', 'contry_of_res_Bahamas', 'contry_of_res_Bangladesh', 'contry_of_res_Belgium', 'contry_of_res_Bolivia', 'contry_of_res_Brazil', 'contry_of_res_Burundi', 'contry_of_res_Canada', 'contry_of_res_Chile', 'contry_of_res_China', 'contry_of_res_Costa Rica', 'contry_of_res_Cyprus', 'contry_of_res_Czech Republic', 'contry_of_res_Ecuador', 'contry_of_res_Egypt', 'contry_of_res_Ethiopia', 'contry_of_res_Finland', 'contry_of_res_France', 'contry_of_res_Germany', 'contry_of_res_Iceland', 'contry_of_res_India', 'contry_of_res_Indonesia', 'contry_of_res_Iran', 'contry_of_res_Ireland', 'contry_of_res_Italy', 'contry_of_res_Jordan', 'contry_of_res_Malaysia', 'contry_of_res_Mexico', 'contry_of_res_Nepal', 'contry_of_res_Netherlands', 'contry_of_res_New Zealand', 'contry_of_res_Nicaragua', 'contry_of_res_Niger', 'contry_of_res_Oman', 'contry_of_res_Pakistan', 'contry_of_res_Philippines', 'contry_of_res_Portugal', 'contry_of_res_Romania', 'contry_of_res_Russia', 'contry_of_res_Saudi Arabia', 'contry_of_res_Serbia', 'contry_of_res_Sierra Leone', 'contry_of_res_South Africa', 'contry_of_res_Spain', 'contry_of_res_Sri Lanka', 'contry_of_res_Sweden', 'contry_of_res_Tonga', 'contry_of_res_Turkey', 'contry_of_res_Ukraine', 'contry_of_res_United Arab Emirates', 'contry_of_res_United Kingdom', 'contry_of_res_United States', 'contry_of_res_Uruguay', 'contry_of_res_Viet Nam', 'relation_Health care professional', 'relation_Others', 'relation_Parent', 'relation_Relative', 'relation_Self']\n"
     ]
    }
   ],
   "source": [
    "#One-hot encode the 'features_minmax_transform' data using pandas.get_dummies()\n",
    "features_final = pd.get_dummies(features_minmax_transform)\n",
    "display(features_final.head(5))\n",
    "\n",
    "\n",
    "# Encode the 'all_classes_raw' data to numerical values\n",
    "asd_classes = asd_raw.apply(lambda x: 1 if x == 'YES' else 0)\n",
    "\n",
    "\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": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wMIWFhWnu3LmlNGXZdqk1Pn+fxx9/XMuWLSuFCcu+S63xN998o969eyssLEwT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\nQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of Class/ASD\n",
    "\n",
    "# 8 bins\n",
    "plt.hist(asd_classes, bins=10)\n",
    "\n",
    "# x-axis limit from 0 to 1\n",
    "plt.xlim(0,1)\n",
    "plt.title('Histogram of Class/ASD')\n",
    "plt.xlabel('Class/ASD from processed data')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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, I 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 487 samples.\n",
      "Testing set has 122 samples.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "np.random.seed(1234)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features_final, asd_classes, train_size=0.80, random_state=1)\n",
    "\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])\n",
    "#asd_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='step3'></a>\n",
    "\n",
    "## Step 3: Models\n",
    "\n",
    "###  Supervised Learning Models\n",
    "**I have applied the following supervised learning models in this project which are currently available in** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html).\n",
    "\n",
    "[(1)](#1): Decision Trees\n",
    "\n",
    "[(2)](#2) Random Forest\n",
    "\n",
    "[(3)](#3) Support Vector Machines (SVM)\n",
    "\n",
    "[(4)](#4) K-Nearest Neighbors (KNeighbors)\n",
    "\n",
    "[(5)](#5) Gaussian Naive Bayes (GaussianNB)\n",
    "\n",
    "[(6)](#6) Logistic Regression\n",
    "\n",
    "[(7)](#7) Linear Discriminant Analysis (LDA)\n",
    "\n",
    "[(8)](#8) Quadratic Discriminant Analysis (QDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In the two figures above, I have used two different depiction techniques to have a quick peek on the ASD dataset we are dealing with. Both of the occasions I have used `factorplot` module from `seaborn` visualization software. In the first case, I used '`swamp`' kind of graph expressing the relationship between several different features present in the data whereas in the second case, '`box`' method was used to present the visual way of showing how different features were associated with each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='1'></a>\n",
    "\n",
    "##  (1) Decision Trees\n",
    "\n",
    "I start with creating a DecisionTreeClassifier and fit it to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=1, splitter='best')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dectree = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "# Train the classifier on the training set\n",
    "dectree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Depiction of Decision Tree algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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qkoPs1B7Egt0YmTufxSM6ajYBXNCqg6fRNx3rcLrnrJpl5MnY54BcccYyz7Fy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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pydotplus \n",
    "\n",
    "\n",
    "dot_data = tree.export_graphviz(dectree,\n",
    "                                out_file=None,\n",
    "                                filled=True,\n",
    "                                rounded=True,\n",
    "                                special_characters=True)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data)  \n",
    "\n",
    "from IPython.display import Image \n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---------------\n",
    "## Evaluating Model Performance\n",
    "\n",
    "### Metrics \n",
    "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",
    "#### 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 where we have \n",
    "- a total of 609 records with \n",
    "- 180 individuals diagonised with ASD and \n",
    "- 429 individuals not diagonised with ASD\n",
    "\n",
    "accuracy by itself is not a very good metric. \n",
    "Thus, in this case 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": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# make class predictions for the testing set\n",
    "y_pred_class = dectree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('True:', array([1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
      "       1, 0]))\n",
      "('False:', array([1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
      "       1, 0]))\n"
     ]
    }
   ],
   "source": [
    "# print the first 25 true and predicted responses\n",
    "print('True:', y_test.values[0:25])\n",
    "print('False:', y_pred_class[0:25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[79  0]\n",
      " [ 0 43]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "# IMPORTANT: first argument is true values, second argument is predicted values\n",
    "# this produces a 2x2 numpy array (matrix)\n",
    "#print(metrics.confusion_matrix(y_test, y_pred_class))\n",
    "\n",
    "# save confusion matrix and slice into four pieces\n",
    "confusion = metrics.confusion_matrix(y_test, y_pred_class)\n",
    "print(confusion)\n",
    "#[row, column]\n",
    "TP = confusion[1, 1]\n",
    "TN = confusion[0, 0]\n",
    "FP = confusion[0, 1]\n",
    "FN = confusion[1, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Metrics computed from a confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Classification Accuracy**: Overall, how often is the classifier correct?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# use float to perform true division, not integer division\n",
    "print((TP + TN) / float(TP + TN + FP + FN))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Classification Error**: Overall, how often is the classifier incorrect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
    "\n",
    "print(classification_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Sensitivity**: When the actual value is positive, how often is the prediction correct?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "sensitivity = TP / float(FN + TP)\n",
    "\n",
    "print(sensitivity)\n",
    "print(metrics.recall_score(y_test, y_pred_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Specificity**: When the actual value is negative, how often is the prediction correct?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "specificity = TN / (TN + FP)\n",
    "\n",
    "print(specificity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**False Positive Rate**: When the actual value is negative, how often is the prediction incorrect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "false_positive_rate = FP / float(TN + FP)\n",
    "\n",
    "print(false_positive_rate)\n",
    "#print(1 - specificity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Precision**: When a positive value is predicted, how often is the prediction correct?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "precision = TP / float(TP + FP)\n",
    "\n",
    "#print(precision)\n",
    "print(metrics.precision_score(y_test, y_pred_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Visualizing the classification prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the first 10 predicted responses\n",
    "# 1D array (vector) of binary values (0, 1)\n",
    "dectree.predict(X_test)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.],\n",
       "       [ 1.,  0.],\n",
       "       [ 1.,  0.],\n",
       "       [ 0.,  1.],\n",
       "       [ 0.,  1.],\n",
       "       [ 1.,  0.],\n",
       "       [ 0.,  1.],\n",
       "       [ 0.,  1.],\n",
       "       [ 1.,  0.],\n",
       "       [ 1.,  0.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the first 10 predicted probabilities of class membership\n",
    "dectree.predict_proba(X_test)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# store the predicted probabilities for class 1\n",
    "y_pred_prob = dectree.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# allow plots to appear in the notebook\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# adjust the font size \n",
    "plt.rcParams['font.size'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mAADYHGEOAIDNEeYAANgcYQ4AgM0R5gAA2BxhDgCAzRHmAADYHGEOAIDNEeYA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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of predicted probabilities\n",
    "\n",
    "# 8 bins\n",
    "plt.hist(y_pred_prob, bins=10)\n",
    "\n",
    "# x-axis limit from 0 to 1\n",
    "plt.xlim(0,1)\n",
    "plt.title('Histogram of predicted probabilities')\n",
    "plt.xlabel('Predicted probability of ASD')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Receiver Operating Characteristic (ROC) Curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It would be nice if we could see how sensitivity and specificity are affected by various thresholds, without actually changing the threshold? Lets plot the ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lpcTvRdnQY9egz85Hj52PHt+c7Ab7gQMHFBAQYDsd/98cBbu3t7eysrJs01ar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\nYgNv6wLm834AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IMPORTANT: first argument is true values, second argument is predicted probabilities\n",
    "\n",
    "# we pass y_test and y_pred_prob\n",
    "# we do not use y_pred_class, because it will give incorrect results without generating an error\n",
    "# roc_curve returns 3 objects fpr, tpr, thresholds\n",
    "# fpr: false positive rate\n",
    "# tpr: true positive rate\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_prob)\n",
    "\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.rcParams['font.size'] = 12\n",
    "plt.title('ROC curve for diabetes classifier')\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Score metric for Model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dectree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Cross-validation:\n",
    "\n",
    "Now instead of a single train/test split, I use K-Fold cross validation to get a better measure of your model's accuracy (K=10). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "dectree = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "cv_scores = cross_val_score(dectree, features_final, asd_classes, cv=10)\n",
    "\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### AUC Score: \n",
    "\n",
    "AUC is the percentage of the ROC plot that is underneath the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/basu/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate cross-validated AUC\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "cross_val_score(dectree, features_final, asd_classes, cv=10, scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### F-beta Score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dectree.fit(X_train, y_train)\n",
    "from sklearn.metrics import fbeta_score\n",
    "predictions_test = dectree.predict(X_test)\n",
    "fbeta_score(y_test, predictions_test, average='binary', beta=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='2'></a>\n",
    "## (2) Random Forest\n",
    "\n",
    "Now I apply a **RandomForestClassifier** instead to see whether it performs better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9933333333333334"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "ranfor = RandomForestClassifier(n_estimators=5, random_state=1)\n",
    "cv_scores = cross_val_score(ranfor, features_final, asd_classes, cv=10)\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "AUC Score: AUC is the percentage of the ROC plot that is underneath the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9988095238095237"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate cross-validated AUC\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "cross_val_score(ranfor, features_final, asd_classes, cv=10, scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "F-beta Score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranfor.fit(X_train, y_train)\n",
    "from sklearn.metrics import fbeta_score\n",
    "predictions_test = ranfor.predict(X_test)\n",
    "fbeta_score(y_test, predictions_test, average='binary', beta=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='3'></a>\n",
    "## (3) SVM\n",
    "\n",
    "Next I try using svm.SVC with a linear kernel and see how well it does in comparison to the decision tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "C = 1.0\n",
    "svc = svm.SVC(kernel='linear', C=C, gamma=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_scores = cross_val_score(svc, features_final, asd_classes, cv=10)\n",
    "\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "AUC Score: AUC is the percentage of the ROC plot that is underneath the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate cross-validated AUC\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "cross_val_score(svc, features_final, asd_classes, cv=10, scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "F-beta Score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_train, y_train)\n",
    "from sklearn.metrics import fbeta_score\n",
    "predictions_test = svc.predict(X_test)\n",
    "fbeta_score(y_test, predictions_test, average='binary', beta=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='4'></a>\n",
    "\n",
    "## (4) K-Nearest-Neighbors (KNN)\n",
    "Next, I explore the K-Nearest-Neighbors algorithm with a starting value of K=10. Recall that K is an example of a hyperparameter - a parameter on the model itself which may need to be tuned for best results on your particular data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94745901639344265"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import neighbors\n",
    "\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=10)\n",
    "cv_scores = cross_val_score(knn, features_final, asd_classes, cv=10)\n",
    "\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "AUC Score: AUC is the percentage of the ROC plot that is underneath the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99300787498461918"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate cross-validated AUC\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "cross_val_score(knn, features_final, asd_classes, cv=10, scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "F-beta Score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91928251121076232"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)\n",
    "from sklearn.metrics import fbeta_score\n",
    "predictions_test = knn.predict(X_test)\n",
    "fbeta_score(y_test, predictions_test, average='binary', beta=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Choosing K is tricky, so I can't discard KNN until we've tried different values of K. Hence we write a for loop to run KNN with K values ranging from 10 to 50 and see if K makes a substantial difference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 0.94745901639344265)\n",
      "(11, 0.95073770491803278)\n",
      "(12, 0.95073770491803278)\n",
      "(13, 0.95404371584699454)\n",
      "(14, 0.95076502732240442)\n",
      "(15, 0.94420765027322395)\n",
      "(16, 0.95076502732240442)\n",
      "(17, 0.95237704918032784)\n",
      "(18, 0.95237704918032784)\n",
      "(19, 0.9540163934426229)\n",
      "(20, 0.95237704918032784)\n",
      "(21, 0.95237704918032784)\n",
      "(22, 0.94745901639344243)\n",
      "(23, 0.94909836065573749)\n",
      "(24, 0.95073770491803256)\n",
      "(25, 0.95073770491803278)\n",
      "(26, 0.95237704918032784)\n",
      "(27, 0.95073770491803278)\n",
      "(28, 0.95073770491803256)\n",
      "(29, 0.95073770491803278)\n",
      "(30, 0.95237704918032784)\n",
      "(31, 0.9474863387978143)\n",
      "(32, 0.94912568306010936)\n",
      "(33, 0.9474863387978143)\n",
      "(34, 0.95076502732240442)\n",
      "(35, 0.94912568306010936)\n",
      "(36, 0.94912568306010914)\n",
      "(37, 0.95076502732240442)\n",
      "(38, 0.95407103825136608)\n",
      "(39, 0.95243169398907079)\n",
      "(40, 0.95243169398907079)\n",
      "(41, 0.95243169398907079)\n",
      "(42, 0.95079234972677573)\n",
      "(43, 0.95079234972677573)\n",
      "(44, 0.95079234972677573)\n",
      "(45, 0.95079234972677573)\n",
      "(46, 0.95243169398907079)\n",
      "(47, 0.95243169398907079)\n",
      "(48, 0.95571038251366114)\n",
      "(49, 0.95243169398907079)\n"
     ]
    }
   ],
   "source": [
    "for n in range(10, 50):\n",
    "    knn = neighbors.KNeighborsClassifier(n_neighbors=n)\n",
    "    cv_scores = cross_val_score(knn, features_final, asd_classes, cv=10)\n",
    "    print (n, cv_scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='5'></a>\n",
    "\n",
    "## (5) Naive Bayes\n",
    "\n",
    "Now I try naive_bayes.MultinomialNB classifier and ask how does its accuracy stack up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88500000000000001"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "#scaler = preprocessing.MinMaxScaler()\n",
    "#all_features_minmax = scaler.fit_transform(all_features)\n",
    "\n",
    "nb = MultinomialNB()\n",
    "cv_scores = cross_val_score(nb, features_final, asd_classes, cv=10)\n",
    "\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "AUC Score: AUC is the percentage of the ROC plot that is underneath the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94450904392764845"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate cross-validated AUC\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "cross_val_score(nb, features_final, asd_classes, cv=10, scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "F-beta Score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83700440528634357"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.fit(X_train, y_train)\n",
    "from sklearn.metrics import fbeta_score\n",
    "predictions_test = nb.predict(X_test)\n",
    "fbeta_score(y_test, predictions_test, average='binary', beta=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='6'></a>\n",
    "\n",
    "## (6) Logistic Regression\n",
    "\n",
    "We've tried all these fancy techniques, but fundamentally this is just a binary classification problem. Try Logisitic Regression, which is a simple way to tackling this sort of thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97043715846994538"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "cv_scores = cross_val_score(logreg, features_final, asd_classes, cv=10)\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "AUC Score: AUC is the percentage of the ROC plot that is underneath the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99740986834010081"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate cross-validated AUC\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "cv_scores_roc = cross_val_score(logreg, features_final, asd_classes, cv=10, scoring='roc_auc').mean()\n",
    "cv_scores_roc.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "F-beta Score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93073593073593064"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X_train, y_train)\n",
    "from sklearn.metrics import fbeta_score\n",
    "predictions_test = logreg.predict(X_test)\n",
    "fbeta_score(y_test, predictions_test, average='binary', beta=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='7'></a>\n",
    "\n",
    "## (7) Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/basu/anaconda/lib/python2.7/site-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.93262295081967217"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "cv_scores = cross_val_score(lda, features_final, asd_classes, cv=10)\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "AUC Score: AUC is the percentage of the ROC plot that is underneath the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9850590623846438"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate cross-validated AUC\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "cv_scores_roc = cross_val_score(lda, features_final, asd_classes, cv=10, scoring='roc_auc').mean()\n",
    "cv_scores_roc.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "F-beta Score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91489361702127669"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.fit(X_train, y_train)\n",
    "from sklearn.metrics import fbeta_score\n",
    "predictions_test = lda.predict(X_test)\n",
    "fbeta_score(y_test, predictions_test, average='binary', beta=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='8'></a>\n",
    "## (8) Quadratic Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/basu/anaconda/lib/python2.7/site-packages/sklearn/discriminant_analysis.py:695: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.35306010928961745"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "\n",
    "qda = QuadraticDiscriminantAnalysis()\n",
    "cv_scores = cross_val_score(qda, features_final, asd_classes, cv=10)\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "AUC Score: AUC is the percentage of the ROC plot that is underneath the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77720253476067425"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate cross-validated AUC\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "cv_scores_roc = cross_val_score(qda, features_final, asd_classes, cv=10, scoring='roc_auc').mean()\n",
    "cv_scores_roc.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "F-beta Score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41414141414141409"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qda.fit(X_train, y_train)\n",
    "from sklearn.metrics import fbeta_score\n",
    "predictions_test = qda.predict(X_test)\n",
    "fbeta_score(y_test, predictions_test, average='binary', beta=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='step5'></a>\n",
    "\n",
    "## Step 5: Model Tuning\n",
    "\n",
    "Now I will fine tune the chosen model. For this I use grid search (GridSearchCV) with at least one important parameter tuned with at least 3 different values. I will need to use the entire training set for this. In the code cell below, I will need to implement the following:\n",
    "\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",
    "\n",
    "\n",
    "Note that, svm.SVC may perform differently with different kernels. The choice of kernel is an example of a \"hyperparamter.\" Here I experiented with different kernels such as 'rbf', 'sigmoid', and 'poly' and found that the best-performing kernel is `linear`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/basu/anaconda/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n",
      "/Users/basu/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.9508\n",
      "F-score on testing data: 0.9302\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 1.0000\n",
      "Final F-score on the testing data: 1.0000\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
    "from sklearn.metrics import fbeta_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "\n",
    "def f_beta_score(y_true, y_predict):\n",
    "    return fbeta_score(y_true, y_predict, beta = 0.5)\n",
    "\n",
    "\n",
    "# TODO: Initialize the classifier\n",
    "clf = SVC(random_state = 1)\n",
    "\n",
    "# TODO: Create the parameters list you wish to tune, using a dictionary if needed.\n",
    "# HINT: parameters = {'parameter_1': [value1, value2], 'parameter_2': [value1, value2]}\n",
    "parameters = {'C':range(1,6),'kernel':['linear','poly','rbf','sigmoid'],'degree':range(1,6)}\n",
    "\n",
    "# TODO: Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(f_beta_score)\n",
    "\n",
    "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "grid_obj = GridSearchCV(estimator = 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",
    "# Report the before-and-afterscores\n",
    "print \"Unoptimized model\\n------\"\n",
    "print \"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5))\n",
    "print \"\\nOptimized Model\\n------\"\n",
    "print \"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions))\n",
    "print \"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In this `GridSearchCV` methos, we came up with the best result when the kernel was selected as `linear`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='step6'></a>\n",
    "## Step 6: Feature Importance\n",
    "\n",
    "An important task when performing supervised learning on a dataset like the autistic 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 has ASD or not.\n",
    "\n",
    "Choose a scikit-learn classifier (e.g., gradientBoosting, 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 ASD dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "###  Extracting Feature Importance\n",
    "Choose a `scikit-learn` supervised learning algorithm that has a `feature_importance_` attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.\n",
    "\n",
    "In the code cell below, I will 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": 66,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.    0.73  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.  ]\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import a supervised learning model that has 'feature_importances_'\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "\n",
    "# TODO: Train the supervised model on the training set using .fit(X_train, y_train)\n",
    "model = GradientBoostingClassifier(random_state=0)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# TODO: Extract the feature importances using .feature_importances_ \n",
    "importances = model.feature_importances_\n",
    "print(importances)\n",
    "# Plot\n",
    "vs.feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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zg66uLoBXH9CnTp2Cn58fZDIZHBwchD9miYmJqFevHurVqyfUpaOjAwcHByQkJBT6Y3Hm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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Import a supervised learning model that has 'feature_importances_'\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "\n",
    "# TODO: Train the supervised model on the training set using .fit(X_train, y_train)\n",
    "model = AdaBoostClassifier(random_state=0)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# TODO: Extract the feature importances using .feature_importances_ \n",
    "importances = model.feature_importances_\n",
    "#print(importances)\n",
    "# Plot\n",
    "vs.feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Feature Selection\n",
    "We need to ask ourselves 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(in order with their weightage factor) contribute more than half of the importance of **all** features present in the data. These 5 features are:\n",
    "- '`result`'\n",
    "- '`relation_self`'\n",
    "- '`country_of_residence`'\n",
    "- '`jundice_no`'\n",
    "- '`jundice_yes`'\n",
    "\n",
    "This hints that we can attempt to *reduce the feature space* and simplify the information required for the model to learn. Although looking at those weight factor it seems like '`result`' feature is clearly dominating its influence on the algorithms over all other features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='step7'></a>\n",
    "\n",
    "## Step 7: Building a MLP  model architecture\n",
    "In this last part I build a model here using sequential model architecture best known as *Multi Layer Perceptron (MLP)*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "\n",
    "\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 8)                 760       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 769\n",
      "Trainable params: 769\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Building the model architecture with one layer of length 4\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(8, activation='relu', input_dim= 94))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
    "   \n",
    "    \n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Compiling the model using categorical_crossentropy loss, and rmsprop optimizer.\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 487 samples, validate on 122 samples\n",
      "Epoch 1/100\n",
      " - 1s - loss: 0.6793 - acc: 0.6591 - val_loss: 0.6715 - val_acc: 0.6393\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.6557 - acc: 0.7187 - val_loss: 0.6541 - val_acc: 0.6475\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.6331 - acc: 0.7187 - val_loss: 0.6349 - val_acc: 0.6475\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.6046 - acc: 0.7187 - val_loss: 0.6134 - val_acc: 0.6475\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.5758 - acc: 0.7187 - val_loss: 0.5882 - val_acc: 0.6475\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.5488 - acc: 0.7290 - val_loss: 0.5602 - val_acc: 0.6639\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.5187 - acc: 0.7474 - val_loss: 0.5277 - val_acc: 0.6885\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.4835 - acc: 0.7741 - val_loss: 0.4956 - val_acc: 0.7131\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.4498 - acc: 0.8234 - val_loss: 0.4659 - val_acc: 0.7623\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.4212 - acc: 0.8583 - val_loss: 0.4334 - val_acc: 0.8279\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.4047 - acc: 0.8645 - val_loss: 0.4056 - val_acc: 0.8525\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.3707 - acc: 0.8645 - val_loss: 0.3750 - val_acc: 0.8770\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.3358 - acc: 0.9076 - val_loss: 0.3460 - val_acc: 0.8689\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.3318 - acc: 0.8994 - val_loss: 0.3203 - val_acc: 0.8770\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.3057 - acc: 0.8912 - val_loss: 0.2977 - val_acc: 0.8852\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.2869 - acc: 0.9138 - val_loss: 0.2788 - val_acc: 0.9016\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.2658 - acc: 0.9199 - val_loss: 0.2591 - val_acc: 0.9180\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.2648 - acc: 0.9076 - val_loss: 0.2465 - val_acc: 0.9180\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.2410 - acc: 0.9220 - val_loss: 0.2345 - val_acc: 0.9098\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.2439 - acc: 0.9240 - val_loss: 0.2238 - val_acc: 0.9180\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.2226 - acc: 0.9199 - val_loss: 0.2126 - val_acc: 0.9262\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.2206 - acc: 0.9179 - val_loss: 0.2037 - val_acc: 0.9262\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.2032 - acc: 0.9343 - val_loss: 0.1992 - val_acc: 0.9180\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.1910 - acc: 0.9466 - val_loss: 0.1907 - val_acc: 0.9262\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.1959 - acc: 0.9343 - val_loss: 0.1832 - val_acc: 0.9262\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.1868 - acc: 0.9363 - val_loss: 0.1749 - val_acc: 0.9426\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.1876 - acc: 0.9261 - val_loss: 0.1698 - val_acc: 0.9426\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.1852 - acc: 0.9343 - val_loss: 0.1650 - val_acc: 0.9426\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.1834 - acc: 0.9363 - val_loss: 0.1612 - val_acc: 0.9426\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.1828 - acc: 0.9343 - val_loss: 0.1573 - val_acc: 0.9426\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.1609 - acc: 0.9363 - val_loss: 0.1513 - val_acc: 0.9508\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.1694 - acc: 0.9179 - val_loss: 0.1478 - val_acc: 0.9508\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.1489 - acc: 0.9466 - val_loss: 0.1454 - val_acc: 0.9426\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.1469 - acc: 0.9363 - val_loss: 0.1415 - val_acc: 0.9508\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.1577 - acc: 0.9281 - val_loss: 0.1396 - val_acc: 0.9508\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.1546 - acc: 0.9446 - val_loss: 0.1381 - val_acc: 0.9508\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.1411 - acc: 0.9446 - val_loss: 0.1348 - val_acc: 0.9508\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.1378 - acc: 0.9528 - val_loss: 0.1327 - val_acc: 0.9508\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.1456 - acc: 0.9363 - val_loss: 0.1304 - val_acc: 0.9508\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.1283 - acc: 0.9610 - val_loss: 0.1275 - val_acc: 0.9508\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.1196 - acc: 0.9548 - val_loss: 0.1265 - val_acc: 0.9590\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.1184 - acc: 0.9569 - val_loss: 0.1225 - val_acc: 0.9508\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.1222 - acc: 0.9487 - val_loss: 0.1207 - val_acc: 0.9508\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.1182 - acc: 0.9610 - val_loss: 0.1184 - val_acc: 0.9508\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.1305 - acc: 0.9487 - val_loss: 0.1167 - val_acc: 0.9508\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.1163 - acc: 0.9528 - val_loss: 0.1152 - val_acc: 0.9508\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.1092 - acc: 0.9528 - val_loss: 0.1130 - val_acc: 0.9590\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.1029 - acc: 0.9569 - val_loss: 0.1109 - val_acc: 0.9590\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.1063 - acc: 0.9610 - val_loss: 0.1091 - val_acc: 0.9590\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.1098 - acc: 0.9589 - val_loss: 0.1078 - val_acc: 0.9590\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.1016 - acc: 0.9630 - val_loss: 0.1066 - val_acc: 0.9590\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0967 - acc: 0.9651 - val_loss: 0.1052 - val_acc: 0.9590\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0956 - acc: 0.9610 - val_loss: 0.1057 - val_acc: 0.9590\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0910 - acc: 0.9671 - val_loss: 0.1045 - val_acc: 0.9590\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.1051 - acc: 0.9528 - val_loss: 0.0998 - val_acc: 0.9590\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.1057 - acc: 0.9569 - val_loss: 0.0991 - val_acc: 0.9590\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0754 - acc: 0.9774 - val_loss: 0.0972 - val_acc: 0.9590\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0978 - acc: 0.9589 - val_loss: 0.0958 - val_acc: 0.9590\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.1028 - acc: 0.9507 - val_loss: 0.0941 - val_acc: 0.9590\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0760 - acc: 0.9713 - val_loss: 0.0924 - val_acc: 0.9590\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0873 - acc: 0.9569 - val_loss: 0.0924 - val_acc: 0.9590\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.1002 - acc: 0.9528 - val_loss: 0.0915 - val_acc: 0.9590\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0740 - acc: 0.9713 - val_loss: 0.0883 - val_acc: 0.9590\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0781 - acc: 0.9630 - val_loss: 0.0894 - val_acc: 0.9590\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0816 - acc: 0.9589 - val_loss: 0.0925 - val_acc: 0.9590\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0790 - acc: 0.9671 - val_loss: 0.0872 - val_acc: 0.9590\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0746 - acc: 0.9692 - val_loss: 0.0906 - val_acc: 0.9590\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0739 - acc: 0.9713 - val_loss: 0.0889 - val_acc: 0.9590\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0734 - acc: 0.9713 - val_loss: 0.0822 - val_acc: 0.9672\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0739 - acc: 0.9671 - val_loss: 0.0843 - val_acc: 0.9672\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0670 - acc: 0.9692 - val_loss: 0.0842 - val_acc: 0.9590\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0617 - acc: 0.9754 - val_loss: 0.0814 - val_acc: 0.9672\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0652 - acc: 0.9713 - val_loss: 0.0840 - val_acc: 0.9590\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0762 - acc: 0.9671 - val_loss: 0.0782 - val_acc: 0.9672\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0726 - acc: 0.9671 - val_loss: 0.0783 - val_acc: 0.9672\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0618 - acc: 0.9774 - val_loss: 0.0784 - val_acc: 0.9672\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0605 - acc: 0.9692 - val_loss: 0.0748 - val_acc: 0.9672\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0620 - acc: 0.9754 - val_loss: 0.0731 - val_acc: 0.9672\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0700 - acc: 0.9713 - val_loss: 0.0774 - val_acc: 0.9672\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0607 - acc: 0.9795 - val_loss: 0.0759 - val_acc: 0.9672\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0739 - acc: 0.9671 - val_loss: 0.0781 - val_acc: 0.9754\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0525 - acc: 0.9897 - val_loss: 0.0719 - val_acc: 0.9672\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0613 - acc: 0.9795 - val_loss: 0.0760 - val_acc: 0.9672\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0573 - acc: 0.9795 - val_loss: 0.0746 - val_acc: 0.9672\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0647 - acc: 0.9815 - val_loss: 0.0754 - val_acc: 0.9672\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0555 - acc: 0.9795 - val_loss: 0.0723 - val_acc: 0.9672\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0511 - acc: 0.9836 - val_loss: 0.0702 - val_acc: 0.9672\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0636 - acc: 0.9774 - val_loss: 0.0687 - val_acc: 0.9672\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0477 - acc: 0.9856 - val_loss: 0.0695 - val_acc: 0.9672\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0446 - acc: 0.9836 - val_loss: 0.0694 - val_acc: 0.9672\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0459 - acc: 0.9877 - val_loss: 0.0648 - val_acc: 0.9672\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0588 - acc: 0.9795 - val_loss: 0.0698 - val_acc: 0.9754\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0485 - acc: 0.9877 - val_loss: 0.0672 - val_acc: 0.9672\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0432 - acc: 0.9959 - val_loss: 0.0638 - val_acc: 0.9672\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0448 - acc: 0.9979 - val_loss: 0.0683 - val_acc: 0.9754\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0588 - acc: 0.9795 - val_loss: 0.0617 - val_acc: 0.9672\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0550 - acc: 0.9836 - val_loss: 0.0626 - val_acc: 0.9754\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0451 - acc: 0.9856 - val_loss: 0.0634 - val_acc: 0.9754\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0427 - acc: 0.9877 - val_loss: 0.0641 - val_acc: 0.9754\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0462 - acc: 0.9897 - val_loss: 0.0676 - val_acc: 0.9754\n"
     ]
    }
   ],
   "source": [
    "# Running and evaluating the model\n",
    "hist = model.fit(X_train, y_train,\n",
    "          batch_size=16,\n",
    "          epochs=100,\n",
    "          validation_data=(X_test, y_test), \n",
    "          verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Evaluating the model\n",
    "This will give you the accuracy of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "487/487 [==============================] - 0s 90us/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "('\\n Training Accuracy:', 1.0)\n",
      "('\\n Testing accuracy: ', 0.97540983606557374)\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model on the training and testing set\n",
    "score = model.evaluate(X_train, y_train)\n",
    "print(\"\\n Training Accuracy:\", score[1])\n",
    "\n",
    "score = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"\\n Testing accuracy: \", score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Free-Flow Visualization:\n",
    "\n",
    "As with \"Feature Selection\" exploration, we have seen the attribute named 'result' has a such a powerful presence in the ASD dataset, all other attributes have little to no contribution in deciding the final class. In the Figure  below, I have drawn a scatterplot with 'result' as x-axis and ASD-class (say 'yes' is 1 and 'no' is 0) as y-axis, and reconfirmed that underlying association between the given variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/basu/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/Users/basu/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:321: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/Users/basu/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:356: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "asd_data['Class/ASD'] = asd_raw.apply(lambda x: 1 if x == 'YES' else 0)\n",
    "#asd_data['result'] = asd_data['result'].apply(lambda x: (np.log(x + 1)))\n",
    "\n",
    "\n",
    "\n",
    "asd_data = pd.DataFrame(data = asd_data)\n",
    "asd_data['result'] = scaler.fit_transform(asd_data['result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#ax = sns.regplot(x= 'result', y='Class/ASD', data = asd_data, fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GkpOTQ2NJSUny+/0NJp8k9erVS82bN49qph8LlzEuLk4tWrSQbduaMWOGLr/8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\nAIahvAEAMMz/B/fZA7OXtMQcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=\"result\", y=\"Class/ASD\", data = asd_data, jitter=True);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#sns.swarmplot(x=\"result\", y=\"Class/ASD\", data=asd_data, palette={'YES': \"r\", 'NO': \"b\"});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "---\n",
    "<a id='step8'></a>\n",
    "\n",
    "## Step 8: Conclusion\n",
    "\n",
    "\n",
    "After exploring my `ASD` dataset with different kind of learning algorithms, I have arrived into this conclusion that all of my model work extremely well with the data. I have used three different `metric` (such as `accuracy`, `AUC score` and `F-score`) to measure the performance of my models, and it seems like all of the `metric` indicated an almost perfect classification of the ASD cases. Here I think the reason of this high performances with different models is the fact that only one of the feature is predominant over all others which I confirmed with the [`Feature Importance`](#step6) section in this notebook. \n",
    "\n",
    "I think to build a more accurate model, we need to have access to more larger datasets. Here the number of instances after cleaning the data were not so sufficient enough so that I can claim that this model is optimum. As this dataset is only publicly available from Decemeber 2017, I think not many works that deal with this dataset is available online. In that consideration, my models can serve as benchmark models for any machine learning researcher/practitioner who will be interested to explore this dataset further. With this fact in mind, I think this are very well developed model that can detect ASD in indivisuals with certain given attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Rebuilding the model without the `'result'` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>jundice</th>\n",
       "      <th>austim</th>\n",
       "      <th>contry_of_res</th>\n",
       "      <th>relation</th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
       "      <th>A6_Score</th>\n",
       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>A9_Score</th>\n",
       "      <th>A10_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.024590</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
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       "      <td>United States</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.019126</td>\n",
       "      <td>m</td>\n",
       "      <td>Latino</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Self</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.027322</td>\n",
       "      <td>m</td>\n",
       "      <td>Latino</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Parent</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049180</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
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       "      <td>yes</td>\n",
       "      <td>United States</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.051913</td>\n",
       "      <td>m</td>\n",
       "      <td>Others</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>United States</td>\n",
       "      <td>Self</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age gender       ethnicity jundice austim  contry_of_res relation  \\\n",
       "0  0.024590      f  White-European      no     no  United States     Self   \n",
       "1  0.019126      m          Latino      no    yes         Brazil     Self   \n",
       "2  0.027322      m          Latino     yes    yes          Spain   Parent   \n",
       "3  0.049180      f  White-European      no    yes  United States     Self   \n",
       "5  0.051913      m          Others     yes     no  United States     Self   \n",
       "\n",
       "   A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n",
       "0         1         1         1         1         0         0         1   \n",
       "1         1         1         0         1         0         0         0   \n",
       "2         1         1         0         1         1         0         1   \n",
       "3         1         1         0         1         0         0         1   \n",
       "5         1         1         1         1         1         0         1   \n",
       "\n",
       "   A8_Score  A9_Score  A10_Score  \n",
       "0         1         0          0  \n",
       "1         1         0          1  \n",
       "2         1         1          1  \n",
       "3         1         0          1  \n",
       "5         1         1          1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the data into features and target label\n",
    "asd_raw = asd_data['Class/ASD']\n",
    "features_raw = asd_data[['age', 'gender', 'ethnicity', 'jundice', 'austim', 'contry_of_res', \n",
    "                      'relation','A1_Score','A2_Score','A3_Score','A4_Score','A5_Score','A6_Score','A7_Score','A8_Score',\n",
    "                      'A9_Score','A10_Score']]\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "numerical = ['age']\n",
    "\n",
    "features_minmax_transform = pd.DataFrame(data = features_raw)\n",
    "features_minmax_transform[numerical] = scaler.fit_transform(features_raw[numerical])\n",
    "features_minmax_transform\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_minmax_transform.head(n = 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>A1_Score</th>\n",
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       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>A9_Score</th>\n",
       "      <th>...</th>\n",
       "      <th>contry_of_res_United Arab Emirates</th>\n",
       "      <th>contry_of_res_United Kingdom</th>\n",
       "      <th>contry_of_res_United States</th>\n",
       "      <th>contry_of_res_Uruguay</th>\n",
       "      <th>contry_of_res_Viet Nam</th>\n",
       "      <th>relation_Health care professional</th>\n",
       "      <th>relation_Others</th>\n",
       "      <th>relation_Parent</th>\n",
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      ],
      "text/plain": [
       "        age  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  \\\n",
       "0  0.024590         1         1         1         1         0         0   \n",
       "1  0.019126         1         1         0         1         0         0   \n",
       "2  0.027322         1         1         0         1         1         0   \n",
       "3  0.049180         1         1         0         1         0         0   \n",
       "5  0.051913         1         1         1         1         1         0   \n",
       "\n",
       "   A7_Score  A8_Score  A9_Score      ...        \\\n",
       "0         1         1         0      ...         \n",
       "1         0         1         0      ...         \n",
       "2         1         1         1      ...         \n",
       "3         1         1         0      ...         \n",
       "5         1         1         1      ...         \n",
       "\n",
       "   contry_of_res_United Arab Emirates  contry_of_res_United Kingdom  \\\n",
       "0                                   0                             0   \n",
       "1                                   0                             0   \n",
       "2                                   0                             0   \n",
       "3                                   0                             0   \n",
       "5                                   0                             0   \n",
       "\n",
       "   contry_of_res_United States  contry_of_res_Uruguay  contry_of_res_Viet Nam  \\\n",
       "0                            1                      0                       0   \n",
       "1                            0                      0                       0   \n",
       "2                            0                      0                       0   \n",
       "3                            1                      0                       0   \n",
       "5                            1                      0                       0   \n",
       "\n",
       "   relation_Health care professional  relation_Others  relation_Parent  \\\n",
       "0                                  0                0                0   \n",
       "1                                  0                0                0   \n",
       "2                                  0                0                1   \n",
       "3                                  0                0                0   \n",
       "5                                  0                0                0   \n",
       "\n",
       "   relation_Relative  relation_Self  \n",
       "0                  0              1  \n",
       "1                  0              1  \n",
       "2                  0              0  \n",
       "3                  0              1  \n",
       "5                  0              1  \n",
       "\n",
       "[5 rows x 93 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93 total features after one-hot encoding.\n",
      "['age', 'A1_Score', 'A2_Score', 'A3_Score', 'A4_Score', 'A5_Score', 'A6_Score', 'A7_Score', 'A8_Score', 'A9_Score', 'A10_Score', 'gender_f', 'gender_m', 'ethnicity_Asian', 'ethnicity_Black', 'ethnicity_Hispanic', 'ethnicity_Latino', 'ethnicity_Middle Eastern ', 'ethnicity_Others', 'ethnicity_Pasifika', 'ethnicity_South Asian', 'ethnicity_Turkish', 'ethnicity_White-European', 'ethnicity_others', 'jundice_no', 'jundice_yes', 'austim_no', 'austim_yes', 'contry_of_res_Afghanistan', 'contry_of_res_AmericanSamoa', 'contry_of_res_Angola', 'contry_of_res_Armenia', 'contry_of_res_Aruba', 'contry_of_res_Australia', 'contry_of_res_Austria', 'contry_of_res_Bahamas', 'contry_of_res_Bangladesh', 'contry_of_res_Belgium', 'contry_of_res_Bolivia', 'contry_of_res_Brazil', 'contry_of_res_Burundi', 'contry_of_res_Canada', 'contry_of_res_Chile', 'contry_of_res_China', 'contry_of_res_Costa Rica', 'contry_of_res_Cyprus', 'contry_of_res_Czech Republic', 'contry_of_res_Ecuador', 'contry_of_res_Egypt', 'contry_of_res_Ethiopia', 'contry_of_res_Finland', 'contry_of_res_France', 'contry_of_res_Germany', 'contry_of_res_Iceland', 'contry_of_res_India', 'contry_of_res_Indonesia', 'contry_of_res_Iran', 'contry_of_res_Ireland', 'contry_of_res_Italy', 'contry_of_res_Jordan', 'contry_of_res_Malaysia', 'contry_of_res_Mexico', 'contry_of_res_Nepal', 'contry_of_res_Netherlands', 'contry_of_res_New Zealand', 'contry_of_res_Nicaragua', 'contry_of_res_Niger', 'contry_of_res_Oman', 'contry_of_res_Pakistan', 'contry_of_res_Philippines', 'contry_of_res_Portugal', 'contry_of_res_Romania', 'contry_of_res_Russia', 'contry_of_res_Saudi Arabia', 'contry_of_res_Serbia', 'contry_of_res_Sierra Leone', 'contry_of_res_South Africa', 'contry_of_res_Spain', 'contry_of_res_Sri Lanka', 'contry_of_res_Sweden', 'contry_of_res_Tonga', 'contry_of_res_Turkey', 'contry_of_res_Ukraine', 'contry_of_res_United Arab Emirates', 'contry_of_res_United Kingdom', 'contry_of_res_United States', 'contry_of_res_Uruguay', 'contry_of_res_Viet Nam', 'relation_Health care professional', 'relation_Others', 'relation_Parent', 'relation_Relative', 'relation_Self']\n"
     ]
    }
   ],
   "source": [
    "#One-hot encode the 'features_minmax_transform' data using pandas.get_dummies()\n",
    "features_final = pd.get_dummies(features_minmax_transform)\n",
    "display(features_final.head(5))\n",
    "\n",
    "\n",
    "# Encode the 'all_classes_raw' data to numerical values\n",
    "asd_classes = asd_raw.apply(lambda x: 1 if x == 'YES' else 0)\n",
    "\n",
    "\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": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 487 samples.\n",
      "Testing set has 122 samples.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "np.random.seed(1234)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features_final, asd_classes, train_size=0.80, random_state=1)\n",
    "\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": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=1, splitter='best')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### (1) Decision Trees\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dectree = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "# Train the classifier on the training set\n",
    "dectree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RieDZ/dprr1kMEZmwTBgPg5ZmNHaTiiZk7UF9oFnB4rfiec3LIB2gFa2mv+Ee7fjNYjQ2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\nTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pydotplus \n",
    "\n",
    "\n",
    "dot_data = tree.export_graphviz(dectree,\n",
    "                                out_file=None,\n",
    "                                filled=True,\n",
    "                                rounded=True,\n",
    "                                special_characters=True)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data)  \n",
    "\n",
    "from IPython.display import Image \n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "dectree = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "cv_scores = cross_val_score(dectree, features_final, asd_classes, cv=10)\n",
    "\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "ranfor = RandomForestClassifier(n_estimators=5, random_state=1)\n",
    "cv_scores = cross_val_score(ranfor, features_final, asd_classes, cv=10)\n",
    "\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import neighbors\n",
    "\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=10)\n",
    "cv_scores = cross_val_score(knn, features_final, asd_classes, cv=10)\n",
    "\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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H0dERuHGA3r17NwEBAcTExODp6Wn+Y7Znzx6qVKlClSpVzO1ycHDA09OTqKioLH8sfvrp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\nQgghhI37D1D1Cu1zm6L8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Import a supervised learning model that has 'feature_importances_'\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "\n",
    "# TODO: Train the supervised model on the training set using .fit(X_train, y_train)\n",
    "model = AdaBoostClassifier(random_state=0)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# TODO: Extract the feature importances using .feature_importances_ \n",
    "importances = model.feature_importances_\n",
    "\n",
    "# Plot\n",
    "vs.feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}