Confusion_Matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f16f86ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b5f9658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments\\\\data_set'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir('F:\\\\Machine-learning\\\\Assignments\\\\data_set')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee843bdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  75.0        0                       582         0                 20   \n",
       "1  55.0        0                      7861         0                 38   \n",
       "2  65.0        0                       146         0                 20   \n",
       "3  50.0        1                       111         0                 20   \n",
       "4  65.0        1                       160         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    1  265000.00               1.9           130    1   \n",
       "1                    0  263358.03               1.1           136    1   \n",
       "2                    0  162000.00               1.3           129    1   \n",
       "3                    0  210000.00               1.9           137    1   \n",
       "4                    0  327000.00               2.7           116    0   \n",
       "\n",
       "   smoking  time  DEATH_EVENT  \n",
       "0        0     4            1  \n",
       "1        0     6            1  \n",
       "2        1     7            1  \n",
       "3        0     7            1  \n",
       "4        0     8            1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv(\"heart failure.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5e03c5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       299 non-null    float64\n",
      " 1   anaemia                   299 non-null    int64  \n",
      " 2   creatinine_phosphokinase  299 non-null    int64  \n",
      " 3   diabetes                  299 non-null    int64  \n",
      " 4   ejection_fraction         299 non-null    int64  \n",
      " 5   high_blood_pressure       299 non-null    int64  \n",
      " 6   platelets                 299 non-null    float64\n",
      " 7   serum_creatinine          299 non-null    float64\n",
      " 8   serum_sodium              299 non-null    int64  \n",
      " 9   sex                       299 non-null    int64  \n",
      " 10  smoking                   299 non-null    int64  \n",
      " 11  time                      299 non-null    int64  \n",
      " 12  DEATH_EVENT               299 non-null    int64  \n",
      "dtypes: float64(3), int64(10)\n",
      "memory usage: 30.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9d445c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename({\"DEATH_EVENT\":\"death\"}, axis=1)  #rename target column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f5e4fdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  75.0        0                       582         0                 20   \n",
       "1  55.0        0                      7861         0                 38   \n",
       "2  65.0        0                       146         0                 20   \n",
       "3  50.0        1                       111         0                 20   \n",
       "4  65.0        1                       160         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    1  265000.00               1.9           130    1   \n",
       "1                    0  263358.03               1.1           136    1   \n",
       "2                    0  162000.00               1.3           129    1   \n",
       "3                    0  210000.00               1.9           137    1   \n",
       "4                    0  327000.00               2.7           116    0   \n",
       "\n",
       "   smoking  time  death  \n",
       "0        0     4      1  \n",
       "1        0     6      1  \n",
       "2        1     7      1  \n",
       "3        0     7      1  \n",
       "4        0     8      1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66b82d63",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('After_Change_Column_Name_heart failure.csv')  #save modified dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea043bbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    203\n",
       "1     96\n",
       "Name: death, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.death.value_counts()  #count each classes, 0 = not death, 1 = death"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd64c5ed",
   "metadata": {},
   "source": [
    "# data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93205256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='death', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(x=\"death\", data=df)  # for x axis graph respected to death attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77580513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='death'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(y=\"death\", data=df) # for y axis graph respected to death attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a517d507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='diabetes', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(x=\"diabetes\", data=df)  # for x axis graph respected to diabetes attribute"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e6e1bfa",
   "metadata": {},
   "source": [
    "### death analysis with respect to diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "783dda6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='diabetes', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(x=\"diabetes\", data=df, hue=\"death\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71046971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    174\n",
       "1    125\n",
       "Name: diabetes, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.diabetes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2f72b2",
   "metadata": {},
   "source": [
    "### death analysis with respect to anaemia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a25d69b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='anaemia', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(x=\"anaemia\", data=df, hue=\"death\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da6b87c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    170\n",
       "1    129\n",
       "Name: anaemia, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.anaemia.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414b62ee",
   "metadata": {},
   "source": [
    "### death analysis with respect to high_blood_pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "41477f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='high_blood_pressure', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWQElEQVR4nO3dfbRddX3n8fcHEowIykMig7lgguADqAhclCIwncJUQA1oRZLpjCCxKUsGrXZq0a4lkZFZRZlhUDo6tCBgmSBilQcBZdAUEAETCQKJFASEmwkQolKqPEj8zh9nZ3Mb8nBJ7jnnJvf9Wuuss/dvP33PWnfdz/ntvc9vp6qQJAlgi34XIEkaOwwFSVLLUJAktQwFSVLLUJAktSb0u4CNMXny5Jo2bVq/y5CkTcrChQsfr6opa1q2SYfCtGnTWLBgQb/LkKRNSpKfr22Zp48kSa2uhUKS85M8luSuNSz78ySVZHIznyRfSHJfkp8k2bdbdUmS1q6bPYULgMNXb0yyC/CHwEPDmo8A9mhec4AvdbEuSdJadO2aQlXdkGTaGhadBXwCuHxY21HARdUZc+OWJNsl2bmqlnWrPknaGL/97W8ZGhri6aef7ncpazVp0iQGBgaYOHHiiLfp6YXmJEcBS6vqjiTDF00FHh42P9S0vSAUksyh05tg11137V6xkrQOQ0NDbLvttkybNo3V/p+NCVXFihUrGBoaYvr06SPermcXmpNsDXwK+PTG7Keqzq2qwaoanDJljXdUSVLXPf300+y4445jMhAAkrDjjju+6J5ML3sKrwGmA6t6CQPAj5O8FVgK7DJs3YGmTZLGrLEaCKtsSH096ylU1Z1V9cqqmlZV0+icItq3qh4BrgA+0NyFdADwhNcTJKn3unlL6jzgh8Drkgwlmb2O1a8G7gfuA/4W+HC36pKkXps7dy5nnnnmi95u/vz53Hzzze388ccfz2WXXTaapb1AN+8+mrWe5dOGTRdwUrdqWZf9/uKifhx2TFr4+Q/0uwRJw8yfP59tttmGAw88sGfH9BfNktQFp59+Oq997Ws56KCDuOeeewD42c9+xuGHH85+++3HwQcfzE9/+lMArrzySt72trexzz77cNhhh/Hoo4/y4IMP8uUvf5mzzjqLt7zlLdx4440A3HDDDRx44IHstttuXek1GAqSNMoWLlzIJZdcwqJFi7j66qv50Y9+BMCcOXP44he/yMKFCznzzDP58Ic7Z8oPOuggbrnlFm6//XZmzpzJ5z73OaZNm8aJJ57Ixz72MRYtWsTBBx8MwLJly7jpppu46qqrOOWUU0a99k16QDxJGotuvPFG3vOe97D11lsDMGPGDJ5++mluvvlmjjnmmHa9Z555Buj85uHYY49l2bJlPPvss+v8XcHRRx/NFltswZ577smjjz466rUbCpLUA7/73e/YbrvtWLRo0QuWnXzyyXz84x9nxowZzJ8/n7lz5651Py95yUva6c7l2NHl6SNJGmWHHHII3/rWt3jqqad48sknufLKK9l6662ZPn06X//614HOP/Q77rgDgCeeeIKpU6cCcOGFF7b72XbbbXnyySd7WruhIEmjbN999+XYY49l77335ogjjmD//fcH4OKLL+a8885j7733Zq+99uLyyztDwM2dO5djjjmG/fbbj8mTJ7f7efe73803v/nNf3WhudvSje5HrwwODtbGPmTHW1Kf5y2p0sgtWbKEN7zhDf0uY73WVGeShVU1uKb17SlIklqGgiSpZShIklqGgiSpZShIklqGgiSp5S+aJWkUjPbt7SO5Rfzaa6/lox/9KCtXruRDH/rQqIyFZE9BkjZBK1eu5KSTTuKaa65h8eLFzJs3j8WLF2/0fg0FSdoE3Xbbbey+++7stttubLXVVsycObP9hfTGMBQkaRO0dOlSdtnl+UfbDwwMsHTpxj/a3lCQJLUMBUnaBE2dOpWHH364nR8aGmpHWt0YhoIkbYL2339/7r33Xh544AGeffZZLrnkEmbMmLHR+/WWVEkaBb0eZXjChAmcc845vOMd72DlypWccMIJ7LXXXhu/31GobY2SnA+8C3isqt7YtH0eeDfwLPAz4INV9atm2SeB2cBK4CNV9Z1u1SZJm4MjjzySI488clT32c3TRxcAh6/Wdh3wxqp6M/BPwCcBkuwJzAT2arb5X0m27GJtkqQ16FooVNUNwC9Wa/tuVT3XzN4CDDTTRwGXVNUzVfUAcB/w1m7VJklas35eaD4BuKaZngo8PGzZUNMmSeqhvoRCkr8CngMu3oBt5yRZkGTB8uXLR784SRrHeh4KSY6ncwH6j+v5B0QvBXYZttpA0/YCVXVuVQ1W1eCUKVO6WqskjTc9DYUkhwOfAGZU1W+GLboCmJnkJUmmA3sAt/WyNklSd29JnQf8PjA5yRBwKp27jV4CXJcE4JaqOrGq7k5yKbCYzmmlk6pqZbdqk6TR9tBpbxrV/e366TvXu84JJ5zAVVddxStf+UruuuuuUTluN+8+mlVVO1fVxKoaqKrzqmr3qtqlqt7SvE4ctv7pVfWaqnpdVV2zrn1LkuD444/n2muvHdV9OsyFJG2iDjnkEHbYYYdR3aehIElqGQqSpJahIElqGQqSpJZDZ0vSKBjJLaSjbdasWcyfP5/HH3+cgYEBPvOZzzB79uyN2qehIEmbqHnz5o36Pj19JElqGQqSpJahIEkb6PkxPcemDanPUJCkDTBp0iRWrFgxZoOhqlixYgWTJk16Udt5oVmSNsDAwABDQ0OM5ee6TJo0iYGBgfWvOIyhIEkbYOLEiUyfPr3fZYw6Tx9JklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSp1bVQSHJ+kseS3DWsbYck1yW5t3nfvmlPki8kuS/JT5Ls2626JElr182ewgXA4au1nQJcX1V7ANc38wBHAHs0rznAl7pYlyRpLboWClV1A/CL1ZqPAi5spi8Ejh7WflF13AJsl2TnbtUmSVqzXl9T2KmqljXTjwA7NdNTgYeHrTfUtEmSeqhvF5qrM97six5zNsmcJAuSLBjLoxNK0qao16Hw6KrTQs37Y037UmCXYesNNG0vUFXnVtVgVQ1OmTKlq8VK0njT61C4AjiumT4OuHxY+weau5AOAJ4YdppJktQjXXueQpJ5wO8Dk5MMAacCfw1cmmQ28HPg/c3qVwNHAvcBvwE+2K26JElr17VQqKpZa1l06BrWLeCkbtUiSRoZf9EsSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKkVl9CIcnHktyd5K4k85JMSjI9ya1J7kvytSRb9aM2SRrPeh4KSaYCHwEGq+qNwJbATOAM4Kyq2h34JTC717VJ0njXr9NHE4CXJpkAbA0sA/4AuKxZfiFwdH9Kk6Txq+ehUFVLgTOBh+iEwRPAQuBXVfVcs9oQMHVN2yeZk2RBkgXLly/vRcmSNG6MKBSSXD+SthHua3vgKGA68CrgZcDhI92+qs6tqsGqGpwyZcqGlCBJWosJ61qYZBKd0zuTm3/maRa9nLV8kx+Bw4AHqmp5c4x/AN4ObJdkQtNbGACWbuD+JUkbaJ2hAPwp8Gd0vtEv5PlQ+GfgnA085kPAAUm2Bp4CDgUWAN8H3gdcAhwHXL6B+5ckbaB1hkJVnQ2cneTkqvriaBywqm5NchnwY+A54HbgXODbwCVJPtu0nTcax5Mkjdz6egoAVNUXkxwITBu+TVVdtCEHrapTgVNXa74feOuG7E+SNDpGFApJvgq8BlgErGyaC9igUJAkjU0jCgVgENizqqqbxUiS+mukv1O4C/g33SxEktR/I+0pTAYWJ7kNeGZVY1XN6EpVkqS+GGkozO1mEZKksWGkdx/9Y7cLkST130jvPnqSzt1GAFsBE4FfV9XLu1WYJKn3RtpT2HbVdJLQGbvogG4VJUnqjxc9Smp1fAt4x+iXI0nqp5GePnrvsNkt6Pxu4emuVCRJ6puR3n307mHTzwEP0jmFJEnajIz0msIHu12IJKn/RvqQnYEk30zyWPP6RpKBbhcnSeqtkV5o/gpwBZ3nKrwKuLJpkyRtRkYaClOq6itV9VzzugDwWZiStJkZ6YXmFUn+IzCvmZ8FrOhOSZIA9vsLR6ZfZeHnP9DvEsaNkfYUTgDeDzwCLKPz2Mzju1STJKlPRtpTOA04rqp+CZBkB+BMOmEhSdpMjLSn8OZVgQBQVb8A9ulOSZKkfhlpKGyRZPtVM01PYaS9DEnSJmKk/9j/O/DDJF9v5o8BTu9OSZKkfhlRT6GqLgLeCzzavN5bVV/d0IMm2S7JZUl+mmRJkt9LskOS65Lc27xvv/49SZJG04hHSa2qxVV1TvNavJHHPRu4tqpeD+wNLAFOAa6vqj2A65t5SVIPveihszdWklcAhwDnAVTVs1X1KzoD7F3YrHYhcHSva5Ok8a7noQBMB5YDX0lye5K/S/IyYKeqWtas8wiw05o2TjInyYIkC5YvX96jkiVpfOhHKEwA9gW+VFX7AL9mtVNFVVU8//hPVlt2blUNVtXglCmOtCFJo6kfoTAEDFXVrc38ZXRC4tEkOwM074/1oTZJGtd6HgpV9QjwcJLXNU2HAovpjMJ6XNN2HHB5r2uTpPGuXz9AOxm4OMlWwP3AB+kE1KVJZgM/pzPWkiSph/oSClW1iM5znld3aI9L0TAPnfamfpcwZuz66Tv7XYLUF/24piBJGqMMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLX6FgpJtkxye5KrmvnpSW5Ncl+SryXZql+1SdJ41c+ewkeBJcPmzwDOqqrdgV8Cs/tSlSSNY30JhSQDwDuBv2vmA/wBcFmzyoXA0f2oTZLGs371FP4n8Angd838jsCvquq5Zn4ImLqmDZPMSbIgyYLly5d3vVBJGk96HgpJ3gU8VlULN2T7qjq3qgaranDKlCmjXJ0kjW8T+nDMtwMzkhwJTAJeDpwNbJdkQtNbGACW9qE2SWPQQ6e9qd8ljBm7fvrOru6/5z2FqvpkVQ1U1TRgJvC9qvpj4PvA+5rVjgMu73VtkjTejaXfKfwl8PEk99G5xnBen+uRpHGnH6ePWlU1H5jfTN8PvLWf9UjSeDeWegqSpD4zFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktTqeSgk2SXJ95MsTnJ3ko827TskuS7Jvc379r2uTZLGu370FJ4D/ryq9gQOAE5KsidwCnB9Ve0BXN/MS5J6qOehUFXLqurHzfSTwBJgKnAUcGGz2oXA0b2uTZLGu75eU0gyDdgHuBXYqaqWNYseAXbqV12SNF71LRSSbAN8A/izqvrn4cuqqoBay3ZzkixIsmD58uU9qFSSxo++hEKSiXQC4eKq+oem+dEkOzfLdwYeW9O2VXVuVQ1W1eCUKVN6U7AkjRP9uPsowHnAkqr6H8MWXQEc10wfB1ze69okabyb0Idjvh34T8CdSRY1bZ8C/hq4NMls4OfA+/tQmySNaz0Phaq6CchaFh/ay1okSf+av2iWJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSa8yFQpLDk9yT5L4kp/S7HkkaT8ZUKCTZEvgb4AhgT2BWkj37W5UkjR9jKhSAtwL3VdX9VfUscAlwVJ9rkqRxY0K/C1jNVODhYfNDwNuGr5BkDjCnmf2XJPf0qLbN3qthMvB4v+sYE05NvyvQMP5tDjM6f5uvXtuCsRYK61VV5wLn9ruOzVGSBVU12O86pNX5t9k7Y+300VJgl2HzA02bJKkHxloo/AjYI8n0JFsBM4Er+lyTJI0bY+r0UVU9l+Q/A98BtgTOr6q7+1zWeOJpOY1V/m32SKqq3zVIksaIsXb6SJLUR4aCJKllKMihRTRmJTk/yWNJ7up3LeOFoTDOObSIxrgLgMP7XcR4YijIoUU0ZlXVDcAv+l3HeGIoaE1Di0ztUy2S+sxQkCS1DAU5tIiklqEghxaR1DIUxrmqeg5YNbTIEuBShxbRWJFkHvBD4HVJhpLM7ndNmzuHuZAktewpSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoaExJMm1NwyQnOS3JYevZdm6S/7Ixx2mWzU8yOLKK13mM45Ocs7H7kXppTD2jWVqbqvp0v2vohyQTmh8YblbH0thlT0Fj0ZZJ/jbJ3Um+m+SlSS5I8j6AJEcm+WmShUm+kOSqYdvu2XzTvz/JR9ZznAlJLk6yJMllSbZefYUks5LcmeSuJGeMoP2DSf4pyW3A29d18OYzfTnJgmabdzXtxye5Isn3gOuTvKx52MxtSW5PclSz3l5N26IkP0myR7Put5Pc0dR2bLPug0kmN9ODSeY303OTfDXJD4CvJpmS5BtJftS81vkZtPmxp6CxaA9gVlX9SZJLgT9atSDJJOB/A4dU1QPNMAjDvR74d8C2wD1JvlRVv13LcV4HzK6qHyQ5H/gwcOawY70KOAPYD/gl8N0kRwO3raX9VuAzTfsTwPeB29fzWafReabFa4DvJ9m9ad8XeHNV/SLJfwO+V1UnJNkOuC3J/wVOBM6uqoubcau2BI4E/l9VvbP5DK9Yz/Gh83Clg6rqqST/Bzirqm5Ksiud4U/eMIJ9aDNhT0Fj0QNVtaiZXkjnH+cqrwfur6oHmvnVQ+HbVfVMVT0OPAbstI7jPFxVP2im/x44aLXl+wPzq2p5c1rlYuCQdbS/bVj7s8DXRvBZL62q31XVvcD9zecDuK6qVj1c5g+BU5IsAuYDk4Bd6YwJ9Kkkfwm8uqqeAu4E/n2SM5IcXFVPjKCGK5ptAQ4DzmmOdQXw8iTbjGAf2kzYU9BY9Myw6ZXASzdi23X9ja8+8Fc/BgJbWw2/HtYW4I+q6p7V1l2S5FbgncDVSf60qr6XZF86PYbPJrm+qk4DnuP5L4GTVtvP8GNtARxQVU9v4OfRJs6egjY19wC7JZnWzB+7EfvaNcnvNdP/AbhpteW3Af82yeTmWdazgH9cR/utTfuOSSYCx4yghmOSbJHkNcBuzedb3XeAk5MEIMk+zftudHpNXwAuB97cnPL6TVX9PfB5OqehAB6kc1oLhp2OW4PvAievmknylhF8Bm1GDAVtUprTHB8Grk2yEHiSzvn7DXEPcFKSJcD2wJdWO9Yy4BQ61wbuABZW1eXraZ9L57TOD+gMRb4+D9EJmWuAE9fyDf2/AhOBnyS5u5kHeD9wV3Oq543ARcCb6FxzWAScCny2WfczwNlJFtDpQa3NR4DB5sL1YjrXLTSOOHS2NjlJtqmqf2m+Of8NcG9VndXvul6sJBcAV1XVZf2uRVrFnoI2RX/SfBO+G3gFnbuRJI0CewrarCXZEbh+DYsOraoVParhr3jh9YWvV9XpvTi+9GIYCpKklqePJEktQ0GS1DIUJEktQ0GS1Pr/v5pT5F2Yg7gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"high_blood_pressure\", data=df, hue=\"death\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e211fcef",
   "metadata": {},
   "source": [
    "### diabetes analysis with respect to anaemia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "30564e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='anaemia', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(x=\"anaemia\", data=df, hue=\"diabetes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6699b577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    174\n",
       "1    125\n",
       "Name: diabetes, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.diabetes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79922869",
   "metadata": {},
   "source": [
    "### split data into x (independent variable)  and  y (dependent variable/ target column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "349b992e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(\"death\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ce1554ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"death\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0163e576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: death, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8654ca35",
   "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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  75.0        0                       582         0                 20   \n",
       "1  55.0        0                      7861         0                 38   \n",
       "2  65.0        0                       146         0                 20   \n",
       "3  50.0        1                       111         0                 20   \n",
       "4  65.0        1                       160         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    1  265000.00               1.9           130    1   \n",
       "1                    0  263358.03               1.1           136    1   \n",
       "2                    0  162000.00               1.3           129    1   \n",
       "3                    0  210000.00               1.9           137    1   \n",
       "4                    0  327000.00               2.7           116    0   \n",
       "\n",
       "   smoking  time  \n",
       "0        0     4  \n",
       "1        0     6  \n",
       "2        1     7  \n",
       "3        0     7  \n",
       "4        0     8  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898a3211",
   "metadata": {},
   "source": [
    "### split data according to training and testing data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "317e045a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f2c3ab70",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, train_size=.70, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "88b9246a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain: (209, 12), xtest: (90, 12) \n",
      "ytrain: (209,),  ytest: (90,)\n"
     ]
    }
   ],
   "source": [
    "print(f\"xtrain: {xtrain.shape}, xtest: {xtest.shape} \\nytrain: {ytrain.shape},  ytest: {ytest.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16ba6081",
   "metadata": {},
   "source": [
    "### Apply dicision tree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6569a088",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b30771b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(xtrain, ytrain)  # train the model with 70% of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4ab7c442",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = dt.predict(xtest)  # pred is corresponding to ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1b1d8fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred   # predicted value of y for xtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8a42f299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np             # for converting dataframe into array\n",
    "np.array(ytest)   # actual value of y for xtest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3edd87e8",
   "metadata": {},
   "source": [
    "### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9f95003e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, plot_roc_curve, RocCurveDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "944f24ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.77      0.73        53\n",
      "           1       0.60      0.49      0.54        37\n",
      "\n",
      "    accuracy                           0.66        90\n",
      "   macro avg       0.64      0.63      0.63        90\n",
      "weighted avg       0.65      0.66      0.65        90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print( classification_report(ytest, pred) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6484a1ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6555555555555556\n"
     ]
    }
   ],
   "source": [
    "print( accuracy_score(ytest, pred) )  # over all accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9102beff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[41, 12],\n",
       "       [19, 18]], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_confusion_matrix = confusion_matrix(ytest, pred)   # confusion metrix\n",
    "\n",
    "test_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f1a14f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAD4CAYAAACt8i4nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAANFklEQVR4nO3dYYhl5X3H8e/Pda2CLZpG7Ga1aKNtsIWuVGyKFMQSak2pBkKJLUHahUmhgtK01eRNY2khgUbbFyUwicYtBI2YBMOStIhZCdJWs0k2G9dtibUpcdm4hCqJb4wz998Xc0wHd3bOnZ3zzL1z9vsJD5l7zr3PfV4sP//8z3POTVUhSWrnrFkvQJLGzqCVpMYMWklqzKCVpMYMWklq7OzWX/D6D15wW4NOct7bfnPWS9AcWvrxsWx2jo1kzs63/sKmv28azYNWkrbUZHnWKziJQStpXGoy6xWcxKCVNC4Tg1aSmqo5rGjddSBpXJaXph9TSLIjyTeT7O9eX57k6STPJ/lsknP65jBoJY3LZHn6MZ07gKOrXn8MuK+qrgBeBvb2TWDQShqXmkw/eiS5BHg38KnudYAbgEe7t+wDbumbx6CVNC6TydQjyUKSg6vGwptm+3vgL4E3UvlngVeq6o2+w4vA7r4leTFM0qhs5GJYVS0Ci2udS/K7wImq+nqS6zezJoNW0rgMt73rOuD3ktwEnAv8DPAPwAVJzu6q2kuAY30T2TqQNC7Lr08/1lFVH6qqS6rqMuB9wFeq6g+BA8B7u7fdBjzWtySDVtK4DHgx7BTuAv4syfOs9Gzv7/uArQNJ49LgzrCqehJ4svv7BeDajXzeoJU0LnN4Z5hBK2lcfNaBJLVVk/Uvcs2CQStpXKxoJakxe7SS1Ji/sCBJjVnRSlJj9mglqbEpH+i9lQxaSeNiRStJbVV5MUyS2rKilaTG3HUgSY1Z0UpSY+46kKTGbB1IUmO2DiSpMYNWkhqzdSBJjXkxTJIas3UgSY3ZOpCkxqxoJakxg1aSGqua9QpOYtBKGpcldx1IUlteDJOkxuawR3vWrBcgSYOqmn6sI8m5SZ5J8q0kR5Lc0x1/MMl/JznUjT19S7KilTQuw1W0rwE3VNWrSXYCTyX5cnfuL6rq0WknMmgljctAQVtVBbzavdzZjdPa0mDrQNKo1PLy1CPJQpKDq8bC6rmS7EhyCDgBPF5VT3en/jbJ4ST3JfmpvjVZ0Uoalw1UtFW1CCyuc34Z2JPkAuALSX4F+BDwfeCc7rN3AX+93vdY0Uoal5pMP6adsuoV4ABwY1UdrxWvAZ8Gru37vEEraVwmNf1YR5KLukqWJOcB7wL+I8mu7liAW4Bn+5Zk60DSuAy362AXsC/JDlaK0keqan+SryS5CAhwCPiTvokMWknjsrw8yDRVdRi4eo3jN2x0LoNW0rjM4Z1hBq2kcenpvc6CQStpXLbjQ2WSvAO4GdjdHToGfLGqjrZcmCSdljmsaNfd3pXkLuBhVq6uPdONAA8luXudz/3kbotP/dNDQ65XktZVk8nUY6v0VbR7gV+uqtdXH0xyL3AE+OhaH1p9t8XrP3hh/v7zImm8Btp1MKS+GxYmwNvWOL6rOydJ82WgGxaG1FfR3gk8keQ7wPe6Yz8PXAHc3nBdknR6ttv2rqr65yS/yMq9vKsvhn2te9iCJM2XObwY1rvroKomwL9vwVokafO24/YuSdpWtmNFK0nbSS3NX1fToJU0Lla0ktSYPVpJasyKVpLaKoNWkhrzYpgkNWZFK0mNGbSS1FaVQStJbVnRSlJjBq0ktVVL3rAgSW3NX84atJLGxRsWJKk1g1aSGrN1IElt2TqQpMZqaf6Ctu/nxiVpe5lsYKwjyblJnknyrSRHktzTHb88ydNJnk/y2STn9C3JoJU0KjWZfvR4Dbihqn4V2APcmOSdwMeA+6rqCuBlYG/fRAatpHEZqKKtFa92L3d2o4AbgEe74/uAW/qWZNBKGpWNVLRJFpIcXDUWVs+VZEeSQ8AJ4HHgv4BXqmqpe8uLwO6+NXkxTNKo/CQCp3lv1SKwuM75ZWBPkguALwDvOJ01GbSSRqXFbzNW1StJDgC/AVyQ5Oyuqr0EONb3eVsHkkZlqIthSS7qKlmSnAe8CzgKHADe273tNuCxvjVZ0Uoal8pQM+0C9iXZwUpR+khV7U/yHPBwkr8Bvgnc3zeRQStpVIZqHVTVYeDqNY6/AFy7kbkMWkmjUpPBKtrBGLSSRmWybNBKUlMtdh1slkEraVRsHUhSY3P4a+MGraRxsaKVpMa8GCZJjVnRSlJjNdydYYMxaCWNitu7JKmxiRWtJLVl60CSGnPXgSQ15q4DSWrMHq0kNWaPVpIa81kHktSYrQNJamzixTBJauuMrGj/+Nf+vPVXaBt698+d9Jt30iC8GCZJjZ2RFa0kbaU53HRg0Eoal+XJWbNewkkMWkmjModPSTRoJY1LYY9WkpqazGGT1qCVNCqTOaxo569rLEmbUGTqsZ4klyY5kOS5JEeS3NEd/0iSY0kOdeOmvjVZ0UoaleXhKtol4INV9Y0kPw18Pcnj3bn7qurvpp3IoJU0KkPtOqiq48Dx7u8fJTkK7D6duWwdSBqVyQZGkoUkB1eNhbXmTHIZcDXwdHfo9iSHkzyQ5MK+NRm0kkZlIz3aqlqsqmtWjcU3z5fkfOBzwJ1V9UPgE8DbgT2sVLwf71uTrQNJozLkUxKT7GQlZD9TVZ8HqKqXVp3/JLC/bx6DVtKoDLW9K0mA+4GjVXXvquO7uv4twHuAZ/vmMmgljcrycFNdB7wf+HaSQ92xDwO3JtnDyvNrvgt8oG8ig1bSqEwyTEVbVU/BmuXxlzY6l0EraVTm8A5cg1bSuPj0LklqbA5/m9GglTQuA96COxiDVtKoWNFKUmP2aCWpMXcdSFJjtg4kqTFbB5LU2LIVrSS1ZUUrSY0ZtJLUmLsOJKkxdx1IUmO2DiSpsQEf/D0Yg1bSqNg6kKTGbB1IUmPuOpCkxiZzGLUGraRR8WKYJDVmj1aSGnPXgSQ1Zo9Wkhqbv5g1aCWNjD1aSWpseQ5rWoNW0qhY0UpSY/N4MeysWS9AkoZUGxjrSXJpkgNJnktyJMkd3fG3JHk8yXe6/7+wb00GraRRmWxg9FgCPlhVVwHvBP40yVXA3cATVXUl8ET3el0GraRRWaamHuupquNV9Y3u7x8BR4HdwM3Avu5t+4Bb+tZk0EoalQk19UiykOTgqrGw1pxJLgOuBp4GLq6q492p7wMX963Ji2GSRmUjl8KqahFYXO89Sc4HPgfcWVU/TP7/Ht+qqiS9X2nQShqVIXcdJNnJSsh+pqo+3x1+KcmuqjqeZBdwom8eWweSRmWoi2FZKV3vB45W1b2rTn0RuK37+zbgsb41WdFKGpUarqK9Dng/8O0kh7pjHwY+CjySZC/wP8Dv90102kGb5I+q6tOnOLcALAD8+lv2cOX5l5/u10jShgx1C25VPQWc6qGLv7WRuTbTOrjnVCeqarGqrqmqawxZSVtpwH20g1m3ok1y+FSnmGJLgyRttUnN3y24fa2Di4HfBl5+0/EA/9pkRZK0CfMXs/1Bux84v6oOvflEkidbLEiSNmMeHyqzbtBW1d51zv3B8MuRpM0ZcNfBYNzeJWlUlgxaSWrLilaSGvMXFiSpsdqG27skaVvZdrsOJGm78VdwJakxK1pJaswerSQ15q4DSWrMfbSS1Jg9WklqbLnmr3lg0EoaFVsHktTYdnzwtyRtK/MXswatpJHxYpgkNWbQSlJj7jqQpMbcdSBJjfmsA0lqzB6tJDVmRStJjS3P4fO7zpr1AiRpSJOqqUefJA8kOZHk2VXHPpLkWJJD3bipbx6DVtKo1Ab+N4UHgRvXOH5fVe3pxpf6JrF1IGlUhnzWQVV9Ncllm53HilbSqGykok2ykOTgqrEw5dfcnuRw11q4sO/NBq2kUdlIj7aqFqvqmlVjcYqv+ATwdmAPcBz4eN8HbB1IGpXWt+BW1Utv/J3kk8D+vs9Y0UoalYEvhp0kya5VL98DPHuq977BilbSqNSAFW2Sh4DrgbcmeRH4K+D6JHtYefTtd4EP9M1j0EoalSFvwa2qW9c4fP9G5zFoJY2Kt+BKUmM+VEaSGluezN+zDgxaSaPig78lqTF7tJLUmD1aSWrMilaSGvNimCQ1ZutAkhqzdSBJjQ354O+hGLSSRsV9tJLUmBWtJDU2afzg79Nh0EoaFS+GSVJjBq0kNTZ/MQuZx/QfqyQLU/7Kps4g/rsYP3+ccWtN+5vxOrP472LkDFpJasyglaTGDNqtZR9Oa/Hfxch5MUySGrOilaTGDFpJasyg3SJJbkzyn0meT3L3rNej2UvyQJITSZ6d9VrUlkG7BZLsAP4R+B3gKuDWJFfNdlWaAw8CN856EWrPoN0a1wLPV9ULVfVj4GHg5hmvSTNWVV8F/nfW61B7Bu3W2A18b9XrF7tjks4ABq0kNWbQbo1jwKWrXl/SHZN0BjBot8bXgCuTXJ7kHOB9wBdnvCZJW8Sg3QJVtQTcDvwLcBR4pKqOzHZVmrUkDwH/BvxSkheT7J31mtSGt+BKUmNWtJLUmEErSY0ZtJLUmEErSY0ZtJLUmEErSY0ZtJLU2P8B6kNBSa7c2B4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test_confusion_matrix)  # 2 dimentin visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b8ec7163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test_confusion_matrix, annot=True)  # annot=True, to show numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8dccb802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([41, 12, 19, 18], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest, pred).reshape(-1)   # reshape array into 1 dimention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "39eb7082",
   "metadata": {},
   "outputs": [],
   "source": [
    "tp, fp, fn, tn = confusion_matrix(ytest, pred).reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5b8b37ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tp: 41, fp: 12, fn: 19, tn: 18\n"
     ]
    }
   ],
   "source": [
    "print(f\"tp: {tp}, fp: {fp}, fn: {fn}, tn: {tn}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "42d3408a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6555555555555556"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = (tp + tn) / (tp+tn+fp+fn)  # apply accuracy law\n",
    "\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "15d01c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we can able to findout precision, recall, f1-score etc matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2951b424",
   "metadata": {},
   "source": [
    "### ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2e516cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1acfce9bb20>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7cyb0jyG2df1Ah2fMWXytGpoOTBeRhsBI4K8iEqWq7f0anTGVyKmsXP61Ko3piR62HzhJ49rVuf/q9oy/NIomdSO8P4ExAVLaO4vbAZ2ANoC1ERgDpBzO4N3FHmYtT+FEZi7dW9bjxdE9GdKjOeGhNvWjqfh8bSN4DhgB7AA+BP6kqkf9GJcxFZqqsnjnIaYlePhm036qiTCoWzPu6h9N76gGNvWjqVR8vSLYAfRV1YP+DMaYiu50dh6frU4jPtHD5n0naFAzjF8ObMv4y9rQvF6NQIdnzHnxNkNZJ1XdDCwHotwxhs6wGcpMsNhz9DTvLk5m1vLdHM3IoVOzOjw3sgfDYlsQEWbVP6Zy83ZF8BAwieJnKrMZykyVpqos9xwhPnEX8zfsR1W5vkszJvSP5tKYhlb9Y6oMbzOUTXIf3qiqmYW3iYh1gzBVUmZOHv9es4f4RA8b9hynbkQoEy+P4ba+bWjVoGagwzOmzPnaRpAIFJ3Ivrh1xlRa+49n8v6SZD5YuptDp7Jp36Q2fx7RjRG9WlKzuk3dYaoub20EzYCWQA0R6YUzvARAXcC+GpkqYeXuI8QneJi3bi95qlzTqQl39o+hX9tGVv1jgoK3rzk3ABOAVsCLhdafAH7vp5iM8bvs3HzmrdvLtEQPa1KOUic8lDv6RXN73za0aVQr0OEZU668tREU3FE8UlU/KaeYjPGb9BNZzFiazIylu0k/kcVFjWsx5eau3NK7FbXDrfrHBCdvVUPjVfV9IFpEHiq6XVVfLGY3YyqctalHiU/w8PnavWTn5TOwYyQT+kUzoH0k1WzqRxPkvH0FKrhGrn0+Ty4ig4CXgRDgLVV9tpgyo4GncLqjrlHVn5zPsYwpKicvn6/W7yM+0cOK5CPUqh7CuD6tuaNfNBdFnteftDFVkreqodfd338s7RO7M5q9ClwHpALLRWSuqm4sVKY98CjQ353+sklpj2NMUYdPZTNz2W7eW5zMvuOZRDWsyRNDu3BrXCvqRoQFOjxjKpzSjDX0NHAa+AroATzoVhudSx9gu6rudJ9jFnAzsLFQmZ8Br6rqEQBVPVDqMzDGtXHPceITd/Hp6j1k5+ZzebvG/HlENwZ2bEKIVf8Yc06+to5dr6q/FZERgAe4BfgOKCkRtARSCi2nApcWKdMBQEQScKqPnlLVr4o+kYhMwrnDmaioqKKbTRDLzcvnm037mZbgYemuw9QIC+HWi1sxoV807ZvWCXR4xlQKviaCgnJDgNmqeqyM+leHAu2BgThdVL8Tke5FRzZV1TeANwDi4uK0LA5sKrejGdl8uDyFdxcnk3b0NC3r1+D3gzsxJi6KejWt+seY0vA1EXwuIptxqoZ+ISKRQKaXfdKA1oWWW7nrCksFlqpqDrBLRLbiJIblPsZlgszW/SeYluBhzqpUMnPyueyihjwxtAvXdm5CaIhN/WjM+fB1hrLJbjvBMVXNE5FTOPX9JVkOtBeRGJwEMBYo2iPoU2AcME1EGuNUFe0sRfwmCOTlKws2H2Ba4i4Sth8iPLQaw2Nbcke/aLq0qBvo8Iyp9HxtLA4DxgMD3CqhRcA/S9pHVXNF5D5gPk79/zuqukFEpgBJqjrX3Xa9iGwE8oBHVPXQeZ+NqVKOZ+bwkVv9s/twBs3qRvDIDR0Z1yeKhrWqBzo8Y6oMUfVe5S4ibwFhwHR31W1AnqpO9GNsxYqLi9OkpKTyPqwpRzvSTzI90cPHK1LJyM4jrk0D7uwfw/VdmxJm1T/GnBcRWaGqccVt87WN4BJV7Vlo+VsRWXPhoRnjyM9XFm1LJz7Bw6Kt6VQPqcbQns25s18M3VvVC3R4xlRpviaCPBFpq6o7AETkIpyqHGMuyMmsXD5Zkcr0RA87D54isk44D17bgZ9cGkVknfBAh2dMUPA1ETwCLBCRnThDUbcB7vRbVKbKSz50iumJycxOSuFEVi49W9fn5bGx3NitOdVDrfrHmPLkNRG4XUWP4dwpXDAExBZVzfJnYKbqUVUSth9iWsIuvt1ygBARhvRozoR+0fSKahDo8IwJWt5GH50I/AXYAcQAk9zePsb4LCM7l3+tTGN6oodtB07SqFZ17r+qHT+9rA1N69qMp8YEmrcrgl8DXVU13W0XmAFYIjA+STmcwXtLkpm1bDfHM3Pp2qIuf7u1J0N7NCciLCTQ4RljXN4SQbaqpgOo6k4RsdY7UyJVZcnOw8Qn7uLrjfsREQZ1bcaE/tHEtWlgUz8aUwF5SwStRGTquZZV9Vf+CctUNpk5eXy2Oo1pCR427ztB/Zph3HNlW267rA0t6tcIdHjGmBJ4SwSPFFle4a9ATOW05+hp3l+SzMxluzmSkUOnZnX468ju3Bzb0qp/jKkkfJmz2JgfUVVWJB9hWoKHrzbsQ1W5rktTJvSL4bKLGlr1jzGVjLdeQ28CL6vq+mK21QLGAFmqOsNP8ZkKJCs3j8/X7GVa4i7Wpx2nbkQod18ew22XtaF1w5qBDs8Yc568VQ29CjwpIt2B9UA6EIEzVHRd4B2cnkSmCjtwPJP3lyTzwbLdHDyZTbsmtXl6eDdu6d2SmtV9vSfRGFNReasaWg2MFpHaQBzQHGdOgk2qusX/4ZlAWrX7CPGJHr5Yu5c8Va7u2IQJ/aO5vF1jq/4xpgrxdT6Ck8BC/4ZiKoLs3Hy+XL+XaQkeVqccpXZ4KLf1bcMdfaOJblwr0OEZY/zArusNAOknspi5bDfvL0nmwIksYhrX4o/DujLy4lbUDrc/E2OqMvsPD3Lr044xLcHDv9fsITsvnwEdIvnrqGiubB9JtWpW/WNMMChVIhCRmqqa4a9gTPl64T9beOXb7dSsHsLYPq25vW807ZrUDnRYxphy5utUlf2At4DaQJSI9ATuUdVf+jM44z8/bDvIK99uZ3hsC/54czfq1QgLdEjGmADxdeD3l4AbgEMAqroGGOCvoIx/HT6VzUMfraZtZC2euaWHJQFjgpzPM4CoakqRVTZDWSWkqvz247Uczcjh5bG9qFHdhoEwJtj5mghS3OohFZEwEXkY2OTHuIyfzFi6m2827ee3gzrSraXNBWyM8T0R/By4F2gJpAGxgLUPVDLb9p/g6S82ckX7xtzVPybQ4RhjKghfew11VNWfFl4hIv2BhLIPyfhDVm4ev5q1mprVQ3nh1p7WNdQYc4avVwSv+LjOVFDPfbWFTXuP8/yoHjSx6SGNMYV4G320L9APiBSRhwptqgtYK2MlsXDLAd7+YRe3923DNZ2bBjocY0wF461qqDrOvQOhQJ1C648Do/wVlCk7B09m8fDstXRoWpvfD+4c6HCMMRWQt9FHFwGLRCReVZPLKSZTRlSVR2av4XhmDu9P7GMzhhljiuVrY3GGiDwPdMWZjwAAVb3aL1GZMvHu4mQWbEnnqZu60KlZ3UCHY4ypoHxtLJ4BbAZigD8CHmC5n2IyZWDzvuP8ed4mruoYyR39ogMdjjGmAvM1ETRS1beBHFVdpKp3AXY1UEFl5uTxwMzV1I0I4/lbe9okMsaYEvlaNZTj/t4rIkOAPUBD/4RkLtSzX25my/4TxN95CY1rhwc6HGNMBefrFcHTIlIP+A3wMM5IpL/2tpOIDBKRLSKyXUQml1BupIioiMT5GI85h2837yc+0cNd/WMY2LFJoMMxxlQCvk5V+bn78BhwFZy5s/icRCQEeBW4DkgFlovIXFXdWKRcHeABYGnpQjdFHTiRySOz19KpWR1+O6hjoMMxxlQSJV4RiEiIiIwTkYdFpJu7bqiIJAL/8PLcfYDtqrpTVbOBWcDNxZT7E/BXILP04ZsC+fnKw7PXcjIrl1fG9bKuosYYn3mrGnobmAg0AqaKyPvA34DnVLWXl31bAoWHrk51150hIr2B1qr6RUlPJCKTRCRJRJLS09O9HDY4TUv08N3WdB4f2oX2Tet438EYY1zeqobigB6qmi8iEcA+oK2qHrrQA4tINeBFYIK3sqr6BvAGQFxcnF7osauaDXuO8dcvN3Nt56aMvzQq0OEYYyoZb1cE2aqaD6CqmcDOUiSBNKB1oeVW7roCdYBuwEIR8QCXAXOtwbh0Tmfn8cCs1dSvGcZzo3pYV1FjTKl5uyLoJCJr3ccCtHWXBVBV7VHCvsuB9iISg5MAxgI/KdioqseAxgXLIrIQeFhVk0p9FkHs6S82sv3ASd67uw8Na1UPdDjGmErIWyI471HKVDVXRO4D5uOMVPqOqm4QkSlAkqrOPd/nNo7/bNjHjKW7mTTgIq5oHxnocIwxlZS3QecuaKA5VZ0HzCuy7slzlB14IccKNvuPZ/K7T9bStUVdHr7euooaY86fz5PXm4ojP1956KPVZObkM3VcL6qH2ttojDl/9glSCb31w04Sth/iyZu60DaydqDDMcZUcj4nAhGpISJWBxFg69OO8fz8LQzq2oyxl7T2voMxxnjhUyIQkZuA1cBX7nKsiFhjbznLyM7lVzNX0ahWOM+O7G5dRY0xZcLXK4KncIaMOAqgqqtx5iYw5WjKvzey69ApXhzTk/o1rauoMaZs+JoIctx+/4XZHb7l6Mt1e5m1PIWfX9mWfm0be9/BGGN85Ot8BBtE5CdAiIi0B34FJPovLFPYnqOnmfyvdfRsVY+HrusQ6HCMMVWMr1cE9+PMV5wFfIAzHPWv/RSTKSTP7Sqak5fPy2N7ERZiHb2MMWXL1yuCTqr6GPCYP4MxZ/vnoh0s2XmY50b1ILpxrUCHY4ypgnz9evmCiGwSkT8VzEtg/G91ylFe+norQ3o059aLWwU6HGNMFeVTIlDVq3BmJksHXheRdSLyuF8jC3Ins3J5YNYqmtQJ5y/DrauoMcZ/fK5wVtV9qjoV+DnOPQXFjhlkysZTczeQcjiDv4/tRb2aYYEOxxhThfl6Q1lnEXlKRNYBr+D0GLK6Cj/595o9fLwilXuvakefmIaBDscYU8X52lj8DvAhcIOq7vFjPEEv9UgGv5+zjl5R9fnVNe0DHY4xJgj4lAhUta+/AzFOV9EHP1yNKrw8xrqKGmPKR4mJQEQ+UtXRbpVQ4TuJfZmhzJTSqwu2s9xzhJfG9CSqUc1Ah2OMCRLerggecH8P9XcgwW5F8hFe/u82bo5twYhe1vxijCk/JdY9qOpe9+EvVTW58A/wS/+HFxxOZObw6w9X0bxeBH8abrdpGGPKl6+V0NcVs+7GsgwkmD352Qb2HM3k5bGx1I2wrqLGmPLlrY3gFzjf/C8SkbWFNtUBEvwZWLD4dFUac1al8eC1Hbi4jXUVNcaUP29tBB8AXwLPAJMLrT+hqof9FlWQ2H0og8c/XU9cmwbce1XbQIdjjAlS3hKBqqpHRO4tukFEGloyOH+5efn8+sNVCPDSmFhCrauoMSZAfLkiGAqswOk+WnjAGwUu8lNcVd7Ub7ezcvdRpo7rReuG1lXUGBM4JSYCVR3q/rZpKcvQcs9h/vHtNm7p3ZJhPVsEOhxjTJDzdayh/iJSy308XkReFJEo/4ZWNR07ncOvZ62mVYOaTLnZuooaYwLP14rp14AMEekJ/AbYAbznt6iqKFXlsTnr2Hfc6SpaO9zXoZ6MMcZ/fE0EuaqqwM3AP1T1VZwupKYUPlmZxudr9/LQdR3oFdUg0OEYYwzg++ijJ0TkUeA24AoRqQbYnU+l4Dl4ij98tp5LYxry8yutq6gxpuLw9YpgDM7E9Xep6j6cuQie91tUVUxOXj4PzFpFSDXhpTGxhFSz2caMMRWHr1NV7gNmAPVEZCiQqarv+jWyKuTv32xlTeoxnh3Zgxb1awQ6HGOM+RFfew2NBpYBtwKjgaUiMsqH/QaJyBYR2S4ik4vZ/pCIbBSRtSLyXxFpU9oTqOgW7zjE/y3cwZi41gzu3jzQ4RhjzFl8bSN4DLhEVQ8AiEgk8A3w8bl2EJEQ4FWcAetSgeUiMldVNxYqtgqIU9UMd1yj53CqoaqEoxnZPPTRaqIb1eLJm7oEOhxjjCmWr20E1QqSgOuQD/v2Abar6k5VzQZm4fQ6OkNVF6hqhru4hCo0D7Kq8vs56zh4MoupY3tRy7qKGmMqKF8/nb4SkfnATHd5DDDPyz4tgZRCy6nApSWUvxtngLuziMgkYBJAVFTluI/to6QU5q3bx+QbO9G9Vb1Ah2OMMefk65zFj4jILcDl7qo3VHVOWQUhIuOBOODKcxz/DeANgLi4OC2uTEWyI/0kT83dSL+2jZh0hQ3HZIyp2LzNR9Ae+BvQFlgHPKyqaT4+dxrQutByK3dd0WNci9MGcaWqZvn43BVWdm4+v561mvCwarw4OpZq1lXUGFPBeavnfwf4HBiJMwLpK6V47uVAexGJEZHqwFhgbuECItILeB0YVqQNotJ64estrEs7xl9H9qBZvYhAh2OMMV55qxqqo6pvuo+3iMhKX59YVXNF5D5gPhACvKOqG0RkCpCkqnNxbkqrDcwWEYDdqjqs1GdRQSRsP8jri3byk0ujuKFrs0CHY4wxPvGWCCLcb+0F9Rs1Ci+raomJQVXnUaRRWVWfLPT42lJHXEEdPuV0FW0bWYsnhlhXUWNM5eEtEewFXiy0vK/QsgJX+yOoykZV+d0nazlyKoe377iEGtVDAh2SMcb4zNvENFeVVyCV2QfLdvP1xv08PqQz3VpaV1FjTOViE+VeoO0HTvCnzzdyRfvG3NXfJnIzxlQ+lgguQFZuHvfPXE3N6qG8cGtP6ypqjKmUbNyDC/D8V1vYtPc4b98RR5O61lXUGFM5+Tr6qLhzFT/pLkeJSB//hlaxfbc1nbd+2MXtfdtwTeemgQ7HGGPOm69VQ/8H9AXGucsncEYWDUoHT2bx0Edr6NC0Nr8f3DnQ4RhjzAXxtWroUlXtLSKrAFT1iHu3cNBRVX738VqOZ+bw/sQ+RIRZV1FjTOXm6xVBjju/gMKZ+Qjy/RZVBfbekmT+u/kAv7+xE52a1Q10OMYYc8F8TQRTgTlAExH5M/AD8Be/RVVBbdl3gqe/2MRVHSO5o190oMMxxpgy4esw1DNEZAVwDc7wEsNVdZNfI6tgMnPy+NXMVdSNCOP5W3vijo1kjDGVnk+JQESigAzg34XXqepufwVW0Tz75Wa27D9B/J2X0Lh2eKDDMcaYMuNrY/EXOO0DAkQAMcAWoKuf4qpQvt28n/hED3f2j2ZgxyaBDscYY8qUr1VD3Qsvi0hv4Jd+iaiCOXAik0dmr6VTszr8blCnQIdjjDFl7ryGmHCHny5p/uEqIT9feWT2Wk5m5fLKuF7WVdQYUyX52kbwUKHFakBvYI9fIqpA4hM9LNqazp+Gd6N90zqBDscYY/zC1zaCwp+CuThtBp+UfTgVx8Y9x3n2y81c27kp4y+NCnQ4xhjjN14TgXsjWR1Vfbgc4qkQTmfn8cCsVdSvGcZzo3pYV1FjTJVWYhuBiISqah7Qv5ziqRD+PG8j2w6c5IXRPWlYKyhH0jDGBBFvVwTLcNoDVovIXGA2cKpgo6r+y4+xBcTXG/fz/pLdTBpwEVe0jwx0OMYY43e+thFEAIdw5iguuJ9AgSqVCPYfz+S3H6+ha4u6PHx9x0CHY4wx5cJbImji9hhaz/8SQAH1W1QBkJ+v/OajNWTm5DN1XC+qh9rkbca/cnJySE1NJTMzM9ChmCokIiKCVq1aERYW5vM+3hJBCFCbHyeAAlUqEbz1w05+2H6QZ27pTtvI2oEOxwSB1NRU6tSpQ3R0tHVIMGVCVTl06BCpqanExPg+h7q3RLBXVadcWGgV3/q0Yzw/fwuDujZj7CWtAx2OCRKZmZmWBEyZEhEaNWpEenp6qfbzVv9R5f9CM7Jz+dWsVTSqFc6zI7vbP6UpV/b3Zsra+fxNebsiuOb8Qqk8/vT5RnYdPMWMiZdSv6Z1FTXGBJ8SrwhU9XB5BRIIX63fy8xlKfz8yrb0a9s40OEYU+727dvH2LFjadu2LRdffDGDBw9m69ateDweunXrVmbHefLJJ/nmm28A+P777+natSuxsbGkpaUxatSoC3puVeXqq6/m+PHjZ9Z9+umniAibN28+s27hwoUMHTr0R/tOmDCBjz/+GHAa7ydPnkz79u3p3bs3ffv25csvv7yg2ACeeeYZ2rVrR8eOHZk/f/45z+Gxxx6jQ4cOdO7cmalTpwLw2Wef0aNHD2JjY4mLi+OHH34AID09nUGDBl1wbAV87T5a5ew9dprffbKOHq3q8eC1HQIdjjHlTlUZMWIEd9xxB7NmzQJgzZo17N+/n9aty7atbMqU/zU1zpgxg0cffZTx48cDnPkg9kVubi6hoT/+2Jo3bx49e/akbt3/TR07c+ZMLr/8cmbOnMkf//hHn577iSeeYO/evaxfv57w8HD279/PokWLfI6tOBs3bmTWrFls2LCBPXv2cO2117J161ZCQn48gGV8fDwpKSls3ryZatWqceDAAQCuueYahg0bhoiwdu1aRo8ezebNm4mMjKR58+YkJCTQv/+F3+8blIkgL1956MM15OTl8/JY6ypqAu+P/97Axj3HvRcshS4t6vKHm849ZciCBQsICwvj5z//+Zl1PXv2BMDj8ZxZ5/F4uO222zh1yrmX9B//+Af9+vVj7969jBkzhuPHj5Obm8trr71Gv379uPvuu0lKSkJEuOuuu3jwwQeZMGECQ4cO5ejRo3z00UfMnz+fL7/8kj//+c8MHTqU9evXk5eXx+TJk1m4cCFZWVnce++93HPPPSxcuJAnnniCBg0asHnzZrZu3fqj85gxYwaTJk06s3zy5El++OEHFixYwE033eRTIsjIyODNN99k165dhIc7E081bdqU0aNHe3+hS/DZZ58xduxYwsPDiYmJoV27dixbtoy+ffv+qNxrr73GBx98QLVqzmdRkybOvCe1a/+vB+OpU6d+VP8/fPhwZsyYYYngfL3+3Q4W7zzEc6N6ENO4VqDDMSYg1q9fz8UXX+y1XJMmTfj666+JiIhg27ZtjBs3jqSkJD744ANuuOEGHnvsMfLy8sjIyGD16tWkpaWxfv16AI4ePfqj55o4cSI//PADQ4cOZdSoUT9KOG+//Tb16tVj+fLlZGVl0b9/f66//noAVq5cyfr164vtEpmQkMDrr79+Zvmzzz5j0KBBdOjQgUaNGrFixQqv57l9+3aioqJ+dFVxLg8++CALFiw4a/3YsWOZPHnyj9alpaVx2WWXnVlu1aoVaWlpZ+27Y8cOPvzwQ+bMmUNkZCRTp06lffv2AMyZM4dHH32UAwcO8MUXX5zZJy4ujscff9xrvL4IukSwJuUoL/5nK0N6NOfWi1sFOhxjAEr85h5oOTk53HfffaxevZqQkJAz38gvueQS7rrrLnJychg+fDixsbFcdNFF7Ny5k/vvv58hQ4ac+SD3xX/+8x/Wrl17pqro2LFjbNu2jerVq9OnT59z9os/fPgwder8b4DkmTNn8sADDwDOh/PMmTO5+OKLz9mbprS9bF566aVSlfdFVlYWERERJCUl8a9//Yu77rqL77//HoARI0YwYsQIvvvuO5544okzbS1NmjRhz56ymQ3Ar3UiIjJIRLaIyHYRmVzM9nAR+dDdvlREov0Zz6msXB6YtYomdcL5y3DrKmqCW9euXVmxYoXXci+99BJNmzZlzZo1JCUlkZ2dDcCAAQP47rvvaNmyJRMmTODdd9+lQYMGrFmzhoEDB/LPf/6TiRMn+hyPqvLKK6+wevVqVq9eza5du84kklq1zn3lHhoaSn5+PuAkhW+//ZaJEycSHR3N888/z0cffYSq0qhRI44cOfKjfQ8fPkzjxo1p164du3fv/lGD87k8+OCDxMbGnvXz7LPPnlW2ZcuWpKSknFlOTU2lZcuWZ5Vr1aoVt9xyC+B88K9du/asMgMGDGDnzp0cPHgQcO5DqVGjhtd4feG3ROAOX/0qcCPQBRgnIl2KFLsbOKKq7YCXgL/6Kx6Ap+ZuYPfhDP4+thf1avp++7UxVdHVV19NVlYWb7zxxpl1a9euPfNNtMCxY8do3rw51apV47333iMvLw+A5ORkmjZtys9+9jMmTpzIypUrOXjwIPn5+YwcOZKnn36alStX+hzPDTfcwGuvvUZOTg4AW7duPdMuUZKOHTuyc+dOwGl4vu2220hOTsbj8ZCSkkJMTAzff/897du3Z8+ePWzatOlM/GvWrCE2NpaaNWty991388ADD5xJdOnp6cyePfus47300ktnklXhn6LVQgDDhg1j1qxZZGVlsWvXLrZt20afPn3OKjd8+PAz1U2LFi2iQwenA8v27dtRdQZxWLlyJVlZWTRq1OjM61NWPbv8eUXQB9iuqjtVNRuYBdxcpMzNwHT38cfANeKnr+mfr93D7BWp3HtVO/rENPTHIYypVESEOXPm8M0339C2bVu6du3Ko48+SrNmzX5U7pe//CXTp0+nZ8+ebN68+cy384ULF9KzZ0969erFhx9+yAMPPEBaWhoDBw4kNjaW8ePH88wzz/gcz8SJE+nSpQu9e/emW7du3HPPPeTm5nrdb8iQISxcuBBwqoVGjBjxo+0jR45k5syZhIeH8/7773PnnXcSGxvLqFGjeOutt6hXrx4ATz/9NJGRkXTp0oVu3boxdOhQn9oMStK1a1dGjx5Nly5dGDRoEK+++uqZHkODBw8+U7UzefJkPvnkE7p3786jjz7KW2+9BcAnn3xCt27diI2N5d577+XDDz88U5OxYMEChgwZckHxFZCCbFPWRGQUMEhVJ7rLtwGXqup9hcqsd8ukuss73DIHizzXJGASQFRU1MXJycmljuf7bem8tziZV3/am7AQ6yVkAm/Tpk107tw50GFUenv37uX222/n66+/DnQo5WrAgAF89tlnNGjQ4Kxtxf1ticgKVY0r7rkqRWOxqr4BvAEQFxd3XpnrivaRNr+AMVVQ8+bN+dnPfsbx48cv+Bt8ZZGens5DDz1UbBI4H/5MBGlA4btSWrnriiuTKiKhQD2ceQ+MMcZnF9rfv7KJjIxk+PDhZfZ8/qwjWQ60F5EYEakOjAXmFikzF7jDfTwK+Fb9VVdlTAVkf+6mrJ3P35TfEoGq5gL3AfOBTcBHqrpBRKaIyDC32NtAIxHZDjwEnN3sbkwVFRERwaFDhywZmDJTMB9BREREqfbzW2Oxv8TFxWlSUlKgwzDmgtkMZcYfzjVDWaVvLDamKgoLCyvVLFLG+Iv1ozTGmCBnicAYY4KcJQJjjAlyla6xWETSgdLfWuxoDBz0WqpqsXMODnbOweFCzrmNqhZ7V22lSwQXQkSSztVqXlXZOQcHO+fg4K9ztqohY4wJcpYIjDEmyAVbInjDe5Eqx845ONg5Bwe/nHNQtREYY4w5W7BdERhjjCnCEoExxgS5KpkIRGSQiGwRke0ictaIpiISLiIfutuXikh0AMIsUz6c80MislFE1orIf0WkTSDiLEvezrlQuZEioiJS6bsa+nLOIjLafa83iMgH5R1jWfPhbztKRBaIyCr373twIOIsKyLyjogccGdwLG67iMhU9/VYKyK9L/igqlqlfoAQYAdwEVAdWAN0KVLml8A/3cdjgQ8DHXc5nPNVQE338S+C4ZzdcnWA74AlQFyg4y6H97k9sApo4C43CXTc5XDObwC/cB93ATyBjvsCz3kA0BtYf47tg4EvAQEuA5Ze6DGr4hVBH2C7qu5U1WxgFnBzkTI3A9Pdxx8D10jBjNCVk9dzVtUFqprhLi7BmTGuMvPlfQb4E/BXoCqM9ezLOf8MeFVVjwCo6oFyjrGs+XLOChTMUVkP2FOO8ZU5Vf0OOFxCkZuBd9WxBKgvIs0v5JhVMRG0BFIKLae664oto84EOseARuUSnX/4cs6F3Y3zjaIy83rO7iVza1X9ojwD8yNf3ucOQAcRSRCRJSIyqNyi8w9fzvkpYLyIpALzgPvLJ7SAKe3/u1c2H0GQEZHxQBxwZaBj8ScRqQa8CEwIcCjlLRSnemggzlXfdyLSXVWPBjIoPxsHxKvqCyLSF3hPRLqpan6gA6ssquIVQRrQutByK3ddsWVEJBTncvJQuUTnH76cMyJyLfAYMExVs8opNn/xds51gG7AQhHx4NSlzq3kDca+vM+pwFxVzVHVXcBWnMRQWflyzncDHwGo6mIgAmdwtqrKp//30qiKiWA50F5EYkSkOk5j8NwiZeYCd7iPRwHfqtsKU0l5PWcR6QW8jpMEKnu9MXg5Z1U9pqqNVTVaVaNx2kWGqWplnufUl7/tT3GuBhCRxjhVRTvLMcay5ss57wauARCRzjiJIL1coyxfc4Hb3d5DlwHHVHXvhTxhlasaUtVcEbkPmI/T4+AdVd0gIlOAJFWdC7yNc/m4HadRZmzgIr5wPp7z80BtYLbbLr5bVYcFLOgL5OM5Vyk+nvN84HoR2QjkAY+oaqW92vXxnH8DvCkiD+I0HE+ozF/sRGQmTjJv7LZ7/AEIA1DVf+K0gwwGtgMZwJ0XfMxK/HoZY4wpA1WxasgYY0wpWCIwxpggZ4nAGGOCnCUCY4wJcpYIjDEmyFkiCAIikiciqwv9RJdQ9mQZHC9eRHa5x1rp3u1Z2ud4S0S6uI9/X2Rb4oXG6D5PweuyXkT+LSL1vZSPPZ+RLUWkuYh87j4eKCLH3ONuEpE/nMfzDSsYhVNEhhe8Tu7yFPfGwQvivoejvJRZWJob9Nxz/9yHcsWOvikifxORq309nvGdJYLgcFpVYwv9eMrhmI+oaiwwGedGtlJR1YmqutFd/H2Rbf0uPDzgf69LN5z7Se71Uj4Wp/92aT0EvFlo+Xv3tYnDGSOnVMMIq+pcVX3WXRyOM+JmwbYnVfWb84ixIokHihsj6RWcvydTxiwRBCERqS3OnAQrRWSdiJw1aqf7Lfa7Qt+Yr3DXXy8ii919Z4tIbS+H+w5o5+77kPtc60Xk1+66WiLyhYiscdePcdcvFJE4EXkWqOHGMcPddtL9PUtEhhSKOV5ERolIiIg8LyLLxRmv/R4fXpbFuAN3iUgf9xxXiUiiiHR072qdAoxxYxnjxv6OiCxzyxY3+inASOCroitV9RSwAmjnXm0sceOdIyIN3Fh+Jf+bR2KWu26CiPxDRPoBw4Dn3ZjaFnoNBonI7EKvzZlv46V9D0XkSfe1XC8ib4j8aKTe2wr9jfRxy/v6uhTrXKNvqmoy0EhEmpXm+YwPAjHetv2U7w/OHaar3Z85OHeU13W3Nca5Q7Hg5sKT7u/fAI+5j0Nwxu5pjPPBXstd/zvgyWKOFw+Mch/fCiwFLgbWAbVw7nDeAPTC+ZB8s9C+9dzfC3HnDyiIqVCZghhHANPdx9VxRmSsAUwCHnfXhwNJQEwxcZ4sdH6zgUHucl0g1H18LfCJ+3gC8I9C+/8FGO8+ro8zrk+tIseIAVYUWh4IfO4+bgR4gK7AWuBKd/0U4O/u4z1AeMExisZR+LUuvOy+x7sLvVevAePP8z1sWGj9e8BNhd6jN93HA3DHzz/X61Lk3OOAt0r4m42mmPH4ca6sRgb6f6qq/VS5ISZMsU6rUxUBgIiEAX8RkQFAPs434abAvkL7LAfecct+qqqrReRKnGqIBPdLYXWcb9LFeV5EHscZ8+VunLFg5qjzLRgR+RdwBc435RdE5K84HxLfl+K8vgReFpFwnKqE71T1tIhcD/QoVMddD2fgtV1F9q8hIqvd898EfF2o/HQRaY8zZEHYOY5/PTBMRB52lyOAKPe5CjTn7HFvrhCRVTiv/bM4A8XVV9VF7vbpOIkJnAQxQ0Q+xRlHyCfqDM3wFXCTiHwMDAF+izPqrK/vYYGrROS3QE2gIU4S/7e7baZ7vO9EpK447Sznel0Kx5cETPT1fAo5ALQ4j/1MCSwRBKefApHAxaqaI87onBGFC7j/2ANwPkDiReRF4AjwtaqO8+EYj6jqxwULInJNcYVUdatbRz4YeFpE/quqU3w5CVXNFJGFwA3AGJxJS8CZuel+VZ3v5SlOq2qsiNTEGcvmXmAqzmQ2C1R1hDgN6wvPsb/gfDvdUtIxKPLa4rQRDD3zJCL1Sth/CM637ZuAx0Skewlli5oF3IdTzZKkqifcah1f30NEJAL4P5yrsxQReYofn0/RMWqUc7wuItK0FLGfSwTOa2rKkLURBKd6wAE3CVwFnDV/sThzGu9X1TeBt3CmzlsC9BeRgjr/WiLSwcdjfg8MF5GaIlILp1rnexFpAWSo6vs4A+MV13Ca416ZFOdDnEG3Cq4uwPlQ/0XBPiLSwT1msdSZue1XwG/kf8OSFwzrO6FQ0RM4VWQF5gP3F9SZizPCa1Fbcao5zklVjwFHxG2HAW4DFokzp0JrVV2AU4VTD6darbCiMRW2COf1/Bn/S5KlfQ8LPvQPum0JRXsSFbTpXI4zCuYxfHtdzlcHoNi5fM35s0QQnGYAcSKyDrgd2FxMmYHAGrcKYwzwsqqm43wwzhSRtThVCp18OaCqrsSpd16G02bwlqquAroDy9wqmj8ATxez+xvAWnEbi4v4D051xzfqTGUITuLaCKwUpwvi63i5+nVjWYszyclzwDPuuRfebwHQpaCxGOfKIcyNbYO7XPR5TwE7Cj54S3AHTnXaWpzeSVNw2i7ed9+nVcBUPXuCmVnAI26jbNsix84DPgdudH9T2vfQPd6bOB++83GqDAvLdF+nf+JUAYIPr4s4HQHeKu6Y4oy+uRjoKCKpInK3uz4Mp+NBZR5KvEKy0UeN8TMRGYFTDfd4oGOpzNzXsbeqPhHoWKoaayMwxs9UdY6IVOY5sSuKUOCFQAdRFdkVgTHGBDlrIzDGmCBnicAYY4KcJQJjjAlylgiMMSbIWSIwxpgg9/95kZjQV3j+HQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot_roc_curve(dt, xtest, ytest)   # works with it's own style;  plot_roc_curve is deprecated\n",
    "\n",
    "RocCurveDisplay.from_predictions(ytest, pred)   # we can use from_predictions() or from_estimator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "02c90764",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "efd563b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1acfcfe76d0>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# plot_roc_curve(dt, xtest, ytest)  ;  plot_roc_curve is deprecated\n",
    "\n",
    "RocCurveDisplay.from_estimator(dt, xtest, ytest)      # we can use from_predictions() or from_estimator()\n",
    "plt.plot([0,1],[0,1],linestyle='dashed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "03f016d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7735849056603774"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Positive Predicted Value ( PPV )\n",
    "PPV = tp / (tp+fp)\n",
    "\n",
    "PPV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e738dd0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}