KNN_for_heart_dis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN for heart-disease\n",
    "\n",
    "### The aim is to Build Heart disease classifier using K-NN algorithm  which will predict the presence of heart disease in a patient or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset contains following features:\n",
    "* age — age in years \n",
    "* sex — (1 = male; 0 = female) \n",
    "* cp — chest pain type \n",
    "* trestbps — resting blood pressure (in mm Hg on admission to the hospital) \n",
    "* chol — serum cholestoral in mg/dl \n",
    "* fbs — (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) \n",
    "* restecg — resting electrocardiographic results \n",
    "* thalach — maximum heart rate achieved \n",
    "* exang — exercise induced angina (1 = yes; 0 = no) \n",
    "* oldpeak — ST depression induced by exercise relative to rest \n",
    "* slope — the slope of the peak exercise ST segment \n",
    "* ca — number of major vessels (0–3) colored by flourosopy \n",
    "* thal — 3 = normal; 6 = fixed defect; 7 = reversable defect \n",
    "* target — have disease or not (1=yes, 0=no)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets start by import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>63</td>\n",
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       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      "age         303 non-null int64\n",
      "sex         303 non-null int64\n",
      "cp          303 non-null int64\n",
      "trestbps    303 non-null int64\n",
      "chol        303 non-null int64\n",
      "fbs         303 non-null int64\n",
      "restecg     303 non-null int64\n",
      "thalach     303 non-null int64\n",
      "exang       303 non-null int64\n",
      "oldpeak     303 non-null float64\n",
      "slope       303 non-null int64\n",
      "ca          303 non-null int64\n",
      "thal        303 non-null int64\n",
      "target      303 non-null int64\n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
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       "      <th>exang</th>\n",
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       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the missing value in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Let's explore the dataset and count the number of patients who have the disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means 165 patients have heart disease. So we can visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe196610ed0>"
      ]
     },
     "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='target', data = data, palette = 'bwr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's classify target variable between male and female and visualize the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "sns.countplot(x='sex', data=data, palette=\"mako_r\")\n",
    "plt.xlabel(\"Sex (0 = female, 1= male)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sex.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So from the above figure, it is evident that in our dataset, 207 males and 96 females are there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('age', 'frequency')\n",
      "58    19\n",
      "57    17\n",
      "54    16\n",
      "59    14\n",
      "52    13\n",
      "51    12\n",
      "62    11\n",
      "44    11\n",
      "60    11\n",
      "56    11\n",
      "64    10\n",
      "41    10\n",
      "63     9\n",
      "67     9\n",
      "55     8\n",
      "45     8\n",
      "42     8\n",
      "53     8\n",
      "61     8\n",
      "65     8\n",
      "43     8\n",
      "66     7\n",
      "50     7\n",
      "48     7\n",
      "46     7\n",
      "49     5\n",
      "47     5\n",
      "39     4\n",
      "35     4\n",
      "68     4\n",
      "70     4\n",
      "40     3\n",
      "71     3\n",
      "69     3\n",
      "38     3\n",
      "34     2\n",
      "37     2\n",
      "77     1\n",
      "76     1\n",
      "74     1\n",
      "29     1\n",
      "Name: age, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# I want to learn frequency of ages\n",
    "print(\"age\",\"frequency\")\n",
    "print(data.age.value_counts(dropna=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe196398990>"
      ]
     },
     "execution_count": 35,
     "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": [
    "data.boxplot(column='age', by='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see on the boxplot, we haven't got any outlier value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Let's alos see the relation between 'Maximum Heart Rata' and 'Age'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=data.age[data.target==1], y=data.thalach[(data.target==1)])#, c= \"bleu\")\n",
    "plt.scatter(x=data.age[data.target==0], y=data.thalach[(data.target==0)], c = \"red\")\n",
    "plt.legend([\"Disease\", \"Not Disease\"])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Maximum Heart Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e65/2IeC7/ufv+dv4x+8P8j8oilIZdPZz/VCJiXKfBT4pIs/h+Rju9PffCSzz938S+FwFyqYoSh40Wql+KGkUU6nRKCZFqQwarVTbVEsUk6IoixCNVqoPVEAoihIJjVZa/GiyPkVRFMWKCghFURTFigoIRVEUxYoKCEVRFMWKCghFURTFigoIRVEUxYoKCEVRFMWKCghFUSrOZHKSQ2OHNOFflaET5RRFqShBqcOVyqIjCEVRKoamDq9uAkcQIrIHy6I9eIv7GGPM2SUplaLUGVGWFq215Uht2FKHHzt+jJHpkZqt02Iin4npyrKUQlHqmCgmlsVilslOHd6WaNPU4VVGoInJGPNi+g+YBgb8v+P+PkVRFkAUE8tiMsu0JlrZdP4mJpITHBg9wERygk3nb9LRQ5VQkJNaRN4HfAl4AM+89Nci8mljzN0lLJuiLHqimFgWm1lGU4dXL4VGMX0eON8YMwwgIiuAfwJUQCjKAohiYlmMZhlNHV6dFBrFFEsLB58jIb6rKIqDtInl2PQxnn75aY5NH8trYlGzjFIuCh1B/EhEfgz8vb/9fuCHpSmSshhYDBE25cJgvFjB9F8BBJlltO2VYlHwmtQicjVwIZ4P4iFjzL2lLFgh6JrU1cliibApB5PJSTbft5m2RFvGXDSRnODmy26O1Llr2yuFUOia1AWbiYwx9xhjPmmM+Q/VIByU6mQxRdiUA5vDeXZ+lpHpkdDX0rZXik1BAkJE3iMiz4rIqIiMici4iIyVunBK7VHMDq8eyHY4A6Eczrn5i6q97TXfUu1RqA/iL4GrjDFPlbIwSu2zGCNsSkna4bxt5zaOHT+WMQvlMy/ZTElrutdUbdur6as2KcgHISI/M8ZcWIbyhEJ9ENXJnqE9bNu5TTuDEIRxLAf5LfYd21fUti9GCpBi+1mUhVOoDyJfLqb3+B93ici3gH8EZtLHjTHfWVAplUWJTnwqLUET5YrZ9sVKAbJ0ydJFNbGvnshnYrrK/2+AKeCyrGMGUAGhWKmHiU/FCicN2xHnM+MFtX2hZc52eKfvsW3ntkCt3/WdrRu3Em+Ic+z4MRpiDcyn5qvG9KUEEyggjDHXAIjIXcDHjTEj/nY3cHPpi6co1UmxbOpROuJi+i1cZS5mCpDZ1CyXrrmU63dcz1xqjsZYIzdefGPVKBA6b8RNoU7qs9PCAcAYc0xE1peoTEqJ0R/EwojSqbuImlcprCkpbJmLmQIkHouzfd92Ll1zaWYEsX3fdq5Ye0XF3z91ngdTcKoNf9QAgIgsRVejq0l2D+1m832b2bJjC5vv28yeoT2VLlLNUcxw0oWEubYmWlnVseqkTnZ4Yphdg7sYnnglO06+MueGoEZJ5+H6zmxqltn5WbqXdNPR1EH3ku6qCL/VeSP5KbSTvxn4uYjcjed7eB/whZKVSikJxdR865lihvJGNRe5uPvJu9myY8sJppyrX3t1YJldWnQUh7ftO5PJyaoMv11sWXFLQUEjCGPMN4CrgSHgJeA9xpj/VcqCKcWnFBOpXJOfFvOkqGIny0t3qlsv3srNl90c2cQxPDHMlh1baIm30NfeR0u8het3XM/wxLCzzECgFu0apQSR+51qTS64kNFbvVCwmcgY8yTwZAnLopSYYk9ic2mexbbrVqPPpNihvMWI+to/tp+51NwJCsDYzBj7x/bT09ZjLfOhsUNl0aIHegfYunEr+8f209/RT09bT9GuHZVij94WI+pHqCOK+YMICmmMasayCYJqdiJG6dRLKez6O/ppjDWeoAA0xhrp7+h3lrlcM993D+3m1odvZTI5SWuilU+88RNV8Rx1zk4wKiDqjGL9IFz22/1j+yNppLuHdnPrI1kdyBs+wZruNVXhM6nUfIew9LT1cOPFN3L9jusZmxnL+CCCtPV8SkMx6j6ZnOTGB2/kmSPPEJMYKZNi64Nb+fq7v14VHXI9zNmJigqIOiTsD8LWSbg0z/6O/tAa6WRykhsfupFnXs7qQB7ayhff+sWKOxGLpfmWK0Dg6tdezUX9F4Uy5biUhmIJtMHxQfYM72HpkqUkGhIk55PsGd7D4Pggpy87PfT1lPKhq8IpgbjCYl2Ox562Hm+FtOPHePqlpzl2PP8KaYPjg+wZ2kNropXO5k5aE63sGdrD1OxURZ2Iac131+Au9h7Zy67BXWx9cGskx3t6xJVoSDA2M0aiIVGyAIGeth429G0IZefPdSyXIgR0PjXPzNwM86n5gr9T6WCHSt/fRbnKVbIRhIg0Aw8BTf597jbG/ImIvBr4JrAUeBz4Q2NMUkSagG8Ar8Nb0vT9xpgXSlU+JT/5tF6X5mkw3rJS6b+ItMRbKupELKbm29XcxVhyjF3P7MqMks5YfkbRAwSgOGahYoaA9rX30d/Zz2ODj2X2va7vdfS19wV+r9L+p0rfvxrKVcoRxAzwVmPMOcC5wBUi8kbgL4AvG2NOB44B1/rnXwscM8asBb7sn6dUkELCYl2aZ3dzN2cuP5Pu5u68mmdfex8DPQNMJicZnR5lMjnJQM+At79IIaALITmXZHxmnORc8oT9obW43MTJBS4vmkuQdl+siZDFDgHtbOqkr72Ple0r6Wvvo7OpM/D8fCOYUmvQ1TqJrtzlKtkIwnh5xCf8zbj/Z4C3Ah/0998F3ADcDrzL/wxwN/BVERFT6JqoStGJEuESRfNsTbSy5S1buOXhW06w9WfH0VfCidjX3kdXUxe/HPql9+YKrO9dT197X2gtbmR6hI6mDt55xjuZnpumubGZoYmhSBq5q40HxweL5ucoZsRbuu5Xrbuq4LoHvUe/OfabkmvQ1TqJrtzlKqmTWkQagMeAtcA24DfAiDFmzj/lILDK/7wKOABgjJkTkVFgGfByzjWvA64D6O/vRykdhXQSueaMqGGTA70DfOXtX6mqcMPJ5CTDU8O0xdsy+4anhhmeHA7dEafbJTmfpKOpo+B2CRMgABS188gX8VaoKStK3YPyOpXD2V+tC1+Vu1wlFRDGmHngXBHpAu4FzrKd5v+3WatPGj0YY+4A7gBvwaAiFVVxENRJuLToqJpntYUb7h/bn0lNnUqliMVizKfmeerlpyKNksK2i6t9Xdfqa++L3Hm4OnvXMwkzgopSd9d30nmdSq1BV+skunKXq6AV5YpyI5E/wVtT4rPAKf4o4QLgBmPM5SLyY//zL0SkEfgtsCLIxKQrylWOfKuEuTqcapwV7eL5Y89z3h3nkYglWBJfwvHZ4yRTSX56zU/560f/OtIKaYW2SyGrsA1PDJ8UzhplNb+w5rKoK8TV4up01fq+LrRcRVlRbiGIyApg1hgzIiJLgLfhOZ53AO/Fi2T6EPBd/yvf87d/4R+/X/0P1Us+W6hN86zWqBAXiYYE63vWs+flPYzPjBOLxVjfs56u5q6ianFBq7Clw2KbG5szAQKtidbICfZsHW5Yk01UO3iUEWLud8qtQVfbqDZNucpVShPTSuAu3w8RA75tjPm+iDwJfFNE/gvwS+BO//w7gf8lIs8BR4EPlLBsygIJawutxUyyXc1dnLHiDM5ccSZzZo5GaSRFiq7mLlZ1rAo9I9026c41W3zrxq2MzYyxazArLHaZFxabry3DmIWiLAdaaft8NeZ1WqyULMzVGLPbGLPeGHO2MeZ3jDFb/f37jDGvN8asNcb8njFmxt8/7W+v9Y/vK1XZlIXjmigXRuushjUBgkjXMUWK+dQ8KVIn1DE3xDeI9KS7Rw89yq9f+jWPHnqUrQ9uZXB80NouozOjIJAyKZLzSVImlfHS5WtL23oQrvDIeCweOpw17LMvNruHdrPlgS3cvvN2tjywRdc0KSGaaqOKqFZ7p4sweZ0qrXVGJUruKttzHBwfZNehXUzNTSEiGGOYmJk4YbZ4bkTS3NxcJvon3hBnbm4uc11XW7rWgwhaDnTT+Zu45ZFbODh2MJMHK189i63Fl3KtbCU6KiCqhFqzz6cp1BZarVEhhRDG3ut6jlPJKY7NHKMl3pKZlX1s5hgYrO3S2dTJ3qN7aYm3sLxlORPJCfYe3Us8Fne25WRyMrMeRLrzvH7H9VzUf1GgUDly/IgXL5j+W0A9oxBmVni1zk9YrKiAqALqRSta7KmVg55jS6KF7uZupmanmDbTGGPobu6mJdHC6ctOt67TsG7ZOg6MHWB0epSGWAPrlq1jNjUL2DX4XYO7nOtBbOjbYBUq4C0Y1L2km9Wdqwt694r5vgZdyzYhbk33GuINcY4dP5ZZ3zp3JFprI/FqRgVEFVBPWlExoy+COgJbCGipCXqOfe19bOjbwNMvP43BIAhnLj8zk48ot126mrvobeulv7M/0xHOzM9kOkKbwzvfehDFWjComO9rlFnhl665lOt3XH+CGS1932pdd6JW0WyuVYAufRieoJxDdz95Nxvv2sgH7/kgG+/ayD1P3pM5VsocPkHPMZ1OZP3K9azpXsP6levZ8pYteSfWzczPMDo9ysz8TMYk58oy25po5caLb2RqdorB8UGmZqdOWg9iMjnJ4YnDmfpHefeK+b66rgUnzwqfnZ9lcHyQ7fu2c+maS7nyjCu5dM2lbN+3ncnkZFGz7yoeOoKoAmrZPl8JgswSQXb4307+tqTaZb7naDDEY3FaGluIx+J5r+cyyQVlmb36tVdz3srzeOrlpzhr+Vm8uvvVmevd/eTd/Of7/zPJ+SSJhgRfeOsXuPq1VxdtlnP6O2FMPOlr5TrJXbPCwRMcp7SdkrnGgdEDjEyPMDU7FSn7brlMUrVo+lIBUSVUs32+2l7sIBPH4YnDVjv83iN7+cojXyn5qmau55gt1DqbO5lPzRdktw9rknM5fIcnhvnM9s8wPjNOQ6yB8ZlxPr3901zUf1Gkd2+gd4DPXvjZkwRRFOe1wZzkJI+STmRqdqrgdsrXXsWmVoNQVEBUEdU4azPfi10J4REUkROPxa12+Hgszp7hPXQ2dWYERKlWNbM9x5HpEYbGh3hh9IWMBn9a52mR11cY6B3gmZef4bgcJ2VSDPQO0NnUyc2/uNk6stp7ZC9Hjh+hI9GR8WkcOX6EvUf20tPWE/rds4XTXrH2itDO60x6eIuT3CW4XKMXV7u41p0oxNlerCVXazUIRQWE4iTfix1FKwr6wRX6YwwycaTt8LnrMi9rWcbM/AwvjLyQmYfQkmgpSjsVUsd4LM4TQ08wPjtOgzQwb+YZnR4tyNSUS2uilS1v3sItj2SlR3/DJwIT2XU0dSAI86n5jIAQhI6mjtD3H54YZsuOLTQ1NNHZ1Mn03DTX77ietd1rA53XtnaJkrLFJThc7RLV2V4srb+Wg1BUQChOgl5sILRWFPSD2z20m1sfufWEH3bQjzHILGJbl3l4YhhBSJkUDdJAyqQQJO/CNRBOi3TVcXRmFIMh5seFxIhhMIzOjNLT1hNaUx3oHeDP3vpnJ9RxMjnpHFl1NXdxft/5PH74cWbmZwA4v+981i5dG7ru+8f2M5GcYCQ1QsqkiEmMxlgjY8kx5/1d7RJ1AqVrxDPQO8BXrigsbXzQvYup9dfqJFFQAaEEEPRih9WKgn5wADc+dCPPvJzlH3hoK19/V7B/IMgs0tPWc0L0zmxqlnN6zuHFsRczJp5XdbwqM6/ARRgtMl8dmxqbWN6yPNOpjifHQ98jX7k2nb+JWx6+hYOjBzOO+HQb/dXlf8WXfvalTOf56Qs/nTlmCwt23WPZkmWMJ8dPyHJ7fO44p3acGjjXwtXZFjtAoxiTN6OE/0a5T7WjAkJxku/FDqMVBQmUqdkp9gzlRJ8MLSz6xLaQUW97L/1d9nkFruvftvM2mhqaaIm35HUs55sHYVuXOchv4DLLBAmioPXAB3oHuP3K20+6Xlh/QqIhwXmnnMfuod1elluJcd4p55FoSESaa1HJAA3XvYut9VdzEEoQKiCUQILsvWG0oqAfXLGjT1zmqnR5Z5OzJ5XXZR8fmhjiwNiBjO1+dcdqp009X6eSXpc5PVGus6mT0ZnR0Etr5ptc1t3czeoO+6zoXO067U/IDQsO8id0NXdx+rLTWbds3UlZbsM++6gUY22JNLYRRyVHNtWECgglL0H23kK1oqAfXF97HwM9AzxzJCv6pCd/9IlNswe3ucpVXpdq5AtIAAAaUUlEQVSwicfi7D3i5UPqbO708iEd8fIhhV1N79DYIeu6zGAfiQUtrVnMJUf3j+1nLjXHksYlzMzNsKRxCWMzY4H+hOxnOZ+aJ9YQy9Qzn3C2dbZhTWzFNMkFUatafzFRAVHjlCPMNOgeYbSiwOiTt2zhloezok+ybOe2hG1D40McGM/S7NtXF2Suyi1vkLlmNjXLuqXrODCelQ9p6TpGZ0ad38lnsshdl7mvvc86USwdkWRbMGhVx6rIS47mtmV/Rz9zqTmefvnpjOmtvamddcvWBXbqtnpOJidDC+ewzuAozuOFOJxrUesvJiogaphyTL6Jeg9XLqTA6JO3nxx9Yrt/b2tvJtNpRrP3M52GJchv4PJbQLCmHtZkYZso1tXc5VwwKN1erjkCrtTdtrZc072G13S/hscPP05qPgXAa7pf4/kG2oI16Nx6Do4PhhLO2e3vWjkvzPOKGs6quFEBUaOUY/JN1Hu41iTIR6Ha/eYLNjsznabNVdlJ8YLMVUH28XSnnjuyyaepu0ZcLq3bNlFs68atJzmZc7dtHa5N2ORry/7Ofs455RzGZsboaOrIK+yiYmuXruYuxpJj7HomSxAuP8PZllH8Gfm+U22ZAqoJFRA1Sjm0okLukfvjcjk9L+q/KHRWVZd2CdDb1svKtpUnOUlbE6184Hc+wOfv/3wmnPUDv/MBZ5ukhcCXfv4lnjnyjBf++aZXwj9tUUFBo4F8I67cDtfVxvvH9tOR6OCdZ7zzBJ9F0PMNmpXsug+Q8V/0tfeF6jxzjwX5kgLbJXcNCn87rJ/HxUKeV72jAqJGKcfkm3z3sKVWnpmfca5JEHZCmEu77Gvvc6Z8nkxOsn3fdi5/zeUZs9D2fdu5Yu0VzvvtPbKXRw89mhEozxx5hoHegVc6XEtUUNBoIMyIy9XG/R39xBviTCYnaYg1ZCbBBfkTgsw1rvu4fCD5Ok/XMZsvCdzzINKzvHMFYVC676i5o4rxvOoNFRA1SinC8MLcI51aOTf53V9e+pfONQkiaWsW7TItBC5dc+lJQiDdSdqyfdraJj3iye4k0iOeoNQVNvNLISOuXN+Mq4172nryrntg8ye4zDUZc5lFENjMUvkm/QV13rm+pKB5EC7nPYT384RFfRP5UQFRw5QjDC9syum51Jw1F1JropXbHgiXGM2lXe4f2+8UAmFHVukwT9uI56zlZ4W6Vr57u3wzLu12+77tbHzVxowZLS0EAWuY79aNW53mGu+jWxDkmqU2X7A5MM1KmM67ED9P2IissH4Dl0Ct1RQY5UIFRI1TjWF4tlxIaS3SFa1ii59P/4Bztcu0+SVMh+Nqo6BV2MJeK0hLz+ebsY1GXJP0AGuY7/6x/XQ0dXDpay49yeEMr2j92enGXYIAPN+Ea2nPMB1rvnYMm7U17Eg0aN5MrabAKBcqIJRAXD/GfKmVc3Mhpf0Jj+x9JLPvrBVnZRKjueLnXeaXKB2OjZ62Hm68+EY+f//nOXr8aGYhnXTZg65l02JdUUSuCWlp30wuQZP0APYe3UtzQzPNjc3MzM2w9+heli1Z5gyNdQkcsHf2QX4ecHfernbJ90xsik6x/AZB82Z0MlwwKiAWKeXIYx8mtTLAyPERDo8fzmyvbFsJBMfPD/QOsHXj1pPmVBTzh336stN5w6o3ZK51xrIzTjhu67xsDvo13WucUUSuCWnp9aJzcU3SSycXXNm2kieGnsh0eOf0nsNcag4E5lPzzDPvXciPvHIJnM6mTmuCP8Dp52lNtDqfS5B2H2W06/LzFDpvIlP3gHkz1TgKrxZUQCxCypXHPkxq5cHxQQ6MHWDt0rUZ7fbA2AEGxwcj18X1w46SgXVF6wpe3f3qgnwjLgf9Fy/5YqAz1jUhzUZQcsHJ5CTPjzwPQGOsEYPh+ZHnmZqdYm5+jnhjPFP3ufm5jInJJXBsobz5nP0um36QQhFFacl16uebN2FjNjXrnDejBKMCYpFRi3nsXfHz+TKd2ghb/3xC0NYRNjc2Wx30U7NTTrv9yPRI4IS0XLLt9rnJBQfHB0mlUjTEGhAEgyGVSjE1O8XeI3tpamiiPdHO9Nx0xizVmmi1Cpx0zqfcUN6tG7c6n33QpLuwiQezn5sty2zuGtpXrL0i0BFvo6u5i962Xvo7C8/kq3iogFhkLCR0L/dHms+5GEZT72vvY3XHah4//Hhm33krz6Ovvc8zV1ni5/OFmRaj/kFC0NURfuR1HwE8U86MmSFlvBFBS7wl0G4fNCHNRpAZrTneTG9bb0aLHp0ZZS41xyktp7D7pd0Z09PZK85mNjXrFDiuNp5NzYZeKyFdxzCJB11CuLe117qG9tqla52O+HzBA65MvoobFRCLjEK0fpu25ursi5VkDaBriafJpTuvriWvlMkWPx+0Qlq++ruib3IJEoKuyKuWRItzbYcgu32xImayR1xpATHQM8CpHafywtgLGGOISQxjDC+MvZCxtdv8BkFtvKpjVai1EtKT7nLrGCTowT6n4j1nvce6hvbQ5JBnYhos3MSUrrs6o8OjAmKRUYjW73Kuujp7m60/rKY+Mj3i2cdjca/Tjr1iH0+fn3ufKJ1qa6I1UIu34XK4uuzdnU2dgWs7uOz2YTspl9B2jbjmUnPMzM0wNTuVKVdzY3NmWVPbs8+XusL27IOeiyvyyCWEnClADPY1tBMdoU1M2eVWwRAOFRCLkCCtP6xzNciJGka7j8fiPDH0RMZkcPT4UcaT43kzsIbtVINmWbu+GzZP0OjMaODaDkGjF1cnZXOEBwlt24jrid8+wdjMGPDKmtdjM2NMzU45n/3X3+1OxR31uYQV9Lb36NxTzrWuob2sZZl18qTOfi4NKiBqnDCrZLlmP6edq2FmrYbV7kdnRkmlUsRiMQBisRipVCqj3QYRRvMLm2ojqCN2zeQGrBP48s0dcLWlTUAtXbI0r9C2tct8ap7Z1GzGeS1+WJLr2Qel4s5HmO+EXZmwp63HuoZ2eoZ1bturw7k0qICoYYoVztoSb4k0azXsJDKbY7XYhB3ZBJnK0tfKTZiXSXAXcu5AmNDQoCgiJwYaYg3EiSMiGGNoiDWcMArKdaxXA673aKB3gJsuu+kk05+t7XX0UBpUQFSAckxisxE0+7k10Vq0bJcuP4fNsepapyEqYUc2+fIEuUYEYecOgN0Z6woNDYoictGSaGF5y3ImkhOkTIqYxGhLtNGSaKGvvc/qWF9I+4d5j11LkaaxjUZcyomt7aOWSwlGBUSZKdckNhutieDZz7k/0ihzBNZ0r3HauoOWFYXi/bCLtVa2y59xUf9FoecO5FuPIUwUkau9+tr7WLt0LY8dfgxjDAbD2qVrM0LA5ljPh+uZuBzermu4Uqm4no1LOdm6caszBXu+Ea8SHhUQZaQaJrGFmf0caY7Aho84bd2uZUWh+MunFsM+7hoRpLPJ5oa/5tP6bQ5sl7kqyM/gaq813Ws8wdK+KjOCSL8PaX9KrmM9LezDhD4HObxtZc23FKkNl0BNt32YkFld3yE6dSkgKjUELWb++YXE1RfaeabvcdPPb+LZI8/S1dzFp970KVoT7slS6SiaMPfOJzhdz6uYz9FWrqDFfMaSYzy699FMR3zmijMzWr8tZDasuSqIoJnMLiHg8qd0NXcF+kZsGVDzObyLQVDbhx2laYRTdEomIERkNfAN4BQgBdxhjLlVRJYC3wJOA14A3meMOSYiAtwKvAOYAj5sjHncdu2FUMkhaLFTV5Rj8s/eI3t55NAjJ6225qrLumXrArO82gj6YbtSNJTjOQYJ4ZHjIxwaP4QxBhHJjDKCtG7b+g4uc1W+7KSupVhdET4uAQVu34grA2pYgpYiDdv2+TL5liM1TD1RyhHEHLDZGPO4iLQDj4nIduDDwE+MMX8uIp8DPgd8Fng7cLr/9wbgdv9/0aj0EoP5tP4oGnGU8MRCNfLMGgaNLXQ3dzMzN3PCGgauH3DYLK8uYeNK0ZC2QxfzObraxCaEnz3yLM8dfY4GaSCFN4J47uhzPHf0OW7fdbszZNaWbjvIZBLkWLel9XbNZA7yp5zde7b1/lOzU84MqPlSvefSmrBP7Mv3rIKim8KEzOroITolExDGmMPAYf/zuIg8BawC3gVs9E+7C3gAT0C8C/iGMcYAD4tIl4is9K9TFKphCOp6ucs1snHdx7Z/Zn7G6zhTIxlTSjwWz6xhEPQDdvk5wsypSKdoyNWUo3Sq+dokTITNVHKKl6deZj4175mEDEzPTTM0MeRMRe1Kt71sybJoWm+uKcrfds0Kd/lTwK51t8RbnBlQ8wU7uNaDcPmfgnApQK79iymlRjVEY5XFByEipwHrgUeA3nSnb4w5LCLpWVKrgANZXzvo7yuagChXdtJ85L7c5RrZ5IsMyd3/71//7xlLjpGIJVgSX8Lx2eMcnzvOsiXLnHUJ2h92TsVkctKa6iLIDh2lTcJG2KTXXJiZn8nMN2iiiY6mDmcq6pHpEWu67URDIrTWOzI9QkfCPpvYZZILmz+pr70vMAOqSwkIErZRRrtRKNd9Skm1RGOVXECISBtwD/AJY8yY52qwn2rZd1KWFRG5DrgOoL/fvtiKi2odgkZZBGUh9yk0MuT43HHW965nz0t7GJ8ZJxaLsb53PYmGROh7FyIErT9sS6qLYj7HKBE2ADGJ0dTQlBEQMYk5ywvB6zsEhbPatEiXwzlf1tQw+ZPglVXjXBlQbYpOaGFbAqpB814IlTaFZ1NSASEicTzh8LfGmO/4u4fSpiMRWQkM+/sPAquzvn4qcNJqMsaYO4A7ADZs2FBgmq5XqMYhaJRFUKLeJ0xkSH9HP2csP4Mzl5+ZcaymSEUqVxTznivVxch0ZZeKbIm3sKJ1BZPJycycgtZEK7OpWWd5V3WsCuxww4y4XA7nfOnRw+RPgvC/lajCtphUi+a9EKrBFJ4mVqoL+1FJdwJPGWP+KuvQ94AP+Z8/BHw3a/8ficcbgdFi+h+yaU20sqpjVVUIhwwRM1SGIa1FTiQnODB6gInkxAmRIa79KVLMp+ZJkYqsqWcLJyBU6u50VE5yPnnCd6I8x8nkJIfGDjGZnAReibCZTE4yOj3KZHIyb4RNX3sfG1ZtoKe1h97WXnpae9iwagPrlq0LLG/aP/CR8z/C1o1bAzuubC1ydedq2hJtbNu5jcnk5AkO5yvPuJJL11zK9n3bicfieds4bJtFaeP51DwzczOej6aMBLVZLRHlt1IqSjmCuBD4Q2CPiPzK3/efgD8Hvi0i1wL7gd/zj/0QL8T1Obww12tKWLaqIkhTLrYQCxsZUixNPYpZqNgmQZd2GTbCJu2kvennN2Xa5VNv+lTeEMww2m2QFglYHc5R0nOkKYZZJmhRqHJQTZr3QqgmU3gpo5h+invazyWW8w2wqVTlqWaCJjGVgrCRIcVy+hU7rXQYguy6USJsDIbGWCNLGpfQGHvlZ1SsBZbS74QrdXjU9Bw2immW6VrSRV973yszuZeUT+utliCUYlAtpvCSmZgUN7lmjrRNefu+7Xz/me9nzAe1pPUUShSTRTFMgjbtMh0IEPYe6c6+e0k3Z644k+4l3SeYMmzXynd/W51d74TLVJjtzwhbl2KYZdLRVVetu4rL117OVeuuoiPREWlyXRTytUutUQ2m8LpMtVFJXGkNwi5yUy3USsRIMbXLKKaMsPfPt/BRsTTMYkbQ5fqMKqHBV4vmvVjQEUQZcWlrg+ODzM7P0r2km46mDrqXdAdql9XC7qHdbL5vM1t2bGHzfZvZM7Qncyx3lLQQinGtYmqXUZyIYe+f7riD3okoGubwxDC7BncxPDGcqctYcowfPPMDdjy/gx888wPGkmOROvWFtHEx35dq0LwXCzqCKCNRUj5HZXhi+KTZtMUkyKbumqwVhWLax6Nql7mjpKhOxDD3L4U9/e4n72bLji0nhMZesfaKokbQRWnjSoem1soouBKogCgjYWezRn1ZbR3B1a+9OnK5bT8gl7AbHB8s2iSfUkwYCutwd3VeUYVNofcvdiRLJq9WvCXTltfvuJ61S9cWPYIuTBtXelJYpYVTtaMCooxEmc0aFldHkE6wFxbXD8gl7ICihRpWOmwxX+dVrOguF8W0p+8f289cau6EthybGWNsZqyifoNKPuNKC6daQH0QZSb9o9968dZMiGWaYthObR3BXGqO/WP783431w4cFOHisjenF5V32efD2JorPWEobORRKQh6J8K0ZX9HP42xxhPasjHWyLpl6yoa+VPJZ1wNz7fa0RFEBSil5pndEaS1osZYI/0dwXmrbCOFpUuWRkrd4BolhR3OV3rCUDXH1Ydty562Hm68+Eau33E9YzNjGdNjT1sPPW09oXJBFZNKPuNqfr7Vgnjz02qTDRs2mF27dlW6GFXHPU/ec1KeniAfxGRyks33bT5hqD2RnGDrxq1seWDLSfsLGYLndiyue0S5VjnZM7THy59URTbqhbRlmOCFfEKomM+lUs+4Gp9vORCRx4wxG/KdpyOIRcjVr72ai/ovKrgjcNmBF5K6IXeUtBBbc6lt/UFUY1z9QtoyPWLIRz77fCXXEC8m1fh8qwkVEIuUQjsCCB5qR0ndEPYeaao13LCSAspGOUwj+XJBLSbnbrU932pCndRKWVIU5LtH0KQ75UTK8byCnMfq3K0f1AehZLBp8MU2JdjusRCbej1T6hGXyz6vz6v2UR+EEprcoXa5JqpVer5DrVKpeRiVji5TyocKCMVJuTpuDTesXlxCSJ279YH6IBQn5ZrEVM40zcVMClfv1ENSvHp/X9QHoQRSzjjxUtvUNe+OEobF/L4U6oNQAaHkpVrDT8OgjlUlDIv9fSlUQKiJSclLMU0JlRqya2imEgZ9XzzUSa2UjUoO2dURroRB3xcPHUEoZaGYax9HoZyOcKX20ffFQ0cQSlmohrkOGpqphEHfFxUQSpmoliG75t1RwlDv74uamJSyoEP24lPvMfpK6dERhFI2dMhePBZzjL5SPegIQikr9TD7ttSkHf5NDU10NnfS1NBUVoe/Uj/oCEJRaoyR6RGGJoY4MHaA+dQ8DbEGVnes1uSGStHREYSi1BjxWJy9R/YC0NncCcDeI3uJx+KVLJayCFEBoSg1xmxqlnVL1wEwOj0KwLql65hNzVayWMoiRE1MilJjdDV30dveS39XPw2xBuZT88zMz9TdLF+l9OgIQlFqjHTI8Mz8DKPTo8zMz2jIsFISdAShKDWIhgwr5UAFhKLUKPU+y1cpPWpiUhRFUayogFAURVGsqIBQFEVRrKiAUBRFUayogFAURVGsiDGm0mWIjIi8BLxYgksvB14uwXVrAa17/VLP9a+3ur/KGLMi30k1LSBKhYjsMsZsqHQ5KoHWvT7rDvVd/3quexBqYlIURVGsqIBQFEVRrKiAsHNHpQtQQbTu9Us917+e6+5EfRCKoiiKFR1BKIqiKFZUQCiKoihW6lpAiEiziDwqIk+IyP8TkT/1979aRB4RkWdF5Fsikqh0WUuFiDSIyC9F5Pv+dj3V/QUR2SMivxKRXf6+pSKy3a//dhHprnQ5S4GIdInI3SLytIg8JSIX1FHd1/nPPP03JiKfqJf6h6GuBQQwA7zVGHMOcC5whYi8EfgL4MvGmNOBY8C1FSxjqfk48FTWdj3VHeBiY8y5WTHwnwN+4tf/J/72YuRW4EfGmDOBc/DegbqouzFmr//MzwVeB0wB91In9Q9DXQsI4zHhb8b9PwO8Fbjb338X8O4KFK/kiMipwDuB/+5vC3VS9wDehVdvWKT1F5EO4M3AnQDGmKQxZoQ6qLuFS4DfGGNepD7rH0hdCwjImFh+BQwD24HfACPGmDn/lIPAqkqVr8TcAnwGSPnby6ifuoOnDNwnIo+JyHX+vl5jzGEA/39PxUpXOtYALwH/0zcv/ncRaaU+6p7LB4C/9z/XY/0DqXsBYYyZ94eapwKvB86ynVbeUpUeEbkSGDbGPJa923Lqoqt7FhcaY84D3g5sEpE3V7pAZaIROA+43RizHpikDs0pvn/td4F/qHRZqpW6FxBp/CH2A8AbgS4RSS/HeiowWKlylZALgd8VkReAb+KZlm6hPuoOgDFm0P8/jGeDfj0wJCIrAfz/w5UrYck4CBw0xjzib9+NJzDqoe7ZvB143Bgz5G/XW/3zUtcCQkRWiEiX/3kJ8DY8Z90O4L3+aR8CvluZEpYOY8x/NMacaow5DW+Yfb8x5g+og7oDiEiriLSnPwOXAb8GvodXb1ik9TfG/BY4ICLr/F2XAE9SB3XP4fd5xbwE9Vf/vNT1TGoRORvPGdWAJyy/bYzZKiJr8LTqpcAvgX9pjJmpXElLi4hsBD5ljLmyXuru1/Nef7MR+DtjzBdEZBnwbaAf2A/8njHmaIWKWTJE5Fy84IQEsA+4Bv83wCKvO4CItAAHgDXGmFF/X108+zDUtYBQFEVR3NS1iUlRFEVxowJCURRFsaICQlEURbGiAkJRFEWxogJCURRFsaICQlEURbGiAkJRFEWxogJCUSIiIv/oJ/r7f+lkfyJyrYg8IyIPiMjXROSr/v4VInKPiOz0/y6sbOkVJT86UU5RIiIiS40xR/00LTuBy4Gf4eU1GgfuB54wxnxURP4OuM0Y81MR6Qd+bIyxJYZUlKqhMf8piqI4+JiI/Av/82rgD4EH0+kZROQfgDP8428DXustuQFAh4i0G2PGy1lgRQmDCghFiYCfv+ptwAXGmCkReQDYiz1dPHjm3AuMMcfLU0JFWTjqg1CUaHQCx3zhcCZemvgW4C0i0u2nTL866/z7gI+mN/xkeYpS1aiAUJRo/AhoFJHdwI3Aw8Ah4IvAI8A/4aXQHvXP/xiwQUR2i8iTwB+Xv8iKEg51UitKERGRNmPMhD+CuBf4H8aYe/N9T1GqER1BKEpxucFf4/zXwPPAP1a4PIoSGR1BKIqiKFZ0BKEoiqJYUQGhKIqiWFEBoSiKolhRAaEoiqJYUQGhKIqiWPn/PwhtwG8vMIsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we want to compare 'age' and 'chol'. Is there any connection with these features?\n",
    "data.plot(kind=\"scatter\", x=\"age\", y=\"chol\", alpha= 0.5, color=\"green\")\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel(\"chol\")\n",
    "plt.title(\"age and chol with Scatter Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": [
    "data.chol.plot(kind=\"line\",color=\"green\",label=\"chol\",grid=True,linestyle=\":\")\n",
    "data.thalach.plot(kind=\"line\",color=\"purple\",label=\"thalach\",grid=True)\n",
    "data.age.plot(kind=\"line\",color=\"pink\",label=\"age\",grid=True)\n",
    "data.trestbps.plot(kind=\"line\",color=\"orange\",label=\"trestbps\",grid=True)\n",
    "plt.legend(loc=\"upper right\") #legend: puts feature label into plot\n",
    "plt.xlabel(\"indexes\")\n",
    "plt.ylabel(\"Features\")\n",
    "plt.title(\"Heart Diseases\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To learn age, gender and chest pain type correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": [
    "sns.boxplot(x='sex', y='age', hue=\"cp\", data=data, palette=\"PRGn\")\n",
    "plt.title(\"0: female, 1:male\",color=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can understand so many info with these plots. According to heart dataset:\n",
    "\n",
    "* The most patients who are heart disease man.\n",
    "* Heart patients are mostly in age from 50 to 60.\n",
    "* chest pain (cp) type is mostly 2 in women whereas it is 0 in men and type 3 is common in men."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": [
    "#Other option is;\n",
    "sns.swarmplot(x=\"sex\", y=\"age\", hue=\"cp\", data=data)\n",
    "plt.title(\"0: female, 1:male\",color=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## After data analisis, let's now labe the data and split it to the training and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,:-1]\n",
    "y=data.iloc[:,-1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next, we split 80% of the data to the training set while 20% of the data to test set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test =  train_test_split(X,y,test_size = 0.25, random_state= 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains features which are highly varying in magnitudes, units, and range. But since most of the machine learning algorithms use Euclidean distance between two data points in their computations, this is a problem. To suppress this effect, we need to bring all features to the same level of magnitudes. This can be achieved by a method called feature scaling.\n",
    "So our next step is to normalize the data which can be done using **StandardScaler()** from sci-kit learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/ipykernel_launcher.py:3: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thenext step is to K-NN model and train it with the training data. Note that n_neighbors is the value of factor K and the metric used is ' minkowski' instead of euclidean distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "classifier = classifier.fit(X_train,y_train)\n",
    "\n",
    "## n_neighbors : int,Number of neighbors to use\n",
    "## metric : by default 'minkowski' isthe distance metric to use for the tree\n",
    "## p : integer, optional (default = 2) Power parameter for the Minkowski metric. When p = 1, this is\n",
    "## equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's train our test data and check its accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "#check accuracy\n",
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "print('Accuracy: {:.2f}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For K=6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors = 6, metric = 'minkowski', p = 2)\n",
    "classifier = classifier.fit(X_train,y_train)\n",
    "#prediction\n",
    "y_pred = classifier.predict(X_test)\n",
    "#check accuracy\n",
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "print('Accuracy: {:.2f}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.87\n"
     ]
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors = 7, metric = 'minkowski', p = 2)\n",
    "classifier = classifier.fit(X_train,y_train)\n",
    "#prediction\n",
    "y_pred = classifier.predict(X_test)\n",
    "#check accuracy\n",
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "print('Accuracy: {:.2f}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.87\n"
     ]
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors = 8, metric = 'minkowski', p = 2)\n",
    "classifier = classifier.fit(X_train,y_train)\n",
    "#prediction\n",
    "y_pred = classifier.predict(X_test)\n",
    "#check accuracy\n",
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "print('Accuracy: {:.2f}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors = 9, metric = 'minkowski', p = 2)\n",
    "classifier = classifier.fit(X_train,y_train)\n",
    "#prediction\n",
    "y_pred = classifier.predict(X_test)\n",
    "#check accuracy\n",
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "print('Accuracy: {:.2f}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors = 10, metric = 'minkowski', p = 2)\n",
    "classifier = classifier.fit(X_train,y_train)\n",
    "#prediction\n",
    "y_pred = classifier.predict(X_test)\n",
    "#check accuracy\n",
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "print('Accuracy: {:.2f}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So as we can see that Accuracy is maximum that is 87% when K=7.\n",
    "Let's also check the confusion matrix and see how many records were predicted correctly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25,  8],\n",
       "       [ 3, 40]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the output, 26 and 40 are correct predictions, and 7 and 3 are incorrect predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Congratulations, you have successfully built a heart disease classifier using K-NN which is capable of classifying heart patient with optimal accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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