EDI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import array\n",
    "import seaborn as sns\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Subject ID</th>\n",
       "      <th>MRI ID</th>\n",
       "      <th>Group</th>\n",
       "      <th>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>M/F</th>\n",
       "      <th>Hand</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>457</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR1</td>\n",
       "      <td>Demented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR2</td>\n",
       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
       "      <td>560</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>1895</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subject ID         MRI ID        Group  Visit  MR Delay M/F Hand  Age  EDUC  \\\n",
       "0  OAS2_0001  OAS2_0001_MR1  Nondemented      1         0   M    R   87    14   \n",
       "1  OAS2_0001  OAS2_0001_MR2  Nondemented      2       457   M    R   88    14   \n",
       "2  OAS2_0002  OAS2_0002_MR1     Demented      1         0   M    R   75    12   \n",
       "3  OAS2_0002  OAS2_0002_MR2     Demented      2       560   M    R   76    12   \n",
       "4  OAS2_0002  OAS2_0002_MR3     Demented      3      1895   M    R   80    12   \n",
       "\n",
       "   SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
       "0  2.0  27.0  0.0  1987  0.696  0.883  \n",
       "1  2.0  30.0  0.0  2004  0.681  0.876  \n",
       "2  NaN  23.0  0.5  1678  0.736  1.046  \n",
       "3  NaN  28.0  0.5  1738  0.713  1.010  \n",
       "4  NaN  22.0  0.5  1698  0.701  1.034  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('oasis_longitudinal.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\n1) Subject ID - ID of the individual subject\\n2) MRI ID - ID of the MRI scan\\n3) Group - Demented / Non Demented i.e. variable to be predicted\\n4) Visit - Count of visit of the subject\\n5) MR Delay - Instrument error\\n6) M/F - Gender\\n7) Hand - Dominant Hand\\n8) Age - Age of Subject\\n9) EDUC - Educational level of subject [years of education]\\n10) SES - Socio Economic Status\\n11) MMSE - Mini Mental State Exam - An all round test of the brain which can yield a max score of 30 being the healthiest \\n12) CDR - Clinical Dementia Rating\\n13) eTIV - Estimated Total IntraCranial Volume\\n14) nWBV - Normalized Whole Brain Volume\\n15) ASF - Atlas Scaling Factor\\n\\n\\n\\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# metadata \n",
    "\n",
    "'''\n",
    "\n",
    "1) Subject ID - ID of the individual subject\n",
    "2) MRI ID - ID of the MRI scan\n",
    "3) Group - Demented / Non Demented i.e. variable to be predicted\n",
    "4) Visit - Count of visit of the subject\n",
    "5) MR Delay - Instrument error\n",
    "6) M/F - Gender\n",
    "7) Hand - Dominant Hand\n",
    "8) Age - Age of Subject\n",
    "9) EDUC - Educational level of subject [years of education]\n",
    "10) SES - Socio Economic Status\n",
    "11) MMSE - Mini Mental State Exam - An all round test of the brain which can yield a max score of 30 being the healthiest \n",
    "12) CDR - Clinical Dementia Rating\n",
    "13) eTIV - Estimated Total IntraCranial Volume\n",
    "14) nWBV - Normalized Whole Brain Volume\n",
    "15) ASF - Atlas Scaling Factor\n",
    "\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Data Types of Columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Subject ID     object\n",
       "MRI ID         object\n",
       "Group          object\n",
       "Visit           int64\n",
       "MR Delay        int64\n",
       "M/F            object\n",
       "Hand           object\n",
       "Age             int64\n",
       "EDUC            int64\n",
       "SES           float64\n",
       "MMSE          float64\n",
       "CDR           float64\n",
       "eTIV            int64\n",
       "nWBV          float64\n",
       "ASF           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\n Data Types of Columns\")\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Data\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(373, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Shape of Data\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Null Values in Each column\n",
      "\n",
      "Subject ID  =  0\n",
      "MRI ID  =  0\n",
      "Group  =  0\n",
      "Visit  =  0\n",
      "MR Delay  =  0\n",
      "M/F  =  0\n",
      "Hand  =  0\n",
      "Age  =  0\n",
      "EDUC  =  0\n",
      "SES  =  19\n",
      "MMSE  =  2\n",
      "CDR  =  0\n",
      "eTIV  =  0\n",
      "nWBV  =  0\n",
      "ASF  =  0\n"
     ]
    }
   ],
   "source": [
    "col = df.columns\n",
    "\n",
    "print(\"\\nNull Values in Each column\\n\")\n",
    "\n",
    "for i in col:\n",
    "    print(i , \" = \", df[i].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill null values with median as it is more robust\n",
    "med_SES = df['SES'].median(skipna = True)\n",
    "med_SES \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['SES'].fillna(med_SES , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no more null values in SES remain\n",
    "df['SES'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Similarly we clean the MMSE column too\n",
    "med_MMSE = df['MMSE'].median(skipna=True)\n",
    "df['MMSE'].fillna(med_MMSE , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no more null values in MMSE remain\n",
    "df['MMSE'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {\"M/F\" : \"Gender\"} , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our data is now ready for visualization and analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Subject ID</th>\n",
       "      <th>MRI ID</th>\n",
       "      <th>Group</th>\n",
       "      <th>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hand</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>457</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR1</td>\n",
       "      <td>Demented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR2</td>\n",
       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
       "      <td>560</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>1895</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subject ID         MRI ID        Group  Visit  MR Delay Gender Hand  Age  \\\n",
       "0  OAS2_0001  OAS2_0001_MR1  Nondemented      1         0      M    R   87   \n",
       "1  OAS2_0001  OAS2_0001_MR2  Nondemented      2       457      M    R   88   \n",
       "2  OAS2_0002  OAS2_0002_MR1     Demented      1         0      M    R   75   \n",
       "3  OAS2_0002  OAS2_0002_MR2     Demented      2       560      M    R   76   \n",
       "4  OAS2_0002  OAS2_0002_MR3     Demented      3      1895      M    R   80   \n",
       "\n",
       "   EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
       "0    14  2.0  27.0  0.0  1987  0.696  0.883  \n",
       "1    14  2.0  30.0  0.0  2004  0.681  0.876  \n",
       "2    12  2.0  23.0  0.5  1678  0.736  1.046  \n",
       "3    12  2.0  28.0  0.5  1738  0.713  1.010  \n",
       "4    12  2.0  22.0  0.5  1698  0.701  1.034  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'F'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Nondemented', 'Demented', 'Converted'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Group'].unique()   # converted refers to people who were initially classified as non demented but later were classified as demented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "males_total  = len(dv[dv['Gender'] == 'M'])\n",
    "females_total = len(dv[dv['Gender'] == 'F'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "males_demented = len(dv[ (dv['Gender'] == 'M')   &  (dv['Group'] != \"Nondemented\")  ])\n",
    "females_demented = len(dv[ (dv['Gender'] == 'F')   &  (dv['Group'] != \"Nondemented\")  ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[160, 213]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot = []\n",
    "tot.append(males_total)\n",
    "tot.append(females_total)\n",
    "tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[99, 84]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem = []\n",
    "dem.append(males_demented)\n",
    "dem.append(females_demented)\n",
    "dem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 2\n",
    "X_axis = np.arange(n)\n",
    "X = ['Males' , 'Females']\n",
    "\n",
    "plt.bar(X_axis - 0.1   , tot , label = 'Total' , width = 0.2)\n",
    "plt.bar(X_axis + 0.1 , dem , label = 'Demented' , width = 0.2)\n",
    "\n",
    "plt.xticks(X_axis , X)\n",
    "plt.xlabel(\"Gender\" , color = \"blue\" , fontweight = \"bold\" , fontsize = 14)\n",
    "plt.ylabel(\"Number of Subjects\" , color = \"blue\" , fontweight = \"bold\" , fontsize = 14 , labelpad = 16)\n",
    "plt.title(\"Gender VS Dementia\", fontweight = \"bold\" , fontsize = 18 , pad = 18)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ses_cat = dv['SES'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ses_cat.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4., 5.])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ses_cat\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ses_dem = []\n",
    "\n",
    "for i in ses_cat : \n",
    "    ses_dem.append(len( dv[ (dv['SES'] == i) & (dv['Group'] != \"Nondemented\") ] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[47, 51, 40, 40, 5]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ses_dem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P6nYIZtajhmzyljQFeAGYC8yJiDGSRgJnAqOBKcBHIuKZbsVoZmbWDUP9tPn2EbF5RIzJw4cB/4qIDYB/5WEzM7NFylBP3tX2AE7N/acCe3YxFjMzs64Yysk7gH9IulHSQblstYh4DCC/rtq16MzMzLpkyF7zBraNiEclrQpcIunushPmZH8QwKhRbhRkZmYLlyF75B0Rj+bX6cA5wNbAE5LWAMiv0+tMe3JEjImIMausskqnQjYzM+uIIZm8JS0jablKP/AeYDJwPrB/rrY/cF53IjQzM+ueoXrafDXgHEmQYvxTRFwk6T/AWZIOBKYBH+5ijGZmZl0xJJN3RDwAvKVG+VPADp2PyMzMbOgY0GlziaUktpB4XasDMjMzs8ZKJW+JQyX+IbGVxLrAf4EbgIck3t7WCM3MzGw+ZY+89wbeBdwDfApYCxCwHHBEe0IzMzOzWsom7zcA0yKYCYwFngTeCTwPbNmm2MzMzKyGssl7GeC53L8hcGMEVwH3ga97m5mZdVLZ5D0deJPE4cA6wO25fCTpKNzMzMw6pGzyvgBYEvgB6Znj50uMBNYG7mxTbGZmZlZD2fu8vw68AmwA/DWCqyTeSvpv7b+1KzgzMzNbUNnkvTLwkwieqBRE8B9g37ZEZWZmZnWVPW0+BfhLdaHE36W+hG5mZmbt158nrKlG2Zqko3IzMzPrkIanzSUuLQxuUjW8DPBm4IV2BDYU/XX99bsdwpDw/vvv73YIZmaLtGbXvMeRWpdDup97XI0617UwHjMzM2uiWfK+gpS830V6mtrNhXEvAXcDP21PaGZmZlZLw+QdkY60JeYBd0awfSeCMjMzs/pK3SoWMbC/DjUzM7PWK/uXoOdLzJHYolC2WS47r33hmZmZWbWyR9TbAFMj+q55R3Ab6f7vsW2Iy8zMzOoom7xXAObUKJ+Xx5mZmVmHlE3ejwNvkNi7UiDxYdL/fD/WjsDMzMystrLJ+++kJ6z9SeJ+ifuBM0i3kV3YruDMzMxsQWWT9xHANFICXy93AqYCE9oSmZmZmdVU9lax6RJbAp8Hts7F1wPHRfB0u4IzMzOzBZX9S1Bykv5BG2MxMzOzEko/fEViG4mLJZ6TuEpiR4lTJN7ezgDNzMxsfqWOvHOCvhQYTrrWvRjwEDCe1GjtmjbFZ2ZmZlXKHnn/EFgCuKRSEME9wAxg2zbEZWZmZnX05wlr04D3VpU/AqzV0ojMzMysof784cisiNf+27ti9VYGY2ZmZs2VTd6TgfUljszDy0v8kpS8b2tLZGZmZlZT2eT9c1JDtW+RGqhtBByS+3/ZntDMzMysllLJO4IzgK8DL5GSuICXgcPyODMzM+uQ/jyk5WcSxwNvIiXvyRG83LbIzMzMrKbSyRsgJ+sb2hSLmZmZlVD3tLnEXImrc/+8PFyrmyPxkMRJkv/b28zMrN0aHXlXrm0Xh+vVWwv4FLAM8InWhGZmZma1NEre3yc9ArXS38jqpEel7tKCmMzMzKyBusk7oi9hF/vrkVgV2LNFcZmZmVkdpRusSawMfA7YNBfdDpwQwYw8fBDwldaGZ2ZmZtXK/qvYdsDfgGULxR8EvibxvgiuiuAp4Kk2xGhmZmYFZZ+w9itgudx/L3Bf7l8WP2HNzMyso8om7zeSnqg2JoKNIngjsFUu26hdwZmZmdmCyl7zvgtYIoKbKwUR3CLxIPBKWyIzMzOzmsoeeX8eWFviAIllcncA6f7uL7YvPDMzM6tW98hbYm6N4t/k7rVqwBWN5mNmZmat1ejIWyU6qP/ktUGTNEzSzZIuyMPrSbpe0r2SzpS0RLuWbWZmNlQ1e8Jat32JdL39dXn4x8CxEXGGpBOBA4ETuhWcmZlZN5R6wlo3SFob2BX4H+ArkgS8G/hYrnIqMAEnbzMzW8SUfUjLOxuNj+CK1oQzn58D36Dv/vKVgGcjYk4efpjUYM7MzGyRUrah2SQg6oyLfsynFEm7AdMj4kZJ4yrFdZZda/qDSI9rZdSoUa0MzWzI2OqmrbodwpBw45Y3DnIObWu200Pq7d5tqOpP0u3kFr4tsLuk9wEjSNe8fw6sIGnxfPS9NvBorYkj4mTgZIAxY8Z4qzQzs4VK2fu8t6/q9gROA+YCn2l1UBHxrYhYOyJGAx8FLo2IjwOXAXvlavsD57V62WZmZkNdqSPvCC6vUXy+xEakRP7rlkZV3zeBMyQdCdwM/LZDyzUzMxsyBnStWkLA+qQGY29uaURVImIS6Zo7EfEAsHU7l2dmZjbUlW1tXutpaxVTWhOKmZmZlVH2yLteY7V5wJEtisXMzMxKKJu8qx/YEsB04LII7mltSGZmZtZI2QZrQ+FRqWZmZkb5W8VeIzFC4m0Sb5L6P72ZmZkNTsPkK7GvxCmVx6NKrEP6o5CrgNuAf0us2f4wzczMrKLZkfP+uXsoD08A1qXvL0G3IP1xiJmZmXVIs+T9RuCJCB7Mw7uSGqv9nvzscGCnNsVmZmZmNTRL3iuRnx8usT6wai7/bgS/IZ1CX7XOtGZmZtYGzZL3y8AauX/b/PpIBNNy/+LAzHYEZmZmZrU1u1XsbmCsxKWkU+gBXAwgsTiwHnBfWyM0MzOz+TQ78j6W1DDtXaQj8LnAr/K43YDhwHVti87MzMwW0PDIO4KzJT5A+lvOxYCTI7g1j14LOBP4Q3tDNDMzs6KmT1iL4Dxq/G92BMcBx7UjKDMzM6vPT0gzMzPrMU7eZmZmPcbJ28zMrMc4eZuZmfUYJ28zM7MeU7e1eeWfxMqI4IrWhGNmZmbNNLpVbBLpiWrNRJP5mJmZWQs1S7rqSBRmZmZWWqPkvX2hfy3gJOAc4CxSUt8L+DDw2bZFZ2ZmZguom7wjuLzSL3E+8HgE+xWq/FViW9KjU09tX4hmZmZWVLa1+Q7AChLLVgpy//JQvmGbmZmZDV7ZhmZPAmsDt0npL0GB9wArAQ+3IzAzMzOrrWzy/hFwPDAaOCiXqTDOzMzMOqTUafMITiT9f/eVwLO5uxJ4fwQntC88MzMzq1b6/uwILgQubGMsZmZmVkLp5C2xPHAIMBaYCpwMbAlMimBae8IzMzOzaqVOm0usBdwCHEk6fT4GWBGYCHyuXcGZmZnZgsreKvYTYF3gKXJDtQiuBF4AdmpPaGZmZlZL2eS9M/A0sEFV+VRSUjczM7MOKZu8lwUejuC5qvIlgaVbG5KZmZk1UjZ5TwHeJLFjHpbEh0lH4g+0IzAzMzOrrWzy/iMwDLiY9BegWwNn5P7T2xOamZmZ1VI2eR8F/I3UWK3YXQT8uD2hmZmZWS2l7vOOYDbwfol3ANuQEve/i/88ZmZmZp1RKnlLHEFqsHYK6bGolfK3ASvmp6+ZmZlZB5Q9bT4B+FSN8mOA81sWjZmZmTXV8MhbYlRhcMmq4WVI93gLMzMz65hmp80fzK8BbF4YLnqipRGZmZlZQ82Sd+WoOqh/hH1y68IxMzOzZpol7wPy6++A+0l/TFLxEnB3BLe3IzAzMzOrrWHyjuBUAIntgfsqw2ZmZtY9Ze/zHg8gsSSwKlWn0Fv9f96SRgBXkJ6dvjhwdkR8T9J6pCe7jQRuAvaNiFmtXLaZmdlQV/b/vDeQuJJ0qnwKqeFapWvHs81fBd4dEW8hNZTbRdJY0tPcjo2IDYBngAPbsGwzM7Mhrex93r8BtmXBx6NWupaKZGYeHJ67AN4NnJ3LTwX2bPWyzczMhrpSp82BrYB5wP8BdwJz2hZRJmkYcCPwBuA4UoO5ZyOisuyHgbXqTHsQcBDAqFGjalUxMzPrWWWT98PA3Ai+2s5giiJiLrC5pBWAc4CNa1WrM+3J5FvYxowZU7OOmZlZryp72vw7wPoS72tnMLVExLPAJGAssIKkyg+OtYFHOx2PmZlZt5VN3keTrm3/VeJpiQcK3f2tDkrSKvmIG0lLATsCdwGXAXvlavsD57V62WZmZkNd2dPm6xb6V8hdRTtOS68BnJqvey8GnBURF0i6EzhD0pHAzcBv27BsMzOzIa1s8j6N9iTpmiLiNmCLGuUPAFt3Kg4zM7OhqF8PaTEzM7PuK3vkjcQw4COkhmNPAH8ARgOTI3i6LdGZmZnZAso+YW154DpSwv48sBuwCakB2RfbFp2ZmZktoGxr86Poe1BL5YlqlwAvA7u0IS4zMzOro2zy3gOYCaxfKYhgLjANeH0b4jIzM7M6yibvlYD7a/x72Dzgda0NyczMzBopm7wfATaS+h5RKrE16ZGlD7cjMDMzM6utbPI+l/Tf2reQ7vfeErgmjzunDXGZmZlZHWWT9xGkJ5oNJzVYWyJPeyvwg/aEZmZmZrWUfUjLTImxwD6kJ5wJ+DdwegSz2hifmZmZVSn9kJYIZpMek3pa+8IxMzOzZvrzhLUdgB2A1ei71xsgIjiw1YGZmZlZbaWSt8R3gQm1RpEasDl5m5mZdUjZI+/PkBL1K8B0OvgPY2ZmZja/ssl7BOlpam+K4MU2xmNmZmZNlL1V7CRgeWBkG2MxMzOzEsoeeX8f2B24V2Iy8HxhXESwQ8sjMzMzs5rKJu+fkf4CFNLT1SBd9640WDMzM7MOKZu89yMl6YdJ177ntC0iMzMza6hs8n4aeDyCDdsZjJmZmTVXtsHa4cDq+RGpZmZm1kVlj7yPJP0pydUSz7Bgg7X1Wx6ZmZmZ1VQ2ea9b6B/J/LeMucGamZlZB5VN3qfhJG1mZjYklP1L0PFtjsPMzMxK6s+/ig0DPgKMBZ4A/gCMBiZH8HRbojMzM7MFlGptLrE8cB0pYX8e2I300JbLgC+2LTozMzNbQNlbxY4CtgLm0fdf3pcALwO7tCEuMzMzq6Ns8t4DmAl9t4RFMJf0tLXXtyEuMzMzq6Ns8l4JuD+CaVXl84DXtTYkMzMza6Rs8n4E2Ehi40qBxNbAxqTnnZuZmVmHlE3e5wJLAreQ7vfeErgmjzunDXGZmZlZHWWT9xHAzaRHpApYIk97K/CD9oRmZmZmtZR9SMvM/Kck+wBbkxL4v4HTI5jVxvjMzMysSumHtEQwm/SY1NPaF46ZmZk1U/YhLftJXCPxgsTzuX+/dgdnZmZmC2p65C1xPHBwZTC/bgNsIzE2gkPaFZyZmZktqOGRt8SuwGdISXsWMBm4E5idyw6WeF+7gzQzM7M+zU6bH5hfLwVGR7BZBJuSnqp2BSmBH9TG+MzMzKxKs+S9OTAH2C+CJyqFETwK7AvMBd7SvvDMzMysWrPkvSowNSfr+UTwEDAFWKUNcZmZmVkdzZL3CGBGg/FP5jpmZmbWIc1amy9GalU+txPBmJmZWXNlHtKiJuOjFYGYmZlZOc2S96kdicLMzMxKa5i8IzigU4EUSVqH9BjW1Un/GX5yRPyfpJHAmcBoUmO5j0TEM92I0czMrFvK/qtYp80BvhoRGwNjgc9J2gQ4DPhXRGwA/CsPm5mZLVKGZPKOiMci4qbc/wJwF7AWsAd9p/JPBfbsToRmZmbdMySTd5Gk0cAWwPXAahHxGKQET7oPvdY0B0m6QdINM2Y0utPNzMys9wzp5C1pWeDPwJcj4vmy00XEyRExJiLGrLKKnyFjZmYLl7rJW2J3ie1y/yiJ1ToXFkgaTkrcf4yIv+TiJyStkcevAUzvZExmZmZDQaMj73OBH+f+KcBf6ldtLUkCfgvcFRHHFEadD+yf+/cHzutUTGZmZkNFo1vF5gJrS2ych0dIrEONh7ZEMK3FcW1L+uOT2yXdksu+DRwFnCXpQGAa8OEWL9fMzGzIa5S8HwLWJf2Hd5D+YWxKjXrRZD79FhFXUf/Jbju0cllmZma9ptFp81+SEmgliapBZ2ZmZh1S94g5gmMlLgU2BX4P3A8c2anAzMzMrLZmj0e9FbhVYifgvgg/69zMzKzbSl2rjmA8gMSuwJhcfEMEf2tTXGZmZlZHqeQtsQxwEfD2qvKrgV0ieKkNsZmZmVkNZZ+wNoF0+1Z1Q7Vtge+1JTIzMzOrqWzy/hDpvu/PAsvn7hDSbWK+19rMzKyDyibvtYB7IjgpghdydyJwdx5nZmZmHVI2eT8PjJJYu1KQn7a2bh5nZmZmHVI2eV8JLAvcJXGRxN+BO4ClgcvbFZyZmZktqOxjTb8L7EhK4DvlMgEvAEe0IS4zM5n9e6IAABeBSURBVDOro9SRdwR3AFsDp5Guc9+d+7eJ4M72hWdmZmbVSv+hSAR3Q3pYi5mZmXVP2WveZmZmNkQ4eZuZmfUYJ28zM7Me4+RtZmbWY5omb4nhEsdI/ERCnQjKzMzM6muavCOYDRwM7BxBtD8kMzMza6TsafNLSI9HXa6dwZiZmVlzZe/zvhrYBbhO4lTg8eLICE5rdWBmZmZWW9nk/WPS339uBPyoalyAk7eZmVmnlH7CGrixmpmZ2VBQNnmv19YozMzMrLRSyTuCqZV+iRWB4RFMb1tUZmZmVlfph7RIfFDiHuBJ4FyJ3SUulXhf+8IzMzOzaqWOvCV2A85i/mR/M/AuUsvzC1sfmpmZmdVS9sj7O6QGa7+pFETwEClxb92GuMzMzKyOssl7M+C+CA6qKn8CWLO1IZmZmVkjZZP3bGDJYoHEMGAdYFargzIzM7P6yibvG4F1JH6fh1cF/gysBPynHYGZmZlZbWWT91H59WOkJ6qtB+ye+49uQ1xmZmZWR6nkHcE/gL2BqaSGawKmAPvkcWZmZtYhpR+PGsHZwNkSKwOKYEb7wjIzM7N6SidviSWAfYBN8/DtwBkRbrBmZmbWSWUf0rIJ6UEs61SN+oHErhHc0fLIzMzMrKayDdZOAkaRrnXPIt06plx2QntCMzMzs1rKJu8xpIT9gQiWimAEsGcue2u7gjMzM7MFlb3mPRWYHcF5lYIIzpf4LzCsLZGZmZlZTWWPvL8JrCexfaUg968HHNaOwMzMzKy2ukfeEg9UFQ0D/inxdB4eSbr+fSxwfnvCMzMzs2qNTpuPrlO+UqF/yQb1zMzMrA0aJe9TOxaFmZmZlVY3eUdwQCcDMTMzs3LKNlgDQGJJiXUkRhW7Vgcl6RRJ0yVNLpSNlHSJpHvz64qtXq6ZmVkvKJW8JTaUuBJ4ifSHJA8WuuqGba0wEdilquww4F8RsQHwL9zK3czMFlFlj7x/C2xL3z+KVXctFRFXwGut2iv2oO86/Kmkh8SYmZktcso+pGUL0m1hPyEdaUfbIqpvtYh4DCAiHpO0ahdiMDMz67qyyftOYLkIjmhnMK0i6SDgIIBRo1p+Sd7MzKyryibvg4GLJU4ELgCeL46M4IpWB1bDE5LWyEfdawDT61WMiJOBkwHGjBnTjbMEZmZmbVM2eS8NzAM+nbui6Md8BuN8YH/gqPx6XuPqZmZmC6eyDdZOBFahQw3WJJ0OXAu8UdLDkg4kJe2dJN0L7JSHzczMFjllj5hfD7wIHEq6VWxOuwICiIh96ozaoZ3LNTMz6wVlk/fFwGYR/LadwZiZmVlzZZP31cB7JS4ELmTBBmuntTowMzMzq61s8j6a1DBt59wVBTh5m5mZdUp/Wom3vGGamZmZ9V/Z5L1eW6MwMzOz0kol7wimtjsQMzMzK6dU8pY4pcHoiODAFsVjZmZmTZQ9bT6e2n9Golzu5G1mZtYhZZP3NOZP3ssDK5AemTqt1UGZmZlZfWWveY+uLpMYR3re+PdaG5KZmZk1UvbZ5guIYBJwA/DtlkVjZmZmTZVtsLZfVdEwYH1gW2B2q4MyMzOz+spe855I/QZr17YsGjMzM2tqsE9Yuxb4VItiMTMzsxIG+oS1AKZH8EqL4zEzM7Mm/IQ1MzOzHtMweUscUmYmERzfmnDMzMysmWZH3r+idkO1ak7eZmZmHVLmtHmzvwItk9zNzMysRZol71p/BboJ8ENgyzx8W0sjMjMzs4YaPmEtgqmVDpgLHAGcR0rcDwL70pfEzczMrAOanjaXGAkcDnwWGAE8ARwJnBTBnPaGZ2ZmZtWatTb/LvBVYDngOVLSPjaClzsQm5mZ9YpmraMWBR1sAdbsyPv79IXzFLAnsKfm/5Aigm1aH5qZmZnVUvYJawJeX+gvcmtzMzOzDmqWvK/AydnMzGxIaZi8IxjXoTjMzMyspIa3ipmZmdnQ4+RtZmbWY5y8zczMeoyTt5mZWY9x8jYzM+sxTt5mZmY9xsnbzMysxzh5m5mZ9RgnbzMzsx7j5G1mZtZjnLzNzMx6jJO3mZlZj3HyNjMz6zFO3mZmZj3GydvMzKzHOHmbmZn1GCdvMzOzHuPkbWZm1mN6LnlL2kXSPZLuk3RYt+MxMzPrtJ5K3pKGAccB7wU2AfaRtEl3ozIzM+usnkrewNbAfRHxQETMAs4A9uhyTGZmZh3Va8l7LeChwvDDuczMzGyRsXi3A+gn1SiLBSpJBwEH5cGZku5pa1SdtTLwZFcjUK2Poed0fT3qhG4uvWW6vx5r7hZ6TpfXo9dhS7R+Na5bb0SvJe+HgXUKw2sDj1ZXioiTgZM7FVQnSbohIsZ0O45e5/XYGl6PreH1OHiL2jrstdPm/wE2kLSepCWAjwLndzkmMzOzjuqpI++ImCPp88DFwDDglIi4o8thmZmZdVRPJW+AiLgQuLDbcXTRQnk5oAu8HlvD67E1vB4Hb5Fah4pYoL2XmZmZDWG9ds3bzMxskefkXYKkSZJ2rir7sqTj27CsKZJWLgyPk3RB7t+92SNhi/Wb1Bsr6XpJt0i6S9KEXD5e0oxcXuk2kbSYpF9Imizpdkn/kbTeIN9uKZJmVg2Pl/Sr3P8ZSfs1mf61+k3q7SbpZkm3SrpT0sG5fIKkR6rWyQqSlpb0x7w+Jku6StKyg3mvAyXpA5JC0kbdWH5/NFpvkuZWrefDcnnNz6aL72Fm81oDmm9Prptu7iNbON+6+7i8zNsL6/4XubzmfrQTeu6ad5ecTmrZfnGh7KPA18tMLEmkSxTzBhNERJxP61rXnwp8JCJuzY+dfWNh3JkR8fliZUn7AGsCm0XEPElrAy+2KJYBi4gTWzEfScNJ18y2joiHJS0JjC5UOTYiflo1zbeAJyLizXn4jcDsVsQzAPsAV5G2ywmDnZmkYRExd7DzqeNL1F9vL0fE5lWxNPtsFia9um6GxD5ykPam8T5u+4iovo+80X60rXzkXc7ZwG75i4Gk0aQP+ao8/PX8K+02Sd+v1Mm/xI4HbgK+K+nYygwlfVrSMf0JouqIc31J1+Xl/qDqSGBZSWdLujv/iq/16IBVgccAImJuRNzZZPFrAI9VvlwR8XBEPNOf+NshHxV/Lfe/NX8G10o6WtLkQtU1JV0k6V5JP6kxq+VIP2afAoiIVyOi2cN91gAeqQxExD0R8erg3lH/5SOzbYEDSTvMSvmZkt5XGJ4o6UOShuX1U9lmK2cYxkm6TNKfgNtz2bmSbpR0h9LDjyrzOlDSf/MR168L2+Uqkv6c5/0fSdvWCLm/620gn03HSVpX0r/yOv2XpFF5XT+gZAVJ8yS9M9e/UtIbqmbTq+umq/tISSPztnpb3i9ulstvz+tdkp5SPksn6feSdqyazUD2cf3dj7ZORLgr0QF/A/bI/YcBR+f+95B++Yr0Y+gC4J2kX7/zgLG53jLA/cDwPHwN8OYay5lC2nHekrv7gAvyuPHAr3L/BcA+uf8zwMzcPw54jvQAm8WAa4HtaiznCOAZ4BzgYGBEYRkzCsu/BVgqz29KHv4ZsEUH1/3cqnimFdbDBOBruX8y8PbcfxQwufCeHgCWB0YAU4F1aiznN8B00lHEx4HFCst4pLD8y3L55rn+tcCRwAZd2jY/Afy2sF1tmfs/AJya+5cgPVp4KdLTB7+Ty5cEbgDWy9vOi8B6hXmPzK9L5fW7EmmnPAUYCQwHrix8Hn+qbG/AKOCuGvHWXW81Puu9G3023erI37eqsr8C++f+TwLn5v6LgDcBu5GeVXF4Xu8PLkzrhs7uI1euKvsl8L3c/27gltx/IrArsGle97/O5fcCy1bNo+4+jgX3y4fm8pr70Y6s725+AXqpI+0gT8/9t9C3g/xp4QOvJNsD84b5YNU8fk3aoW4E/KfOcubbMEk71FrJ+ylg8dz/OuZP3pcUpj8B+ESdZa0PfBa4HJhUvYwa9Zck/aPb0cDTwA4dWvczq4aL62EC8DVgBWBqoc5mzJ+8f10Y93dq/KDJ494MHArcDEwsLqNO/WWBDwLHA88CG3dh2/wbsFPu/yJ9O80RpIS9JOkPfP6Yy88G/lvYZh8k7WDHkX+YFOY9Abg1d88BY4E9yT8KCsusfB7TmT/BPAIsV3a9VX/WzT6bbnW14iQ9mrOSeIYDT+b+w/P37Cf5Pf8d2A44qz/b1FBfN3RpH5nLbgZeXxh+iPRj/ePAj4FDSD+oriP9H8b1deZdcx9Xa5mFaRbYj3ai8zXv8s4FjpG0JbBURNyUywX8KCJOKlbOp42qrwn/Bvg2cDfwuzbGWjzNNpc6bRsi4n7gBEm/BmZIWqnRTCOdvvs78HdJT5B24v9qTciD1uypwmXXye3A7ZJ+T0pq4xvNNCJmAn8B/iJpHvA+4K6SMQ9a/szeDWwqKUgPLwpJ34iIVyRNAnYmXc87vTIZ8IWIuLhqXuMobLN5eEfgbRHxUp7XCBqv68Vy/ZcbxT2Q9dbfz2YIqNyHeyXp7NiapCO1r5N+KF1Rc6LeXTfd3EfW+9+LK4DPkc4CHU76YbAX6TNZcIIB7ONq7Ucj4ql+xD4gvuZdUv5CTQJOoW8nCKmBxifV1yJ0LUmr1pnH9aRns3+sah4DcR3wodz/0UYVa5G0a+Fa+AakhPZsg/pbSloz9y9GOrKd2t/ltkuka1MvSBqbi/q1TiQtm5NVxeY0eX+StpW0Yu5fgvQf851eJ3sBp0XEuhExOiLWIe28t8vjzwAOAN5BX2Oii4HPKjV2QtKGkpapMe/lgWdy4t6IdNQN8G/gXZJWlLQ4fdshwD+A1xo7SpqvgVUu69d6G8hn0yXX0LfdfZx8vRe4Hng7MC8iXiEdfR5MjQTSy+umy/vIK0jrvPKj88mIeD4iHiL9YckGEfEA6TP5GrXXfb/3cf3dj7aSj7z753TSL+LXEkNE/EPSxsC1+TOcSTp9VK+l7lnA5jH4xl5fBv4g6auk06bP9XP6fYFjJb0EzAE+HhFz83vYW9J2hbqHkE7N/7rSIIW0A296+1WHHUiK8UXSTqQ/60TANySdBLxMOiIYXxh/qKRPFIb3JJ0uOyF/eRcjfQ5/HnD0A7MP6fp+0Z9JO78rScn0NOD8iJiVx/+GdMryphz7DNL7qXYR8BlJtwH3kH4wEhGPSPpfUlJ6FLiTvnX9ReC4PM3ipJ3qZ6rm22i9LSXplqoY/ofGn003LC3p4cLwMaT3foqkr5PW6QGQjuYkPURef6TPZR9yo8Aqvb5uOrWPvC2flajUnwD8Lm93LwH7F+peTzojBWnd/4i+H1ZFq9J4H3eZpErMt0XEftTZjzaIu2X8hLUOU7oH+9iIGNTpZklLk24dCUkfJTVe26MlQfYoScvmX/8o3QO7RkR8qcthLZQq6zofeZ9D+p+Bc7odl/W+Vu0jF3Y+bd4h+XaF/5ISbis2yq2AW/IvzUOAr7Zgnr1uV6WHJUwmnSY+stsBLcQm5KPAyaTT9Od2OR7rcW3YRy7UfORtZmbWY3zkbWZm1mOcvM3MzHqMk7eZmVmPcfI2MzPrMU7eZmZmPcbJ28zMrMc4eZuZmfUYJ28zM7Me4+RtZmbWY5y8zczMeoyTt5mZWY9x8jYzM+sxTt5mZmY9xsnbzMysxzh5m7WBxBSJkJjY7Vh6QV5XITGh27GY9QInb1skSAyXOEziTokXJZ6XuF/iPImt27DIm4Hrgfv7Gef4QiKr7ia1Ic6h4vrcPTyYmUisLnGCxIMSr0g8LTFZ4o8SKxbqTRrMOpWYmKefMph4zQZq8W4HYNYhPwG+nPvvA14G1gV2B84B/t3KhUXwgRbM5i7g+cLwnS2Y55AUwdjBzkNiMeBvwJbAHNL6GgG8AXgTcDjwzGCXYzYU+MjbFhX75NcfRrBBBJsBKwBvoypxSxwgcaPEy/ko/TqJD1fVWU7iJxL3Sbyaj/D+KTEyj1/gtLnESIlfSUyTmC0xXeJ0ifXrxHxIBGML3SGFeS0h8W2JO/IR5nMSV0tsWPZ9SIwuHNV/VeIPEi9IPCLxnar32zR2iQmF+b1H4i6JlyTOyuvrUIlHJWZI/FLqO3ioddpcYjWJE/MyZ+Vl/rXuJwwbkhI3wM4RvCWCNwLLA+8nJ26JAN6V672rsOzREutK/F3iobzeXs5H7l+WUOWzBfbP069bmH5c7l4brvf+JIZJHJm3n5clnpG4ReKoBu/PrE9EuHO30HcQ0yEC4lqI90OsXqfed3K9gJgG8Whh+OBcZwmIG6vq3QMxF2J0rjMlj5uYh0dA3J7L5kDcAfFyHp4BsXauN74w33EN3s9fC/Ueh7gTYnZlmpLvY3ShbFauM6NQtlM/Y59QmPZ5iLsLw3dAvARxf6Hs04X3UymbkIdXKqzDgLgX4kGIaLBONinUnwjxTojlatS7LsdXifO63K0BMSaXPwRxE8QThXl+Lk9/TmE9vVqYfkuIcbU+vxrv7wuF9Xlr3n5egbiv298Vd73RdT0Ad+460VUllkp3D8QPIJbOdZbJCSYgzoNYDGI4xBW5bHou268wj28VlrFBJVmwYPI+oDDNXrls07zzDoif5bJi8q7uvpzrvLNQdjzEsFy+JsTq/XgfxeR9LelHycqkRB4QR/Uz9uI6/nguu6pQtm1ebmXdnFFYd9XJ7YhC2ccK9bZq8BkL4tKqdTYnJ9bxECrUnZTHT6qaxwrkH2B5eDGIy3PdKwvlE3PZlKrpyybvX+bh3xbqLAXx9m5/V9z1RufT5rZIiGAC8EHgPPquI28IfBc4LQ+/CVgq958ZwbwIZgNn57JVSNfJt8nDs4GjC8u4N4IX6oTw1vw6C/hzrj8ZuC2Xj6kxzV30NeS6Hngsl29TqPPjCObm+T0aweP9eB9FZ0YwK4Ingem5bLVBxF45vT0lvz4TwdURzAOmVs2/lsp7nBLBnyqFEdxYb4IIAtiF1LbhqhzvsDyv3wGfa7C8itnANySmSswG5gLvzOPWLDF9WRcAAXxS4jGJy4H/Yf42DmZ1OXnbIiOCcyLYk3StewxwUx61W27sNF/1doXRj7rV17zPbOPyni30z8mvGuC8iHgtCVXmVUxKlflUz3/Q8g+Q/4vgHcDrgF2B5/LoPUrM4ufAZ4FRwIOkH01P5nHDyoRQ6B8GILF8jTgvJl2f/1/gRlKjukOBayXWKbEcW8Q5edsiQeJoKR3N5bNONwJ359Ev5iPCO0it0AE+KrGYxHBgr1w2g3TUeH0eHg58pbCM9SWWrRPCf/LrksCHcv1Ngc1y+Q39eDvXF/q/XvnhoXSb1Gr9eB9ltTL2sirvcbTERyqFEm+pN4HEOrkR2GiACF4FLgWeylWKPyBeyq/LVM2m0ur9HxFsCIwDHqmxuMr0S1casmXTC/2VxnwL3HkgsRkwPYLDI9itsNxloS23LtpCxsnbFhX7Atfl1tS3SUwFPpbH/QkgghdJR0KQbiGbkrt35LLv5iR/Bn1H7T/Op1jvBu4BVq6z/NOBybn/DIk7SK3ch5GO7I6tMc3xuYV4pTsnx3kF6bQrpFPBj0hMJiXkjfvxPsoaSOyDdRx9PzDOlLhX4n7S/fP1LEO6HezB3KL9JlLifX0ef3qhbuWH25i8PVyUhyuXAt4jcQ/wENQ8Eq5Mvwpwd/58lgLuBablccdIXAacUGP6jwAP55b0N9K3Pc0l/fgya8jJ2xYV3yFd755BOiJanbSj/V/ga5VKERwJfJK0M10FWJF0FLh3BCflOrNIR2RHAw8AawCrApdT55plBK+Qbk86jnTtekPgReBMYGxEzYeTbEy6XlvptiiM+xApUd0FjCSd5r0BeLTs+yhrgLEPSgRPkY5GTyIl0NGk0+AXNpjsEeAIYBLwKuna/wjSejkg4rVr/gA/Bf4JzATeTN91+6+QtpOZwHKkz7jW7WmnkK7/P0daH9sAwyKYA+xN+pExjPTZfKjG9Jfn9yJgU9IzN64BPhTx2g8Ds7oU0a5Le2ZmZtYOPvI2MzPrMU7eZmZmPcbJ28zMrMc4eZuZmfUYJ28zM7Me4+RtZmbWY5y8zczMeoyTt5mZWY9x8jYzM+sx/x9UJmFdLTkjdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "X = ['Very High SES' , 'High SES' , 'Average SES' , 'Low SES' , 'Very Low SES']\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(X , ses_dem , color = ['firebrick' , 'royalblue' , 'limegreen' , 'yellow' , 'magenta'])\n",
    "ax.set_xlabel('SocioEconomic Status' , color = 'blue' , fontweight = 'bold' , fontsize = 14 , labelpad = 15)\n",
    "ax.set_ylabel('Number of Demented Subjects' , color = 'blue' , fontweight = 'bold' , fontsize = 14 , labelpad = 15)\n",
    "plt.title(\"SocioEconomic Status VS Dementia\", fontweight = \"bold\" , fontsize = 18 , pad = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = df['Age']\n",
    "norm_brain = df['nWBV']\n",
    "norm_brain = norm_brain*1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.scatter(age,norm_brain , color = 'limegreen')\n",
    "ax.set_xlabel('Age' , color = 'blue' , fontweight = 'bold' , fontsize = 14 , labelpad = 15)\n",
    "ax.set_ylabel('Whole Brain Volume [mL]' , color = 'blue' , fontweight = 'bold' , fontsize = 14 , labelpad = 15)\n",
    "plt.title('Age VS Whole Brain Volume' , fontweight = \"bold\" , fontsize = 18 , pad = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. , 2. ])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdr = df['CDR'].unique()\n",
    "cdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdr_dem = []\n",
    "cdr_tot = []\n",
    "for i in cdr :\n",
    "    cdr_dem.append(len(df[ (df['CDR'] == i)  &  (df['Group'] != 'Nondemented') ]))\n",
    "    cdr_tot.append(len(df[df['CDR'] == i]))\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8.74, 98.37, 100.0, 100.0]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdr_perc = []\n",
    "for i in range(4) : \n",
    "    cdr_perc.append( round(  ((cdr_dem[i] / cdr_tot[i])*100) , 2 ) ) \n",
    "\n",
    "\n",
    "cdr_perc \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = ['None' , 'Very Mild' , 'Mild' , 'Moderate/Severe']\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(X , cdr_perc , color = ['firebrick' , 'royalblue' , 'limegreen' , 'yellow' , 'magenta'])\n",
    "ax.set_xlabel('Clinical Dementia Rating' , color = 'blue' , fontweight = 'bold' , fontsize = 14 , labelpad = 15)\n",
    "ax.set_ylabel('Percentage of Subjects with Dementia' , color = 'blue' , fontweight = 'bold' , fontsize = 14 , labelpad = 15)\n",
    "plt.title(\"Dementia dependence on CDR\", fontweight = \"bold\" , fontsize = 18 , pad = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_dem = df.Age[((df['Group'] != \"Nondemented\") & (df['Gender'] == \"M\"))]\n",
    "f_dem = df.Age[ (df['Group'] != \"Nondemented\") & (df['Gender'] == \"F\")]\n",
    "m_nondem = df.Age[ (df['Group'] == \"Nondemented\") & (df['Gender'] == \"M\")]\n",
    "f_nondem = df.Age[ (df['Group'] == \"Nondemented\") & (df['Gender'] == \"F\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = [m_dem , f_dem , m_nondem , f_nondem]\n",
    "lab = [\"Demented Males\" , \"Demented Females\" , \"Non Demented Males\" , \"Non Demented Females\"]\n",
    "plt.figure(figsize = (10,8))\n",
    "colors = ['deepskyblue' , 'mediumspringgreen' , 'hotpink' , 'yellow']\n",
    "bplot = plt.boxplot(plot_data , labels = lab , patch_artist = True )\n",
    "\n",
    "\n",
    "    \n",
    "for patch, color in zip(bplot['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "plt.title(\"Age Comparision\", fontweight = \"bold\" , fontsize = 18 , pad = 18)\n",
    "\n",
    "plt.ylabel('Years' , color = 'blue' , fontweight = 'bold' , fontsize = 14 , labelpad = 15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\nThe analysis that has been carried out from the various graphs based on the data in the dataset are : \\n\\n1) Higher percentage of males are demented than females\\n2) The SES has little or no effect on dementia as almost all the different SES categories have the same number of demented subjects [very poor being the exception]\\n3) The Whole Brain Volume has a lot of variance for a given age however it has a slightly negative linear relation with age\\n4) Clinical Dementia Rating is highly correlated to our target variable. 100% of people with a CDR higher than 0.5 have been classified as demented.\\n\\n'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "\n",
    "The analysis that has been carried out from the various graphs based on the data in the dataset are : \n",
    "\n",
    "1) Higher percentage of males are demented than females\n",
    "2) The SES has little or no effect on dementia as almost all the different SES categories have the same number of demented subjects [very poor being the exception]\n",
    "3) The Whole Brain Volume has a lot of variance for a given age however it has a slightly negative linear relation with age\n",
    "4) Clinical Dementia Rating is highly correlated to our target variable. 100% of people with a CDR higher than 0.5 have been classified as demented.\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hand'] = df['Hand'].replace(['R','L'], [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Subject ID</th>\n",
       "      <th>MRI ID</th>\n",
       "      <th>Group</th>\n",
       "      <th>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hand</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>457</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR1</td>\n",
       "      <td>Demented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR2</td>\n",
       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
       "      <td>560</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>1895</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>368</td>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>OAS2_0185_MR2</td>\n",
       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
       "      <td>842</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>OAS2_0185_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>2297</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1688</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>370</td>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371</td>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>763</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1327</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>372</td>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR3</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>3</td>\n",
       "      <td>1608</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>373 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Subject ID         MRI ID        Group  Visit  MR Delay Gender Hand  Age  \\\n",
       "0    OAS2_0001  OAS2_0001_MR1  Nondemented      1         0      M    0   87   \n",
       "1    OAS2_0001  OAS2_0001_MR2  Nondemented      2       457      M    0   88   \n",
       "2    OAS2_0002  OAS2_0002_MR1     Demented      1         0      M    0   75   \n",
       "3    OAS2_0002  OAS2_0002_MR2     Demented      2       560      M    0   76   \n",
       "4    OAS2_0002  OAS2_0002_MR3     Demented      3      1895      M    0   80   \n",
       "..         ...            ...          ...    ...       ...    ...  ...  ...   \n",
       "368  OAS2_0185  OAS2_0185_MR2     Demented      2       842      M    0   82   \n",
       "369  OAS2_0185  OAS2_0185_MR3     Demented      3      2297      M    0   86   \n",
       "370  OAS2_0186  OAS2_0186_MR1  Nondemented      1         0      F    0   61   \n",
       "371  OAS2_0186  OAS2_0186_MR2  Nondemented      2       763      F    0   63   \n",
       "372  OAS2_0186  OAS2_0186_MR3  Nondemented      3      1608      F    0   65   \n",
       "\n",
       "     EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
       "0      14  2.0  27.0  0.0  1987  0.696  0.883  \n",
       "1      14  2.0  30.0  0.0  2004  0.681  0.876  \n",
       "2      12  2.0  23.0  0.5  1678  0.736  1.046  \n",
       "3      12  2.0  28.0  0.5  1738  0.713  1.010  \n",
       "4      12  2.0  22.0  0.5  1698  0.701  1.034  \n",
       "..    ...  ...   ...  ...   ...    ...    ...  \n",
       "368    16  1.0  28.0  0.5  1693  0.694  1.037  \n",
       "369    16  1.0  26.0  0.5  1688  0.675  1.040  \n",
       "370    13  2.0  30.0  0.0  1319  0.801  1.331  \n",
       "371    13  2.0  30.0  0.0  1327  0.796  1.323  \n",
       "372    13  2.0  30.0  0.0  1333  0.801  1.317  \n",
       "\n",
       "[373 rows x 15 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'] = df['Gender'].replace(['F','M'], [0,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Subject ID</th>\n",
       "      <th>MRI ID</th>\n",
       "      <th>Group</th>\n",
       "      <th>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hand</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>457</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR1</td>\n",
       "      <td>Demented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR2</td>\n",
       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>1895</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>368</td>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>OAS2_0185_MR2</td>\n",
       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
       "      <td>842</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>OAS2_0185_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>2297</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1688</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>370</td>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371</td>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1327</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>372</td>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR3</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>3</td>\n",
       "      <td>1608</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>373 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Subject ID         MRI ID        Group  Visit  MR Delay  Gender Hand  Age  \\\n",
       "0    OAS2_0001  OAS2_0001_MR1  Nondemented      1         0       1    0   87   \n",
       "1    OAS2_0001  OAS2_0001_MR2  Nondemented      2       457       1    0   88   \n",
       "2    OAS2_0002  OAS2_0002_MR1     Demented      1         0       1    0   75   \n",
       "3    OAS2_0002  OAS2_0002_MR2     Demented      2       560       1    0   76   \n",
       "4    OAS2_0002  OAS2_0002_MR3     Demented      3      1895       1    0   80   \n",
       "..         ...            ...          ...    ...       ...     ...  ...  ...   \n",
       "368  OAS2_0185  OAS2_0185_MR2     Demented      2       842       1    0   82   \n",
       "369  OAS2_0185  OAS2_0185_MR3     Demented      3      2297       1    0   86   \n",
       "370  OAS2_0186  OAS2_0186_MR1  Nondemented      1         0       0    0   61   \n",
       "371  OAS2_0186  OAS2_0186_MR2  Nondemented      2       763       0    0   63   \n",
       "372  OAS2_0186  OAS2_0186_MR3  Nondemented      3      1608       0    0   65   \n",
       "\n",
       "     EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
       "0      14  2.0  27.0  0.0  1987  0.696  0.883  \n",
       "1      14  2.0  30.0  0.0  2004  0.681  0.876  \n",
       "2      12  2.0  23.0  0.5  1678  0.736  1.046  \n",
       "3      12  2.0  28.0  0.5  1738  0.713  1.010  \n",
       "4      12  2.0  22.0  0.5  1698  0.701  1.034  \n",
       "..    ...  ...   ...  ...   ...    ...    ...  \n",
       "368    16  1.0  28.0  0.5  1693  0.694  1.037  \n",
       "369    16  1.0  26.0  0.5  1688  0.675  1.040  \n",
       "370    13  2.0  30.0  0.0  1319  0.801  1.331  \n",
       "371    13  2.0  30.0  0.0  1327  0.796  1.323  \n",
       "372    13  2.0  30.0  0.0  1333  0.801  1.317  \n",
       "\n",
       "[373 rows x 15 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Group'] = df['Group'].replace([\"Nondemented\" , \"Converted\" , \"Demented\"] , [0,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Subject ID</th>\n",
       "      <th>MRI ID</th>\n",
       "      <th>Group</th>\n",
       "      <th>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hand</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>457</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1895</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
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       "      <td>OAS2_0185</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>842</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
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       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>OAS2_0185_MR3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2297</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>1688</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
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       "    <tr>\n",
       "      <td>370</td>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
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       "    <tr>\n",
       "      <td>371</td>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1327</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>372</td>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1608</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>373 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Subject ID         MRI ID  Group  Visit  MR Delay  Gender Hand  Age  EDUC  \\\n",
       "0    OAS2_0001  OAS2_0001_MR1      0      1         0       1    0   87    14   \n",
       "1    OAS2_0001  OAS2_0001_MR2      0      2       457       1    0   88    14   \n",
       "2    OAS2_0002  OAS2_0002_MR1      1      1         0       1    0   75    12   \n",
       "3    OAS2_0002  OAS2_0002_MR2      1      2       560       1    0   76    12   \n",
       "4    OAS2_0002  OAS2_0002_MR3      1      3      1895       1    0   80    12   \n",
       "..         ...            ...    ...    ...       ...     ...  ...  ...   ...   \n",
       "368  OAS2_0185  OAS2_0185_MR2      1      2       842       1    0   82    16   \n",
       "369  OAS2_0185  OAS2_0185_MR3      1      3      2297       1    0   86    16   \n",
       "370  OAS2_0186  OAS2_0186_MR1      0      1         0       0    0   61    13   \n",
       "371  OAS2_0186  OAS2_0186_MR2      0      2       763       0    0   63    13   \n",
       "372  OAS2_0186  OAS2_0186_MR3      0      3      1608       0    0   65    13   \n",
       "\n",
       "     SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
       "0    2.0  27.0  0.0  1987  0.696  0.883  \n",
       "1    2.0  30.0  0.0  2004  0.681  0.876  \n",
       "2    2.0  23.0  0.5  1678  0.736  1.046  \n",
       "3    2.0  28.0  0.5  1738  0.713  1.010  \n",
       "4    2.0  22.0  0.5  1698  0.701  1.034  \n",
       "..   ...   ...  ...   ...    ...    ...  \n",
       "368  1.0  28.0  0.5  1693  0.694  1.037  \n",
       "369  1.0  26.0  0.5  1688  0.675  1.040  \n",
       "370  2.0  30.0  0.0  1319  0.801  1.331  \n",
       "371  2.0  30.0  0.0  1327  0.796  1.323  \n",
       "372  2.0  30.0  0.0  1333  0.801  1.317  \n",
       "\n",
       "[373 rows x 15 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorical data has been converted into numerical data \n",
    "# 0 -> Non Demented    1 -> Demented   [for prediction] \n",
    "# 0 -> Male    1 -> Female   ,   0 -> Right Hand       1 -> Left Hand\n",
    "# now we remove some irrelevant columns like visit , MRI ID , Subject Id and MR Delay as they arent necessary for the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Subject ID' , 'MRI ID' , 'Visit' , 'MR Delay'], axis = 1  , inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     Group  Gender Hand  Age  EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF\n",
       "0        0       1    0   87    14  2.0  27.0  0.0  1987  0.696  0.883\n",
       "1        0       1    0   88    14  2.0  30.0  0.0  2004  0.681  0.876\n",
       "2        1       1    0   75    12  2.0  23.0  0.5  1678  0.736  1.046\n",
       "3        1       1    0   76    12  2.0  28.0  0.5  1738  0.713  1.010\n",
       "4        1       1    0   80    12  2.0  22.0  0.5  1698  0.701  1.034\n",
       "..     ...     ...  ...  ...   ...  ...   ...  ...   ...    ...    ...\n",
       "368      1       1    0   82    16  1.0  28.0  0.5  1693  0.694  1.037\n",
       "369      1       1    0   86    16  1.0  26.0  0.5  1688  0.675  1.040\n",
       "370      0       0    0   61    13  2.0  30.0  0.0  1319  0.801  1.331\n",
       "371      0       0    0   63    13  2.0  30.0  0.0  1327  0.796  1.323\n",
       "372      0       0    0   65    13  2.0  30.0  0.0  1333  0.801  1.317\n",
       "\n",
       "[373 rows x 11 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Mapping for Feature Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_correlation_map( df ):\n",
    "    plt.figure(figsize=(16, 6))\n",
    "    heatmap = sns.heatmap(df.corr(), vmin=-1, vmax=1, annot=True, cmap='coolwarm')\n",
    "    heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':18}, pad=12);\n",
    "    heatmap.set_ylim(10.0, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_correlation_map(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from the corr. mapping we find the highly correlated features [ threshold here taken as |0.75| ]\n",
    "\n",
    "# Group -> CDR\n",
    "# ASF -> eTIV\n",
    "\n",
    "# so we remove CDR and eTIV from our dataset \n",
    "\n",
    "# CDR is highly correlated with our target variable\n",
    "# However a CDR test is taken generally after a person is confirmed as demneted or has some symptoms\n",
    "# Since its  post prediction data we cannot use it to predict dementia and hence it will be removed\n",
    "\n",
    "# ASF is a way of normalizing eTIV for different subjects\n",
    "# Hence they are highly correlated\n",
    "# Since ASF is standardized medical data we will be retaining it in our dataset \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Group</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hand</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>368</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>372</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>373 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group  Gender Hand  Age  EDUC  SES  MMSE   nWBV    ASF\n",
       "0        0       1    0   87    14  2.0  27.0  0.696  0.883\n",
       "1        0       1    0   88    14  2.0  30.0  0.681  0.876\n",
       "2        1       1    0   75    12  2.0  23.0  0.736  1.046\n",
       "3        1       1    0   76    12  2.0  28.0  0.713  1.010\n",
       "4        1       1    0   80    12  2.0  22.0  0.701  1.034\n",
       "..     ...     ...  ...  ...   ...  ...   ...    ...    ...\n",
       "368      1       1    0   82    16  1.0  28.0  0.694  1.037\n",
       "369      1       1    0   86    16  1.0  26.0  0.675  1.040\n",
       "370      0       0    0   61    13  2.0  30.0  0.801  1.331\n",
       "371      0       0    0   63    13  2.0  30.0  0.796  1.323\n",
       "372      0       0    0   65    13  2.0  30.0  0.801  1.317\n",
       "\n",
       "[373 rows x 9 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['CDR' , 'eTIV'], axis = 1  , inplace = True)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Normalization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we plan to deploy this model , we will be dividing the data with fixed values rather than the max "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = df['Age'] / 100\n",
    "df['EDUC'] = df['EDUC'] / 100\n",
    "df['MMSE'] = df['MMSE'] / 30 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
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       "<p>373 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group  Gender Hand   Age  EDUC  SES      MMSE   nWBV    ASF\n",
       "0        0       1    0  0.87  0.14  2.0  0.900000  0.696  0.883\n",
       "1        0       1    0  0.88  0.14  2.0  1.000000  0.681  0.876\n",
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       "4        1       1    0  0.80  0.12  2.0  0.733333  0.701  1.034\n",
       "..     ...     ...  ...   ...   ...  ...       ...    ...    ...\n",
       "368      1       1    0  0.82  0.16  1.0  0.933333  0.694  1.037\n",
       "369      1       1    0  0.86  0.16  1.0  0.866667  0.675  1.040\n",
       "370      0       0    0  0.61  0.13  2.0  1.000000  0.801  1.331\n",
       "371      0       0    0  0.63  0.13  2.0  1.000000  0.796  1.323\n",
       "372      0       0    0  0.65  0.13  2.0  1.000000  0.801  1.317\n",
       "\n",
       "[373 rows x 9 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
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       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
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       "    <tr>\n",
       "      <td>370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.331</td>\n",
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       "      <td>371</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
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       "    <tr>\n",
       "      <td>372</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>373 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Hand   Age  EDUC  SES      MMSE   nWBV    ASF\n",
       "0         1    0  0.87  0.14  2.0  0.900000  0.696  0.883\n",
       "1         1    0  0.88  0.14  2.0  1.000000  0.681  0.876\n",
       "2         1    0  0.75  0.12  2.0  0.766667  0.736  1.046\n",
       "3         1    0  0.76  0.12  2.0  0.933333  0.713  1.010\n",
       "4         1    0  0.80  0.12  2.0  0.733333  0.701  1.034\n",
       "..      ...  ...   ...   ...  ...       ...    ...    ...\n",
       "368       1    0  0.82  0.16  1.0  0.933333  0.694  1.037\n",
       "369       1    0  0.86  0.16  1.0  0.866667  0.675  1.040\n",
       "370       0    0  0.61  0.13  2.0  1.000000  0.801  1.331\n",
       "371       0    0  0.63  0.13  2.0  1.000000  0.796  1.323\n",
       "372       0    0  0.65  0.13  2.0  1.000000  0.801  1.317\n",
       "\n",
       "[373 rows x 8 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('Group' , axis = 1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "368    1\n",
       "369    1\n",
       "370    0\n",
       "371    0\n",
       "372    0\n",
       "Name: Group, Length: 373, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Group']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Group</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hand</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>368</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>372</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>373 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group  Gender Hand   Age  EDUC  SES      MMSE   nWBV    ASF\n",
       "0        0       1    0  0.87  0.14  2.0  0.900000  0.696  0.883\n",
       "1        0       1    0  0.88  0.14  2.0  1.000000  0.681  0.876\n",
       "2        1       1    0  0.75  0.12  2.0  0.766667  0.736  1.046\n",
       "3        1       1    0  0.76  0.12  2.0  0.933333  0.713  1.010\n",
       "4        1       1    0  0.80  0.12  2.0  0.733333  0.701  1.034\n",
       "..     ...     ...  ...   ...   ...  ...       ...    ...    ...\n",
       "368      1       1    0  0.82  0.16  1.0  0.933333  0.694  1.037\n",
       "369      1       1    0  0.86  0.16  1.0  0.866667  0.675  1.040\n",
       "370      0       0    0  0.61  0.13  2.0  1.000000  0.801  1.331\n",
       "371      0       0    0  0.63  0.13  2.0  1.000000  0.796  1.323\n",
       "372      0       0    0  0.65  0.13  2.0  1.000000  0.801  1.317\n",
       "\n",
       "[373 rows x 9 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    " X_train, X_test, y_train, y_test  =   sklearn.model_selection.train_test_split(X, y ,  test_size=0.3, train_size=0.7, random_state=None, shuffle=True, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(261, 8) (112, 8) (261,) (112,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape , X_test.shape , y_train.shape , y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = DecisionTreeClassifier(random_state = 5 , criterion = \"gini\" , max_depth = 4 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = tree.fit(X_train , y_train )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "def measure_performance(X_test,y_test, clf_mdl, show_accuracy=True , show_confusion_matrix = True , show_precision = True , show_recall = True , show_roc_auc = True):\n",
    "    y_pred=clf_mdl.predict(X_test)   \n",
    "    if show_accuracy:\n",
    "        print (\"Accuracy: {0:.3f}\".format(metrics.accuracy_score(y_test,y_pred)),\"\\n\")\n",
    "    \n",
    "         \n",
    "    if show_precision : \n",
    "        print(\"Precision :  {0:.3f}\".format(metrics.precision_score(y_test,y_pred)),\"\\n\")\n",
    "        \n",
    "        \n",
    "        \n",
    "    if show_recall : \n",
    "        print(\"Recall :  {0:.3f}\".format(metrics.recall_score(y_test,y_pred)),\"\\n\")\n",
    "        \n",
    "    \n",
    "            \n",
    "    if show_roc_auc : \n",
    "        print(\"ROC AUC Score :  {0:.3f}\".format(metrics.roc_auc_score(y_test,y_pred)),\"\\n\")\n",
    "        \n",
    "        \n",
    "    if show_confusion_matrix:\n",
    "        print (\"Confusion matrix\\n\\n\")\n",
    "        metrics.confusion_matrix(y_test,y_pred)\n",
    "        print(pd.crosstab(y_test, y_pred, rownames = ['Actual'], colnames =['Predicted'], margins = True))\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.839 \n",
      "\n",
      "Precision :  0.863 \n",
      "\n",
      "Recall :  0.800 \n",
      "\n",
      "ROC AUC Score :  0.839 \n",
      "\n",
      "Confusion matrix\n",
      "\n",
      "\n",
      "Predicted   0   1  All\n",
      "Actual                \n",
      "0          50   7   57\n",
      "1          11  44   55\n",
      "All        61  51  112\n"
     ]
    }
   ],
   "source": [
    "measure_performance(X_test , y_test , tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = RandomForestClassifier( criterion = \"gini\" , n_estimators = 15 )  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = random_forest.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.875 \n",
      "\n",
      "Precision :  0.956 \n",
      "\n",
      "Recall :  0.782 \n",
      "\n",
      "ROC AUC Score :  0.873 \n",
      "\n",
      "Confusion matrix\n",
      "\n",
      "\n",
      "Predicted   0   1  All\n",
      "Actual                \n",
      "0          55   2   57\n",
      "1          12  43   55\n",
      "All        67  45  112\n"
     ]
    }
   ],
   "source": [
    "measure_performance(X_test , y_test , random_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "guass_model = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guass_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.821 \n",
      "\n",
      "Precision :  0.927 \n",
      "\n",
      "Recall :  0.691 \n",
      "\n",
      "ROC AUC Score :  0.819 \n",
      "\n",
      "Confusion matrix\n",
      "\n",
      "\n",
      "Predicted   0   1  All\n",
      "Actual                \n",
      "0          54   3   57\n",
      "1          17  38   55\n",
      "All        71  41  112\n"
     ]
    }
   ],
   "source": [
    "measure_performance(X_test , y_test , guass_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model = svm.SVC(kernel = 'rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.634 \n",
      "\n",
      "Precision :  0.635 \n",
      "\n",
      "Recall :  0.600 \n",
      "\n",
      "ROC AUC Score :  0.633 \n",
      "\n",
      "Confusion matrix\n",
      "\n",
      "\n",
      "Predicted   0   1  All\n",
      "Actual                \n",
      "0          38  19   57\n",
      "1          22  33   55\n",
      "All        60  52  112\n"
     ]
    }
   ],
   "source": [
    "measure_performance(X_test , y_test , svm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scikit-learn inbuilt Multi Layer Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "inbuilt_neural_net = MLPClassifier(random_state=1, max_iter=100 , activation = 'tanh' , batch_size = 30 , learning_rate = 'adaptive' , solver = 'lbfgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='tanh', alpha=0.0001, batch_size=30, beta_1=0.9,\n",
       "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "              hidden_layer_sizes=(100,), learning_rate='adaptive',\n",
       "              learning_rate_init=0.001, max_iter=100, momentum=0.9,\n",
       "              n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "              random_state=1, shuffle=True, solver='lbfgs', tol=0.0001,\n",
       "              validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inbuilt_neural_net.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.839 \n",
      "\n",
      "Precision :  0.836 \n",
      "\n",
      "Recall :  0.836 \n",
      "\n",
      "ROC AUC Score :  0.839 \n",
      "\n",
      "Confusion matrix\n",
      "\n",
      "\n",
      "Predicted   0   1  All\n",
      "Actual                \n",
      "0          48   9   57\n",
      "1           9  46   55\n",
      "All        57  55  112\n"
     ]
    }
   ],
   "source": [
    "measure_performance(X_test , y_test , inbuilt_neural_net )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras Neural Net "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model=Sequential()\n",
    "\n",
    "model.add(Dense(units=32,kernel_initializer='normal',activation='relu',input_dim=X_train.shape[1]))\n",
    "\n",
    "model.add(Dense(units=64,kernel_initializer='normal',activation='relu'))\n",
    "\n",
    "model.add(Dense(units=128,kernel_initializer='normal',activation='relu'))\n",
    "\n",
    "model.add(Dense(units=1,kernel_initializer='normal',activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_30 (Dense)             (None, 32)                288       \n",
      "_________________________________________________________________\n",
      "dense_31 (Dense)             (None, 64)                2112      \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "dense_33 (Dense)             (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 10,849\n",
      "Trainable params: 10,849\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 261 samples, validate on 112 samples\n",
      "Epoch 1/500\n",
      "261/261 [==============================] - 1s 3ms/step - loss: 0.6932 - accuracy: 0.5517 - val_loss: 0.6924 - val_accuracy: 0.5982\n",
      "Epoch 2/500\n",
      "261/261 [==============================] - 0s 341us/step - loss: 0.6923 - accuracy: 0.5441 - val_loss: 0.6913 - val_accuracy: 0.6250\n",
      "Epoch 3/500\n",
      "261/261 [==============================] - 0s 333us/step - loss: 0.6905 - accuracy: 0.5556 - val_loss: 0.6889 - val_accuracy: 0.6429\n",
      "Epoch 4/500\n",
      "261/261 [==============================] - 0s 345us/step - loss: 0.6857 - accuracy: 0.6245 - val_loss: 0.6843 - val_accuracy: 0.5982\n",
      "Epoch 5/500\n",
      "261/261 [==============================] - 0s 330us/step - loss: 0.6803 - accuracy: 0.6169 - val_loss: 0.6757 - val_accuracy: 0.6339\n",
      "Epoch 6/500\n",
      "261/261 [==============================] - 0s 331us/step - loss: 0.6696 - accuracy: 0.6207 - val_loss: 0.6664 - val_accuracy: 0.6071\n",
      "Epoch 7/500\n",
      "261/261 [==============================] - 0s 333us/step - loss: 0.6614 - accuracy: 0.6284 - val_loss: 0.6659 - val_accuracy: 0.5982\n",
      "Epoch 8/500\n",
      "261/261 [==============================] - 0s 303us/step - loss: 0.6573 - accuracy: 0.6207 - val_loss: 0.6564 - val_accuracy: 0.6071\n",
      "Epoch 9/500\n",
      "261/261 [==============================] - 0s 330us/step - loss: 0.6508 - accuracy: 0.6207 - val_loss: 0.6532 - val_accuracy: 0.6071\n",
      "Epoch 10/500\n",
      "261/261 [==============================] - 0s 303us/step - loss: 0.6559 - accuracy: 0.6169 - val_loss: 0.6539 - val_accuracy: 0.6429\n",
      "Epoch 11/500\n",
      "261/261 [==============================] - 0s 310us/step - loss: 0.6477 - accuracy: 0.6437 - val_loss: 0.6515 - val_accuracy: 0.6071\n",
      "Epoch 12/500\n",
      "261/261 [==============================] - 0s 352us/step - loss: 0.6443 - accuracy: 0.6169 - val_loss: 0.6460 - val_accuracy: 0.6071\n",
      "Epoch 13/500\n",
      "261/261 [==============================] - 0s 318us/step - loss: 0.6448 - accuracy: 0.6360 - val_loss: 0.6451 - val_accuracy: 0.6071\n",
      "Epoch 14/500\n",
      "261/261 [==============================] - 0s 276us/step - loss: 0.6540 - accuracy: 0.6360 - val_loss: 0.6425 - val_accuracy: 0.6607\n",
      "Epoch 15/500\n",
      "261/261 [==============================] - 0s 291us/step - loss: 0.6327 - accuracy: 0.6437 - val_loss: 0.6348 - val_accuracy: 0.6429\n",
      "Epoch 16/500\n",
      "261/261 [==============================] - 0s 303us/step - loss: 0.6266 - accuracy: 0.6437 - val_loss: 0.6294 - val_accuracy: 0.6429\n",
      "Epoch 17/500\n",
      "261/261 [==============================] - 0s 314us/step - loss: 0.6241 - accuracy: 0.6398 - val_loss: 0.6249 - val_accuracy: 0.6518\n",
      "Epoch 18/500\n",
      "261/261 [==============================] - 0s 310us/step - loss: 0.6383 - accuracy: 0.6475 - val_loss: 0.6495 - val_accuracy: 0.5893\n",
      "Epoch 19/500\n",
      "261/261 [==============================] - 0s 314us/step - loss: 0.6236 - accuracy: 0.6398 - val_loss: 0.6219 - val_accuracy: 0.6607\n",
      "Epoch 20/500\n",
      "261/261 [==============================] - 0s 310us/step - loss: 0.6109 - accuracy: 0.6513 - val_loss: 0.6097 - val_accuracy: 0.6339\n",
      "Epoch 21/500\n",
      "261/261 [==============================] - 0s 284us/step - loss: 0.6157 - accuracy: 0.6590 - val_loss: 0.6014 - val_accuracy: 0.6339\n",
      "Epoch 22/500\n",
      "261/261 [==============================] - 0s 287us/step - loss: 0.6004 - accuracy: 0.6590 - val_loss: 0.6041 - val_accuracy: 0.6875\n",
      "Epoch 23/500\n",
      "261/261 [==============================] - 0s 299us/step - loss: 0.6097 - accuracy: 0.6705 - val_loss: 0.5977 - val_accuracy: 0.7054\n",
      "Epoch 24/500\n",
      "261/261 [==============================] - 0s 314us/step - loss: 0.5856 - accuracy: 0.6743 - val_loss: 0.5808 - val_accuracy: 0.6786\n",
      "Epoch 25/500\n",
      "261/261 [==============================] - 0s 318us/step - loss: 0.5816 - accuracy: 0.6705 - val_loss: 0.5681 - val_accuracy: 0.6696\n",
      "Epoch 26/500\n",
      "261/261 [==============================] - 0s 298us/step - loss: 0.5880 - accuracy: 0.7126 - val_loss: 0.5594 - val_accuracy: 0.6964\n",
      "Epoch 27/500\n",
      "261/261 [==============================] - 0s 322us/step - loss: 0.5619 - accuracy: 0.6858 - val_loss: 0.5654 - val_accuracy: 0.7143\n",
      "Epoch 28/500\n",
      "261/261 [==============================] - 0s 291us/step - loss: 0.5601 - accuracy: 0.6935 - val_loss: 0.5342 - val_accuracy: 0.7500\n",
      "Epoch 29/500\n",
      "261/261 [==============================] - 0s 299us/step - loss: 0.5559 - accuracy: 0.7203 - val_loss: 0.5186 - val_accuracy: 0.7500\n",
      "Epoch 30/500\n",
      "261/261 [==============================] - 0s 314us/step - loss: 0.5369 - accuracy: 0.7011 - val_loss: 0.5163 - val_accuracy: 0.7411\n",
      "Epoch 31/500\n",
      "261/261 [==============================] - 0s 322us/step - loss: 0.5485 - accuracy: 0.6820 - val_loss: 0.5267 - val_accuracy: 0.7232\n",
      "Epoch 32/500\n",
      "261/261 [==============================] - 0s 330us/step - loss: 0.5535 - accuracy: 0.7011 - val_loss: 0.5150 - val_accuracy: 0.7321\n",
      "Epoch 33/500\n",
      "261/261 [==============================] - 0s 314us/step - loss: 0.5262 - accuracy: 0.7203 - val_loss: 0.4839 - val_accuracy: 0.7500\n",
      "Epoch 34/500\n",
      "261/261 [==============================] - 0s 318us/step - loss: 0.5608 - accuracy: 0.6705 - val_loss: 0.6429 - val_accuracy: 0.6429\n",
      "Epoch 35/500\n",
      "261/261 [==============================] - 0s 330us/step - loss: 0.5932 - accuracy: 0.6475 - val_loss: 0.5803 - val_accuracy: 0.6696\n",
      "Epoch 36/500\n",
      "261/261 [==============================] - 0s 322us/step - loss: 0.5496 - accuracy: 0.6820 - val_loss: 0.4830 - val_accuracy: 0.7500\n",
      "Epoch 37/500\n",
      "261/261 [==============================] - 0s 314us/step - loss: 0.5701 - accuracy: 0.6973 - val_loss: 0.4824 - val_accuracy: 0.7768\n",
      "Epoch 38/500\n",
      "261/261 [==============================] - 0s 345us/step - loss: 0.5535 - accuracy: 0.7011 - val_loss: 0.5473 - val_accuracy: 0.6964\n",
      "Epoch 39/500\n",
      "261/261 [==============================] - 0s 314us/step - loss: 0.5040 - accuracy: 0.7739 - val_loss: 0.4738 - val_accuracy: 0.7768\n",
      "Epoch 40/500\n",
      "261/261 [==============================] - 0s 310us/step - loss: 0.5077 - accuracy: 0.7471 - val_loss: 0.4765 - val_accuracy: 0.7500\n",
      "Epoch 41/500\n",
      "261/261 [==============================] - 0s 330us/step - loss: 0.4926 - accuracy: 0.7816 - val_loss: 0.4594 - val_accuracy: 0.7946\n",
      "Epoch 42/500\n",
      "261/261 [==============================] - 0s 314us/step - loss: 0.4932 - accuracy: 0.7318 - val_loss: 0.4503 - val_accuracy: 0.7768\n",
      "Epoch 43/500\n",
      "261/261 [==============================] - 0s 337us/step - loss: 0.4974 - accuracy: 0.7510 - val_loss: 0.4510 - val_accuracy: 0.7857\n",
      "Epoch 44/500\n",
      "261/261 [==============================] - 0s 322us/step - loss: 0.4886 - accuracy: 0.7739 - val_loss: 0.4461 - val_accuracy: 0.8036\n",
      "Epoch 45/500\n",
      "261/261 [==============================] - 0s 322us/step - loss: 0.5136 - accuracy: 0.7586 - val_loss: 0.4666 - val_accuracy: 0.7768\n",
      "Epoch 46/500\n",
      "261/261 [==============================] - 0s 337us/step - loss: 0.5228 - accuracy: 0.7241 - val_loss: 0.4890 - val_accuracy: 0.7500\n",
      "Epoch 47/500\n",
      "261/261 [==============================] - 0s 322us/step - loss: 0.4953 - accuracy: 0.7165 - val_loss: 0.4540 - val_accuracy: 0.8125\n",
      "Epoch 48/500\n",
      "261/261 [==============================] - 0s 272us/step - loss: 0.5206 - accuracy: 0.7241 - val_loss: 0.4403 - val_accuracy: 0.7946\n",
      "Epoch 49/500\n",
      "261/261 [==============================] - 0s 307us/step - loss: 0.4755 - accuracy: 0.7778 - val_loss: 0.4447 - val_accuracy: 0.8036\n",
      "Epoch 50/500\n",
      "261/261 [==============================] - 0s 307us/step - loss: 0.4804 - accuracy: 0.7586 - val_loss: 0.4343 - val_accuracy: 0.7946\n",
      "Epoch 51/500\n",
      "261/261 [==============================] - 0s 326us/step - loss: 0.4924 - accuracy: 0.7625 - val_loss: 0.4422 - val_accuracy: 0.7768\n",
      "Epoch 52/500\n",
      "261/261 [==============================] - 0s 287us/step - loss: 0.5249 - accuracy: 0.6973 - val_loss: 0.4554 - val_accuracy: 0.8036\n",
      "Epoch 53/500\n",
      "261/261 [==============================] - 0s 314us/step - loss: 0.4931 - accuracy: 0.7318 - val_loss: 0.4697 - val_accuracy: 0.7679\n",
      "Epoch 54/500\n",
      "261/261 [==============================] - 0s 307us/step - loss: 0.4883 - accuracy: 0.7548 - val_loss: 0.4298 - val_accuracy: 0.8125\n",
      "Epoch 55/500\n",
      "261/261 [==============================] - 0s 322us/step - loss: 0.4808 - accuracy: 0.7663 - val_loss: 0.4304 - val_accuracy: 0.7946\n",
      "Epoch 56/500\n",
      "261/261 [==============================] - 0s 299us/step - loss: 0.4870 - accuracy: 0.7625 - val_loss: 0.4667 - val_accuracy: 0.7768\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/500\n",
      "261/261 [==============================] - 0s 326us/step - loss: 0.5032 - accuracy: 0.7625 - val_loss: 0.5097 - val_accuracy: 0.7143\n",
      "Epoch 58/500\n",
      "261/261 [==============================] - 0s 345us/step - loss: 0.4808 - accuracy: 0.7548 - val_loss: 0.4220 - val_accuracy: 0.7946\n",
      "Epoch 59/500\n",
      "261/261 [==============================] - 0s 318us/step - loss: 0.5050 - accuracy: 0.7318 - val_loss: 0.4451 - val_accuracy: 0.8125\n",
      "Epoch 60/500\n",
      "261/261 [==============================] - 0s 287us/step - loss: 0.4929 - accuracy: 0.7510 - val_loss: 0.4281 - val_accuracy: 0.7946\n",
      "Epoch 61/500\n",
      "261/261 [==============================] - 0s 318us/step - loss: 0.4842 - accuracy: 0.7510 - val_loss: 0.4900 - val_accuracy: 0.7679\n",
      "Epoch 62/500\n",
      "261/261 [==============================] - 0s 318us/step - loss: 0.4793 - accuracy: 0.7625 - val_loss: 0.4229 - val_accuracy: 0.8036\n",
      "Epoch 63/500\n",
      "261/261 [==============================] - 0s 314us/step - loss: 0.4638 - accuracy: 0.7778 - val_loss: 0.4512 - val_accuracy: 0.7946\n",
      "Epoch 64/500\n",
      "261/261 [==============================] - 0s 345us/step - loss: 0.5221 - accuracy: 0.7395 - val_loss: 0.5177 - val_accuracy: 0.6964\n",
      "Epoch 65/500\n",
      "261/261 [==============================] - 0s 287us/step - loss: 0.4977 - accuracy: 0.7433 - val_loss: 0.4501 - val_accuracy: 0.7946\n",
      "Epoch 66/500\n",
      "261/261 [==============================] - 0s 345us/step - loss: 0.5255 - accuracy: 0.7241 - val_loss: 0.4873 - val_accuracy: 0.7500\n",
      "Epoch 67/500\n",
      "261/261 [==============================] - 0s 303us/step - loss: 0.5254 - accuracy: 0.7165 - val_loss: 0.4318 - val_accuracy: 0.8036\n",
      "Epoch 68/500\n",
      "261/261 [==============================] - 0s 264us/step - loss: 0.4727 - accuracy: 0.7471 - val_loss: 0.4258 - val_accuracy: 0.8036\n",
      "Epoch 00068: early stopping\n"
     ]
    }
   ],
   "source": [
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1 , patience = 10)\n",
    "history=model.fit(X_train,y_train,batch_size=16,epochs=500 , callbacks = [es] , validation_data = (X_test , y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)   \n",
    "n= 0 \n",
    "for i in y_pred :\n",
    "    if i < 0.5 : \n",
    "        y_pred[n] = 0\n",
    "        n = n + 1\n",
    "    else :\n",
    "        y_pred[n] = 1\n",
    "        n = n + 1\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = array(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.804 \n",
      "\n",
      "Precision :  0.851 \n",
      "\n",
      "Recall :  0.727 \n",
      "\n",
      "ROC AUC Score :  0.802 \n",
      "\n",
      "Confusion matrix\n",
      "\n",
      "\n",
      "[[50  7]\n",
      " [15 40]]\n"
     ]
    }
   ],
   "source": [
    "print (\"Accuracy: {0:.3f}\".format(metrics.accuracy_score(y_test,y_pred)),\"\\n\")\n",
    "print(\"Precision :  {0:.3f}\".format(metrics.precision_score(y_test,y_pred)),\"\\n\")\n",
    "print(\"Recall :  {0:.3f}\".format(metrics.recall_score(y_test,y_pred)),\"\\n\")\n",
    "print(\"ROC AUC Score :  {0:.3f}\".format(metrics.roc_auc_score(y_test,y_pred)),\"\\n\")\n",
    "print (\"Confusion matrix\\n\\n\")\n",
    "print(metrics.confusion_matrix(y_test,y_pred))\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparision of Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\"Decision Tree\" , \"Random Forest\" , \"Naive Bayes\" , \"Support Vector Machine\" , \"Inbuilt MLP\" , \"Neural Net\"]\n",
    "Accuracy = [0.839 , 0.875 , 0.821 , 0.634 , 0.839 , 0.804]\n",
    "Precision = [0.863 , 0.956 , 0.927 , 0.635 , 0.836 , 0.851]\n",
    "Recall = [0.800 , 0.782 , 0.691 , 0.600 , 0.836 , 0.727]\n",
    "ROC_Score = [0.839 , 0.873 , 0.819 , 0.633 , 0.839 , 0.802]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>ROC Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Inbuilt MLP</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Neural Net</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy  Precision  Recall  ROC Score\n",
       "0           Decision Tree     0.839      0.863   0.800      0.839\n",
       "1           Random Forest     0.875      0.956   0.782      0.873\n",
       "2             Naive Bayes     0.821      0.927   0.691      0.819\n",
       "3  Support Vector Machine     0.634      0.635   0.600      0.633\n",
       "4             Inbuilt MLP     0.839      0.836   0.836      0.839\n",
       "5              Neural Net     0.804      0.851   0.727      0.802"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'Model': labels, 'Accuracy': Accuracy , 'Precision' : Precision , 'Recall' : Recall , 'ROC Score' : ROC_Score}\n",
    "model_rep = pd.DataFrame(data=d)\n",
    "model_rep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_chart(y , para):\n",
    "    fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "    ax = fig.add_axes([0,0,1,1])\n",
    "    ax.bar(labels , y , color = ['deepskyblue' , 'mediumspringgreen' , 'hotpink' , 'yellow' , 'salmon' , 'mediumorchid'])\n",
    "    ax.set_xlabel('Models' , color = 'blue' , fontweight = 'bold' , fontsize = 14 , labelpad = 15)\n",
    "    ax.set_ylabel(para , color = 'blue' , fontweight = 'bold' , fontsize = 14 , labelpad = 15)\n",
    "    plt.title(\"Model \" + para , fontweight = \"bold\" , fontsize = 18 , pad = 18)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_chart(Accuracy , \"Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_chart(Precision , \"Precision\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_chart(Recall , \"Recall\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_chart(ROC_Score , \"ROC Score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}