Liver_Disease_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business Problem \n",
    "### Problem Context\n",
    "Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors.\n",
    "\n",
    "### Content\n",
    "This data set contains 416 liver patient records and 167 non liver patient records collected from North East of Andhra Pradesh, India. The \"Dataset\" column is a class label used to divide groups into liver patient (liver disease) or not (no disease). This data set contains 441 male patient records and 142 female patient records.\n",
    "\n",
    "Any patient whose age exceeded 89 is listed as being of age \"90\".\n",
    "\n",
    "### Features:\n",
    "\n",
    "* Age of the patient\n",
    "* Gender of the patient\n",
    "* Total Bilirubin\n",
    "* Direct Bilirubin\n",
    "* Alkaline Phosphotase\n",
    "* Alamine Aminotransferase\n",
    "* Aspartate Aminotransferase\n",
    "* Total Protiens\n",
    "* Albumin\n",
    "* Albumin and Globulin Ratio\n",
    "* Dataset: field used to split the data into two sets (patient with liver disease, or no disease)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Mapping business problem to ML problem\n",
    "\n",
    "###  Type of Machine Learning Problem\n",
    "\n",
    "It is a binary classification problem, where given the above set of features, we need to predict if a given patient has liver disease or not\n",
    "\n",
    "###  Evaluation Metric (KPI)\n",
    "\n",
    "Since this is binary classification problem, we use the following metrics:\n",
    "* **Confusion matrix** - For getting a better clarity of the no of correct/incorrect predictions by the model\n",
    "* **ROC-AUC** - It considers the rank of the output probabilities and intuitively measures the likelihood that model can distinguish between a positive point and a negative point. (**Note:** ROC-AUC is typically used for binary classification only). We will use AUC to select the best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for numerical computing\n",
    "import numpy as np\n",
    "\n",
    "# for dataframes\n",
    "import pandas as pd\n",
    "\n",
    "# for easier visualization\n",
    "import seaborn as sns\n",
    "\n",
    "# for visualization and to display plots\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# import color maps\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# Ignore Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from math import sqrt\n",
    "\n",
    "# to split train and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# to perform hyperparameter tuning\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "# Machine Learning Models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score, confusion_matrix\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.metrics import accuracy_score\n",
    "#import xgboost\n",
    "import os\n",
    "mingw_path = 'C:\\\\Program Files\\\\mingw-w64\\\\x86_64-7.2.0-posix-seh-rt_v5-rev0\\\\mingw64\\\\bin'\n",
    "os.environ['PATH'] = mingw_path + ';' + os.environ['PATH']\n",
    "from xgboost import XGBClassifier\n",
    "from xgboost import plot_importance  # to plot feature importance\n",
    "\n",
    "# to save the final model on disk\n",
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('indian_liver_patient.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(583, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',\n",
       "       'Alkaline_Phosphotase', 'Alamine_Aminotransferase',\n",
       "       'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',\n",
       "       'Albumin_and_Globulin_Ratio', 'Dataset'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65  Female              0.7               0.1                   187   \n",
       "1   62    Male             10.9               5.5                   699   \n",
       "2   62    Male              7.3               4.1                   490   \n",
       "3   58    Male              1.0               0.4                   182   \n",
       "4   72    Male              3.9               2.0                   195   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                        16                          18             6.8   \n",
       "1                        64                         100             7.5   \n",
       "2                        60                          68             7.0   \n",
       "3                        14                          20             6.8   \n",
       "4                        27                          59             7.3   \n",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0      3.3                        0.90        1  \n",
       "1      3.2                        0.74        1  \n",
       "2      3.3                        0.89        1  \n",
       "3      3.4                        1.00        1  \n",
       "4      2.4                        0.40        1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes[df.dtypes=='object']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3IAAANgCAYAAABpyVpxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMv8EyyzxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26b02a166a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram grid\n",
    "df.hist(figsize=(15,15), xrot=-45, bins=10) ## Display the labels rotated by 45 degress\n",
    "\n",
    "# Clear the text \"residue\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>583.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44.746141</td>\n",
       "      <td>3.298799</td>\n",
       "      <td>1.486106</td>\n",
       "      <td>290.576329</td>\n",
       "      <td>80.713551</td>\n",
       "      <td>109.910806</td>\n",
       "      <td>6.483190</td>\n",
       "      <td>3.141852</td>\n",
       "      <td>0.947064</td>\n",
       "      <td>1.286449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.189833</td>\n",
       "      <td>6.209522</td>\n",
       "      <td>2.808498</td>\n",
       "      <td>242.937989</td>\n",
       "      <td>182.620356</td>\n",
       "      <td>288.918529</td>\n",
       "      <td>1.085451</td>\n",
       "      <td>0.795519</td>\n",
       "      <td>0.319592</td>\n",
       "      <td>0.452490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>2110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4929.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "count  583.000000       583.000000        583.000000            583.000000   \n",
       "mean    44.746141         3.298799          1.486106            290.576329   \n",
       "std     16.189833         6.209522          2.808498            242.937989   \n",
       "min      4.000000         0.400000          0.100000             63.000000   \n",
       "25%     33.000000         0.800000          0.200000            175.500000   \n",
       "50%     45.000000         1.000000          0.300000            208.000000   \n",
       "75%     58.000000         2.600000          1.300000            298.000000   \n",
       "max     90.000000        75.000000         19.700000           2110.000000   \n",
       "\n",
       "       Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "count                583.000000                  583.000000      583.000000   \n",
       "mean                  80.713551                  109.910806        6.483190   \n",
       "std                  182.620356                  288.918529        1.085451   \n",
       "min                   10.000000                   10.000000        2.700000   \n",
       "25%                   23.000000                   25.000000        5.800000   \n",
       "50%                   35.000000                   42.000000        6.600000   \n",
       "75%                   60.500000                   87.000000        7.200000   \n",
       "max                 2000.000000                 4929.000000        9.600000   \n",
       "\n",
       "          Albumin  Albumin_and_Globulin_Ratio     Dataset  \n",
       "count  583.000000                  579.000000  583.000000  \n",
       "mean     3.141852                    0.947064    1.286449  \n",
       "std      0.795519                    0.319592    0.452490  \n",
       "min      0.900000                    0.300000    1.000000  \n",
       "25%      2.600000                    0.700000    1.000000  \n",
       "50%      3.100000                    0.930000    1.000000  \n",
       "75%      3.800000                    1.100000    2.000000  \n",
       "max      5.500000                    2.800000    2.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems there is outlier in Aspartate_Aminotransferase as the max value is very high than mean value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset i.e output value has '1' for liver disease and '2' for no liver disease so let's make it 0 for no disease to make it convinient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## if score==negative, mark 0 ;else 1 \n",
    "def partition(x):\n",
    "    if x == 2:\n",
    "        return 0\n",
    "    return 1\n",
    "\n",
    "df['Dataset'] = df['Dataset'].map(partition)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender\n",
       "count     583\n",
       "unique      2\n",
       "top      Male\n",
       "freq      441"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar plots for categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b029ec358>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAAFACAYAAACGOHZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAD25JREFUeJzt3XuMpXV9x/H3x11WVmpRYVUK4qCu\nVagIZaVYGrMQNdTWSytpRCPaEqFGQW2r0ZIQ1PSK8daaBrREpFat2CpiFZVbqyYsu4LsKqLY2ool\nRYqgRYtl+faP86yO24E5sHPmO3vO+5VM5jzPec7M9zfAmzPPuUyqCklSnwd0DyBJs84QS1IzQyxJ\nzQyxJDUzxJLUzBBLUjNDLEnNDLEkNTPEktRsdfcAK8G+++5bc3Nz3WNImjJbtmy5parWLXacIQbm\n5ubYvHlz9xiSpkySfxvnOE9NSFIzQyxJzQyxJDUzxJLUzBBLUjNDLEnNDLEkNTPEktTMEEtSM0Ms\nSc0MsSQ1M8SS1Mw3/QGuu/G/OOK17+seY6psOevE7hGk3Yb3iCWpmSGWpGaGWJKaGWJJamaIJamZ\nIZakZoZYkpoZYklqZoglqZkhlqRmhliSmhliSWpmiCWpmSGWpGaGWJKaGWJJamaIJamZIZakZoZY\nkpoZYklqZoglqZkhlqRmhliSmhliSWpmiCWpmSGWpGaGWJKaGWJJamaIJamZIZakZoZYkpoZYklq\nZoglqZkhlqRmhliSmhliSWpmiCWpmSGWpGaGWJKaGWJJamaIJamZIZakZoZYkpoZYklqZoglqZkh\nlqRmhliSmhliSWpmiCWpmSGWpGYTC3GS7UmumfcxN8Hv9dIkfzmpry9Jk7R6gl/7h1V12AS/viRN\nhWU9NZFkVZKzklyV5Nokpwz7Nya5IsnfJflakj9N8qIkm5JsTfLY4bhnJ7kyydVJPpvkEQt8j3VJ\nPjJ8j6uSHL2ca5Sk+2qSIV4777TEPwz7TgJur6qnAE8BXpbkoOG6JwOvAp4EvBh4fFUdCbwHOHU4\n5nPAUVV1OPBB4HULfN93AG8bvsfzh9v/P0lOTrI5yea7fvD9XV6sJN1fy31q4pnAoUmOH7b3BtYD\nPwKuqqqbAJJ8A/j0cMxW4Jjh8gHAh5LsB6wB/nWB7/t04OAkO7Z/NsmDq+qnaltV5wDnAOz1yIPq\n/i1RknbdJEO8kACnVtXFP7Uz2QjcOW/X3fO27+Ync/4F8NaqunC4zZkLfI8HAE+tqh8u3diSNDnL\n/fS1i4GXJ9kDIMnjk+x1H26/N/Dt4fJL7uGYTwOv3LGRxAcMJa1oyx3i9wBfAb6YZBtwNvftXvmZ\nwIeT/DNwyz0ccxqwYXgw8CvA7+7CvJI0cany9OhejzyonvDiN3aPMVW2nHVi9whSuyRbqmrDYsf5\nyjpJamaIJamZIZakZoZYkpoZYklqZoglqZkhlqRmhliSmhliSWpmiCWpmSGWpGaGWJKaGWJJamaI\nJamZIZakZoZYkpoZYklqZoglqZkhlqRmhliSmhliSWpmiCWpmSGWpGaGWJKaGWJJamaIJamZIZak\nZoZYkpoZYklqZoglqZkhlqRmhliSmhliSWpmiCWpmSGWpGaGWJKaGWJJamaIJamZIZakZoZYkpoZ\nYklqZoglqZkhlqRmhliSmhliSWpmiCWp2aIhTrIqyWeXYxhJmkWLhriqtgM/SLL3MswjSTNn9ZjH\n/Q+wNclngDt27Kyq0yYy1TJ74gH7sPmsE7vHkDSjxg3xJ4YPSdISGyvEVXVekrXAgVV1/YRnkqSZ\nMtazJpI8G7gG+NSwfViSCyc5mCTNinGfvnYmcCRwG0BVXQMcNKGZJGmmjBviu6rq9p321VIPI0mz\naNwH67YleSGwKsl64DTgC5MbS5Jmx7j3iE8FDgHuBD4AfA949aSGkqRZMu6zJn4AnD58SJKW0L2G\nOMnHuZdzwVX1nCWfSJJmzGL3iN8yfP5N4JHA3wzbJwDfnNBMkjRT7jXEVXUFQJI3V9XT5l318ST/\nNNHJJGlGjPtg3bokj9mxkeQgYN1kRpKk2TLu09deA1ye5F+G7TnglIlMJEkzZtxnTXxqeP7wE4Zd\nX62qOyc3liTNjnHvEQMcweie8GrgyUmoqvdNZCpJmiFjhTjJ+cBjGb3xz/ZhdwGGWJJ20bj3iDcA\nB1eV7y8hSUts3GdNbGP0PGJJ0hIb9x7xvsBXkmxi9H4TgK+sk6SlMG6Iz5zkEJI0y8Z9+toVSR4N\nrK+qzyZ5ELBqsqNJ0mwY908lvQy4ADh72LU/8NFJDSVJs2TcB+teARzN6H2IqaqvAw+f1FCSNEvG\nDfGdVfWjHRtJVuOfSpKkJTFuiK9I8ofA2iTPAD4MfHxyY0nS7Bg3xK8HvgNsBU4GPlFV/rUOSVoC\ni/2FjucCB1TVu4B3Dw/arQOOSHJbVV2wHENO2o9u+jL//qYndY8haQU78IytE/vai90jfh1w4bzt\nNYze/Gcj8PIJzSRJM2Wx5xGvqapvzdv+XFXdCtyaZK8JziVJM2Oxe8QPnb9RVa+ct+lf6JCkJbBY\niK8czgv/lCSnAJsmM5IkzZbFTk28BvhokhcCXxz2HQE8EHjeJAeTpFmx2F9xvhn45STHAocMuz9R\nVZdOfDJJmhHjvunPpYDxlaQJGPcFHZKkCTHEktTMEEtSM0MsSc0MsSQ1M8SS1MwQS1IzQyxJzQyx\nJDUzxJLUzBBLUjNDLEnNDLEkNTPEktTMEEtSM0MsSc0MsSQ1M8SS1MwQS1IzQyxJzQyxJDUzxJLU\nzBBLUjNDLEnNDLEkNTPEktTMEEtSM0MsSc0MsSQ1M8SS1MwQS1IzQyxJzQyxJDUzxJLUzBBLUjND\nLEnNDLEkNTPEktTMEEtSM0MsSc0MsSQ1M8SS1MwQS1IzQyxJzVZUiJNUkvPnba9O8p0kFy1yu42L\nHSNJK9WKCjFwB/ALSdYO288Avt04jyRN3EoLMcAngV8bLp8AfGDHFUmOTPKFJFcPn39+5xsn2SvJ\nuUmuGo577jLNLUn3y0oM8QeBFyTZEzgUuHLedV8FnlZVhwNnAH+8wO1PBy6tqqcAxwBnJdlr54OS\nnJxkc5LNt96xfckXIUnjWt09wM6q6tokc4zuDf/jTlfvDZyXZD1QwB4LfIlnAs9J8gfD9p7AgcB1\nO32fc4BzAA7df20t1fySdF+tuBAPLgTeAmwE9pm3/83AZVX1G0OsL1/gtgGeX1XXT3ZESVoaK/HU\nBMC5wJuqautO+/fmJw/evfQebnsxcGqSACQ5fCITStISWZEhrqobq+odC1z158CfJPk8sOoebv5m\nRqcsrk2ybdiWpBUrVZ4ePXT/tXXRKY/rHkPSCnbgGTv/gr64JFuqasNix63Ie8SSNEsMsSQ1M8SS\n1MwQS1IzQyxJzQyxJDUzxJLUzBBLUjNDLEnNDLEkNTPEktTMEEtSM0MsSc0MsSQ1M8SS1MwQS1Iz\nQyxJzQyxJDUzxJLUzBBLUjNDLEnNDLEkNTPEktTMEEtSM0MsSc0MsSQ1M8SS1MwQS1IzQyxJzQyx\nJDUzxJLUzBBLUjNDLEnNDLEkNTPEktTMEEtSM0MsSc0MsSQ1M8SS1MwQS1IzQyxJzQyxJDUzxJLU\nzBBLUjNDLEnNDLEkNTPEktTMEEtSs9XdA6wEa/Y7hAPP2Nw9hqQZ5T1iSWpmiCWpmSGWpGaGWJKa\nGWJJamaIJamZIZakZoZYkpoZYklqZoglqZkhlqRmhliSmhliSWqWquqeoV2S7wPXd8+xjPYFbuke\nYhnN2nph9ta8Utf76Kpat9hBvg3myPVVtaF7iOWSZLPrnW6ztubdfb2empCkZoZYkpoZ4pFzugdY\nZq53+s3amnfr9fpgnSQ18x6xJDUzxJLUbKZDnOS4JNcnuSHJ67vnWSpJzk1yc5Jt8/Y9LMlnknx9\n+PzQYX+SvHP4GVyb5Bf7Jr9/kjwqyWVJrkvy5SSvGvZP5ZqT7JlkU5IvDet947D/oCRXDuv9UJI1\nw/4HDts3DNfPdc5/fyVZleTqJBcN21Oz3pkNcZJVwLuAXwUOBk5IcnDvVEvmvcBxO+17PXBJVa0H\nLhm2YbT+9cPHycBfLdOMS+ku4Per6onAUcArhn+W07rmO4Fjq+rJwGHAcUmOAv4MeNuw3u8CJw3H\nnwR8t6oeB7xtOG539Crgunnb07PeqprJD+CpwMXztt8AvKF7riVc3xywbd729cB+w+X9GL2IBeBs\n4ISFjttdP4CPAc+YhTUDDwK+CPwSo1eWrR72//jfb+Bi4KnD5dXDceme/T6u8wBG/zM9FrgIyDSt\nd2bvEQP7A9+at33jsG9aPaKqbgIYPj982D9VP4fh19DDgSuZ4jUPv6ZfA9wMfAb4BnBbVd01HDJ/\nTT9e73D97cA+yzvxLns78Drg7mF7H6ZovbMc4iywbxafyzc1P4ckPwN8BHh1VX3v3g5dYN9uteaq\n2l5VhzG6p3gk8MSFDhs+79brTfLrwM1VtWX+7gUO3W3XO8shvhF41LztA4D/aJplOfxnkv0Ahs83\nD/un4ueQZA9GEX5/Vf39sHuq1wxQVbcBlzM6N/6QJDveP2b+mn683uH6vYFbl3fSXXI08Jwk3wQ+\nyOj0xNuZovXOcoivAtYPj7yuAV4AXNg80yRdCLxkuPwSRudRd+w/cXgmwVHA7Tt+nd9dJAnw18B1\nVfXWeVdN5ZqTrEvykOHyWuDpjB7Eugw4fjhs5/Xu+DkcD1xawwnU3UFVvaGqDqiqOUb/nV5aVS9i\nmtbbfZK68wN4FvA1RufXTu+eZwnX9QHgJuB/Gd07OInRObJLgK8Pnx82HBtGzx75BrAV2NA9//1Y\n768w+tXzWuCa4eNZ07pm4FDg6mG924Azhv2PATYBNwAfBh447N9z2L5huP4x3WvYhbVvBC6atvX6\nEmdJajbLpyYkaUUwxJLUzBBLUjNDLEnNDLEkNTPE0oQkeXWSB3XPoZXPp69JEzK8EmxDVa3EP/Ou\nFcR7xJppSU4c3pP4S0nOT/LoJJcM+y5JcuBw3HuTHD/vdv89fN6Y5PIkFyT5apL3D6/YOw34OeCy\nJJf1rE67i9WLHyJNpySHAKcDR1fVLUkeBpwHvK+qzkvyO8A7gect8qUOBw5h9F4Hnx++3juT/B5w\njPeItRjvEWuWHQtcsCOUVXUro/e1/dvh+vMZvXx6MZuq6saqupvRy6vnJjCrppgh1iwLi7894o7r\n72L472V4k6E18465c97l7fibpu4jQ6xZdgnwW0n2gdHfuAO+wOgdvgBeBHxuuPxN4Ijh8nOBPcb4\n+t8HHrxUw2p6+X9uzayq+nKSPwKuSLKd0TuanQacm+S1wHeA3x4OfzfwsSSbGAX8jjG+xTnAJ5Pc\nVFXHLP0KNC18+pokNfPUhCQ1M8SS1MwQS1IzQyxJzQyxJDUzxJLUzBBLUrP/A4pjRIwcZZCfAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26b06fad9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.countplot(y='Gender', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dataset Gender\n",
       "1        1   Male\n",
       "2        1   Male\n",
       "3        1   Male\n",
       "4        1   Male\n",
       "5        1   Male"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Gender'] == 'Male'][['Dataset', 'Gender']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUIAAAEYCAYAAAApuP8NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSOgpCEUk9P4/PvxPDoB7aCMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26b07c30320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot (x=\"Age\", y=\"Gender\", hue=\"Dataset\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age seems to be a factor for liver disease for both male and female genders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients that are male:  441\n",
      "Number of patients that are female:  142\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26b073e3198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x = 'Gender', label='Count')\n",
    "\n",
    "M, F = df['Gender'].value_counts()\n",
    "print('Number of patients that are male: ',M)\n",
    "print('Number of patients that are female: ',F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are more male patients than female patients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label Male as 0 and Female as 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## if score==negative, mark 0 ;else 1 \n",
    "def partition(x):\n",
    "    if x =='Male':\n",
    "        return 0\n",
    "    return 1\n",
    "\n",
    "df['Gender'] = df['Gender'].map(partition)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2-D Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26b073e3748>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAFgCAYAAABKY1XKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhChEGSACTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26b073e6390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')   ## Background Grid\n",
    "sns.FacetGrid(df, hue = 'Dataset', size = 5).map(plt.scatter, 'Total_Bilirubin', 'Direct_Bilirubin').add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a9ffea7b00>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAFgCAYAAABHfSWNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3XtcVHXeB/DPmWGAARVC1BLU0NSo\ntBAr28ws23BL01wlyzCf3MryydSVzFYNzRuxW7t2sbJan7AbFZG1m1bWbpeV1khFW8RNzQRveQ8d\nmGHm9/xxmGGGuZ0Z5nIOfN6vly/lzJkzX6bk6znnM9+fJIQQICIiUkAX7QKIiEg72DSIiEgxNg0i\nIlKMTYOIiBRj0yAiIsWi0jQqKiqCet6PP/4Y2kJCRK11AawtWKwtcGqtC1B3bVqjqTMNk8kU7RI8\nUmtdAGsLFmsLnFrrAtRdm9ZoqmkQEVF0sWkQEZFibBpERKQYmwYRESnGpkFERIqxaRARkWJsGkRE\npBibBhERKcamQUREirFpEBGRYmwaRESkGJsGEREpFhPtAgLRad96YN1NgOm4vMGYAvymEBiYG93C\niIjaCe00jcoSnPfvpYDN0rzNdBwoe0D+MxsHEVHYaefy1MbF0Dk3DDubBdi4OPL1EBG1Q9ppGqdq\ngnuMiIhCRjtNIyk9uMeIiChktNM0RiyETWdw364zACMWRr4eIqJ2SDtNY2AuDl7xBzkxZWdMAcY+\nx5vgREQRop30FIDTvUYibeSsaJdBRNRuaedMg4iIoo5Ng4iIFGPTICIixdg0iIhIMTYNIiJSjE2D\niIgUY9MgIiLF2DSIiEgxNg0iIlKMTYOIiBRj0yAiIsXYNIiISDE2DSIiUoxNg4iIFGPTICIixdg0\niIhIMTYNIiJSjE2DiIgUY9MgIiLF2DSIiEgxNg0iIlKMTYOIiBSLiXYBgSrbUouiDdU4cNKE7slG\n5Of0x9istGiXRUTULoStaYwdOxYdO3YEAKSnp2P58uWtPuZne37BM+X7YLJYAQC1J02YV7pdfj02\nDiKisAtL02hoaAAAFBcXh/S4//fdCUfDsDNZrCjaUM2mQUQUAZIQQoT6oNu2bcPDDz+MtLQ0NDY2\nYvbs2bjsssscj1dUVCAhISHg4970f3vgqVgJwN/v6h18wa1UX1+P+Pj4qL2+L6wtOKwtcGqtC1BP\nbZmZmdEuodXCcqYRHx+PqVOnYsKECfjxxx9xzz33YP369YiJaX65YN68Lok/4ciZRrft3ZONUf2P\nUVVVpdr/GVhbcFhb4NRaF6Du2rQmLOmpjIwM3HLLLZAkCRkZGUhOTsbPP//c6uPeNegcGA16l21G\ngx75Of1bfWwiIvIvLGca77zzDnbt2oWCggIcPnwYdXV16NKlS6uPe/vxVZimL4NOZ4MVOrxuHYFd\n2Y9hrP5r4KnFwKkaICkdGLEQGJgbgu+EiIichaVpjB8/HvPmzcPtt98OSZKwbNkyl0tTQflwNs7Z\nXQoJACQgBjbk6T/B198dROP2HxBjrZf3O7Uf+GCG/Gc2DiKikApL04iNjcWf/vSn0B60Yo3cMJxI\nEnA1dkCytnjAYgI2LmbTICIKMe18Ily07Ax+nKoJTx1ERO2YdpqGpPe/j7Ok9PDUQUTUjmmnaWRP\ncfuMhhDA1+ISNOpb5K8NRvlmOBERhZR2msaoJ3GizzjYAAjIDeMM4lHSOAxLpGk4azwPgAQk9QBG\nr+T9DCKiMNBO0wBg6jIQOoMREuSb4B2keqwwvITjZ83IrvszysZ8D8zawYZBRBQmmmoaXSqfl5NR\nThIkMx6OKXHMoCIiovDRVNMwnD3scXt36RgA4MBJk8fHiYgoNDTVNCwJ3TxuPyA6A5BnUBERUfho\nqmn8PHCanIxyclbE4onGXM6gIiKKAE01jdO9RsrJqKQeEJBwCF0wz/I7VHT6NZaPG8A1NYiIwkxT\ny7122rceDRVPIdZyChBAvFSPO4ecj7/ccn20SyMiahe0c6ZRWYJu5Y8jznLKEblNxi+4tOJRbF73\nQrSrIyJqF7TTNDYuRgzc50/FSo3o8V1RFAoiImp/tNM0fAwg7CqORrAQIqL2SztNw8cAwiNSagQL\nISJqv7TTNEYsRCPcJ92aRQz2D8qPQkFERO2PdprGwFyc7jNGHlgo5F82AbxhHY6Z/+mLsi210a6Q\niKjN007ktrIESXvWyV2uaQk/CcBE/eeoON0P80rNAMDPahARhZF2zjQ2LoZeNLptjpOsHFhIRBQh\n2mkaPtJTHFhIRBQZ2mkaPtJTHFhIRBQZ2mkaIxbCKrnfgmkQeg4sJCKKEM3cCC+zXo0K3I+5YjUS\nUQ8AsAF403odvoy/Dq9etheX/2MO8H6NfFZiXyN842L50pZ9G1f1IyIKmiaaRtmWWswr3Y5fW63Q\nGWyQmtJTegAT9F9AZ5GQte1LwCo3E5zaD5Q9IA+ospqbt30wQ/4zGwcRUVA0cXmqaEM1TBY5JZUg\nmV0eS5DMuF23ETH2hmFnszQ3DDuLST7zICKioGiiadhTUd0lzzOm9LApP5iPFBYREfmmiaZhT0Ud\nEJ5nTFkD+TZ8pLCIiMg3TTSN/Jz+MBrklNRZEevy2FkRizdsI9Coj3d9ks4A6F33hcHYfIOciIgC\npokb4fbRIMs+lPCICXg4pgTdpWM4IDpjj+iGO/Qbobc6XaJK6sH0FBFRGGiiaQBy41j8/jassw3F\nOvNQAMCimFcwWf+pfRRVs743NjcHNgkiopDRxOUpu+P1wuXrSfrPHPFbFxVrIlIPEVF7o6mm0ZLX\n1JRwXxaWiIhaT9NNw2tqSnJfrImIiFpPU00jJd71WtRr1ushhIcds6dEpB4iovZGMzfCAWBN6uu4\n5OcPXG5823uGY1uH84BRT0a2MCKidkIzZxq7/3ofBvz8AXSSPFLK/ksnwTU9VXcQ+L9bolUmEVGb\nppmm0WtfieeklCd7/xnWWoiI2ivNNA29CGC+FBERhYVmmoZV0kypRERtlmZ+Eu/rles5KeVJxrVh\nrYWIqL3STNPYftlCFFtvgBBw/9W0jwDwDQYio+o+XL3iM5RtqY1myUREbY4mIrdlW2ox862teNVw\nEADcbojbIOHbQYWYvLkXTBb50+C1J02YV7odQPPAQyIiah1NnGkUbagGAFyj+95jgkoHgR7fFTka\nhp3JYnU8l4iIWk8TTcO+cp8vXYXnVf2UPJeIiJTRRNOwr9znyxHJ86p+Sp5LRETKaKJp5Of0BwB8\nabvYY4LKBgn7B+XDaHAdVGg06B3PJSKi1tPEjXD7jezJb/0BO2LvQiIszQ9KwA9Iw4R/pQOwQicB\nNgGkJRuRn9O/7d8Eryzh6oREFDGaONMAgGc//y8+is1HomRxmT0lAegravCqYSkAuWEY9FL7aRgf\nzABO7Qcg5N8/mCFvJyIKA800jf8eOYMLpVqP6SlJkpNVdharaB+pqY2LAUuLG/0Wk7ydiCgMNNM0\nAtUuUlOnagLbTkTUSm22abSL1FRSemDbiYhaSTNNo2/XROwUaR7TU0LIySo7+z2NNm/EQsDQojka\njPJ2IqIwCFt66tixYxg3bhxeeeUV9OnTp9XH++SGIzjz7jEA8Ng4rtF9j71xdzhWZJI+TgH0hfIX\nGxfLN4klPSCsQFIP15SRpwSS43lRSCUpTUTZtzE9RUQREpamYbFYsHDhQsTHx4fmgJUlsJZOQ6Jk\n9b+vnek48N40QKcHrGZ5m2h6vj1lZPfBjOYbyqf2A2UPyHfX7c9z3j/cP5DtiSjneny99sBcNgki\nipiwXJ4qLCzExIkT0bVr19AccONi6BFAw7AT1uYf/C3ZU0aeEkg2i/vzIpVKYiKKiFQs5GcapaWl\nSElJwTXXXIMXX3zR635VVVWKj3nhqRooXek1EKIpZaT02OJUDXZ6qLu+vj6g78cXb9+rt9f2J5S1\nhRprC45aa1NrXYB6asvMzIx2Ca0W8qbx7rvvQpIkbNq0CVVVVZg7dy5WrVqFLl26uOwX0JuXlN70\nAbbQkuwpI4XHlpLSPdZdVVUVuv8ZvHyv3l7bn5DWFmKsLThqrU2tdQHqrk1rQn556rXXXsPatWtR\nXFyMzMxMFBYWujWMgI1YCCv0/vdrSdID+ljPj9lTRp4SSDqD+/MilUpiIoqIVEwTs6cwMBd6ANbS\ne6HzteZr01gRAXmIoWSzwiRikAAJEpye1zI9BagnPcVEFBGpWFibRnFxcciOdfifL6OrEB7HiNgJ\nAezqMBg96iphlMyABCSiwXUn+7/anX8Ie0sgResHNRNRRKRSmvlwX9dj5T4bBiCnZPvWfSs3DG+Y\nRCIiCppmmgZ8XJUKGGczEREFRTtNI5SZW85mIiIKimaaxpHOQzyOD3EmBPDfDoNhEl4SUwCTSERE\nraCZptHtwQ04Ft8LQsDrr0a9Ef1y7sOO7CU4hC6wCQlnEAfhGEilBy69gzeZiYiCpJmmgcoSJDf8\n5LpqX4tfBpsJ+GAGLj//HJxb8AN0v30RiQZdc9xWWIFtr3NlOyKiIGmnaWxcjBgld8Od01Gc40RE\nFFLaaRqBJJ7s+3JlOyKikNJO0wgk8WTflyvbERGFlHaaxoiFaFSSu3VOR3GOExFRSGmnaQzMhQkJ\nHlNTZuE0WSrG6PIcjF4pz5qCJP8+eqV7eqqyBHjqEqAgWf6dN8qJiDzSxsBCACce74NkccZtlIgQ\nQExzqFZesc95pTt/c5wCXSmPiKgd08yZRnLjUY+zpyQJ0LXcHkhCigkrIiLFNNM0AqY0IcWEFRGR\nYm23aShNSDFhRUSkmGaaxsmYVI+zp4QAbC23B5KQYsKKiEgxzTSNcxbsxnFdilty6owwYLFhJs4a\nz4PPhJQ3ShNWRESknaYBAM/q7oC1xbYEnQUFjX9GgumgfFf81H7gvWlAQZLy+OzAXGDWDqDgpHyG\nsXEx47dERB5oJnK7bMUizG98xi0p5fKlsDX93tRaAo3PMn5LROSTZs40Jp991T1aqwTjt0REIaOZ\nptFdOhr8kxm/JSIKCc00jQMiNfgnM35LRBQSmmkaryZMdo/WKsH4LRFRyGimaTz6yGN43XqDz+Ve\nHT1Favq2/MVnWw4qBNQZv43EQMXKEmBpdzl1VpAELDoH+HB26F+HiDRNM+mpZ/+8FPfrP/U4f8pN\nTJz/H/beklKjV8rxW7WIRKKrsgQovQ+ArXmbsAHfviz/edSToXkdItI8zZxpjDn+ivL0lJLEk1aS\nUpGoc+NiuDQMZxVrQvc6RKR5mmkaAaen/CWetJKUikSdvo4lWn6ckojaM800jYDTU/4ST1pJSkWi\nTl/HkvShex0i0jy/TePgwYNYvXo1nnnmGcevaHg/5W7l6SkliSetJKUiUeeIhfD6v0L2lNC9DhFp\nnt+m8dBDD6Gurg6pqamOX9EwfeYfYJV8JKfs6SlJD1x6B/BTObAopSkJlOKeBNLKoMJw11lZ4vme\nhqQDBk/lTXAicuE3PZWYmIhZs2ZFohbfFqUiBvCfnhJW+eat87V4YfWcBPK3FKxahKvOlsksQD6L\nUWPzJCJV8Hum0bdvX/ztb3/Dnj17sHfvXuzduzcSdbkTFigePeXt5i2TQK60kiAjItXwe6ZRVVWF\nqqoqx9eSJOHVV18Na1FhwySQK60kyIhINfw2jeLi4kjUERlMArlKSpc/LOhpOxGRB16bxowZM7By\n5UoMHTrU7bGvvvoqrEV5JBkglF6ikvSezyqYBHI1YqHnexpqS5ARkWp4bRorV64EEKUG4cljRyEK\nkpwGTDmRnBZjknRywzAkApazcHnC9+/Jv//3Y/kSTFK6/AOy5U1fe6LI1z6BCscxW3tc+34fzQVM\nx+U/xxi9709E7Z7fy1OfffYZSktL0dDQ4Ni2evXqsBblye6CS9BbKElPNUVHLWfcHzMdb05RAZ7n\nOIVj1lO45keF6riNTmcapuNcrZCIvPLbNAoLC7F48WIkJSVFoh6veov9yoYVBsqeFrL/gPSVKAr2\nh2g4jhmq44arNiJqk/w2jb59++LKK6+MRC3R45wWCkeiKFwppVAclwkqIgqA36YxYsQI3Hbbbejd\nu7dj2/Lly8NaVMQ5p4XCkSgKV0opFMdlgooo6r755hvMnDkTF1xwAYQQaGxsxOTJk3HTTTd53P/A\ngQPYuXMnrr/++la/9smTJ/Hll19i9OjRivb3++G+4uJi3HXXXbjpppscv6Jhj9QDIpiV+/xpmRYK\nx6yncM2PCsVxtTKDi6iNGzJkCIqLi7F27Vq8/PLLeOmll1w+I+esvLwc3333XUhet7q6Gp999pni\n/f2eaaSmpkatUTjrU7AD5oVJMLTYLgDYoIMeNjRIMYhDo/dYrjEFuPjWpvTUfjlpZTEBpffICSL7\nYxZTc2w3qYfyRJK3JJPz/ZJQpqdCcdxw1UZEQUtMTMRtt92Gv//971i7di0OHTqEEydOYNiwYXjw\nwQfx4osvor6+HllZWejYsaNjkGx9fT0KCwvRvXt3x9zA+vp65Ofn48orr8RHH32ENWvWQKfTITs7\nG3PmzMHzzz+PnTt34q233sJtt93mtza/TSM+Ph5Tp07FRRddBKnpTvTs2ZFfBvRoQQY6Sx7SUwL4\nly0Te8V5mOxrZT99LPCbQvmHYWUJ8P50wGpufrxlskpYm//FrbRh+EoyhWt+VCiOq5UZXETtSOfO\nnfHxxx9j5MiRmDBhAhoaGjBs2DDMnDkT9957L/bs2YMRI0bgtddeQ1FREbp164bnn38e69evxw03\n3ICjR49izZo1OHbsGH788UecPHkSTz/9NN59910YjUbk5+fj66+/xrRp0/Dmm28qahiAgqZx3XXX\ntfqbD4XO4rjHhiBJwDW67/ErVPlOV1nNzYmgjYtdG4Y3gaSImEIiohA6cOAAsrKysH37dpSXl6ND\nhw4wm91/bnXr1g1Lly5FQkICDh8+jEGDBqFv376YNGkSZs+ejcbGRuTl5eGnn37C8ePHce+99wIA\nzpw5g/379yMjIyOguvw2jfR0bdwQ1XtbrtSZPREUinSR0v2YQiKiANXV1eHtt9/G+PHjYTKZsHjx\nYuzbtw8lJSUQQkCn08Fmk3/mzZ8/H59++ik6dOiAuXPnQgiB6upqnDlzBi+++CKOHDmCiRMn4p13\n3sF5552HV155BQaDAaWlpcjMzERdXZ3jWEr4bRpvvPEGAEAIgR9++AFpaWm4/PLLg3wrwscKHWL8\nNQ57IshbYsjXc5TsxxQSEQWpvLwceXl50Ol0sFqtePDBB5GRkYHZs2ejoqICRqMRvXr1wpEjR9Cv\nXz+sWrUKF198McaMGYPc3Fx06tQJqampOHLkCM4//3w8++yzKCsrg8FgwIwZM5CSkoIpU6YgLy8P\nVqsVaWlp+M1vfoPTp09j165dWLNmDaZMmeK3TkkI5Zkks9mMmTNn4rnnnmvNe4OKigpkZ2cH9Jyj\nBRkeL1EJAXxpu1jZPY0xz3q/p+GJwrUlqqqqkGnZrsq1KaqqqpCZmRm11/eFtQVHrbWptS5A3bVp\nTUBrhFutVuzfr/Bf6CGWWrAXZySDx9X6huq+xwjdVvxXSodA83ZHNzSmNDcMQP59zLPydud9Mq51\nmoQryemq0nuBpy6RG01lifznguTmbXZaWQlQS3y930QUFX4vTzlPuW1sbMRdd90V1oK82bRyCoYI\ni9uZhP3LdOkohH02ldM+VskAvT015axlYsiefnJMxxWAuWl+1an98pmJEIDN0rzNno4yDPB8TApe\nuOZ1EVGr+G0aaplye/mx9/3OnvL0uF5YlCWYPKWfnHm6lGVPR4182/exKXBMoxGpkt/LU9u3b8e4\nceMwbNgwTJw4Ebt27YpEXW4UpaO8UZJgCjblxHRUeDCNRqRKfpvG0qVL8cQTT+CLL77AokWLUFBQ\nEIGy3FkDu/3iSkmCKdiUE9NR4eHtfeX7TRRVfn8Sx8XF4YILLgAA9O/fHwZDy0EekbG58xi/s6c8\nPW6VDMrmKHmaweRMHwvoWnzvnNEUPpyJRaRKXu9pvPXWW/IOMTEoKCjA5ZdfjsrKSnTo0MHvQa1W\nK+bPn4+9e/dCr9dj+fLl6NmzZ6sKvWrGGpwp+BAJwuJzP+fGUa8zwph9hzxXqvQeeaMxxXWciPPM\npfQrgB+/aroZLgF6Q/O9DL2hadCVxf04XoaKRVXT93ahVudJcSYWkUPZlloUbajGgZMmdE82Ij+n\nP8ZmpbXqmDabDQUFBaiurkZsbCyWLFmCXr16+X2e16bx888/AwCysrIAAHv37kXHjh0VZZ0///xz\nAMCbb76Jb775BsuXL8eqVasUfSPenHi8D5I9pKd80dnMsH67Bno4rRduOg6UPQD8VA5se901nePy\n4TzhevPb3GIlwEYfN82jzSl5JAHaTR4xjUaEsi21mFe6HSaL/HOs9qQJ80q3A0CrGsenn34Ks9mM\nt956C1u3bsWKFSsU/Zz22jRuvvnmoIu54YYbMHz4cADy/JTU1NSgj2WX3Hg04JX74iSr5wdsFqBi\njVO8NghqTvIweUTUZhRtqHY0DDuTxYqiDdWtahoVFRW45pprAACXXXYZduzYoeh5XpvGwoULIUkS\nhBCO6bYnTpzAjz/+iO3bt/s/cEwM5s6di08++QQrV650e9zbnHhvLgxob/+EsHofoa70GKdqsLOq\nCvX19QF/P+F04akaj9+bvV61UNv75oy1BU6tdQHqqS2YT6UfOOn5qoa37UrV1dW53G7Q6/VobGxE\nTIzvT2J4fbS4uNjx58rKSqxduxa7d+/G+PHjFRdVWFiIOXPmIDc3F3/729+QkJDgeCzQNy/U6y9J\n9vUyWnOMpHRkZmaqb0SBlzlY9nrVQnXvmxPWFji11gWouzZ/uicbUeuhQXRP9hHcUaBDhw44c6b5\nsrvNZvPbMAAf6Smz2Yz33nsPEyZMwIoVK7Bz505s3LgRjz32mN+DlpWV4YUXXgAAGI1GSJIEvV7v\n51m+nYxJDXjlvgahhxUeXldnALKn+E5L+aPmJA+TR0RtRn5OfxgNrj/HjAY98nP6t+q4gwYNwhdf\nfAEA2Lp1K/r166foeV6bxvXXX4/q6moUFRXh9ddfR9euXREfH6/ooDfeeCP+85//YNKkSZg6dSoe\nffRRxMXFKXquN+cs2I0GSec+e8rLrzoRB0nSud4EBwBDohyf/fblpuv+TRdyknoAg6e6zqMyJAKx\niU5PdtpXzXOlnOZgCc7BItK0sVlpWD5uANKSjZAApCUbsXzcgFanp379618jNjYWEydOxPLlyzFv\n3jxFz/N6LjJ58mR8+OGHqK2txfjx4xHAMFwkJCTgL3/5i+L9lahcei0GCJvim+GJaPB8z8JyFq4X\nu4R85mH/V/i21532bZGYgghsNb9oakoe7dTwaTkRycZmpbW6SbSk0+mwePHiwJ/n7YF7770X69at\nQ15eHj788EPs2LEDRUVFURsjMsC8NaD0lPddPTQ/W9N8Kn/zp4DmFBIRUTvk9xPhV1xxBYqKivDJ\nJ5/g3HPPxcMPPxyJuiLvVE3rV+kjImrjFA906tSpE/Ly8lBWVhbOeqInKT2wVfqIiNqhVkwBjKzt\nsZcFlJ7yvquHC1f2exr+5k8BTCERUbummaYx8A//xG4p3XNaCq5fnzB0gzR4KqCLdT1IxrXAuBdd\nE1KQmu9p/FQOxDg1DWOKnKhquRof4LKiXKd965ufU1kCFGYABUnyr8KM4Faci+SqdVwhj4gU8v9J\nDpXYvO4FZIka7zfD7dsNRpwzeon8522vw2UZjpp/A1l3AnP3el4Z7tuXXY/ZaAJ6DgFGPdm8zcPz\nztu8AujelGxoufa4fdYVoDxxFclV67hCHhEFQDNnGj2+K0KMkvSUPd3ka/4SEHxSysPzdNb65tf0\ntMKf/UxGKX+1h1IkX4uIghOmqwHbtm1DXl5eQM/RzJlGV/GzrxytK1/pJvtjwSalgl1RLpDEVSRX\nreMKeUTqFqarAatXr8a6detgNAY2GUMzZxpHpC7Kd/aVhLJvDzYp5eu4vo4ZSOIqkqvWcYU8InUL\n09WAnj174umnnw74eZppGvsH5aNRSXrKnm7yN38p2KSUh+fZ9PHNr6lvcfMdcP3EuRKRnB3FOVVE\n6hamqwE5OTmKBhS2pJmmcfkt9+FQTA/PySk0/9lqaZBX6XtvmutsKQCw1MsJKUA+rbv0DkBqGgQm\n6eV0VcukVMvTP6e5ToAEGFNg08cBpffKnT8rzzWdZUwBxj4X2Glky9cI5+yoSL5WC532rQ9N0oyo\nLVPZ1QDN3NM4/HQO0hr3+01P6e1xKcfY8xZzpuwJqZ5D5HSVfT9hldNVSn5g2leUa7rWGON8rXHb\n66H5oRvJVeuisUJeZQnO+2YJIBqbt5mOy+kze01EJJ/1O9/TAKJ6NUAzZxpdj5UHvHKfVxVrQnOd\nkMmj4G1cDJ1zw7Czmvn+ETmL4tUATzRzpgEB5ekpv8eyhuY6IZNHwVOScCMiWZiuBqSnp6OkJLBL\nwpo50whZwwDk+xehuE6osmuNmhKqpBkRRZRmmsaRzkMCXrnPq+wpoUkNMXkUvBELYZM8nOjqY/n+\nEamYZppGtwc3oE5K9JuecusrsYlwnKZIemDwVMxv/B/0eaMDZpz5H9SK1OBXt2u61mhOOBd+rzUG\n+olOb/s7ticBi1Lk37U4L2pgLg5eOd89aTbmWe3cBOfMLmqHNHNP4/DTOegqzvifPeVMHwuM+rPL\nD6H5ZduxtvwnAMA6MRTrGoYCAO68rCeWDBwQeGEDc7HbMMD36niBfqLT2/4/lcvpLPt2e/JLo/Oi\nTvcaibSRs6JdRnA4s4vaKc2caQSVnvKQxHnjm/0ed/W2PSQCTVl5279ijfd5WUxtRRaTc9ROaaZp\n+Fggw7cWSRyrlxsj3raHRKApK2/bHZ89CfB1KPSYnKN2SjtNI9j0VIskjt7L6Yq37SERaMrK23b7\np9cDfR0KPSbnqJ3STNMIKj3lIYlz+5U9PO7qbXtIBJqy8rZ/9hTv87KY2oosJueondJM0+iWcTEg\neVi1zylF5ULSyXOgnG9KVpZgyd7bsTd+Er6Om4FbdF9BL0m4c0hPLBkbxE1wpQL9RKe3/Uc96bQd\nzWceLY/nlOrp88HY0KR6mBSFrNrFAAAYOElEQVRypbJP6VKE8O+BRtJTH86G+PZl+QqV0qtIwiYn\njXoOcZkTBYsJEoA06ShWJv4VK0dnAcGkpgIV6Cc6ve3v7zgtUj2xZw+1PtXDpJBn0ZjZRdHDvwcA\ntHKmUbEmuFsa/lbqa4tpl3B8n+3lvSPyhX8PAGilafhLDfnib6W+tpZ2Ccf32V7eOyJf+PcAgFaa\nhr/UkC/+Vupra2mXcHyf7eW9I/KFfw8AaKVpZE8J7mMa/lbqa4tpl3B8n+3lvSPyhX8PAGilaYx6\nEtLgqbDBe3rK5jxfCnBPs7SXtEuL79OccG7rv8/28t4R+cK/BwC0kp4CgFFP4sCWj5HW2DzuwyZJ\n0BvPAUwnICWlA31vBL5/T14B7tR+ednXD2cC+jh5m6SX748k9ZD/dRDIf+zKEvmG16ka+XTU2/Nb\n7tf3RuC/H/t/XiD81eKU6tldVeV7LpZSTAoR8e8BNNQ0Di67zG25Vz2E3AwAuUnYl3J1Zj4D4Iz8\n52AH/CmN2nnaz7mmUET0GPsjoijSxuUpAOc27A3dcq9AYFE5pVE7T/u15nVbUwsRURhopmmEhdKo\nnNKoXWuPF8paiIjCoH03DaVROaVRu9YeL5S1EBGFgWaaxqG4jNAt9woEFpVTGrXztF9rXrc1tRAR\nhYFmboSf9+hW7Cq4GH2F02UYCZD0sYDVIv9LO6U38OOX8twpu9hEr+mpMuvVKFrxGQ6cNKF7shHX\nXdgFn+/8GbUnTdBLEqxCIC3ZiPycqzF29Epl6akYY/M9B2MKcPGtoU1P2Z+rMMnVZ/0C4K3DoUtu\n+aM0ZUZEmqSZpjFp9Sbc2Hgh+uprXG6IC6sZ0uCp8mDCD2a4NgyD0W25V7uyLbWYV7odJoucqKo9\naXIsAws0L8pUe9KEeaXbgXFXY+ysHd4LbJlqAoBGk1zXqCeD+6a9URL7a6onNpIpKya7iNo8zVye\n+nr3cUzSf+aWoJIAeRnUAFNFRRuqHQ3DH5PFiqIN1b53UluqKRr1qO09IKKQ00zTAAA9bJ4fENaA\nU0UHTvqJxga6v9pSTdGoR23vARGFnKaahtVbuZI+4FRR92Q/N6wD3V9tqaZo1KO294CIQk4zTePq\nPil4zXq9W4JKAPIyqAGmivJz+sNoUDY912jQIz+nv++d1JZqikY9ansPiCjkNHMj/LV7rsKwZfcg\nw3QQ1+i+d3lMfPsyzNveRdyldzQllfbLZx/26+k/lbslmMZmyTdmizZUK0hP9cfYrDTfBQaSavKk\nsgT4aG7zWBRATl/9pjC4m8hNzzGvX4DYsxFKT7X2PSAi1dNM0yjbUotBZ/6JwTH/9ThOJM5yEtaK\nV6HPniwv86pg/tPYrFz/zSAQwQ4zqywByh4AbBbX7abj8nb7sYOoZ7dhQGgGFgbwmmwSRG2XZi5P\nFW2oxhx9CRIks9d99MIiJ6nCPf8p1DYudm8YdjaLumolonZNM03jwEkTuktH/e+odGlYNSV6/NWi\nplqJqF3TTNPonmzEAZHqf0elS8OqKdHjrxY11UpE7ZpmmkZ+Tn/80ZqLsyLW6z5WySAnqcI9/ynU\nRiwEdAbPj+kM6qqViNo1zTSNsVlpGJv8I+Jh9rzkKwB9TKw8tqPlkoyDp8pJJLvGBnlVv6cukW9C\nh0pliXzMguTAjx3X0X2bMQUY+1x4byy3puZW6rRvfdRem4iCo5n01O6/3odr6/7ueyEmyxk5bTT2\nOcB5TlRliZyosrPPpwrlbKRg5y55mlllMEZm7eFozoqqLMF5m1cA1vrIvzYRBU0zZxq99pUoW7nP\nU9rI14p6oUpSBTt3KZrzmqL82jp7w4j0axNR0DTTNPTCy9wpTwJdUS8U6aRg5y5Fc15Te31tIgqa\nZpqGVQqg1EBX1AtFOinYuUvRnNfUXl+biIKmmaaxr1euspX7PKWNfK2oF6okVbBzl6I5rynKr23T\nx0fntYkoaCG/EW6xWPDoo4+itrYWZrMZ999/P0aMGNHq4/b5nxdwumAdOoqz7g9K9t8koNev5BlO\npffIG+3zmxwr7+13W8FP8Y3XD2fLnzgXVvkY2VOaF1hqzdyllqv9Oc+bajmTSsk8KqfV8/okdAMs\nj3vePxTzsuzPNZ4jbzOdUHacgbk4eKAWaVUvc04VkYaEvGmsW7cOycnJKCoqwokTJ3DrrbeGpGkc\nXHYZzhVn/dwMF8Def7puss9vapmoCtSHs11nWAlr89d9mhpUoHOXvK325/x4y5lUpuPA+9ObX8/P\nMWPPHvKdSmrNvCzn2p0HLSpMQp3uNRJpI2cF/tpEFDUhvzw1cuRIPPTQQ46v9XqFn9D249yGvcrS\nU56EYn5TxZrAtivhL73kbSaV1ez9+4lUIspXIi1cr0lEURfyM43ExEQAQF1dHWbMmIGZM2d63K+q\nqiqg417YyrrEqRrsDPA1XV5fWOGpZwlhRX19fcDfDwBceKrG8zGbavX2uPM+gR4zVHzVpvQ1g33f\nIoG1BU6tdQHqqS2iE6fDJCwf7jt48CCmT5+OO+64A6NHj/a4T6BvnpJ74L5ISemt+w9mvw/ScrOk\nR3x8fHDHTkqXL+W0PKa9Vi+Pu+wT6DFDxUdtSl+zqqpKtX+JWFvg1FoXoO7atCbkl6eOHj2Ku+++\nG/n5+Rg/fnzIjnsoLkNZesqTUMxvyp4S2HYl/KWXvM2k0sd6/34ilYjylUgL12sSUdSFvGk8//zz\nOH36NJ577jnk5eUhLy8P9fX1/p/ox3mPbsVR6RyPM6ccvcQ+4db5Mx2SrvmehtLZRp7mMY16Up5h\n5XgNvfy1PT0VjIG57nOynMeHDMyVb+A7z80ypgBjnvV+g7nFMc0J54ZnJEnL2o0pTXV6+D6IqM2Q\nhAj63+9Bq6ioQHZ2dkDP2bzuBVxSMR9GL4swCcD1GrvOAEiSfNPYTslMpyBmQan51Je1BYe1BU6t\ndQHqrk1rNPPhvh7fFXltGADcb8raLK4NA1D/LCgiIpXTTNPoKn4OzYHUPAuKiEjlNNM0jkhdQnMg\nNc+CIiJSOc00jf2D8mHysWqf240ZnUFOGTlT+ywoIiKV00zTuPyW+/Bm6oM4Ljo4klPWpvSUDb7u\naTQ94inR4ykl5SkVFGMESu/1vbpctFbAi+LKe0TU/mhm5b6yLbXYcbget+nNjnEieshNw3fnE4BO\n7z4Mz9+qdQNzFa9s12nfeqCiMPIr4EVz5T0iapc0c6ZRtKEas3RvIaFFgkrROCqbVdlqfi1TUgqT\nVF0qn49O4opJLyKKMM00jQMnTeguHQ3+AEpX83PerjBJZTh7WNlrhhqTXkQUYZppGt2TjTggUoM/\ngNLV/Jy3K0xSWRK6KXvNUGPSi4giTDNNIz+nP56y3YazLRJUij7Obr+n4UxJSkphkurngdOik7hi\n0ouIIkwzTWNsVhp6XzUWTxgeQI0tFTZIOGs8D3t6TcRJdHSfQ2UXmwiMfd79xrCSlJS/2VBNTvca\nqWi/kFNYHxFRqGgmPQUA1/fuiMybFwFYBAD4eEst5pVuh8lyC27RfYUVhpdcb5QbjMCoP/se7qck\nJaXkh3CwK+C1VrRel4jaJc2caXhStKEaJou8xsXDMSVuySrFSSKmkIiIFNF00zhwsvkHvddklZIk\nEVNIRESKaLppdE9uvgnsNVmlJEnEFBIRkSKabhr5Of1hNMiLIj3RmOuWrFKcJGIKiYhIEc3cCC/b\nUotNZWvxoHgT3aWjOCSlIm3Qw3j1cqDvd48jSfwCSE6LMUl64NI7lN/EBuR7GKdqAOM58tel98rb\nWo4gCZXKkubXTEoP3+sQEYWIJppG2ZZa/PPdZ7FUvxoJOvlmd3ccRWrFo4iRrNBBuM8TEVZg2+tA\nzyGBpZ8iNc+Jc6OISIM0cXmqaEM1fu9h7lSs1Cg3DG+CSUBFKknFxBYRaZAmmkar5k4FmoCKVJKK\niS0i0iBNNI1WzZ0KNAEVqSQVE1tEpEGaaBr5Of3xJw9zp8wiBjZfw9GDSUBFKknFxBYRaZAmmsbY\nrDRc+9vpWIR75blTAmiEDgapETrjOYAh0WlvpyYSYwR+Kg9sZbtIzXPi3Cgi0iBNpKcAuXH0j78T\n6ZZLgQ9mQGe/iWw6Lv8Lfdxq+WvnRJLpOPDty80HUZpQitQ8J86NIiKN0UzTcPCXOmr5WEv2ffnD\nmogoYNprGqFIHTGhREQUFE3c03DhK3WkNHnEhBIRUVC01zR8pY48PdYSE0pEREHTVNP4bM8vKFj3\nPY416B2r9DUYklEg7kPG64m4+u+p2DxgkWsiafBUJpSIiEJEM/c0yrbUYs+mMjzuNH8KAKzmszhu\nMUMAqD1pwuTNvbB83AaMzUqLXrFERG2UZs40ijZUY5aH+VMJkhkPxzR/9sJksaJoQ3WkyyMiahc0\n0zR8zZ/qLh1z25eIiEJPM03D1/ypA6Kz275ERBR6mmka+Tn98ZSH+VNnRSyeaGy+sW006JGf0z/S\n5RERtQuaaRpjs9LQ+6qxeMLwgDx/ChLOGs/D99lLUNHp15AApCUbsXzcAN4EJyIKE82kpwDg+t4d\nkXnzIgCLAAAJAC4H8PUt0ayKiKj90MyZBhERRR+bBhERKcamQUREirFpEBGRYmwaRESkGJsGEREp\nxqZBRESKsWkQEZFibBpERKQYmwYRESnGpkFERIqxaRARkWJsGkREpBibBhERKcamQUREirFpEBGR\nYmwaRESkWNiaxrZt25CXlxeuwwOVJcBTlwAFyfLvlSXhey0iIgIQpuVeV69ejXXr1sFoNIbj8HKD\n+GAGYDHJX5/aL38NAANzw/OaREQUnjONnj174umnnw7HoWUbFzc3DDuLSd5ORERhIwkhRDgOXFNT\ng9mzZ6OkxP2yUUVFBRISEgI+Zn19PeLj43HhW1dBgnvZAhJ23rYpqHpbw16XGrG24LC2wKm1LkA9\ntWVmZka7hFYLy+UpJYJ586qqquTnJaXLl6RakJLSo/IfxVGXCrG24LC2wKm1LkDdtWmNNtNTIxYC\nhhb3SwxGeTsREYWNNpvGwFxg9EogqQcASf599Er5sdYkqpjIIiLyKWyXp9LT0z3ezwiZgbmuSanW\nJqqYyCIi8kubZxqetDZRxUQWEZFfbadpnKoJbHuon09E1A60naaRlB7Y9lA/n4ioHWg7TaO1iSom\nsoiI/Go7TcNbokrpTezWPp+IqB2I2of7wqJloirSzyciauPazpkGERGFHZsGEREpxqZBRESKsWkQ\nEZFibBpERKQYmwYRESnGpkFERIqxaRARkWJsGkREpBibBhERKcamQUREiklCCBHpF62oqIj0SxIR\nqUJ2dna0S2iVqDQNIiLSJl6eIiIixdg0iIhIMTYNIiJSTPWLMNlsNhQUFKC6uhqxsbFYsmQJevXq\nFe2ysG3bNvzxj39EcXEx9u3bh0ceeQSSJKFv37547LHHoNNFvh9bLBY8+uijqK2thdlsxv33348L\nLrhAFbVZrVbMnz8fe/fuhV6vx/LlyyGEUEVtAHDs2DGMGzcOr7zyCmJiYlRTFwCMHTsWHTt2BACk\np6fjtttuw9KlS6HX6zF06FD87//+b1TqeuGFF/DZZ5/BYrHg9ttvxxVXXKGK9620tBTvvfceAKCh\noQFVVVUoLi5WxXvWJgiV27Bhg5g7d64QQogtW7aIadOmRbkiIV588UUxatQoMWHCBCGEEPfdd58o\nLy8XQgixYMEC8fHHH0elrnfeeUcsWbJECCHE8ePHxbXXXqua2j755BPxyCOPCCGEKC8vF9OmTVNN\nbWazWTzwwAPixhtvFD/88INq6hJCiPr6ejFmzBiXbbfccovYt2+fsNls4ne/+53YsWNHxOsqLy8X\n9913n7BaraKurk6sXLlSVe+bXUFBgXjzzTdV8Z61Faq/PFVRUYFrrrkGAHDZZZdhx44dUa4I6Nmz\nJ55++mnH199//z2uuOIKAMCwYcPwr3/9Kyp1jRw5Eg899JDja71er5rabrjhBjz++OMAgAMHDiA1\nNVU1tRUWFmLixIno2rUrAPX89wSAnTt3wmQy4e6778bkyZOxefNmmM1m9OzZE5IkYejQodi0aVPE\n6/rqq6/Qr18/TJ8+HdOmTcPw4cNV9b4BwPbt2/HDDz/g5ptvVsV71laovmnU1dWhQ4cOjq/1ej0a\nGxujWBGQk5ODmJjmK3tCCEiSBABITEzEL7/8EpW6EhMT0aFDB9TV1WHGjBmYOXOmamoDgJiYGMyd\nOxePP/44cnJyVFFbaWkpUlJSHP8wAdTz3xMA4uPjMXXqVLz88stYtGgR5s2bB6PR6Hg8WvWdOHEC\nO3bswF/+8hcsWrQIc+bMUdX7BsiXz6ZPn+72M0QNtWmZ6u9pdOjQAWfOnHF8bbPZXH5gq4Hzddsz\nZ86gU6dOUavl4MGDmD59Ou644w6MHj0aRUVFqqkNkP9VP2fOHOTm5qKhocGxPVq1vfvuu5AkCZs2\nbUJVVRXmzp2L48ePR70uu4yMDPTq1QuSJCEjIwMdO3bEyZMno15fcnIyevfujdjYWPTu3RtxcXE4\ndOhQ1OuyO336NPbs2YMhQ4agrq7O5WdItGvTOtWfaQwaNAhffPEFAGDr1q3o169flCtyd9FFF+Gb\nb74BAHzxxRcYPHhwVOo4evQo7r77buTn52P8+PGqqq2srAwvvPACAMBoNEKSJFxyySVRr+21117D\n2rVrUVxcjMzMTBQWFmLYsGFRr8vunXfewYoVKwAAhw8fhslkQkJCAn766ScIIfDVV19Fpb7s7Gx8\n+eWXEEI46rrqqqtU875t3rwZv/rVrwDI//A0GAxRf8/aCtV/Ityentq1axeEEFi2bBn69OkT7bJQ\nU1OD2bNno6SkBHv37sWCBQtgsVjQu3dvLFmyBHq9PuI1LVmyBB999BF69+7t2PaHP/wBS5YsiXpt\nZ8+exbx583D06FE0NjbinnvuQZ8+fVTxvtnl5eWhoKAAOp1ONXWZzWbMmzcPBw4cgCRJmDNnDnQ6\nHZYtWwar1YqhQ4di1qxZUantiSeewDfffAMhBGbNmoX09HTVvG8vvfQSYmJiMGXKFADyPzjV8J61\nBapvGkREpB6qvzxFRETqwaZBRESKsWkQEZFibBpERKQYmwYRESnGpkERt2LFCuTl5WHkyJEYPnw4\n8vLyMGPGDI/71tTU4PPPP/d6rH379uH222/3+Xh2djby8vJw55134re//S22bt0KAFi1ahV27NiB\nt99+G0899RQOHz7sGHWixLBhw9ymE/zjH//A22+/rfgYRFqjro9WU7vwyCOPAJBHeOzZswdz5szx\nuu+mTZtQU1OD6667LujX69evH4qLiwEAP/zwA37/+9/j/fffx/333w8AqKqqAgB069YNCxYsCPp1\nAGD48OGtej6R2rFpkGosXbrUcRYwZswY5Obm4qWXXoLZbEZWVhbi4uKwatUqAPLIa+cRKUqdOnUK\naWlpAIA5c+Zg3LhxjsfsI+7feOMN3HzzzTj//PMRHx+P9PR0pKenY8KECdi1axeWLVuGNWvWAADm\nz5+PmpoadOnSBStWrMC6detQU1ODcePGYe7cuejatSv279+PQYMGtbohEakBmwapwqeffoojR46g\npKQEFosFEydOxJAhQ/C73/0ONTU1GD58OIqLi/Hkk08iNTUVzzzzDNavX4+cnBy/x961axfy8vLQ\n2NiIqqoqLFu2zO9zfvnlF8yYMQP9+/fHU0895XW/SZMmYcCAAVi+fDneeecdxMbGOh7bt28fXnnl\nFcTGxuL666/H9OnTkZKSouwNIVIpNg1Shd27d2Pw4MGQJAmxsbG49NJLsXv3bpd9unXrhsWLFyMh\nIQGHDh1yjOH2x/ny1OHDh3Hrrbdi0KBBfp+XkZHhts15gEJ8fDwGDBgAAMjKysK3336L/v37Ox7v\n1asXEhISAACpqakwm82K6iVSM94IJ1Xo06cPKioqAMjzlrZu3eqY7mr/Qb1gwQKsWLECK1asQOfO\nnRHMBJxzzjkHcXFxsFqtfve1Ty+Oi4vDkSNHAAD/+c9/HI83NDRg165dAIBvv/3WbZimfUw4UVvC\nMw1ShREjRuDf//43Jk6cCLPZjFGjRuHCCy+ExWLB6tWrkZmZidGjR2P8+PHo1KkTOnfu7PhB7o/9\n8pQkSTCZTJg0aZLjvoYSo0aNwsyZM1FeXo6LLrrIsT0+Ph5//etfsW/fPvTo0QPjxo1zLDNK1FZx\nYCERESnGMw1qE1auXInNmze7bS8sLET37t2jUBFR28QzDSIiUow3womISDE2DSIiUoxNg4iIFGPT\nICIixdg0iIhIsf8HanuPn9zl6LgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9ffe8c198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')   ## Background Grid\n",
    "sns.FacetGrid(df, hue = 'Dataset', size = 5).map(plt.scatter, 'Total_Bilirubin', 'Albumin').add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a9863af828>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAFgCAYAAACov06zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEgYhhBBZKGEQQgiR5f8D5XhiPfVMb0EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9863ed438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')   ## Background Grid\n",
    "sns.FacetGrid(df, hue = 'Dataset', size = 5).map(plt.scatter, 'Total_Protiens', 'Albumin_and_Globulin_Ratio').add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations\n",
    "* Finally, let's take a look at the relationships between numeric features and other numeric features.\n",
    "* ***Correlation*** is a value between -1 and 1 that represents how closely values for two separate features move in unison.\n",
    "* Positive correlation means that as one feature increases, the other increases; eg. a child's age and her height.\n",
    "* Negative correlation means that as one feature increases, the other decreases; eg. hours spent studying and number of parties attended.\n",
    "* Correlations near -1 or 1 indicate a strong relationship.\n",
    "* Those closer to 0 indicate a weak relationship.\n",
    "* 0 indicates no relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.056560</td>\n",
       "      <td>0.011763</td>\n",
       "      <td>0.007529</td>\n",
       "      <td>0.080425</td>\n",
       "      <td>-0.086883</td>\n",
       "      <td>-0.019910</td>\n",
       "      <td>-0.187461</td>\n",
       "      <td>-0.265924</td>\n",
       "      <td>-0.216408</td>\n",
       "      <td>0.137351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>-0.056560</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.089291</td>\n",
       "      <td>-0.100436</td>\n",
       "      <td>0.027496</td>\n",
       "      <td>-0.082332</td>\n",
       "      <td>-0.080336</td>\n",
       "      <td>0.089121</td>\n",
       "      <td>0.093799</td>\n",
       "      <td>0.003424</td>\n",
       "      <td>-0.082416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <td>0.011763</td>\n",
       "      <td>-0.089291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.874618</td>\n",
       "      <td>0.206669</td>\n",
       "      <td>0.214065</td>\n",
       "      <td>0.237831</td>\n",
       "      <td>-0.008099</td>\n",
       "      <td>-0.222250</td>\n",
       "      <td>-0.206267</td>\n",
       "      <td>0.220208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <td>0.007529</td>\n",
       "      <td>-0.100436</td>\n",
       "      <td>0.874618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.234939</td>\n",
       "      <td>0.233894</td>\n",
       "      <td>0.257544</td>\n",
       "      <td>-0.000139</td>\n",
       "      <td>-0.228531</td>\n",
       "      <td>-0.200125</td>\n",
       "      <td>0.246046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <td>0.080425</td>\n",
       "      <td>0.027496</td>\n",
       "      <td>0.206669</td>\n",
       "      <td>0.234939</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.125680</td>\n",
       "      <td>0.167196</td>\n",
       "      <td>-0.028514</td>\n",
       "      <td>-0.165453</td>\n",
       "      <td>-0.234166</td>\n",
       "      <td>0.184866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <td>-0.086883</td>\n",
       "      <td>-0.082332</td>\n",
       "      <td>0.214065</td>\n",
       "      <td>0.233894</td>\n",
       "      <td>0.125680</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.791966</td>\n",
       "      <td>-0.042518</td>\n",
       "      <td>-0.029742</td>\n",
       "      <td>-0.002375</td>\n",
       "      <td>0.163416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <td>-0.019910</td>\n",
       "      <td>-0.080336</td>\n",
       "      <td>0.237831</td>\n",
       "      <td>0.257544</td>\n",
       "      <td>0.167196</td>\n",
       "      <td>0.791966</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.025645</td>\n",
       "      <td>-0.085290</td>\n",
       "      <td>-0.070040</td>\n",
       "      <td>0.151934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Protiens</th>\n",
       "      <td>-0.187461</td>\n",
       "      <td>0.089121</td>\n",
       "      <td>-0.008099</td>\n",
       "      <td>-0.000139</td>\n",
       "      <td>-0.028514</td>\n",
       "      <td>-0.042518</td>\n",
       "      <td>-0.025645</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.784053</td>\n",
       "      <td>0.234887</td>\n",
       "      <td>-0.035008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albumin</th>\n",
       "      <td>-0.265924</td>\n",
       "      <td>0.093799</td>\n",
       "      <td>-0.222250</td>\n",
       "      <td>-0.228531</td>\n",
       "      <td>-0.165453</td>\n",
       "      <td>-0.029742</td>\n",
       "      <td>-0.085290</td>\n",
       "      <td>0.784053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.689632</td>\n",
       "      <td>-0.161388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <td>-0.216408</td>\n",
       "      <td>0.003424</td>\n",
       "      <td>-0.206267</td>\n",
       "      <td>-0.200125</td>\n",
       "      <td>-0.234166</td>\n",
       "      <td>-0.002375</td>\n",
       "      <td>-0.070040</td>\n",
       "      <td>0.234887</td>\n",
       "      <td>0.689632</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.163131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <td>0.137351</td>\n",
       "      <td>-0.082416</td>\n",
       "      <td>0.220208</td>\n",
       "      <td>0.246046</td>\n",
       "      <td>0.184866</td>\n",
       "      <td>0.163416</td>\n",
       "      <td>0.151934</td>\n",
       "      <td>-0.035008</td>\n",
       "      <td>-0.161388</td>\n",
       "      <td>-0.163131</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Age    Gender  Total_Bilirubin  \\\n",
       "Age                         1.000000 -0.056560         0.011763   \n",
       "Gender                     -0.056560  1.000000        -0.089291   \n",
       "Total_Bilirubin             0.011763 -0.089291         1.000000   \n",
       "Direct_Bilirubin            0.007529 -0.100436         0.874618   \n",
       "Alkaline_Phosphotase        0.080425  0.027496         0.206669   \n",
       "Alamine_Aminotransferase   -0.086883 -0.082332         0.214065   \n",
       "Aspartate_Aminotransferase -0.019910 -0.080336         0.237831   \n",
       "Total_Protiens             -0.187461  0.089121        -0.008099   \n",
       "Albumin                    -0.265924  0.093799        -0.222250   \n",
       "Albumin_and_Globulin_Ratio -0.216408  0.003424        -0.206267   \n",
       "Dataset                     0.137351 -0.082416         0.220208   \n",
       "\n",
       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "Age                                 0.007529              0.080425   \n",
       "Gender                             -0.100436              0.027496   \n",
       "Total_Bilirubin                     0.874618              0.206669   \n",
       "Direct_Bilirubin                    1.000000              0.234939   \n",
       "Alkaline_Phosphotase                0.234939              1.000000   \n",
       "Alamine_Aminotransferase            0.233894              0.125680   \n",
       "Aspartate_Aminotransferase          0.257544              0.167196   \n",
       "Total_Protiens                     -0.000139             -0.028514   \n",
       "Albumin                            -0.228531             -0.165453   \n",
       "Albumin_and_Globulin_Ratio         -0.200125             -0.234166   \n",
       "Dataset                             0.246046              0.184866   \n",
       "\n",
       "                            Alamine_Aminotransferase  \\\n",
       "Age                                        -0.086883   \n",
       "Gender                                     -0.082332   \n",
       "Total_Bilirubin                             0.214065   \n",
       "Direct_Bilirubin                            0.233894   \n",
       "Alkaline_Phosphotase                        0.125680   \n",
       "Alamine_Aminotransferase                    1.000000   \n",
       "Aspartate_Aminotransferase                  0.791966   \n",
       "Total_Protiens                             -0.042518   \n",
       "Albumin                                    -0.029742   \n",
       "Albumin_and_Globulin_Ratio                 -0.002375   \n",
       "Dataset                                     0.163416   \n",
       "\n",
       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "Age                                          -0.019910       -0.187461   \n",
       "Gender                                       -0.080336        0.089121   \n",
       "Total_Bilirubin                               0.237831       -0.008099   \n",
       "Direct_Bilirubin                              0.257544       -0.000139   \n",
       "Alkaline_Phosphotase                          0.167196       -0.028514   \n",
       "Alamine_Aminotransferase                      0.791966       -0.042518   \n",
       "Aspartate_Aminotransferase                    1.000000       -0.025645   \n",
       "Total_Protiens                               -0.025645        1.000000   \n",
       "Albumin                                      -0.085290        0.784053   \n",
       "Albumin_and_Globulin_Ratio                   -0.070040        0.234887   \n",
       "Dataset                                       0.151934       -0.035008   \n",
       "\n",
       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
       "Age                        -0.265924                   -0.216408  0.137351  \n",
       "Gender                      0.093799                    0.003424 -0.082416  \n",
       "Total_Bilirubin            -0.222250                   -0.206267  0.220208  \n",
       "Direct_Bilirubin           -0.228531                   -0.200125  0.246046  \n",
       "Alkaline_Phosphotase       -0.165453                   -0.234166  0.184866  \n",
       "Alamine_Aminotransferase   -0.029742                   -0.002375  0.163416  \n",
       "Aspartate_Aminotransferase -0.085290                   -0.070040  0.151934  \n",
       "Total_Protiens              0.784053                    0.234887 -0.035008  \n",
       "Albumin                     1.000000                    0.689632 -0.161388  \n",
       "Albumin_and_Globulin_Ratio  0.689632                    1.000000 -0.163131  \n",
       "Dataset                    -0.161388                   -0.163131  1.000000  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9f73fbf60>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqYAAAK3CAYAAABX8Y2hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3X1cVHX+///nCAyCQF4lXqJAmdpm\nirbZhWah1ceLLAyFitJMtwttszRMxAhB0TQ/qUl4vYIS4lqrme1GtbFfS1Ncbc0So6IoFczLQWGA\nmd8ffZrVn6kMEucQj/vtxu3mnHPmvF8zjvryed7vMxan0+kUAAAAYLBGRhcAAAAASDSmAAAAMAka\nUwAAAJgCjSkAAABMgcYUAAAApkBjCgAAAFOgMQUAAMA59uzZo5iYmPO2f/DBBxo+fLhGjhypdevW\nSZLKyso0YcIEPfDAAxo7dqyOHj1a43FpTAEAAOCydOlSTZs2TeXl5edsr6io0KxZs7RixQqlp6cr\nKytLJSUlyszMVOfOnbV27Vrde++9Wrx4cY3HpjEFAACAS1BQkBYuXHje9oKCAgUFBemKK66Q1WpV\nr169tHPnTuXl5alv376SpH79+umTTz6p8dieNX4mGqyKI18bXYJbHug10egS3OZU/ftCttYWH6NL\ncMun9kNGl+C2lp5NjC7BLbtOfmN0CW47cvqk0SW4zd9av/7sBfo2M7oEt31ZvKNOx6vrf2e9Woac\n8/iuu+5SUVHRecfZbDb5+/u7Hjdp0kQ2m+2c7U2aNNGpU6dqXAuJKYDLVt+aUgCA+/z8/FRaWup6\nXFpaKn9//3O2l5aWKiAgoMZj0JgCAADgkkJDQ1VYWKjjx4/Lbrdr586d6tmzp8LCwvTRRx9JknJz\nc9WrV68aj8GlfAAAADNxVBldwTk2bdqk06dPa+TIkZoyZYrGjBkjp9Op4cOHKzAwUNHR0YqNjVV0\ndLS8vLw0b968Go9lcTqd9W8yGwzFHNPfXn2bY1ofL+Uzx/S3xxzTusEc099enc8xLT5Qp+N5tbq6\nTse7GBJTAAAAM3E6jK7AMMwxBQAAgCmQmAIAAJiJg8QUAAAAMBSJKQAAgIk4mWMKAAAAGIvEFAAA\nwEyYYwoAAAAYi8YUAAAApsClfAAAADNh8RMAAABgLBJTAAAAM3FUGV2BYUhMAQAAYAokpgAAAGbC\nHFMAAADAWCSmAAAAZsIN9gEAAABjkZgCAACYiJM5pgAAAICxaEwbkCVLlujWW29VeXm50aUAAIAL\ncTjq9sdEaEwbkE2bNmnQoEHavHmz0aUAAACchzmmDcT27dsVFBSkqKgoTZ48WREREfrss8/00ksv\nqUmTJmrRooW8vb2VkpKi9PR0vf3227JYLBo0aJAefvhho8sHAKDhYI4pfu+ys7MVGRmpkJAQWa1W\n7dmzRy+++KJSUlK0evVqBQUFSZK++uorvfPOO1q7dq3Wrl2rnJwcff311wZXDwAAGgIS0wbgxIkT\nys3N1dGjR5Weni6bzaaMjAwVFxfr6quvliT16tVL77zzjvLz8/Xjjz9q1KhRrud+9913CgkJMfAV\nAADQgDiqjK7AMDSmDcDGjRs1fPhwxcbGSpLOnDmj8PBwNW7cWF999ZWuuuoq7dmzR5IUEhKiq666\nSsuWLZPFYtGqVavUuXNnI8sHAAANBI1pA5Cdna05c+a4Hvv4+OjOO+9Uy5YtNXXqVPn6+srLy0uB\ngYHq0qWLbrrpJkVHR8tut6t79+4KDAw0sHoAANBQ0Jg2ABs3bjxvW0JCgtasWaPXX39dzZs31/z5\n8+Xl5SVJeuyxx/TYY4/VdZkAAEBq0IufaEwbsBYtWujRRx+Vr6+v/P39lZKSYnRJAACgAaMxbcDu\nvvtu3X333UaXAQAAzmaym97XJW4XBQAAAFMgMQUAADCTBjzHlMQUAAAApkBiCgAAYCbMMQUAAACM\nRWIKAABgIk5nw/1KUhJTAAAAmAKJKQAAgJmwKh8AAAAwFokpAACAmbAqHwAAADAWiSkAAICZMMcU\nAAAAMBaNKQAAAEyBS/kAAABm4uAG+wAAAIChSEwBAADMhMVPAAAAgLFITAEAAMyEG+wDAAAAxiIx\nhdse6DXR6BLcsjZvvtEluK3qwHajS3DbihGbjS7BLdc16mB0CW7zrmcLdb0C6l/28WHFPqNLcJtH\no/r1Ph88fdToEsyPOaYAUHP1rSkFAJgTiSkAAICZMMcUAAAAMBaJKQAAgJmQmAIAAADGIjEFAAAw\nEaeznt2CoxaRmAIAAMAUSEwBAADMhDmmAAAAgLFoTAEAAGAKXMoHAAAwE76SFAAAADAWiSkAAICZ\nsPgJAAAAMBaJKQAAgJkwxxQAAAAwFokpAACAmTDHFAAAADAWiSkAAICZMMcUAAAAMBaJKQAAgJkw\nxxQAAAAwFokpAACAmZCYAgAAAMYiMQUAADATVuXjUlJSUhQTE6O7775b/fv3V0xMjJ5++ulfPbao\nqEgffvjhBc9VWFio6Ojoi+7v1auXYmJi9NBDD2n48OHavXu3JCk1NVV79+5Vdna25s+fr8OHD2vG\njBnVfh39+vVTZWXlOdv++c9/Kjs7u9rnAAAA+C2QmFbTlClTJEkbNmzQ119/rUmTJl3w2E8++URF\nRUW6/fbbazxe586dlZ6eLkn66quv9Nxzz+lvf/ubnnjiCUnSF198IUkKDAxUfHx8jceRpP79+1/W\n8wEAAGoDjellSk5OdqWZw4YN04gRI7Rs2TLZ7Xb17NlT3t7eSk1NlSSVl5fr5ZdfdnuMEydOqF27\ndpKkSZMmKSIiwrWvsLBQU6ZMUWZmpgYPHqxOnTqpcePGat++vdq3b6/IyEjl5+dr5syZWrVqlSRp\n2rRpKioq0pVXXqmUlBRt3LhRRUVFioiIUGxsrFq1aqXvv/9eYWFhl930AgAANzXgxU80ppchJydH\nxcXFWrdunSoqKhQVFaU+ffroscceU1FRkfr376/09HS98soratmypRYtWqR3331Xd9111yXPnZ+f\nr5iYGFVWVuqLL77QzJkzL/mcU6dO6emnn9Y111yj+fPnX/C4Bx98UNddd51mzZql9evXy2q1uvYV\nFhZqxYoVslqtuuOOO/TUU0+pefPm1XtDAAAALgON6WUoKChQ7969ZbFYZLVadf3116ugoOCcYwID\nA5WYmChfX18dOnRIf/zjH6t17rMv5R8+fFj33XefwsLCLvm84ODg87Y5nU7Xrxs3bqzrrrtOktSz\nZ0/t3LlT11xzjWt/x44d5evrK0lq2bKl7HZ7teoFAAC1hMVPqInQ0FDl5eVJkux2u3bv3q2OHTvK\nYrG4msH4+HilpKQoJSVFLVq0OKdJrK5mzZrJ29tbVVVVlzy2UaOff0u9vb1VXFwsSdq3b59rf3l5\nufLz8yVJO3fuVOfOnc95vsVicbs+AACA2kBiehnCw8P16aefKioqSna7XUOGDFGXLl1UUVGhpUuX\nqmvXrho6dKjuv/9+BQQEqEWLFq5m8VJ+uZRvsVh05swZPfjgg655ptUxZMgQPfPMM9q2bZu6devm\n2t64cWOtXLlShYWF6tChgyIiIvTmm2+6/doBAMBvpAHPMbU4axLhoUGL7DjM6BLcsjbvwvNtzarq\nwHajS3DLihGbjS7BbR718G8+73pW85sex40uwW0f/rTv0geZjNWjfmVMFY5LX/0zmxO2gksfVIvO\nvJlSp+P53DelTse7mPr1af6dWbBggXbs2HHe9tmzZ6tt27YGVAQAAAzXgOeY0pga6EI36AcAAGiI\naEwBAADMpAHPMWVVPgAAAEyBxBQAAMBMSEwBAAAAY5GYAgAAmEkDvpMniSkAAABMgcQUAADATJhj\nCgAAABiLxhQAAACmwKV8AAAAM+FSPgAAAGAsElMAAAAzcZKYAgAAAIYiMQUAADAT5pgCAAAAxiIx\nBQAAMBMDv5LU4XAoISFB+/fvl9VqVVJSkjp27ChJ+uKLLzRz5kzXsbt379Zrr72m7t2766677lLn\nzp0lSQMGDNAjjzxSo/FpTAEAACBJysnJkd1uV1ZWlnbv3q2UlBSlpqZKkrp27ar09HRJ0pYtW9Sq\nVSv169dPH3/8sYYMGaL4+PjLHp/GFAAAwEwMnGOal5envn37SpJ69OihvXv3nnfM6dOntXDhQmVk\nZEiS9u7dq88//1wPPfSQmjdvrmnTpqlVq1Y1Gp85pgAAAJAk2Ww2+fn5uR57eHiosrLynGPWr1+v\nu+++W82bN5ckhYSE6Omnn1ZGRoYGDBigpKSkGo9PYgq3OWXc3JeaqDqw3egS3OZx9Y1Gl+CWsf++\nUWuun250Gb973gbOO6uJRrIYXYLbHPXsPZYki6V+vc/llRVGl2B+Biamfn5+Ki0tPasUhzw9z20X\nN23apAULFrge9+nTRz4+PpKkgQMHnrPPXSSmAC4bTSkA/D6EhYUpNzdX0s+Lm35Z0PSLU6dOyW63\nq02bNq5t06ZN09///ndJ0ieffKJrr722xuOTmAIAAJiJgd/8NHDgQG3dulVRUVFyOp2aOXOmVq5c\nqaCgIIWHh+ubb75Ru3btznnOc889p6lTpyozM1M+Pj5cygcAAMDla9SokRITE8/ZFhoa6vp19+7d\ntXjx4nP2d+jQwbVa/3LRmAIAAJiI01H/5jrXFuaYAgAAwBRITAEAAMzEwFX5RiMxBQAAgCnQmAIA\nAMAUuJQPAABgJgbeLspoJKYAAAAwBRJTAAAAM+F2UQAAAICxSEwBAADMhNtFAQAAAMYiMQUAADAT\nElMAAADAWCSmAAAAZuJkVT4AAABgKBJTAAAAM2GOKQAAAGAsElMAAAAz4ZufAAAAAGORmAIAAJiJ\nkzmmAAAAgKFoTC9i+/btuummmxQTE6OHHnpIUVFReuedd/TFF19o0aJFl3XurKwsVVRUXHD/woUL\ndddddykmJkbR0dEaP368bDabJGn8+PGSpJiYGBUUFGjDhg16//33q/2aJk6ceN725ORk/fjjjzV4\nJQAAALWDxvQS+vTpo/T0dGVkZGj58uVatmyZpP82hzWVlpYmxyVuBzFq1Cilp6crMzNTISEhysrK\nkqTzmuKIiAiFh4dfVj1xcXFq27btZZ0DAADUAoezbn9MhDmmbmjSpIlGjhypxMREtW7dWvPnz9ft\nt9+ukJAQhYSE6NFHH1V8fLzKy8vl7e2tGTNmqE2bNlq8eLFycnJUVVWl6OhoeXh4qKSkRBMnTtTi\nxYurNfaJEyfUrVs3SdItt9yirVu3uvYtXLhQLVu2VEhIiObOnSsvLy+NGDFCCxYs0JYtW+Tt7a25\nc+cqJCRE7dq1U2FhocaMGaNjx44pOjpakZGRiomJUUJCgt555x0VFRXpp59+0o8//qgXXnhBffv2\n/U3eTwAAgLPRmLqpRYsWOnbsmFq3bi1JOnjwoDZs2KBmzZrpmWeeUUxMjG677TZ98sknmjt3rsaM\nGaPc3FxlZ2fLbrdr3rx5iouLU2pqqubPn3/RsVatWqV33nlHx48f1+nTp/Xkk09esr7y8nJlZ2dL\nkhYsWPCrx1RUVCg1NVUOh0PDhg07L221Wq1atmyZtm7dqhUrVtCYAgBQh5wN+Ab7NKZu+vHHH3XP\nPffowIEDkqRmzZqpWbNmkqT8/HylpaVp2bJlcjqd8vLy0jfffKPu3bvLw8NDPj4+mjZtWrXHGjVq\nlKKjoyVJ2dnZio2N1apVqy76nODg4F/d7jzre3d79Oghq9UqSQoNDVVRUdE5x3bt2lWS1Lp1a9nt\n9mrXCwAAcDmYY+oGm82m7OxsNW/e3LWtUaP/voUhISGaNGmS0tPT9dJLL+muu+5SSEiI9u3bJ4fD\noYqKCo0ePVp2u10Wi+WSc0zP1rZt24sulvq1eqxWq4qLi+V0OvXll1+6tu/bt0+VlZU6ffq0CgoK\nFBQUdM45LBZLtesCAAC1jDmmuJBt27YpJiZGjRo1UlVVlSZMmKArrrhC27dvP+/Y2NhYJSQkqLy8\nXGVlZYqLi1PXrl3Vt29fRUdHy+FwKDo6WlarVb1799a4ceO0evXqCzaCv1zK9/DwUFlZmaZOnepW\n7Y899pjGjRundu3aKSAgwLXd29tbY8eO1cmTJzVhwgQ1bdrUvTcFAADgN2Bxnn2NF6iG+zveY3QJ\nbsl4I9roEtzmcfWNRpfgljXXTze6hAbBu579db3O86TRJbjtvSN7jS7BbT5eVqNLcMup8jNGl+C2\nsrLv6nS80qSH6nS8JtMy6nS8iyExNZDdbteYMWPO2x4cHKzExEQDKgIAADAOjamBrFar0tPTjS4D\nAACYicnmfdYlFj8BAADAFEhMAQAAzKQB38eUxBQAAACmQGIKAABgJswxBQAAAIxFYgoAAGAmTuaY\nAgAAAIaiMQUAAIApcCkfAADATFj8BAAAABiLxBQAAMBEnNxgHwAAADAWiSkAAICZMMcUAAAAMBaJ\nKQAAgJmQmAIAAADGIjEFAAAwE76SFAAAADAWiSkAAICZNOA5pjSmcFtri4/RJbhlxYjNRpfgNl9H\n/av5wT2JRpfgloTe04wuwW3+zvp1kavMUWl0CW5r4eNvdAluO3LmpNEluKVZYz+jS4CJ0ZgCuGz1\nrSkFADNzNuDEtH799xsAAAC/WySmAAAAZkJiCgAAABiLxhQAAACmwKV8AAAAM3Fwg30AAADAUCSm\nAAAAZsLiJwAAAMBYJKYAAABmQmIKAAAAGIvEFAAAwEScThJTAAAAwFAkpgAAAGbCHFMAAADAWCSm\nAAAAZkJiCgAAABiLxBQAAMBEnCSmAAAAgLFITAEAAMyExBQAAAAwFo0pAAAATIFL+QAAAGbiMLoA\n49SLxHTJkiW69dZbVV5eLkmKiYlRQUGBFi5cqMzMzGqd44477lB5ebmWLFmizz77rFbqWrhwoe66\n6y7FxMQoJiZGUVFR2r59uyTplltuqZUxzrZ9+3ZNnDix2sdnZWWpoqKi1usAAAD4LdSLxHTTpk0a\nNGiQNm/erIiIiMs617hx42qpqp+NGjVK0dHRkqSCggJNmjRJb775Zq2OUVNpaWm69957jS4DAAC4\noSHfLsr0jen27dsVFBSkqKgoTZ48+Vcb08LCQj377LNKTk5W06ZNlZCQoPLych0/flxPPfWUBgwY\n4Dp2ypQpGjRokI4cOaKPPvpIZWVl+u677zR27FhFRERo//79SkpKkiQ1bdpUM2fOlL+/f7VqPX78\nuHx9fSVJdrtdzz33nH788Uc1bdpUCxYs0JkzZzR58mTZbDZVVVXpz3/+s2666SbNnz9f27Ztk8Ph\n0ODBgzVq1CjFxMQoODhY33zzjZxOp+bPn+96rY899piOHj2q22+/XRMmTNC+ffs0Y8YMeXh4yNvb\nWzNmzNDWrVtVUlKiiRMnauHChZo+fboOHTqkY8eOqV+/fnrmmWf0j3/8Q0uXLpWnp6fatWunOXPm\nqLS0VHFxcTp27Jgkadq0abrmmmsu6/cQAACgOkzfmGZnZysyMlIhISGyWq3as2fPOfu/+eYb/fWv\nf9W8efPUqVMnffzxxxo9erRuvPFG7dq1SwsXLjynMT2bzWbT8uXL9e233+rxxx9XRESE4uPjNXPm\nTF111VXKzs7WsmXLLnr5fNWqVXrnnXfUqFEjBQQEaMaMGZKk06dPa+LEiWrfvr1iYmL0xRdfaMuW\nLbr55pv1yCOP6PDhw4qOjlZOTo7eeustZWRkKDAwUBs2bHCdOywsTImJiVqzZo3S0tI0cOBAlZeX\na/HixaqqqlL//v01YcIETZs2TcnJyeratatycnKUkpKiBQsWKDU1VfPnz9fBgwfVo0cPRUZGqry8\n3NWYvv322xo1apQGDx6st956SzabTWlpaerTp48eeOABffvtt3rhhReqPV0CAADUAhJTczpx4oRy\nc3N19OhRpaeny2azKSMj45xjcnNz5enpKQ8PD0nSlVdeqdTUVK1fv14Wi0WVlZUXPH+XLl0kSW3a\ntJHdbpf08+X4l156SZJUUVGh4ODgi9Z49qX8s11xxRVq3769JKlly5Y6c+aMCgoKNHToUElSYGCg\n/Pz8dPToUb3yyit65ZVXdOTIEfXt29d1jj59+kj6uUH94IMPJElXX321rFarJMnT8+ffvuLiYnXt\n2lWSdMMNN2jevHnn1NK0aVP95z//0bZt2+Tn5+d6rS+88ILS0tKUmZmpkJAQDRgwQPn5+dq2bZu2\nbNkiSTp58uRFXz8AAEBtMXVjunHjRg0fPlyxsbGSpDNnzig8PFzNmjVzHfPII4+oY8eOev7555WR\nkaFXX31VkZGRuu222/TXv/71ovM9LRbLeduCg4M1e/ZstW3bVnl5eSopKalR7b927tDQUO3cuVPd\nunXT4cOHdfLkSQUEBOjdd9/VK6+8IqfTqcGDB2vw4MGSpL1796p169batWuXrrrqqguet1WrVvry\nyy/VpUsX7dixQ506dXId63A4tGHDBvn7+ysxMVGFhYVat26dnE6nsrKyNGHCBLVo0ULTp0/Xe++9\np5CQEN1zzz0aOnSofvrpJ2VnZ9fo9QMAgBpqwKvyTd2YZmdna86cOa7HPj4+uvPOO7V+/fpzjrv5\n5pv17rvvaunSpbr77ruVnJystLQ0tWnTxjVXsroSEhIUGxurqqoqSVJycvLlv5D/86c//UlTp07V\n3//+d5WVlSkxMVFWq1VXXHGFhg0bpiuuuEK33HKL2rZtK0l68803tWrVKvn4+GjOnDnKz8//1fMm\nJSVpxowZcjqd8vDw0MyZMyVJvXv31rhx4zR9+nQ9++yzysvLk4+Pjzp27Kji4mJ1795do0ePVtOm\nTdWkSRP1799f/fv3V1xcnNatWyebzabx48fX2usHAAC4GIvT6Wy4ExlMLCYmRgkJCQoNDTW6lPOM\n7zTS6BLc0q3Ky+gS3OZbz/63/OCeRKNLcFtC72lGl+A2f2e9uMOfy0eOn4wuwW37Tv9gdAluO3Km\nfk25CrD6Gl2C2w4e31en4x2L7F+n4zXL/medjncxpk5MzcBut2vMmDHnbQ8ODlZiYv37xxgAAMCs\naEwvwWq1Kj09vc7HNWJMAABgAvXsqlltql/XhQAAAPC7RWIKAABgIg35m59ITAEAAGAKJKYAAABm\nwhxTAAAAwFg0pgAAADAFLuUDAACYiJNL+QAAAICxSEwBAADMhMQUAAAAMBaJKQAAgIkwxxQAAAAw\nGIkpAACAmZCYAgAAAMYiMQUAADCRhjzHlMYUAAAAkiSHw6GEhATt379fVqtVSUlJ6tixo2t/UlKS\ndu3apSZNmkiSFi9erIqKCk2aNEllZWVq1aqVZs2aJR8fnxqNT2MKAABgIkYmpjk5ObLb7crKytLu\n3buVkpKi1NRU1/7PP/9cy5YtU/PmzV3bkpKSNGTIEEVERGjJkiXKysrSqFGjajQ+c0wBAAAgScrL\ny1Pfvn0lST169NDevXtd+xwOhwoLCzV9+nRFRUVp/fr15z2nX79++vjjj2s8PokpAACAiRiZmNps\nNvn5+bkee3h4qLKyUp6enjp9+rQeeughjR49WlVVVXr44Yf1hz/8QTabTf7+/pKkJk2a6NSpUzUe\nn8YUAAAAkiQ/Pz+Vlpa6HjscDnl6/twu+vj46OGHH3bNH+3Tp4++/PJL13MaN26s0tJSBQQE1Hh8\nGlO47VP7IaNLcMt1jToYXcLv3prrp+uAV/1aRpqwM8noEtxW+c+1RpfglpyJ/zK6BLd18W1ndAlu\n823S8dIHmciu0kKjSzA/p8WwocPCwvThhx9q0KBB2r17tzp37uza9+2332rixIl688035XA4tGvX\nLt13330KCwvTRx99pIiICOXm5qpXr141Hp/GFMBlq29NKQDg1w0cOFBbt25VVFSUnE6nZs6cqZUr\nVyooKEjh4eEaOnSoRowYIS8vLw0bNkxXX321nnjiCcXGxmrdunVq1qyZ5s2bV+PxaUwBAAAgSWrU\nqJESExPP2RYaGur69dixYzV27Nhz9rds2VLLly+vlfFpTAEAAEykId9gn9tFAQAAwBRITAEAAEzE\n6TBu8ZPRSEwBAABgCiSmAAAAJsIcUwAAAMBgJKYAAAAm4jTwBvtGIzEFAACAKZCYAgAAmAhzTAEA\nAACDkZgCAACYCPcxBQAAAAxGYgoAAGAiTqfRFRiHxBQAAACmQGIKAABgIswxBQAAAAxGYwoAAABT\n4FI+AACAiXApHwAAADAYiSkAAICJcLsoAAAAwGAkpgAAACbCHFMAAADAYLXemC5ZskS33nqrysvL\nJUkxMTEqKCio8fk2bNig999/v7bKkyS9+OKLuvfee+ukjvfee0+HDx92+3mXUlVVpTFjxig6Olon\nTpyo9fMDAABjOJ2WOv0xk1pvTDdt2qRBgwZp8+bNtXK+iIgIhYeH18q5JOnMmTPatWuXQkNDtX37\n9t+8jtWrV8tms7n9vEspKSnRsWPHlJmZqSuuuKLWzw8AAFDXanWO6fbt2xUUFKSoqChNnjxZERER\nrn2HDh1SQkKCysvLdfz4cT311FMaMGCAhg4dqt69eys/P1/BwcFq0aKFdu7cKavVqiVLluj1119X\ny5YtFRISoqVLl8rLy0tFRUUaNGiQnnjiCR08eFDx8fEqLy+Xt7e3ZsyYoTZt2lywxi1btuimm25S\nv379tGbNGt14442SdNl1FBUVKS4uTpWVlbJYLJo2bZoOHTqkL774QrGxsXr55Zf19NNPq2nTpurX\nr5+uv/56LVq0SJJUVlam2bNny8vLS88995xat26t77//Xtddd51eeukl5eXlafbs2fL09FRAQIDm\nzp2r+Ph4ffvtt5o+fbomT56suLg4HTt2TJI0bdo0XXPNNbr99tsVEhKikJAQRUZGKiUlRQ6HQydP\nntS0adMUFhamKVOm6LvvvlN5ebnGjBmjQYMG6dNPP9X8+fPl4eGhDh06KDExUV5eXrX5UQEAABfg\ndBhdgXFqNTHNzs5WZGSkQkJCZLVatWfPHte+r7/+WqNHj9bKlSsVHx+vNWvWSJJKS0s1ZMgQrVmz\nRjt37lRYWJjWrFmjiooKffXVV+ec/8cff9TChQuVlZWlZcuWSZJmz56tmJgYpaena8yYMZo7d261\narz55pu1b98+12X2y61jzpw3riczAAAgAElEQVQ5iomJ0Zo1axQXF6epU6eqf//+6tq1q6vpLCkp\n0fLlyzV27FgdOHBAL7/8slavXq077rhD7777riTp22+/VXJysrKzs5Wbm6uSkhLl5ORo4MCBysjI\n0P3336+TJ0/qxRdf1FVXXaXExES9/vrr6tOnj9LT0zVjxgwlJCRIkg4ePKi5c+cqLi5OX331lWJj\nY7Vq1SqNHj1aGzZskM1m0/bt27Vo0SItXbpUVVVVcjqdio+P16JFi5SRkaHAwEC9+eabNfxEAAAA\nVF+tJaYnTpxQbm6ujh49qvT0dNlsNmVkZLj2X3nllUpNTdX69etlsVhUWVnp2nfttddKkgICAhQa\nGur69S/zVH/RuXNneXp6ytPTU40bN5Yk5efnKy0tTcuWLZPT6bxosldQUKADBw4oJSVFkmSxWJSZ\nmalnnnnmsusoKCjQDTfcIEnq2rWrDh06dN747du3l9VqlSQFBgYqOTlZvr6+Onz4sMLCwiRJQUFB\n8vPzc71n5eXlevzxx/X666/rkUceUWBgoLp37y673e46b35+vrZt26YtW7ZIkk6ePClJatasmZo1\nayZJatWqlRYvXqzGjRurtLRUfn5+8vPzU3x8vOLj42Wz2XTPPffo6NGjKi4udr0nZWVluuWWWy74\nngIAgNrlMNm8z7pUa43pxo0bNXz4cMXGxkr6eS5neHi4qzF69dVXFRkZqdtuu01//etfz0nhLJbq\n/Qb82nEhISF69NFHFRYWpoKCAu3YseOCz8/OztbEiRP14IMPSvo5+Rw5cqSefPLJy64jNDRUO3fu\nVHh4uL744gu1bNnSdazz/+6U26jRfwPqadOmKScnR35+foqNjXUd82vn3rRpk+677z7FxsYqLS1N\n69atO2fxVkhIiO655x4NHTpUP/30k7Kzs88bLzk5WXPnzlVoaKgWLFigH374QcXFxfr888/12muv\nqby8XLfddpuGDh2q1q1ba/HixfL399f7778vX1/far0vAAAAl6PWGtPs7GzNmTPH9djHx0d33nmn\n1q9fL0m6++67lZycrLS0NLVp08Y1H/JyxcbGuuaulpWVKS4u7lePs9vt2rx5s/72t7+5trVt21Zd\nunTR3//+98uu4/nnn1d8fLxWrFihyspKJScnS5J69uyp559/XjNmzDjn+GHDhmnEiBEKCAhQy5Yt\nVVxcfMFzX3fddZoyZYp8fX3l5eWlxMTEc/Y//vjjiouL07p162Sz2TR+/PjzznHPPffoySefVIsW\nLdS6dWsdO3ZMV155pUpKSnTvvffK19dXjz76qKxWq+Li4jRu3Dg5nU41adLknN9XAADw2zLbSvm6\nZHE6G/IXX6Em/tj2NqNLcMuYRh2MLsFt3vXsT+UBr/o3Uz9hZ5LRJbit8p9rjS7BLUMn/svoEtzm\nUQ9v7+1rqV/flbOrtNDoEtxW+NNndTre/i7/U6fjXfPlljod72Lq16e5msaPH3/evT39/PyUmppq\nUEUAAADV05C/+el32Zj+chsmAAAA1B/175oFAAAAfpd+l4kpAABAfdWQV/+QmAIAAMAUSEwBAABM\npCEvfiIxBQAAgCmQmAIAAJhIQ/5KUhJTAAAAmAKJKQAAgIk05K8kJTEFAACAKZCYAgAAmAj3MQUA\nAAAMRmIKAABgIqzKBwAAAAxGYgoAAGAirMoHAAAADEZiCgAAYCKsygcAAAAMRmIKAABgIqzKBwAA\nAAxGYgq3tfRsYnQJbvGuMroC93nXswlGf7BbVOhVv/6HX/nPtUaX4DbP/g8YXYJbvLTV6BLc9lPV\naaNLcJvN4mF0CW7xtNB64ML4dAC4bPWtKQUAM+N2UQAAAIDBSEwBAABMhMVPAAAAgMFITAEAAEyk\nfi1/rV0kpgAAADAFElMAAAATYY4pAAAAYDASUwAAABPhPqYAAACAwUhMAQAATMRhdAEGIjEFAACA\nKZCYAgAAmIhTzDEFAAAADEViCgAAYCKOBvzVTySmAAAAMAUaUwAAAJgCl/IBAABMxMHiJwAAAMBY\nJKYAAAAmwu2iAAAAAIORmAIAAJgIX0kKAAAAGKxGjemSJUt06623qry8vLbrkSRlZWWpoqLigvt/\n/PFHffDBBzU+/+OPP67HH3/crecsWbJEn332mdtjXeq11NTJkyc1cuRIPfroo7V+bgAAYBynLHX6\nYyY1akw3bdqkQYMGafPmzbVdjyQpLS1NDseFg+xt27Zp165dNTr3wYMHdfr0aZ04cULff/99tZ83\nbtw4de/e3e3xLvVaaio/P1+tWrXSihUrav3cAAAARnB7jun27dsVFBSkqKgoTZ48WREREVqzZo3e\neustNWrUSGFhYYqNjdWUKVPkdDpdjeDs2bMVGhqqefPmae/evSotLVVoaKhmzZqlhQsX6t///rdO\nnz6toUOHqqSkRBMnTtTChQs1ffp0HTp0SMeOHVO/fv00YcIELVmyRGVlZerZs6fat2+vpKQkSVLT\npk01c+ZM+fv7X7D+9evXKzw8XI0bN9batWsVGxsrSRo4cKB69uypwsJC9enTR6dOndJnn32m4OBg\nvfzyy5oyZYoGDRqkI0eO6KOPPlJZWZm+++47jR07VhEREdq3b59mzJghDw8PeXt7a8aMGdq6davr\ntTzyyCOaO3euvLy8NGLECDVu3Fhr1qxx1fXqq6/qwIEDWrp0qby8vFRUVKRBgwbpiSee0D/+8Q8t\nXbpUnp6eateunWbOnKkZM2aouLhYCxYsUGRkpOLj41VeXu4au6qqSk888YSaNm2qfv366frrr9ei\nRYskSWVlZZo9e7batm2rP//5z7LZbCorK9PkyZN14403asuWLVq1apUaNWqkXr16adKkSe5+TAAA\nQA0xx9QN2dnZioyMVEhIiKxWq/bs2aMNGzYoLi5OWVlZ6tChgyorKyVJHTp00OrVqzVhwgS9/PLL\nstlsCggI0MqVK/XGG29o9+7dOnz4sCQpJCREb7zxhh588EFdeeWVmj9/vg4ePKgePXpo+fLlyszM\nVGZmpjw8PDRu3DgNGTJE4eHhio+P14svvqj09HT169dPy5Ytu2DtDodDb7/9toYNG6bBgwfrnXfe\nUVlZmSTphx9+0DPPPKOMjAytXr1aDzzwgLKzs5WXl6eTJ0+ecx6bzaa0tDSlpqZqyZIlkqRp06Zp\n+vTpysjIUHR0tFJSUhQZGel6LZJUXl6utWvX6t5779W3336rJUuWKD09XcHBwfp//+//Sfp5msLC\nhQuVlZXlei1vv/22Ro0apczMTN16660qKyvT1KlT1adPHz399NOaPXu2YmJilJ6erjFjxmju3LmS\npJKSEi1fvlxjx47VgQMH9PLLL2v16tW644479O677+q7777TkSNH9Prrr2vevHkqKyvT8ePHtXDh\nQq1atUqZmZk6fPiwtm7d6u7HBAAAwG1uJaYnTpxQbm6ujh49qvT0dNlsNmVkZGjWrFlasWKF5s6d\nqx49esjpdEqS+vTpI0nq2bOnZs6cKW9vbx09elTPPvusfH19dfr0adf8y+Dg4PPGa9q0qf7zn/9o\n27Zt8vPzk91uP++YgoICvfTSS5KkioqKXz3PL/71r3+ptLRUzz33nKSfG9VNmzYpMjJSTZs2Vdu2\nbSVJvr6+uuqqqyRJ/v7+582l7dKliySpTZs2rpqKi4vVtWtXSdINN9ygefPmnTf+2bW1aNFCsbGx\natKkib7++mv16NFDktS5c2d5enrK09NTjRs3liS98MILSktLU2ZmpkJCQjRgwIBzzpufn6+0tDQt\nW7ZMTqdTXl5ekqT27dvLarVKkgIDA5WcnCxfX18dPnxYYWFhuvrqq/Xggw/q2WefVWVlpWJiYvTd\nd9/p6NGjGjdunCSptLTUrSkPAADg8jTkxNStxnTjxo0aPny46/L3mTNnFB4eLj8/P7300kvy9vbW\nmDFj9O9//1uS9Pnnn6t3797atWuXrr76auXm5urgwYP63//9Xx09elTvvfeeq4lt1Oi/4a3FYpHD\n4dCGDRvk7++vxMREFRYWat26dXI6nWrUqJFr3mZwcLDrsnReXp5KSkouWP/69euVlJSk/v37S5Ly\n8vKUlJSkyMhIWSzVn/z7a8e2atVKX375pbp06aIdO3aoU6dO57yWs1/jqVOntGDBAv3zn/+UJI0e\nPdr1PvzaubOysjRhwgS1aNFC06dP13vvvedqoqWf0+ZHH31UYWFhKigo0I4dO84ZT/o50c3JyZGf\nn59iY2PldDq1f/9+lZaWasmSJSouLlZUVJTWr1+vNm3aaMWKFfLy8tKGDRtcDTcAAMBvya3GNDs7\nW3PmzHE99vHx0Z133qkWLVro/vvvV7NmzRQYGKjrr79eGzZsUG5urt5//305HA7NmjVLjRs31uLF\nizVixAhZrVZ16NBBxcXF543Tu3dvjRs3TtOnT9ezzz6rvLw8+fj4qGPHjiouLlbnzp2Vmpqqa6+9\nVgkJCYqNjVVVVZUkKTk5+Vdr/+mnn7Rnzx7XZXVJ6tWrl8rLy2u8kOpsSUlJmjFjhpxOpzw8PDRz\n5sxzXstTTz3lOtbPz09hYWG677775Ovrq4CAABUXF6t9+/a/eu7u3btr9OjRatq0qZo0aaL+/fsr\nPz/ftT82NlYJCQkqLy9XWVmZ4uLizjvHsGHDNGLECAUEBKhly5YqLi5Wp06d9Nprr+mtt96Sl5eX\nnn76aTVv3lyjRo1STEyMqqqq1K5dO/3P//zPZb8/AACgesy2Ur4uWZy/RHW17JfFQv369fstTg8D\nDQoaZHQJbhlR1dzoEtzm/dv8sfzNFHrVv79E/zyvi9EluM2z/wNGl+CWe3o+demDTOanqtNGl+A2\nL4uH0SW45VD5caNLcFvBkcsPsNyxOTC6TscbfDizTse7mN/dNz/Z7XaNGTPmvO3BwcFKTEw0oCIA\nAIDqc9S//+vXmt+sMU1JSfmtTn1RVqtV6enphowNAACAmuMrSQEAAGAKv7tL+QAAAPWZowEvfiIx\nBQAAgCmQmAIAAJhI/bovS+0iMQUAAIApkJgCAACYSEP+SlISUwAAAJgCiSkAAICJOCysygcAAAAM\nRWIKAABgIqzKBwAAAAxGYgoAAGAirMoHAAAADEZiCgAAYCIOAxflOxwOJSQkaP/+/bJarUpKSlLH\njh1d+1etWqXNmzdLkm677TaNHz9eTqdT/fr1U6dOnSRJPXr00HPPPVej8WlMAQAAIEnKycmR3W5X\nVlaWdu/erZSUFKWmpkqSvv/+e23cuFHZ2dmyWCx64IEHNGDAAPn4+Ojaa6/V66+/ftnj05gCAACY\niEPGRaZ5eXnq27evpJ+Tz71797r2tW7dWsuWLZOHh4ckqbKyUt7e3vr88891+PBhxcTEqHHjxnrh\nhRcUEhJSo/GZYwoAAABJks1mk5+fn+uxh4eHKisrJUleXl5q3ry5nE6nZs+erW7duik4OFhXXnml\nxo0bp/T0dP3pT3/S5MmTazw+iSkAAAAkSX5+fiotLXU9djgc8vT8b7tYXl6uqVOnqkmTJnrxxRcl\nSX/4wx9cKWrv3r11+PBhOZ1OWWrwDVYkpgAAACbirOOfs4WFhSk3N1eStHv3bnXu3Pm/dTmdevLJ\nJ3XNNdcoMTHR1YwuWrRIf/nLXyRJX375pdq2bVujplQiMQUAAMD/GThwoLZu3aqoqCg5nU7NnDlT\nK1euVFBQkBwOhz799FPZ7Xb961//kiQ9++yzGjdunCZPnqyPPvpIHh4emjVrVo3Htzidzob8zVeo\ngdZNuxpdgltuDAg1ugS3NTJw4ntNlTkrjS7BLRX18BbWXvXsItfGf79mdAluq/wgw+gS3JY1Md/o\nEtyS43na6BLcllG4oU7HW93uoTod7+EfzPO5r19/ywEwpfrWlAIAzIlL+QAAACZS/67n1B4SUwAA\nAJgCiSkAAICJNOTFPySmAAAAMAUSUwAAABNx1L8bs9QaElMAAACYAokpAACAibAqHwAAADAYiSkA\nAICJkJgCAAAABiMxBQAAMBEnq/IBAAAAY9GYAgAAwBS4lA8AAGAiLH4CAAAADEZiCgAAYCIkpgAA\nAIDBSEwBAABMxGl0AQYiMQUAAIApkJgCAACYiIMb7AMAAADGIjEFAAAwEVblAwAAAAYjMa2GlJQU\nff755yopKVFZWZk6dOigZs2aacGCBecdW1RUpAMHDuj222//1XMVFhZqypQpyszMvOD+iIgIdevW\nTZJkt9t18803689//nO1aj17/BkzZmjcuHEKDAys5isFAABGa8iJKY1pNUyZMkWStGHDBn399dea\nNGnSBY/95JNPVFRUdMHGtDo6d+6s9PR0SZLD4dCIESM0ePBgXXXVVZd87tnjx8fH17gGAACAukZj\nehmSk5O1e/duSdKwYcM0YsQILVu2THa7XT179pS3t7dSU1MlSeXl5Xr55ZfdHqOsrEx2u13e3t6a\nNGmSbDabjh8/rmXLlunVV1+96PhpaWlKSUlR06ZNFRcXpxMnTshisWj69Onq1KmThgwZouuvv17f\nfPONAgMD9eqrr+qbb75RXFycPD095enpqTlz5qhVq1a196YBAICLasj3MaUxraGcnBwVFxdr3bp1\nqqioUFRUlPr06aPHHntMRUVF6t+/v9LT0/XKK6+oZcuWWrRokd59913dddddlzx3fn6+YmJiJEke\nHh4aM2aMOnToIEm65ZZbFBMTU63x09LSJEmpqanq16+fRowYoYKCAiUkJGjlypX6/vvv9Ze//EWB\ngYEaMWKEPv/8c+3atUvdu3fX888/rx07dujEiRM0pgAAoE7QmNZQQUGBevfuLYvFIqvVquuvv14F\nBQXnHBMYGKjExET5+vrq0KFD+uMf/1itc599Kf//Lzg4uNrj/yI/P187d+7Upk2bJEnHjx+XJDVv\n3tw1/7R169YqLy/XyJEjtXTpUo0ZM0YBAQF69tlnq1UzAACoHdzHFG4LDQ1VXl6epJ8XKO3evVsd\nO3aUxWKR0/lzCB8fH6+UlBSlpKSoRYsWru2Xo1GjRtUe/xchISF69NFHXQnu0KFDJUkWy/mf/Jyc\nHN144436y1/+ovDwcC1fvvyyawYAAKgOEtMaCg8P16effqqoqCjZ7XYNGTJEXbp0UUVFhZYuXaqu\nXbtq6NChuv/++xUQEKAWLVqouLi4Tsf/xZNPPqm4uDhlZmaqtLRUTz/99AXPe+211yo2Nlaenp7y\n8PDQ1KlTa61mAACAi7E4ayPGQ4PSumnXSx9kIjcGhBpdgtsaqX5dxylzVhpdgtsq6uENWbzq2UWu\njf9+zegS3Fb5QYbRJbgta2K+0SW4JcfztNEluC2jcEOdjpfS8aE6HW9KoXk+9ySmBlmwYIF27Nhx\n3vbZs2erbdu2BlQEAABgLBpTg1zscjoAAGi4GvKl7Pp1XQgAAAC/WySmAAAAJuJowJkpiSkAAABM\ngcQUAADAROrfPUNqD4kpAAAATIHEFAAAwEQa7gxTElMAAACYBIkpAACAiTDHFAAAADAYiSkAAICJ\nOCxGV2AcElMAAACYAokpAACAifDNTwAAAIDBaEwBAABgClzKBwAAMJGGeyGfxBQAAAAmQWIKAABg\nItxgHwAAADAYiSkAAICJNOTbRdGYwm1HTp80ugS3fFixz+gS3OZw1r+/lFr4+Btdglu6+LYzugS3\n/VR12ugS3FL5QYbRJbjN846HjC7BbSOeSTK6BLccXGR0BTAzGlMAl62+NaUAYGb1L5qoPcwxBQAA\ngCmQmAIAAJgIq/IBAAAAg5GYAgAAmEhDXpVPYgoAAABTIDEFAAAwkYabl5KYAgAAwCRITAEAAEyE\nVfkAAACAwWhMAQAAYApcygcAADARZwNe/kRiCgAAAFMgMQUAADARFj8BAAAABiMxBQAAMBG+khQA\nAAAwGIkpAACAiTTcvJTEFAAAACZBYgoAAGAizDEFAAAADEZiCgAAYCLcxxQAAAAwGIkpAACAiTiZ\nYwqzWrJkiW699VaVl5dLkmJiYlRQUKCFCxcqMzOz1sYZP358rZ0LAACgJmhMTW7Tpk0aNGiQNm/e\n/JuOs2jRot/0/AAAoHocdfxjJlzKN7Ht27crKChIUVFRmjx5siIiIs7Zn5OToy1btqisrEzTpk1T\n9+7ddcstt2jr1q2SpIkTJyoqKko//PCDPvzwQ5WVlamkpEQPP/yw3n//fR04cEDPP/+8BgwY4Hpe\nTEyMunTpogMHDshms+nVV19Vu3btjHj5AACggSExNbHs7GxFRkYqJCREVqtVe/bsOWd/u3bttHr1\naiUnJ+vFF1+86LlKS0u1dOlSjR07VpmZmVq0aJESExO1YcOG847t3r27Vq1apVtuueU3T2oBAAB+\nQWJqUidOnFBubq6OHj2q9PR02Ww2ZWRknHPMDTfcIEm6+uqrVVJSct45nM7/Tp7u2rWrJMnf31+h\noaGyWCy64oorXHNXz9atWzdJUuvWrXXkyJFae00AAODSGvLiJxpTk9q4caOGDx+u2NhYSdKZM2cU\nHh6uZs2auY757LPPNHToUO3fv19t27aVJFVWVqq0tFReXl766quvXMdaLJa6fQEAAABuojE1qezs\nbM2ZM8f12MfHR3feeafWr1/v2lZUVKSHH35YdrtdiYmJkqSHH35YI0eOVPv27V3NKgAAqD/MtiCp\nLlmcZ1/vBarB01q/FkP5enkbXYLbHPXsj2ULH3+jS3BbF9/69TmWpGNVp40uwS0f/W+40SW4zfOO\nh4wuwW0VK5OMLsEtry6yG12C214ozLj0QbXokU7D63S8v3z71zod72JITAEAAEykvoUTtYlV+QAA\nADAFElMAAAATabh5KYkpAAAATILEFAAAwEQcDTgzJTEFAACAKZCYAgAAmEhD/uYnElMAAACYAokp\nAACAiTTkb34iMQUAAIApkJgCAACYCKvyAQAAAIPRmAIAAMAUuJQPAABgIg35dlE0pgAAAJAkORwO\nJSQkaP/+/bJarUpKSlLHjh1d+9etW6c33nhDnp6eeuKJJ3T77bfr6NGjmjRpksrKytSqVSvNmjVL\nPj4+NRqfS/kAAAAm4qjjn7Pl5OTIbrcrKytLzz33nFJSUlz7SkpKlJ6erjfeeEPLly/XK6+8Irvd\nrsWLF2vIkCFau3atunXrpqysrBq/dhpTAAAASJLy8vLUt29fSVKPHj20d+9e177PPvtMPXv2lNVq\nlb+/v4KCgvTll1+e85x+/frp448/rvH4XMoHAAAwEafTuDmmNptNfn5+rsceHh6qrKyUp6enbDab\n/P39XfuaNGkim812zvYmTZro1KlTNR6fxBQAAACSJD8/P5WWlroeOxwOeXp6/uq+0tJS+fv7n7O9\ntLRUAQEBNR6fxhQAAMBEHHLW6c/ZwsLClJubK0navXu3Onfu7NrXvXt35eXlqby8XKdOnVJBQYE6\nd+6ssLAwffTRR5Kk3Nxc9erVq8avnUv5AAAAkCQNHDhQW7duVVRUlJxOp2bOnKmVK1cqKChI4eHh\niomJ0QMPPCCn06mJEyfK29tbTzzxhP6/9u49Luf7/x/446ITFUlJYp0U8WGbU3OYLfXBxyks5FBm\nwgzJeZKz8iGnOc9pJlFJDGnzERN+cxhrDEMH51RUUqmrw/X7o1vXV6uuini9r3rcd3Nbva9c10NS\nz/fr8HzNmTMHISEhaNCgAVavXv3Gr8/ClIiIiEhC/rlT/n2qVasWlixZUuyatbW18u2hQ4di6NCh\nxR43MjLCzp07q+T1WZhSpelrvVlvMlFq11K/FSsymUx0hErJystBVm6O6BiVUlfXvPwPkpgMWW3R\nESoleNod0REqbajXMtERKk1zjI/oCJWzcYHoBCRhLEyJ6K2pW1FKRCRlNfnkJ/UbSiIiIiKiaokj\npkREREQS8s+d8jUJR0yJiIiISBI4YkpEREQkISJPfhKNI6ZEREREJAkcMSUiIiKSEJF9TEXjiCkR\nERERSQILUyIiIiKSBE7lExEREUkIG+wTEREREQnGEVMiIiIiCWGDfSIiIiIiwThiSkRERCQhbLBP\nRERERCQYR0yJiIiIJIRrTImIiIiIBOOIKREREZGEsI8pEREREZFgHDElIiIikpAC7sonIiIiIhKL\nI6ZEREREElJzx0s5YkpEREREEsERUyIiIiIJYR/TCti2bRu6deuGnJwcAICbmxtiY2OxYcMG7N+/\nv8oCTZ48ucqeqyK6du2q8vG8vDxs3LgRQ4YMwahRozBq1CgEBwcDAC5evIhp06aV+XvDwsKwatWq\nCuV4/bne5HMQFhaGzz//HG5ubnBzc4OzszMWL16s8vfcvn0bly9fBgBMmzYNcrm80q9LREREVFUq\nPGJ69OhR9OnTB+Hh4Rg8ePA7C7Rx48Z39txvYu3atSgoKEBQUBBq166NzMxMTJgwAR06dHhnr/mm\nn4N+/fph5syZAICCggKMGDEC169fR5s2bUr9+BMnTsDIyAgdO3bE2rVr3zgvERERUVWoUGF68eJF\nfPDBB3B1dcWsWbNKFKYnT55EREQEsrOz4ePjg7Zt26Jr1644f/48gMLROFdXVzx+/BinT59GdnY2\nkpOT4e7ujsjISNy9exezZ8+Gk5OT8ve5ubmhZcuWuHv3LjIyMvDdd9/BzMys1HxPnz7FokWLkJOT\ng7S0NEyaNAlOTk7o378/OnXqhNu3b0Mmk2Hz5s2oW7cu5s+fj5iYGDRr1kzlKGFeXh4iIiJw4sQJ\n1K5dGwCgq6uLgIAAyGQyPHv2TPmxR44cwY8//ggtLS1YWFhgyZIlAIDo6GiMHj0aGRkZmDJlCj7/\n/HP06NEDERER0NbWxqpVq2BlZVXsz/Ymn4N/yszMxMuXL6Gvr4+MjAzMmzcPL1++RGpqKoYMGQJH\nR0ccOnQImpqaaN26Nby8vBAREYHk5GTMmzcPeXl5kMlk8PHxQcuWLSv0mkRERPT2avJUfoUK0wMH\nDmDIkCGwsrKClpYW/qUHgTYAACAASURBVPzzz2KPm5mZYcmSJcoC89ChQ2U+V2ZmJnbt2oXw8HDs\n3r0bISEhuHjxIvbs2QMnJ6diH9u2bVvMmzcPa9euRXh4OMaPH1/qc8bFxWHMmDGwt7fH1atXsWHD\nBjg5OSEzMxN9+/bF/PnzMWPGDERFRaFu3brIyclBSEgInjx5gl9++aXMrKmpqahfvz40NAo/Tfv2\n7UNERAQyMzMxYMAA2NnZKT9uw4YNOHToEPT09ODn54fg4GDUrVsXderUwbZt25CSkoIhQ4age/fu\nFfmUV/pzAADHjh1DdHQ0kpOToauri6+//hoWFha4ceMG+vbti549eyIxMRFubm4YMWIEBg0aBCMj\nI7Rt21b5HCtXroSbmxucnJxw69YteHt7IywsrFKZiYiIiN5EuYXpixcvEBUVhZSUFAQEBCAjIwN7\n9+4t9jEdO3YEANjY2CA5ObnEcyheaxRbVMzp6+vD2toaMpkM9evXV65dfV2rVq0AAI0bNy42OvlP\nxsbG2LJlC0JDQyGTyZCXl1fiOUxNTZGTk4PHjx8rC7EmTZrA1NS0zOc1MDBAWloa8vPzUbt2bYwY\nMQIjRozA/v37i+V5+PAhmjdvDj09PeXn49y5c/jwww/Rvn17yGQyNGzYEPr6+khLSyvzc1Oain4O\ngP+byn/48CE8PDxgYWEBADAyMsKPP/6IEydOQE9Pr9jn559iY2OVf592dnZ4+vSpytckIiKiqlVe\nbVCdlbv56ciRI/jiiy+wa9cu7Ny5EyEhITh//jxSUlKUH3Pt2jUAhZtpmjRpAqBwGjwzMxNyuRwx\nMTHKj5XJZFX9Z8B3330HZ2dn+Pv7w97evthf6D9fz8rKCtHR0QCAxMREJCYmlvm8mpqa6NmzJ9at\nW4eCggIAQE5ODv78889iz9u0aVPExsYiKysLAHDp0iVYWloCAK5fvw4ASE5ORlZWFho0aAAtLS0k\nJSVBoVDg77//roLPQHHNmjXDwoULMXXqVLx69Qq7du3CRx99hFWrVqF3797Kz49MJlP+uYpYW1vj\n999/BwDcunULRkZGVZ6PiIiIqDTljpgeOHAAK1euVL5fp04d9OzZE6Ghocprjx49gru7O+RyuXJt\npbu7O4YNG4amTZsqi9V3pXfv3vD19cX3338PU1NTpKamlvmxTk5OuHLlCoYMGYImTZqgQYMGKp97\n1qxZ2LFjB0aOHAkNDQ1kZGTAyckJY8aMURadhoaGmDJlCtzd3VGrVi188MEHmDlzJsLDw5GdnQ13\nd3dkZWVhyZIlkMlk8PDwwPjx42FmZoZ69epV6eeiSJcuXdClSxesX78eDg4OWLRoEY4ePQoDAwPU\nrl0bcrkc//rXv7By5UpYW1srf9/s2bMxf/587Nq1C3l5efD19X0n+YiIiKh0NXmNqUxRk8eL6Y00\n0GsuOkKl1K6lfudIvIuZhXcpK7fkUhyp62lUercKKUvKzxQdoVLG4d0OSrwLQ720REeoNM0xPqIj\nVMqq9gtER6i0uff3lv9BVahTk8/e6+tdenLmvb6eKmrVYH/y5Ml48eJFsWt6enrYsmXLWz1vZGQk\ndu/eXeK6u7s7/v3vf7/Vc1e1d/U5ICIiImlQ1OARU7UqTN9Vj1NHR0c4Ojq+k+eualLr80pERERU\nVdSqMCUiIiKq7mryKkv1W3xHRERERNUSR0yJiIiIJKQm78rniCkRERERSQJHTImIiIgkhGtMiYiI\niIgE44gpERERkYRwjSkRERERkWAsTImIiIhIEjiVT0RERCQhNflIUo6YEhEREZEkcMSUiIiISEIK\n2C6KiIiIiEgsjpgSERERSQjXmBIRERERCcYRUyIiIiIJ4RpTIiIiIiLBOGJKREREJCFcY0pERERE\nJJhMoajBCxnojbRs1FF0hEpJyEoRHaHScvJyRUeotAY6eqIjVIpWbfWbMNKQqVfmzroWoiNUWmvU\nFR2h2pt5ZYnoCJWmaWT1Xl/P1rjDe329O8m/v9fXU4UjpkT01tStKCUiImlSr9tvIiIiomqOa0yJ\niIiIiATjiCkRERGRhLCPKRERERGRYCxMiYiIiEgSOJVPREREJCHc/EREREREJBhHTImIiIgkRKEo\nEB1BGI6YEhEREZEkcMSUiIiISEIKuMaUiIiIiEgsjpgSERERSYiCDfaJiIiIiMTiiCkRERGRhHCN\nKRERERGRYBwxJSIiIpIQrjElIiIiIhKMI6ZEREREElLAEVMiIiIiIrE4YkpEREQkIQruyiciIiIi\nEouFKRERERFJAqfyiYiIiCSE7aJIsi5evIjOnTvDzc0No0aNgqurK44fP17mxz958gSnTp2qktdO\nS0vD0aNHq+S5iIiIiMrDEVM18Mknn2Dt2rUAgMzMTLi5ucHS0hJ2dnYlPvbChQuIi4tDjx493vp1\nb9++jVOnTqF///5v/VxERERUMTX5SFIWpmpGV1cXw4YNw/Hjx7F37148ffoUqamp6N69O6ZMmYJt\n27YhOzsbH3/8MfT19bFx40YAQHZ2NlasWIEmTZpg6tSpyMjIQHZ2NmbNmgV7e3tERERg9+7dqFWr\nFtq3b4+ZM2di69at+PvvvxEcHIxhw4YJ/pMTERFRdcfCVA01bNgQJ06cQO/evTFkyBDk5OSge/fu\n8PLywvjx4xEXFwdHR0cEBgbC398fJiYm2Lp1K37++Wc4OTnh2bNn2L17N54/f4579+4hLS0NGzZs\nwMGDB1GnTh3MmjUL58+fx9dff42goCAWpURERO9RTV5jysJUDT158gQff/wxrl+/jgsXLkBPTw9y\nubzEx5mYmMDX1xd169ZFYmIi2rVrBxsbG4wcORLTp09HXl4e3Nzc8ODBA6SkpGD8+PEACpcLPHz4\nEJaWlu/7j0ZEREQ1GAtTNZORkYEDBw7AxcUFr169wpIlS3D//n2EhIRAoVCgVq1aKCgoAAD4+Pjg\n5MmT0NPTw5w5c6BQKHD79m1kZmZi27ZtSEpKgqurK0JDQ2Fqaopdu3ZBU1MTYWFhsLOzQ0ZGhvK5\niIiI6P2oyUeSsjBVAxcuXICbmxtq1aqF/Px8TJkyBZaWlpg+fTquXLmCOnXqwNzcHElJSbC1tcWW\nLVvQunVrODs7Y+jQoahXrx6MjIyQlJQECwsLbNq0CYcPH4ampiY8PT1haGiIL7/8Em5ubsjPz4eZ\nmRn+85//ID09HXfu3MHu3bvx5Zdfiv40EBERUTUnU9TkhQz0Rlo26ig6QqUkZKWIjlBpOXm5oiNU\nSgMdPdERKk2rtvrdl2vI1CtzZ10L0REqrTXqio5Q7c28skR0hErTNLJ6r6/XQK/5e3291IyY9/p6\nqrCPKRERERFJgnrdfhMRERFVczW5jylHTImIiIhIEjhiSkRERCQhNXn7D0dMiYiIiEgSOGJKRERE\nJCE1uY8pR0yJiIiISBJYmBIRERGRJHAqn4iIiEhCFGwXRUREREQkFkdMiYiIiCSEm5+IiIiIiATj\niCkRERGRhEixwX52djZmzZqF58+fQ1dXFytWrIChoWGxj1mxYgWuXr2KvLw8DBs2DEOHDkVaWhp6\n9eoFW1tbAICTkxNGjx5d5uuwMCUiIiIilfbv3w9bW1tMmTIF4eHh2Lx5M3x8fJSPX7hwAQ8ePEBw\ncDDkcjn69u2LXr164ebNm+jXrx/mz59fodfhVD4RERGRhCje838VceXKFXz66acAgO7du+O3334r\n9vjHH38MPz8/5fv5+fnQ0NDAX3/9hRs3bmDUqFHw9PREUlKSytfhiCkRERERKR04cAA//vhjsWsN\nGzaEvr4+AEBXVxcvX74s9ri2tja0tbWRm5uLb7/9FsOGDYOuri6srKzwr3/9C126dMGRI0ewbNky\nrF+/vszXZmFKREREJCGi15gOGTIEQ4YMKXZt8uTJyMzMBABkZmaiXr16JX7fixcv4OnpiU6dOmHC\nhAkAgE8++QR16tQBAPz73/9WWZQCnMonIiIionK0a9cOZ86cAQBERUWhffv2xR7Pzs7Gl19+iS++\n+AKTJk1SXvfx8cEvv/wCAPjtt9/QunVrla8jU4guy0nttGzUUXSESknIShEdodJy8nJFR6iUBjp6\noiNUmlZt9Zsw0pCpV+bOuhaiI1Raa9QVHaHam3lliegIlaZpZPV+X0/L7L2+Xq78cbkf8+rVK8yZ\nMwfJycnQ1NTE6tWrYWxsjJUrV6J37964evUqNm7cCDs7O+XvKVpz6u3tDQCoU6cOli1bhkaNGpX5\nOixMqdJYmL57LEzfPRam7x4LUyoNC9MKvJ4EC9P3Rb2+yxERERFVczV5xJBrTInoraVmZ4iOQERE\n1QCn8omIiIhIEjhiSkRERESSwMKUiIiIiCSBhSkRERERSQILUyIiIiKSBLaLIuHu3buH+/fvo0WL\nFjAxMYFMJhMdiQS7c+cOFi1ahJcvX6J///6wsbGBg4OD6Fgq5efnIywsDAkJCbC3t4eNjQ0MDQ1F\nx1Lpt99+w8OHD9G2bVtYWlpCW1tbdKRqJSEhAceOHUNOTo7y2uTJkwUmqpi///4b8+bNw9OnT2Fs\nbAxfX99yT+shqiocMSWh9u7di4ULF2Lt2rX4+eefsXTpUtGRKiwtLQ2JiYnKX1J3/vx5eHh4wN3d\nXflLqnx9fbF8+XIYGBjAxcUFGzZsEB2pXAsWLMCTJ09w/vx5ZGZmYs6cOaIjqbRmzRocOnQIwcHB\nuHXrFubOnSs6UoU8e/YMsbGxuH//PhYsWIC///5bdKQyTZ06FRkZGTAyMlL+Uge+vr7w9fXF+fPn\nsXz5csl/Xz5w4ECx9/fs2SMoCVUFjpiSUOHh4di3bx/c3d2VZ+yqg2XLluF///sfjI2NoVAoIJPJ\nEBoaKjqWSsuXL4e3tzcaN24sOkqFmJubQyaTwdDQELq6uqLjlOvBgwfw9fXFlStX0KNHD2zbtk10\nJJWuXLmCwMBAuLm5YdCgQdi/f7/oSBUye/ZsjBs3Dvv374ejoyP8/PwkW4jo6upi2rRpomNUmkKh\nQMuWLQEAdnZ20NCQZqlw7NgxnDp1ChcvXsSFCxcAFM5c3L17V9I33qSaNL/aqMYoaqNbNH2vpaUl\nMk6FXb16FadOnULt2rVFR6kwU1NTdOnSRXSMCqlfvz6CgoLw6tUrhIeHo169eqIjlSs/Px8pKYXH\n32ZkZKBWLWlPSOXn5yMnJwcymQz5+fmSz1skNzcX9vb2+P777+Hs7IyQkBDRkcpkY2OD8PBw2NnZ\nKb/HWVpaCk5VPg0NDZw+fRodOnTA5cuXJft9+dNPP4WxsTHS0tIwbNgwAECtWrXQrFkzwcnobbAw\nJaH69u2LkSNH4smTJxg3bhycnJxER6qQDz74AHl5eWpVmDZs2BALFixAq1atlD8ki76ZS42fnx+2\nbt2KBg0a4K+//oKvr6/oSOXy8vLC8OHDkZycjGHDhsHb21t0JJVGjx6NwYMHIyUlBUOGDMGXX34p\nOlKF5ObmYuXKlcqiKS8vT3SkMt26dQu3bt1Svi+TySQ7uvs6X19frFixAqtXr4a1tbVkp/Lr168P\ne3t72NvbF1svbWBgIDoavQWe/ETCxcbG4s6dO7CyskKLFi1Ex6mQ4cOH48GDBzA3NwdQ+AMnMDBQ\ncCrVNm7cWOKalDdivHz5EjKZDCdPnoSDgwPq168vOlKFpKSkoF69epKd/nzdixcvcP/+fTRr1gwN\nGjQQHadC4uLicO7cOQwbNgwnT55E69atYWFhITpWtZCXlwcNDQ3I5fISj0l11BQoXC/99OlTxMbG\nYtSoUTh79izWrFkjOha9IRamJNQ/N1xoamqicePGGDlypKQLkQcPHpS49sEHHwhIUr6nT5+icePG\niI+PL/GYVKcVZ8+eja5du+KPP/5AQUEBnj9/jk2bNomOpVJERAQKCgogl8vh7++PsWPHYuzYsaJj\nleny5ct49eoVFAoFli5diqlTp6J///6iY1XIjRs3iu10b9euncA0JXl6emL9+vXo1q1bicfOnTsn\nIFHFzJgxA6tXr0aPHj2UsypFa+gjIyMFpyvbyJEjleulAwICMHToUEkv8SDVpH9LT9VaTk4OmjVr\nhg4dOuDPP//E9evXYWhoiDlz5mDr1q2i45Vw8OBBfPHFFwgLCyvR1mrq1KmCUqn2ww8/YO7cuViw\nYEGJHzZSnVZ8/PgxnJ2dERoaioCAAIwePVp0pHLt2rUL27Ztw/Tp0/Hrr7/iq6++knRh6u/vj1Wr\nVmHx4sXYv38/vLy81KIw9fT0RFJSEho1agSgcLZCaoXp+vXrAUi7CC3N6tWrAQDr1q1D27Ztldcv\nXrwoKlKFqOt6aSodC1MSKiUlRTnl8umnn+Krr76Cl5cXRo4cKThZ6YravZiZmQlOUnFFo9IBAQFI\nSUnB48ePYW5uLukNRbm5uTh+/DiaN2+OlJQUpKWliY5UrqIeoLq6utDS0kJmZqbgRKppa2ujYcOG\n0NDQgLGxcanTt1KUmJiI4OBg0TEq5NSpUwgLCys2urt9+3aBiVT7/fffERMTg927d2PMmDEAgIKC\nAgQGBuLYsWOC05VNXddLU+lYmJJQGRkZiI2NhbW1NWJjY5GVlYXU1FRkZWWJjlaqzz77DACUo3n3\n7t2DjY2NWrS5OnjwILZv3w5ra2vExcVhypQp6NOnj+hYpfLw8EB4eDjmzp2LgIAAeHl5iY5UrqZN\nm+KLL77A/PnzsXHjxmIjTlKkp6eHMWPGYMSIEQgMDISpqanoSBViZWWFZ8+eqUVP0BUrVmDJkiWS\nXpb0unr16uHZs2eQy+VITk4GUDgiPWvWLMHJVPvPf/6DLl26qN16aSod15iSUNeuXcOiRYuQlJQE\nHR0dDBo0CAYGBjAyMkKvXr1ExyvTjBkz0KhRI3z00Uf4/fffkZGRgeXLl4uOpZKLiwsCAwOhra2N\nrKwsjB49ukRjaql6fepWyjIzM6Grq4vk5GQYGxuLjqOSXC7HgwcP0Lx5c9y5cwcWFhaS3uBS5D//\n+Q8ePnyIhg0bAigsnH799VexocowefLkUjcdSl1iYiJMTEyU7+fm5kJTU1NgItXUeb00lcQRUxKq\nbdu2WLRoEfbu3Yvz58/j+fPnmDRpkuhY5UpKSlKux+rVq5dklx68zsDAQLlTXEdHR9JT+d999x32\n79+P3NxcZGdnw8LCAuHh4aJjqRQdHY2wsDDk5uYCKPwa2blzp+BUZUtISEBkZCR+/vlnAIV5lyxZ\nIjhV+aQ8pfxPjo6OGDZsGKysrJTXpH4DCwCnT5/GDz/8gLy8PCgUCmhqauKXX34RHatM6rpemkrH\nwpSEkMvlCA8PR2BgILS0tJCRkYHIyEjo6OiIjqZSQUEBgMJp25s3b6JVq1aIiYlRto2SounTp0Mm\nkyElJQWDBw/Ghx9+iJs3b0r6c3327FlERUXBz88PY8aMweLFi0VHKteyZcvw5Zdf4pdffoGtra3k\n12zOmTMHDg4OuHr1Kho1aiTZ5TP/dO/ePSxatAjp6eno168fbG1tlUtspCYgIAAeHh7Q19cXHaVS\nQkJCEBAQgC1btqB379748ccfRUdSSV3XS1PpWJiSED169EC/fv2watUqWFhYwMPDQ9KFUhEHBwfI\nZDIoFAr8v//3/6Cjo4OcnBxJZ3d1dS1xrV+/fgKSVJyBgYFyA5G5uTlevXolOlK56tWrh379+uH8\n+fOYMmUKRo0aJTqSSjo6OpgwYQLu3buH5cuXY8SIEaIjVcjSpUuxePFiLFy4EIMGDcKECRMkW5ga\nGRlJdh23Kg0aNECjRo2QmZkJe3t7ZZcBqVLX9dJUOhamJIS7uzuOHTuGx48fw8XFBeqy1PnMmTOi\nI1Rap06dAACHDx8WnKTiGjdujNDQUNSpUwerV69GRkaG6EjlkslkuHv3Ll69eoW4uDjl5hGpUigU\nSE5ORlZWFrKysvDixQvRkSqkoKAAVlZWkMlkMDIyQt26dUVHKpOOjg7Gjh1b7LS16dOnC05VPn19\nfZw8eRIymQxBQUHKo3al6rvvviu2XnrIkCGiI9Fb4OYnEurSpUs4cOAAoqKi4OLiAmdnZ9ja2oqO\nVa4xY8aU6GO6a9cuQWkqpmhNrEKhwK1bt2BgYKC8JjUFBQVISEhA/fr1cejQIXTp0gXW1taiY6l0\n9+5d3L17FyYmJvD19cWAAQMk3bbm8uXLiImJQaNGjeDj44OBAwdizpw5omOVa8qUKejevTsOHDiA\nr776CkeOHMHmzZtFxyrVoUOHSlwbNGiQgCSVk5GRgQcPHsDIyAi7du1Cjx49lDe4UnT//n38/PPP\nxdZ3q8N6aSodC1OShPT0dPz00084ePCgWozs3b17F0BhkXfjxg3cuXNHLX6oF1EoFJgwYQK2bdsm\nOkox+fn5yM/Px/Tp07F27VooFAoUFBRg/Pjxkj0MoMjy5ctLnGQmZTt37pT0AQBlSU9Px5YtW5TH\nGE+cOBGGhoaiY5Xq8uXLJa517NhRQJKKu3HjBvT19ZUn2WVnZ2PDhg2Sbhnl6uoKBwcHXLx4Uble\nWurLD6hsnMonSahXrx7c3Nzg5uYmOkqF2NjYKN+2tbVVi5OJXt8QkJycjEePHglMU7qDBw9i69at\nePbsGXr37g2FQoHatWujffv2oqOVKzY2Funp6ZLudvC6M2fO4Msvv0Tt2rVFR6mQopZhL168KLYe\nNjMzU7KF6f79+wEU3gjGxMTAzMxM0oXp0qVLcfPmTWRkZGDixIkwNzeHp6cnunbtKjqaSuq6XppK\nx8KU6A2EhoYq305OTlaLNZC9e/dWbtwqWvsmNUOHDsXQoUMRGhoKFxcX0XEqJTY2Fvb29jA0NFQu\n85DykZSpqan49NNP0bRpU8hkMuV6Qqnatm0bfHx8MGfOHOXXMVC4tjcwMFBwutIVnWoHFN4YSv2g\niKtXr+LQoUN4+fIlRo8ejVevXmHZsmXo3Lmz6Ggqqet6aSodC1OiN/D6aKO2tjbWrVsnME3FTJ06\nFc7OzqJjVEjXrl3h6emJ2NhYWFhYYO7cuWjatKnoWCqdPn1adIRK2bp1q+gIleLj4wMAJXbhS7m/\n5uvy8/Px8OFD0TFU0tPTA1C4+SkzMxM7d+6U/L87oPAgg//9738YMGAAHB0dMXDgQNGR6C2wMCV6\nA48fP4a/v7/oGJVy4MABtSlM58+fj+HDh6Njx464dOkS5s2bJ/leinfv3sXChQvx8uVL9O/fHzY2\nNnBwcBAdq0waGhrw9/dHamoqevXqhRYtWsDMzEx0rDL9+uuviI6OxpEjR5RfxwUFBThx4oRkT4nr\n1q2b8u28vDzJL/l5fUNno0aN1KIoBQo3axVN3zs6OuL48eOCE9HbYGFK9Aays7MRExMDCwsL1KpV\nCwCU/5cquVyOgQMHwtLSUjl1K9Vd+Tk5OXB0dAQAODk5Yffu3WIDVcCyZcuwfPly+Pj4wMXFBR4e\nHpIuTOfPn48xY8Zg8+bN6NChA7799luEhISIjlUmGxsbJCcnQ0tLC02aNAFQ+G9OyjeIUl7KUZqH\nDx9izZo1UCgUePToUbGlCFJsc3X69GlcvXoV4eHh+OOPPwAU3qxERkaqZf9YKsTClOgNxMXFFVuj\nKeXzuovMnDlTdIQKy8/Px+3bt9GiRQvcvn1bdJwKMzc3h0wmg6GhIXR1dUXHUSknJwedO3fGli1b\nYGVlBW1tbdGRVDIzM8OQIUMwcOBAPHjwAHFxcTA3N5d0e7nr169j4cKFePbsGZo0aYIlS5ZIOq+n\np2epb0tVy5YtkZaWBm1tbVhaWgIo/F7ct29fwcnobbAwJXoDUj+3/XWnT5+Gg4MD4uPjSzwm1d6E\nPj4+8Pb2RlJSEkxMTLB06VLRkcpVv359BAUF4dWrVwgPD5f87nwtLS2cPXsWBQUFiI6OhpaWluhI\nFRISEoKwsDB8+OGH2LJli6T7xfr6+mLlypVo3rw5bt++jUWLFmHfvn2iY5WpvB6rkyZNwqZNm95T\nmvKZmppi0KBBcHZ2LjZjlZSUJDAVvS0WpkSVsGzZMvj4+GDEiBElGuxLdWdwWloaAEj+JKLXtWrV\nCgcPHhQdo0JevnwJfX19+Pn5YevWrWjQoAH++usv+Pr6io6m0tKlS7FixQqkpqZi165dWLRokehI\nFXLkyBEEBQVBU1MTcrkcw4cPl2xhqq2tjebNmwMAWrRoAU1NTcGJ3k56erroCKXauHEj9u3bh9zc\nXGRnZ8PCwkKtBg+oOBamRJUwbtw4AMB///tfwUkq7qOPPkJ8fLxaTW9t3LgRgYGBxXpsSnW93tdf\nf43AwED4+/tj8eLFouOUa+zYsdi5cydCQ0Oxdu1a0XEqTaFQKAs8LS0tSRZ7wcHBAAo3mC1atAgd\nO3bEtWvXlLve1dU/b8alIioqClFRUfDz88OYMWPU4t8hlY2FKVEl7N+/v8xvzlOnTn3PaSpmwYIF\npV6XyWSSPU3p9OnTOH36NHR0dERHKZeOjg6++OIL3L9/v8R6WCn2Bc3MzISnpyeuXLlSYnmHVDfD\nve6jjz7CtGnT0KFDB1y5cgUffvih6EglFM1OfPzxxwCA+Ph46Ovrw87OTmSsasvAwABaWlrIzMyE\nubk5Xr16JToSvQUWpkSVIOV2OmUJCAgQHaHSGjZsCA0N9fj2tH37diQlJWHBggVYuHCh6Djl2r59\nO27fvo0HDx5g2LBhouNUmqenJy5cuIC4uDj06dMHTk5OoiOVoE6zE9VB48aNERoaijp16mDVqlVq\nceAJlU2mKDo+g4jKdfPmTbRq1Qq//fZbicekejqKp6cn1q9fX6ynYhGpTY9Pnz4dMpkM8fHxyM3N\nVR79KuXWVkUyMzPx8uVLaGhoIDg4GAMHDpT0jUxubi5ycnIgk8nwv//9Dw4ODqhfv77oWOUaMWKE\npDcQAYCbm5vydKqiGZbU1FTcu3cP169fF5zuzU2ZMgUbNmwQHaOYv//+G8ePH8fNmzfRrl076Ovr\no3Pnzsq1vaR+YqHWZQAAIABJREFUWJgSVcL333+PCRMmYNasWcWuy2QyrFy5UlCq6uPSpUtlPibV\nDgJFvvnmGwwePBgnTpxA8+bNcfHiRezcuVN0rDLNnj0bXbt2xR9//IGCggI8f/5cUjuuyzJx4kR0\n69ZN2Y8XkO5NIQBcu3YNe/fuxfnz59GzZ0+1GFVPSEjAsWPHkJOTo7w2efJkgYlKFxERge3bt2P4\n8OEwNDTEkydPEBISgqlTp0pyJJ0qRj3myogkYsKECQCgbOqdmJiIgoICmJqaioylUlZWFoKDg9G0\naVN8+OGHmDNnDvLy8vDtt9+idevWouMVU1R8Pn/+HFu2bMG9e/dgY2ODr7/+WnCy8qWnp8PR0RF7\n9uzBypUrcfbsWdGRVHr8+DGcnZ0RGhqKgIAAyZ9KVERPTw/R0dGIjo4GUHhTKLXCVC6XIzw8HPv2\n7YOmpiYyMjIQGRmpFmumgcL18p07d5b09zUA2LNnD/bu3Yu6desqrw0aNAgTJ05kYarGWJgSVcK1\na9ewYMECNG3aFH379sWyZcugra0NNzc3jBkzRnS8Un377bewtLTE/fv3sXLlSkyYMAGmpqZYtmwZ\n9u/fLzpeqby8vNCnTx+4uLjgypUrmD17Nr7//nvRsVTKzc3Frl270Lp1a8TExCAzM1N0JJVyc3Nx\n/PhxNG/eHCkpKcq2YlIWGxsLLy8vSS+RAIAePXqgX79+8Pf3h4WFBTw8PNSmKAUAXV1dTJs2TXSM\ncmloaBQrSoHCG5fXu3mQ+mFhSlQJS5cuxerVq5GWlgYPDw+cOHEC9evXl3Rh+uzZM6xfvx4AMGDA\nALi4uAAAduzYITJWuYYPHw6g8HSXn3/+WXCa8s2ePRuRkZGYOHEijh49Kvm+oB4eHggPD8fcuXMR\nEBAALy8v0ZFU2rBhA6KiopCbmwsPDw/069dPdKQyubu749ixY3j8+DFcXFygbivmbGxsEB4eDjs7\nO+VyiaKTlaSkrA4pBQUF7zkJVSUWpkSVoKOjA2trawCAnZ0djI2Nldel6vXd7QYGBsq38/PzRcSp\nECsrKxw5cgT29va4ceMGDAwMlK2NpPgDEgDat28PCwsLZGRkwMHBQXSccvXs2RM9e/YEIN1WZ687\nf/48Dhw4gKysLEyePFnShen48eMxfvx4XLp0CQcOHMBff/0Ff39/ODs7S/pI0iK3bt3CrVu3lO9L\ntbVcTEwMZsyYUeyaQqFAbGysoERUFViYElXC63forxd8Uh4RSUxMRHBwMBQKRbG3pXxsX1xcHOLi\n4nDgwAHltQULFkj2ByQALFq0CFFRUWjUqJFyN7YU+5gW2bp1K3bs2FHspkpqXRpeV3Rkat26dZGX\nlyc4TcV06tQJnTp1Qnp6On766SfMnj0bhw8fFh2rXOrSYm7dunWlXnd1dX3PSagqsTAlqoSbN29i\n5MiRUCgUuHPnjvLtu3fvio5Wpv79+ysbfr/+tpRHnF7/wZiQkCD5TRhA4frjkydPFjuzW8oiIiJw\n9uxZ1KlTR3SUaq9evXpwc3ODm5ub6CgqqVNrOUD6nTrozbBdFFElPHjwoMzHPvjgAzx9+hSNGzd+\nj4ne3sKFCyV3hN+ePXugo6OD9PR0hIWF4dNPP8XcuXNFx1Jp2rRp8PPzU5tC75tvvsGmTZske8zk\nP3Xo0AEtWrRQ3hQWvS2TyRAYGCg6HhFVEY6YElXCBx98oPLx2bNnS3aquSz/PJZSCsLDwxEQEKDc\noKMOrYwSEhLg4OAAc3NzAJD8VH5ubi769++vXPMo9UMMwsLCVD6ujjeFUlN0wEVppPy1QdULC1Oi\nKsQJiKohk8mQnJwMIyMjyGQyvHjxQnSkcqnbD+5x48aJjlAp1fGmUGq4NpOkgIUpURVSl2lRqbO3\nt8eoUaOwevVq+Pn5KXePS1leXh5+/vln5ObmAgCSkpKwZMkSwanKZmtri3PnziEvL0+5GU6d1+zx\npvDtFf39q8MGLaq+WJgSkeRMmzYNXl5eSE1NxaxZs6CpqSk6UrnmzJkDBwcHXL16FY0aNUJWVpbo\nSCp5enrCwsICd+7cgba2ttqsjS0LbwqrTlG7JYVCgVu3bsHAwAADBw4UnIpqCvXYPkqkJtRx1EaK\nmS9evAgnJyeMGTMGvXr1wvnz50VHKpeOjg4mTJgAExMT/Pe//8WzZ89ERyrXkiVLYGlpiR9++EEt\nlkvQ+zFjxgzMmDEDM2fOxI4dO/Dy5UvRkagGYWFKVIWkOBWan58PuVyOyZMnIzc3F3K5HDk5OXB3\ndwcA7Nq1S3DCktatW4d9+/bhp59+wv79+8vsVyglCoUCycnJyMrKQlZWlloUejk5OXj16hVkMpnk\nR3jLI8UbLHUll8uVv548eYJHjx6JjkQ1CKfyiSphxIgRJaYMX29ZM2XKFEHJynbw4EFs3boVz549\nQ+/evaFQKFCrVi106NABACQ5TV67dm2YmJgAAExMTKCtrS04UfkmT56MkydPYsCAAXB0dJT81OfI\nkSPx448/omvXrvjss8/Qvn170ZHeihRvCtVV7969IZPJoFAooKOjg7Fjx4qORDUI+5gSVUJ5fUyl\nLDQ0FC4uLgAKR0SKTtKRoq+//hpdu3ZFx44dcfnyZVy4cAGbNm0SHUulnTt3qtUP8CNHjmDAgAEA\ngIyMDOjp6QlOpFp5N4VUtRQKBVJTU2FoaCg6CtUwLEyJ3sDDhw/xyy+/FNvRvHDhQtGxVAoODkZs\nbCy8vb3x1VdfYcCAAZId1Xv58iU2b96MuLg4WFtbY8KECahfv77oWCq5u7vjhx9+QO3atUVHqZBR\no0Zh7969omNUmDrfFKqbM2fOYOnSpdDX10dWVhaWLFkCe3t70bGohmBhSvQGXF1d8emnn+L3339H\nw4YNkZOTgw0bNoiOpdKgQYMQFBQEbW1t5ObmYtSoUQgODhYdq1T5+fm4efOmcv0jAHTs2FFwKtX6\n9++P58+fo2nTppDJZJJvsD906FDI5XJYWloqj1FVh16s6nhTqG6GDBmC77//HoaGhkhOTsakSZMQ\nEhIiOhbVEFxjSvQGdHR0MGnSJMydOxfLly/HiBEjREcqV61atZRrNTU1NSXdXsfT0xMvX76EsbGx\ncrpW6oXphg0biq3Xlfrmp5kzZ4qO8EZmzZpV4qaQqpaurq5yCt/Y2FjtW4mRemFhSvQGFAoFUlJS\nkJWVhezsbMkXIQDg6OiIESNGoG3btrhx4wZ69OghOlKZUlNTsW/fPtExKiQ5ORkZGRmYM2cOVq5c\nCYVCgYKCAixYsAChoaGi45WQn5+P/Px87NmzB2vXrlXmHT9+vFqcnKSON4XqYs2aNQAKv0YmTJiA\n9u3b49q1a5Jej07VDwtTojcwceJEHD9+HP369cPnn3+O/v37i45Urm+++QYODg6Ij4/HwIED0bJl\nS9GRytSkSRMkJCTA1NRUdJRy/fnnn/jxxx8RHx+P+fPnAygcne7WrZvgZKUrr0uD1KnjTaG6sLS0\nLPZ/mUwGR0dHkZGoBuIaU6I3cObMGXz22WfK93/55Rf06tVLYKLyJSYmwt/fH6mpqejVqxdatGiB\nDz/8UHSsYoqKOblcjqysLBgYGCgfO3funKhYFfLPrwmpe71Lgzq5cOECYmJiYGJigvnz56N///6Y\nN2+e6FjVSl5eHoKDgxETEwMLCwsMHz6co6b03rAwJaqEX3/9FdHR0Thy5AicnZ0BAAUFBThx4gQi\nIiIEp1Nt/PjxGDNmDDZv3ozFixfj22+/5YaGKvT3339j3rx5SExMhJGREfz8/NCqVSvRscqUkJCA\n5cuXIzY2FhYWFpg7dy6aNm0qOla51PGmUN14e3ujXr166NChAy5duoS0tDSsXLlSdCyqITiVT1QJ\nNjY2SE5OhpaWFpo0aQKgcNrW399fcLLy5eTkoHPnztiyZQusrKwk2bT+8uXLWL58OXR1dbFs2TKY\nm5uLjlRhvr6+8PX1RcuWLXHr1i0sXrxY0rvy58+fj+HDh6Njx464dOkS5s2bhx9//FF0rDKpuilk\nYVq17t+/r+wN6+TkBFdXV8GJqCbhkaRElWBmZoYhQ4bg6NGjaNeuHQwMDNCmTRv861//Eh2tXFpa\nWjh79iwKCgoQHR0tyam5NWvWwN/fH15eXmrRuuh1CoVCuW7Xzs4OGhrSvu/PycmBo6Mj6tWrBycn\nJ+Tn54uOpJKNjQ3MzMyUN4VNmjTBBx98oBY3heqm6KhaAMjOzpb81wZVLyxMid5ASEgIZs+ejd9+\n+w3ffvstdu/eLTpSuZYuXYqwsDCkpqZi165dWLRokehIJWhqasLa2hrt27dHenq66DiVoqGhgdOn\nT+Ply5c4deqUJAv/1+Xn5+P27dsAoPy/lKnzTaG6cXd3h7OzMyZNmgRnZ2eMHj1adCSqQaR9S08k\nUUeOHEFQUBA0NTUhl8sxfPhwfPnll6JjqfTDDz9g7dq1omNUWEFBgegIleLr64sVK1Zg9erVsLa2\nxtKlS0VHUsnHxwfe3t5ISkqCiYmJ5PMWCQkJQVhYGD788ENs2bIFAwYMkPy/PXUzYMAAdO/eHQ8f\nPkTTpk3RoEED0ZGoBmFhSvQGFAqFspm6lpZWscbqUhUbG4v09HTUq1dPdJQyJSYmIjg4GAqFQvl2\nkWHDhglMVj4zMzOsX79edIwKa9WqFQ4ePCg6RqWp402hupg+fXqZB2+o29IaUl8sTInewEcffYRp\n06ahQ4cOuHLliuTaLpUmNjYW9vb2MDQ0VP7wkVoLpv79+yM5ObnE2+pg69at2LFjB3R0dJTXpPb5\nfd3hw4exbdu2YicnRUZGCkxUMep4U6guuMmJpIDtoogqwcvLC+vWrQMAnDx5EnFxcbCysoKTk5Pg\nZDXDwoULsXjxYtExSuXs7IygoCC1Ob6xb9++2Lx5c7FDDKS+LhYA/Pz8kJycrLwpNDY2xty5c0XH\nqjbi4+NhaWmJPXv2ID09HTKZDGPHji12w0X0LnHElKgSUlJSlG+rSzG6efNmfPPNN5gxY0aJx9Rt\nei4+Pl50hDKZmZmp1Q/vZs2aqVU7rqKbQm9vb+VNYZ8+fdTm36E6CAkJwdGjRxEQEIDDhw9j6NCh\n+PPPP/HDDz9g4sSJouNRDcHClKgSHj58qDxP+p+mT5/+ntNUTI8ePfD3338jISEBqampcHZ2hqGh\nISwsLERHq1Zyc3PRv39/2NraAig8zlHKhb+Ojg48PDxgZ2enXNoh1a9hQD1vCtXN0aNHsW3bNgCA\nrq4uXF1dMWjQIAwfPpyFKb03LEyJKkFHR0d5jrS6iI+Px/bt2+Hq6oqGDRviyZMnCAgIwNSpU0VH\nq1bGjRsnOkKlqNPxqYB63hSqo6KlKH369AEAaGtrQ1dXV2QkqmFYmBJVgpGREQYNGiQ6RqXs2bMH\ne/fuRd26dZXXBg0ahIkTJ3LkqQqcPn0aDg4OpS4z6NSpk4BEql2/fh1t2rSBsbGx6CiVoo43heom\nJycHCoUCMpkMw4cPB1C42YwN9ul9YmFKVAnq2MxbQ0OjWFEKAHp6eqhdu7agRG9Oins109LSAEBt\nOgj89ttvaNOmDcLDw0s81q1bNwGJKkYdbwrVTffu3bFq1SrMmDEDtWrVgkKhwLp169RudJ3UG3fl\nE1Vz7u7u2LNnT4WvS0FGRga2b9+O5ORkfP7552jRogXMzc2Rm5sr2fZAeXl5iImJgVwuV15r27at\nwETly8jIKNYuqmHDhgLTqLZixQrMmTNHdIxqLT8/H2vXrsXx48dhYGCAtLQ09OrVC7NmzUKtWjwo\nkt4PFqZE1VyXLl3QuXPnYtcUCgUuXryI8+fPC0qlmqenJ7p3746wsDDMnDkTa9aswd69e0XHUmns\n2LGQy+XKAwxkMhk2btwoOFXZ5syZgytXrkBfX185fXvo0CHRsUgC8vPzkZqaCgMDA2ho/N/E6smT\nJ7n8h945TuUTVXNFfVf/ScrNtNPS0uDi4oIjR46gXbt2kpzC/6ecnBzJF8+vi4uLw8mTJ0XHIAmq\nXbs2jIyMSlzfs2cPC1N651iYElVzUtyAUxGxsbEAgKdPn6rFNGKHDh1w9uxZWFtbK681adJEYCLV\n2rZtqzwggqgi1OEGkdQfC1MikhwfHx94e3sjNjYWnp6eWLhwoehI5Xr+/Dn8/PyKTeUHBQUJTlU2\nPT09uLi4FNsYJ+UjVEm8on63RO8SC1MikhxbW1sEBweLjlEp8fHxiIiIEB2jwi5evIhLly4VW0NI\nRCQavyMRkeQcPnwY27ZtK7ZjPDIyUmCi8tna2iI6OhqtWrVSXpPy2fMWFhZ4/vw5TExMREchNcGp\nfHofuCufiCSnb9++2Lx5M0xNTZXXpFzkAUD//v2RmZmpfF8mk0m6mO7ZsyceP36MBg0aKK9xKp9U\nOXXqFHr06CE6BlVzHDElIslp1qwZzM3NRceolKNHjyrfzs3NxS+//CIwTflOnDhR7P0//vhDUBKS\niqIDFnJzc/Hq1SuYmpri6dOnaNiwIYtSem9YmBKR5Ojo6MDDwwN2dnbKDRfqcB56UlISgoKCcPDg\nQbRs2RL9+vUTHUkluVyOo0ePIjAwEHK5HMeOHRMdiQQqGjGfOXMmZsyYAVNTUyQmJmL58uWCk1FN\nwsKUiCRH3Y5AvHTpEvbu3Ytbt26hVq1aCAoKKrYMQWoePXqEwMBAREREQKFQYO3atWjXrp3oWCQR\njx49Un79mpiYICEhQXAiqklYmBKRZFy/fh1t2rSBsbGx6CgVNnjwYFhZWcHV1RWffPIJxo8fL+mi\ndOLEiUhPT8fAgQNx7NgxeHl5sSilYqytrTFr1iy0bdsW0dHRaN++vehIVIOwMCUiyfjtt9/Qpk0b\nhIeHl3isaP2b1LRp0wZ//PEHoqKiYGJiIvlejwqFAhoaGsjOzkZBQYHk89L7t3TpUkRFReHu3bvo\n06cPHB0dRUeiGoS78olIMuRyeZmPSXlXfnZ2NiIiInDgwAHcvXsX06ZNQ58+fWBgYCA6WqmePn2K\n0NBQHD16FFlZWfD19UW3bt3U4oQtevcyMjIQFRVV7N/jwIEDBSaimoSFKRFJRo8ePUqM4CkUCsm3\nXnpdbGwsQkNDcfz4cZw5c0Z0HJUUCgXOnj2L0NBQXLt2Db/++qvoSCQB7u7uaNSokXJJikwmU4vN\nh1Q9sDAlInoHcnNzoampiUmTJmHTpk2i45QrJSUFhoaGWLhwIRYvXiw6Dgnk5uaGgIAA0TGohuIa\nUyKSnMjISOzbtw+5ublQKBRIS0sr1idUHWhqagIA0tPTBSepGENDQwCFR6tSzdaiRQv8+eefsLOz\nU16T8lIaql5YmBKR5GzatAnz589HUFAQ7O3tcf78edGR3hg3F5G6uXTpEk6dOqV8X52W0pD6Y2FK\nRJLToEEDfPzxxwgKCsLgwYMRFhYmOhJRjXHkyBHREagGY2FKRJKjqamJy5cvIy8vD2fPnkVycrLo\nSEQ1RnVYSkPqi71BiEhyFi9ejLy8PEycOBEhISGYOnWq6EhvrH79+qIjVAr3w9KmTZswefJkmJqa\nYtCgQbC1tRUdiWoQFqZEJDkvXrxA586d0bx5c3z33Xd49OiR6EjlSkxMxMyZMzF27FiEhITgzz//\nBABs2LBBcLLSZWRkYO3atfD29saJEydw//59AMCuXbsEJyPRipbSAIUnmyUmJgpORDUJC1Mikhxv\nb288fPgQjx49wqhRo/D48WPRkco1f/58fPHFF5DL5ejQoQN8fX1FR1LJ29sbzZo1w71792BkZIR5\n8+YB+L9uAlRzcSkNicTClIgkZ82aNZg+fTomTpyIKVOmqEVfzZycHHTu3BkymQxWVlbQ1tYWHUml\ntLQ0uLi4QENDA+3ateMUPin9cymNp6en6EhUg3DzExFJRnBwsPLtdu3aISoqCg8ePMCDBw8wbNgw\ngcnKp6WlhbNnz6KgoADR0dFq0fcxNjYWQOERpTyOlIqYmJjAxMQEQPGlKOpyWASpNxamRCQZr08Z\n6uvro2/fvmozjbh06VKsWLECqamp2LVrFxYtWiQ6kko+Pj7w9vZGbGwsPD09JZ+XxFOXwyJIvbEw\nJSLJ6Nu3r+gIb6xx48ZYu3at6BgV9vjx42Ij1MePH0erVq0EJiKp42ER9D6wMCUiyViwYAFkMhkU\nCoXyh2Bqairu3buH69evC06n2tatW7Fjxw7o6Ogor507d05gotKdPn0aV69eRXh4OP744w8AQEFB\nASIjI9GnTx/B6YiopmNhSkSSERAQoHz72rVr2Lt3L2JjY+Hi4iIwVcVERETg7NmzqFOnjugoKrVs\n2RJpaWnQ1taGpaUlgMKRMHUerSai6oOFKRFJhlwuR3h4OPbt2wdNTU1kZGQgMjKy2CikVJmZmalF\nzqKm6c7OzsU2PCUlJQlMRepA3Q6LIPUkU7BHCBFJRLdu3dCvXz+4urrCwsICHh4e2LFjh+hYFTJu\n3DgkJCQoT8mRyWRYvXq14FRlW79+vfLYyezsbFhYWCA8PFx0LJKAhIQEHDt2DDk5OcprkydPFpiI\nahKOmBKRZLi7u+PYsWN4/PgxXFxc1Kq35rhx40RHqJSoqChERUXBz88PY8aMUYtesfR+TJ06FZ07\nd4apqanoKFQDsTAlIskYP348xo8fj0uXLuHAgQP466+/4O/vD2dnZ8me13369Gk4ODggPj6+xGOd\nOnUSkKhiDAwMoKWlhczMTJibm+PVq1eiI5FE6OrqYtq0aaJjUA3FwpSIJKdTp07o1KkT0tPT8dNP\nP2H27Nk4fPiw6FilSktLAwC16bdapHHjxggNDUWdOnWwevVqZGRkiI5EEmFjY4Pw8HDY2dkpu2MU\nbZQjete4xpSIqArk5eUhJiYGcrlcea1t27YCE6lWUFCAhIQE1K9fH4cOHUKXLl1gbW0tOhZJgJub\nW7H3ZTIZ9uzZIygN1TQsTImIqsDYsWMhl8tRr149AIU/zDdu3Cg4VUn5+fnIz8/H9OnTsXbtWigU\nChQUFGD8+PEsPohIOE7lExFVgZycHOzdu1d0jHIdPHgQW7duxbNnz9C7d28oFArUrl0b7du3Fx2N\nBPP09MT69evRrVu3Eo9J8bAIqp44YkpEVAXWrVuH9u3bF5sOb9KkicBEqgUGBmLkyJGiYxARFcMR\nUyKiKvD8+XP4+fkVm8oPCgoSnKpsx44dY2FKpTp16hTCwsKK9THdvn27wERUk7AwJSKqAvHx8YiI\niBAdo8Lq1q0LPz8/WFpaKk+AGjZsmOBUJAUrVqzAkiVLeNITCcHClIioCtja2iI6OhqtWrVSXtPS\n0hKYSLWPP/4YQOFIL9HrbGxsYG9vLzoG1VBcY0pEVAX69++PzMxM5fsymQyRkZECE5UvKSkJeXl5\nUCgUSEpKUharVLMdOnQIQUFBsLKyUl5bvny5wERUk7AwJSKqgby9vREdHY1Xr14hOzsbzZo1Q0hI\niOhYJAGDBw+Gh4cH9PX1ldc+/fRTgYmoJuFUPhFRFYiMjMS+ffuQm5sLhUKBtLQ0HD16VHSsMsXF\nxSE8PBwLFizAtGnTMHXqVNGRSCKMjIzQp08f0TGohmJhSkRUBTZt2oT58+cjKCgI9vb2OH/+vOhI\nKunq6kImkyErKwuGhobIzc0VHYkkQkdHB2PHjkWrVq2UR5JOnz5dcCqqKViYEhFVgQYNGuDjjz9G\nUFAQBg8ejLCwMNGRVGrdujV27tyJRo0aYdq0acjPzxcdiSTCwcFBdASqwViYEhFVAU1NTVy+fBl5\neXk4e/YskpOTRUdSafr06cjMzISOjg7OnDmDtm3bio5EEtG0aVPREagGY2FKRFQFFi9ejLi4OEyc\nOBHfffcdPD09RUdSKT4+HitXrsS9e/dgY2ODFi1aiI5EErF//34AgEKhQExMDMzMzNCxY0fBqaim\n4K58IqK3EB8fX+KaQqGATCaDpaWlgEQVM3ToUEyaNAnt2rXDlStXsHPnTgQEBIiORRIjl8vh5eWF\nzZs3i45CNQRHTImI3sKCBQuUbxdtFElNTcW9e/dw/fp1UbHKVadOHXz22WcAgM8//xw//PCD4EQk\nRfn5+Xj48KHoGFSDsDAlInoLr48yXrt2DXv37kVsbCxcXFwEpiqfqakpNm/ejE8++QQ3btyAlpYW\nzp07BwDo1q2b4HQk0ut//3l5eRg9erTANFTTcCqfiOgtyOVyhIeHY9++fdDU1ERGRgZCQkKgo6Mj\nOppKc+fOLfMxnvJDRKKwMCUiegvdunVDv3794OrqCgsLC3h4eGDHjh2iY1VIfn4+FAoFoqOj0bZt\nW2hpaYmORBJw/fp1LFy4EM+ePUOTJk2wZMkS2Nraio5FNQSn8omI3oK7uzuOHTuGx48fw8XFBepy\nr+/v749mzZrhyZMnuHHjBoyNjfHf//5XdCySAF9fX6xcuRLNmzfH7du3sWjRIuzbt090LKohaokO\nQESkzsaPH48jR47Azc0Nx44dw19//QV/f3/cuXNHdDSVrly5AldXV/zxxx/YuXMnEhISREciidDW\n1kbz5s0BAC1atICmpqbgRFSTcMSUiKgKdOrUCZ06dUJ6ejp++uknzJ49G4cPHxYdq0wFBQW4du0a\nmjZtCrlcjpSUFNGRSLDg4GAAgIaGBhYtWoSOHTvi2rVr0NPTE5yMahKuMSUiqoECAwNx+PBhLF++\nHPv370fDhg3xzTffiI5FAm3cuLHMxyZPnvwek1BNxhFTIqIaaOTIkWjTpg22b9+Oc+fOoVevXqIj\nkWB9+/YVHYGII6ZERDVJUXurwMBAaGlpqU17K3r33NzcIJPJlCeXAepxWARVLxwxJSKqQXr06IF+\n/fph1apVyvZWLEoJUN/DIqh6YWFKRFSDqGt7K3r3SjssIjIykjcu9F5xKp+IqAa6dOkSDhw4gKio\nKLi4uMDZ2ZlN1Gs4dT4sgqoPjpgSEdVA6tbeit49jqaTFHDElIiIiJQ4mk4isTAlIiKiEopG0w8e\nPMjRdHpRNneiAAAAQUlEQVRvWJgSERERkSTUEh2AiIiIiAhgYUpEREREEsHClIiIiIgkgYUpERER\nEUkCC1MiIiIikgQWpkREREQkCf8fP3/2cGpcjyUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a986bd1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp4AAAK3CAYAAAAs1U9tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9fe973860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask=np.zeros_like(df.corr())\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "plt.figure(figsize=(10,10))\n",
    "with sns.axes_style(\"white\"):\n",
    "    ax = sns.heatmap(df.corr()*100, mask=mask, fmt='.0f', annot=True, lw=1, cmap=ListedColormap(['green', 'yellow', 'red','blue']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(570, 11)\n"
     ]
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "print( df.shape )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were 13 duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b073e3d30>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEBCAYAAADbxHY7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9H9cr6FktBtzIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26b0743f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.Aspartate_Aminotransferase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "135    4929\n",
       "117    2946\n",
       "118    1600\n",
       "207    1500\n",
       "119    1050\n",
       "Name: Aspartate_Aminotransferase, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Aspartate_Aminotransferase.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.Aspartate_Aminotransferase <=3000 ]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b074e5668>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV4AAAEBCAYAAAAw8804AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZQQvAFhG8AKAZQQvAFj2f6/gIvKXSgPrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26b074a0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.Aspartate_Aminotransferase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117    2946\n",
       "118    1600\n",
       "207    1500\n",
       "199    1050\n",
       "119    1050\n",
       "Name: Aspartate_Aminotransferase, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Aspartate_Aminotransferase.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(568, 11)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.Aspartate_Aminotransferase <=2500 ]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=df.dropna(how='any')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(564, 11)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65       1              0.7               0.1                   187   \n",
       "1   62       0             10.9               5.5                   699   \n",
       "2   62       0              7.3               4.1                   490   \n",
       "3   58       0              1.0               0.4                   182   \n",
       "4   72       0              3.9               2.0                   195   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                        16                          18             6.8   \n",
       "1                        64                         100             7.5   \n",
       "2                        60                          68             7.0   \n",
       "3                        14                          20             6.8   \n",
       "4                        27                          59             7.3   \n",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0      3.3                        0.90        1  \n",
       "1      3.2                        0.74        1  \n",
       "2      3.3                        0.89        1  \n",
       "3      3.4                        1.00        1  \n",
       "4      2.4                        0.40        1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create separate object for target variable\n",
    "y = df.Dataset\n",
    "\n",
    "# Create separate object for input features\n",
    "X = df.drop('Dataset', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split X and y into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=1234,\n",
    "                                                    stratify=df.Dataset)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(451, 10) (113, 10) (451,) (113,)\n"
     ]
    }
   ],
   "source": [
    "# Print number of observations in X_train, X_test, y_train, and y_test\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data standardization\n",
    "* In Data Standardization we perform zero mean centring and unit scaling; i.e. we make the mean of all the features as zero and the standard deviation as 1.\n",
    "* Thus we use **mean** and **standard deviation** of each feature.\n",
    "* It is very important to save the **mean** and **standard deviation** for each of the feature from the **training set**, because we use the same mean and standard deviation in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_mean = X_train.mean()\n",
    "train_std = X_train.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Standardize the train data set\n",
    "X_train = (X_train - train_mean) / train_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.510000e+02</td>\n",
       "      <td>4.510000e+02</td>\n",
       "      <td>4.510000e+02</td>\n",
       "      <td>4.510000e+02</td>\n",
       "      <td>4.510000e+02</td>\n",
       "      <td>4.510000e+02</td>\n",
       "      <td>4.510000e+02</td>\n",
       "      <td>4.510000e+02</td>\n",
       "      <td>4.510000e+02</td>\n",
       "      <td>4.510000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.517454e-17</td>\n",
       "      <td>2.067821e-17</td>\n",
       "      <td>-1.839499e-16</td>\n",
       "      <td>-1.493878e-15</td>\n",
       "      <td>3.938707e-17</td>\n",
       "      <td>-5.452647e-17</td>\n",
       "      <td>-3.064806e-17</td>\n",
       "      <td>2.291589e-15</td>\n",
       "      <td>-4.290729e-16</td>\n",
       "      <td>2.939260e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.459710e+00</td>\n",
       "      <td>-5.980292e-01</td>\n",
       "      <td>-4.613674e-01</td>\n",
       "      <td>-5.013974e-01</td>\n",
       "      <td>-9.009611e-01</td>\n",
       "      <td>-3.943772e-01</td>\n",
       "      <td>-4.865614e-01</td>\n",
       "      <td>-3.476087e+00</td>\n",
       "      <td>-2.818030e+00</td>\n",
       "      <td>-2.023543e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-7.595886e-01</td>\n",
       "      <td>-5.980292e-01</td>\n",
       "      <td>-3.996190e-01</td>\n",
       "      <td>-4.658144e-01</td>\n",
       "      <td>-4.636908e-01</td>\n",
       "      <td>-3.157860e-01</td>\n",
       "      <td>-4.017376e-01</td>\n",
       "      <td>-6.503894e-01</td>\n",
       "      <td>-7.121980e-01</td>\n",
       "      <td>-6.338007e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.975347e-02</td>\n",
       "      <td>-5.980292e-01</td>\n",
       "      <td>-3.687448e-01</td>\n",
       "      <td>-4.302314e-01</td>\n",
       "      <td>-3.270438e-01</td>\n",
       "      <td>-2.432402e-01</td>\n",
       "      <td>-3.056039e-01</td>\n",
       "      <td>7.882282e-02</td>\n",
       "      <td>3.103678e-02</td>\n",
       "      <td>1.145223e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.583769e-01</td>\n",
       "      <td>1.668451e+00</td>\n",
       "      <td>-1.063141e-01</td>\n",
       "      <td>-7.440091e-02</td>\n",
       "      <td>2.042996e-02</td>\n",
       "      <td>-9.814860e-02</td>\n",
       "      <td>-7.092474e-02</td>\n",
       "      <td>6.257320e-01</td>\n",
       "      <td>8.362078e-01</td>\n",
       "      <td>4.199603e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.762091e+00</td>\n",
       "      <td>1.668451e+00</td>\n",
       "      <td>1.105471e+01</td>\n",
       "      <td>6.472879e+00</td>\n",
       "      <td>7.090935e+00</td>\n",
       "      <td>9.701578e+00</td>\n",
       "      <td>8.504762e+00</td>\n",
       "      <td>2.813368e+00</td>\n",
       "      <td>2.880103e+00</td>\n",
       "      <td>5.612406e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age        Gender  Total_Bilirubin  Direct_Bilirubin  \\\n",
       "count  4.510000e+02  4.510000e+02     4.510000e+02      4.510000e+02   \n",
       "mean   8.517454e-17  2.067821e-17    -1.839499e-16     -1.493878e-15   \n",
       "std    1.000000e+00  1.000000e+00     1.000000e+00      1.000000e+00   \n",
       "min   -2.459710e+00 -5.980292e-01    -4.613674e-01     -5.013974e-01   \n",
       "25%   -7.595886e-01 -5.980292e-01    -3.996190e-01     -4.658144e-01   \n",
       "50%    2.975347e-02 -5.980292e-01    -3.687448e-01     -4.302314e-01   \n",
       "75%    7.583769e-01  1.668451e+00    -1.063141e-01     -7.440091e-02   \n",
       "max    2.762091e+00  1.668451e+00     1.105471e+01      6.472879e+00   \n",
       "\n",
       "       Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
       "count          4.510000e+02              4.510000e+02   \n",
       "mean           3.938707e-17             -5.452647e-17   \n",
       "std            1.000000e+00              1.000000e+00   \n",
       "min           -9.009611e-01             -3.943772e-01   \n",
       "25%           -4.636908e-01             -3.157860e-01   \n",
       "50%           -3.270438e-01             -2.432402e-01   \n",
       "75%            2.042996e-02             -9.814860e-02   \n",
       "max            7.090935e+00              9.701578e+00   \n",
       "\n",
       "       Aspartate_Aminotransferase  Total_Protiens       Albumin  \\\n",
       "count                4.510000e+02    4.510000e+02  4.510000e+02   \n",
       "mean                -3.064806e-17    2.291589e-15 -4.290729e-16   \n",
       "std                  1.000000e+00    1.000000e+00  1.000000e+00   \n",
       "min                 -4.865614e-01   -3.476087e+00 -2.818030e+00   \n",
       "25%                 -4.017376e-01   -6.503894e-01 -7.121980e-01   \n",
       "50%                 -3.056039e-01    7.882282e-02  3.103678e-02   \n",
       "75%                 -7.092474e-02    6.257320e-01  8.362078e-01   \n",
       "max                  8.504762e+00    2.813368e+00  2.880103e+00   \n",
       "\n",
       "       Albumin_and_Globulin_Ratio  \n",
       "count                4.510000e+02  \n",
       "mean                 2.939260e-16  \n",
       "std                  1.000000e+00  \n",
       "min                 -2.023543e+00  \n",
       "25%                 -6.338007e-01  \n",
       "50%                  1.145223e-01  \n",
       "75%                  4.199603e-01  \n",
       "max                  5.612406e+00  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check for mean and std dev.\n",
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Note: We use train_mean and train_std_dev to standardize test data set\n",
    "X_test = (X_test - train_mean) / train_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>113.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.109816</td>\n",
       "      <td>-0.216940</td>\n",
       "      <td>-0.062052</td>\n",
       "      <td>-0.033465</td>\n",
       "      <td>-0.048222</td>\n",
       "      <td>-0.017203</td>\n",
       "      <td>0.007819</td>\n",
       "      <td>-0.082507</td>\n",
       "      <td>-0.175052</td>\n",
       "      <td>-0.218486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.937258</td>\n",
       "      <td>0.851421</td>\n",
       "      <td>0.835512</td>\n",
       "      <td>1.046110</td>\n",
       "      <td>0.734043</td>\n",
       "      <td>0.661163</td>\n",
       "      <td>0.874507</td>\n",
       "      <td>0.962887</td>\n",
       "      <td>0.922474</td>\n",
       "      <td>0.851370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.338273</td>\n",
       "      <td>-0.598029</td>\n",
       "      <td>-0.445930</td>\n",
       "      <td>-0.501397</td>\n",
       "      <td>-0.768218</td>\n",
       "      <td>-0.394377</td>\n",
       "      <td>-0.469597</td>\n",
       "      <td>-2.655723</td>\n",
       "      <td>-2.198667</td>\n",
       "      <td>-1.718105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.577433</td>\n",
       "      <td>-0.598029</td>\n",
       "      <td>-0.399619</td>\n",
       "      <td>-0.465814</td>\n",
       "      <td>-0.444170</td>\n",
       "      <td>-0.321831</td>\n",
       "      <td>-0.401738</td>\n",
       "      <td>-0.741541</td>\n",
       "      <td>-0.836070</td>\n",
       "      <td>-0.801792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.090472</td>\n",
       "      <td>-0.598029</td>\n",
       "      <td>-0.384182</td>\n",
       "      <td>-0.430231</td>\n",
       "      <td>-0.342661</td>\n",
       "      <td>-0.261377</td>\n",
       "      <td>-0.316914</td>\n",
       "      <td>-0.012329</td>\n",
       "      <td>-0.216708</td>\n",
       "      <td>-0.190916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.940533</td>\n",
       "      <td>-0.598029</td>\n",
       "      <td>-0.198937</td>\n",
       "      <td>-0.181150</td>\n",
       "      <td>-0.018612</td>\n",
       "      <td>-0.007466</td>\n",
       "      <td>-0.034168</td>\n",
       "      <td>0.625732</td>\n",
       "      <td>0.526527</td>\n",
       "      <td>0.114522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.851312</td>\n",
       "      <td>1.668451</td>\n",
       "      <td>4.231512</td>\n",
       "      <td>5.974717</td>\n",
       "      <td>3.147694</td>\n",
       "      <td>4.834965</td>\n",
       "      <td>4.263572</td>\n",
       "      <td>2.175308</td>\n",
       "      <td>2.136869</td>\n",
       "      <td>2.558026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age      Gender  Total_Bilirubin  Direct_Bilirubin  \\\n",
       "count  113.000000  113.000000       113.000000        113.000000   \n",
       "mean     0.109816   -0.216940        -0.062052         -0.033465   \n",
       "std      0.937258    0.851421         0.835512          1.046110   \n",
       "min     -2.338273   -0.598029        -0.445930         -0.501397   \n",
       "25%     -0.577433   -0.598029        -0.399619         -0.465814   \n",
       "50%      0.090472   -0.598029        -0.384182         -0.430231   \n",
       "75%      0.940533   -0.598029        -0.198937         -0.181150   \n",
       "max      1.851312    1.668451         4.231512          5.974717   \n",
       "\n",
       "       Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
       "count            113.000000                113.000000   \n",
       "mean              -0.048222                 -0.017203   \n",
       "std                0.734043                  0.661163   \n",
       "min               -0.768218                 -0.394377   \n",
       "25%               -0.444170                 -0.321831   \n",
       "50%               -0.342661                 -0.261377   \n",
       "75%               -0.018612                 -0.007466   \n",
       "max                3.147694                  4.834965   \n",
       "\n",
       "       Aspartate_Aminotransferase  Total_Protiens     Albumin  \\\n",
       "count                  113.000000      113.000000  113.000000   \n",
       "mean                     0.007819       -0.082507   -0.175052   \n",
       "std                      0.874507        0.962887    0.922474   \n",
       "min                     -0.469597       -2.655723   -2.198667   \n",
       "25%                     -0.401738       -0.741541   -0.836070   \n",
       "50%                     -0.316914       -0.012329   -0.216708   \n",
       "75%                     -0.034168        0.625732    0.526527   \n",
       "max                      4.263572        2.175308    2.136869   \n",
       "\n",
       "       Albumin_and_Globulin_Ratio  \n",
       "count                  113.000000  \n",
       "mean                    -0.218486  \n",
       "std                      0.851370  \n",
       "min                     -1.718105  \n",
       "25%                     -0.801792  \n",
       "50%                     -0.190916  \n",
       "75%                      0.114522  \n",
       "max                      2.558026  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check for mean and std dev. - not exactly 0 and 1\n",
    "X_test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Model-1 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000], 'penalty': ['l1', 'l2']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_params = {'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000], 'penalty': ['l1', 'l2']}\n",
    "model = GridSearchCV(LogisticRegression(), tuned_params, scoring = 'roc_auc', n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train set results\n",
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Test set results\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get just the prediction for the positive class (1)\n",
    "y_pred_proba = model.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.56318892,  0.57919181,  0.62369434,  0.66472788,  0.60945037,\n",
       "        0.9939173 ,  0.5074297 ,  0.63805623,  0.80857905,  0.47259005])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display first 10 predictions\n",
    "y_pred_proba[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For test point 28, actual class = 1, precited class = 1, predicted probability = 0.9285583846220501\n"
     ]
    }
   ],
   "source": [
    "i=28  ## Change the value of i to get the details of any point (56, 213, etc.)\n",
    "print('For test point {}, actual class = {}, precited class = {}, predicted probability = {}'.\n",
    "      format(i, y_test.iloc[i], y_pred[i], y_pred_proba[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4],\n",
       "       [31, 77]], dtype=int64)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate ROC curve from y_test and pred\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAHoCAYAAABpZ8cPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ81aCCGEMDhp1kIIIYTB/Qc5njZ9FMRLtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a98680d9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ROC curve\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot(fpr, tpr, label='l1')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "# Diagonal 45 degree line\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "\n",
    "# Axes limits and labels\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.581751737359\n"
     ]
    }
   ],
   "source": [
    "# Calculate AUC for Train set\n",
    "print(roc_auc_score(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.710262345679\n"
     ]
    }
   ],
   "source": [
    "# Calculate AUC for Test set\n",
    "print(auc(fpr, tpr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Building the model again with the best hyperparameters\n",
    "model = LogisticRegression(C=1, penalty = 'l2')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The features in order of importance are:\n",
      "--------------------------------------------------\n",
      "Alamine_Aminotransferase\n",
      "Direct_Bilirubin\n",
      "Aspartate_Aminotransferase\n",
      "Albumin\n",
      "Total_Protiens\n",
      "Total_Bilirubin\n",
      "Alkaline_Phosphotase\n",
      "Age\n",
      "Gender\n",
      "Albumin_and_Globulin_Ratio\n"
     ]
    }
   ],
   "source": [
    "indices = np.argsort(-abs(model.coef_[0,:]))\n",
    "print(\"The features in order of importance are:\")\n",
    "print(50*'-')\n",
    "for feature in X.columns[indices]:\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-2 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=15, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': [100, 200, 300, 400, 500], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_params = {'n_estimators': [100, 200, 300, 400, 500], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4]}\n",
    "model = RandomizedSearchCV(RandomForestClassifier(), tuned_params, n_iter=15, scoring = 'roc_auc', n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=4, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get just the prediction for the positive class (1)\n",
    "y_pred_proba = model.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.61562769,  0.60077365,  0.62661236,  0.58193463,  0.34953728,\n",
       "        0.99093564,  0.51492834,  0.50708841,  0.95323413,  0.42825976])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display first 10 predictions\n",
    "y_pred_proba[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6, 11],\n",
       "       [26, 70]], dtype=int64)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate ROC curve from y_test and pred\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAHoCAYAAABpZ8cPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUjg2ayIiIoVjsyYiIlK4fwHN61EICk6+VwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a98698ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ROC curve\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot(fpr, tpr, label='l1')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "# Diagonal 45 degree line\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "\n",
    "# Axes limits and labels\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88846153846153852"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate AUC for Train set\n",
    "roc_auc_score(y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.726466049383\n"
     ]
    }
   ],
   "source": [
    "# Calculate AUC for Test set\n",
    "print(auc(fpr, tpr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=4, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Building the model again with the best hyperparameters\n",
    "model = RandomForestClassifier(n_estimators=500, min_samples_split=2, min_samples_leaf=4)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The features in order of importance are:\n",
      "--------------------------------------------------\n",
      "Alkaline_Phosphotase\n",
      "Total_Bilirubin\n",
      "Aspartate_Aminotransferase\n",
      "Alamine_Aminotransferase\n",
      "Direct_Bilirubin\n",
      "Age\n",
      "Albumin\n",
      "Albumin_and_Globulin_Ratio\n",
      "Total_Protiens\n",
      "Gender\n"
     ]
    }
   ],
   "source": [
    "indices = np.argsort(-model.feature_importances_)\n",
    "print(\"The features in order of importance are:\")\n",
    "print(50*'-')\n",
    "for feature in X.columns[indices]:\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-3 XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1),\n",
       "          fit_params=None, iid=True, n_iter=15, n_jobs=-1,\n",
       "          param_distributions={'max_depth': [1, 2, 3, 4, 5], 'learning_rate': [0.01, 0.05, 0.1], 'n_estimators': [100, 200, 300, 400, 500], 'reg_lambda': [0.001, 0.1, 1.0, 10.0, 100.0]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_params = {'max_depth': [1, 2, 3, 4, 5], 'learning_rate': [0.01, 0.05, 0.1], 'n_estimators': [100, 200, 300, 400, 500], 'reg_lambda': [0.001, 0.1, 1.0, 10.0, 100.0]}\n",
    "model = RandomizedSearchCV(XGBClassifier(), tuned_params, n_iter=15, scoring = 'roc_auc', n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.05, max_delta_step=0,\n",
       "       max_depth=1, min_child_weight=1, missing=None, n_estimators=500,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1.0, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get just the prediction for the positive class (1)\n",
    "y_pred_proba = model.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.70944315,  0.57030702,  0.55026132,  0.57963443,  0.14151166,\n",
       "        0.96893185,  0.52726376,  0.62016237,  0.9243142 ,  0.2954419 ], dtype=float32)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display first 10 predictions\n",
    "y_pred_proba[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Just as above we can use these probabilities to get model interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7, 13],\n",
       "       [25, 68]], dtype=int64)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate ROC curve from y_test and pred\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAHoCAYAAABpZ8cPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSOL+B9GUk54+Un2QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a98698b470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ROC curve\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot(fpr, tpr, label='l1')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "# Diagonal 45 degree line\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "\n",
    "# Axes limits and labels\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68413611310807565"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate AUC for Train\n",
    "roc_auc_score(y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.715470679012\n"
     ]
    }
   ],
   "source": [
    "# Calculate AUC for Test\n",
    "print(auc(fpr, tpr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.05, max_delta_step=0,\n",
       "       max_depth=1, min_child_weight=1, missing=None, n_estimators=500,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBClassifier(max_depth=1,learning_rate=0.05,n_estimators=500, reg_lambda=1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def my_plot_importance(booster, figsize, **kwargs): \n",
    "    from matplotlib import pyplot as plt\n",
    "    from xgboost import plot_importance\n",
    "    fig, ax = plt.subplots(1,1,figsize=figsize)\n",
    "    return plot_importance(booster=booster, ax=ax, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9869b3550>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAAJVCAYAAAAhjxiSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBgAAAAz6f1+PgsILNKnkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a986637278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot_importance(model, (10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-4 KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The optimal number of neighbors is 17.\n"
     ]
    }
   ],
   "source": [
    "# creating odd list of K for KNN\n",
    "neighbors = list(range(1,20,2))\n",
    "# empty list that will hold cv scores\n",
    "cv_scores = []\n",
    "\n",
    "#  10-fold cross validation , 9 datapoints will be considered for training and 1 for cross validation (turn by turn) to determine value of k\n",
    "for k in neighbors:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_val_score(knn, X_train, y_train, cv=5, scoring='accuracy')\n",
    "    cv_scores.append(scores.mean())   \n",
    "\n",
    "# changing to misclassification error\n",
    "MSE = [1 - x for x in cv_scores]\n",
    "\n",
    "# determining best k\n",
    "optimal_k = neighbors[MSE.index(min(MSE))]\n",
    "print('\\nThe optimal number of neighbors is %d.' % optimal_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE.index(min(MSE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEBCAYAAAB8NQKFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQQghhMq/AC3txoKKoWKLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a98680d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot misclassification error vs k \n",
    "plt.plot(neighbors, MSE)\n",
    "plt.xlabel('Number of Neighbors K')\n",
    "plt.ylabel('Misclassification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=17, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors = optimal_k)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_pred = classifier.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.911504424778755"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = accuracy_score(y_test, y_pred, normalize=True) * float(100)  ## get the accuracy on testing data\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  6],\n",
       "       [28, 75]], dtype=int64)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf=confusion_matrix(y_test,y_pred).T\n",
    "cnf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get just the prediction for the positive class (1)\n",
    "y_pred_proba = classifier.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.76470588,  0.64705882,  0.94117647,  0.64705882,  0.41176471,\n",
       "        1.        ,  0.41176471,  0.52941176,  0.64705882,  0.52941176])"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display first 10 predictions\n",
    "y_pred_proba[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate ROC curve from y_test and pred\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAHoCAYAAABpZ8cPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9867f04a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ROC curve\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot(fpr, tpr, label='l1')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "# Diagonal 45 degree line\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "\n",
    "# Axes limits and labels\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.63769470404984419"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate AUC for Train\n",
    "roc_auc_score(y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.628279320988\n"
     ]
    }
   ],
   "source": [
    "# Calculate AUC for Test\n",
    "print(auc(fpr, tpr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-5 Descision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best'),\n",
       "          fit_params=None, iid=True, n_iter=15, n_jobs=-1,\n",
       "          param_distributions={'min_samples_split': [2, 3, 4, 5, 7], 'min_samples_leaf': [1, 2, 3, 4, 6], 'max_depth': [2, 3, 4, 5, 6, 7]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_params = {'min_samples_split': [2, 3, 4, 5, 7], 'min_samples_leaf': [1, 2, 3, 4, 6], 'max_depth': [2, 3, 4, 5, 6, 7]}\n",
    "model = RandomizedSearchCV(DecisionTreeClassifier(), tuned_params, n_iter=15, scoring = 'roc_auc', n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=6, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_proba = model.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.95652174,  0.54193548,  0.54193548,  0.54193548,  0.        ,\n",
       "        1.        ,  0.66666667,  0.54193548,  1.        ,  0.        ])"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_proba[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  3],\n",
       "       [31, 78]], dtype=int64)"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAHoCAYAAABpZ8cPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a986d5ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ROC curve\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot(fpr, tpr, label='l1')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "# Diagonal 45 degree line\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "\n",
    "# Axes limits and labels\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60209681284447636"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate AUC for Train\n",
    "roc_auc_score(y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.667438271605\n"
     ]
    }
   ],
   "source": [
    "print(auc(fpr, tpr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=6, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Building the model again with the best hyperparameters\n",
    "model = DecisionTreeClassifier(min_samples_split=2, min_samples_leaf=6, max_depth=4)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The features in order of importance are:\n",
      "--------------------------------------------------\n",
      "Total_Bilirubin\n",
      "Alkaline_Phosphotase\n",
      "Age\n",
      "Albumin_and_Globulin_Ratio\n",
      "Albumin\n",
      "Aspartate_Aminotransferase\n",
      "Gender\n",
      "Direct_Bilirubin\n",
      "Alamine_Aminotransferase\n",
      "Total_Protiens\n"
     ]
    }
   ],
   "source": [
    "indices = np.argsort(-model.feature_importances_)\n",
    "print(\"The features in order of importance are:\")\n",
    "print(50*'-')\n",
    "for feature in X.columns[indices]:\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-6 SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "def svc_param_selection(X, y, nfolds):\n",
    "    Cs = [0.001, 0.01, 0.1, 1, 10]\n",
    "    gammas = [0.001, 0.01, 0.1, 1]\n",
    "    param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "    grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    grid_search.best_params_\n",
    "    return grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svClassifier=SVC(kernel='rbf',probability=True)\n",
    "svClassifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 1}"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_param_selection(X_train,y_train,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###### Building the model again with the best hyperparameters\n",
    "model = SVC(C=1, gamma=1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Test results\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  4],\n",
       "       [29, 77]], dtype=int64)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_proba = model.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate ROC curve from y_test and pred\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAHoCAYAAABpZ8cPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a986d1b908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ROC curve\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot(fpr, tpr, label='l1')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "# Diagonal 45 degree line\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "\n",
    "# Axes limits and labels\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77828900071890716"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate AUC for Train\n",
    "roc_auc_score(y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.667438271605\n"
     ]
    }
   ],
   "source": [
    "print(auc(fpr, tpr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-7 Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: \n",
      " 90.02\n",
      "Test Score: \n",
      " 69.03\n",
      "Accuracy: \n",
      " 0.690265486726\n",
      "[[13 16]\n",
      " [19 65]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.45      0.41      0.43        32\n",
      "          1       0.77      0.80      0.79        81\n",
      "\n",
      "avg / total       0.68      0.69      0.69       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Import Library\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset\n",
    "# Create Gradient Boosting Classifier object\n",
    "gbclass = GradientBoostingClassifier(\n",
    "                    random_state = 1000,\n",
    "                    verbose = 0,\n",
    "                    n_estimators = 10,\n",
    "                    learning_rate = 0.9,\n",
    "                    loss = 'deviance',\n",
    "                    max_depth = 3\n",
    "                   )\n",
    "#gbclass = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)\n",
    "# Train the model using the training sets and check score\n",
    "gbclass.fit(X_train, y_train)\n",
    "#Predict Output\n",
    "predicted= gbclass.predict(X_test)\n",
    "\n",
    "gbclass_score = round(gbclass.score(X_train, y_train) * 100, 2)\n",
    "gbclass_score_test = round(gbclass.score(X_test, y_test) * 100, 2)\n",
    "print('Score: \\n', gbclass_score)\n",
    "print('Test Score: \\n', gbclass_score_test)\n",
    "print('Accuracy: \\n', accuracy_score(y_test,predicted))\n",
    "print(confusion_matrix(predicted,y_test))\n",
    "print(classification_report(y_test,predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = gbclass.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Test results\n",
    "y_pred = gbclass.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_proba = gbclass.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate ROC curve from y_test and pred\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAHwCAYAAAC/hfaiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns5JwUTczMyuJ/wN0d+ue+WmoiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18f2399dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ROC curve\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot(fpr, tpr, label='l1')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "# Diagonal 45 degree line\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "\n",
    "# Axes limits and labels\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.861250898634076"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train,y_train_pred )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.707175925926\n"
     ]
    }
   ],
   "source": [
    "# Calculate AUC for Test\n",
    "print(auc(fpr, tpr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Score: \n",
      " 77.83\n",
      "Neural Test Score: \n",
      " 69.91\n",
      "Accuracy: \n",
      " 0.699115044248\n",
      "[[ 5  7]\n",
      " [27 74]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.42      0.16      0.23        32\n",
      "          1       0.73      0.91      0.81        81\n",
      "\n",
      "avg / total       0.64      0.70      0.65       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Neural Networks# Neural \n",
    "neural = MLPClassifier(hidden_layer_sizes=40,\n",
    "                     activation='relu',\n",
    "                     solver='adam',\n",
    "                     alpha=0.001,\n",
    "                     batch_size='auto',\n",
    "                     max_iter=200,\n",
    "                     random_state=137,\n",
    "                     tol=0.0001,\n",
    "                     early_stopping=False,\n",
    "                     validation_fraction=0.1,\n",
    "                     beta_1=0.9,\n",
    "                     beta_2=0.999,\n",
    "                     epsilon=1e-08,\n",
    "                     learning_rate='constant',\n",
    "                     power_t=0.5,\n",
    "                     momentum=0.8,\n",
    "                     nesterovs_momentum=True,\n",
    "                     shuffle=True,\n",
    "                     learning_rate_init=0.001)\n",
    "neural.fit(X_train, y_train)\n",
    "#Predict Output\n",
    "predicted = neural.predict(X_test)\n",
    "\n",
    "neural_score = round(neural.score(X_train, y_train) * 100, 2)\n",
    "neural_score_test = round(neural.score(X_test, y_test) * 100, 2)\n",
    "print('Neural Score: \\n', neural_score)\n",
    "print('Neural Test Score: \\n', neural_score_test)\n",
    "print('Accuracy: \\n', accuracy_score(y_test, predicted))\n",
    "print(confusion_matrix(predicted,y_test))\n",
    "print(classification_report(y_test,predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Train results\n",
    "y_train_pred = neural.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Predict Test results\n",
    "y_pred = neural.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_proba = neural.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate ROC curve from y_test and pred\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAHoCAYAAABpZ8cPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiJSODZrIiIihWOzJiIiUrj/Ao5CF2tU43prAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26b113aa048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ROC curve\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot(fpr, tpr, label='l1')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "# Diagonal 45 degree line\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "\n",
    "# Axes limits and labels\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.66544452432302892"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train,y_train_pred )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.692515432099\n"
     ]
    }
   ],
   "source": [
    "# Calculate AUC for Test\n",
    "print(auc(fpr, tpr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Save the winning model to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "win_model = XGBClassifier(max_depth=1,learning_rate=0.05,n_estimators=500, reg_lambda=1)\n",
    "win_model.fit(X_train, y_train)\n",
    "with open('LiverDisease.pkl', 'wb') as pickle_file:\n",
    "      joblib.dump(win_model, 'LiverDisease.pkl') "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}