alzheimer's disease classification algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy.io import arff\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, roc_curve, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier, \n",
    "                              GradientBoostingClassifier, ExtraTreesClassifier)\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "# import xgboost as xgb\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "FOLDS =10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\balu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (1.1.0)\n",
      "Requirement already satisfied: scipy in c:\\users\\balu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from xgboost) (1.4.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\balu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from xgboost) (1.18.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def age(field):\n",
    "    plt.figure()\n",
    "    g = None\n",
    "    if field == \"Age\":\n",
    "        df_query_mri = df[df[\"Age\"] > 0]\n",
    "        g = sns.countplot(df_query_mri[\"Age\"])\n",
    "        g.figure.set_size_inches(18.5, 10.5)\n",
    "    else:\n",
    "        g = sns.countplot(df[field])\n",
    "        g.figure.set_size_inches(18.5, 10.5)\n",
    "    \n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to graph number of people per state [Demented, Nondemented]\n",
    "def cont_Dementes(field):\n",
    "    plt.figure()\n",
    "    g = None\n",
    "    if field == \"Group\":\n",
    "        df_query_mri = df[df[\"Group\"] >= 0]\n",
    "        g = sns.countplot(df_query_mri[\"Group\"])\n",
    "        g.figure.set_size_inches(2, 7)\n",
    "    else:\n",
    "        g = sns.countplot(df[field])\n",
    "        g.figure.set_size_inches(2, 7)\n",
    "    \n",
    "sns.despine()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def report_performance(model):\n",
    "\n",
    "    model_test = model.predict(X_test)\n",
    "\n",
    "    print(\"Confusion Matrix\")\n",
    "    print(\"{0}\".format(metrics.confusion_matrix(y_test, model_test)))\n",
    "    print(\"\")\n",
    "    print(\"Classification Report\")\n",
    "    print(metrics.classification_report(y_test, model_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv (r'https://raw.githubusercontent.com/balupeddireddy08/Alzheimer-s_Detection_Using_Machine_Learning/master/oasis_longitudinal.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Visit</th>\n",
       "      <th>MR Delay</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",
       "      <th>count</th>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.882038</td>\n",
       "      <td>595.104558</td>\n",
       "      <td>77.013405</td>\n",
       "      <td>14.597855</td>\n",
       "      <td>2.460452</td>\n",
       "      <td>27.342318</td>\n",
       "      <td>0.290885</td>\n",
       "      <td>1488.128686</td>\n",
       "      <td>0.729568</td>\n",
       "      <td>1.195461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.922843</td>\n",
       "      <td>635.485118</td>\n",
       "      <td>7.640957</td>\n",
       "      <td>2.876339</td>\n",
       "      <td>1.134005</td>\n",
       "      <td>3.683244</td>\n",
       "      <td>0.374557</td>\n",
       "      <td>176.139286</td>\n",
       "      <td>0.037135</td>\n",
       "      <td>0.138092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1106.000000</td>\n",
       "      <td>0.644000</td>\n",
       "      <td>0.876000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1357.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.099000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>0.729000</td>\n",
       "      <td>1.194000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>873.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>0.756000</td>\n",
       "      <td>1.293000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2639.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>0.837000</td>\n",
       "      <td>1.587000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Visit     MR Delay         Age        EDUC         SES  \\\n",
       "count  373.000000   373.000000  373.000000  373.000000  354.000000   \n",
       "mean     1.882038   595.104558   77.013405   14.597855    2.460452   \n",
       "std      0.922843   635.485118    7.640957    2.876339    1.134005   \n",
       "min      1.000000     0.000000   60.000000    6.000000    1.000000   \n",
       "25%      1.000000     0.000000   71.000000   12.000000    2.000000   \n",
       "50%      2.000000   552.000000   77.000000   15.000000    2.000000   \n",
       "75%      2.000000   873.000000   82.000000   16.000000    3.000000   \n",
       "max      5.000000  2639.000000   98.000000   23.000000    5.000000   \n",
       "\n",
       "             MMSE         CDR         eTIV        nWBV         ASF  \n",
       "count  371.000000  373.000000   373.000000  373.000000  373.000000  \n",
       "mean    27.342318    0.290885  1488.128686    0.729568    1.195461  \n",
       "std      3.683244    0.374557   176.139286    0.037135    0.138092  \n",
       "min      4.000000    0.000000  1106.000000    0.644000    0.876000  \n",
       "25%     27.000000    0.000000  1357.000000    0.700000    1.099000  \n",
       "50%     29.000000    0.000000  1470.000000    0.729000    1.194000  \n",
       "75%     30.000000    0.500000  1597.000000    0.756000    1.293000  \n",
       "max     30.000000    2.000000  2004.000000    0.837000    1.587000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e49e7e0588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 8)) \n",
    "corr = df.corr(method = 'pearson') \n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True), \n",
    "            square=True, ax=ax) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Visit</th>\n",
       "      <th>MR Delay</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",
       "      <th>Visit</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920009</td>\n",
       "      <td>0.183213</td>\n",
       "      <td>0.024615</td>\n",
       "      <td>-0.051622</td>\n",
       "      <td>-0.029078</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>0.117428</td>\n",
       "      <td>-0.126682</td>\n",
       "      <td>-0.120399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MR Delay</th>\n",
       "      <td>0.920009</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.205357</td>\n",
       "      <td>0.051630</td>\n",
       "      <td>-0.030813</td>\n",
       "      <td>0.065844</td>\n",
       "      <td>-0.062915</td>\n",
       "      <td>0.119624</td>\n",
       "      <td>-0.105586</td>\n",
       "      <td>-0.123545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.183213</td>\n",
       "      <td>0.205357</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027886</td>\n",
       "      <td>-0.046857</td>\n",
       "      <td>0.055612</td>\n",
       "      <td>-0.026257</td>\n",
       "      <td>0.042348</td>\n",
       "      <td>-0.518359</td>\n",
       "      <td>-0.035067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUC</th>\n",
       "      <td>0.024615</td>\n",
       "      <td>0.051630</td>\n",
       "      <td>-0.027886</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.722647</td>\n",
       "      <td>0.194884</td>\n",
       "      <td>-0.153121</td>\n",
       "      <td>0.257015</td>\n",
       "      <td>-0.012200</td>\n",
       "      <td>-0.241752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SES</th>\n",
       "      <td>-0.051622</td>\n",
       "      <td>-0.030813</td>\n",
       "      <td>-0.046857</td>\n",
       "      <td>-0.722647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.149219</td>\n",
       "      <td>0.076160</td>\n",
       "      <td>-0.261575</td>\n",
       "      <td>0.090095</td>\n",
       "      <td>0.255576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMSE</th>\n",
       "      <td>-0.029078</td>\n",
       "      <td>0.065844</td>\n",
       "      <td>0.055612</td>\n",
       "      <td>0.194884</td>\n",
       "      <td>-0.149219</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.686519</td>\n",
       "      <td>-0.032084</td>\n",
       "      <td>0.341912</td>\n",
       "      <td>0.040052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDR</th>\n",
       "      <td>0.002325</td>\n",
       "      <td>-0.062915</td>\n",
       "      <td>-0.026257</td>\n",
       "      <td>-0.153121</td>\n",
       "      <td>0.076160</td>\n",
       "      <td>-0.686519</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022819</td>\n",
       "      <td>-0.344819</td>\n",
       "      <td>-0.029340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eTIV</th>\n",
       "      <td>0.117428</td>\n",
       "      <td>0.119624</td>\n",
       "      <td>0.042348</td>\n",
       "      <td>0.257015</td>\n",
       "      <td>-0.261575</td>\n",
       "      <td>-0.032084</td>\n",
       "      <td>0.022819</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.210122</td>\n",
       "      <td>-0.988877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nWBV</th>\n",
       "      <td>-0.126682</td>\n",
       "      <td>-0.105586</td>\n",
       "      <td>-0.518359</td>\n",
       "      <td>-0.012200</td>\n",
       "      <td>0.090095</td>\n",
       "      <td>0.341912</td>\n",
       "      <td>-0.344819</td>\n",
       "      <td>-0.210122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASF</th>\n",
       "      <td>-0.120399</td>\n",
       "      <td>-0.123545</td>\n",
       "      <td>-0.035067</td>\n",
       "      <td>-0.241752</td>\n",
       "      <td>0.255576</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>-0.029340</td>\n",
       "      <td>-0.988877</td>\n",
       "      <td>0.213476</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Visit  MR Delay       Age      EDUC       SES      MMSE  \\\n",
       "Visit     1.000000  0.920009  0.183213  0.024615 -0.051622 -0.029078   \n",
       "MR Delay  0.920009  1.000000  0.205357  0.051630 -0.030813  0.065844   \n",
       "Age       0.183213  0.205357  1.000000 -0.027886 -0.046857  0.055612   \n",
       "EDUC      0.024615  0.051630 -0.027886  1.000000 -0.722647  0.194884   \n",
       "SES      -0.051622 -0.030813 -0.046857 -0.722647  1.000000 -0.149219   \n",
       "MMSE     -0.029078  0.065844  0.055612  0.194884 -0.149219  1.000000   \n",
       "CDR       0.002325 -0.062915 -0.026257 -0.153121  0.076160 -0.686519   \n",
       "eTIV      0.117428  0.119624  0.042348  0.257015 -0.261575 -0.032084   \n",
       "nWBV     -0.126682 -0.105586 -0.518359 -0.012200  0.090095  0.341912   \n",
       "ASF      -0.120399 -0.123545 -0.035067 -0.241752  0.255576  0.040052   \n",
       "\n",
       "               CDR      eTIV      nWBV       ASF  \n",
       "Visit     0.002325  0.117428 -0.126682 -0.120399  \n",
       "MR Delay -0.062915  0.119624 -0.105586 -0.123545  \n",
       "Age      -0.026257  0.042348 -0.518359 -0.035067  \n",
       "EDUC     -0.153121  0.257015 -0.012200 -0.241752  \n",
       "SES       0.076160 -0.261575  0.090095  0.255576  \n",
       "MMSE     -0.686519 -0.032084  0.341912  0.040052  \n",
       "CDR       1.000000  0.022819 -0.344819 -0.029340  \n",
       "eTIV      0.022819  1.000000 -0.210122 -0.988877  \n",
       "nWBV     -0.344819 -0.210122  1.000000  0.213476  \n",
       "ASF      -0.029340 -0.988877  0.213476  1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method = 'pearson') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e49e927748>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K0/+f2+U91w3Tvu0+uudGN6ObKH8J8KF+WsEZ1d/E+pqq+ti0Kz9wu+OBn1fVB0ZS2BRm/JRvVf0I+EGSJ/RD+9Ndmz0PWNyPLaabDFmSNPPu6ieQfzLdPLh/ALx3TLU8CvjjMX33OhnHNVSAo4CzklwLPB34S7rOBC9IcjPd/4AnrmV7SdIMqKpVdJfZ3tJPzzcvyUl9a7Nrk/wRQJL9+lZs5yT5bpITkxzWt1e7Lsnj+/UWpGsB+q3+Z1E/fnyS0/rWbbf2sxlBlwWP71uundSve+zA9//5RK1J/izJTUn+L93k+zNqLJMzVNU1wMIpFu0/07VIktauqm7tp/fbHjgQ+ElVPSvJ5sClSb7ar/o04EnAncCtwCerau9+Cr+jgGPoHpv8m6r6erpeqhf02wA8kW6u34cDNyX5ON0lwT0nJrFP8kJgd2Bvusc4z0vyXOAXdPPvPoMu266i6zQzY5ztSJI0jIlrry8EntrPwwvwSLqAuwf41kCbtX8BJoL2Ou6fFP/5wB4DLdMekWTiift/qqq7gbuTrAImWsANemH/c3X/eev++x8OnFtVv+y//7wpth0pA1WStFZJHkfXBm0VXbAeVVUXTFpnPx7cZm2wBdtE3jwE2KeqBnus0gfs4PZrarsW4K+q6m8nbX8M69FyraVxXUOVJM0BSRYAnwA+0vcYvQB4c5LN+uW/088lMKyvMtC9Jsma+pFO+E3Ltd4FwOv7BuEk2alv4XYJ8PIkW/ZHvC9dh5qa8AhVkmaxYR5zGYEtk1zD/Y/NnAl8sF/2Sbo5169Kd1i5GjhoHfZ9NPDR/qbU+XRB+KY1rVxVP05yaZLrga9U1bFJngRc1h/V/hw4vKquSvJZ4Bq6Fm7/vA41NWGgSpIeoKrmrWXZr+nmXn/XpEUX9z8T6+038P43y6rqDuDVU+z3+Emf9xx4/5pJy06mu7lp8j5OYAMat2woT/lKktSAgSpJUgMGqiRJDRiokiQ1YKBKktSAgSpJUgM+NiNJs9iiUxY1nf3n0qMuHaZ9228DHwKeRTd70feBY6rquy1rGaKO1wFfraofrsM2uwJfHnzsZqZ4hCpJ+o1+soZzgYur6vFVtQfdM6dTzas7yjrmAa8DHj2T37shDFRJ0qDnAb+qqk9MDPQdwr7et227vm/H9mr4Tdu2i5N8Lsl3kpzVt3l7UZJzJvbRr/eP/fsXJrksyVVJ/mFgGsHvJ3lPkq8Dh9J1JTurb922ZZK9+hZxVya5IMmO/XZ7Jfl2ksuAI2fqH2oyA1WSNGhPpm579gq6/tVPo+sYc9JEoNG1TDsG2AN4HLAIuBB49sA8v68GPptkO+DdwPOr6pnAMuB/DHzPf1bVvlX16X7ZYX3rtnuBU4CDq2ov4DTunxXpU8DRVbXPBv/2G8BrqJKkYewLnF1V9wG3J/ka3TXWnwJXVNUKgH4O4F37fqfnAy9N8jngxcDbgd+lC95L+7l4HwpcNvA9n13D9z+BLuwv7LebB6xM8kjgUVX1tX69M4EXNfqd14mBKkkatBw4eIrxtd3MtKa2a5+lOwV7J12v1J/112gvrKpD17CvX6xhPMDyyUehSR7FmNu2TfCUryRp0EXA5kneODGQ5FnAvwOvTjKvb+n2XOCKafZ1MfBM4I3cf+T5TWBRkv/S73urJL+zhu0HW7fdBCxIsk+/3WZJnlxV/wH8JMm+/XqHDf+rtuURqiTNYsM85tJSVVWSlwMfSnIc8J/0j80AWwPfpjsifHtV/SjJE9eyr/uSfJnubt3F/djq/nGYs5Ns3q/6bmCqR3JOBz6R5C5gH7oj5w/3p3nn0z3asxz4Q+C0JL+k65c6FgaqJOkB+uc+XzXFomP7n8F1L+aBbdveMmn5WxhoKN6PXUR3/XXy9+466fPngc8PDF1Dd2Q8ebsr6W6WmnD8FLWPnKd8JUlqwECVJKkBA1WSpAYMVEmSGjBQJUlqwECVJKkBA1WSpAYMVEmSGjBQJUlqwECVJKkBA1WSpAYMVEmSGjBQJUlqwECVJKkBA1WSpAYMVEmSGjBQJUlqYGyBmmRekquTfLn/vG2SC5Pc3L9uM67aJElaV+M8Qn0rcOPA5+OApVW1O7C0/yxJ0pwwlkBNsjPwYuCTA8MHAkv690uAg2a6LkmS1te4jlA/BLwd+PXA2A5VtRKgf91+qg2THJFkWZJlq1evHn2lkiQNYcYDNclLgFVVdeX6bF9Vp1bVwqpauGDBgsbVSZK0fuaP4TsXAS9L8gfAFsAjknwauD3JjlW1MsmOwKox1CZJ0nqZ8SPUqnpnVe1cVbsChwAXVdXhwHnA4n61xcCXZro2SZLW12x6DvVE4AVJbgZe0H+WJGlOGMcp39+oqouBi/v3Pwb2H2c9kiStr9l0hCpJ0pxloEqS1ICBKklSAwaqJEkNGKiSJDVgoEqS1ICBKklSAwaqJEkNGKiSJDVgoEqS1ICBKklSAwaqJEkNGKiSJDVgoEqS1ICBKklSAwaqJEkNGKiSJDVgoEqS1ICBKklSAwaqJEkNGKiSJDVgoEqS1ICBKklSAwaqJEkNGKiSJDVgoEqS1ICBKklSAwaqJEkNGKiSJDVgoEqS1MD8cRcgSdNZdMqicZcwrUuPunTcJWjMPEKVJKkBA1WSpAYMVEmSGjBQJUlqwECVJKmBGQ/UJI9J8v+S3JhkeZK39uPbJrkwyc396zYzXZskSetrHEeo9wJ/WlVPAp4NHJlkD+A4YGlV7Q4s7T9LkjQnzHigVtXKqrqqf/8z4EZgJ+BAYEm/2hLgoJmuTZKk9TXWa6hJdgWeAVwO7FBVK6ELXWD7NWxzRJJlSZatXr16pkqVJGmtxhaoSbYGPg8cU1U/HXa7qjq1qhZW1cIFCxaMrkBJktbBWAI1yWZ0YXpWVX2hH749yY798h2BVeOoTZKk9TGOu3wD/B1wY1V9cGDRecDi/v1i4EszXZskSetrHJPjLwL+O3Bdkmv6sXcBJwLnJHkDcBvwyjHUJknSepnxQK2qrwNZw+L9Z7IWSZJacaYkSZIaMFAlSWrAQJUkqQEDVZKkBgxUSZIaMFAlSWrAQJUkqQEDVZKkBgxUSZIaMFAlSWpgHHP5aiOz6JRF4y5hWpcedem4S5C0kfMIVZKkBgxUSZIaMFAlSWrAQJUkqQEDVZKkBgxUSZIaMFAlSWrAQJUkqQEDVZKkBgxUSZIaMFAlSWrAQJUkqQEDVZKkBgxUSZIaMFAlSWrAQJUkqQEDVZKkBgxUSZIaMFAlSWrAQJUkqQEDVZKkBgxUSZIaMFAlSWrAQJUkqQEDVZKkBgxUSZIamHWBmuSAJDcluSXJceOuR5KkYcyqQE0yD/go8CJgD+DQJHuMtypJkqY3qwIV2Bu4papurap7gM8AB465JkmSpjV/3AVMshPwg4HPK4D/OrhCkiOAI/qPP09y0wzVNhaPbb/L7YA72u92dsvRGXcJs9YI/sZgE/w7a/A3dn5VHdCiFo3HbAvUqf4i6wEfqk4FTp2ZcjY+SZZV1cJx16GNm39n2hTNtlO+K4DHDHzeGfjhmGqRJGlosy1QvwXsnmS3JA8FDgHOG3NNkiRNa1ad8q2qe5O8BbgAmAecVlXLx1zWxsbT5ZoJ/p1pk5Oqmn4tSZK0VrPtlK8kSXOSgSpJUgMG6iYgSSU5c+Dz/CSrk3x5nHVp45PkviTXDPzsOu6apJkyq25K0sj8AtgzyZZVdRfwAuDfxlyTNk53VdXTx12ENA4eoW46vgK8uH9/KHD2GGuRpI2Ogbrp+AxwSJItgKcCl4+5Hm2cthw43XvuuIuRZpKnfDcRVXVtfz3rUOD/jLcabcQ85atNloG6aTkP+ACwH/Bb4y1FkjYuBuqm5TTgJ1V1XZL9xl2MJG1MDNRNSFWtAE4edx2StDFy6kFJkhrwLl9JkhowUCVJasBAlSSpAQNVkqQGDFRJkhowULXJG6YbT5LNklzZv7ejiqQH8TlUabhuPPsC3+jfO72epAfxCFXqTNeN54B+HUmakoEqdabrxvM84OL+vR1VJD2Ip3wl1t6NJ8mjgTur6pf9kKd8JT2IgSrdb03deF4EXDCOgiTNHZ7yle53GvC+qrpu0rjXTyVNyyNUqTdVN54k84Ddq+o746lK0lxhtxlpLZLsCxxeVW8ady2SZjcDVZKkBryGKklSAwaqJEkNGKiSJDVgoEqS1ICBKklSAwaqJEkN/H/dN4q/j8d+MAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 466.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "sb.catplot('M/F',data=df,hue='Group',kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age(\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "  </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",
       "\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  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Group'] = df['Group'].replace(['Converted'], ['Demented'])\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nondemented</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>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nondemented</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>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Demented</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>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Group  MR Delay M/F Hand  Age  EDUC  SES  MMSE  eTIV   nWBV    ASF\n",
       "0  Nondemented         0   M    R   87    14  2.0  27.0  1987  0.696  0.883\n",
       "1  Nondemented       457   M    R   88    14  2.0  30.0  2004  0.681  0.876\n",
       "2     Demented         0   M    R   75    12  NaN  23.0  1678  0.736  1.046"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Subject ID'], axis = 1, inplace = True, errors = 'ignore')\n",
    "df.drop(['MRI ID'], axis = 1, inplace = True, errors = 'ignore')\n",
    "df.drop(['Visit'], axis = 1, inplace = True, errors = 'ignore')\n",
    "#for this study the CDR we eliminated it\n",
    "df.drop(['CDR'], axis = 1, inplace = True, errors = 'ignore')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>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>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</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>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</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>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group  MR Delay M/F Hand  Age  EDUC  SES  MMSE  eTIV   nWBV    ASF\n",
       "0      0         0   M    R   87    14  2.0  27.0  1987  0.696  0.883\n",
       "1      0       457   M    R   88    14  2.0  30.0  2004  0.681  0.876\n",
       "2      1         0   M    R   75    12  NaN  23.0  1678  0.736  1.046"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 = Demented, 0 = Nondemented\n",
    "df['Group'] = df['Group'].replace(['Converted'], ['Demented'])\n",
    "\n",
    "df['Group'] = df['Group'].replace(['Demented', 'Nondemented'], [1,0])    \n",
    "df.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cont_Dementes(\"Group\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>457</td>\n",
       "      <td>1</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>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>R</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group  MR Delay  M/F Hand  Age  EDUC  SES  MMSE  eTIV   nWBV    ASF\n",
       "0      0         0    1    R   87    14  2.0  27.0  1987  0.696  0.883\n",
       "1      0       457    1    R   88    14  2.0  30.0  2004  0.681  0.876\n",
       "2      1         0    1    R   75    12  NaN  23.0  1678  0.736  1.046"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['M/F'] = df['M/F'].replace(['M', 'F'], [1,0])  \n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()\n",
    "encoder.fit(df.Hand.values)\n",
    "list(encoder.classes_)\n",
    "#Transoformamos\n",
    "encoder.transform(df.Hand.values)\n",
    "df[['Hand']]=encoder.transform(df.Hand.values)\n",
    "encoder2=LabelEncoder()\n",
    "encoder2.fit(df.Hand.values)\n",
    "list(encoder2.classes_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Lost proportion (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SES</th>\n",
       "      <td>5.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMSE</th>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Lost proportion (%)\n",
       "SES                  5.09\n",
       "MMSE                 0.54"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_na = (df.isnull().sum() / len(df)) * 100\n",
    "data_na = data_na.drop(data_na[data_na == 0].index).sort_values(ascending=False)[:30]\n",
    "missing_data = pd.DataFrame({'Lost proportion (%)' :round(data_na,2)})\n",
    "missing_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</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>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</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>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</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>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</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>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group  MR Delay  M/F  Hand  Age  EDUC  SES  MMSE  eTIV   nWBV    ASF\n",
       "0      0         0    1     0   87    14  2.0  27.0  1987  0.696  0.883\n",
       "1      0       457    1     0   88    14  2.0  30.0  2004  0.681  0.876\n",
       "2      1         0    1     0   75    12  2.0  23.0  1678  0.736  1.046\n",
       "3      1       560    1     0   76    12  2.0  28.0  1738  0.713  1.010\n",
       "4      1      1895    1     0   80    12  2.0  22.0  1698  0.701  1.034"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute  import SimpleImputer\n",
    "# We perform it with the most frequent value \n",
    "imputer = SimpleImputer ( missing_values = np.nan,strategy='most_frequent')\n",
    "\n",
    "imputer.fit(df[['SES']])\n",
    "df[['SES']] = imputer.fit_transform(df[['SES']])\n",
    "\n",
    "# We perform it with the median\n",
    "imputer = SimpleImputer ( missing_values = np.nan,strategy='median')\n",
    "\n",
    "imputer.fit(df[['MMSE']])\n",
    "df[['MMSE']] = imputer.fit_transform(df[['MMSE']])\n",
    "from sklearn.impute  import SimpleImputer\n",
    "# We perform it with the median\n",
    "imputer = SimpleImputer ( missing_values = np.nan,strategy='median')\n",
    "\n",
    "imputer.fit(df[['MMSE']])\n",
    "df[['MMSE']] = imputer.fit_transform(df[['MMSE']])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdata=df[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\balu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "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>M/F</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</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": [
       "   M/F  Age  EDUC  SES  MMSE  eTIV   nWBV    ASF\n",
       "0    1   87    14  2.0  27.0  1987  0.696  0.883\n",
       "1    1   88    14  2.0  30.0  2004  0.681  0.876\n",
       "2    1   75    12  2.0  23.0  1678  0.736  1.046\n",
       "3    1   76    12  2.0  28.0  1738  0.713  1.010\n",
       "4    1   80    12  2.0  22.0  1698  0.701  1.034"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdata.drop(['Group'], axis = 1, inplace = True, errors = 'ignore')\n",
    "rdata.drop(['MR Delay'], axis = 1, inplace = True, errors = 'ignore')\n",
    "rdata.drop(['Hand'], axis = 1, inplace = True, errors = 'ignore')\n",
    "rdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "df_norm = df\n",
    "scaler = StandardScaler()\n",
    "df_norm[['Age','MR Delay','M/F','Hand','EDUC','SES','MMSE','eTIV','nWBV','ASF']]=scaler.fit_transform(df[['Age','MR Delay','M/F','Hand','EDUC','SES','MMSE','eTIV','nWBV','ASF']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2.932703</td>\n",
       "      <td>-1.309643</td>\n",
       "      <td>-2.316501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.937715</td>\n",
       "      <td>1.153798</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.079409</td>\n",
       "      <td>0.173429</td>\n",
       "      <td>-1.083784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group  MR Delay       M/F  Hand       Age      EDUC       SES      MMSE  \\\n",
       "0      0 -0.937715  1.153798   0.0  1.308738 -0.208132 -0.394466 -0.095686   \n",
       "1      0 -0.217613  1.153798   0.0  1.439787 -0.208132 -0.394466  0.721664   \n",
       "2      1 -0.937715  1.153798   0.0 -0.263856 -0.904394 -0.394466 -1.185486   \n",
       "\n",
       "       eTIV      nWBV       ASF  \n",
       "0  2.836059 -0.905169 -2.265742  \n",
       "1  2.932703 -1.309643 -2.316501  \n",
       "2  1.079409  0.173429 -1.083784  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.446765</td>\n",
       "      <td>-1.344830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2.048264</td>\n",
       "      <td>1.153798</td>\n",
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       "      <td>1.193108</td>\n",
       "      <td>-0.770344</td>\n",
       "      <td>-1.170800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group  MR Delay       M/F  Hand       Age      EDUC       SES      MMSE  \\\n",
       "0      0 -0.937715  1.153798   0.0  1.308738 -0.208132 -0.394466 -0.095686   \n",
       "1      0 -0.217613  1.153798   0.0  1.439787 -0.208132 -0.394466  0.721664   \n",
       "2      1 -0.937715  1.153798   0.0 -0.263856 -0.904394 -0.394466 -1.185486   \n",
       "3      1 -0.055315  1.153798   0.0 -0.132806 -0.904394 -0.394466  0.176764   \n",
       "4      1  2.048264  1.153798   0.0  0.391392 -0.904394 -0.394466 -1.457936   \n",
       "\n",
       "       eTIV      nWBV       ASF  \n",
       "0  2.836059 -0.905169 -2.265742  \n",
       "1  2.932703 -1.309643 -2.316501  \n",
       "2  1.079409  0.173429 -1.083784  \n",
       "3  1.420506 -0.446765 -1.344830  \n",
       "4  1.193108 -0.770344 -1.170800  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_norm.to_csv('DatasetSelectionAttributes.csv', sep=',',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Hand'], axis = 1, inplace = True, errors = 'ignore')\n",
    "df.drop(['MR Delay'], axis = 1, inplace = True, errors = 'ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Group</th>\n",
       "      <th>M/F</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.153798</td>\n",
       "      <td>1.308738</td>\n",
       "      <td>-0.208132</td>\n",
       "      <td>-0.394466</td>\n",
       "      <td>-0.095686</td>\n",
       "      <td>2.836059</td>\n",
       "      <td>-0.905169</td>\n",
       "      <td>-2.265742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.153798</td>\n",
       "      <td>1.439787</td>\n",
       "      <td>-0.208132</td>\n",
       "      <td>-0.394466</td>\n",
       "      <td>0.721664</td>\n",
       "      <td>2.932703</td>\n",
       "      <td>-1.309643</td>\n",
       "      <td>-2.316501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.153798</td>\n",
       "      <td>-0.263856</td>\n",
       "      <td>-0.904394</td>\n",
       "      <td>-0.394466</td>\n",
       "      <td>-1.185486</td>\n",
       "      <td>1.079409</td>\n",
       "      <td>0.173429</td>\n",
       "      <td>-1.083784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.153798</td>\n",
       "      <td>-0.132806</td>\n",
       "      <td>-0.904394</td>\n",
       "      <td>-0.394466</td>\n",
       "      <td>0.176764</td>\n",
       "      <td>1.420506</td>\n",
       "      <td>-0.446765</td>\n",
       "      <td>-1.344830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.153798</td>\n",
       "      <td>0.391392</td>\n",
       "      <td>-0.904394</td>\n",
       "      <td>-0.394466</td>\n",
       "      <td>-1.457936</td>\n",
       "      <td>1.193108</td>\n",
       "      <td>-0.770344</td>\n",
       "      <td>-1.170800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group       M/F       Age      EDUC       SES      MMSE      eTIV  \\\n",
       "0      0  1.153798  1.308738 -0.208132 -0.394466 -0.095686  2.836059   \n",
       "1      0  1.153798  1.439787 -0.208132 -0.394466  0.721664  2.932703   \n",
       "2      1  1.153798 -0.263856 -0.904394 -0.394466 -1.185486  1.079409   \n",
       "3      1  1.153798 -0.132806 -0.904394 -0.394466  0.176764  1.420506   \n",
       "4      1  1.153798  0.391392 -0.904394 -0.394466 -1.457936  1.193108   \n",
       "\n",
       "       nWBV       ASF  \n",
       "0 -0.905169 -2.265742  \n",
       "1 -1.309643 -2.316501  \n",
       "2  0.173429 -1.083784  \n",
       "3 -0.446765 -1.344830  \n",
       "4 -0.770344 -1.170800  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>M/F</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.153798</td>\n",
       "      <td>1.308738</td>\n",
       "      <td>-0.208132</td>\n",
       "      <td>-0.394466</td>\n",
       "      <td>-0.095686</td>\n",
       "      <td>2.836059</td>\n",
       "      <td>-0.905169</td>\n",
       "      <td>-2.265742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.153798</td>\n",
       "      <td>1.439787</td>\n",
       "      <td>-0.208132</td>\n",
       "      <td>-0.394466</td>\n",
       "      <td>0.721664</td>\n",
       "      <td>2.932703</td>\n",
       "      <td>-1.309643</td>\n",
       "      <td>-2.316501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.153798</td>\n",
       "      <td>-0.263856</td>\n",
       "      <td>-0.904394</td>\n",
       "      <td>-0.394466</td>\n",
       "      <td>-1.185486</td>\n",
       "      <td>1.079409</td>\n",
       "      <td>0.173429</td>\n",
       "      <td>-1.083784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        M/F       Age      EDUC       SES      MMSE      eTIV      nWBV  \\\n",
       "0  1.153798  1.308738 -0.208132 -0.394466 -0.095686  2.836059 -0.905169   \n",
       "1  1.153798  1.439787 -0.208132 -0.394466  0.721664  2.932703 -1.309643   \n",
       "2  1.153798 -0.263856 -0.904394 -0.394466 -1.185486  1.079409  0.173429   \n",
       "\n",
       "        ASF  \n",
       "0 -2.265742  \n",
       "1 -2.316501  \n",
       "2 -1.083784  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_test.drop([\"Group\"],axis=1)\n",
    "y = data_test[\"Group\"].values\n",
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We divide our data into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74.80% Train\n",
      "25.20% Test\n"
     ]
    }
   ],
   "source": [
    "print(\"{0:0.2f}% Train\".format((len(X_train)/len(data_test.index)) * 100))\n",
    "print(\"{0:0.2f}% Test\".format((len(X_test)/len(data_test.index)) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Demented : 183 (49.06%)\n",
      "Original Nondemented : 190 (50.94%)\n",
      "\n",
      "Training Demented : 139 (49.82%)\n",
      "Training Nondemented : 140 (50.18%)\n",
      "\n",
      "Test Demented : 44 (46.81%)\n",
      "Test Nondemented : 50 (53.19%)\n"
     ]
    }
   ],
   "source": [
    "print(\"Original Demented : {0} ({1:0.2f}%)\".format(len(df_norm.loc[df_norm['Group'] == 1]), 100 * (len(df_norm.loc[df_norm['Group'] == 1]) / len(df_norm))))\n",
    "print(\"Original Nondemented : {0} ({1:0.2f}%)\".format(len(df_norm.loc[df_norm['Group'] == 0]), 100 * (len(df_norm.loc[df_norm['Group'] == 0]) / len(df_norm))))\n",
    "print(\"\")\n",
    "print(\"Training Demented : {0} ({1:0.2f}%)\".format(len(y_train[y_train[:] == 1]), 100 * (len(y_train[y_train[:] == 1]) / len(y_train))))\n",
    "print(\"Training Nondemented : {0} ({1:0.2f}%)\".format(len(y_train[y_train[:] == 0]), 100 * (len(y_train[y_train[:] == 0]) / len(y_train))))\n",
    "print(\"\")\n",
    "print(\"Test Demented : {0} ({1:0.2f}%)\".format(len(y_test[y_test[:] == 1]), 100 * (len(y_test[y_test[:] == 1]) / len(y_test))))\n",
    "print(\"Test Nondemented : {0} ({1:0.2f}%)\".format(len(y_test[y_test[:] == 0]), 100 * (len(y_test[y_test[:] == 0]) / len(y_test))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = [0.001, 0.10, 0.1, 10, 25, 50,65,70,80,90, 100, 1000,2000,10000,20000,25000,30000,40000]\n",
    "\n",
    "kernel =  ['rbf']\n",
    "    \n",
    "gamma =[1e-2, 1e-3, 1e-4, 1e-5,1e-6,1e-7,1e-8,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "parametros_svm = {'C': C,\n",
    "            'gamma': gamma,\n",
    "             'kernel': kernel}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 20 folds for each of 144 candidates, totalling 2880 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    3.0s\n",
      "[Parallel(n_jobs=-1)]: Done 408 tasks      | elapsed:    6.1s\n",
      "[Parallel(n_jobs=-1)]: Done 1220 tasks      | elapsed:   14.7s\n",
      "[Parallel(n_jobs=-1)]: Done 2352 tasks      | elapsed:   22.9s\n",
      "[Parallel(n_jobs=-1)]: Done 2880 out of 2880 | elapsed:   30.0s finished\n",
      "c:\\users\\balu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning:\n",
      "\n",
      "The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=20, error_score='raise-deprecating',\n",
       "             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'C': [0.001, 0.1, 0.1, 10, 25, 50, 65, 70, 80, 90, 100,\n",
       "                               1000, 2000, 10000, 20000, 25000, 30000, 40000],\n",
       "                         'gamma': [0.01, 0.001, 0.0001, 1e-05, 1e-06, 1e-07,\n",
       "                                   1e-08, 1],\n",
       "                         'kernel': ['rbf']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='roc_auc', verbose=2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm = SVC()\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "svm_random = GridSearchCV(model_svm, parametros_svm,  cv = 20, \n",
    "                               verbose=2, n_jobs = -1, scoring='roc_auc')\n",
    "svm_random.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = range(10,250)\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = range(1,40)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = range(3,60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "parametro_rf = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:    9.0s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   27.3s\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:   53.8s\n",
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:  1.4min finished\n",
      "c:\\users\\balu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning:\n",
      "\n",
      "The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                    class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features='auto',\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    n_estimators='warn',\n",
       "                                                    n_jobs=-1, oob_score=False,\n",
       "                                                    random_state=None,\n",
       "                                                    verbose=0,\n",
       "                                                    warm_start=False),\n",
       "                   iid='warn', n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={'max_depth': range(1, 40),\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_split': range(3, 60),\n",
       "                                        'n_estimators': range(10, 250)},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_absolute_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FOLDS=10;\n",
    "model_forest = RandomForestClassifier(n_jobs=-1)\n",
    "forest_random = RandomizedSearchCV(estimator = model_forest, param_distributions = parametro_rf, n_iter = 100, cv = FOLDS, \n",
    "                               verbose=2, random_state=42, n_jobs = -1, scoring='neg_mean_absolute_error')\n",
    "forest_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 10,\n",
       " 'min_samples_split': 5,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 34}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=37, max_features='sqrt', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=8,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=60,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf = forest_random.best_estimator_\n",
    "model_rf =  RandomForestClassifier(n_estimators=60,min_samples_split=8,max_features='sqrt',max_depth= 37)\n",
    "model_rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.897723704866562"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score = cross_val_score(model_rf, X_train, y_train, cv=FOLDS, scoring='roc_auc').mean()\n",
    "test_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8029100529100528"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score = cross_val_score(model_rf, X_train, y_train, cv=FOLDS, scoring='accuracy').mean()\n",
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_rf= model_rf.predict(X_test)\n",
    "test_recall = recall_score(y_test, Predicted_rf, pos_label=1)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, Predicted_rf, pos_label=1)\n",
    "test_auc = auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = range(50,280)\n",
    "# Maximum number of levels in tree\n",
    "max_depth =  range(1,40)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_leaf = [3,4,5,6,7,8,9,10,15,20,30,40,50,60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "parametro_Et = {'n_estimators': n_estimators,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_leaf': min_samples_leaf}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:    7.6s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   23.7s\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:   44.8s\n",
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:  1.1min finished\n",
      "c:\\users\\balu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning:\n",
      "\n",
      "The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise-deprecating',\n",
       "                   estimator=ExtraTreesClassifier(bootstrap=False,\n",
       "                                                  class_weight=None,\n",
       "                                                  criterion='gini',\n",
       "                                                  max_depth=None,\n",
       "                                                  max_features='auto',\n",
       "                                                  max_leaf_nodes=None,\n",
       "                                                  min_impurity_decrease=0.0,\n",
       "                                                  min_impurity_split=None,\n",
       "                                                  min_samples_leaf=1,\n",
       "                                                  min_samples_split=2,\n",
       "                                                  min_weight_fraction_leaf=0.0,\n",
       "                                                  n_estimators='warn',\n",
       "                                                  n_jobs=-1, oob_score=False,\n",
       "                                                  random_state=None, verbose=0,\n",
       "                                                  warm_start=False),\n",
       "                   iid='warn', n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={'max_depth': range(1, 40),\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_split': range(3, 60),\n",
       "                                        'n_estimators': range(10, 250)},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='roc_auc', verbose=2)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_et = ExtraTreesClassifier(n_jobs=-1)\n",
    "et_random = RandomizedSearchCV(estimator = model_et, param_distributions = parametro_rf, n_iter = 100, cv = FOLDS, \n",
    "                               verbose=2, random_state=42, n_jobs = -1, scoring='roc_auc')\n",
    "et_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 46,\n",
       " 'min_samples_split': 3,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "et_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = range(10,200)\n",
    "\n",
    "learning_rate = [0.0001, 0.001, 0.01, 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random grid\n",
    "parametros_ada = {'n_estimators': n_estimators,\n",
    "               'learning_rate': learning_rate}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:    8.3s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   17.8s\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:   32.0s\n",
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   50.6s finished\n",
      "c:\\users\\balu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning:\n",
      "\n",
      "The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise-deprecating',\n",
       "                   estimator=AdaBoostClassifier(algorithm='SAMME.R',\n",
       "                                                base_estimator=None,\n",
       "                                                learning_rate=1.0,\n",
       "                                                n_estimators=50,\n",
       "                                                random_state=None),\n",
       "                   iid='warn', n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={'learning_rate': [0.0001, 0.001, 0.01,\n",
       "                                                          0.1, 0.2, 0.3, 0.4,\n",
       "                                                          0.5, 0.6, 0.7, 0.8,\n",
       "                                                          0.9, 0.95, 1],\n",
       "                                        'n_estimators': range(10, 200)},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='roc_auc', verbose=2)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ada = AdaBoostClassifier()\n",
    "\n",
    "ada_random = RandomizedSearchCV(estimator = model_ada, param_distributions = parametros_ada, n_iter = 100, cv = FOLDS, \n",
    "                               verbose=2, random_state=42, n_jobs = -1, scoring='roc_auc')\n",
    "ada_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_random.predict([[1,72,12,4,17,1483,0.695,1.184]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 110, 'learning_rate': 0.6}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "parametros_gb = {\n",
    "    \"loss\":[\"deviance\"],\n",
    "    \"learning_rate\": [0.01, 0.025, 0.005,0.5, 0.075, 0.1, 0.15, 0.2,0.3,0.8,0.9],\n",
    "    \"min_samples_split\": [0.01, 0.025, 0.005,0.4,0.5, 0.075, 0.1, 0.15, 0.2,0.3,0.8,0.9],\n",
    "    \"min_samples_leaf\": [1,2,3,5,8,10,15,20,40,50,55,60,65,70,80,85,90,100],\n",
    "    \"max_depth\":[3,5,8,10,15,20,25,30,40,50],\n",
    "    \"max_features\":[\"log2\",\"sqrt\"],\n",
    "    \"criterion\": [\"friedman_mse\",  \"mae\"],\n",
    "    \"subsample\":[0.5, 0.618, 0.8, 0.85, 0.9, 0.95, 1.0],\n",
    "    \"n_estimators\":range(1,100)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=-1)]: Done 276 tasks      | elapsed:    3.6s\n",
      "[Parallel(n_jobs=-1)]: Done 682 tasks      | elapsed:   10.4s\n",
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   14.0s finished\n",
      "c:\\users\\balu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning:\n",
      "\n",
      "The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise-deprecating',\n",
       "                   estimator=GradientBoostingClassifier(criterion='friedman_mse',\n",
       "                                                        init=None,\n",
       "                                                        learning_rate=0.1,\n",
       "                                                        loss='deviance',\n",
       "                                                        max_depth=3,\n",
       "                                                        max_features=None,\n",
       "                                                        max_leaf_nodes=None,\n",
       "                                                        min_impurity_decrease=0.0,\n",
       "                                                        min_impurity_split=None,\n",
       "                                                        min_samples_leaf=1,\n",
       "                                                        min_samples_split=2,\n",
       "                                                        min_weight_fraction_leaf=0.0,\n",
       "                                                        n_estimators=100,\n",
       "                                                        n_...\n",
       "                                        'max_features': ['log2', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 3, 5, 8, 10,\n",
       "                                                             15, 20, 40, 50, 55,\n",
       "                                                             60, 65, 70, 80, 85,\n",
       "                                                             90, 100],\n",
       "                                        'min_samples_split': [0.01, 0.025,\n",
       "                                                              0.005, 0.4, 0.5,\n",
       "                                                              0.075, 0.1, 0.15,\n",
       "                                                              0.2, 0.3, 0.8,\n",
       "                                                              0.9],\n",
       "                                        'n_estimators': range(1, 100),\n",
       "                                        'subsample': [0.5, 0.618, 0.8, 0.85,\n",
       "                                                      0.9, 0.95, 1.0]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='roc_auc', verbose=2)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gb= GradientBoostingClassifier()\n",
    "\n",
    "\n",
    "gb_random = RandomizedSearchCV(estimator = model_gb, param_distributions = parametros_gb, n_iter = 100, cv = FOLDS, \n",
    "                               verbose=2, random_state=42, n_jobs = -1, scoring='roc_auc')\n",
    "gb_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_random.predict(rdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.95,\n",
       " 'n_estimators': 96,\n",
       " 'min_samples_split': 0.15,\n",
       " 'min_samples_leaf': 5,\n",
       " 'max_features': 'log2',\n",
       " 'max_depth': 50,\n",
       " 'loss': 'deviance',\n",
       " 'learning_rate': 0.15,\n",
       " 'criterion': 'friedman_mse'}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_random.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_xgb = {\n",
    "        'silent': [False],\n",
    "        'max_depth': [6, 10, 15, 20],\n",
    "        'learning_rate': [0.001, 0.01, 0.1, 0.2, 0,3],\n",
    "        'subsample': [0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "        'colsample_bytree': [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "        'colsample_bylevel': [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "        'min_child_weight': [0.5, 1.0, 3.0, 5.0, 7.0, 10.0],\n",
    "        'gamma': [0, 0.25, 0.5, 1.0],\n",
    "        'reg_lambda': [0.1, 1.0, 5.0, 10.0, 50.0, 100.0],\n",
    "        'n_estimators': [50,100,120]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done 276 tasks      | elapsed:    2.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:41:29] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    9.1s finished\n",
      "c:\\users\\balu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning:\n",
      "\n",
      "The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise-deprecating',\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, gamma=None,\n",
       "                                           gpu_id=None, importance_type='gain',\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None,\n",
       "                                           max_delta_step=None, max_depth=None,\n",
       "                                           min_child_weight=None, missing=nan,\n",
       "                                           monotone_c...\n",
       "                                        'gamma': [0, 0.25, 0.5, 1.0],\n",
       "                                        'learning_rate': [0.001, 0.01, 0.1, 0.2,\n",
       "                                                          0, 3],\n",
       "                                        'max_depth': [6, 10, 15, 20],\n",
       "                                        'min_child_weight': [0.5, 1.0, 3.0, 5.0,\n",
       "                                                             7.0, 10.0],\n",
       "                                        'n_estimators': [50, 100, 120],\n",
       "                                        'reg_lambda': [0.1, 1.0, 5.0, 10.0,\n",
       "                                                       50.0, 100.0],\n",
       "                                        'silent': [False],\n",
       "                                        'subsample': [0.5, 0.6, 0.7, 0.8, 0.9,\n",
       "                                                      1.0]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='roc_auc', verbose=2)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "import xgboost as xgb\n",
    "model_xgb = xgb.XGBClassifier()\n",
    "xgb_random = RandomizedSearchCV(estimator = model_xgb, param_distributions = param_xgb, n_iter = 100, cv = FOLDS, \n",
    "                               verbose=2, random_state=42, n_jobs = -1, scoring='roc_auc')\n",
    "xgb_random.fit(X_train.values, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.5,\n",
       " 'silent': False,\n",
       " 'reg_lambda': 1.0,\n",
       " 'n_estimators': 100,\n",
       " 'min_child_weight': 0.5,\n",
       " 'max_depth': 15,\n",
       " 'learning_rate': 0.2,\n",
       " 'gamma': 1.0,\n",
       " 'colsample_bytree': 0.7,\n",
       " 'colsample_bylevel': 0.7}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "parametro_rf = forest_random.best_params_\n",
    "\n",
    "parametro_et = et_random.best_params_\n",
    "\n",
    "parametro_ada = ada_random.best_params_\n",
    "\n",
    "parametro_gb = gb_random.best_params_\n",
    "\n",
    "parametro_svm = svm_random.best_params_\n",
    "\n",
    "parametro_xgb= xgb_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_rf = forest_random.best_estimator_\n",
    "\n",
    "model_et = et_random.best_estimator_\n",
    "\n",
    "model_ada = ada_random.best_estimator_\n",
    "\n",
    "model_gb = gb_random.best_estimator_\n",
    "\n",
    "model_svc = svm_random.best_estimator_\n",
    "\n",
    "model_xgb= xgb_random.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:41:30] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:30] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:31] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:32] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:32] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:33] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:33] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:34] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:34] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:35] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=FOLDS, random_state = 0, shuffle = True)\n",
    "for i, (train_index, val_index) in enumerate(kf.split(X_train, y_train)):\n",
    "    Xtrain, Xval = X_train.values[train_index], X_train.values[val_index]\n",
    "    ytrain, yval = y_train[train_index], y_train[val_index]\n",
    "    \n",
    "    model_rf.fit(Xtrain, ytrain)\n",
    "    model_et.fit(Xtrain, ytrain)\n",
    "    model_ada.fit(Xtrain, ytrain)\n",
    "    model_gb.fit(Xtrain, ytrain)\n",
    "    model_svc.fit(Xtrain, ytrain)\n",
    "    model_xgb.fit(Xtrain, ytrain)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_feature = model_rf.feature_importances_\n",
    "ada_feature = model_ada.feature_importances_\n",
    "gb_feature = model_gb.feature_importances_\n",
    "et_feature = model_et.feature_importances_\n",
    "xbg_feature = model_xgb.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = X.columns.tolist()\n",
    "# Create a dataframe with features\n",
    "feature_dataframe = pd.DataFrame( {'features': cols,\n",
    "     'Random Forest feature importances': rf_feature,\n",
    "      'AdaBoost feature importances': ada_feature,\n",
    "    'Gradient Boost feature importances': gb_feature,\n",
    "    'Extra Trees  feature importances': et_feature,\n",
    "    'Xgboost feature importances': xbg_feature,\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
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    "xbg_feature"
   ]
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   ],
   "source": [
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['Random Forest feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['Random Forest feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Random Forest Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')\n",
    "\n",
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['Extra Trees  feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['Extra Trees  feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Extra Trees Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')\n",
    "\n",
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['AdaBoost feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['AdaBoost feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'AdaBoost Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')\n",
    "\n",
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['Gradient Boost feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['Gradient Boost feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Gradient Boosting Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')\n",
    "\n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['Xgboost feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['Xgboost feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'XgboostFeature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>Random Forest feature importances</th>\n",
       "      <th>AdaBoost feature importances</th>\n",
       "      <th>Gradient Boost feature importances</th>\n",
       "      <th>Extra Trees  feature importances</th>\n",
       "      <th>Xgboost feature importances</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M/F</td>\n",
       "      <td>0.043327</td>\n",
       "      <td>0.054545</td>\n",
       "      <td>0.046592</td>\n",
       "      <td>0.072816</td>\n",
       "      <td>0.087991</td>\n",
       "      <td>0.061054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.075610</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>0.060605</td>\n",
       "      <td>0.088611</td>\n",
       "      <td>0.089419</td>\n",
       "      <td>0.084667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EDUC</td>\n",
       "      <td>0.091190</td>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.093795</td>\n",
       "      <td>0.127054</td>\n",
       "      <td>0.120726</td>\n",
       "      <td>0.101099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  features  Random Forest feature importances  AdaBoost feature importances  \\\n",
       "0      M/F                           0.043327                      0.054545   \n",
       "1      Age                           0.075610                      0.109091   \n",
       "2     EDUC                           0.091190                      0.072727   \n",
       "\n",
       "   Gradient Boost feature importances  Extra Trees  feature importances  \\\n",
       "0                            0.046592                          0.072816   \n",
       "1                            0.060605                          0.088611   \n",
       "2                            0.093795                          0.127054   \n",
       "\n",
       "   Xgboost feature importances      mean  \n",
       "0                     0.087991  0.061054  \n",
       "1                     0.089419  0.084667  \n",
       "2                     0.120726  0.101099  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the new column that contains the average of the values.\n",
    "feature_dataframe['mean'] = feature_dataframe.mean(axis= 1) # axis = 1 computes the mean row-wise\n",
    "feature_dataframe.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Barplots of Mean Feature Importance\"}, \"yaxis\": {\"gridwidth\": 2, \"ticklen\": 5, \"title\": {\"text\": \"Feature Importance\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('49ef2e61-81ca-492e-adb5-2e42690ae001');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = feature_dataframe['mean'].values\n",
    "x = feature_dataframe['features'].values\n",
    "data = [go.Bar(\n",
    "            x= x,\n",
    "             y= y,\n",
    "            width = 0.5,\n",
    "            marker=dict(\n",
    "               color = feature_dataframe['mean'].values,\n",
    "            colorscale='Portland',\n",
    "            showscale=True,\n",
    "            reversescale = False\n",
    "            ),\n",
    "            opacity=0.6\n",
    "        )]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Barplots of Mean Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='bar-direct-labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RandomForest</th>\n",
       "      <th>AdaBoost</th>\n",
       "      <th>GradientBoost</th>\n",
       "      <th>ExtraTrees</th>\n",
       "      <th>SVM</th>\n",
       "      <th>XGB</th>\n",
       "      <th>Real value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RandomForest  AdaBoost  GradientBoost  ExtraTrees  SVM  XGB  Real value\n",
       "0             1         1              1           1    1    1           1\n",
       "1             1         1              1           1    1    1           1\n",
       "2             1         0              0           0    0    1           0\n",
       "3             0         1              0           0    1    0           1\n",
       "4             0         0              0           1    0    0           0\n",
       "5             1         1              1           1    1    1           1\n",
       "6             0         1              0           0    0    0           0\n",
       "7             1         1              1           1    1    1           1\n",
       "8             1         0              1           1    0    1           1\n",
       "9             1         1              1           1    1    1           1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predicted_rf= model_rf.predict(X_test)\n",
    "Predicted_ada = model_ada.predict(X_test)\n",
    "Predicted_gb = model_gb.predict(X_test)\n",
    "Predicted_et = model_et.predict(X_test)\n",
    "Predicted_svm= model_svc.predict(X_test)\n",
    "Predicted_xgb= model_xgb.predict(X_test.values)\n",
    "base_predictions_train = pd.DataFrame( {'RandomForest': Predicted_rf.ravel(),\n",
    "      'AdaBoost': Predicted_ada.ravel(),\n",
    "      'GradientBoost': Predicted_gb.ravel(),\n",
    "      'ExtraTrees': Predicted_et.ravel(),\n",
    "      'SVM': Predicted_svm.ravel(),\n",
    "      'XGB': Predicted_xgb.ravel(),\n",
    "     'Real value': y_test                                \n",
    "                                        \n",
    "    })\n",
    "base_predictions_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = [] # list to store all performance metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "model='Random Forest'\n",
    "test_score = cross_val_score(model_rf, X_train, y_train, cv=FOLDS, scoring='accuracy').mean() # Get recall for each parameter setting\n",
    "test_recall = recall_score(y_test, Predicted_rf, pos_label=1)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, Predicted_rf, pos_label=1)\n",
    "test_auc = auc(fpr, tpr)\n",
    "acc.append([model,test_score, test_recall, test_auc, fpr, tpr, thresholds])\n",
    "\n",
    "model='AdaBoost'\n",
    "test_score = cross_val_score(model_ada, X_train, y_train, cv=FOLDS, scoring='accuracy').mean() # Get recall for each parameter setting\n",
    "test_recall = recall_score(y_test, Predicted_ada, pos_label=1)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, Predicted_ada, pos_label=1)\n",
    "test_auc = auc(fpr, tpr)\n",
    "acc.append([model, test_score,test_recall, test_auc, fpr, tpr, thresholds])\n",
    "\n",
    "model='Gradient Boosting'\n",
    "test_score = cross_val_score(model_gb, X_train, y_train, cv=FOLDS, scoring='accuracy').mean() # Get recall for each parameter setting\n",
    "test_recall = recall_score(y_test, Predicted_gb, pos_label=1)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, Predicted_gb, pos_label=1)\n",
    "test_auc = auc(fpr, tpr)\n",
    "acc.append([model, test_score,test_recall, test_auc, fpr, tpr, thresholds])\n",
    "\n",
    "model='ExtraTrees'\n",
    "test_score = cross_val_score(model_et, X_train, y_train, cv=FOLDS, scoring='accuracy').mean() # Get recall for each parameter setting\n",
    "test_recall = recall_score(y_test, Predicted_et, pos_label=1)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, Predicted_et, pos_label=1)\n",
    "test_auc = auc(fpr, tpr)\n",
    "acc.append([model, test_score, test_recall, test_auc, fpr, tpr, thresholds])\n",
    "\n",
    "model='SVM'\n",
    "test_score = cross_val_score(model_svc, X_train, y_train, cv=FOLDS, scoring='accuracy').mean() # Get recall for each parameter setting\n",
    "test_recall = recall_score(y_test, Predicted_svm, pos_label=1)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, Predicted_svm, pos_label=1)\n",
    "test_auc = auc(fpr, tpr)\n",
    "acc.append([model, test_score, test_recall, test_auc, fpr, tpr, thresholds])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "[[40 10]\n",
      " [ 7 37]]\n",
      "\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.80      0.82        50\n",
      "           1       0.79      0.84      0.81        44\n",
      "\n",
      "    accuracy                           0.82        94\n",
      "   macro avg       0.82      0.82      0.82        94\n",
      "weighted avg       0.82      0.82      0.82        94\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report_performance(model_et)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.806614</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.759091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.813757</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.727727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.817460</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.861818</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ExtraTrees</td>\n",
       "      <td>0.878042</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>0.820455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.799471</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.787727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Xgboost</td>\n",
       "      <td>0.824339</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.789091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model  Accuracy    Recall       AUC\n",
       "0      Random Forest  0.806614  0.818182  0.759091\n",
       "1           AdaBoost  0.813757  0.795455  0.727727\n",
       "2  Gradient Boosting  0.817460  0.863636  0.861818\n",
       "3         ExtraTrees  0.878042  0.840909  0.820455\n",
       "4                SVM  0.799471  0.795455  0.787727\n",
       "5            Xgboost  0.824339  0.818182  0.789091"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame(acc, columns=['Model', 'Accuracy', 'Recall', 'AUC', 'FPR', 'TPR', 'TH'])\n",
    "result[['Model', 'Accuracy', 'Recall', 'AUC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename='algo_gb.pkl'\n",
    "pickle.dump(model_gb,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename='algo_rf.pkl'\n",
    "pickle.dump(model_rf,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename='algo_et.pkl'\n",
    "pickle.dump(model_et,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename='algo_ada.pkl'\n",
    "pickle.dump(model_ada,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename='algo_svc.pkl'\n",
    "pickle.dump(model_svc,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename='algo_xgb.pkl'\n",
    "pickle.dump(model_xgb,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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