ASD_Adolescents_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Autism Spectrum Disorder in Adolescents***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Import Required libraries***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "from scipy.io import arff\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Setting Max Rows and Columns limits for display*** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',25)\n",
    "pd.set_option('display.max_rows',110)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Read the Adolescents data***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd = pd.read_excel('Autism_Child_data_in_excel.xlsx',sheet_name='Autism_Adolscent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'ethnicity', 'jundice', 'autism', 'relation',\n",
       "       'contry_of_res', 'used_app_before', 'age_desc', 'A1_Score', 'A2_Score',\n",
       "       'A3_Score', 'A4_Score', 'A5_Score', 'A6_Score', 'A7_Score', 'A8_Score',\n",
       "       'A9_Score', 'A10_Score', 'result', 'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd.columns = ['age', 'gender', 'ethnicity', 'born_with_jaundice', 'family_member_with_PDD', 'whos_completing_test', 'country', 'used_screening_app_before', 'age_desc', \n",
    "                           'Q1_Score','Q2_Score','Q3_Score','Q4_Score','Q5_Score','Q6_Score','Q7_Score','Q8_Score','Q9_Score','Q10_Score', 'screening_score', 'ASD_Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>m</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Austria</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NO</td>\n",
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       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>m</td>\n",
       "      <td>Black</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Austria</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</td>\n",
       "      <td>0</td>\n",
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       "      <td>?</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>?</td>\n",
       "      <td>AmericanSamoa</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Self</td>\n",
       "      <td>'United Kingdom'</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>7</td>\n",
       "      <td>YES</td>\n",
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       "      <td>?</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>?</td>\n",
       "      <td>Albania</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age gender       ethnicity born_with_jaundice family_member_with_PDD  \\\n",
       "0   15      m        Hispanic                yes                    yes   \n",
       "1   15      m           Black                 no                     no   \n",
       "2   12      f               ?                 no                     no   \n",
       "3   14      f  White-European                 no                     no   \n",
       "4   16      f               ?                 no                     no   \n",
       "\n",
       "  whos_completing_test           country used_screening_app_before  \\\n",
       "0               Parent           Austria                        no   \n",
       "1             Relative           Austria                        no   \n",
       "2                    ?     AmericanSamoa                        no   \n",
       "3                 Self  'United Kingdom'                        no   \n",
       "4                    ?           Albania                        no   \n",
       "\n",
       "        age_desc  Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  \\\n",
       "0  '12-16 years'         0         0         0         1         1         1   \n",
       "1  '12-16 years'         0         0         0         0         0         0   \n",
       "2  '12-16 years'         0         0         0         0         0         0   \n",
       "3  '12-16 years'         0         1         1         1         1         1   \n",
       "4  '12-16 years'         1         1         1         1         1         1   \n",
       "\n",
       "   Q7_Score  Q8_Score  Q9_Score  Q10_Score  screening_score ASD_Label  \n",
       "0         1         1         1          0                6        NO  \n",
       "1         0         0         1          1                2        NO  \n",
       "2         0         0         1          1                2        NO  \n",
       "3         0         1         1          0                7       YES  \n",
       "4         1         0         0          0                7       YES  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Initial Data Analysis (IDA)***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                          0\n",
       "gender                       0\n",
       "ethnicity                    6\n",
       "born_with_jaundice           0\n",
       "family_member_with_PDD       0\n",
       "whos_completing_test         6\n",
       "country                      0\n",
       "used_screening_app_before    0\n",
       "age_desc                     0\n",
       "Q1_Score                     0\n",
       "Q2_Score                     0\n",
       "Q3_Score                     0\n",
       "Q4_Score                     0\n",
       "Q5_Score                     0\n",
       "Q6_Score                     0\n",
       "Q7_Score                     0\n",
       "Q8_Score                     0\n",
       "Q9_Score                     0\n",
       "Q10_Score                    0\n",
       "screening_score              0\n",
       "ASD_Label                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.isin(['?']).sum()            # Just checking the number of NULL values in features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Check-1: Features DataType Mismatch***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 104 entries, 0 to 103\n",
      "Data columns (total 21 columns):\n",
      "age                          104 non-null int64\n",
      "gender                       104 non-null object\n",
      "ethnicity                    104 non-null object\n",
      "born_with_jaundice           104 non-null object\n",
      "family_member_with_PDD       104 non-null object\n",
      "whos_completing_test         104 non-null object\n",
      "country                      104 non-null object\n",
      "used_screening_app_before    104 non-null object\n",
      "age_desc                     104 non-null object\n",
      "Q1_Score                     104 non-null int64\n",
      "Q2_Score                     104 non-null int64\n",
      "Q3_Score                     104 non-null int64\n",
      "Q4_Score                     104 non-null int64\n",
      "Q5_Score                     104 non-null int64\n",
      "Q6_Score                     104 non-null int64\n",
      "Q7_Score                     104 non-null int64\n",
      "Q8_Score                     104 non-null int64\n",
      "Q9_Score                     104 non-null int64\n",
      "Q10_Score                    104 non-null int64\n",
      "screening_score              104 non-null int64\n",
      "ASD_Label                    104 non-null object\n",
      "dtypes: int64(12), object(9)\n",
      "memory usage: 17.2+ KB\n"
     ]
    }
   ],
   "source": [
    "adolescents_asd.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### **Features datatypes appears to be correct, however, we need to handle the residing data.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Check-2: Individual features handling***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>AmericanSamoa</td>\n",
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       "      <td>'12-16 years'</td>\n",
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       "      <th>3</th>\n",
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       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Self</td>\n",
       "      <td>'United Kingdom'</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>?</td>\n",
       "      <td>Albania</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age gender       ethnicity born_with_jaundice family_member_with_PDD  \\\n",
       "0   15      m        Hispanic                yes                    yes   \n",
       "1   15      m           Black                 no                     no   \n",
       "2   12      f               ?                 no                     no   \n",
       "3   14      f  White-European                 no                     no   \n",
       "4   16      f               ?                 no                     no   \n",
       "\n",
       "  whos_completing_test           country used_screening_app_before  \\\n",
       "0               Parent           Austria                        no   \n",
       "1             Relative           Austria                        no   \n",
       "2                    ?     AmericanSamoa                        no   \n",
       "3                 Self  'United Kingdom'                        no   \n",
       "4                    ?           Albania                        no   \n",
       "\n",
       "        age_desc  Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  \\\n",
       "0  '12-16 years'         0         0         0         1         1         1   \n",
       "1  '12-16 years'         0         0         0         0         0         0   \n",
       "2  '12-16 years'         0         0         0         0         0         0   \n",
       "3  '12-16 years'         0         1         1         1         1         1   \n",
       "4  '12-16 years'         1         1         1         1         1         1   \n",
       "\n",
       "   Q7_Score  Q8_Score  Q9_Score  Q10_Score  screening_score ASD_Label  \n",
       "0         1         1         1          0                6        NO  \n",
       "1         0         0         1          1                2        NO  \n",
       "2         0         0         1          1                2        NO  \n",
       "3         0         1         1          0                7       YES  \n",
       "4         1         0         0          0                7       YES  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Fixing values in GENDER variable***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['m', 'f'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    54\n",
       "m    50\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['gender'] = adolescents_asd['gender'].apply(lambda val: 0 if val == 'm' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    54\n",
       "0    50\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Fixing values in BORN_WITH_JAUNDICE variable***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Austria</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Austria</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>?</td>\n",
       "      <td>AmericanSamoa</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Self</td>\n",
       "      <td>'United Kingdom'</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>?</td>\n",
       "      <td>Albania</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender       ethnicity born_with_jaundice family_member_with_PDD  \\\n",
       "0   15       0        Hispanic                yes                    yes   \n",
       "1   15       0           Black                 no                     no   \n",
       "2   12       1               ?                 no                     no   \n",
       "3   14       1  White-European                 no                     no   \n",
       "4   16       1               ?                 no                     no   \n",
       "\n",
       "  whos_completing_test           country used_screening_app_before  \\\n",
       "0               Parent           Austria                        no   \n",
       "1             Relative           Austria                        no   \n",
       "2                    ?     AmericanSamoa                        no   \n",
       "3                 Self  'United Kingdom'                        no   \n",
       "4                    ?           Albania                        no   \n",
       "\n",
       "        age_desc  Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  \\\n",
       "0  '12-16 years'         0         0         0         1         1         1   \n",
       "1  '12-16 years'         0         0         0         0         0         0   \n",
       "2  '12-16 years'         0         0         0         0         0         0   \n",
       "3  '12-16 years'         0         1         1         1         1         1   \n",
       "4  '12-16 years'         1         1         1         1         1         1   \n",
       "\n",
       "   Q7_Score  Q8_Score  Q9_Score  Q10_Score  screening_score ASD_Label  \n",
       "0         1         1         1          0                6        NO  \n",
       "1         0         0         1          1                2        NO  \n",
       "2         0         0         1          1                2        NO  \n",
       "3         0         1         1          0                7       YES  \n",
       "4         1         0         0          0                7       YES  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['yes', 'no'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['born_with_jaundice'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     88\n",
       "yes    16\n",
       "Name: born_with_jaundice, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['born_with_jaundice'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['born_with_jaundice'] = adolescents_asd['born_with_jaundice'].apply(lambda val: 1 if val == 'yes' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88\n",
       "1    16\n",
       "Name: born_with_jaundice, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['born_with_jaundice'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Fixing values in FAMILY_MEMBER_WITH_PDD variable***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['yes', 'no'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['family_member_with_PDD'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     90\n",
       "yes    14\n",
       "Name: family_member_with_PDD, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['family_member_with_PDD'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['family_member_with_PDD'] = adolescents_asd['family_member_with_PDD'].apply(lambda val: 1 if val == 'yes' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    90\n",
       "1    14\n",
       "Name: family_member_with_PDD, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['family_member_with_PDD'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Fixing values in USED_SCREENING_APP_BEFORE variable***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Relative</td>\n",
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       "      <td>no</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>AmericanSamoa</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>'United Kingdom'</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>Albania</td>\n",
       "      <td>no</td>\n",
       "      <td>'12-16 years'</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender       ethnicity  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   15       0        Hispanic                   1                       1   \n",
       "1   15       0           Black                   0                       0   \n",
       "2   12       1               ?                   0                       0   \n",
       "3   14       1  White-European                   0                       0   \n",
       "4   16       1               ?                   0                       0   \n",
       "\n",
       "  whos_completing_test           country used_screening_app_before  \\\n",
       "0               Parent           Austria                        no   \n",
       "1             Relative           Austria                        no   \n",
       "2                    ?     AmericanSamoa                        no   \n",
       "3                 Self  'United Kingdom'                        no   \n",
       "4                    ?           Albania                        no   \n",
       "\n",
       "        age_desc  Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  \\\n",
       "0  '12-16 years'         0         0         0         1         1         1   \n",
       "1  '12-16 years'         0         0         0         0         0         0   \n",
       "2  '12-16 years'         0         0         0         0         0         0   \n",
       "3  '12-16 years'         0         1         1         1         1         1   \n",
       "4  '12-16 years'         1         1         1         1         1         1   \n",
       "\n",
       "   Q7_Score  Q8_Score  Q9_Score  Q10_Score  screening_score ASD_Label  \n",
       "0         1         1         1          0                6        NO  \n",
       "1         0         0         1          1                2        NO  \n",
       "2         0         0         1          1                2        NO  \n",
       "3         0         1         1          0                7       YES  \n",
       "4         1         0         0          0                7       YES  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no', 'yes'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['used_screening_app_before'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     100\n",
       "yes      4\n",
       "Name: used_screening_app_before, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['used_screening_app_before'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['used_screening_app_before'] = adolescents_asd['used_screening_app_before'].apply(lambda val: 1 if val == 'yes' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100\n",
       "1      4\n",
       "Name: used_screening_app_before, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['used_screening_app_before'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Fixing values in AGE_DESC variable***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Austria</td>\n",
       "      <td>0</td>\n",
       "      <td>'12-16 years'</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Austria</td>\n",
       "      <td>0</td>\n",
       "      <td>'12-16 years'</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>AmericanSamoa</td>\n",
       "      <td>0</td>\n",
       "      <td>'12-16 years'</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>'United Kingdom'</td>\n",
       "      <td>0</td>\n",
       "      <td>'12-16 years'</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>'12-16 years'</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender       ethnicity  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   15       0        Hispanic                   1                       1   \n",
       "1   15       0           Black                   0                       0   \n",
       "2   12       1               ?                   0                       0   \n",
       "3   14       1  White-European                   0                       0   \n",
       "4   16       1               ?                   0                       0   \n",
       "\n",
       "  whos_completing_test           country  used_screening_app_before  \\\n",
       "0               Parent           Austria                          0   \n",
       "1             Relative           Austria                          0   \n",
       "2                    ?     AmericanSamoa                          0   \n",
       "3                 Self  'United Kingdom'                          0   \n",
       "4                    ?           Albania                          0   \n",
       "\n",
       "        age_desc  Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  \\\n",
       "0  '12-16 years'         0         0         0         1         1         1   \n",
       "1  '12-16 years'         0         0         0         0         0         0   \n",
       "2  '12-16 years'         0         0         0         0         0         0   \n",
       "3  '12-16 years'         0         1         1         1         1         1   \n",
       "4  '12-16 years'         1         1         1         1         1         1   \n",
       "\n",
       "   Q7_Score  Q8_Score  Q9_Score  Q10_Score  screening_score ASD_Label  \n",
       "0         1         1         1          0                6        NO  \n",
       "1         0         0         1          1                2        NO  \n",
       "2         0         0         1          1                2        NO  \n",
       "3         0         1         1          0                7       YES  \n",
       "4         1         0         0          0                7       YES  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"'12-16 years'\", \"'12-15 years'\"], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['age_desc'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12-16 years'    97\n",
       "'12-15 years'     7\n",
       "Name: age_desc, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['age_desc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['age_desc'] = adolescents_asd['age_desc'].apply(lambda val: 0 if val == \"'12-16 years'\" else 1 if val == \"'12-15 years'\" else 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Fixing values in ASD_Label variable***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Parent</td>\n",
       "      <td>Austria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>NO</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
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       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Austria</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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>NO</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>AmericanSamoa</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>'United Kingdom'</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>Albania</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender       ethnicity  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   15       0        Hispanic                   1                       1   \n",
       "1   15       0           Black                   0                       0   \n",
       "2   12       1               ?                   0                       0   \n",
       "3   14       1  White-European                   0                       0   \n",
       "4   16       1               ?                   0                       0   \n",
       "\n",
       "  whos_completing_test           country  used_screening_app_before  age_desc  \\\n",
       "0               Parent           Austria                          0         0   \n",
       "1             Relative           Austria                          0         0   \n",
       "2                    ?     AmericanSamoa                          0         0   \n",
       "3                 Self  'United Kingdom'                          0         0   \n",
       "4                    ?           Albania                          0         0   \n",
       "\n",
       "   Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  Q7_Score  \\\n",
       "0         0         0         0         1         1         1         1   \n",
       "1         0         0         0         0         0         0         0   \n",
       "2         0         0         0         0         0         0         0   \n",
       "3         0         1         1         1         1         1         0   \n",
       "4         1         1         1         1         1         1         1   \n",
       "\n",
       "   Q8_Score  Q9_Score  Q10_Score  screening_score ASD_Label  \n",
       "0         1         1          0                6        NO  \n",
       "1         0         1          1                2        NO  \n",
       "2         0         1          1                2        NO  \n",
       "3         1         1          0                7       YES  \n",
       "4         0         0          0                7       YES  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NO', 'YES'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['ASD_Label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YES    63\n",
       "NO     41\n",
       "Name: ASD_Label, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['ASD_Label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['ASD_Label'] = adolescents_asd['ASD_Label'].apply(lambda val: 1 if val == 'YES' else 0 if val == 'NO' else 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    63\n",
       "0    41\n",
       "Name: ASD_Label, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['ASD_Label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Finding NULL values in the dataset***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                          0\n",
       "gender                       0\n",
       "ethnicity                    6\n",
       "born_with_jaundice           0\n",
       "family_member_with_PDD       0\n",
       "whos_completing_test         6\n",
       "country                      0\n",
       "used_screening_app_before    0\n",
       "age_desc                     0\n",
       "Q1_Score                     0\n",
       "Q2_Score                     0\n",
       "Q3_Score                     0\n",
       "Q4_Score                     0\n",
       "Q5_Score                     0\n",
       "Q6_Score                     0\n",
       "Q7_Score                     0\n",
       "Q8_Score                     0\n",
       "Q9_Score                     0\n",
       "Q10_Score                    0\n",
       "screening_score              0\n",
       "ASD_Label                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.isin(['?']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Standardizing COUNTRY variable***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'United Kingdom'          29\n",
       "'United States'           12\n",
       "Argentina                 11\n",
       "India                      7\n",
       "Austria                    5\n",
       "Indonesia                  3\n",
       "Canada                     3\n",
       "Albania                    2\n",
       "Croatia                    2\n",
       "AmericanSamoa              2\n",
       "Afghanistan                2\n",
       "Belgium                    2\n",
       "Australia                  2\n",
       "Netherlands                2\n",
       "Egypt                      2\n",
       "Bahamas                    1\n",
       "Brazil                     1\n",
       "Portugal                   1\n",
       "Ireland                    1\n",
       "Norway                     1\n",
       "Jordan                     1\n",
       "Anguilla                   1\n",
       "'United Arab Emirates'     1\n",
       "'South Africa'             1\n",
       "Bangladesh                 1\n",
       "France                     1\n",
       "'New Zealand'              1\n",
       "Bahrain                    1\n",
       "Azerbaijan                 1\n",
       "'Viet Nam'                 1\n",
       "Comoros                    1\n",
       "Finland                    1\n",
       "Greenland                  1\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['country'] = adolescents_asd['country'].str.replace(\"'\",'').str.capitalize().str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United kingdom          29\n",
       "United states           12\n",
       "Argentina               11\n",
       "India                    7\n",
       "Austria                  5\n",
       "Canada                   3\n",
       "Indonesia                3\n",
       "Belgium                  2\n",
       "Netherlands              2\n",
       "Afghanistan              2\n",
       "Australia                2\n",
       "Albania                  2\n",
       "Croatia                  2\n",
       "Americansamoa            2\n",
       "Egypt                    2\n",
       "Jordan                   1\n",
       "Viet nam                 1\n",
       "South africa             1\n",
       "Norway                   1\n",
       "Ireland                  1\n",
       "Portugal                 1\n",
       "Bahamas                  1\n",
       "Greenland                1\n",
       "New zealand              1\n",
       "Anguilla                 1\n",
       "Bangladesh               1\n",
       "France                   1\n",
       "Bahrain                  1\n",
       "Azerbaijan               1\n",
       "Finland                  1\n",
       "Brazil                   1\n",
       "Comoros                  1\n",
       "United arab emirates     1\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Standardizing ETHNICITY variable and replacing '?' with NaN***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White-European       40\n",
       "Asian                16\n",
       "Others               12\n",
       "'Middle Eastern '     9\n",
       "Black                 8\n",
       "?                     6\n",
       "Hispanic              5\n",
       "Latino                5\n",
       "'South Asian'         3\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['ethnicity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['ethnicity'] = adolescents_asd['ethnicity'].apply(lambda val: np.nan if val == '?' else str(val).replace(\"'\",'').strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White-European    40\n",
       "Asian             16\n",
       "Others            12\n",
       "Middle Eastern     9\n",
       "Black              8\n",
       "Hispanic           5\n",
       "Latino             5\n",
       "South Asian        3\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['ethnicity'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Standardizing WHOS_COMPLETING_TEST and replacing '?' with NaN***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self                          45\n",
       "Parent                        36\n",
       "Relative                       8\n",
       "?                              6\n",
       "'Health care professional'     6\n",
       "Others                         3\n",
       "Name: whos_completing_test, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['whos_completing_test'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['whos_completing_test'] = adolescents_asd['whos_completing_test'].apply(lambda val: np.nan if val == '?' else str(val).replace(\"'\",'').strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self                        45\n",
       "Parent                      36\n",
       "Relative                     8\n",
       "Health care professional     6\n",
       "Others                       3\n",
       "Name: whos_completing_test, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['whos_completing_test'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Fixing NULLs in 'WHOS_COMPLETING_TEST'***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "</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>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Americansamoa</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belgium</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Argentina</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender ethnicity  born_with_jaundice  family_member_with_PDD  \\\n",
       "2    12       1       NaN                   0                       0   \n",
       "4    16       1       NaN                   0                       0   \n",
       "5    13       1       NaN                   0                       0   \n",
       "6    16       1       NaN                   0                       0   \n",
       "28   13       0       NaN                   0                       0   \n",
       "97   15       1       NaN                   0                       0   \n",
       "\n",
       "   whos_completing_test        country  used_screening_app_before  age_desc  \\\n",
       "2                   NaN  Americansamoa                          0         0   \n",
       "4                   NaN        Albania                          0         0   \n",
       "5                   NaN        Belgium                          0         0   \n",
       "6                   NaN    Afghanistan                          0         0   \n",
       "28                  NaN    Afghanistan                          0         0   \n",
       "97                  NaN      Argentina                          0         1   \n",
       "\n",
       "    Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  Q7_Score  \\\n",
       "2          0         0         0         0         0         0         0   \n",
       "4          1         1         1         1         1         1         1   \n",
       "5          1         0         0         0         0         1         0   \n",
       "6          0         0         0         1         1         1         1   \n",
       "28         1         1         0         0         0         0         1   \n",
       "97         1         0         0         0         1         0         0   \n",
       "\n",
       "    Q8_Score  Q9_Score  Q10_Score  screening_score  ASD_Label  \n",
       "2          0         1          1                2          0  \n",
       "4          0         0          0                7          1  \n",
       "5          0         1          0                3          0  \n",
       "6          1         1          0                6          0  \n",
       "28         0         1          0                4          0  \n",
       "97         1         0          1                4          0  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd[adolescents_asd['ethnicity'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Health care professional</th>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Others</th>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Parent</th>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Relative</th>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Self</th>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              gender\n",
       "whos_completing_test     age        \n",
       "Health care professional 12        2\n",
       "                         13        1\n",
       "                         15        2\n",
       "                         16        1\n",
       "Others                   13        1\n",
       "                         16        2\n",
       "Parent                   12       12\n",
       "                         13        6\n",
       "                         14        5\n",
       "                         15        7\n",
       "                         16        6\n",
       "Relative                 12        3\n",
       "                         15        1\n",
       "                         16        4\n",
       "Self                     12        7\n",
       "                         13        6\n",
       "                         14        9\n",
       "                         15        7\n",
       "                         16       16"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(adolescents_asd.groupby(['whos_completing_test','age']).count()['gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "relationship_df = pd.DataFrame(adolescents_asd.groupby(['whos_completing_test','age']).count()['gender'])\n",
    "relationship_df.columns = ['number_of_entries']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "relationship_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>age</th>\n",
       "      <th>number_of_entries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Health care professional</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Health care professional</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Health care professional</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Parent</td>\n",
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       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Parent</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
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       "      <td>14</td>\n",
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       "      <th>9</th>\n",
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       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Parent</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Relative</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Relative</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Relative</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Self</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Self</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Self</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Self</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Self</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        whos_completing_test  age  number_of_entries\n",
       "0   Health care professional   12                  2\n",
       "1   Health care professional   13                  1\n",
       "2   Health care professional   15                  2\n",
       "3   Health care professional   16                  1\n",
       "4                     Others   13                  1\n",
       "5                     Others   16                  2\n",
       "6                     Parent   12                 12\n",
       "7                     Parent   13                  6\n",
       "8                     Parent   14                  5\n",
       "9                     Parent   15                  7\n",
       "10                    Parent   16                  6\n",
       "11                  Relative   12                  3\n",
       "12                  Relative   15                  1\n",
       "13                  Relative   16                  4\n",
       "14                      Self   12                  7\n",
       "15                      Self   13                  6\n",
       "16                      Self   14                  9\n",
       "17                      Self   15                  7\n",
       "18                      Self   16                 16"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relationship_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_wise_max_entries = pd.DataFrame(relationship_df.groupby(['age']).max()['number_of_entries'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_wise_max_entries.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>number_of_entries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
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       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
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       "      <th>4</th>\n",
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  number_of_entries\n",
       "0   12                 12\n",
       "1   13                  6\n",
       "2   14                  9\n",
       "3   15                  7\n",
       "4   16                 16"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_wise_max_entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "relationship_entries_max_with_age = pd.merge(age_wise_max_entries,relationship_df,how='left',on=['age','number_of_entries'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>number_of_entries</th>\n",
       "      <th>whos_completing_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>Parent</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>Self</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>Self</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>Parent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>Self</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>Self</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  number_of_entries whos_completing_test\n",
       "0   12                 12               Parent\n",
       "1   13                  6               Parent\n",
       "2   13                  6                 Self\n",
       "3   14                  9                 Self\n",
       "4   15                  7               Parent\n",
       "5   15                  7                 Self\n",
       "6   16                 16                 Self"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relationship_entries_max_with_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Here, we have found the MAXIMUM number of entries of the relative or self as per the AGE of Adolescents.*** \n",
    "\n",
    "#### ***Now, the logic is that if we have multiple entries of an AGE with the same number of entries then we will find the probability of every class.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>whos_completing_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Parent</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Relative</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Health care professional</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Others</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      index  whos_completing_test\n",
       "0                      Self                    45\n",
       "1                    Parent                    36\n",
       "2                  Relative                     8\n",
       "3  Health care professional                     6\n",
       "4                    Others                     3"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(adolescents_asd['whos_completing_test'].value_counts()).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_whos_completing_probs(df_name,col_name,df_relationship_max_entries_age=None):\n",
    "    \"\"\"\n",
    "    Description: This function calculates the probability based upon the number_of_entries of a relative_class on basis of AGE.\n",
    "    \n",
    "    Input: Accepts 3 inputs:\n",
    "    df_name : The DataFrame containing all the records\n",
    "    col_name : Column of DataFrame having RELATIVE classes\n",
    "    df_relationship_max_entries_age : DataFrame with Maximum of count of every class w.r.t AGE\n",
    "    \n",
    "    Returns : It returns the probability of the appearance of a relative class inplace of NULL\n",
    "    \"\"\"\n",
    "    rows, cols = df_name[~df_name[col_name].isnull()].shape\n",
    "    whos_completing_test_counts = pd.DataFrame(df_name[col_name].value_counts())\n",
    "    whos_completing_test_counts.reset_index(inplace=True)\n",
    "    whos_completing_test_counts.columns = ['relative_class','number_of_times_in_data']\n",
    "    all_relationship_class_probs = pd.merge(df_relationship_max_entries_age,whos_completing_test_counts,how='left',left_on='whos_completing_test',right_on='relative_class')\n",
    "    all_relationship_class_probs['max_records'] = rows\n",
    "    all_relationship_class_probs['probability'] = (all_relationship_class_probs['number_of_times_in_data']/all_relationship_class_probs['max_records'])*all_relationship_class_probs['number_of_entries']\n",
    "    all_relationship_class_probs.drop(['max_records','relative_class','number_of_times_in_data'],axis=1,inplace=True)\n",
    "    relationship_class_probs = all_relationship_class_probs.groupby(['age']).max()\n",
    "    relationship_class_probs.reset_index(inplace=True)\n",
    "    return relationship_class_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "relationship_class_probs = find_whos_completing_probs(adolescents_asd,'whos_completing_test',relationship_entries_max_with_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>number_of_entries</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>Parent</td>\n",
       "      <td>4.408163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>Self</td>\n",
       "      <td>2.755102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>Self</td>\n",
       "      <td>4.132653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>Self</td>\n",
       "      <td>3.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>Self</td>\n",
       "      <td>7.346939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  number_of_entries whos_completing_test  probability\n",
       "0   12                 12               Parent     4.408163\n",
       "1   13                  6                 Self     2.755102\n",
       "2   14                  9                 Self     4.132653\n",
       "3   15                  7                 Self     3.214286\n",
       "4   16                 16                 Self     7.346939"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relationship_class_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Americansamoa</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belgium</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Argentina</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender ethnicity  born_with_jaundice  family_member_with_PDD  \\\n",
       "2    12       1       NaN                   0                       0   \n",
       "4    16       1       NaN                   0                       0   \n",
       "5    13       1       NaN                   0                       0   \n",
       "6    16       1       NaN                   0                       0   \n",
       "28   13       0       NaN                   0                       0   \n",
       "97   15       1       NaN                   0                       0   \n",
       "\n",
       "   whos_completing_test        country  used_screening_app_before  age_desc  \\\n",
       "2                   NaN  Americansamoa                          0         0   \n",
       "4                   NaN        Albania                          0         0   \n",
       "5                   NaN        Belgium                          0         0   \n",
       "6                   NaN    Afghanistan                          0         0   \n",
       "28                  NaN    Afghanistan                          0         0   \n",
       "97                  NaN      Argentina                          0         1   \n",
       "\n",
       "    Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  Q7_Score  \\\n",
       "2          0         0         0         0         0         0         0   \n",
       "4          1         1         1         1         1         1         1   \n",
       "5          1         0         0         0         0         1         0   \n",
       "6          0         0         0         1         1         1         1   \n",
       "28         1         1         0         0         0         0         1   \n",
       "97         1         0         0         0         1         0         0   \n",
       "\n",
       "    Q8_Score  Q9_Score  Q10_Score  screening_score  ASD_Label  \n",
       "2          0         1          1                2          0  \n",
       "4          0         0          0                7          1  \n",
       "5          0         1          0                3          0  \n",
       "6          1         1          0                6          0  \n",
       "28         0         1          0                4          0  \n",
       "97         1         0          1                4          0  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd[adolescents_asd['whos_completing_test'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix_whos_completing_test = pd.merge(relationship_class_probs,adolescents_asd[adolescents_asd['whos_completing_test'].isnull()],how='right',on=['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>used_screening_app_before</th>\n",
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       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Americansamoa</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>Self</td>\n",
       "      <td>2.755102</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>Self</td>\n",
       "      <td>2.755102</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</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",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>4</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>Self</td>\n",
       "      <td>3.214286</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Argentina</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>Self</td>\n",
       "      <td>7.346939</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>Self</td>\n",
       "      <td>7.346939</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  number_of_entries whos_completing_test_x  probability  gender  \\\n",
       "0   12                 12                 Parent     4.408163       1   \n",
       "1   13                  6                   Self     2.755102       1   \n",
       "2   13                  6                   Self     2.755102       0   \n",
       "3   15                  7                   Self     3.214286       1   \n",
       "4   16                 16                   Self     7.346939       1   \n",
       "5   16                 16                   Self     7.346939       1   \n",
       "\n",
       "  ethnicity  born_with_jaundice  family_member_with_PDD  \\\n",
       "0       NaN                   0                       0   \n",
       "1       NaN                   0                       0   \n",
       "2       NaN                   0                       0   \n",
       "3       NaN                   0                       0   \n",
       "4       NaN                   0                       0   \n",
       "5       NaN                   0                       0   \n",
       "\n",
       "  whos_completing_test_y        country  used_screening_app_before  age_desc  \\\n",
       "0                    NaN  Americansamoa                          0         0   \n",
       "1                    NaN        Belgium                          0         0   \n",
       "2                    NaN    Afghanistan                          0         0   \n",
       "3                    NaN      Argentina                          0         1   \n",
       "4                    NaN        Albania                          0         0   \n",
       "5                    NaN    Afghanistan                          0         0   \n",
       "\n",
       "   Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  Q7_Score  \\\n",
       "0         0         0         0         0         0         0         0   \n",
       "1         1         0         0         0         0         1         0   \n",
       "2         1         1         0         0         0         0         1   \n",
       "3         1         0         0         0         1         0         0   \n",
       "4         1         1         1         1         1         1         1   \n",
       "5         0         0         0         1         1         1         1   \n",
       "\n",
       "   Q8_Score  Q9_Score  Q10_Score  screening_score  ASD_Label  \n",
       "0         0         1          1                2          0  \n",
       "1         0         1          0                3          0  \n",
       "2         0         1          0                4          0  \n",
       "3         1         0          1                4          0  \n",
       "4         0         0          0                7          1  \n",
       "5         1         1          0                6          0  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix_whos_completing_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix_whos_completing_test.drop(['number_of_entries','whos_completing_test_y','probability'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix_whos_completing_test['whos_completing_test'] = fix_whos_completing_test['whos_completing_test_x']\n",
    "fix_whos_completing_test.drop(['whos_completing_test_x'],axis=1,inplace=True)\n",
    "fix_whos_completing_test = fix_whos_completing_test[['age', 'gender', 'ethnicity', 'born_with_jaundice',\n",
    "       'family_member_with_PDD', 'whos_completing_test', 'country',\n",
    "       'used_screening_app_before', 'age_desc', 'Q1_Score', 'Q2_Score',\n",
    "       'Q3_Score', 'Q4_Score', 'Q5_Score', 'Q6_Score', 'Q7_Score', 'Q8_Score',\n",
    "       'Q9_Score', 'Q10_Score', 'screening_score', 'ASD_Label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Belgium</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Afghanistan</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Argentina</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Albania</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Afghanistan</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender ethnicity  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   12       1       NaN                   0                       0   \n",
       "1   13       1       NaN                   0                       0   \n",
       "2   13       0       NaN                   0                       0   \n",
       "3   15       1       NaN                   0                       0   \n",
       "4   16       1       NaN                   0                       0   \n",
       "5   16       1       NaN                   0                       0   \n",
       "\n",
       "  whos_completing_test        country  used_screening_app_before  age_desc  \\\n",
       "0               Parent  Americansamoa                          0         0   \n",
       "1                 Self        Belgium                          0         0   \n",
       "2                 Self    Afghanistan                          0         0   \n",
       "3                 Self      Argentina                          0         1   \n",
       "4                 Self        Albania                          0         0   \n",
       "5                 Self    Afghanistan                          0         0   \n",
       "\n",
       "   Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  Q7_Score  \\\n",
       "0         0         0         0         0         0         0         0   \n",
       "1         1         0         0         0         0         1         0   \n",
       "2         1         1         0         0         0         0         1   \n",
       "3         1         0         0         0         1         0         0   \n",
       "4         1         1         1         1         1         1         1   \n",
       "5         0         0         0         1         1         1         1   \n",
       "\n",
       "   Q8_Score  Q9_Score  Q10_Score  screening_score  ASD_Label  \n",
       "0         0         1          1                2          0  \n",
       "1         0         1          0                3          0  \n",
       "2         0         1          0                4          0  \n",
       "3         1         0          1                4          0  \n",
       "4         0         0          0                7          1  \n",
       "5         1         1          0                6          0  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix_whos_completing_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd.dropna(subset=['whos_completing_test'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98, 21)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd = pd.concat([adolescents_asd,fix_whos_completing_test],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104, 21)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                          0\n",
       "gender                       0\n",
       "ethnicity                    6\n",
       "born_with_jaundice           0\n",
       "family_member_with_PDD       0\n",
       "whos_completing_test         0\n",
       "country                      0\n",
       "used_screening_app_before    0\n",
       "age_desc                     0\n",
       "Q1_Score                     0\n",
       "Q2_Score                     0\n",
       "Q3_Score                     0\n",
       "Q4_Score                     0\n",
       "Q5_Score                     0\n",
       "Q6_Score                     0\n",
       "Q7_Score                     0\n",
       "Q8_Score                     0\n",
       "Q9_Score                     0\n",
       "Q10_Score                    0\n",
       "screening_score              0\n",
       "ASD_Label                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Belgium</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",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Afghanistan</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Argentina</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Albania</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Afghanistan</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  gender ethnicity  born_with_jaundice  family_member_with_PDD  \\\n",
       "98    12       1       NaN                   0                       0   \n",
       "99    13       1       NaN                   0                       0   \n",
       "100   13       0       NaN                   0                       0   \n",
       "101   15       1       NaN                   0                       0   \n",
       "102   16       1       NaN                   0                       0   \n",
       "103   16       1       NaN                   0                       0   \n",
       "\n",
       "    whos_completing_test        country  used_screening_app_before  age_desc  \\\n",
       "98                Parent  Americansamoa                          0         0   \n",
       "99                  Self        Belgium                          0         0   \n",
       "100                 Self    Afghanistan                          0         0   \n",
       "101                 Self      Argentina                          0         1   \n",
       "102                 Self        Albania                          0         0   \n",
       "103                 Self    Afghanistan                          0         0   \n",
       "\n",
       "     Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  Q7_Score  \\\n",
       "98          0         0         0         0         0         0         0   \n",
       "99          1         0         0         0         0         1         0   \n",
       "100         1         1         0         0         0         0         1   \n",
       "101         1         0         0         0         1         0         0   \n",
       "102         1         1         1         1         1         1         1   \n",
       "103         0         0         0         1         1         1         1   \n",
       "\n",
       "     Q8_Score  Q9_Score  Q10_Score  screening_score  ASD_Label  \n",
       "98          0         1          1                2          0  \n",
       "99          0         1          0                3          0  \n",
       "100         0         1          0                4          0  \n",
       "101         1         0          1                4          0  \n",
       "102         0         0          0                7          1  \n",
       "103         1         1          0                6          0  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd[adolescents_asd['ethnicity'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Here, we got the values populated in WHOS_COMPLETING_TEST***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Fixing NULLs in 'ETHNICITY'***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicity_val_counts = pd.DataFrame(adolescents_asd['ethnicity'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicity_val_counts.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicity_val_counts.columns = ['ethnicity','ethnicity_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>ethnicity_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>White-European</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asian</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Others</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Latino</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>South Asian</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ethnicity  ethnicity_count\n",
       "0  White-European               40\n",
       "1           Asian               16\n",
       "2          Others               12\n",
       "3  Middle Eastern                9\n",
       "4           Black                8\n",
       "5        Hispanic                5\n",
       "6          Latino                5\n",
       "7     South Asian                3"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethnicity_val_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Americansamoa', 'Belgium', 'Afghanistan', 'Argentina', 'Albania'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(adolescents_asd[adolescents_asd['ethnicity'].isnull()]['country'].nunique())\n",
    "\n",
    "adolescents_asd[adolescents_asd['ethnicity'].isnull()]['country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicity_max_vals = pd.DataFrame(adolescents_asd.groupby(['country','ethnicity']).count()['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicity_max_vals.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicity_max_vals.sort_values(['country','age'],ascending=[True,False],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Americansamoa</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Latino</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>White-European</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Austria</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Latino</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Latino</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Others</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Canada</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>White-European</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Finland</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>France</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Greenland</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>India</td>\n",
       "      <td>Asian</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Asian</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>White-European</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>New zealand</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Norway</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>South africa</td>\n",
       "      <td>White-European</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>United arab emirates</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>United kingdom</td>\n",
       "      <td>White-European</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>United kingdom</td>\n",
       "      <td>Others</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>United kingdom</td>\n",
       "      <td>Asian</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>United kingdom</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>United kingdom</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>United states</td>\n",
       "      <td>White-European</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>United states</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>United states</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>United states</td>\n",
       "      <td>Others</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Viet nam</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country       ethnicity  age\n",
       "0                Albania  White-European    1\n",
       "1          Americansamoa           Black    1\n",
       "2               Anguilla  Middle Eastern    1\n",
       "4              Argentina        Hispanic    4\n",
       "5              Argentina          Latino    3\n",
       "6              Argentina  White-European    2\n",
       "3              Argentina           Black    1\n",
       "7              Australia  Middle Eastern    1\n",
       "8              Australia  White-European    1\n",
       "13               Austria  White-European    1\n",
       "12               Austria  Middle Eastern    1\n",
       "11               Austria          Latino    1\n",
       "10               Austria        Hispanic    1\n",
       "9                Austria           Black    1\n",
       "14            Azerbaijan     South Asian    1\n",
       "15               Bahamas           Asian    1\n",
       "16               Bahrain           Black    1\n",
       "17            Bangladesh           Asian    1\n",
       "18               Belgium  White-European    1\n",
       "19                Brazil          Latino    1\n",
       "20                Canada          Others    2\n",
       "21                Canada  White-European    1\n",
       "22               Comoros     South Asian    1\n",
       "23               Croatia  White-European    2\n",
       "24                 Egypt  Middle Eastern    2\n",
       "25               Finland  White-European    1\n",
       "26                France           Black    1\n",
       "27             Greenland  White-European    1\n",
       "28                 India           Asian    7\n",
       "29             Indonesia           Asian    3\n",
       "30               Ireland  White-European    1\n",
       "31                Jordan  Middle Eastern    1\n",
       "32           Netherlands  White-European    2\n",
       "33           New zealand  White-European    1\n",
       "34                Norway  White-European    1\n",
       "35              Portugal           Asian    1\n",
       "36          South africa  White-European    1\n",
       "37  United arab emirates  Middle Eastern    1\n",
       "42        United kingdom  White-European   16\n",
       "40        United kingdom          Others    9\n",
       "38        United kingdom           Asian    2\n",
       "39        United kingdom           Black    1\n",
       "41        United kingdom     South Asian    1\n",
       "46         United states  White-European    7\n",
       "43         United states           Black    2\n",
       "44         United states  Middle Eastern    2\n",
       "45         United states          Others    1\n",
       "47              Viet nam           Asian    1"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethnicity_max_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicity_max_vals = pd.merge(ethnicity_max_vals,ethnicity_val_counts,how='left',on=['ethnicity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows,cols = adolescents_asd[~adolescents_asd['ethnicity'].isnull()].shape\n",
    "ethnicity_max_vals['probability'] = (ethnicity_max_vals['ethnicity_count']/rows)*ethnicity_max_vals['age']\n",
    "ethnicity_max_vals.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicity_max_vals = ethnicity_max_vals.groupby(['country'])['ethnicity','probability'].max()\n",
    "ethnicity_max_vals.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0.408163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Americansamoa</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.081633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>0.091837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0.816327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0.408163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Austria</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0.408163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>0.030612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>Asian</td>\n",
       "      <td>0.163265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.081633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Asian</td>\n",
       "      <td>0.163265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country       ethnicity  probability\n",
       "0        Albania  White-European     0.408163\n",
       "1  Americansamoa           Black     0.081633\n",
       "2       Anguilla  Middle Eastern     0.091837\n",
       "3      Argentina  White-European     0.816327\n",
       "4      Australia  White-European     0.408163\n",
       "5        Austria  White-European     0.408163\n",
       "6     Azerbaijan     South Asian     0.030612\n",
       "7        Bahamas           Asian     0.163265\n",
       "8        Bahrain           Black     0.081633\n",
       "9     Bangladesh           Asian     0.163265"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethnicity_max_vals.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['fix_ethnicity'] = adolescents_asd[['ethnicity','country']].apply(lambda val: ethnicity_max_vals[ethnicity_max_vals['country'] == str(val['country']).capitalize()]['ethnicity']\n",
    "                                               if str(val['ethnicity']).lower() == \"nan\" else val['ethnicity'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['fix_ethnicity'] = adolescents_asd['fix_ethnicity'].apply(lambda val : str(val).replace(\"[\",'').replace(\"]\",'').replace(\"'\",''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White-European    43\n",
       "Asian             16\n",
       "Others            12\n",
       "Black              9\n",
       "Middle Eastern     9\n",
       "Hispanic           5\n",
       "Latino             5\n",
       "South Asian        3\n",
       "                   2\n",
       "Name: fix_ethnicity, dtype: int64"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['fix_ethnicity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "      <th>fix_ethnicity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Afghanistan</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>Afghanistan</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  gender ethnicity  born_with_jaundice  family_member_with_PDD  \\\n",
       "100   13       0       NaN                   0                       0   \n",
       "103   16       1       NaN                   0                       0   \n",
       "\n",
       "    whos_completing_test      country  used_screening_app_before  age_desc  \\\n",
       "100                 Self  Afghanistan                          0         0   \n",
       "103                 Self  Afghanistan                          0         0   \n",
       "\n",
       "     Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  Q7_Score  \\\n",
       "100         1         1         0         0         0         0         1   \n",
       "103         0         0         0         1         1         1         1   \n",
       "\n",
       "     Q8_Score  Q9_Score  Q10_Score  screening_score  ASD_Label fix_ethnicity  \n",
       "100         0         1          0                4          0                \n",
       "103         1         1          0                6          0                "
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd[adolescents_asd['fix_ethnicity'] == '']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['fix_ethnicity'] = adolescents_asd[['fix_ethnicity','country']].apply(lambda val: 'Asian' if val['fix_ethnicity'] == '' and val['country'] == 'Afghanistan' \n",
    "                                                                                      else val['fix_ethnicity'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White-European    43\n",
       "Asian             18\n",
       "Others            12\n",
       "Black              9\n",
       "Middle Eastern     9\n",
       "Hispanic           5\n",
       "Latino             5\n",
       "South Asian        3\n",
       "Name: fix_ethnicity, dtype: int64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['fix_ethnicity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd.drop(['ethnicity'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***First-hand Cleaned DataFrame***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                          0\n",
       "gender                       0\n",
       "born_with_jaundice           0\n",
       "family_member_with_PDD       0\n",
       "whos_completing_test         0\n",
       "country                      0\n",
       "used_screening_app_before    0\n",
       "age_desc                     0\n",
       "Q1_Score                     0\n",
       "Q2_Score                     0\n",
       "Q3_Score                     0\n",
       "Q4_Score                     0\n",
       "Q5_Score                     0\n",
       "Q6_Score                     0\n",
       "Q7_Score                     0\n",
       "Q8_Score                     0\n",
       "Q9_Score                     0\n",
       "Q10_Score                    0\n",
       "screening_score              0\n",
       "ASD_Label                    0\n",
       "fix_ethnicity                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104, 21)"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "      <th>fix_ethnicity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Austria</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Austria</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>United kingdom</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>White-European</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Australia</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Middle Eastern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Bahrain</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Azerbaijan</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>South Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Parent</td>\n",
       "      <td>United arab emirates</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Middle Eastern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>United kingdom</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>White-European</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>United kingdom</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Parent</td>\n",
       "      <td>United kingdom</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   15       0                   1                       1   \n",
       "1   15       0                   0                       0   \n",
       "2   14       1                   0                       0   \n",
       "3   15       1                   0                       0   \n",
       "4   12       0                   1                       1   \n",
       "5   12       1                   0                       0   \n",
       "6   13       1                   1                       0   \n",
       "7   13       1                   0                       0   \n",
       "8   12       1                   0                       0   \n",
       "9   12       1                   0                       0   \n",
       "\n",
       "  whos_completing_test               country  used_screening_app_before  \\\n",
       "0               Parent               Austria                          0   \n",
       "1             Relative               Austria                          0   \n",
       "2                 Self        United kingdom                          0   \n",
       "3               Parent             Australia                          0   \n",
       "4               Parent               Bahrain                          0   \n",
       "5               Parent            Azerbaijan                          0   \n",
       "6               Parent  United arab emirates                          0   \n",
       "7                 Self        United kingdom                          0   \n",
       "8                 Self        United kingdom                          0   \n",
       "9               Parent        United kingdom                          0   \n",
       "\n",
       "   age_desc  Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  \\\n",
       "0         0         0         0         0         1         1         1   \n",
       "1         0         0         0         0         0         0         0   \n",
       "2         0         0         1         1         1         1         1   \n",
       "3         0         1         1         0         1         1         0   \n",
       "4         0         1         1         1         1         1         1   \n",
       "5         0         0         1         1         0         0         1   \n",
       "6         0         1         0         0         0         1         1   \n",
       "7         0         1         0         0         0         1         1   \n",
       "8         0         1         0         0         1         1         1   \n",
       "9         0         1         1         1         1         1         1   \n",
       "\n",
       "   Q7_Score  Q8_Score  Q9_Score  Q10_Score  screening_score  ASD_Label  \\\n",
       "0         1         1         1          0                6          0   \n",
       "1         0         0         1          1                2          0   \n",
       "2         0         1         1          0                7          1   \n",
       "3         1         1         0          1                7          1   \n",
       "4         0         0         0          0                6          0   \n",
       "5         0         0         1          0                4          0   \n",
       "6         0         1         1          0                5          0   \n",
       "7         0         0         1          1                5          0   \n",
       "8         0         1         1          1                7          1   \n",
       "9         0         1         1          1                9          1   \n",
       "\n",
       "    fix_ethnicity  \n",
       "0        Hispanic  \n",
       "1           Black  \n",
       "2  White-European  \n",
       "3  Middle Eastern  \n",
       "4           Black  \n",
       "5     South Asian  \n",
       "6  Middle Eastern  \n",
       "7  White-European  \n",
       "8          Others  \n",
       "9          Others  "
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Labelling the Categorical Variables***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Using LabelEncoder for labelling the classes in WHOS_COMPLETING_TEST variable***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self                        50\n",
       "Parent                      37\n",
       "Relative                     8\n",
       "Health care professional     6\n",
       "Others                       3\n",
       "Name: whos_completing_test, dtype: int64"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['whos_completing_test'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ***Import LabelEncoder***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder as LE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ***Instantiate LabelEncoder***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LE()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ***Applying LabelEncoder on WHOS_COMPLETING_TEST***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['label_whos_completing_test'] = pd.Series(label_encoder.fit_transform(adolescents_asd['whos_completing_test']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "      <th>fix_ethnicity</th>\n",
       "      <th>label_whos_completing_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Austria</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Austria</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>United kingdom</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>White-European</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Australia</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Bahrain</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   15       0                   1                       1   \n",
       "1   15       0                   0                       0   \n",
       "2   14       1                   0                       0   \n",
       "3   15       1                   0                       0   \n",
       "4   12       0                   1                       1   \n",
       "\n",
       "  whos_completing_test         country  used_screening_app_before  age_desc  \\\n",
       "0               Parent         Austria                          0         0   \n",
       "1             Relative         Austria                          0         0   \n",
       "2                 Self  United kingdom                          0         0   \n",
       "3               Parent       Australia                          0         0   \n",
       "4               Parent         Bahrain                          0         0   \n",
       "\n",
       "   Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  Q7_Score  \\\n",
       "0         0         0         0         1         1         1         1   \n",
       "1         0         0         0         0         0         0         0   \n",
       "2         0         1         1         1         1         1         0   \n",
       "3         1         1         0         1         1         0         1   \n",
       "4         1         1         1         1         1         1         0   \n",
       "\n",
       "   Q8_Score  Q9_Score  Q10_Score  screening_score  ASD_Label   fix_ethnicity  \\\n",
       "0         1         1          0                6          0        Hispanic   \n",
       "1         0         1          1                2          0           Black   \n",
       "2         1         1          0                7          1  White-European   \n",
       "3         1         0          1                7          1  Middle Eastern   \n",
       "4         0         0          0                6          0           Black   \n",
       "\n",
       "   label_whos_completing_test  \n",
       "0                           2  \n",
       "1                           3  \n",
       "2                           4  \n",
       "3                           2  \n",
       "4                           2  "
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Health care professional', 'Others', 'Parent', 'Relative', 'Self'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Using LabelEncoder for labelling the classes in ETHNICITY variable***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White-European    43\n",
       "Asian             18\n",
       "Others            12\n",
       "Black              9\n",
       "Middle Eastern     9\n",
       "Hispanic           5\n",
       "Latino             5\n",
       "South Asian        3\n",
       "Name: fix_ethnicity, dtype: int64"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd['fix_ethnicity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['label_ethnicity'] = pd.Series(label_encoder.fit_transform(adolescents_asd['fix_ethnicity']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
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       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
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       "      <th>Q10_Score</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Austria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Austria</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>United kingdom</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>White-European</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Australia</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Bahrain</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   15       0                   1                       1   \n",
       "1   15       0                   0                       0   \n",
       "2   14       1                   0                       0   \n",
       "3   15       1                   0                       0   \n",
       "4   12       0                   1                       1   \n",
       "\n",
       "  whos_completing_test         country  used_screening_app_before  age_desc  \\\n",
       "0               Parent         Austria                          0         0   \n",
       "1             Relative         Austria                          0         0   \n",
       "2                 Self  United kingdom                          0         0   \n",
       "3               Parent       Australia                          0         0   \n",
       "4               Parent         Bahrain                          0         0   \n",
       "\n",
       "   Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  Q7_Score  \\\n",
       "0         0         0         0         1         1         1         1   \n",
       "1         0         0         0         0         0         0         0   \n",
       "2         0         1         1         1         1         1         0   \n",
       "3         1         1         0         1         1         0         1   \n",
       "4         1         1         1         1         1         1         0   \n",
       "\n",
       "   Q8_Score  Q9_Score  Q10_Score  screening_score  ASD_Label   fix_ethnicity  \\\n",
       "0         1         1          0                6          0        Hispanic   \n",
       "1         0         1          1                2          0           Black   \n",
       "2         1         1          0                7          1  White-European   \n",
       "3         1         0          1                7          1  Middle Eastern   \n",
       "4         0         0          0                6          0           Black   \n",
       "\n",
       "   label_whos_completing_test  label_ethnicity  \n",
       "0                           2                2  \n",
       "1                           3                1  \n",
       "2                           4                7  \n",
       "3                           2                4  \n",
       "4                           2                1  "
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Asian', 'Black', 'Hispanic', 'Latino', 'Middle Eastern', 'Others',\n",
       "       'South Asian', 'White-European'], dtype=object)"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Using LabelEncoder for labelling the classes in COUNTRY variable***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd['label_country'] = pd.Series(label_encoder.fit_transform(adolescents_asd['country']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>whos_completing_test</th>\n",
       "      <th>country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "      <th>fix_ethnicity</th>\n",
       "      <th>label_whos_completing_test</th>\n",
       "      <th>label_ethnicity</th>\n",
       "      <th>label_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Austria</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Austria</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self</td>\n",
       "      <td>United kingdom</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>White-European</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Australia</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Parent</td>\n",
       "      <td>Bahrain</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   15       0                   1                       1   \n",
       "1   15       0                   0                       0   \n",
       "2   14       1                   0                       0   \n",
       "3   15       1                   0                       0   \n",
       "4   12       0                   1                       1   \n",
       "\n",
       "  whos_completing_test         country  used_screening_app_before  age_desc  \\\n",
       "0               Parent         Austria                          0         0   \n",
       "1             Relative         Austria                          0         0   \n",
       "2                 Self  United kingdom                          0         0   \n",
       "3               Parent       Australia                          0         0   \n",
       "4               Parent         Bahrain                          0         0   \n",
       "\n",
       "   Q1_Score  Q2_Score  Q3_Score  Q4_Score  Q5_Score  Q6_Score  Q7_Score  \\\n",
       "0         0         0         0         1         1         1         1   \n",
       "1         0         0         0         0         0         0         0   \n",
       "2         0         1         1         1         1         1         0   \n",
       "3         1         1         0         1         1         0         1   \n",
       "4         1         1         1         1         1         1         0   \n",
       "\n",
       "   Q8_Score  Q9_Score  Q10_Score  screening_score  ASD_Label   fix_ethnicity  \\\n",
       "0         1         1          0                6          0        Hispanic   \n",
       "1         0         1          1                2          0           Black   \n",
       "2         1         1          0                7          1  White-European   \n",
       "3         1         0          1                7          1  Middle Eastern   \n",
       "4         0         0          0                6          0           Black   \n",
       "\n",
       "   label_whos_completing_test  label_ethnicity  label_country  \n",
       "0                           2                2              6  \n",
       "1                           3                1              6  \n",
       "2                           4                7             30  \n",
       "3                           2                4              5  \n",
       "4                           2                1              9  "
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Americansamoa', 'Anguilla', 'Argentina',\n",
       "       'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n",
       "       'Bangladesh', 'Belgium', 'Brazil', 'Canada', 'Comoros', 'Croatia',\n",
       "       'Egypt', 'Finland', 'France', 'Greenland', 'India', 'Indonesia',\n",
       "       'Ireland', 'Jordan', 'Netherlands', 'New zealand', 'Norway',\n",
       "       'Portugal', 'South africa', 'United arab emirates',\n",
       "       'United kingdom', 'United states', 'Viet nam'], dtype=object)"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Removing the columns 'WHOS_COMPLETING_TEST', 'COUNTRY' and 'FIX_ETHNICITY'***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd.drop(['whos_completing_test','country','fix_ethnicity'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Cleaned/Pre-processed DataFrame***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "</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>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "      <th>label_whos_completing_test</th>\n",
       "      <th>label_ethnicity</th>\n",
       "      <th>label_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
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       "      <td>1</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</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>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>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1</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>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",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</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>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>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   15       0                   1                       1   \n",
       "1   15       0                   0                       0   \n",
       "2   14       1                   0                       0   \n",
       "3   15       1                   0                       0   \n",
       "4   12       0                   1                       1   \n",
       "\n",
       "   used_screening_app_before  age_desc  Q1_Score  Q2_Score  Q3_Score  \\\n",
       "0                          0         0         0         0         0   \n",
       "1                          0         0         0         0         0   \n",
       "2                          0         0         0         1         1   \n",
       "3                          0         0         1         1         0   \n",
       "4                          0         0         1         1         1   \n",
       "\n",
       "   Q4_Score  Q5_Score  Q6_Score  Q7_Score  Q8_Score  Q9_Score  Q10_Score  \\\n",
       "0         1         1         1         1         1         1          0   \n",
       "1         0         0         0         0         0         1          1   \n",
       "2         1         1         1         0         1         1          0   \n",
       "3         1         1         0         1         1         0          1   \n",
       "4         1         1         1         0         0         0          0   \n",
       "\n",
       "   screening_score  ASD_Label  label_whos_completing_test  label_ethnicity  \\\n",
       "0                6          0                           2                2   \n",
       "1                2          0                           3                1   \n",
       "2                7          1                           4                7   \n",
       "3                7          1                           2                4   \n",
       "4                6          0                           2                1   \n",
       "\n",
       "   label_country  \n",
       "0              6  \n",
       "1              6  \n",
       "2             30  \n",
       "3              5  \n",
       "4              9  "
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'born_with_jaundice', 'family_member_with_PDD',\n",
       "       'used_screening_app_before', 'age_desc', 'Q1_Score', 'Q2_Score',\n",
       "       'Q3_Score', 'Q4_Score', 'Q5_Score', 'Q6_Score', 'Q7_Score', 'Q8_Score',\n",
       "       'Q9_Score', 'Q10_Score', 'screening_score', 'ASD_Label',\n",
       "       'label_whos_completing_test', 'label_ethnicity', 'label_country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "adolescents_asd = adolescents_asd[['age', 'gender', 'born_with_jaundice', 'family_member_with_PDD','label_whos_completing_test', 'label_ethnicity', 'label_country',\n",
    "                                   'used_screening_app_before', 'age_desc', 'Q1_Score', 'Q2_Score', 'Q3_Score', 'Q4_Score', 'Q5_Score', 'Q6_Score', 'Q7_Score', 'Q8_Score',\n",
    "                                   'Q9_Score', 'Q10_Score', 'screening_score', 'ASD_Label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104, 21)"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 104 entries, 0 to 103\n",
      "Data columns (total 21 columns):\n",
      "age                           104 non-null int64\n",
      "gender                        104 non-null int64\n",
      "born_with_jaundice            104 non-null int64\n",
      "family_member_with_PDD        104 non-null int64\n",
      "label_whos_completing_test    104 non-null int32\n",
      "label_ethnicity               104 non-null int32\n",
      "label_country                 104 non-null int32\n",
      "used_screening_app_before     104 non-null int64\n",
      "age_desc                      104 non-null int64\n",
      "Q1_Score                      104 non-null int64\n",
      "Q2_Score                      104 non-null int64\n",
      "Q3_Score                      104 non-null int64\n",
      "Q4_Score                      104 non-null int64\n",
      "Q5_Score                      104 non-null int64\n",
      "Q6_Score                      104 non-null int64\n",
      "Q7_Score                      104 non-null int64\n",
      "Q8_Score                      104 non-null int64\n",
      "Q9_Score                      104 non-null int64\n",
      "Q10_Score                     104 non-null int64\n",
      "screening_score               104 non-null int64\n",
      "ASD_Label                     104 non-null int64\n",
      "dtypes: int32(3), int64(18)\n",
      "memory usage: 16.0 KB\n"
     ]
    }
   ],
   "source": [
    "adolescents_asd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>label_whos_completing_test</th>\n",
       "      <th>label_ethnicity</th>\n",
       "      <th>label_country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>screening_score</th>\n",
       "      <th>ASD_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.00000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>104.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.134615</td>\n",
       "      <td>0.519231</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.134615</td>\n",
       "      <td>2.894231</td>\n",
       "      <td>4.317308</td>\n",
       "      <td>19.567308</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.067308</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.701923</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.519231</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.788462</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>6.721154</td>\n",
       "      <td>0.605769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.576882</td>\n",
       "      <td>0.502050</td>\n",
       "      <td>0.362548</td>\n",
       "      <td>0.342965</td>\n",
       "      <td>1.214142</td>\n",
       "      <td>2.777386</td>\n",
       "      <td>11.026679</td>\n",
       "      <td>0.193239</td>\n",
       "      <td>0.251767</td>\n",
       "      <td>0.445708</td>\n",
       "      <td>0.500933</td>\n",
       "      <td>0.478047</td>\n",
       "      <td>0.459629</td>\n",
       "      <td>0.43511</td>\n",
       "      <td>0.423365</td>\n",
       "      <td>0.502050</td>\n",
       "      <td>0.488860</td>\n",
       "      <td>0.410377</td>\n",
       "      <td>0.478047</td>\n",
       "      <td>2.200868</td>\n",
       "      <td>0.491051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.750000</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>0.75000</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>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</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>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</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>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>32.000000</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>1.00000</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>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age      gender  born_with_jaundice  family_member_with_PDD  \\\n",
       "count  104.000000  104.000000          104.000000              104.000000   \n",
       "mean    14.134615    0.519231            0.153846                0.134615   \n",
       "std      1.576882    0.502050            0.362548                0.342965   \n",
       "min     12.000000    0.000000            0.000000                0.000000   \n",
       "25%     13.000000    0.000000            0.000000                0.000000   \n",
       "50%     14.000000    1.000000            0.000000                0.000000   \n",
       "75%     16.000000    1.000000            0.000000                0.000000   \n",
       "max     16.000000    1.000000            1.000000                1.000000   \n",
       "\n",
       "       label_whos_completing_test  label_ethnicity  label_country  \\\n",
       "count                  104.000000       104.000000     104.000000   \n",
       "mean                     2.894231         4.317308      19.567308   \n",
       "std                      1.214142         2.777386      11.026679   \n",
       "min                      0.000000         0.000000       0.000000   \n",
       "25%                      2.000000         1.000000       7.750000   \n",
       "50%                      3.000000         5.000000      21.000000   \n",
       "75%                      4.000000         7.000000      30.000000   \n",
       "max                      4.000000         7.000000      32.000000   \n",
       "\n",
       "       used_screening_app_before    age_desc    Q1_Score    Q2_Score  \\\n",
       "count                 104.000000  104.000000  104.000000  104.000000   \n",
       "mean                    0.038462    0.067308    0.730769    0.538462   \n",
       "std                     0.193239    0.251767    0.445708    0.500933   \n",
       "min                     0.000000    0.000000    0.000000    0.000000   \n",
       "25%                     0.000000    0.000000    0.000000    0.000000   \n",
       "50%                     0.000000    0.000000    1.000000    1.000000   \n",
       "75%                     0.000000    0.000000    1.000000    1.000000   \n",
       "max                     1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         Q3_Score    Q4_Score   Q5_Score    Q6_Score    Q7_Score    Q8_Score  \\\n",
       "count  104.000000  104.000000  104.00000  104.000000  104.000000  104.000000   \n",
       "mean     0.653846    0.701923    0.75000    0.769231    0.519231    0.615385   \n",
       "std      0.478047    0.459629    0.43511    0.423365    0.502050    0.488860   \n",
       "min      0.000000    0.000000    0.00000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.75000    1.000000    0.000000    0.000000   \n",
       "50%      1.000000    1.000000    1.00000    1.000000    1.000000    1.000000   \n",
       "75%      1.000000    1.000000    1.00000    1.000000    1.000000    1.000000   \n",
       "max      1.000000    1.000000    1.00000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         Q9_Score   Q10_Score  screening_score   ASD_Label  \n",
       "count  104.000000  104.000000       104.000000  104.000000  \n",
       "mean     0.788462    0.653846         6.721154    0.605769  \n",
       "std      0.410377    0.478047         2.200868    0.491051  \n",
       "min      0.000000    0.000000         1.000000    0.000000  \n",
       "25%      1.000000    0.000000         6.000000    0.000000  \n",
       "50%      1.000000    1.000000         7.000000    1.000000  \n",
       "75%      1.000000    1.000000         8.000000    1.000000  \n",
       "max      1.000000    1.000000        10.000000    1.000000  "
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Finding Co-relation b/w the variables***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21.0, 0.0)"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,12))\n",
    "fig = sns.heatmap(adolescents_asd.corr(),cmap='coolwarm',annot=True)\n",
    "top,bottom = fig.get_ylim()\n",
    "fig.set_ylim(top+0.5,bottom-0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***As SCREENING_SCORE is highly co-related with ASD_Label, hence, removing SCREENING_SCORE from the DataFrame.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4097: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "adolescents_asd.drop(['screening_score'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***DataFrame to be used for Model Fit***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>label_whos_completing_test</th>\n",
       "      <th>label_ethnicity</th>\n",
       "      <th>label_country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "      <th>ASD_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>30</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>1</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>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   15       0                   1                       1   \n",
       "1   15       0                   0                       0   \n",
       "2   14       1                   0                       0   \n",
       "3   15       1                   0                       0   \n",
       "4   12       0                   1                       1   \n",
       "\n",
       "   label_whos_completing_test  label_ethnicity  label_country  \\\n",
       "0                           2                2              6   \n",
       "1                           3                1              6   \n",
       "2                           4                7             30   \n",
       "3                           2                4              5   \n",
       "4                           2                1              9   \n",
       "\n",
       "   used_screening_app_before  age_desc  Q1_Score  Q2_Score  Q3_Score  \\\n",
       "0                          0         0         0         0         0   \n",
       "1                          0         0         0         0         0   \n",
       "2                          0         0         0         1         1   \n",
       "3                          0         0         1         1         0   \n",
       "4                          0         0         1         1         1   \n",
       "\n",
       "   Q4_Score  Q5_Score  Q6_Score  Q7_Score  Q8_Score  Q9_Score  Q10_Score  \\\n",
       "0         1         1         1         1         1         1          0   \n",
       "1         0         0         0         0         0         1          1   \n",
       "2         1         1         1         0         1         1          0   \n",
       "3         1         1         0         1         1         0          1   \n",
       "4         1         1         1         0         0         0          0   \n",
       "\n",
       "   ASD_Label  \n",
       "0          0  \n",
       "1          0  \n",
       "2          1  \n",
       "3          1  \n",
       "4          0  "
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adolescents_asd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 104 entries, 0 to 103\n",
      "Data columns (total 20 columns):\n",
      "age                           104 non-null int64\n",
      "gender                        104 non-null int64\n",
      "born_with_jaundice            104 non-null int64\n",
      "family_member_with_PDD        104 non-null int64\n",
      "label_whos_completing_test    104 non-null int32\n",
      "label_ethnicity               104 non-null int32\n",
      "label_country                 104 non-null int32\n",
      "used_screening_app_before     104 non-null int64\n",
      "age_desc                      104 non-null int64\n",
      "Q1_Score                      104 non-null int64\n",
      "Q2_Score                      104 non-null int64\n",
      "Q3_Score                      104 non-null int64\n",
      "Q4_Score                      104 non-null int64\n",
      "Q5_Score                      104 non-null int64\n",
      "Q6_Score                      104 non-null int64\n",
      "Q7_Score                      104 non-null int64\n",
      "Q8_Score                      104 non-null int64\n",
      "Q9_Score                      104 non-null int64\n",
      "Q10_Score                     104 non-null int64\n",
      "ASD_Label                     104 non-null int64\n",
      "dtypes: int32(3), int64(17)\n",
      "memory usage: 15.2 KB\n"
     ]
    }
   ],
   "source": [
    "adolescents_asd.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Import the Machine Learning Packages***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts, cross_val_score as crs_val\n",
    "from sklearn.metrics import accuracy_score as acc_scr, recall_score as rec_scr, precision_score as prec_scr\n",
    "from sklearn.naive_bayes import BernoulliNB as BNB, GaussianNB as GNB\n",
    "from sklearn.ensemble import RandomForestClassifier as RFC, GradientBoostingClassifier as GBC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Seggregating the Pre-processed DataFrame into X and y child DataFrames***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_adolescents = adolescents_asd.iloc[:,0:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 104 entries, 0 to 103\n",
      "Data columns (total 19 columns):\n",
      "age                           104 non-null int64\n",
      "gender                        104 non-null int64\n",
      "born_with_jaundice            104 non-null int64\n",
      "family_member_with_PDD        104 non-null int64\n",
      "label_whos_completing_test    104 non-null int32\n",
      "label_ethnicity               104 non-null int32\n",
      "label_country                 104 non-null int32\n",
      "used_screening_app_before     104 non-null int64\n",
      "age_desc                      104 non-null int64\n",
      "Q1_Score                      104 non-null int64\n",
      "Q2_Score                      104 non-null int64\n",
      "Q3_Score                      104 non-null int64\n",
      "Q4_Score                      104 non-null int64\n",
      "Q5_Score                      104 non-null int64\n",
      "Q6_Score                      104 non-null int64\n",
      "Q7_Score                      104 non-null int64\n",
      "Q8_Score                      104 non-null int64\n",
      "Q9_Score                      104 non-null int64\n",
      "Q10_Score                     104 non-null int64\n",
      "dtypes: int32(3), int64(16)\n",
      "memory usage: 14.3 KB\n"
     ]
    }
   ],
   "source": [
    "X_adolescents.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>born_with_jaundice</th>\n",
       "      <th>family_member_with_PDD</th>\n",
       "      <th>label_whos_completing_test</th>\n",
       "      <th>label_ethnicity</th>\n",
       "      <th>label_country</th>\n",
       "      <th>used_screening_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>Q1_Score</th>\n",
       "      <th>Q2_Score</th>\n",
       "      <th>Q3_Score</th>\n",
       "      <th>Q4_Score</th>\n",
       "      <th>Q5_Score</th>\n",
       "      <th>Q6_Score</th>\n",
       "      <th>Q7_Score</th>\n",
       "      <th>Q8_Score</th>\n",
       "      <th>Q9_Score</th>\n",
       "      <th>Q10_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
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       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>30</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  born_with_jaundice  family_member_with_PDD  \\\n",
       "0   15       0                   1                       1   \n",
       "1   15       0                   0                       0   \n",
       "2   14       1                   0                       0   \n",
       "3   15       1                   0                       0   \n",
       "4   12       0                   1                       1   \n",
       "\n",
       "   label_whos_completing_test  label_ethnicity  label_country  \\\n",
       "0                           2                2              6   \n",
       "1                           3                1              6   \n",
       "2                           4                7             30   \n",
       "3                           2                4              5   \n",
       "4                           2                1              9   \n",
       "\n",
       "   used_screening_app_before  age_desc  Q1_Score  Q2_Score  Q3_Score  \\\n",
       "0                          0         0         0         0         0   \n",
       "1                          0         0         0         0         0   \n",
       "2                          0         0         0         1         1   \n",
       "3                          0         0         1         1         0   \n",
       "4                          0         0         1         1         1   \n",
       "\n",
       "   Q4_Score  Q5_Score  Q6_Score  Q7_Score  Q8_Score  Q9_Score  Q10_Score  \n",
       "0         1         1         1         1         1         1          0  \n",
       "1         0         0         0         0         0         1          1  \n",
       "2         1         1         1         0         1         1          0  \n",
       "3         1         1         0         1         1         0          1  \n",
       "4         1         1         1         0         0         0          0  "
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_adolescents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_adolescents = adolescents_asd.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    104.000000\n",
       "mean       0.605769\n",
       "std        0.491051\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        1.000000\n",
       "75%        1.000000\n",
       "max        1.000000\n",
       "Name: ASD_Label, dtype: float64"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_adolescents.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104,)"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_adolescents.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Creating Training and Testing Datasets***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = tts(X_adolescents,y_adolescents,test_size=0.25,random_state=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(78, 19) (26, 19) (78,) (26,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Bernoulli Naive Bayes***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [],
   "source": [
    "bernaulli_nb = BNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_bnb = bernaulli_nb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_bnb = model_bnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8846153846153846"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_scr(y_predict_bnb,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec_scr(y_predict_bnb,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8235294117647058"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_scr(y_predict_bnb,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91666667, 0.90909091, 0.90909091, 1.        , 1.        ,\n",
       "       0.9       , 0.7       , 1.        , 1.        , 0.9       ])"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crs_val(model_bnb,X_adolescents,y_adolescents,cv=10,n_jobs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9234848484848485"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crs_val(model_bnb,X_adolescents,y_adolescents,cv=10,n_jobs=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Gaussian Naive Bayes***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussian_nb = GNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_gnb = gaussian_nb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_gnb = model_gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7692307692307693"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_scr(y_predict_gnb,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9285714285714286"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec_scr(y_predict_gnb,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7222222222222222"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_scr(y_predict_gnb,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.83333333, 0.81818182, 1.        , 1.        , 0.8       ,\n",
       "       1.        , 0.6       , 0.9       , 0.9       , 0.7       ])"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crs_val(model_gnb,X_adolescents,y_adolescents,cv=10,n_jobs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8551515151515151"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crs_val(model_gnb,X_adolescents,y_adolescents,cv=10,n_jobs=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Random Forest Classifier***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RFC(n_estimators=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_rfc = rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_rfc = model_rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8846153846153846"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_scr(y_predict_rfc,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec_scr(y_predict_rfc,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8235294117647058"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_scr(y_predict_rfc,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.83333333, 0.90909091, 1.        , 1.        , 0.9       ,\n",
       "       0.9       , 0.5       , 0.8       , 0.9       , 0.9       ])"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crs_val(model_rfc,X_adolescents,y_adolescents,cv=10,n_jobs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8942424242424242"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crs_val(model_rfc,X_adolescents,y_adolescents,cv=10,n_jobs=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Gradient Boosting Classifier***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbc = GBC(n_estimators=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_gbc = gbc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_gbc = model_gbc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8076923076923077"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_scr(y_predict_gbc,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec_scr(y_predict_gbc,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7368421052631579"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_scr(y_predict_gbc,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91666667, 0.81818182, 1.        , 1.        , 0.8       ,\n",
       "       0.9       , 0.7       , 0.8       , 0.9       , 0.9       ])"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crs_val(model_gbc,X_adolescents,y_adolescents,cv=10,n_jobs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8551515151515152"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crs_val(model_gbc,X_adolescents,y_adolescents,cv=10,n_jobs=5).mean()"
   ]
  }
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