Liver Patient.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "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 import linear_model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC,LinearSVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the CSV data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>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": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df = pd.read_csv(r'E:\\Downloads\\archive (1)\\indian_liver_patient.csv')\n",
    "liver_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 583 entries, 0 to 582\n",
      "Data columns (total 11 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   Age                         583 non-null    int64  \n",
      " 1   Gender                      583 non-null    object \n",
      " 2   Total_Bilirubin             583 non-null    float64\n",
      " 3   Direct_Bilirubin            583 non-null    float64\n",
      " 4   Alkaline_Phosphotase        583 non-null    int64  \n",
      " 5   Alamine_Aminotransferase    583 non-null    int64  \n",
      " 6   Aspartate_Aminotransferase  583 non-null    int64  \n",
      " 7   Total_Protiens              583 non-null    float64\n",
      " 8   Albumin                     583 non-null    float64\n",
      " 9   Albumin_and_Globulin_Ratio  579 non-null    float64\n",
      " 10  Dataset                     583 non-null    int64  \n",
      "dtypes: float64(5), int64(5), object(1)\n",
      "memory usage: 50.2+ KB\n"
     ]
    }
   ],
   "source": [
    "liver_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#details about the dataset\n",
    "liver_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                           0\n",
       "Gender                        0\n",
       "Total_Bilirubin               0\n",
       "Direct_Bilirubin              0\n",
       "Alkaline_Phosphotase          0\n",
       "Alamine_Aminotransferase      0\n",
       "Aspartate_Aminotransferase    0\n",
       "Total_Protiens                0\n",
       "Albumin                       0\n",
       "Albumin_and_Globulin_Ratio    4\n",
       "Dataset                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if there is some null values or not\n",
    "liver_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# on the Albumin_and_Globulin_Ratio column there are null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients diagnosed with liver disease 416\n",
      "Number of patients not diagnosed with liver disease 167\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.countplot(data=liver_df, x ='Dataset', label = 'Count')\n",
    "\n",
    "LD,NLD = liver_df['Dataset'].value_counts()\n",
    "print('Number of patients diagnosed with liver disease', LD)\n",
    "print('Number of patients not diagnosed with liver disease', NLD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the number of male and female patient\n",
    "sns.countplot(data=liver_df, x ='Gender', label = 'Count')\n",
    "\n",
    "M,F = liver_df['Gender'].value_counts()\n",
    "print('Number of patients that are male', M)\n",
    "print('Number of patients that are female', F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1042d110550>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 407.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting patient age vs gender\n",
    "sns.catplot(x=\"Age\", y=\"Gender\", hue=\"Dataset\", data=liver_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Disease by Gender and Age')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting age vs gender\n",
    "g = sns.FacetGrid(liver_df, col = \"Dataset\", row=\"Gender\", margin_titles = True)\n",
    "g.map(plt.hist, \"Age\", color = \"red\")\n",
    "plt.subplots_adjust(top=0.9)\n",
    "g.fig.suptitle('Disease by Gender and Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting gender along with Total bilirubin and Direct Bilirubin\n",
    "g = sns.FacetGrid(liver_df, col = \"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Direct_Bilirubin\",\"Total_Bilirubin\", edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting gender along with Aspartate_Aminotransferase and Alamine_Aminotransferase\n",
    "g = sns.FacetGrid(liver_df, col = \"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Aspartate_Aminotransferase\",\"Alamine_Aminotransferase\", edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting gender along with Alkaline_Phosphotase and Alamine_Aminotransferase\n",
    "g = sns.FacetGrid(liver_df, col = \"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Alkaline_Phosphotase\",\"Alamine_Aminotransferase\", edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QeqqZOW5QZ3sNajmA7wEIZ/k6QpbQl3s+8OBNWD17Aja+fQTLXnkvJWeHpjGDsGhj8np9lzeEJZt2x6XSfv3qYRhQXaZ98xRKh2yshybbZk8wjEUbW9E0ZhDueVZZi/rs8PPiYlo9JlEcq+MhNv4dDodhmy6nEwcevAmPz2k0nHQKxFXiHWauZeZ+sQ8Au41OyNooJ6KbAZxk5h0JjptHRC1E1HLq1KlsdUdIEisjTAcRqstdpkXTOr0BhJnR6Q2gwuXQtPOp4wZjxMCalHbgm+n71eWuopmcJPZTIxvroYmcSkLhMDq9AVSVOdE8dTQuqavWjq8/3zimVecIwDyO1XOry12oKnNiYL9ybLnrWlSXG7tLVJtk+qnjtUCcVMpSfS+bI/0qAFOJ6DCAjQCmENH62IOYeTUzNzJzY11dXRa7IyTCKKVVdWpQMdv93uUNRqXUtnf78cjMBkwdNxhLbhiV8g78D9vNjy8WJPZTIxtuIR6feTw/9ceDOHrWq8V18+Z9iow2pR4AcOzjHkvnCAA42eE1PUZdS9WnsJulkHf7gnF914/X9050FcJ4CRBRXKAT0XkAAkYnZG2CYubvM/OFzDwUwAwArzLzrGxdT+g7ycgERmngK2Y2YM0bh+JkiatH1GHx9SNxz7N7Ut6BX1PhxNLp40oilVZIjmy4hTgciHMqeXj6WPQEQlFynhrXd21sxZzPDcOVwwfAQfHnrpjRgC17j2u/96t0GzqhbNl7HCtmNKDK7YxKYQcYK2Y0xLVZZrCOpB+vj77WZjq+bMQ6AOuJaLj6AhENA7Ah8l4cUm6jhInLsnM7LGUC9fj+1W6snj1By+KrdDsMZb/qcpcmWai73punjkb9+TXoieyIV+WK/lW9bXb2BBEMhzCwXzlWzboctRXuuOOF0kO/HtqXTDV93Fe4nVi6Zb8Wl20nu7B0y3787LYG1NWWG46H2kqXFquLn2nVzu30BlBb7sLXrx6GBVPqlbFR5sS9z+2POqam3IU7rx6OSrcDTqcDVQ7SrlNd7sbOIyfxy9snoKbChS5vEDuPtOPakefHfQ69rKduw2ieOhojBtbYMouPmX9ORLUA3iIiF5TcBAbwCyj5CnHkZIJi5q0AtubiWkJyGGX9rJjZgIVT6rHslfe041SZoMrtNM0S6vYHTVPRiUh7b/PuY9i8+xiuHD4Aj89pjEoxb/cEotp+aNpY/PC/dxseL5QuWmFA9KZXp0Js3L+y+PM40eFD0/Jt2jFXDh+ALl8QZ7v9pnFdW+FGly+IEx0+PPpaG5bcMAr3PLsnLlPvjquGGba/evYEbS1Vv5Wj7WQXfvm/h/DmwZao4xuHDoj7vLFbQDbvPoZTnT48Pqcxrb9NLmDm+wHcr0p9zGy5+Focq81CyhjJeYs2tOKOq4YZygRW8l+V22koSyRriGlmfiluEUKmScaMeMXMBqx945ChLL1iRgMqXUosqrGtythGmXpGrg/q2FDRj5FVW9vi+mMW/4VmkExE9US0jIj+HYAPQCcRXWx5jpjFliZGpqyq4aRqSKmXCayOdxAhFArDEwhpmwvNNiEaSQ8J+2IzqSIBYhZrY6zMiFXnksoyB0bd9yKCYcbUcYOxYHK9JktXuBxwuXq/14fDDBAM43f/Azdh1H0vYP8DN8LjNx4b+nbUMeINhBAO9xrQWsW/DTfnml6ciHYDWANgEIC/A/BNAK8w87Vm58gdVIlilRFlZF6ZKIPK6XSgtsINBxFqK9wppYKru/S33HUt3v/xF7HlrmuxMKLhi+mrkA7BYDhq20MwFEaXL2iYtXeiw4ejH/dg1hNvweGIjvXNu4+hafk2zHriLbgcQE8wFNWmNxhCt884807N1OsJhBOODf1WjqoyF2oqkjM9LjCD5G5m/jkzfw9AAzP7AVRZnZDU/yJE9Dki+hoRzVYfmeitkD9SlQfSlROSSV1Xd+k3b96nFWCbccUQVLrk+5OQOsFgGO0ef9y2hz8eOIlQOGwouR081YnHZl2Obl8QT79+KE7ae3z2BHTEbKXo8gXR0RPE6++dMpS4D57qtLXklgdeI6KvR/xZQ0RUn+iEhBIfEf0KwCUAWgGo+cbMzAv72Nk4RObILanKA+nICV2+IOaubYlaaFaTHtSF3GSOKSBE4ssznd4A5q3bERdPq2Zdjvnrd2L5jAZ87AloWXtb9h7H168eBgJh7jolDvXSnsev7EGKbXPrkkn4/u/eQfPU0diy93iUJ5/aZnWZ7e9qMo2VxNcBoBrKPOKH4tH6bWb+s9k5yYz+RgCXsp0Wq4SMYJURZbamlGoGlT4VNlbPV10qCmQXvFAgmLkx9Kt0Y/vhdpxXU47P/LjXfNXlIHzruhHacQC0jFOtECCguT2ok9CF51ZqrhCrTndHXe/g6W7N/cGG60R5IWJplBLJ/C+zF8qC1vGUeyQUJKFQGGe6/XGGlel44Kl6fl1teVwqrpqm7gkYO6ara1CCkArqmlBsPHX0BDBxaH982O6JOl5bS2UYnnfkjAfn1ZRhSdMo3L1pT9SYWDilHh990hP33sPTx8LrD6HCYntGqU1Skb1PYwHU6l7+DwDfB3CImT+IOycJie81AA0A3oaSGggAYOapfe9yNCJz2AMziWT17Akpl7hQ16C6fUF8/3fvGMp4VnusCnAQi8SXZ9Q1qNgvWDs+aEfj0P4oczrwz+t3xsWaNxhCe7c/bqL56Yv7ce8X/x53PdMaF7+P3T4BHl8Qi3+z23C8EFExydfJYCXxvQLACaBT9/I1AF4H8J/MvCH2nGT+Qs0pdlAocMwkEtUQVpP9ypzoCYS1ReBYGUN9rX+1GwNqykzb7PYFcW6luyQKsAnZx+VyoH9Vmeb20O0LotLlxNUj6hT3E18QT85pREUk9tRYq3A70XayU3Nx6PYFseODdmzefQw//2qDsatEhQu1FeYGr+rz2PdKVL4ewMzj9S8Q0U5mvsXshIR6DTP/r9EjE70V7IlR2uzCKfU43RWdGXX0rBdP/fEgOr0BnOn2xWTq+dDpDWDu2haMuu9FU7PY9050KVlWHj8qXY5CSZcVbI7L1bvtobrMhbM9AS1250bibfEzrZHYCyAcZvgDIVw66FP4p1/1xvilgz6F5lsu1VLG9Uwc2h+d3qBpbHf7glkxuC1g1hi8ttbqBNMJiohej/zsJKIO3aMzko0hFClGzhBzPjcMd8XUqVF3y5/1BLBwQ2ytp1ac9QQsd+w/NG0sHn2tTTOXtVntGqFIMHIquXvTHsyfVB/liBIIs2EtplvHX2Do8LB8RmLHiUJze8gyv1edJIioHxFVAHje6gRTiY+Zr478rDU7RrAHmcwSCocZPcEwBtT0SiRHz/agttJYxlBr3xi9d1H/3j14m3cfg4OAx2c3oqrcifdOdGHpS/s1k0tVElEz+wQhU5hliV5SV43WH1yPfpVuRQZ0xx9335c+DaeDsOyrDcoeqTsaUeZ2oqNHMX09eLobCybX44JzK7Fq1uWoKXfBGwijssyhyN8OZ0YMbouE36HXSeIXUJwkfgWgb04SRHQuEY0losvVRwY6K2SAZDbCptPWqPtexLx1O9DjD4EIpjJG28ku09pNsdlSJzp8ON3lQ7cviObN+7TJST3+yBlP2n0XBDOMZLaFU+rR7vFj/vqdcTXMVP7ft69CXW1FlKzd4Q3iV28exvz1O+ENhLU6TqPuewHz1+/ERx1edPuDGHXfi9pYBFBIbg/ZJPNOEkT0IwB7ADwC4GeRx9IMdFbIAJks9WzUVkdPAHdvMq7npNa1ObfKjZUxu/NXzmzAuVXuuOOXvXzAcOe9+p4Ny1QLBY6RzHbHVcOwaEO8nHf1iDrtuCEDqg0lvy83XIA3D55Bpzeg1XHSS4f+YNjOZdfzydZUnSSSyeK7DcAlkdlOsBmZ3ORq1Nb5/SpM6zlVljlw5zXDNT09VsYAFEmvssyp1NmJSHp/eOc43v3RjYbvuSIbdwUhU8TVkfKFUFVuPG6qy3trPamvxR7Tr1LZaqGOjdj3B59TGfW7xLPGAgA1AH4JxUliA4BvWZ2Q7EbdcwCc7GPnhCwQWxMGSH+Tq2raqrdsUUtWG9VzcjocqCnvvQmviawhAQBIac/hAGY98VZc/7zBMEC9700dN1jbpd/tC5aiRYyQImmvvVL0Zl69w0m3LwhnpA2rDb8ANGk79n19yfeJQ/sr7uSMlPtZbA4UzPypVM9JZqNuI4D/gjJRyUZdm2FUeDDdTa5GDhKrZl0OfyiMRRsSu0oY96XBdGMkAJzp9mPDWx/g1vEXGrpMFOCAlI26OSCVuDc69vHZE+Dxh7Dx7SNxsffw9LFYumU/5nzuYlxwTlXcht8X9h7Hi3tPmI6NjW8fwcpX2zBxaH88Fjlmoe6YZGI7k+M6x1ht1J0NYCszHyGiawBMhLJB9yPTc5KYoPZBuSV7B0qJXgDK/qgUO54QGaTpkalvWmamrUbmmndeMzzuDs3U9HV2I0DG3yDDYUa3P2joXFGgu+1lgsoBqRgMmx379B2NCITZMPaap45G0/JteO5frkT9+bXahl+Xg1DudqLLq2zw9YfCCIQYtZUudPQEcOh0l3a8ap+kGtAm6me6n89mWE1Q70BxJaoDsA3A0wBuZObPm52TzCc9zcwrU+ykkEOSLYOdaCLTr0E133Ipbh1/gZaC+8I7x7Wy1bc2DAZYKf6mb8d0PazcCQeRYf8cDrJ0rhAEIxKtvepjHQzcOGagtn760Sc9CDPgdjkQ9Iex/pufQdvJLjz6Whs27z4WtX1i+mN/1sxix9//clwRw3AQ+L//tRcAsGByPS674Bz0RJIimDlhbJuNySI1UA4wc4iIvgRgPTP/hIimW52QzAS1g4h+AmAzoiW+nX3rq5BLkpEM1PWsptED8cXLBmG+TpZbMaMBALDzyMdY0jQKc9e1ZMz0NZPraEJpYBUzRt6OqvS26nQ3ljSNwnM7/xYn7T00bSwA4FSnT1tHUttkZnPT4xkN8IcYz+74MK7NX94+wbifviCqylymY7JIDZQ7iWgRgDsBzCYiQoI5KFmz2FiYmaek3U0TRObIHslIBuok5nYS5q/faVhP52NPIOOmrwWstxshEl8OsIoZTyBkGOvNU0cDAJo370Pz1NFo3rwv7piffOUyuJyEpVv240SHT2uTWbmexx+Ki399XajYNhd/YQRmfGZI1DrVw9PHon9VGcKA6ZgsYANlK4nvYgCLAOxk5vVEVA1lP9QbZucknIqZeXJa3RRsRVWZM66ezaqtbagqc2ruDQ4HoX+VG+Qg0/RataZO7HtVZYqMl86u+bg04CLIWBKyixqrq2dP0GJGXScyk9VGDFRku8dun2Bq8DpkQBXOdPmw7KsN8PhDqHA60O1X2qwscxqaHl/Uv0qTBWPfW/lqGxZMqdfkxbaTXVi6ZT+WfbVBuyYQXysNQNGNCWb+gIh+DOCzEZnvz1aTE5DEBEVEPzC52P3pdVPIB15/yLBmzcfdfoQYWlZduydgmV77sSdgKT0kux4WS7rnCaVJOMxo9wQMM0Afm2Usqx0548EXlv2vpfR25IwHLifBGwih0uWIr4s2U6kBteyV97Tz1HRz1VA2ts1Ob1BbvwWUu6RuXxBElLBWWjGNCSKaBMXq6A0ANwD4CxE9wMwvm52TjNVRt+4RAnATgKF966qQa0LMhrvenU6HtttddZIwM798ftdRQ9PXEja/FPKEGqtNYwbhnmej43rtnw5heYxTycPTFacS9Zg1bxzCipnGbiZ3b9qDcFi5RpyTxIZW3HHVsKjzVCeVLXuPG44b9bn+tSp3r5Hs4utHxn2GInWg+CmA65j5HwF8COBGAD+0OiEZie9n+t+JaCmUhAlLIk612wCUR67zW2b+90TnCdnBTPZQpQ41O8jMNeK3Oz5E83//BQCiTF+LQXoQCg81y81MVvuXyfWaU0mPP4T7nn8nyvsxVnrr8Yfwb8+90+tmUt47HvRsP9yOmgqXoWvKndcMR6XbEVWHSn0v9jV1D+GA6jLLWmlFRjkzvx95TszcQ0RlViekc99YBWB4Esf5AExh5i4icgN4nYheYOY/p3HNkifVvU6hUBieQK8uDxiXs1bLYKv1aWKPAYBgOIydRz7Wfm8aPRAMZRJjZiiJNjJBCZknNo4rXU54Q2FUlTnxyuLP49jHPaaSM0WcSpqnjsaJDl9Uu3rpbfEXRuDrVw/D8hkNuP/Lo/Hm+6fhDYQQCjMOPHATOryKc7nHHwQi2yXUuGdmeAMhlLsdYGYQEYgIYMUDUO07AHCY4ypSOxyELhNJvcAz9oxgIqpk5h4AbiL6HoD3rU5IJovvHQDqQU4om6zuZ+ZfJNsrIqqCUtZ3PjO/ZXacZDIZk2qWm5EjhLpz3rgM9oCEzg7q7vqm0QMx4eL+ce0YOUuUKJLFlyGM4jjWqeGRrzXAF2As2bQ76pgylwPr/nQYt46/EM/vik8pVx0hPvEEMOOKIXHrTOUx7ieP/uN4BMMclY330LSxeH7X3/APjRfB7SAs3Kh3i2iA2+mI26qRvANLQWTsGWGVxXczgP3M/B4RrQZwBMDPmbnb9JwkJqiLdb8GAZxg5mBSPVVca3cAqAfwKDPfY3W8DFJjUt1V3ukNGO6OV3fOa99G3U54g+HknR1mN4JhvPN+9ewJcd8OSxSZoDKEWRyrLg/q70/OaUSIe+PaSYRvRMaLmh13SV01eiJ3M53eINwO0kq+G13jJ1+5DJOWbtVeU1PJjfrSvHlf3PFGbViNkyLy3Uup00T0bWZ+xOz9ZEq+fwBgAIAvA/gKgMuSvTgzh5i5AcCFAK4gojEGHZxHRC1E1HLq1Klkmy4pYneVTx03GM1TR6OqzIkuXzCuflJ1uUtLKX//x1/ElruuxcB+5ShzO7Uy2LUVbricjrj6NJbODuVO0/eqi0uKyAmlGPvhMCsxy6zFrtFrgPm6qeryoI6Dcnf0f2PlbocW/z+PpHMv2bS7V2pjxqYdH1o6PeiLbQK9qeRGfVGPjx1vsW1YjRM1i7WYa0YR0beIaDcRHVIfAJZGni8yOifZNPPpUKohAsAaItrEzA8k2zFm/piItkLJ2tgb895qAKsB5Vtksm2WEvpd81PHDTZNSVWD2iyl3OsPoSqJicTjM9nF7guBwYbvdfuCcgeVIqUW+0ZSlpWZarffeG2m7WSXNg6e3/U3VMbKdzMbcN/Nf49v/2f05tgefwjjfviSdkynN2i6bSK22KaVc/nEof3RFSnCqb/e6a74da8SHycLoGSBd0R+ZwBbAUwG4DE6IZlFg5kAJjLzv0ey8D4L4B8TnUREdUR0TuR5JYAvAHg3iesJMegLri2YXJ8wJTVsklIeTiDnqjgciEslf3j6WDgcSl9iiw2qabOCYIVRQcyzngAWxhQOVOPZLNa27D2ujQOjNPNFG1rR5Q3FxX9PIBR1zMeegOmWitoKV9Rr/SpdhmnpW/Yex4oZDVj7xqG461W6nTJOojnGzIeZuT3yOAvF67Wdmb1GJySjyxwGUAFAbaAcCTIvIgwCsDayDuUA8Btm/n0S55UsZjp0rNNCopTUKp1sod+h7guEFBfmBOnhFW4nlm7Zb7j7XXWLMEubFQQzjNxMjKSzgf3KAQYoInvpY63S5cTXrx6mSXNGaeZGEt32w+3oX12GqeMGa4awF55biQWT6zH4nAr88vYJqKlQrrHjcDt+t+uYFv8ev7Je6wuGouO+TOlLVZkTK19ti7teTYVLxokOZr4umdf0mE5QRPQIlFswH4B9RPRy5PfroWTkJerMHgDjEx0nKCTK5FE16mRSUlVJUL9DfWC/8jjZzyxTyOMP4USHL273u3oNp9OB2shAK2G5QkgRbyBeen4sxtFh6rjBhmbE1WVAtc5ctXnqaEv3hliJTnWJWHLDKABAfV012rv9UbKcmpV36/gLAQBNy7fhyuED8NN/GIt7t+yNk9aXz2jAM28fQdOYQaZjUh0fMk7SwzSLj4jmWJ3IzGsz3ZlSzmRKNlMvmZRU9ZhuX1DLPNpy17WG5phGmYBFlvaaaySLz4QubzCuNlKsmeoriz9vakYM9Jqr6tegjFLIHQ5ErUE9NG0slr60H6c6ffjJVy5D/+oy/NOvjDME9WayD08fC7fTgY89AcPx89jtE7D2jUPFVHCzr2T0A5veQWVjAhLMSbb+SzLGquox+h3qZlKI0W51MW8tfvKR1lxVbmxYPKC6DI/PnqAl8MRmrC6YXI8KpwM9wRB+PfczmtTnC4Xxtc9ejHMjprF66e27v9mtOUm0nezC0pf2ay4RF/WvBBEZ9kUdJyMG1qB56mhN2q6rLTc1dv3mNcNRrnOQSOfvWURp5hnFSuLTb9CNhZl5XHa6VJqkUhMpGWPV2B3qZlKI2W51MW8tXvJ1h2wk8SnZbn4QgEq3Ez3BcFzG6pluL9o98Rt2j37swQXnVCEQDOPp13vvYlTniLMeP+au2xMV8wun1ONMtx/MMOzLR58ozhTvnejSJD69y4qZsWtFZAsHkPp4KTXFgoiWMfNi9afVsVYrdjcDuCXmMRXAvwA4mqnOCgr6TL1MmbDq21y1tU1MXgUAxtl0uTAnDYdhmF3qD4axaGOrlrkXm7F6SV1tvGnrxlbtdX+Yo7L5Hn2tDQ9NG4sylyMu5udcNQyLNrTCHwwb9sVBhIenj8WqrW1RYySbxq75+vfII2oJp0mJDrSS+D5QnxNRA4CvAbgNwCEAz/ape0Ic2ZDVYtv0BkJi8irkrZx4VbnxdQefU6ltYo2tKaZmw5mZtm4/3I6acleUhK2awi6f0YDFz7RGZaPWRLL/1GvGtjnwUxXwBkJaPSj9GMmWsWuRlnfPCFYS30gAM6DsgzoD4BkoSRVSwDBLZFJW02vaABTzyrLeNkW2K11SkZMzhWqhtf+Bm9B2sguPvtaGzbuPKZtcvUG88a9Kge6uSDq2mrH6yMwGdPuCOPDgTejoCeD5XUfR/N9/0Ta9qud1egN4ZGYDrrzkPPSrdCu1y7r9cdmoW5dMssz+0/8NjP4WZrXSvIEQwqxMNuo6WE8grCkUZutLVn+XIjSLTRmrT/8ugD8CuIWZ2wCAiL6Tk14JfaLUNG0hNVS5KjY+siX3GsXjQ9PGor6uGreOvxBr/3QIX5lwIRY/0xpVZr3C6cCEi/tj3rodUWtPA/uVY8LF/dF2shN1tRVY/Ewr5nzuYky4uH+0OevMBqyadXnUa+dEajdteOsIHpo2Nm4tyexvoH6GDW99EHfeY7MuR7cvGOWGoaasz/ncUFOnDACmf5eZn7lY5HdYp5n/Hyh3UJ8D8CKAjQCeYOZh2epMKaTa5oJUzWWFjFIQaea5zBozi8dVsy7HD/5rHzbvPhZlAqvGKrO5MfGOw+24Y02Ldt7AfuWYv36n4bEAojbLEhE8gRAq3Q6tVHyiv4H+M+iz+Dz+IAgUlz6v9qvc5UgqbT62z9VlBevHl7DTRLSTmS9Xf1oda7UG9RyA54ioGsCtAL4DYCARrQLwHDO/lFq/hb5g9B8KEC8dAMlr2pLaWrr0VU5OJXbM4rG2wq2tF20/3I5L6qq1tO+eyMRhZkx8x5oW7ff682tABMO0cXVdKxyO1GpyKOXcY/OTmRkMRpfP+LPoP8Pm3ce0lPUDD96k9SO2n6qprdVYNEtdT/VvXGBsjPlpSjJu5t3M/GtmvhmKK3krgH/tU/eElFDlhblrWzDy3hcwd20LznT70OkNRL3W6Q3gTLcPR84oxpZ69EUJzdv0xzmjC0IsqcaOuualR10HUlk4pV5zdhh13wuYu65FW++JPa+jJxDXjscXxJKmUdr5zZv3YUnTKHj9oaj+Ln6mFe3dfjz1+kEcPevFvHU7MPLeFzBv3Q4cPevFU388aPhZzD6Dxx+y/HyqyazVeWo6vf6zG43vYhmfzPxT/U8rUjKGipj6/ZKZp6TbOSF1jNNQW3HWEzA03jQywIzV10swtVXIEKnGjtEWCtX0NSr9OyaV/PX3Thmaxb75/ukoE+NVW9vQEwiZGiTr+zt/Uj3u3mRsMquazxp9FqttIEbvqUay50bWvKzOM05djx/fpTg+ZUGiADCTSNQaNLHGm8HItyw1vbbHr8gEenlAUluFZImTmlKMnbgtFL4Qnt35IZrGDMKCKSPw0Sc9cBJh/Tc/E5XJ9u0NrXj3/huxatblWmZeR48fV4+ow4EHb0K3LwhmxrJIzSfjGmYueHwhxYAWvY4qZs4ql9RVR9VaMzNsVjP1uv2Kq4XRe1+/ehgqXU7UOsh0+4hV6rqR4W2pjc/StdYtIMwkBLUGjSppdOkkkc27j6Fp+TbMeuItgGBoCJtIBhQEIzmvy2ssvVnFjr4gH8B4ce8JNC3fhu8804pQGJi7rqVXmrthFKaOG4yJQ/vjbx/3oOH+lzH8+3/A87uOosLtipLlvIEwPL6gqaz93okuzF3XgiVNSptqern6U4/qMtG8eZ+hrOZwECqcDpzu9GHeuh0Ydd+LmLduB9o9yjGVLuP3mNm0GKHDQaZj0cjwttTGp0xQBYChRDIzvgbN2jcOxdWsMUudzYZzhVB8GMl5a1KIMyNUtwaz+mb3PLsHi68fiZUzG3BulVu7zq3jLzB0lAiG2VDWfmjaWDz6Wpsm9y2+fqTmqLJl7/G44+d8bhju2mhcm0qlJxgy7ENPMARPwPi9RLKc8ViM/uzFPD6JqIqI+hu+Z5Zmng8kzdycWJml0u3AqPte1OQ8AHA5CPsfuFHZIJhE5k8RZwnlk4JIM0+WMDNG3vtCn+LMqM3Fz7Ri/qR6jBhYY9j+gQdv0jLt9BvOzY4d/v0/aJlwIwbW4L0TvVJhVJtQPAHDYaCyrDfNvNsXRHW5y7R95c7P/O+htp3ofNO/SZJZugUwPk07SESHjLYpEdHnADxglNsgd1AFgl4iqSl3oScQNpQFegJhUzkhG4TDjC5fEGGO/CyCLCOhFzP5ySrOEsVEty+oOTy8dyJealNdIkCAN9ibEp4oq0+VtY+c8aB58z5tclKP8/hDcBChqsyFmgoXnA4HaivccBChtsKdlOxt1oduX9DyvUTEjm99DbhcjeUcUEtEc2IfAMYAmGh0gkxQBUpfJbpMpJlLqnrxk2qcJRMT+lLuRibGK2Y04PX3Tmkp4XPXKW09/fohw6w+9bn60ypzrq+ftdJlXIa+0uU0LVFfjLJcmpQBaAQwIeYxBsDTRieIxFfA9EWiy4TbhDhWGFJUEh+QWpwlGxOhUBieQMTFwRdEty+E82rL0XayC1v2HkfTmEEAEFckcPEXRmgl32OdITIhjyXzWYPBMHqCoagy9C6XI+5zlWiZdyuJ7wgzD0mlsZL9X6QY6IsbQCbSzCVVvTSwirN0U9CdTgdqnQ6EmdFw/8tR6zY/nHopBp9TgepyF5qnjo5aS1r5ahu+dd0IOIhQXebSrs3M0H/ZTmVsRH2GSMkPVVYzwuVyoDYyIcWWclc/l9F7Am5I9YSSmtqFXjKRZu7xmbThK61U2FIlEynosXHYfMuluGnMIC2VXJ92rrXlC8VdO5ETRCqfQWTq7MDM76Z6jkxQJUom0swdDsStHzw8fSwcElUlQSZS0GPj0CiV/J5n92DB5Pqo+DK6tpUTRCqfoRQdG+yKSHxZoBDStzNRILHC7cTSLfujCsIt3bJf29kvFDdGct7KV9uwYEp9wrjSj5FKtwNPzmlEudsBIjKUCEcMrEHz1NFafDGzoTms6hBR6XYoThAmNZjUa8d+BjVVPdZJwoxCGOuFjExQGaaQajH11dHa4w/FFYS7cvgAKbRWIpgVPlRT0AHjuDIaIz+7bRzcPgIIhm2eisTZlcMH4MgZD86pcuN7N34a3/3Nbq2Nh6ePxUef9GiOEIuSqMH0yuLPa9dTTVtja0SZjd1CGuuFiogxGaaUJANxoyht0v33Nxoj3/3NbnT7Q6h0Ow1l4+pyp+YOsezlA/jYE8B3f7M7zhzWQYQ7rhqGRRuMHSFir73s5QOWrhZWY7eUxnq+yNrXXCK6CMA6AH8HIAxgNTOvyNb1co16ax9X9MxmmW3ZlCAyIRMKhYvDQehf5VYK7EXSqp2k3AVZyWNW5sdEwH3P7zWUjX95+wTUVLjwwK1jUF3uxKpZl2sl4NU2Bn6qQnsOxNdZqixzRF178+5jcBDw+OxGVJVbj10jNxc7jfV0sbNMmc07qCCA7zLz3wP4LIAFRHRpFq+XM9Rb+6f+GF1TJl0jzWz3M5sZSkW4211IknCY0e4JRJm3tnv8WPxMq2WsmWV/ftjuQUdPQJONL/m3P6Bp+Tac6PCh0xvEP/1Kuc4//UrJ2Fv3p8P44mWD0HzLpVobR854tDFoXGfJj4VT6qOufaLDB5B1ZqvZWIptq9AMXe2exZi1CYqZjzPzzsjzTgB/BXBBtq6XS9Rbe6OaMn010sxGP0WCELKBUXzdvWkP5k+qt4w1o+zPn902Dp+qcuPN90/HuzEYGCOrGXuLNrbi1vEXRMl/6hg0qrO0aEMr7rhqWNJ1ndT3jD6rVVuFgt3/j8jJSjYRDQUwHsBbBu/NAzAPAIYMSWmTcd5QJQqjmjLJZjHlsp96ClGCKFYKMfb1mMWXvtS5UawZZX/+9MV38bPbxuGaEeejwuXQ5LyOngBqyl1Y+Wqb4XW2H25Hv0q3kuH30n6tFPuCKfU4r8Y4I7CmwmVZn8noPbPPatVWIWD3/yOyniRBRDUAngVwFzN3xL7PzKuZuZGZG+vq6rLdnYygSgFGNWUmDu0PXzAcd04+TFWl5pO9KcTY15OolLtZrHn8IQw/rzrqteHnVeN0lx/BcBjkIBz/xIujZ3swf/1OvH+q2/Q6qlls0/JtmtuEUSn2qeMGY8td12L/AzdpFkRm9ZmMJGursVTIErfd/4/IqhcfEbkB/B7AFmZeluh4O/uR6VF12w1vfYBbx18YlZb62KzL4Q+FsTAqxbUBZU4H/nn9zpymo0oabF4oOi8+M4zi6+HpY7F0y36c6PCZxlooFFbSwDf2jpEVkTEyXz9GZjQgEGb8tuXDuHH20LSxeH7X3zDjiiGoKnNi7rodvW3NaED/KuW6ZuM01XFQrGMpC58ro3+MrE1QREQA1gJoZ+a7kjmnkAapWRYfWKkOGmuW+ZOvXIZJS7dGvZYLU1U7Z+gUKSUzQQEx8eULweFQJDyrWDMzlDUaI0/OaUSY42s36eOZGabmreEwo9sfxLx1O/psalysYynDnyujf5BsSnxXAbgdwBQiao08vpjF62UNI3lOlQIIBNIVIzNLVb2of1Xca6novPo+ePxBdHmTkwsly07IJvr4qipzIhnl2si9oXnqaAwZUIUtd12r+e5tP9yOijJn3H95RASCUsPJ6XQo5q26uk7q5KT2r7rclZF1lmIdS3b+XFn7+s7MryPDs2k+sLoFBhD33i9vn2C4E/7Ddk9Uu3r9OpU+DOxXjiVNo3D3pvTlCkHINKlIRXoHCiP3hoemjQUAnOr0ocsbxJo3DvVJolPT2mPHpMcXQk2FOJ7YGXGSSIBVGmayZplLp49DdZkz7XRU/XXmT6rH3ZuS3+0uCLkglXRlfTq3kXvDPc/uweLrR2LFzAaseeOQ4XaOVGJeTI0LF/n6kIBEaZimaeazG1FZ5kTbyS489KLiMt88dTRGDKxJWefV98Eotd1OaaFC8ZDK2kQq6cqxDiRG5w0ZUAVmjoynESnHvL7v4TDwP389IabGBYhMUAkwM8RU0zDNzDJBwKwn3op671SnL63ECH0f1NRao/6IQauQKVLN7rIaJ0Zxqa57dHoDhud1+4Igoqh08mTbNssu/OmL+7VUdDE1LgzkJjcBVrvL032vL31YtbUtTq4otN3rgv1J1WEg3XivcjvjnSNmNESNoS17j+OhacnHvJnDxeLrR8qYKTCyug8qVeyaamsldaT7Xl/64A2EEA4rGYPFlO5aJBRFmnmYGSPvfSGqFLvLQTjw4E1wkPFHTDfeQ6EwPAFdCrnbCaezN03c0JTZom2rvgOQMZNdMvpHlfvbJLCqm9TXmkrp9KGqrPc6IlGUDrnch+Pxh7BwSj2axgzS1m227D2uyWJmfUl2LOjP7wmGUV3m0tLE9ejbrK1wJNV2IrlR9j4VDvK/W5Yo1p3nQn7IdTxVuhyYccWQaLeHGQ2odDn63JdsfxZVGoxtP1VJT8Zw/hGJL0uY7ZbPhXuEkFeyIvHlOp6srgegT33JxWfJxJ2PjOG0KBgniZLG7i7BQmFhFU/ZMCG2ul5fYztTn8XKgDkT7ggyhvOPTFBZwu4uwUJhYRZPXd5gVorNWcVvX2M7E58lF4X2ZAznH5mgskQm08wFwSieVKeFbLiKZHMLRSY+Sy4K7ckYzj+yBpVFJAOoJMlamnlsPFW6HRh134sppYIn27b6n3C2tlD09bOkkwafDjKGU0bWoAoFO7sEC4VHbDz1BMIZkaDM5DIApvHb19ju62fJlfwmYzi/yAQlCAVKpiSoXMhliUj1s4j8VhpIrqQgFCixpqvpSlB2yFZL9bNk6rML9kbuoAShgMmEBGWXbLVUP4vIb8WPTFCCUOKIXCbYFZH4BKHEEblMsCsyQQmCkDPTY0FIBZH4BEEQBFsiE5QgCIJgS2SCEgRBEGyJTFCCIAiCLZEJShAEQbAlWZugiOgpIjpJRHuzdY2+YFVLRhAEQcg/2byDWgPgxiy2nza5qCUjCIIg9I2sTVDMvA1Ae8ID84AdzDEFQRAEa/K+BkVE84iohYhaTp06lZNr2sEcUxDyEfuCUEjkfYJi5tXM3MjMjXV1dTm5pl3MMYXSJtnYz8Z6qazBCoVA3ieofCDmmEKhkI31UlmDFQqFkpyg9OaYBx68CY/PacSA6jIxxxRsRzbWS2UNVigUsplmvgHAmwBGEdHfiOgb2bpWOkgtGaEQyMZ6qazBCoVCNrP4ZjLzIGZ2M/OFzPxktq4lCMVKNtZLZQ1WKBRKUuIThEIhG+ulsgYrFApS+EUQbEw2iglKgUKhUJAJShBsTjaKCUqBQqEQEIlPEARBsCUyQQmCIAi2hJjtszmPiE4B+CDf/TDgPACn892JNJG+55bTzJyySbKNYx8ozH8HFel7bkkr/s2w1QRlV4iohZkb892PdJC+C32lkP8dpO+FjUh8giAIgi2RCUoQBEGwJTJBJcfqfHegD0jfhb5SyP8O0vcCRtagBEEQBFsid1CCIAiCLZEJShAEQbAlMkEJgiAItkQmKEEQBMGWyAQlCIIg2BKZoARBEARbIhOUIAiCYEtkghIEQRBsia0mqBtvvJEByEMehfxIC4l9eRTJI6PYaoI6fbrQnOUFITNI7AtCPLaaoARBEARBRSYoQRAEwZbIBCUIgiDYEpmgBEEQBFsiE5QgCIJgS2SCEgRBEGyJTFCCIAiCLZEJShAEQbAlMkEJgiAItkQmKEEQBMGWyAQlCIIg2BKZoARBEARbIhOUIAiCYEtkghIEQRBsiUxQgiAIgi2RCUoQBEGwJTJBCYIgCLZEJihBEATBlriy2TgRHQbQCSAEIMjMjdm8niAIglA8ZHWCijCZmU/n4DqCIAhFRzjM8ARCqCpzwuMPocrthMNB+e5WTsjFBCUIgiCkQTjMONPtx8INu7D9cDsmDu2PlTPHY0B1WUlMUtleg2IALxHRDiKal+VrCYIgFBWeQAgLN+zCmwfPIBhmvHnwDBZu2AVPIJTvruWEbN9BXcXMx4jofAAvE9G7zLxNf0Bk4poHAEOGDMlyd4RioRhkD4l9IRFVZU5sP9we9dr2w+2oKnPmqUe5Jat3UMx8LPLzJIDnAFxhcMxqZm5k5sa6urpsdkcoElTZY+7aFoy89wXMXduCM91+hMOc766lhMS+kAiPP4SJQ/tHvTZxaH94/KVxB5W1CYqIqomoVn0O4AYAe7N1PaF0KHXZQygdqtxOrJw5HlcOHwCXg3Dl8AFYOXM8qtylcQeVTYlvIIDniEi9zn8y84tZvJ5QIpS67CGUDg4HYUB1GR6f02goZxeD1G1F1iYoZj4IYFy22hdKF1X2ePPgGe01VfaoKZfEVKG4cDhIi2t9fJdChp84SQgFR6nLHoIAlIbULV83hYySC8khkewhCKVAKUjdcgclZIxcZtepsoeDIj9lchJKjFLI8JMJSsgYpSA5CIJdKAWpWyQ+IWOUguQgCHahFKRuuYMSMkahSg7hMKPLF0SYIz8LbMOvUFykEo/FLnXLBCVkjEKUHIrFlUIoDiQeoyFm+3zwxsZGbmlpyXc3hD5QaBsHu3xBzF3bErWn6srhA/D4nMZ091Sl9WEl9gUgK/GYazI62OUOSugTsXIEgLQkh3zJbLJuJvSFTMetxGM0MkEJaZMpOSKfskahrpsJ+ScbcSvxGI1MUELaZCqtPJ/p6YW4bibYg2zErcRjNAUhagr2IG59KUNyRCrtxPah0uVATzCc9ppXKaTqCtlBH7dTxw3Ggsn1uKSuGj2BEMLMGYvHSpejoNZ1M4ncQQlJYSRndHmDGZEjkpU1Yvvw1B8PZkRiKfZUXSE7qHE7ddxgLLlhFLbsPY5jH3sxb92OjMVjlduJdk+gZLP6ZIISksJIzljzxiGsmNnQZzkiWVkjtg9NYwZh0cZWca4Q8oIat4uvH4l7nt2DpjGDcM+zezIaj6XuziISn5AURjLcylfbsGBKfZ/lsWRlttg+1J9fIxlPQt5Q43ZATRm2H27PSjyWelaf3EEJSWEmw/UEwknLY1YpucnIbLF9aDvZJRlPQl5xOEiLS9N49IXi4l0dC6FwGJ3egGmaeqln9ckEJSRFX7OLMpGSG9uHLXuPY8WMvkuMgtAX1Ljcsvc4Hpo2NioeH54+Fvc9/05UvKtj4ak/HsTRs9ZrVqWe1SdOEkLS9MUlIlM75DOdxZcFxEmiBFHjstLtgMcfQnW5C0fOeLDs5QPYvPsYgN54B4C5a1vQPHU0mjfvSzgmCsydJaMdkzUoIWmMSk8nO3gypaUb9aHG6Yj6XRByhT7+AYBAqK1wI8yM5a8cwILJ9Vh22zh0+YKorXCjxx9CZZkj4ZqVehelbxuc+rgrdETiE9ImFdmu1LV0ofiwin9vIIQlTb2p5/PX78So+17A3HXKMQun1JuuWR0548GZbh86vcbp5aVkKCsTlJA2qaTAlrqWLhQfVvEfDgN3bzJOPV+0oRV3XDXMcM3qoWljsezlA1i4oRVnPQHDtksp9Vw0ESFtEsl2UTJEIIT+VW5xbBAKlmScVAb2KwcYqCp3YtWsy1FT7jIcIzUVLtx59XBUuB14fHYjKsucaDvZhaUv7cfm3cfgchAu6l8Vd546tkol9VzuoIS0sZLtjGSIdk9AmZTEsUEoMJJxUpk6bjCWNI3C3HXKMfPX70S7R5Hz9GjSNgG3P/k2znr8mPXEW2havk1LqJg4tD9OdnjjzjtyxpMxB5dCQCYoIW2sZLtSkiGE4icZJ5XF14/E3Zvi5bw5Vw0zHCPq+ClzOfDw9Pj09H6VbkP5L1MOLoVA1iU+InICaAFwlJlvzvb1hOyjlzoq3U48eUcjKtzRsl2yWXulko0kFDZGxrD159fAFwhpEh2RsfRWW+HCqlmXa1l8VWW9MT6gugwgYPEzrWieOhr159fgo096EGagssyJ1bMnoNLtxPunuqPkv0w4uBQCubiDWgTgrzm4jpAD4qSOdS3o9oW0FFh1kCSTtVdK2UhCYRNrDNu8eR9G3fcCvrG2BWFmzHriLRw922MY853eIBrufxmznngLIERNJKoTxYkOH5qWb8N3nmlFKAx877d7MPLeFzBv3Q60d/vx5vuno+S/VBxcCpmsTlBEdCGALwF4IpvXEXJHstJdMll7IgMKhUKsMayR1Gcm1fmDIUsZTj9WFkyuj8/629iKW8dfUPByHhEFiKiTiDoMHoaDPtsS33IA3wNQa3YAEc0DMA8AhgwZkuXuCH0lWekuGQPYUjfClNgvHGKNYfWopslEFCXVtZ3swtIt+7Hsqw3KODCR4WLHitGY6FfpxoEHbyp0Oe8dZr7c6A0i2mn0etbuoIjoZgAnmXmH1XHMvJqZG5m5sa6uLlvdETKEqXSnGmLqTC8TGcCm0lYxIrGfPayMiVM5NhTqNXPt9gfhD5ibJuulukv+7Q9oWr4NJzp88PhDmgNEIrPkbp9xhl63L1gMcl5Zqu9lU+K7CsBUIjoMYCOAKUS0PovXE3KAsXTXgFA4nPJaUibbEgSVVNY2rY4NhcI40+3XzFznrduBDm8Qj8+eEBWzK2Y0oNLlsJS1k+1TpcsZZ4CstF8UqkKAiOK+iRHReQACRifkxCyWiCYBWJIoi08MMwuD2Mw7BwHfWJOeEWwm27IJYhabZ1IxJrY6lpkxb92OuPceu30CPvrEq8l4W/Yex53XDEdNucs0KzWVPgWDYfQEFcPZbl8QlS4nXK6C2RFkGv9E9B0ANwKYz8wHI68NA7AawB+Y+eex5xTEiBfsTbnL0aeUcr35a5i5pNelhL5j5fAQZtZiz+zYRI4NtRUujPvhNu01l4PwretGADA2M07mOvqx4Q2FUV2mSOO1Fe60/w52g5l/TkS1AN4iIheAMAAG8Aso+Qpx5GRaZuatsgeqODCTKkx3yyc4Twq0CZkmNoZiHR7mrm1BpzeAM90+HDnjMYy3Lm8QHT0Bw/c6egJxryWKz1RdV4pV1mbm+5m5DsBIAJ9m5vOYuZlNpLyCuW8U7IFRarhqfpmJlHIxlRX6SmwMGTk8nPUEsHBDK5a9fCDOsHXFzAaseeMQnt911HA9iICU41NcVwAiqieiZUT07wB8ADqJ6GKrc0TiE1LCTKqoqXD1KaVcL3FUlzvx+OxGVJUX9y55ITvEpm0D8VLdRf2rsP1wO4KRuxQ1NVyt1/SpKje+3HABaspcWD17grYetONwO36365h2fCAYQiDMAAGd3gAqXYpEFzsOrLZdlNB2i2cBrAEwCIqs900AvwJwrdkJcgclpISVVJFWSrmBxPGNNS3oCcS7UwhCsui3OBjF3oftvdLe5t3H0LR8m+b0EAiGcdOYQVj3p8M49ok3KotveJ2ypbNp+Ta8tO84OrzBqPfbPUopdyOpzmzbRQnJ2t3M/HNm/h6ABmb2A6iyOiGpCYqIPkdEXyOi2eojE70VCo90JTiROIR8YRR751a5sdLEcDUQZiza2GpYy+meZ/dgweR6XDl8AK4eUYdFG1vjXB+axgxKKY5LSNZ+jYi+HvFnDRFRfaITEkp8RPQrAJcAaAWg/qUZwLo+dFQoUJJxiEj1vBKSOIQ8YBZ7AAzjsTpSw8msJPuIgTWWrg/159dE/Z4ojtMdUwXItwFUA/glAD+ADQC+ZXVCMmtQjQAuNcuyEIqLbLiL69sEoEl3KqrEod8jopcNBaGv6NO/1bv2qjInmLn3AUaXLwRmxsSh/bWS7GZx2ekNGL7fdrLL8Phk+1esMc/M/VI9JxmJby+Av0u9O0KhkUy6a6opsckcX0ISh5BnYuNx3rodOHrWi6dfP4SjZ7146o8HQQBWzGgwLMmuj8sqt7Hrw5a9xyWODSAiFxFdTkSf1z3eJKJJZtl8CZ0kiOg1AA0A3oaSGggAYOapGew7ANlNn2+S2e2eyo74VI4vorpQ4iRhY8zisXnqaDRv3qf9fPqORgTCrMVjdbnLMC5DoTA8AeX9jp4AWo+cxaBzqlB/fg08/qCy4bYw4zhdrJwkXgHgBNCpe/kaAK8D+E9m3hB7TjL3ks0pdlAoQMKRwZhoLcjsmEq3A12+YNrrS6UgcQjZJZkvOVbrRvqfbpcDwUDY8Dr6SckTCKHS7cTIe1/QUtYBxV3iwIM3Kav1gsoAZh6vf4GIdjLzLWYnJJT4mPl/jR6Z6K1gD1TZw2xXvT7d1SglduGUelMZr4RSaIU80lenEnW96djHPVo8P/XHgzh6tjfNfO7aFnj8wTgD2fZuPx6Z2RDX5pEznqJ1hEiTNQavrbU6wXSCIqLXIz9jC0x1ElFH3/op2Ak1zdtoV32shm60XnTHVcOwaEOrYZq4rC8JuaAvTiUPTRuLLXuP4+HpY1HmcmjxbJRmHoykoMemll89oi6uzWUvH5DtEtH8XnWSIKJ+RFQB4HmrE0y1FGa+OvLTtNigUByosofRrvqqsmiZxDAl1kLGc1DJpNAKeSTlQpqzG1FZ5kSnN4CacheaxgzSiguq58ammU8dNxj9Kt2G16kud2lttp3swtKX9mPz7mNwRWRuAQDwO6ToJJGU2E9E5wK4SH88MxtWQBQKD32a9+bdx7B59zEtkcGsAqh+vagrUmTNLB1X1peEbJPKVgWHgwACZj3xVlyyhCo9x6aZTx03GEtuGKXJ4LHX6fYFQURxbcp2iSi61ZIaRLSHmf1E1DcnCSL6EYA9AB4B8LPIY2kGOivYhL7KcCLjCfkm1Ri0Ol59T59mvmByPe55do+xueyMBrgdJOMgMVtTdZJIJs18P4DLIr5JWUVSbTNPsunbyRxndUwRpYn3FUkzT4JsbwjXu0UomXaOuHRxZtay8bp9QVS5nXA6HVFt6c9TM/WmjhuMBZPrNRn82Z0f4vYrh8JBJOPAOs38EwA1UByJ/AD+CuDbzPxns3OSue/cC+AcACdT6qaQd9TMpoUbdmH74XZMHNofK2eOx4DqsrhBk0iGS9SWyHhCsqQSl6kQV/wycp0Nb32AW8dfiHue3aNd77FZl8MfCmPhhtaE8Vxb4YiSsfUyePPU0Xhx7wlMm3CRyNkJYOZPpXpOMk4SPwGwi4i2ENFm9ZF694Rck0kTVjF0FTJFrmJJvY5RNp5aDyrZPlhl/4mMlxwRo/EhkefXENFiIrJ0KUpmil8L4CEA70Ap0SsUCJk0YRVDVyFT5CqW1OsYmb6q9aCS7UNs9mp3ZFP6ndcML0UZL13uBvDryKT0JICnATwD4PNmJyQzQZ1m5pWZ6Z+QK8JhRrffOrsuFb1cDF2FTJFOLKWzRuqg3my8hVPq0TRmEOrPr8FHn/SgyxfE/gduQtvJLrz5/mlcecl5qD+/Jm4tSk+07OcGANSUZ7+kXhGtawWYOUREXwKwnpl/QkTTrU5I5q+7g4h+QkRXRoz+LieiyzPTXyEbqNr7068fMt14m6rpq2QoCZki1VhK18S42xfEY7Mux8FTnZhxxRA0b96H7/6mFQzgn3+1A6PuewHNm/fhi5cNwpa9xzHqPsUZ4ky3H6GQPcSiVMepzekkokUAFgL4LyIiJLhJStYsNhZm5ilpd9OEUstkyhZ6Q0x9xpHevDJV01egqL7JZRPJ4kuCVGKpTybGsxvBYMxbtwNvHjyDLXddi+bN+wzNYpuWb9N+Xz17gnaXlE/SGad5xiqL72IAiwDsZOb1RFQNpbLuG2bnJPyEzDw5rW4KeSEcZoCB9d/8jCZfqJAudlJZB0hUz0kQUiWVbLeqMicG9ivHlruuRf35NWg72YVVW9uSMjGuKndqzwGYFiE0KjJoZH6cCpn4QldMa7/M/AER/RjAZyMy35+tJicguYq6PzC52P3pdVPIFkbpuytmNGDj20fwpVfbolJpPYHk1gGylRIsCMniDYSwpGkU7t7Umyb+8PSx8AZCqCpTYtVsXeu9E10odzm098yKEOqLDKpmsYtMUtCTIVPjppjWfoloEhSrozcA3ADgL0T0ADO/bHZOMmtQ3bpHCMBNAIb2ratCNjBK3120UTG9TNfEVdLLhXwTDgN3b4pOE7970x6EdctEZmngj77WhmUvH8DD05W12FVb27TnZkUGrcyPkyVT46bI1n5/CuA6Zv5HAB8CuBHAD61OSEbi+5n+dyJaCiDhPqiIU+02AOWR6/yWmf890XlCYvT1aPRZR0ZywMB+5bjgnEq8/+MvRkkjyZq4FpPEIBQmVeXm8p0mwwVC6F/l1uL5vROKYevlQ87BreMvQG2FC6tnT9DGzJNzGlERSRevdDnx9auH4VvXjYDXH0JYJ5E/+lobNu8+FhXzfak7leq4MTRnLty133Jmfj/ynJi5h4jKrE5I5x6xCsDwJI7zAZjCzF1E5AbwOhG9YGVrISQmFAor8sPG1igZb0B1GXqC4Sg5YOq4wVjSNApz17UYSiPJrAMUk8QgFCZmMdjlDeKffrUjTkLz+EJo3rwPTaMH4ouXDcK6Px2Oc5J4ePpYLH1uP050+LTzwEC3PxQlyz00bSwA4FSnT5sckpHuMjluisidgomokpl7ALiJ6HsA3rc6IRmz2HeIaE/ksQ/AfgArEvZEQRV23ZFHQeZG2glPIGRYj8ZItlt8/ciE0kgiikxiEAoQoxhcMbMBa944ZCihORzAw9PH4tbxF2gSd6yTxN2b9mD+pPqo84xkuXue3YPF14/UYr4vdadk3OA+ABdGnv8JQBmAO61OSGY6vln3PAjgBDMHk+lNxLV2B4B6AI8y81sGx8wDMA8AhgwZkkyzJU11ucu0Hk2sbKe+F3usmtmUDEUmMdgKif3kMIrBSrcDK19tizpOL6GptZ3MnCT0mXv684yOGzKgCmClHynXnZJxo8HMv9c9nwcARPRtKJUyDEmm5PsHAAYA+DKArwC4LIUOhZi5AcqseQURjTE4ZjUzNzJzY11dXbJNlyzdEdNKPWo9GqNjF06pjzvWqtx6OMzo8gURZkanN4BQOKztymdmMDNAyv6MAt0saBsk9qPRx54aX+pr2g6JyBaHnkDYcBx4/CF4/CEMP68aXd5gVF2n2GPVzD39eWZtqhOL1TGxqNKcg0gzki1liOhbRLSbiA6pDwBLI88XGZ2TjMT3Ayh+fAMAnAdgDRHdl0rHmPljAFuhZG0IfaDK7cSKGQ1xWUhG7hDz1u3AjCuGYPEXRqS1Y3/euh04etaLp14/iI89AbR3+TFv3Y5i2NEu2Awjx4RObwBnun2GLgpWElqly4EZVwzBG22ntAw9oxpOfz542rAOlJUsJ9Jdn1gA5UZnQuRxOYB3I89/aXRCMk4SfwUwnpm9kd8roewE/vsE59VB8V76OHLOSwAe0t/mxVJqu+nTxSyLz2zXuZq9lO6O/eapo9G8eR9+8pXLMGnp1qj3bLyjPV+Ik0QaGMXe1iWT8P3fvWPqomCWTae21Tx1NLbsPY6mMYNwSV01unxB9Ktwo8MbwLo/HcadVw8HCCnXNRNHFUusnCT+h5mvS/SanmT+ZzkMoAKAN/J7ORJkXkQYBGBtZB3KAeA3VpOTkDxOpwO1ETNLvR2LmT5erZMZrDA7X9XwL+pfFfeepJsLmcAo9hI5jhvVf1LTztW4/dKrbVj2ynva+S4H4d0f3YimMYNQVe6Mk+aSyZgroqy6nGI0EVlNToCFxEdEjxDRSijp4vuIaA0RPQ2lgGGX2Xm6C+9h5vHMPJaZx4jzRPZJRR9P5XxVw/+w3ZN224JghVHsnezwGsezLz7m9BLheye6TNeeVJeI5s37RKouAKzWoFqgZOA9B+DfALwGZR3pXgAvZL1nQsr0VR+3Ksr2s9vGobrMKdq7kBWMYq9fpTvO9eHh6WPhMPhfS5/+/ehrbVrcxq49zfncMNy1sW8uEULuSLgGlUtKXYe30raT1b37qo/rz1eLsvX4w3A4gHKXAx5/KKn1rBJG1qDSJC52y5xY/Ewr5k+qjzKJXfbVBjgoZl2IGSPvfQHByJ1Q8y2X4tbxF6BfpVuLYzV29ccBiux34MGb4toU0iKjf0RTAZWI3oH5xlpm5nGZ7EipY2UuCSBp48m+6uOGRdkqer+y1kaei/YuZJrY2O3yBXGiw6eVwQCUJAkjNwa9c8PUcYMx5dMDMX/9zniXCXFGyTtEtIyZF6s/rY61kvhuBnBLzGMqgH8BcDRTnRUUrHaoi2GrUIqkIlnrj10wuT7OOSJVk2Qhq6glnCYlOtD0K0Nkgy4AgIgaAHwNwG0ADgF4tk/dE+Kw2qHOzGLYKhQFRhI0Mxtum0jWjUFtc0BNGVbPnmA5lpI1SRbsgVUW30gi+kFkH9QvoNijEzNPZuZf5KyHJYJZBt2RMx5F3kvREUIQ7IbRZlyPP4gz3b0bwGNLridyYzDaXJ5ovIjDQ+FgJfG9C+A6ALcw89XM/AiUelBCFjDLoFv28gEs2tCKO64aJrKEUNAYSdXBMJuaH6fbpoyX4sFqVXAagBkAXiOiFwFsRIYzNIReNDljdiMqy5xoO6nUs9m8+xhcDkJNhUtkCaGgMZLe+lW6TTeXp9vm9sPtMl7sTdKp46Z3UMz8HDN/FcCnoex/+g6AgUS0iohu6HMXSxQjQ0wVZgaDMeuJt9C0fBs27z4GIDrLyEiWsGpTEOyCkYzd0RNI2vwYiI91j898c7rIeLZlY8xPU5JxM+9m5l8z881QXMlbAfxrn7pXohhp8OoudrUQ4dOvH4rbXJiKwavsjBfsipGM7XKQqflxLEaxHgqHsXJmg8h5BQQz/1T/0wrZqJtDzMxYH5/TCGbGvHU7tH0cCyYrmxM9/iCqy8y/AVq1Kfs68oJs1LUglSy+WMxi/ck7GhFmiJxnD3KzUVfIPImKnanvbd59TFt7SrTDPdkCaoJgB4w3klOU+bHe9FU/4ZjFernLgZ5ACmWihYIhocQnZA4rM9dUChEm26YgFBpWkrVRrKvmryJxFycyQeUQq13sVoUI021TEAoNK9cUo1i/46phWLRBzF8LASK6l4g+ZfD6OCL6guE5sgaVW6zMXM0KESY6Twqo2QpZg+oDsaavQK+Zq8cfQoXLgR7dGElk/ipjI+dYFSzshFJL8GZm/pvu9b8D8Htmbow9R+6gcozVLnan04HaCjccRKitcEdNTlYyhuyMF4oFs7TxLm8QT/3xII597I1ynej0GkvjHn9IMlztx3sAfgjgf4hotPoiM38EwG10gkxQBYCYxQqlgsMBwxpQPYEQmsYMijOBXfvGISyfYZxmLuPGdhAzPwdgDoDniegfACAyWfmNTpAsvgJAMvWEUqHC7cTSLfvRPHW0VgNq6Zb9+NltDTivpjxuHKx8tQ3/MrleO74nUkfKKutPxk3eYABg5j8T0fUAVhPRKihV2283OkHuoDJIthwdJFNPKAXCYUa3L4if3dYAAPjOM61oWr4NJzp8+LDdY1jCfeLQ/nj/VDealm/DrCfeAkiRvEOhMLp9QRx48Ca0/uB6NN9yqXa8jJu8oTkQMfNhZr4BwGBmvpCZXzM6Qe6gMoRVwcG+rgmp2UuxbUumnlAsGI2fh6aNRX1dNWZ+ZgjKnA6s/dNhPDRtLO55do92zMPTx2Lplv1R0p7qyrJoY6t23IoZDRjYrxyNQwfIuMkTzHza4LWA1TmSxZchsu3oINlIBYNk8aWB2fhZPXsCqsuU8eMJhFDpdmil2z2+EBwORRbUj4lOb0BzZTFqS8ZNVhEnCTuSbb27r6XcBSFXpPNlymz8VEeyU4HeuK+tUFYmaip6x4F+TFSXuzCwXzm23HWtto61amtbVFtCYSBrUBlC1okEIX3z4kyOH68/hCVNo9C8eR9G3fcCmjfvw5KmUfDKWCw4ZILKEOLoIAjpb4nI5PgJM+PuTdHp6Hdv2oOwjZYzhOTImlZERBcBWAfg7wCEAaxm5hXZul6+0QoOplEkTdaXhGLBSuo2MoBV6cv4ietDucu4DyKNFxzZvIMKAvguM/89gM8CWEBEl2bxenknHUcH2e0uFBNmUl2XN5gwxjPliCJye/GQtQmKmY8z887I804AfwVwQbauV6jIbnehmDCS6lbMbMCaNw7lLMZFbi8ecnLPS0RDAYwH8JbBe/MAzAOAIUOG5KI7eSVWzqt0O2S3e4li19jvi+RsJNVVuh1Y+Wpb1HHZjPFMyoVCfsl6kgQR1QB4FsBdzNwR+z4zr2bmRmZurKury3Z38oqZnLdwSn3UcSJHlAZ2jP1MSM6xUl1PIJxzyU0MlIuDrE5QROSGMjn9mpl/l81rFQJGct6iDa2446phIkcItiAbkrNIbkK6ZDOLjwA8CeCvzLwsW9cpJMwynGoqXCJHCLYgGxvORXIT0iWbd1BXQXGonUJErZHHF7N4PdtjlV0kcoRgB7KVAZeM5JYts2WhcMlmFt/rzEzMPJaZGyKPP2TreoWASB2C3clXjMp2C8EIMYvNMbIpt+gpeLPYfMRots2WhZwhZrGFjJi+CkI8UlxQMEK8+ARB0MiX1CbuD4IRMkEJgqCRL2cTWZ8VjBCNSRBKmLj1pjxJbZlIRZf13eJD7qAEoUQxkvO6vMG8SW19cX+QLMDiRCYoQShRjOS8NW8cwoqZDQUntYnpcnEiEp8glChGct7KV9uwYEp9wbk+SBZgcSJ3UIJQophlzvUEwgXnbCJZgMWJTFCCUKIUU+ZcMX0WoReR+AShRCkmE9di+ixCL3IHlUHE7FIoNDRnEzVUCQUbu1IDqviQO6gMoaa5LtywC9sPt2Pi0P5YOXM8BlSXyUARbI3ErmBX5A4qQ0iaq1CoSOwKdkUmqAwhaa5CoSKxK9gVmaAyhKS5CoWKxK5gV2SCyhCS5ioUKhK7gl2RJIkMIWmuQqEisSvYFZmgMogUIxQKFYldwY6IxCcIgiDYEpmgBEEQBFsiE5QgCIJgS2SCEgRBEGxJ1iYoInqKiE4S0d5sXUMQBEEoXrJ5B7UGwI1ZbF8QBEEoYrI2QTHzNgDtCQ8UBEEQBANkDUoQBEGwJXmfoIhoHhG1EFHLqVOn8t0dQcgZEvuCYE3eJyhmXs3MjczcWFdXl+/uCELOkNgXBGvyPkEJgiAIghHZTDPfAOBNAKOI6G9E9I1sXUsQBEEoPrLmCsnMM7PVtiAIglD8iMQnCIIg2BKZoARBEARbIhOUIAiCYEtkghIEQRBsiUxQgiAIgi2RCUoQBEGwJcTM+e6DBhGdAvBBvvthwHkATue7E2kifc8tp5k5ZRd/G8c+UJj/DirS99ySVvybYasJyq4QUQszN+a7H+kgfRf6SiH/O0jfCxuR+ARBEARbIhOUIAiCYEtkgkqO1fnuQB+Qvgt9pZD/HaTvBYysQQmCIAi2RO6gBEEQBFsiE1QCiMhJRLuI6Pf57ksqENE5RPRbInqXiP5KRFfmu0/JQkTfIaJ9RLSXiDYQUUW++1SqSPznHon/XmSCSswiAH/NdyfSYAWAF5n50wDGoUA+AxFdAGAhgEZmHgPACWBGfntV0kj85xCJ/2hkgrKAiC4E8CUAT+S7L6lARP0AXAvgSQBgZj8zf5zXTqWGC0AlEbkAVAE4luf+lCQS/3lD4j+CTFDWLAfwPQDhPPcjVYYDOAXg6Yg88wQRVee7U8nAzEcBLAVwBMBxAJ8w80v57VXJshwS/zlF4j8amaBMIKKbAZxk5h357ksauABcDmAVM48H0A3gX/PbpeQgonMBfBnAMACDAVQT0az89qr0kPjPDxL/0cgEZc5VAKYS0WEAGwFMIaL1+e1S0vwNwN+Y+a3I77+FMmALgS8AOMTMp5g5AOB3AD6X5z6VIhL/+UHiX4dMUCYw8/eZ+UJmHgplkfJVZi6IbzLM/BGAD4loVOSl6wD8JY9dSoUjAD5LRFVERFD6XhAL3MWExH/ekPjX4cp3B4Ss8W0AvyaiMgAHAXw9z/1JCmZ+i4h+C2AngCCAXZAd9ULqSPwXAeIkIQiCINgSkfgEQRAEWyITlCAIgmBLZIISBEEQbIlMUIIgCIItkQlKEARBsCUyQQmCIAi2RCaoHEBEA4ioNfL4iIiO6n4vizn2LiKqSqLNrUTUaPH+YSJ6h4h2E9FLRPR3KfR3KBF9Tfd7IxGtTPZ8QdAj8S+ki0xQOYCZzzBzAzM3AHgMwM/V35nZH3P4XVAcjDPBZGYeB6AFwL/p3yAFs3//oQC0AcrMLcy8MEN9EkoMiX8hXWSCyhNEdF3EafkdInqKiMqJaCEUg8jXiOi1yHGriKglUsDsh2lebhuA+sg3w78S0f8HZaf6RUT0cKQw2jtE9NXI8f8B4JrIN9zvENEktWAdEVVH+rs90v8vR16/g4h+R0QvEtF7RPTTyOtOIlqju8Z30v+rCcWCxL+QFMwsjxw+ADQDuA/AhwBGRl5bB+CuyPPDAM7THd8/8tMJYCuAsZHft0IpamZ2Ha0dAL8A8BCUb4ZhAJ+NvD4NwMuRtgdC8QEbBGASgN/r2tJ+B/BjALMiz88BcABANYA7oFjKfApABYAPAFwEYAKAl3VtnZPvfwN55O8h8S/xn8pD7qDygxOKY/GByO9roRRYM+I2ItoJxZNrNIBLU7jOa0TUCqAfgJ9EXvuAmf8ceX41gA3MHGLmEwD+F8DEBG3eAOBfI+1uhTIYh0Te+x9m/oSZvVDMOS+GMmiHE9EjRHQjgI4U+i8UJxL/QlKIWWx+6E7mICIaBmAJgInMfJaI1kAZEMkymZlP69o7J+balEJb+nOmMfP+mL5+BoBP91IIgCvS73EAmgAsAHAbgDvTuK5QPEj8C0khd1D5oQLAUCKqj/x+O5RvbwDQCaA28rwflAH1CRENBHBThvuxDcBXIzp5HZRvsW/H9CGWLQC+TUQEAEQ03uoCRHQeAAczPwvg/6Jw6vII2UPiX0gKuYPKD14o9v+biMgFYDuU7CZAsdZ/gYiOM/NkItoFYB8UqeCNDPfjOQBXAtgNgAF8j5k/IqIzAIJEtBvAGijyisqPoJQC3xMZpIcB3GxxjQuglN5Wvwx9P5MfQChIJP6FpJByG4IgCIItEYlPEARBsCUi8RU4RPQWgPKYl29n5nfy0R9ByCUS/8WNSHyCIAiCLRGJTxAEQbAlMkEJgiAItkQmKEEQBMGWyAQlCIIg2BKZoARBEARb8v8DrG2sskyFOz0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting gender along with Total_Protiens and Albumin\n",
    "g = sns.FacetGrid(liver_df, col = \"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Total_Protiens\",\"Albumin\", edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1042ed2bee0>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting total protine vs albumin\n",
    "sns.jointplot(\"Total_Protiens\",\"Albumin\",data=liver_df,kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting gender along with Albumin and Albumin_and_Globulin_Ratio\n",
    "g = sns.FacetGrid(liver_df, col = \"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Albumin\",\"Albumin_and_Globulin_Ratio\", edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting gender along with Albumin_and_Globulin_Ratio and Total_Protiens\n",
    "g = sns.FacetGrid(liver_df, col = \"Gender\", row=\"Dataset\", margin_titles=True)\n",
    "g.map(plt.scatter,\"Albumin_and_Globulin_Ratio\",\"Total_Protiens\", edgecolor=\"w\")\n",
    "plt.subplots_adjust(top=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>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": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender_Female  Gender_Male\n",
       "0              1            0\n",
       "1              0            1\n",
       "2              0            1\n",
       "3              0            1\n",
       "4              0            1"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(liver_df['Gender'], prefix = 'Gender').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing the categorical value to numerical value\n",
    "liver_df = pd.concat([liver_df,pd.get_dummies(liver_df['Gender'], prefix = 'Gender')], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Direct_Bilirubin</th>\n",
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       "      <th>Alamine_Aminotransferase</th>\n",
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       "      <th>Total_Protiens</th>\n",
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       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
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       "      <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",
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       "      <td>0</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",
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       "      <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",
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       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "</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  Gender_Female  Gender_Male  \n",
       "0      3.3                        0.90        1              1            0  \n",
       "1      3.2                        0.74        1              0            1  \n",
       "2      3.3                        0.89        1              0            1  \n",
       "3      3.4                        1.00        1              0            1  \n",
       "4      2.4                        0.40        1              0            1  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>230</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>180</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>312</th>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>106</td>\n",
       "      <td>25</td>\n",
       "      <td>54</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "209   45  Female              0.9               0.3                   189   \n",
       "241   51    Male              0.8               0.2                   230   \n",
       "253   35  Female              0.6               0.2                   180   \n",
       "312   27    Male              1.3               0.6                   106   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "209                        23                          33             6.6   \n",
       "241                        24                          46             6.5   \n",
       "253                        12                          15             5.2   \n",
       "312                        25                          54             8.5   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  Gender_Female  Gender_Male  \n",
       "209      3.9                         NaN        1              1            0  \n",
       "241      3.1                         NaN        1              0            1  \n",
       "253      2.7                         NaN        2              1            0  \n",
       "312      4.8                         NaN        2              0            1  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df[liver_df['Albumin_and_Globulin_Ratio'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling the null values with the mean value\n",
    "liver_df[\"Albumin_and_Globulin_Ratio\"] = liver_df.Albumin_and_Globulin_Ratio.fillna(liver_df['Albumin_and_Globulin_Ratio'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10.9</td>\n",
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       "      <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>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>7.3</td>\n",
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       "      <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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65              0.7               0.1                   187   \n",
       "1   62             10.9               5.5                   699   \n",
       "2   62              7.3               4.1                   490   \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",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Gender_Female  Gender_Male  \n",
       "0      3.3                        0.90              1            0  \n",
       "1      3.2                        0.74              0            1  \n",
       "2      3.3                        0.89              0            1  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = liver_df.drop(['Gender','Dataset'],axis=1)\n",
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = liver_df['Dataset']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corelation between all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.000000</td>\n",
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       "      <td>0.007529</td>\n",
       "      <td>0.080425</td>\n",
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       "      <td>0.011763</td>\n",
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       "      <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>-0.206159</td>\n",
       "      <td>-0.089291</td>\n",
       "      <td>0.089291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <td>0.007529</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>-0.200004</td>\n",
       "      <td>-0.100436</td>\n",
       "      <td>0.100436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <td>0.080425</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>-0.233960</td>\n",
       "      <td>0.027496</td>\n",
       "      <td>-0.027496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <td>-0.086883</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>-0.002374</td>\n",
       "      <td>-0.082332</td>\n",
       "      <td>0.082332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <td>-0.019910</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>-0.070024</td>\n",
       "      <td>-0.080336</td>\n",
       "      <td>0.080336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Protiens</th>\n",
       "      <td>-0.187461</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>0.233904</td>\n",
       "      <td>0.089121</td>\n",
       "      <td>-0.089121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albumin</th>\n",
       "      <td>-0.265924</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>0.686322</td>\n",
       "      <td>0.093799</td>\n",
       "      <td>-0.093799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <td>-0.216089</td>\n",
       "      <td>-0.206159</td>\n",
       "      <td>-0.200004</td>\n",
       "      <td>-0.233960</td>\n",
       "      <td>-0.002374</td>\n",
       "      <td>-0.070024</td>\n",
       "      <td>0.233904</td>\n",
       "      <td>0.686322</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003404</td>\n",
       "      <td>-0.003404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Female</th>\n",
       "      <td>-0.056560</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>0.003404</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Male</th>\n",
       "      <td>0.056560</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>-0.003404</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Age  Total_Bilirubin  Direct_Bilirubin  \\\n",
       "Age                         1.000000         0.011763          0.007529   \n",
       "Total_Bilirubin             0.011763         1.000000          0.874618   \n",
       "Direct_Bilirubin            0.007529         0.874618          1.000000   \n",
       "Alkaline_Phosphotase        0.080425         0.206669          0.234939   \n",
       "Alamine_Aminotransferase   -0.086883         0.214065          0.233894   \n",
       "Aspartate_Aminotransferase -0.019910         0.237831          0.257544   \n",
       "Total_Protiens             -0.187461        -0.008099         -0.000139   \n",
       "Albumin                    -0.265924        -0.222250         -0.228531   \n",
       "Albumin_and_Globulin_Ratio -0.216089        -0.206159         -0.200004   \n",
       "Gender_Female              -0.056560        -0.089291         -0.100436   \n",
       "Gender_Male                 0.056560         0.089291          0.100436   \n",
       "\n",
       "                            Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
       "Age                                     0.080425                 -0.086883   \n",
       "Total_Bilirubin                         0.206669                  0.214065   \n",
       "Direct_Bilirubin                        0.234939                  0.233894   \n",
       "Alkaline_Phosphotase                    1.000000                  0.125680   \n",
       "Alamine_Aminotransferase                0.125680                  1.000000   \n",
       "Aspartate_Aminotransferase              0.167196                  0.791966   \n",
       "Total_Protiens                         -0.028514                 -0.042518   \n",
       "Albumin                                -0.165453                 -0.029742   \n",
       "Albumin_and_Globulin_Ratio             -0.233960                 -0.002374   \n",
       "Gender_Female                           0.027496                 -0.082332   \n",
       "Gender_Male                            -0.027496                  0.082332   \n",
       "\n",
       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "Age                                          -0.019910       -0.187461   \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                   -0.070024        0.233904   \n",
       "Gender_Female                                -0.080336        0.089121   \n",
       "Gender_Male                                   0.080336       -0.089121   \n",
       "\n",
       "                             Albumin  Albumin_and_Globulin_Ratio  \\\n",
       "Age                        -0.265924                   -0.216089   \n",
       "Total_Bilirubin            -0.222250                   -0.206159   \n",
       "Direct_Bilirubin           -0.228531                   -0.200004   \n",
       "Alkaline_Phosphotase       -0.165453                   -0.233960   \n",
       "Alamine_Aminotransferase   -0.029742                   -0.002374   \n",
       "Aspartate_Aminotransferase -0.085290                   -0.070024   \n",
       "Total_Protiens              0.784053                    0.233904   \n",
       "Albumin                     1.000000                    0.686322   \n",
       "Albumin_and_Globulin_Ratio  0.686322                    1.000000   \n",
       "Gender_Female               0.093799                    0.003404   \n",
       "Gender_Male                -0.093799                   -0.003404   \n",
       "\n",
       "                            Gender_Female  Gender_Male  \n",
       "Age                             -0.056560     0.056560  \n",
       "Total_Bilirubin                 -0.089291     0.089291  \n",
       "Direct_Bilirubin                -0.100436     0.100436  \n",
       "Alkaline_Phosphotase             0.027496    -0.027496  \n",
       "Alamine_Aminotransferase        -0.082332     0.082332  \n",
       "Aspartate_Aminotransferase      -0.080336     0.080336  \n",
       "Total_Protiens                   0.089121    -0.089121  \n",
       "Albumin                          0.093799    -0.093799  \n",
       "Albumin_and_Globulin_Ratio       0.003404    -0.003404  \n",
       "Gender_Female                    1.000000    -1.000000  \n",
       "Gender_Male                     -1.000000     1.000000  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_corr = X.corr()\n",
    "liver_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the data into Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(408, 11)\n",
      "(408,)\n",
      "(175, 11)\n",
      "(175,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state =42)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Training score: \n",
      " 71.08\n",
      "Logistic Regression Test score: \n",
      " 72.57\n",
      "Accuracy:\n",
      " 0.7257142857142858\n",
      "Confusion Matrix:\n",
      " [[117  11]\n",
      " [ 37  10]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           1       0.76      0.91      0.83       128\n",
      "           2       0.48      0.21      0.29        47\n",
      "\n",
      "    accuracy                           0.73       175\n",
      "   macro avg       0.62      0.56      0.56       175\n",
      "weighted avg       0.68      0.73      0.69       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "logreg_pred = logreg.predict(X_test)\n",
    "\n",
    "logreg_score = round(logreg.score(X_train, y_train)* 100,2)\n",
    "logreg_score_test = round(logreg.score(X_test, y_test)* 100,2)\n",
    "\n",
    "# equation coefficient and intercept\n",
    "print('Logistic Regression Training score: \\n',logreg_score)\n",
    "print('Logistic Regression Test score: \\n',logreg_score_test)\n",
    "\n",
    "print('Accuracy:\\n',accuracy_score(y_test,logreg_pred))\n",
    "print('Confusion Matrix:\\n',confusion_matrix(y_test,logreg_pred))\n",
    "print('Classification Report:\\n',classification_report(y_test,logreg_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.K-Nearest Neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " K-Nearest Neighbours Training score: \n",
      " 73.53\n",
      "K-Nearest Neighbours Test score: \n",
      " 74.29\n",
      "Accuracy:\n",
      " 0.7428571428571429\n",
      "Confusion Matrix:\n",
      " [[117  11]\n",
      " [ 34  13]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           1       0.77      0.91      0.84       128\n",
      "           2       0.54      0.28      0.37        47\n",
      "\n",
      "    accuracy                           0.74       175\n",
      "   macro avg       0.66      0.60      0.60       175\n",
      "weighted avg       0.71      0.74      0.71       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 6)\n",
    "knn.fit(X_train,y_train)\n",
    "knn_pred = knn.predict(X_test)\n",
    "\n",
    "knn_score = round(knn.score(X_train, y_train)* 100,2)\n",
    "knn_score_test = round(knn.score(X_test, y_test)* 100,2)\n",
    "\n",
    "# equation coefficient and intercept\n",
    "print(' K-Nearest Neighbours Training score: \\n',knn_score)\n",
    "print('K-Nearest Neighbours Test score: \\n',knn_score_test)\n",
    "\n",
    "print('Accuracy:\\n',accuracy_score(y_test,knn_pred))\n",
    "print('Confusion Matrix:\\n',confusion_matrix(y_test,knn_pred))\n",
    "print('Classification Report:\\n',classification_report(y_test,knn_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Decision Tree Training score: \n",
      " 75.98\n",
      "Decision Tree Test score: \n",
      " 74.29\n",
      "Accuracy:\n",
      " 0.7371428571428571\n",
      "Confusion Matrix:\n",
      " [[116  12]\n",
      " [ 34  13]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           1       0.77      0.91      0.83       128\n",
      "           2       0.52      0.28      0.36        47\n",
      "\n",
      "    accuracy                           0.74       175\n",
      "   macro avg       0.65      0.59      0.60       175\n",
      "weighted avg       0.71      0.74      0.71       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dtree = DecisionTreeClassifier(max_depth = 4 , random_state = 1 , min_samples_leaf =20)\n",
    "dtree.fit(X_train,y_train)\n",
    "dtree_pred = dtree.predict(X_test)\n",
    "\n",
    "dtree_score = round(dtree.score(X_train, y_train)* 100,2)\n",
    "dtree_score_test = round(knn.score(X_test, y_test)* 100,2)\n",
    "\n",
    "# equation coefficient and intercept\n",
    "print(' Decision Tree Training score: \\n',dtree_score)\n",
    "print('Decision Tree Test score: \\n',dtree_score_test)\n",
    "\n",
    "print('Accuracy:\\n',accuracy_score(y_test,dtree_pred))\n",
    "print('Confusion Matrix:\\n',confusion_matrix(y_test,dtree_pred))\n",
    "print('Classification Report:\\n',classification_report(y_test,dtree_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = [0.00001, 0.0001, 0.001, 0.01, 0.1]\n",
    "hist = []\n",
    "for val in grid:\n",
    "    clf = SVC(gamma=val)\n",
    "    cross_val = cross_val_score(clf, X, y, cv=5)\n",
    "    hist.append(np.mean(cross_val))\n",
    "plt.plot([str(i) for i in grid], hist)\n",
    "plt.title('Cross Validations score for SVC')\n",
    "plt.xlabel('gamma')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Decision Tree Training score: \n",
      " 70.59\n",
      "Decision Tree Test score: \n",
      " 73.14\n",
      "Accuracy:\n",
      " 0.7314285714285714\n",
      "Confusion Matrix:\n",
      " [[128   0]\n",
      " [ 47   0]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           1       0.73      1.00      0.84       128\n",
      "           2       0.00      0.00      0.00        47\n",
      "\n",
      "    accuracy                           0.73       175\n",
      "   macro avg       0.37      0.50      0.42       175\n",
      "weighted avg       0.53      0.73      0.62       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(kernel= \"linear\",C=0.025, gamma=0.01)\n",
    "svm.fit(X_train,y_train)\n",
    "svm_pred = svm.predict(X_test)\n",
    "\n",
    "svm_score = round(svm.score(X_train, y_train)* 100,2)\n",
    "svm_score_test = round(svm.score(X_test, y_test)* 100,2)\n",
    "\n",
    "# equation coefficient and intercept\n",
    "print(' Decision Tree Training score: \\n',svm_score)\n",
    "print('Decision Tree Test score: \\n',svm_score_test)\n",
    "\n",
    "print('Accuracy:\\n',accuracy_score(y_test,svm_pred))\n",
    "print('Confusion Matrix:\\n',confusion_matrix(y_test,svm_pred))\n",
    "print('Classification Report:\\n',classification_report(y_test,svm_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Decision Tree Training score: \n",
      " 54.17\n",
      "Decision Tree Test score: \n",
      " 57.14\n",
      "Accuracy:\n",
      " 0.5714285714285714\n",
      "Confusion Matrix:\n",
      " [[54 74]\n",
      " [ 1 46]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           1       0.98      0.42      0.59       128\n",
      "           2       0.38      0.98      0.55        47\n",
      "\n",
      "    accuracy                           0.57       175\n",
      "   macro avg       0.68      0.70      0.57       175\n",
      "weighted avg       0.82      0.57      0.58       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, y_train)\n",
    "gaussian_pred = gaussian.predict(X_test)\n",
    "\n",
    "gauss_score = round(gaussian.score(X_train, y_train)* 100,2)\n",
    "gauss_score_test = round(gaussian.score(X_test, y_test)* 100,2)\n",
    "\n",
    "# equation coefficient and intercept\n",
    "print(' Decision Tree Training score: \\n',gauss_score)\n",
    "print('Decision Tree Test score: \\n',gauss_score_test)\n",
    "\n",
    "print('Accuracy:\\n',accuracy_score(y_test,gaussian_pred))\n",
    "print('Confusion Matrix:\\n',confusion_matrix(y_test,gaussian_pred))\n",
    "print('Classification Report:\\n',classification_report(y_test,gaussian_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Decision Tree Training score: \n",
      " 100.0\n",
      "Decision Tree Test score: \n",
      " 74.29\n",
      "Accuracy:\n",
      " 0.7428571428571429\n",
      "Confusion Matrix:\n",
      " [[113  15]\n",
      " [ 30  17]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           1       0.79      0.88      0.83       128\n",
      "           2       0.53      0.36      0.43        47\n",
      "\n",
      "    accuracy                           0.74       175\n",
      "   macro avg       0.66      0.62      0.63       175\n",
      "weighted avg       0.72      0.74      0.73       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators = 100)\n",
    "random_forest.fit(X_train, y_train)\n",
    "rf_pred = random_forest.predict(X_test)\n",
    "\n",
    "random_forest_score = round(random_forest.score(X_train, y_train)* 100,2)\n",
    "random_forest_score_test = round(random_forest.score(X_test, y_test)* 100,2)\n",
    "\n",
    "# equation coefficient and intercept\n",
    "print(' Decision Tree Training score: \\n',random_forest_score)\n",
    "print('Decision Tree Test score: \\n',random_forest_score_test)\n",
    "\n",
    "print('Accuracy:\\n',accuracy_score(y_test,rf_pred))\n",
    "print('Confusion Matrix:\\n',confusion_matrix(y_test,rf_pred))\n",
    "print('Classification Report:\\n',classification_report(y_test,rf_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# From the models K-Nearest Neighbours perform the best on this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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