heart_disease_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "669025da",
   "metadata": {},
   "source": [
    "### Importing Health Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e46d632a",
   "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>condition</th>\n",
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       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
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       "<p>297 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     69    1   0       160   234    1        2      131      0      0.1   \n",
       "1     69    0   0       140   239    0        0      151      0      1.8   \n",
       "2     66    0   0       150   226    0        0      114      0      2.6   \n",
       "3     65    1   0       138   282    1        2      174      0      1.4   \n",
       "4     64    1   0       110   211    0        2      144      1      1.8   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "292   40    1   3       152   223    0        0      181      0      0.0   \n",
       "293   39    1   3       118   219    0        0      140      0      1.2   \n",
       "294   35    1   3       120   198    0        0      130      1      1.6   \n",
       "295   35    0   3       138   183    0        0      182      0      1.4   \n",
       "296   35    1   3       126   282    0        2      156      1      0.0   \n",
       "\n",
       "     slope  ca  thal  condition  \n",
       "0        1   1     0          0  \n",
       "1        0   2     0          0  \n",
       "2        2   0     0          0  \n",
       "3        1   1     0          1  \n",
       "4        1   0     0          0  \n",
       "..     ...  ..   ...        ...  \n",
       "292      0   0     2          1  \n",
       "293      1   0     2          1  \n",
       "294      1   0     2          1  \n",
       "295      0   0     0          0  \n",
       "296      0   0     2          1  \n",
       "\n",
       "[297 rows x 14 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"./heart_cleveland_upload.csv\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dea7b61",
   "metadata": {},
   "source": [
    "### Identifying Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "35c8f264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "sex          0\n",
       "cp           0\n",
       "trestbps     0\n",
       "chol         0\n",
       "fbs          0\n",
       "restecg      0\n",
       "thalach      0\n",
       "exang        0\n",
       "oldpeak      0\n",
       "slope        0\n",
       "ca           0\n",
       "thal         0\n",
       "condition    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sumofnull = df.isnull().sum()\n",
    "sumofnull"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc607220",
   "metadata": {},
   "source": [
    "### Examining Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f15319af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            int64\n",
       "sex            int64\n",
       "cp             int64\n",
       "trestbps       int64\n",
       "chol           int64\n",
       "fbs            int64\n",
       "restecg        int64\n",
       "thalach        int64\n",
       "exang          int64\n",
       "oldpeak      float64\n",
       "slope          int64\n",
       "ca             int64\n",
       "thal           int64\n",
       "condition      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datatype = df.dtypes\n",
    "datatype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2106ae",
   "metadata": {},
   "source": [
    "### Identifying Numerical and Categorical Features\n",
    "Need to classify the features within our health data into two categories: numerical and categorical. This classification is crucial for our data analysis and modeling efforts. Understanding the nature of these features is essential for the project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "087f5790",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numeric Features:\n",
      "['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'condition']\n",
      "\n",
      "Categorical Features:\n",
      "['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal']\n"
     ]
    }
   ],
   "source": [
    "numeric_features = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'condition']\n",
    "cat_features = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal']\n",
    "\n",
    "print(\"Numeric Features:\")\n",
    "print(numeric_features)\n",
    "\n",
    "print(\"\\nCategorical Features:\")\n",
    "print(cat_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e56848b",
   "metadata": {},
   "source": [
    "### Converting Features to Categorical Data Types\n",
    "we transform selected features into categorical data types. Specifically, we convert 'sex,' 'cp,' 'fbs,' 'restecg,' 'exang,' 'slope,' 'ca,' and 'thal' into categorical variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "42e8e8b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            int64\n",
       "sex           object\n",
       "cp            object\n",
       "trestbps       int64\n",
       "chol           int64\n",
       "fbs           object\n",
       "restecg       object\n",
       "thalach        int64\n",
       "exang         object\n",
       "oldpeak      float64\n",
       "slope         object\n",
       "ca            object\n",
       "thal          object\n",
       "condition      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = cat_features\n",
    "df[lst] = df[lst].astype(object)\n",
    "dtype = df.dtypes\n",
    "\n",
    "dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2288e10",
   "metadata": {},
   "source": [
    "### Exploring Feature Correlations\n",
    "By generating a heatmap using the 'sns.heatmap' function, we visualize the relationships between numerical features . This visualization is pivotal for understanding how these features interact and impact each other within the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "90e139ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "selected_columns = df[numeric_features]\n",
    "\n",
    "# Calculating correlation\n",
    "corr_data = selected_columns.corr()\n",
    "\n",
    "# Creating heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_data, annot=True, cmap='RdBu', linewidths=0.1)\n",
    "plt.title('Correlation Between Numeric Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b59fff",
   "metadata": {},
   "source": [
    "### Visualizing Health Conditions\n",
    "Visualize the distribution of health conditions within our dataset. By generating this plot, we can gain a clear overview of the prevalence of different health conditions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e3cb6bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "condition_ax = sns.countplot(x=df[\"condition\"], palette='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322ba8bc",
   "metadata": {},
   "source": [
    "### Analyzing Health Conditions by Gender\n",
    "we generate a countplot to analyze health conditions with respect to gender. By creating this plot, we gain insights into how different health conditions are distributed among males and females. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9cb1e496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_ax = sns.countplot(x=df[\"sex\"], hue=df['condition'],  palette='bwr')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930db80f",
   "metadata": {},
   "source": [
    "### Examining Chest Pain Types and Health Conditions\n",
    "countplot to examine the relationship between different types of chest pain ('cp') and health conditions. By visualizing this data, we gain insights into how various chest pain types are associated with different health conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "59142a1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp_ax = sns.countplot(x=df[\"cp\"], hue=df['condition'], palette='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f3c94e",
   "metadata": {},
   "source": [
    "### Investigating Fasting Blood Sugar Levels and Health Conditions\n",
    "countplot to investigate the relationship between fasting blood sugar levels ('fbs') and health conditions. By visualizing this data, we gain insights into how different fasting blood sugar levels are associated with various health conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "23b5bcaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fbs_ax = sns.countplot(x=df[\"fbs\"], hue=df['condition'], palette='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dfece26",
   "metadata": {},
   "source": [
    "### Analyzing Resting Electrocardiographic Results and Health Conditions\n",
    "countplot to analyze the connection between resting electrocardiographic results ('restecg') and health conditions. By visualizing this data, we gain insights into how different resting electrocardiographic outcomes are linked to various health conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ff3f40cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "restecg_ax = sns.countplot(x=df[\"restecg\"], hue=df['condition'], palette='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19a99888",
   "metadata": {},
   "source": [
    "### Examining Exercise-Induced Angina and Health Conditions\n",
    "countplot to examine the relationship between exercise-induced angina ('exang') and health conditions. By visualizing this data, we gain insights into how the presence or absence of exercise-induced angina is associated with various health conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8046142d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exang_ax = sns.countplot(x=df[\"exang\"], hue=df['condition'], palette='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe959bc",
   "metadata": {},
   "source": [
    "### Investigating the Slope of the ST Segment and Health Conditions\n",
    "countplot to investigate the relationship between the slope of the ST segment ('slope') and health conditions. By visualizing this data, we gain insights into how different ST segment slopes are associated with various health conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1a794ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope_ax = sns.countplot(x=df[\"slope\"], hue=df['condition'], palette='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cffa4066",
   "metadata": {},
   "source": [
    "### Analyzing the Number of Major Vessels Colored by Fluoroscopy and Health Conditions\n",
    "countplot to analyze the relationship between the number of major vessels colored by fluoroscopy ('ca') and health conditions. By visualizing this data, we gain insights into how the number of colored vessels is associated with various health conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bc8ea375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ca_ax = sns.countplot(x=df[\"ca\"], hue=df['condition'], palette='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b13d8de1",
   "metadata": {},
   "source": [
    "### Examining Thalassemia and Health Conditions\n",
    "countplot to examine the relationship between thalassemia ('thal') and health conditions. By visualizing this data, we gain insights into how different thalassemia categories are associated with various health conditions. This analysis is essential for understanding the impact of thalassemia on health outcomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a18e9208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thal_ax = sns.countplot(x=df[\"thal\"], hue=df['condition'], palette='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68679504",
   "metadata": {},
   "source": [
    "### Visualizing Age Distribution\n",
    "create a histogram to visualize the distribution of age in our dataset. The histogram is constructed using the 'age' column from the dataset and is presented with 20 bins for better visualization. This plot helps us gain insights into the distribution of ages within the dataset and is a critical aspect of our project's analysis to understand the age demographics of the individuals in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8360c297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_col = df['age']\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(age_col, bins=20, color='skyblue', alpha=0.7, ec='blue')\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Age Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e569ac31",
   "metadata": {},
   "source": [
    "### Visualizing Resting Blood Pressure Distribution\n",
    " create a histogram to visualize the distribution of resting blood pressure ('trestbps') in our dataset. The histogram is constructed using the 'trestbps' column from the dataset and is presented with 20 bins for better visualization. This plot helps us gain insights into the distribution of resting blood pressure levels within the dataset and is an important aspect of our project's analysis to understand the distribution of this health-related feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7f8b6eac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAGDCAYAAAD6aR7qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAdOklEQVR4nO3df7RdZX3n8fcHghL8BZSAMRBjLf4qU5EGR8cqqMVfVaB2oTXUpi0VUx1X7dBapLbV1XaK2ukPR6cxFjVVo2KrgtaOIlZpHQWRgoLgYCECl0gAdfjRCJJ854+zrz3Ge3MP4e7n3Hvyfq1119n72fvs53uehNwPz95n71QVkiRJ6t9e4y5AkiRpT2HwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpLGIsnmJD+7AOpYmeSOJHvP0/HWJ/n9bvnYJDfMx3G74z01ydfn63iS2jN4SQL6DUJJ3p3kj/s49hz9/kqS7V2wuiPJtUneleRR0/tU1XVV9cCq2j7Csf5lrj6ral1V/dE81V9JfmLo2P9cVY+ej2NLGg+Dl6SRJFky7hp20xeq6oHAQ4CfBbYBX05yxHx3NF+zZpIml8FLEkneA6wEPtbNDL0myapuxuWUJNcBn+n2/bUkVyb5TpJPJnl4154kf5Fka5L/l+QrSY5IcipwMvCa7tgfG+r66CRf6471riT7dsc6NskNSc5Icks3G3fyUL3P6953e5KpJL8912esqu1V9W9V9Qrgc8Dru2NNf84l3fqvJLmmO/a1SU5O8lhgPfDk7jN8t9v33Un+OsknktwJPH2m2b1dfI7PJvn1ofUfzKoluaBrvqzr88U7n7pM8tjuGN9NckWS44e2vTvJ25L8Q/dZLkzyyLnGSVK/DF6SqKqXAtcBL+hOu71paPMxwGOBZyc5ETgDeCGwDPhn4P3dfs8CngY8CtgfeDFwa1VtAN4HvKk79guGjn0y8Gzgkd37Xje07aHAQcAKYC2wIcn0abazgJdX1YOAI+hC4b3wYeCpOzcmeQDwFuC53bH/C3BpVV0JrKObPauq/Yfetgb4E+BBwEynInf1OWZVVU/rFh/f9fnBnWrdB/gY8CngYOBVwPt2OvZLgDcABwDf6OqUNEYGL0lzeX1V3VlV24CXA39aVVdW1T3AfweO7Ga9vs8gfDwGSLfPljmO/daqur6qvs0gFLxkp+2/X1V3VdXngH8AXtS1fx94XJIHV9V3quqSe/mZbgQOnGXbDuCIJEuraktVXTHHsc6pqs9X1Y6q+t4s+8z2Oe6LJwEPBM6sqrur6jPAx/nhMfxwVV3U/Vm9DzhyHvqVdB8YvCTN5fqh5YcDf9Wd2vou8G0gwIruF/9bgbcBNyXZkOTB9+LY3wQeNrT+naq6c5btvwA8D/hmks8lefK9/Ewrutp/SNffixnMbm3pTtM95l58hpns6nPcFw8Drq+qHTsde8XQ+reGlv+dQVCTNEYGL0nTaoT26xmc4tt/6GdpVf0fgKp6S1X9NPCTDE4d/s4cxz5saHklg5moaQd0p/5+ZHtVfamqTmBwiu2jwNlzfrof9vMMTpP+iKr6ZFUdBywHrgLeMcdnmK192qyfA7gT2G9o20PnONawG4HDkgz/O74SmLoXx5DUmMFL0rSbgB+fY5/1wGuT/CRAkockOalbPjrJf+6uPboT+B4wfYuG2Y79yiSHJjmQwbVjH9xp+xuS3C/JU4HnAx/q1k9O8pCq+j5w21A/s0qyd5JHJPmfwLEMrn3aeZ9DkhzfBaW7gDt2+gyHJrnfXH3N4Ec+R9d+KfDCJPtlcNuIU3Z6367+TC5kMM6vSbJPkmOBFwAf2I36JDVi8JI07U+B13WnEWf8lmBVfQR4I/CBJLcBlwPP7TY/mMHs0HcYnPK6FfizbttZDK7J+m6Sjw4dchODi8Ov6X6Gvw34re5YNzK4PmldVV3VbXspsLmrYR3wS7v4XE9OcgeDgPbZrs6jq+qrM+y7F3Ba1+e3GXyx4BXdts8AVwDfSnLLLvrb2a4+x18AdzMIWBu77cNeD2zsxu2HrgurqruB4xmM/y3A/wJ+eejYkhagVM01Sy5JbXWzN++tqkPHXIokzStnvCRJkhoxeEmSJDXiqUZJkqRGnPGSJElqxOAlSZLUyJJxFzCKgw46qFatWjXuMiRJkub05S9/+ZaqWjbTtkURvFatWsXFF1887jIkSZLmlOSbs23zVKMkSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRnp9ZFCSzcDtwHbgnqpaneRA4IPAKmAz8KKq+k6fdUiSJC0ELWa8nl5VR1bV6m79dOD8qjocOL9blyRJmnjjONV4ArCxW94InDiGGiRJkprr9VQjUMCnkhTw9qraABxSVVsAqmpLkoNnemOSU4FTAVauXNlzmVK/dqxZA1NT/Xe0YgV7bdrUfz+SpN3Sd/B6SlXd2IWr85JcNeobu5C2AWD16tXVV4FSE1NTbFu3rvdulq5f33sfkqTd1+upxqq6sXvdCnwEeCJwU5LlAN3r1j5rkCRJWih6C15JHpDkQdPLwLOAy4FzgbXdbmuBc/qqQZIkaSHp81TjIcBHkkz3s6mq/neSLwFnJzkFuA44qccaJEmSFozegldVXQM8fob2W4Fn9tWvJEnSQuWd6yVJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhpZMu4CpF3ZsWYNTE3128mKFey1aVO/fUiShMFLC93UFNvWreu1i6Xr1/d6fEmSpnmqUZIkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpkSXjLkDS4rJjzRqYmuq/oxUr2GvTpv77kaSGDF6S7p2pKbatW9d7N0vXr++9D0lqzVONkiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiO9B68keyf51yQf79YPTHJekqu71wP6rkGSJGkhaDHj9ZvAlUPrpwPnV9XhwPnduiRJ0sTrNXglORT4OeBvhppPADZ2yxuBE/usQZIkaaHoe8brL4HXADuG2g6pqi0A3evBM70xyalJLk5y8c0339xzmZIkSf3rLXgleT6wtaq+vDvvr6oNVbW6qlYvW7ZsnquTJElqr8+HZD8FOD7J84B9gQcneS9wU5LlVbUlyXJga481SJIkLRi9zXhV1Wur6tCqWgX8IvCZqvol4FxgbbfbWuCcvmqQJElaSMZxH68zgeOSXA0c161LkiRNvD5PNf5AVX0W+Gy3fCvwzBb9SpIkLSTeuV6SJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0sGXcBWpx2rFkDU1O991ObN/fehyRJrRi8tHumpti2bl3v3ex72mm99yFJUiueapQkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIa6S14Jdk3yUVJLktyRZI3dO0HJjkvydXd6wF91SBJkrSQ9DnjdRfwjKp6PHAk8JwkTwJOB86vqsOB87t1SZKkiddb8KqBO7rVfbqfAk4ANnbtG4ET+6pBkiRpIRkpeCU5YncOnmTvJJcCW4HzqupC4JCq2gLQvR48y3tPTXJxkotvvvnm3elekiRpQRl1xmt9d73WK5LsP+rBq2p7VR0JHAo88d4EuKraUFWrq2r1smXLRn2bJEnSgjVS8KqqnwFOBg4DLk6yKclxo3ZSVd8FPgs8B7gpyXKA7nXrvaxZkiRpURr5Gq+quhp4HfC7wDHAW5JcleSFM+2fZNn07FiSpcDPAlcB5wJru93WAufsdvWSJEmLyJJRdkryU8CvAj8HnAe8oKouSfIw4AvAh2d423JgY5K9GQS8s6vq40m+AJyd5BTgOuCkefgckiRJC95IwQt4K/AO4Iyq2jbdWFU3JnndTG+oqq8AT5ih/VbgmbtRqyRJ0qI2avB6HrCtqrYDJNkL2Leq/r2q3tNbdZIkSRNk1Gu8Pg0sHVrfr2uTJEnSiEYNXvsO3QyVbnm/fkqSJEmaTKMGrzuTHDW9kuSngW272F+SJEk7GfUar1cDH0pyY7e+HHhxLxVJkiRNqJGCV1V9KcljgEcDAa6qqu/3WpkkSdKEGXXGC+BoYFX3nickoar+tpeqJEmSJtCoN1B9D/BI4FJge9dcgMFLkiRpRKPOeK0GHldV1WcxkiRJk2zUbzVeDjy0z0IkSZIm3agzXgcBX0tyEXDXdGNVHd9LVZIkSRNo1OD1+j6LkCRJ2hOMejuJzyV5OHB4VX06yX7A3v2WJkmSNFlGusYrycuAvwPe3jWtAD7aU02SJEkTadSL618JPAW4DaCqrgYO7qsoSZKkSTRq8Lqrqu6eXkmyhMF9vCRJkjSiUYPX55KcASxNchzwIeBj/ZUlSZI0eUYNXqcDNwNfBV4OfAJ4XV9FSZIkTaJRv9W4A3hH9yNJkqTdMOqzGq9lhmu6qurH570iSZKkCXVvntU4bV/gJODA+S9HkiRpco10jVdV3Tr0M1VVfwk8o9/SJEmSJsuopxqPGlrdi8EM2IN6qUiSJGlCjXqq8X8MLd8DbAZeNO/VSJIkTbBRv9X49L4LkSRJmnSjnmr8b7vaXlV/Pj/lSJIkTa57863Go4Fzu/UXABcA1/dRlCRJ0iQaNXgdBBxVVbcDJHk98KGq+vW+CpMkSZo0oz4yaCVw99D63cCqea9GkiRpgo064/Ue4KIkH2FwB/ufB/62t6okSZIm0KjfavyTJP8IPLVr+tWq+tf+ypIkSZo8o55qBNgPuK2q/gq4IckjeqpJkiRpIo16O4k/ZPDNxkcD7wL2Ad4LPKW/0iSpXzvWrIGpqX47WbGCvTZt6rcPSYvGqNd4/TzwBOASgKq6MYmPDJK0uE1NsW3dul67WLp+fa/Hl7S4jHqq8e6qKgYX1pPkAf2VJEmSNJlGDV5nJ3k7sH+SlwGfBt7RX1mSJEmTZ85TjUkCfBB4DHAbg+u8/qCqzuu5NkmSpIkyZ/Cqqkry0ar6acCwJUmStJtGPdX4xSRH91qJJEnShBv1W41PB9Yl2QzcCYTBZNhP9VWYJEnSpNll8EqysqquA57bqB5JkqSJNdeM10eBo6rqm0n+vqp+oUFNkiRJE2mua7wytPzjfRYiSZI06eYKXjXLsiRJku6luU41Pj7JbQxmvpZ2y/AfF9c/uNfqJEmSJsgug1dV7d2qEEmSpEk36n28JEmSdB8ZvCRJkhoxeEmSJDVi8JIkSWpk1EcGSZPrmmvYccwxvXZRmzf3evwfmKTPIkkTyOClPV5t38731q3rtY99Tzut1+NPm6TPIkmTyFONkiRJjfQWvJIcluSfklyZ5Iokv9m1H5jkvCRXd68H9FWDJEnSQtLnjNc9wGlV9VjgScArkzwOOB04v6oOB87v1iVJkiZeb8GrqrZU1SXd8u3AlcAK4ARgY7fbRuDEvmqQJElaSJpc45VkFfAE4ELgkKraAoNwBhzcogZJkqRx6z14JXkg8PfAq6vqtrn2H3rfqUkuTnLxzTff3F+BkiRJjfQavJLswyB0va+qPtw135Rkebd9ObB1pvdW1YaqWl1Vq5ctW9ZnmZIkSU30+a3GAGcBV1bVnw9tOhdY2y2vBc7pqwZJkqSFpM8bqD4FeCnw1SSXdm1nAGcCZyc5BbgOOKnHGiRJkhaM3oJXVf0LkFk2P7OvfiVJkhYq71wvSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGloy7AEma0TXXsOOYY3rtojZv7vX4Le1YswampvrtZMUK9tq0qd8+pAln8JK0INX27Xxv3bpe+9j3tNN6PX5TU1Ns63m8lq5f3+vxpT2BpxolSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRnoLXknemWRrksuH2g5Mcl6Sq7vXA/rqX5IkaaHpc8br3cBzdmo7HTi/qg4Hzu/WJUmS9gi9Ba+qugD49k7NJwAbu+WNwIl99S9JkrTQtL7G65Cq2gLQvR7cuH9JkqSxWbDPakxyKnAqwMqVK8dczeLS4mG5k/RwYUmSWmkdvG5KsryqtiRZDmydbceq2gBsAFi9enW1KnAiNHhY7kQ9XFiSpEZan2o8F1jbLa8FzmncvyRJ0tj0eTuJ9wNfAB6d5IYkpwBnAscluRo4rluXJEnaI/R2qrGqXjLLpmf21ackSdJC5p3rJUmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYW7LMaJ5XPUZQkac9l8GrN5yhKkrTH8lSjJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjS8ZdgCRJ03asWQNTU/13tGIFe23a1H8/0k4MXpKkhWNqim3r1vXezdL163vvQ5qJpxolSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNeIjgySpT9dcw45jjum9m9q8ufc+JkqLP5cbb4SHPazfPnzm5KJj8JKkHtX27XyvwbMH9z3ttN77mCQt/lz2Pe203vvwmZOLj6caJUmSGjF4SZIkNWLwkiRJasTgJUmS1IgX13d2rFkDU1O99+M3jyQtWg2+Cei/kZp0Bq9pU1Ns85tHkjSrVt8ElCaZpxolSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEb/VKEmSdqnJLZdaPFQcxv5gcYOXJEnatQa3XGrxUHEY/4PFx3KqMclzknw9yTeSnD6OGiRJklprHryS7A28DXgu8DjgJUke17oOSZKk1sYx4/VE4BtVdU1V3Q18ADhhDHVIkiQ1NY7gtQK4fmj9hq5NkiRpoqWq2naYnAQ8u6p+vVt/KfDEqnrVTvudCpzarT4a+HrTQts4CLhl3EXswRz/8XHsx8vxHy/Hf7xajP/Dq2rZTBvG8a3GG4DDhtYPBW7ceaeq2gBsaFXUOCS5uKpWj7uOPZXjPz6O/Xg5/uPl+I/XuMd/HKcavwQcnuQRSe4H/CJw7hjqkCRJaqr5jFdV3ZPkvwKfBPYG3llVV7SuQ5IkqbWx3EC1qj4BfGIcfS8wE30qdRFw/MfHsR8vx3+8HP/xGuv4N7+4XpIkaU/lQ7IlSZIaMXj1KMk7k2xNcvlQ24FJzktydfd6wNC213aPUfp6kmePp+rJMMvYvznJVUm+kuQjSfYf2ubYz6OZxn9o228nqSQHDbU5/vNotvFP8qpujK9I8qahdsd/nszyb8+RSb6Y5NIkFyd54tA2x34eJTksyT8lubL7e/6bXfvC+d1bVf709AM8DTgKuHyo7U3A6d3y6cAbu+XHAZcB9wceAfwbsPe4P8Ni/Zll7J8FLOmW3+jYtx3/rv0wBl+s+SZwkOPfbvyBpwOfBu7frR/s+Dcb+08Bz+2Wnwd81rHvbfyXA0d1yw8C/m83zgvmd68zXj2qqguAb+/UfAKwsVveCJw41P6Bqrqrqq4FvsHg8UraDTONfVV9qqru6Va/yOAecuDYz7tZ/u4D/AXwGmD44lLHf57NMv6/AZxZVXd1+2zt2h3/eTTL2Bfw4G75IfzHvSsd+3lWVVuq6pJu+XbgSgZPx1kwv3sNXu0dUlVbYPAXBDi4a/dRSm39GvCP3bJj30CS44Gpqrpsp02OfxuPAp6a5MIkn0tydNfu+Pfv1cCbk1wP/Bnw2q7dse9RklXAE4ALWUC/ew1eC0dmaPMrpz1I8nvAPcD7pptm2M2xn0dJ9gN+D/iDmTbP0Ob4z78lwAHAk4DfAc5OEhz/Fn4D+K2qOgz4LeCsrt2x70mSBwJ/D7y6qm7b1a4ztPX6Z2Dwau+mJMsButfp6f6RHqWk+ybJWuD5wMnVneDHsW/hkQyun7gsyWYGY3xJkofi+LdyA/DhGrgI2MHgmXWOf//WAh/ulj/Ef5zKcux7kGQfBqHrfVU1Pe4L5nevwau9cxn8R0j3es5Q+y8muX+SRwCHAxeNob6JleQ5wO8Cx1fVvw9tcux7VlVfraqDq2pVVa1i8I/dUVX1LRz/Vj4KPAMgyaOA+zF4ULDj378bgWO65WcAV3fLjv0862ZxzwKurKo/H9q0YH73juXO9XuKJO8HjgUOSnID8IfAmQym+E8BrgNOAqiqK5KcDXyNwWmwV1bV9rEUPgFmGfvXMvjmynmD/zb5YlWtc+zn30zjX1VnzbSv4z//Zvn7/07gnd1tDu4G1nazvo7/PJpl7F8G/FWSJcD3gFPBv/s9eQrwUuCrSS7t2s5gAf3u9c71kiRJjXiqUZIkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeElaNJLsn+QV83SsM4aWV3W3WZCkXhm8JC0m+wM/EryS7L0bxzpj7l0kaX4ZvCQtJmcCj0xyaZIvJfmnJJsY3Cxx7yRv7tq/kuTlMHg8SJILuvdcnuSpSc4ElnZt08/sXJJkY/fev+ueL0mSzUnemOSi7ucnuvaTuuNdluSCcQyGpMXHG6hKWjSSrAI+XlVHJDkW+AfgiKq6NsmpwMFV9cdJ7g98nsHdqV8I7FtVf9LNjO1XVbcnuaOqHjh03GuBn6mqzyd5J/C1qvqz7tmS7+je/8vAi6rq+Um+CjynqqaS7F9V3204FJIWKWe8JC1mF1XVtd3ys4Bf7h4TciHwYwyeu/Yl4FeTvB74T1V1+yzHur6qPt8tvxf4maFt7x96fXK3/Hng3UleBuzOqU5JeyCDl6TF7M6h5QCvqqoju59HVNWnquoC4GnAFPCebtZqJjtP/9eulqtqHfA64DDg0iQ/dl8+iKQ9g8FL0mJyO/CgWbZ9EviNJPsAJHlUkgckeTiwtareAZwFHNXt//3pfTsrk0zPZr0E+JehbS8eev1Cd/xHVtWFVfUHwC0MApgk7dKScRcgSaOqqluTfL679cM24KahzX8DrAIuSRLgZuBE4Fjgd5J8H7gDmJ7x2gB8JcklwO8BVwJrk7wduBr466Fj3z/JhQz+Z/UlXdubkxzOYKbtfOCy+f20kiaRF9dL0i50F9evrqpbxl2LpMXPU42SJEmNOOMlSZLUiDNekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqZH/D+tGOXn1yDGeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trestbps_col = df['trestbps']\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(trestbps_col, bins=20, color='lightcoral', alpha=0.7, ec='red')\n",
    "\n",
    "plt.xlabel('trestbps')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('trestbps Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9601bed",
   "metadata": {},
   "source": [
    "### Visualizing Cholesterol Distribution\n",
    "create a histogram to visualize the distribution of cholesterol levels ('chol') in our dataset. The histogram is constructed using the 'chol' column from the dataset and is presented with 20 bins for better visualization. This plot helps us gain insights into the distribution of cholesterol levels within the dataset, which is a critical aspect of our project's analysis to understand the distribution of this health-related feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ec9891fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chol_col = df['chol']\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(chol_col, bins=20, color='lightgreen', alpha=0.7, ec='green')\n",
    "\n",
    "# Label x-axis and y-axis, and set title\n",
    "plt.xlabel('Cholesterol (chol)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Cholesterol Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c157c8ff",
   "metadata": {},
   "source": [
    "### Visualizing Maximum Heart Rate Distribution\n",
    "create a histogram to visualize the distribution of maximum heart rate ('thalach') in our dataset. The histogram is constructed using the 'thalach' column from the dataset and is presented with 20 bins for better visualization. This plot helps us gain insights into the distribution of maximum heart rate levels within the dataset, which is a critical aspect of our project's analysis to understand the distribution of this health-related feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9a06f949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thalach_col = df['thalach']\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(thalach_col, bins=20, color='cyan', alpha=0.7, ec=\"darkblue\")\n",
    "\n",
    "# Label x-axis and y-axis, and set title\n",
    "plt.xlabel('Maximum Heart Rate (thalach)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Maximum Heart Rate Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6bc45f2",
   "metadata": {},
   "source": [
    "### Visualizing ST Depression Distribution\n",
    "create a histogram to visualize the distribution of ST depression ('oldpeak') in our dataset. The histogram is constructed using the 'oldpeak' column from the dataset and is presented with 20 bins for better visualization. This plot helps us gain insights into the distribution of ST depression levels within the dataset, which is a critical aspect of our project's analysis to understand the distribution of this health-related feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "78ad75cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oldpeak_col = df['oldpeak']\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(oldpeak_col, bins=20, color='orange', alpha=0.7, ec=\"darkred\")\n",
    "\n",
    "# Label x-axis and y-axis, and set title\n",
    "plt.xlabel('ST depression')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of ST depression')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6049aac",
   "metadata": {},
   "source": [
    "### Analyzing Fasting Blood Sugar Levels and Health Conditions\n",
    "use a countplot to analyze the relationship between fasting blood sugar levels ('fbs') and health conditions. By generating this plot, we gain insights into how different fasting blood sugar levels are associated with various health conditions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "39ccdc4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 414.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplt = sns.catplot(x='fbs', hue='condition', kind='count', alpha=0.85, data=df, palette='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8485fb9b",
   "metadata": {},
   "source": [
    "### Visualizing Chest Pain Types, Age, and Health Conditions\n",
    "use a violin plot to visualize the relationship between different chest pain types ('cp'), age, and health conditions. By generating this plot, we gain insights into how chest pain types are distributed across different age groups and their association with health conditions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "41061a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UanNVtE2xvx/5XC6UeTD1wrsZN8NiuXv3Lj7+iU/gc7/4iy0tvpypqSlocs+m+3SlF5OT/ndYeP3112GaJnK7an+2+XNOnjzp+z4a5c7kFHT7PTNU62dYrY7u3r0LPZFoq959rU5HCMuVK1dQLOQda6QqJEOLjePcu+/6uof3P/wQua1uMJt2aBSo6zpAkvUP4c4Gv3XrFgr5PKanptqigO3W7Ulo6mbL1Ij0YWlxwfesrJMnTwLxOIplKu63YqTTMPr68Morr/i6h0YxTRMzM9MwVOv7wX+G0eoon89bFf/JpNPDLyiXZSfREcLyri0SroQFQCG2GxcvXPStUjqTyeDOrVsVv/xaby+gKC1dGW0YBkCy0/8qTGEpbUgYZnPCejAMA3dnpmFsFRb7tp/te3Rdx8nXXkNmYgKQ3H2VMxMTOHv2bKiV7bVYWFiArmmOoDApDsixUFodcfHS02krxqKqbdFiqdXpHGGJdMNQelw9X4vtgWHovlkQ58+f39bOfBOShGJfn+/uNz+xhIWc2owwXWGlKaCtdEIsx8zMjDUIrWT0NQDo9u3bPrbvOXPmDLKZDHK73V0wAUBu926YponXXnvNt300CncB6xFbjIlgqH2huIadtdNpa910uqVd0u1CRwjLmXfOIh/ZVbF4bCtF27I5d+6cL+tzi6lQxV2RHxzE+QsXWjYobhgGiGTHFRZmrKM0S6fVM3Zu3boFYPuEUsO+zR/3g5dffhlQFOTHxly/pjg4CJZMWq9tEbjYGmrJHBm1Hzdu+vdeVcKZKGu3WSqk07gWwojync6OF5bZ2VnM3p1BMb7H9WuYkgKLDvgqLEZvL1g0WvE5haEhaMWikz3WahiGAWZqSM39GEC4Fks7CQs/UfEuvRwmxwE15VtGFmMMP3npJWTHxsDU8i2KykKEzK5dePW111qm2/GtW7cAkpBYeg3p2R8CsETm7sx04BdaN2/eBOJxJ9VY6+7G9NSUSKdvkB0vLFwcvAgLAOSie/DO2XMNF2kZhoF3zp4t2868lKLdKPDs2bMNrRcUhmEAzIRasAKbYVosCwsLYJGI83src/PmTUCJw5TT2x4rKgO+TSi9ePEi5u7e9eQG4+R270Yhn8fbb7/ty14a5ebNmzAj/VALM1DzViaYHhmAaZqBxzuuXL2KQkk3DK23F6ZhtGRKdjsRmLAQ0WEiOlvyb5WI/jER9RHRC0R02f5ZPlXKJ86dOwfIEbsPmHu0+B6sra40/AG7evUqsuvryJdpZ16KkUjA7OrCO++809B6QWEJCW25HQ7z8/NW6xtZDqzztF9cu3YdujpY1u2qR4Zw9Zo/rXu4K8tNmvFW8qOjgKq2jDvs6rXrKKpbXYfWhViQo8JN07QyRktqy3gB8+XLwc1l6gQCExbG2EXG2IOMsQcBPAwgC+B7AL4O4EXG2EEAL9q3A+PMmXdQjO5ygs5uKSb2Amjcgjhz5gwAlJ2TsZXs8DBOnznTkrUaG1lgtOV28EzbdQYsmWz54rWr166hqJa3TvXIIDJrq75UlL/08ssoDg+XrYuqiSwjOz6Ol155pemfNV3XcefO7W2jLHRbWILsoTc5OYl8LodiSRmA1t0NSFLLd8JodcJyhX0SwFXG2E0AnwfwnH3/cwCeCWrR9fV1XL582bMbDLADiWqyYWE5deoUzK6ujamRVciPjiKzttaSV0uWS5BfhVNofZwYY5i9exd6MoliItHSNQYrKytYXlqEHh0q+zi/v9G+cDMzM7h86RLW67BWONndu7G0sND02qnbt2/DNAxHSDhMjgGRHly7di2wtfnfvilbU5ah9fU1/X1pd8ISli8C+EP792HG2DQA2D/Lfwt94L333gNjZl3CAiLko3tw5p2zda9vGAZOnT6NbJnhS+UojFp9zFrF913KJguFKDSLZWVlBcVCAUYqBT2ZxJ0QiubqhV9d65EKwmJflTd6snzV7vdVLb7S8+ab6HnzzYqP5ycmAKKmV+FzkS03LrygDuHyleCas54/f94Skp6eTe9Xvr8fH5w/33Rrrp0JXFiIKALgFwH8fx5f9ywRnSKiU/W6P6zAPUGLeQ9wAlbAf2ryTt0B40uXLiGztuY6HdRIJmF2d7eksDDG4NgoJIX2pePBWz2Vgp5OY2F+vmVTsnmjUT1a/kLCVLoAOdawsJx89VWYXV3Qt4xgKCWyuIhIlWFZZiyG4tAQXmpynMUSFtpmsQCWEN+4cT2wDK1333vP6nohy5ver+LgILKZjK+p4Z1GGBbLZwCcYYzxBjx3iWgUAOyfZYdUMMa+yRg7wRg7MeiiXUU53jl7FkZs1DKr60CzLZ16047feustAPBUZ7A+OopTp0+33Mlzc/A+PFdYaQGbnk6DmWYorT7q4dq1a4AchVmpEJcIWmQYVxpwheXzefzs7bexblscjZCdmMCVy5ebGre6fPkyzOhA2amuWnQEuqb5WlTK0XUdH54/j3yZcwuvN2vlguVWJwxh+WVsuMEA4AcAvmL//hUAzwexqK7reO+995Cv01oBAC06DkiKU+DolTfeeAN6X5+nAGt+fByFfL7uNYNik4VCFJrFwk8qeleXVR0Nf6vX/eTKlauWu6vKCV+LDOPKlat1C/M777wDrVBAzkU341rkJqzZRM2c0XL+wkUUIuUTW3R7xEUQgfQLFy5AKxRQGNruttR7eoBYzLc6tk4kUGEhogSApwH8Scndvw3gaSK6bD/220GsfeXKFRTyecfqKEd69odOQVZZJAVadALv1BFnyWazOHvuHLIerBXATgUlwptV/OPNoFRICFJoMZZbt26BpVJgimJl7MDf6nU/uXL1KoqR6vE0PTqMzNpq3e7VN954A5BlJx7XCFpfH1gi4QwKC5tMJoO7M9OOgGxFjwwCJAWSzMKTcgrl4p9EyA4O4u0WnbbZDgQqLIyxLGOsnzG2UnLfAmPsk4yxg/bPyo7gBthoPFlZWNT8tFOQVYlCfDcuXvTekPLUqVMwdN3zlSWLRFAYHsarLdTLCcDmK+wGXTBeuH7jBgq2pWLGYkAsFmhtQ70sLi5ao68rBO45PIBfb2bY62+8gfzwMJhSfa6QK4iwPjaGt372s6YEqjdiUhVEUlJgRIcDsVhOnz4Ns7sbZoXhaIWREUzevt3ydVOtyo6tvH/vvfeASJfrxpOV0GK7oesaLly44Ol1b7zxBqCqrupXtpIbH8flS5da6kNtWSgbMZYwLBbGGG7cuGEVRwIAEYpdXbjegsLCA/KVAvccnnJcTwB/fn4eN65fR86jFVyN/NgY1lZXm1K3wdsXVZvqWoyM4MPz3r57tdB1HadOn8Z6le8mtwhPnTrl69qdwo4VlrPn3vXUeLISvNW+l0AeYwyvvPoqsiMjgOytMBNoDd/3VkzTLNGVcGIsc3NzyGWzG8ICoNjdjWsBFs3Vy0babHVhMeUUoCbrslhO266Zgo/Cwk+gp5vg9rl06RKgJK1suQpo0VEsLy362srnwoULyGWzVd2Jxb4+IBptyQzNdmBHCsvS0hKmpyahxeovIOOYShos0utpVPGNGzdwd3oa+ToL2LS+PrBksqVam5cKCQspxsKv6kuFRevtxcrSUss1o7x+/XrFHmGbIEJRHcK1Olq7nD59GohEyo63rhee4t6MK/MLFy6iEBmtevHHWzH5aVE52ZrV4lSShOzwMN54663QMiB3EjtSWD744AMAQDHeuLAAQD46gXPvurdYnAI22/LwDBHWx8fx2uuvt0yXVcMwwLjJQlIoXzZ+Vb9JWOzfG61e95tr165Dq9AjbCu6Ooir1655fg9PnTmD3NCQ66FebskODeHM2bOhxll0XcfVq1crx1dstKglLH52/X7zrbeg9/fXzNbMj49jdmamZbMQW5kdLCwEPdZ4SiYAaLFdmJu969ocP3nyJPS+PldtXCqRm5hALpttmW7HW9ONw7BYrl69arU0LzkB8CaBrSYsV69dh1YjcM/Ro0NYz6x56hm2srKCWzdulM9iapDCyAjW19ZCTYq4ceMGdF2DVqM5LJPjQLTXN4slk8ngnMtszbydeNOsrLl2ZkcKy/nz52HGhsCkyvNPvKDFJpzj1iKTyeDsuXNWAVsD5MfHAUlqGXeYZTnxq3E5FEvq8pUryJdYK4DVBRrRaKA9pLyytraGleXFstXj5eDP89I5m8f4ytVdNAo/Zpi1U24C95y8OupbAP/tt9+GaRiuvAl6VxfM7m68JoTFMztOWBhjeO/9D1GI+BfgtPy85Gom/ZtvvgnTMKxeTA3AVBX5kRG83OReThxd37BQGEmBC4tpmrh2/bpjoTgQodDTg8t2qmorsDHcq8Lo6S3U0xL+/ffft+IzlcZbN4De1QXEYp7iiI1y6dIlQFJdvWdadBSTd24jl8s1vO7rr78ORCKuBXp9fBynTp1CPp9veO1OYscJy/z8PFaWF6H55AYDACZFYUYHXJnjJ0+eBGIxX64sc7t24daNGy0xg9tqMWNZLAzBC8v09DQKW1qac4q2sLRKUNUZR6y6FBalG5AUT4WeH3zwgTWF1Mu0SLcQId/Xh/fs2GQYXLx40cqgczHOQo+OgjHm1L3UC2MML588abnBXGZr5iYmoBWLIu3YIztOWDZMbP8sFgAoREbx4fnqAUTTNHHytdewPjbmS4CVD3HiyQDNRCtxhbEQXGH8JLLNYrHvy2YyLTObxQruEgzVZbYWSTAjfa6DwowxfHD+PHL9/bWfXCeFgQFcv3o1lHHFjDFcvHgZxYi77gE8wN9oBf7FixexOD/vaThafmQEUNWmd4FuN3acsHCrola2iVf06Bhm705jbW2t4nM+/PBDrC4v1zXVr+yaXV0wurtbQ1i0DSFh1HxhKX1Os7lz5w4Q7QEk99XwRbkPt267s0Tn5uawurzsa5rxtv0MWKOAw0iKmJ+fx9raStlW+eUw1B5AjjUsLK+88goAj9maioLs2BhefuWVlrGQ24EdJyxXrlwBor11dzSuhBat3Yrj1VdftdwKDcZXSlmfmMCp06d98S83QrFYBLNTaU3IgXdfvnLlClhXV1nXj2afYFtlINqdO5Moyt4mbBtqH6YmJ12drPjfqQVosYT5njp/j0thAUnQIsMN7+2ll19GcWjI89TN3O7dmJ+b8zXleaez44Tl8uUryCv+p2Tyq6tqV8knX3sNxcFBmFF/stEA6+pK17Sm+3iLxSIcVxjJyAfsMrl0+TJyFeaNmNEokKyvej0I7kxOQle8Cksv8vkcVlZWaj6Xn1DLxZv8Qk+nAVUNRVhqza0phxYZxqXL9cfVZmZmcOniRaxXGY5WiZw9ouDlJs+uaSd2lLDouo5bt25VHA3bCKbSDcixijO4FxcXcfH8eWR9tFYAq8YAqtr0XHpNKxUWBcVCMbC1isUibt+65VxFlyPX04OLLWCxFItFLC0uWO4aDxiqJRJuRi1fvXrV6vDs4wXLNoig9fSEItbXrl0D1C4wOen6NXp0GNn1+uNqL730EgAgt8f7NFkzHkdheBgv/uQnda3diewoYZmamoJh6DBcFqp5ggh6ZKCisDhtInwWFsgyciMjeLXJwlIoFACyPi5MiiCfD85iuX79OkzTLBtf4Wh9fbh5Pbjpgm6ZnbXm1JmehcV6vithuXYN+SrTIv2i0NODyyEIy5Wr11BQvQ3v412hK33/avHTn/4URm9v1amb1cju3o1rV6+KKnyX7Chh4XUBbgvVvKKpg7h67UbZx958800gFrNGnfpMbnwc05OTTUs7ZoxBKxbgFEiSglyAef2O66eKxVLs7YWu601voc+FxVC8nbB440X++orPM02rw3MIwqJ1d2N5cRGZTCawNRhjuHH9es3xAltppCv08vIyzrzzDjINJNVkbUvnpz/9ad3H6CR2lLA40wZdFqp5RVf7sTA/u61YijGG1998E+uj1RvqcXrefBM9HgZ58dYSzRr+VSgU7Jn3titMiiCfDy6ZwOp6q1iFexXggexmtHsvZUNYKu+1HKacBEiuKSxzc3MoFgp1X2l7ga8RpFjPzs4in895iq8AVldoUhKeuhVwXn75ZTDTRHbvXs+v5RjpNPSBAfzViy/WfYxOYkcJy507dwAlDiaXH97TKEbEOplNTk5uuv/GjRtYWlhwPds+sriIyKL7+WZ6VxdYKtW0AL4jpNwVRhEUArRYLly4YLnBqtQCad3dgCw3XVj4zJxqrd/LQhKgpmvO3OEXS6FYLLaQB2kZc1eW4dWrQARNHairK/SLP/kJzHS64ay6zJ49+PCDD3D37t2GjtMJ7ChhmZycdF+kVgf82FuF5Wc/+xmAGm24G4EI6yMjTZv0l81m+UYAAKYUQbFYCKQRpWmauHDxIvK1TgKSBK2vz1X/tiBZWFgAJAVM8p7erssp18JSzXrzC92e1BlkHMHpUlCHV0FTB3DDo8WytraGt956C5k9exqezcQtHuEOq82OEpY7k1PQ5J7Ajl8pk+fMmTNgqRSMAL/8hdFRrK2uNqX54vr6OgCrRxgAp7nnhuD4x507d5BdX3cVq8r39+PD8+ebIrachYUFQEnXddLSpTRm56oLy+TkJCBJVvPNoFEUIJUKVFhu374NkqO159aUQY8MYHFh3tPn7uTJkzB0vSE3mLN+dzeMvj688Fd/1fCxdjo7RlgYY7g7M+M67TM9+0MohWkohWn03fom0rM/rPkaU04BkoLp6elN654+cwbZANqZl5K3j9+MNvpcWPjHhV+db9zvH7wRYnGwtqukODiIXDbb1AD+4uIidA9ps6WYSrJm6/zJyUmY6bSnFkE9b76JyMICIgsLGPqzP/MUzyukUpiamnL9fK/cvn0butpXnxDbVo4XV91fvfgiWCrl6vPkhrU9e/DuuXMtNTa8FdkxwrK+vo5CIe86O0fNT0My85DMPCK561Dz07VfRASo3Zs+VHfu3MHy0lIgczJKMdJpsGSyKcLiZAnZFotpdzUIInvovffeAyKRTcO9KtGMdu9bWVhYhC7VKSxyCivLS1UtrjuTkygkvR0/srgISdMgaRpiMzOe4nlaKoU7AQrLzVu3oSn1uasruaIrsb6+jtdffx2Z3bsbdoNxcnv3gjEm3GE12DHCwgunTMW7ie0FTUrhbkkmT5BzMjZBhNzAAM424STK+6NtuMLim+73k1NnziA/MODqCl3v6gLicZw7d873fbhlcWnJsmTrwJSTME2zqkBPz8w0NDDOK0YyiYX5+UDqg0zTxPT0FHS1viA6Fxa3Fstrr70GXdN8cYNxtJ4eGN3doliyBjtGWJzsnDp8t14w5DRmZzeqf99//31AVV1dYTdKYWgIM1NTniYP+sFG2xFusVjCsrq66us6y8vLuH71qtVR1g1EyA4N4a23325ag8C11dW6sxBN+3WV2roUi0UsLy5C92ixNIKeSoGZZiCdoxcWFqBrmhOr9AqT4yAl7tpV95Of/AQsHvf3oo8ImT17cOb0aSwvL/t33B3GjhEWfrI1lWC/hKaS3PSBOn/+PAr9/b7PIS8HH/J04YI/0/Tc4gjIFovFTZ8rL7z99tsA4Dptmz93dmamKcWj+XwexWLBEQiv8NdVOkHxk7sRorDwJIFa9TX1wAXBa/ubUnSld1OMsxL5fB4nX33VcoP5/N3M7t0L0zSdbsmC7ewYYeFfTrPOQKpbTDmJzNoqdF2HYRi4eOkSCgG2My+FV6KHLSwrKysgJQ4n3bjGlXa9vP7660A06mlKIi8ebcYI543PXH3Cwmq8j46whJERZsNFLAiLhWdTmnVaLACgKd2YnKotLG+//TYK+XxdvcFq7qG/HyyVcvqPCbazY4SFX1XX+yV3Cz/++vo6pqamUCwUqjZL9BMWjYKl06F39V1aWtpkCTIpCkiKr64AwzDw8smTnoek8bnkzRjExAWhfovFek8rvY/cvRuqsNgt5YPIeuLC4rX9TSmG0uOqv9pLL70ERCLB1JYRIbN7N954882mj7NoVXaMsGQyGUCOuBp12gimnWq7trbm1JSEEV/h5Lu7caUJwqJTycmNCFCSWPSQbVSLc+fOWUPS6rjCzOzejbdPnfI95lOLRq3kWq6whYUFABsn+zAwYzFAkpy1/WR2dhZQYg3NSjKVbuSy61VT3U3TtC5SxsddjyD2Snb3bmjFouO+FWxmxwjL+vo64PNwr3LwGo5MJuPUT4QpLFp3N27euhVqsHpufgHGlswnQ/ZXWF544QVAUeqavpndtw+mYYSeAsotlnqD90yKASRVdIU5ccMg2+VvhQiIx339v+XMzs5a4ycawFCtIuRqrroLFy5geXHR9WepnrqfwvAwEImIkcUV2DHCks1mwSgS+DpMstbI5/NWwDgeB4sEvy5H7+qCViiEWqC1uLi4LaVWl1KYn/fnqlbXdfz4L/4C67t2lZ0YWYtifz/M7m786Z/9mS/7cUujMRbL8ktUtFiWlpYA24IIEyMaDSTjaebuLDSpsaxN7kar1q+Lzy5yO8KirrofWUZ2bAwnX3tNjCwuw44Rlnw+D0beT0pe4Wvk83lMTk6iGGKNAbDRzymsLChd17G2ugJT2WqxpDHnk7CcPHkSa6urWL/nnvoOQITV/ftx5vTpQKvGt9JojMV6bbKixbKysmK5pkJGi0QCEZbZ2dmG4ivARrPPahdWr7/xBrSBgcDfu/zYGOZnZ+vquLzT2THCUiwWYZIS+DpM2hCW6ZmZUGsMgI2snSDSQcuxuLgIxti2tvCmksby0oIvfbr+5HvfA0smnQyvelg/eBAgwvPPP9/wftyytLRkZcs1ENfTKVGxLmltbQ16iNYwx4xGseJz8atpmlhaWmy4gJm/vpIrLJvN4v3330fWQ8p6veTszysf8ifYYMcIi6ZpMBFs4B6AcxLRdR3z8/OhZuwAgG6vF0Q6aDkqdTQwlbR9smisWHNychJvvvEGVg8ebMjlY6RSyI2P40++9z1omtbQntyyNVuuHkw5iYXF8u/h6toajDpcg41iRiK+d1VYWVmBaRgNCwuToiA5WjG54N1334VpGNZI74Ax0mmwVArvvPNO4Gu1GztGWIpFDSzgjDAAzhr5fB65bBZGyK4KFokAkhRa1a9TS7HFYuEujUYF7rvf/S4YgMzhww0dBwDWjh7F0uIifhJSu43FxSXo1KCwVMmuW89mwZTgrfCtMEVB3uc0Wi4E9ba/KcVUKo8bOHfuHEAUfIslm+zQEE6dOSPiLFvYMcJiMhPO6NxAsdbg6Y6hZuwAVsA3FvO9OLESzkz3Lb5xw+Vo3Wrkcjn88fe+h/U9e3ypLs9PTMDs7sYf/OEfNnwsN8wvbM+W84ohp7CeWSvbmyufzzdFWExF8X2QGxdPQ2lcWHQpVdFSfu+996D39oaWUFMYGsLy4qIY/rWFHSMsYVMoFABYboOwMSORQOeSlzI7OwuQvK1Ww1S7Nx6vkx/96EfIZjJYO3asoT06EGH56FF88P77TnPQIFlYWIDhQ8yAMVb2RKkVi2AB1WFURZah67qvV+FcWCpZLF7GWBhyCvML2608xhg+OH++9pA4H2lWm6VWJ1BhIaIeIvouEV0govNE9DgR9RHRC0R02f5Zf3+HJsL9+E25opTljXHBAXP37l0g0u30CdvYQwogqe4rNcMw8Hvf+ha0oSEUfXRbrB88CESj+P1vfcu3Y5ajWCwis7basGuHN00t51LUDQMs5FRjAM6afnY4dmpyKhSTehljYcpJLJWJS83Pz2NtZcXVkDi/4F03Ll68GNqa7UDQn9rfAfBjxtgRAMcBnAfwdQAvMsYOAnjRvt0wiqwACGGSILPW4NlQXq8oGxnCxDFl2bGYgmZ6ehpFqUyKKElAxF17jXKcPHkSU3fuYPnYMd9mZQAAU1WsHDqEn/zkJ67ndtSDk9Sg+lPwV87yM03T1/fGNfaaflosVkyQwOTGuwiYcgJrayvb9sfTfrXuxv5PvMAUBayryxm5LLAITFiIqAvAEwB+FwAYY0XG2DKAzwN4zn7acwCe8WM9WZZALHhhIVu8JH4l6fGL38gQJg4L8WQzNTUDo8LJsyh1ueo0W47f+/3fh5lOB9IkMHPvvWAAvv3tb/t+bE6lpAavmD7EqtqB5eVlkJrYZvnWA59jszVzjQuLHqKwAEA+ncb1Jk4xbUWCtFj2A5gD8P8S0TtE9B+IKAlgmDE2DQD2z7J+ECJ6lohOEdEpN5lHkUgEEgwft18BZq0hN8P37ewhnAwUK6V6FoZS3ltpqD2uOs1u5cKFCzh39ixWjh4NpKrcSCaxvm8fvvf884GMTwb8aagIcJeiXNalKMsy4EOdkPdNmRvr+8Tq6qpvDWIrzQOy4oEUfglAKoUZEbzfRJDCogD4CID/mzH2EIB1eHB7Mca+yRg7wRg7MehiXrUlLP5PvdsKMWuNGE8zbsIXX2IMSgixnfn5eZimWXF+hqH0YmF+zrMv/jvf+Q6gqsgcPOjDLsuzdu+9yGez+LOA2rxstIDvaexAJIFVcCmqqgpqwueLDAOSJPkuLDr5k5pfadzA3NwckEiE3gLHjMextrISyNTNdiXI/4E7AO4wxnhZ6ndhCc1dIhoFAPunLz6ASCTinPSDhEwraJ+wr4qoCR8myTAQDSHNmbu5KgqL2gPT47TBTCaDP/+Lv0Bm3z6wAP+G4uAgtIEB/H/f/W4gNQZTU1OAkrBGCJTBS5aTJndjcnJ7K5poNNqUzxcZBlSf/29WVlZhSv50aTYrjMZeXl6G3oQWOEY8DsZYaCUA7UBgwsIYmwFwm4h45dsnAXwI4AcAvmLf9xUAvvTgSCQSILPox6GqwtfosTsaSx6/+FQsIh6P45d/+ZcRj8dBRe97lnR9w2IKkA1hqeQK6930PDf85V/+JbRCwZeCyFqsHTqEa1evBpIKOjk5BV2tPIfHS5aTrvbhTplEg1QqBSmkLgKlSMWic+HkFyura87IiUbhbfe3ptxns1kYTSooBSBms5QQtM343wD4FhG9C+BBAP8SwG8DeJqILgN42r7dMJawBJ8pRcxao89OM5Q8CoNULOKZZ57Br//6r+Pzn/+859cDABUK6OpqLGjsBmeUrNJT9nFuyXhp/PjnP/4xjJ4eT1Mi62V93z5AlvHnf/7nvh/71u3b0BR/BrwZah+Wlxa3nZi60+m6Ph+NImkaUj73wFtfzzgjJxrFlMoLSy6fh9mE2KdpC0tYJQDtQKDCwhg7a8dJHmCMPcMYW2KMLTDGPskYO2j/9GXwQzKZBNMLTjpwLcjIb7YcDHcfCjIsYRkaGgJJEiSPab9mJILvf//7+MY3voHnn3/ee4GlaQIhCcvMzAygpgCpfL8qLjhuU46Xl5dx9uxZZPbuDSWNlkWjyI2N4cWf/tRXd5iu67g7Mw0j4k+9hB6xRPb27dub7u/p6YFaz4VHg1axlM+j3+epqLlstqLb0Cv8OFsTM6gZqdkAyP5sNWv9VmTHVN6nUikAzLU7jMz8JsuBTHfCItnP6+7uRld3N+Rs1tM+WSSCXC6Hb3/728jlcp5bT0j2VVF/CEVgU1NT0CtkhFmbUYGI+5Tjt956C8w06xrmVS+5Xbtwd3p620m7ESYnJ2GaJnTVH6vLUK3/y621EL29vZDqcK80ahWrhQJ6e/2rWzYMA5pWhCn506WCz0TKbvnuyZLknORDxV6zqZmiLUb4DsmASNtzSsjMuRp9yqQYvv/974Mxhueffx5M6nG1Dpk5KIqKaDSKocFBzITsV+VCNhCCK+nO5DS0Cm4wji53u3aFnTlzBohEPFVG97z55qZan2JfH5Yfe8z16/nM8zNnzmD37t2uX1cNPjnUiPgkLPZxbmyphRgcHAQKBZCue+rwwK1i/tk2PY42lrNZuMnEdAt3ETGfhAUkA7S9SDgej0NuRrKDviVTVLBzLJZuuyhKMtxZEEyObbYcXI41lows0rYbanRkBGpAdRKVUGy/8vDwcKDrmKaJ2dkZmDWERVN6MDnlzhX2Ie/j5CEdNLK4iNjMjPPPa0Gp3tUFRKM4f/68p9dVgwuAHvGnFQ2TIkCkd9vAKH5ylz1+xhqxiknTgEIBQz622XFiD34O4pMj22JSXV1dkJvhOrSfH4Z7ul3oWGGpF8nIOmuNjY1ZJ/oQzW8uLGMBDzJaWlqCrmkVU405htJrCZCLeoubt26F2m4DAEAEzeeWGzdu3AAiXa4vRtxQUAZw5erVTfeN2taWHFLDUWDj8zXi4zyTon3iZRVidXVBinNcTk9Pj+Mq9kKjrkM5n4eiKIh7tAx3MjvGFeak/xrBWhCSkUVfrxXYHB8fB4pFSPm8Z3dDvShra4gnEoFfHdWqYeGYajd0TcPi4mJV91yhUEA2kwm9KhoAtEQCs1VG2XrlypWrKCj+uYoAQI8O4eaNUzBN02kXNG5PKFTW1hBOZzhrrdK1/cARFj/nJUnKtoFuw8PDluuwWPRkpTXsOsxkMDg8LIL3JewYi4UHG4MWFpWtO4Fz7rNXQyyMUpeXsWfPnsA/xLzFSKVUYw5/vFaXY+62MJsxEVFVfWvtYpomrl27Bj3irytSjwyjWCxsilcNDg5CURSoPk9zrAYXlomJCd+OuVGR7p+wMMjbhIVbeIpHC6/RhBo1k8G4vbbAYscIS1dXFyRZhqQH6zYgPeOI2N69ewEASojCEl1bwz573SBxhKVG917+eC1h4VfhXuXQj4JSYmyjaWiDTE9Po1DIQ4/6LCz28a5cueLcJ8syxicmQv18KSsrSKbTjgfADwzD6q/HfGhA6UC0zf26x25oqoY0XRUAwBgiKyvOuUBgsWOERZIk9PT0QdIDvLozNUDPOS6f0dFRRGMxRBqc++4WKhRAmQwOHDgQ+Fp3794FJBVMqu664k0YawlLMpkEEXmu+/GjoFQqFtHtk+vwqh0HCUpYrm6Js+zftw+xLc0Wg0RdWcFeny3iDQHw73TDIDmCxdm7dy9IkkIVFjmbBYrFUL6T7cSOibEAwNDgAKZvBfcllG3R4sIiSRIOHDiAlTpa39cDF7CDATZv5MzOzoKp3TULGZmcBCSlZtt3WZbR29+PNY9uikb93wAQWV/HiE9XlNyi8NsVxqQoWLRvk8UCAPv27YP00kuAYQAh1EnEVlZw4JFHgjl4wCGIaDSKiV27sL6wEOxCJaj2WmF8J9uJHWOxAMDQ0CBUMziLRdIt0SoNUh8+dAixpaVQMsNUW8DC+BDPzc1Bk1yM3SUC1C7MuwiO33PggPVeeaAwMoLleBy/96MfYTkeR8FrtpKuQ15exv79+729rgJXrlwBon2+ZoRxCuowLl3eLCz79+8HGAsljifl80A269t7FTTluik8cN99SCwshJapGZ2fhyRJOBxC77t2YkdZLMPDw5D1U4EdX7aFpbSG5MiRI8D3vgc5k4GRbmz+eS0iCwvo7u31tXitEndn52Ao7goZNSntalDVA/ffj5/97Geesna8FEOWIzo/D5gm7r///oaOw7l0+Qryin81HqXokWHcvvUKisUiIvb7c8899wAA1KUlZwxuUKi26PM1fcfHkz2hfNzs2LFj+NM//dNQvo8AEJ2dxb79+0Wq8RZ2mMUyBKZnA+tyLOnLAMoIC4CIj+mslYgtLODY0aOhpDUuLizAVNx9MQ05hbn52u6HRx99FGAMsQBHBm8lducOJEnCww8/3PCxNE3DrVs3oUf9q/EoRY+OwDTNTTU3e/bsgSTLzkk/SPgaflssG61O/JwtU15YHnroIQBArM6R2Z4wTcTm5vDwRz4S/Fptxo4SFn7Cl/Rg3AaytoJ4Imn3JbM4ePAgZEWxrowDhDQNytISjh07Fug6gNWDqVDIw5Q3hKXafBFDSbtyhT3wwANId3UhEdYYV8aQvnkTD33kI07Ln0a4efMmTMPwPXDPKZcZpqoqdu/eHUqCiLq0hGQ67btFzIfS+Tk6nJhRdtjdgQMHkEqnEQ1BWCJzc4Cm+XLRstPYUcLCq4VlLZgvoawvb2ulEolEcPDgwcCFJWL7je+9995A1wGARTuWYyobAlptvogpp5Bdz2yrK9iKoij49F//60jevg3ymB1WD5HZWUgrK/jMpz/ty/GuXbsGwL9WLlvRIwMASdsyww4dPIhYCDGW6NISDh444LtFrPLaJebj6HBmbBy3BEmS8OgjjyA5NRV4nCU2NQUiEsJShh0lLE4LDG05kOOrxjLGx7YXQh1/4AHHlx8UEXtK43333RfYGpwl++rYlN3N5ODPW3aR5vn5z38e0HWktmQ/BUH6wgXE4nE8/fTTvhzv+vXrAAh6JKAYFylg0f5tzSgPHDgAWl21+ngFBWOILC8HkhjCBcDXCa9MLyssAPD444+D1tehBJx2nJicxOEjR3yt+dkp7ChhGRwctGZ1ByQssrZUtkfX/fffD2haoH7w6OwsxsbHfW1nXgnPwmJbNksu/v4jR47gvvvvR/f584EKsZTNInn9On7xc59D0qehVTdu3ACL9lWcT+MHBWUIV65e23Qfr5EI8vMlr68HVo/Bx2j7KSxk6hXHcz/++OMAgPidO76ttxWpUEBkbg4f/9jHAlujndlRwqIoCgaHhiFr/teVkJED9FxlYQEQ9TD73ROMITE3h+MPPBDM8bewahfkuRUWJic2va4Wf/fLX4a0uhporKXrgw9AAH7lV37Ft2PeuHETRZeZcvWiRwYwNTm5qfjPEZYAr8D5sQMVFtMni4sxMFOrKCwjIyPYf+AAEgEKS+zOHYAx/NzP/Vxga7QzO0pYAGDXxDgU3f8rOx63GS3TE2hsbAw9vb2I1qg+r3vtTAbIZnH8+PFAjr+VFdufb8ruUihNKb7pdbV48sknsWvPHvS8+24gfnApn0fXhQt4+lOf8q3nFWMMt2/fdqY9BoURGYBh6JuGp42NjUFR1VCEZd++fb4fe8Ni8cuVZwDMrDr/5OefeALRmZnAYnnxW7fQ1dMTSsyzHdlxwjIxMQFV999i4VZQuRMVEeHB48cRD8hiido1In7VYtRibW0NALkeJcsFyK3FIkkSnv0H/wDK4iLiAVgt6fffB3QdX/3qV3075tLSEvL5HAw12FoS3T7+ZElKtizL2L1nT6BFkuryMtLd3YHECyRJQiQS9a0MgFs+1WpHnnjiCYCxYNxhhoHk5CSefOIJ33rQ7TR23LsyPj4OaBmQ6e+VilJc2Dh+GY4fPw5pdbWuUbK1iN69i1giEVzh2hYymQygxACXTQOZZF05eukg/Nf+2l/Drj170Hf2rK+xFimXQ/f583j6U5/y1a3Duw4HLSyG2rtpPc7+vXsRDbBnmLq6iv0BNlKMxeMgVllYyMhvbjZqVJ6rwgUqUWUEw7Fjx9Db14fEluFpfhCbmQGKRTz55JO+H3unsOOEhVsUctFfq0XWFpHu6q5YD8HdVEG4w+Jzc3jgvvtCm6mdyWQADy1LrJGzZL3OJbIs49eefRby0hIS16/XscvydL33HkjX8Q//4T/07ZgAMGPXRdQaI9AoptIFkLTJFQYAu3btgrS6GljCQ3RtzbfRzeWIx+NVL/bIzG9qNkpmNWEpOMeshCRJ+IUnn0RictIZHewX8Zs3EYlGrYJfQVl2nLDs2rULACBr/jaik7UF7LaPXY4jR45AUVXHbeUXVCxCWVwMLb4CWJaHSe7cYAAAkkBK1PPMk6effhr79u9Hr09Wi5TNouvCBXzmM5/xvY35nO3mNNSAx8+SDKjbC04nJiYAxgKZJkm6Dqyv+zqDZSuJRKKqK4xJMXz/+9/HN77xDTz//POOFVwOyRaWahYLAPzCL/wCoGmIbbH+GoIxpG7fxsc/9jEx474KnoSFiPzJ2wwQ/uVQiv4WLEb1RezeXVlYIpEIjh49ipjPwhKZmwMYC1VYstksDPI27IhJUWSz3sZCS5KE/+rXfg3yygoS167VfkENut57D5Jp4ld/9VcbPtZW5ubmAEmpOUbAD3Q55QgZx5kmGYCwyCGMu04lk1UtFibHNg/bqmIxk0thOXHiBBLJJOI+usMic3Og9XVLtAQVcSUsRPQxIvoQwHn79nEi+veB7qxOUqkUurt7/bVYTA0oLtd0FTx4/LhVIW/4V2EcnZsDEYVSGMlZz2ZhehaWiGdhAawMsf333IPed99tyGqRcjl0XbyIz3zmM47V6ifLy8uAkqw5RsAPDCmJpaXlTffxrhJBCAs/ZrmMR79IpZKQmT9xTy4steqTVFXFz33iE0jdvu2bCzFx8yYkWcYnPvEJX463U3FrsfzvAP46gAUAYIydA/BEUJtqlD17dkPxUVgUbREAq3nCuv/++wHDsMTFJ6Kzs9izb9+m/mRBk83mwDwWAZqkIp+v7BevhCRJ+NWvfhXy8nJDV5bpDz4AGQb+/t//+3UfoxorKytOWnXQmFICy1sywIaGrDYych3iXQt+zK3tivwkmUxCrhK894JbYQFsd1g+70/skzGkbt3CiRMn0OXT4LidimtXGGPs9pa7fGz84y979uxGRPfv5C7bbrVaFovvhZKMIT4/j+MhpRlz8vk8GHkUFqgo1Fkz8NRTT2F8YgI9779fV10LaRq6L17ELzz1lDOe1m8ymQx0CsenbsoxZNY2WyaRSASpri6rQt5n+DH7+4Mr/rRiLP5YLG5jLADwsY99DIqiIF7SMbpelJUVSCsr+IUWzgYjov9IRL9k//4fiOhe+/d/vuV5rwe5D7fCcpuIPgaAEVGEiP4ZbLdYK7Jnzx6guOrbB1lxKSyDg4PoHxx0+no1vO7qKpDPh1a/wikUCt6FhRRk60y1lmUZf+dLX4I6N4dIHTGq5OXLQKGAv/vlL9e1vhuyubyd/RY8TIqgUNhu/fX390MOIJ1dzuWQSqedGTBBkEwmASNcVxhgic8jjz5qucMaLMbl4vTEEy3rrNkEY+wfMMY+tG/+8y2PBdqLxq2w/BqAfwRgHMAdAA/at1sSLgCyTwF8WZtHd0+vK3fUA/fdh7hPnY75jJewq3sLhQIgeZsBx0hBIV//ieOzn/0s4okE0hcueHshY+i+eBFH77030DhULpfzLLZ1Qyo0rVgyK95isL8fSh3uxlrI+XzgPegcYfGh0wKZBUiSVLGly1Z+/oknIK2uNtyUMnn7Ng4eOuSry5CI/i4RvUtE54jo94hoDxG9aN/3IhHttp/3H4no3xLR60R0rcQqISL6d0T0IRH9KYChkmO/REQniOi3AcSJ6CwRfct+LFPy+n9NRO8T0XtE9Lft+5+0X/9dIrpARN8iD22vXQkLY2yeMfYlxtgwY2yIMfZ3GGPhDZb2CBcWvzLDVG0e+/a6c7Hce++9kFZXfWklEZmfRyQaDX1UrKZpYORxuCipKBTrb9kRj8fx2b/5N5G8ccPTexeZnYW8tIQv/NIv1b22GwzDAHNZMAp4K/jbCl9nq7D09vZCKfo/xE7K59Ef8HRKy23FqhZJuoXMAmKxuOv2/jzQXqkKv9jXB1NVYaoq8iMjKJZ5L6R8HpHZWfy8j9YKER0D8D8CeIoxdhzA1wD8OwD/iTH2AIBvAfi3JS8ZBfAJAJ8F8Nv2ff8ZgMMA7gfwqwC2WSKMsa8DyDHGHmSMfWnLw/85LEPhOIBPAfjXRMSzOB4C8I8B3AtgP4CPu/3b3GaF/dsy//5XIvq824XCZGJiAkQEueiPS0rV5l3XRRw9ehSAPxMlowsLOHToUNmBRkGi1yEsjGQUGzzp/eIv/iJgGJ4KJpNXriASjeKTn/xkQ2vXwjrJu88I81Lwt53ywtLd3Q0pgN5XarGI7u5u349bCo+H+NHWhcwi4i7iKxynKeXtrWFii+XHHkOxvx/F/n7M/o2/UXYcdmxyEmDM72ywpwB8lzE2DwCMsUUAjwP4A/vx34MlJJzvM8ZM273FzaYnAPwhY8xgjE0B+InHPXyi5PV3AbwM4BH7sZ8xxu4wxkwAZwHsdXtQt5dgMViqdtn+9wCAPgBfJaL/w+1iYRGLxTA4OOyLxUJGDtAyrlNYnVHFiw1W/jOG6OIijtrHCwvGGAxDBzwLiwJdb6zJ4JEjRzCxaxeSboXFNJG+eRNP/vzP+9Ya3y+8FPyVeXXZe9PpNJDP+964Uy4WA89y4lXyfsQ9iRVdBe5L+cTHP47o7GzdM21ik5NId3U5F44+Qaj0n71B6eOlbx5VeE49e6hE6XoGANcnBbfCcg8sc+3/ZIz9n7BMpqOwzLC/5naxMNm3by9UHzLDuDi5zTbq6enBwNBQRWFxY3YDgLK2BhSLOHz4cH0brxPdbn/ByGP7GJKd19YLEeHpT30KMZddaaOzs0A+j6eeeqqhdd2gqirIwwRELwV/219srbPVUk2lUgBjvrcoQaEQeDr7hrA03uGYzCISVdq5lOOxxx6zZtRvaZXjCsaQnJ7GYx/9qN9tlV4E8LeIqB8AiKgPwOsAvmg//iUAr9Y4xisAvkhEsu3CqlS5qRGVDRK+AuBv268fhGUB/czj37ENt8IyDqD0kjAJYIwxZmCzqrUMe/bstkShwtWdFhuFKcVgSjEU4/ugxcoXh8kehQUAjhw6hFgFYXFjdgOAar/+0KFDrtf1Az5e2KuwMJJqjiZ2wyc+8Qkrzbqku28lYrdvQ5JlZ7BTkERUFfBzAmIViBmQJGlb51x+8pf8jLOYJqBp4QmLDzEWySwikfAmLMePH4caiSBah7AoKyug9XV89KMf9fzaajDGPgDwLwC8TETnAPwbAP8tgP+SiN4F8GVYcZdqfA+WF+k9AP83LFdWOb4J4F0evN/y+ncBnIPlRvvvGWMzdfw5m3Br2vxvAM4S0UuwTKcnAPxLu8XLXzW6iSDYs2cPYOQhGRmYyvbGkWtDn3Pmti/ufrbicRRtHkSSpz5KBw8exKuvv25V4Nd5haMuLYGIQg/cb1gdHtvI+WCxAFZX2lgigej0NLI1/vbEzAzuu+++UNxgVh1G8HPnAeuKPBbbfuLkf2ctd06xr88p0i3291e0igE41k/Q7yHvq+WHxSJBq9qAshzRaBQP3H8/3rpxA8se1+NWzokTJzy+sjaMsecAPLfl7m0mOGPs7225nbJ/MgD/dYVjP1ny+28A+I0Kr//v7H+lr30JwEslt8uuUQm3WWG/Cysj4AIshfufAFxijK0zxv67Sq8joht2CttZIjpl39dHRC8Q0WX7ZyB5jtzCaDSALxfnMTI6WnG+djkOHDgAmCbUBtqcR5aWMDI2FnqjO0ccPFssshWbaRBFUfDQ8eNI1KhnIU2DOj+PEw8/3PCabkgmE5BDMs6JFRGLb48hOBZLDWFxaxUDG9ZPaMLiw7AviVUeS1yNEydOQFlYAHm0+KIzM+gfGKg4MkOwHbdZYf8AwF8A+Dqs9LPfBfCbLtf4BTvNjcv91wG8yBg7CMvH+HUvG3bLRspxY3GWiDaPvXu8tRPnU/gayZuPrqzgYABjYmvBR+J6Sa21kGHo/jRjeOCBByAvLVW9Mo8sLACMhVY8mkqlfOt1VQvJyKErvd015WRW+eBy5PBjBS0sfo4nlphW1wXXA/Zob6+dMRLz83jowQddpzcL3Ps7vgYrBe0mY+wXYOU312sKfB4bpt9zAJ6p8zhVGR4ehqKojRVJMgZZm/fcJoQ/v+5RsqYJeXXV99bvbqjXYgFJYMwE8yFjyUnZrtJzjT/mc5ZORbq6uiDp/vfpKgcZubLpv1xY/IyxcOvHa5aVV5yqfj/GEzO9ri4Bx44dA0mSp75hcjYLWltzREngDrfCkmeM5QGAiKKMsQuwinJqwQD8JRGdJiIeyBhmjE0DgP1zqNwLiehZIjpFRKe2thB3gyzLGB+fgKLVLyySsQYYRc/dcmOxGAaGhup2hcmZDGAYgQ5eqoSTFeYxxsKD/X7EWfikzGrCrC4tIdXVFWh/q1K6u7vB9BzAghm0VYrKsuju3p7+69YV5gV+rKCD9xtz7xv/fJCp1SUsqVQKu3fv9tQktlndL9odt2ePO0TUA+D7AF4goucBuJme83HG2EcAfAbAPyIi12WrjLFvMsZOMMZODA4Oun3ZJvbubawZpWy70eppbLhn1y6oa2t1rctfF0T791pwV5jbscQbWM/3Q1iGhoYQiUaruhLVlRUc2LcvNPeENQuegYzgrRbJzKKvTMA9iKwwHm8IWlh4jNJLynZFmOEp5lnKvUePIra05Pr56sICiCj0tP92x23w/j9jjC0zxn4TwP8MK8byjIvXTdk/Z2EF/R8FcJe3DLB/+jsZq4Rdu3ZBKi7WfZXJW+/XM1lv165diNQpLIr9uiAn+lWi3joWHpMxfJhFI0kSdtUQ5qBH6W6Fn+hlw//uwptgJqBlyvbu4id/r8HnavBK/kojt/3CEQIfLBbWgLAcPnwYlMlActlzLbK4iPFduzxnoXU6nkcTM8ZeZoz9gLHqCelElCSiNP8dViHl+wB+AOAr9tO+AuB5r3twy8TEBGBqkPT6TvBycQGSLDtDlrwwOjoK5HJ1FbPJa2uQFQX1WmqNsCEMXtON/bNYAGDXxAQiFYZaka4D2WyoWTrc5Sbp/g/aKoUMy91WzsWnKAriiQRkH9u6cOsn6Mr7DYulQVciY4BZX4wF2EiscRv/jDUpiSZsiOjTRHSRiK4QUcMJVUE2oRoG8D3bVaEA+APG2I+J6G0A3yGirwK4BeALQW2AX/HL2gJM1XsvJFlbwPDwaF29uviYVzmTgd7T4+m1yvo6BoeGthXIhcGGK2yzxcKbKj7zzDP4/ve/D21LU0VWob9VvYyOjkJ+7TXrRLLF3cXnh9Qj+PXiCItRfwq5G2TDuggaGBgo+3hXdzfmfexwLOfzUCORwK/I+Xeo8RiL9fmqtwKe14Wpy8so1Pr8GAaklZXQk2geeuypVxipvpnjxLRb77z5k4phCCKSAfxfAJ6G1b3+bSL6QUnLfc8EJiyMsWuwOmZuvX8BQLAdA224sCjFBWgJ74WGqr6IPVXm3FejdOJfPcIy1qSc+Y10480nczLzeOa/sJoqMsbw+9/9i80vtIXID1cYYE8z1DRQsQi2pWahGcLCT/T1Wr9ukXRLuColJfT39eGGhxhBzfXyeXR7/HzWtY4kgUhqPPmhQrsbtwwNDUFRFMfdXA1lfR1gLPQkGkbq7vn9/8y3iXUD175R6ymPArhin7NBRN+Glb3besLSCoyMjECSJMhafV9ERVvCxER97UK4sCjr657L6iK5nPP6sKlUec+bKjLG7KaKPZtf6LMrjLsBlWwW2lZhsUfphvkeJZNJRKIxyIELi3X8Sm7QwYEBROrpd1UBOZfDYAXryG9kRUajg2e5K61eYZFlGSNjY1h1IyxNjHWGzDiA0tbPdwA01L8mfF9LiCiKgoHBYcia907DZOQAPeu4tLzCr3A9zyhnDJTNVnSFBE0lV1itporcFeaXxcJPrOXeP35fmDEoIkJ/f79jUQQFF65KFsvg4KCvc+8j2SyGQxJoWVYazwpr0GIBgImxMaguRjzLdoxvdLR8H8EdRLnUyoYK0na0sADA7l3jUHTvFgu3cuoVlng8jmgs5jr7hEOaBuh62XTTMHBcYXUG7/0SlmrCrGSziMXjoWfqDA0OQDaCDd5L+hriiWTFv21kZATI5/2pvmcM8vq6rxMRqyHLcsOuMEJjFgtgibPqQpyV9XUQUdMu8kLkDoBSn/8E3JWTVGTHC8vo6ChUY9nz6xoVFgDo7evzPKNctoWo2cKyNWBem2CERSrz/knZLAaakDE3ODgI1QzYFWasVS365FfPSoWMOU9rFQpAsdjQZ9wLiqL4kBXWWPAesF2o2azV2bkKUi6HdHd36IP2msDbAA4S0T4iisBq2/+DRg7YEcKC4ipgevP9y/oygAaFpafH88Q/buH0hBBQLUe9Fkulcbr1kkgkEIvHoZSzWHI5DDVBWCxXWLAWi6yvYWiw8hWyk5DSQINTDo8hhJW2LcsKGo+xWK+vt44FsEY8g7GahaZyLte0C7wwYYzpsDok/wWA8wC+Y7f0r5sdL8U8c0jWV2BE3Lf/kLVlRCLRhka29vb0QPHYjoYLUdCjYitRf+W9ZeH4FbwHgIHBQSyWEZZIiAHnUvr6+qy2LqYGSPWf2KqhsnX09x+s+DjvAqGsNN7Cnx8jrHRaVVVBDTcqbTzGwi/apHweZpVmlnKhgL4mfM6IabdcZHJ5Ol6t5zDG/gzAn/m15o4XFu4/9ioskr6CwaGhhlqGdHV1QfFYJR1WwVolNiyO5sZYAGB4aAjXbm35TtjJDc3ImtuoZcnAlAKZ9gDS16peJadSKfT29yPTQOdsjrq8DFlRQst6UlUFyDZosdieh0YsFt5loJbFohSLTbnAq1Zz0i7seFcYFxZJW/b0OkVfwdhoY3US6XTac/sN/mEPusVGJRxh8SiozLZY/OhuzBkaHIS6JcYiFQqArjelKwFvsyIF1dbF1AE9X7adSymHDh701O+qEpGlJezesye0GEIkEmm8QNJ+fb2V94D7nmtyCJM1dyo7XlicQkWP9QeqsdrwVXEymQS8xlhCGrxUCS4MrGwGYhXIf2EZHh4Gra9vCrLy4shmWCyOsAQUZ+GC5UZYlOVla0JpA8QWF3EkxNHXUR+Ehb++EYuFf7dqNvPUtKZ9D9udHS8siUQC8UQSku7BJ81MQGtcWBKJhHVS9HACIE2DJMsNXZE1woYrzKsLkLa8vnGGh4et2TQlKdtKE6ruORsWSzAdjt0Ky5EjRwDDqH/eD6zMOlpft44VErFYrOFBX3wCZSOTVXkqN9X6XmreRyALLHa8sABW6qoXi0Uy1gFmNpy/7gxm8lBzIOk6YvG4mFaHksSLktTaZrRz4XB/e3DCkt20TiWOHTsGwPskxFKi9pwRfqwwiMdjkNCgsNgxlnpGE2/swxaWat9LwwBMs6F1OpmOEJahwUGnuZ8buKujUWFx5nx7yJQiXQ99zr2f+OkKK1ezoWQyUBSlKWmgqVQKkiQ1XVjGx8fR1dODyGz9Eycis7OQZDlUiyUej0OqMEFSi43ClGIwpRiK8X3QYuWr3ckeD93IxEtn6FgVi4U/1s7fxWay47PCAGBgoB+Kec318yWjelsNt9QrLO18leSnpcWFZZPFkslgcHi4KZ2fJUlCIplCxgxGWMi0EhVqCQsR4aHjx7F89iwqNSsq1hDe2OwsDh8+HOqJM5lMQmLlY45rQ5+Dmrd6oC3ufrbscwCAzMZjkF6EpVku6bAhov8HwGcBzDLG7mv0eB0hLH19fZB095k83GJp9KrYERYvMZYmWywbJ2yvlgfb8vrGSSaTSKXTWCsRFjWTwS57pkYz6Orqwtyqt24KbpEM67huMgIfeughvPzyy5CyWZhlrt6XH3us8ot1HdG5OTz8qU/Vvdd6SKVSgN5Yy3/+HjWSrSXLMoioZYXlwSeeeIUpin9t83X91tlXXqmVwvwfAfw7AP/JjzU7Qlj6+/vBjAJgFgGp9geFB1EbFRb+ofQkLIaBWBNTHLnFQV6FxXaB+R0bGhsbw1yJsETW10NrQVKO7u5uSEvBCAsZOahqxNWFxcMPPwwAiM3MILvf20iI6NwcYBjOMcIinU4DRt5qJOlxQimHzBwURW3IqiciKKoKqpJowh9rhrAwRdk9/Uu/5Fvb/NHvfrf2moy9QkR7/VqzI2IsTqWty/oDychAUdSGUw3rFpYmusKcmgaPPZ0abWdeifGxMUTtgD2fHNlMYenp7oLM/Bu0VYpk5pByWb906NAhJJJJxKa89wqMTU+DJAkPPvig59c2Ai/65VZHPUhGFql0uuELGEVVXVksjaQ1dzIdJizufOOSkUW6q7vhD28lYSn29VX0gUum2VS/7sakP681Eo232ijH2NgYpLU1qxNvC7QxT6VSkFlwrrB0yp2wyLKMR06cQGJmxvM68elpHD58OPQiXD8KTCVjvWY6thtURanehFIIS0N0mLC4tViy6OlpvJWDM+d7ywd4+bHHKvrAyTSb+mHesFi8CQv5MCejHKOjo4CuQ8rnneywZgpLV1eXNasnAMjMobvbfSufRx99FNLqKmQPDSmpWERkbg6PfbShOU51wV3LjRSYysY6BvobzwhUI5HqFksTXWE7gY4Qlo36A3cnBMnIoq+3p+F1HYHwUDQoNVlYnIwZjxXS/Pl+Z7TxehUlk2kZYYGec2JKfqJ4FJaP2uIQ9+AOi83MAKaJx6oF9wOCt+GRjPo7M6vmmi/tfCK1hKXDssL8piOEhft2yaUrTGE5X5pAOm4lLzEW02zq/AfHfee19YbZeA+ncjgpx+vrkDMZSLLc1MFLXV1dADNBzFsPODfILO/pc7dnzx4MDA15irPEJicRiUbxwAMP1LPFhnCmgmp1CgszgWLjHTEA6wLIjbC0c+q/F4joDwG8AeAwEd0hoq82cryOyApzgoamS4vF9PYFr4QjLB4slmYLi5ORVKGQrRK81UZgFsv6OpT1dfQPDDQ05KlRSqvvDcnnk46e9dRNl4jw8ccfx/yPf2xZxS5SvZNTUzhx4kRTrsSTySQSyRTW7VlHXpH0NYAZvnRdiEWjoCouRF571oz3iXT9lptMLi/Hq/Ucxtgv+7YgOkRYVFVFJBKFZLjM5jFyvgQ2HYFoI2Fx2tCYHptnmo1XRJejq6sLajRqWSzr6xgNaYxuJfiJn4x1QPWxdb6pA0bec5v2xx9/HM8//zwi8/Mo1riSl9fWIK2s4PEmuME4Y2NjWJyqrzMzn+rqhys0HouBqnSIbmblvYuak5anI1xhAJBIJkGmC2FhBmAUfWmXXY/FghYRFvIoLGQWEY3GfK+I5zPH5fV1RHK50OazV8JJBKlRcOu2RQmHJ5Z4nRz6yCOPgCQJ8Tt3aj43NjkJwBKjZrFrYhxRo1K/gOrImvU6PyZexuNxyNVcYbbFIlq61EfHCEsqlXIlLGRbNX60y64neI8mB+/5301urTsbMvOI+2ytcIYHByHnctas+ybGV4CSzCajembT2tDnoEdHoUdHsbj7WawNfa7q8912Nt5Kd3c3Dh8+7CqAH5+awsDgoDOFshns2rULVFj0nHUIAIo2DyLJP2Gp0mqJN4712wLvFDpGWNKplCv3Dn9O0ywWw2iqxaIoimV5bIlH1boCl4wcutLBTL0cGBhAZG0N0LSG+7c1Cl9f9nkmi2R3365HOD/2+OOIzM1VHypnmkhMT+Pxxx5raufs3bt3A8yA7HHwHgDIxXkMj4z4cuGVSCSqdh3nnY9F2/z66BhhSaWSkNxYLPZz/LhSqVTHUpUmWywArMrmLRZLrStwrzUYXujt7YW07k+bnUZJJBKIxuINpcyWQ26g8emjjz4KMGalElcgsrAAFApOinKz2Gf3eVOK3jszR7U53HPAW/uaSqRSKaCKEEvFIiLRaFMv8tqZjhGWZDIJ2UWKKI8t+OEK85xuzFjTLRbA8vN7bQ2vmP4UlZaj1D3kR9V1owwNDdWfMlsBSVtxju2VBx54AGo0WjXtmD/2yCOP1LdBn9hv9zVTCh6FhRmQivOOMDWKIywVLvokTUNSjCWum44RlkQiUbFldyl+CoskSZBk2X2MxS66a7bF0t/XC9n01nZDNrOBnfRLA9pes6aCYGx0BIrhYSKpC2R9Bd09fXWlt0YiETx4/DjiVSyW6MwM9u7b13RXYjqdRv/AEJSCt1Y0cnEeMHXcc889vu0DqDyeWCoWkRbCUjcdIyzJZBJkuImxWB80v3yriqK4doW1SuO7vr4+KF6EhTFAywQmLKU1RX7UFzXK6OgoFL2+lNlKyNoSxsbqT6N95MQJKIuLkPJl3L2mifjsLB45caKBHfrHkcMHEdW8CYtqC9HBgwd92YNT21Yof06Q8nn0eszQE2zQMcISj8fBTBeuMNb4IKFS1BpdVDetbQtQKwgL6Wuu25aQmQWYEdjVcGlNUSsIy8TEBKBlPKdkVyNqLGL3rom6X/+Rj3zEOs7du9seiywsAJoWepv8Shw6dAhSYRYw3RfhKoUpyLKCvXv3+rIHJ228grCoxaLn1G/BBh0jLIlEwipCq5HmyE8WflkstZrdbaJF+hMNDAwARtH1iVNuIKPJDaUZen4JfiNMTFgCIBcX/DmgqQOFJee49XD06FEro6/MuGIuNsePH6/7+H5y+PBhgJlQittFsBJqfgr79+/37bvhCEs5Cw+AnM+3RDyvXemYlAdnmqOpgVVpCUKmv2mGtZrdbVq7RYTFaRaor8KQaxeISboVyA5KWO655x48+eSTVtpxCzQF5FfNSnEWeqzx2TCKNg+ANRSYjkajOHL0KM6UsViis7MYHhnxpXmjHxw5cgSAJRZ6zIWYMoaoNo2jRz/p2x54dqFcTlgYA4SwNETHCAsXCjILYFVOlmQWQCT5dgKLRiKOJVKLVml8xzOTZH0VRrR2lpJsC0tQVfHJZBLf+MY3Ajl2PezevRtEUl0ps+WQi3MA0LCb54H778f7589v6xsWn5/H8Y99rKFj+8n4+DgSyRSy+Um46d4n6cuAto57773Xtz04wpLbvgPJzhYTwlI/HeMKc4SlRsoxsSKisZhvRWSxWAySW2HRg2k97xUuELLuLvOpkVTZdiQSiWBi1y4oBfeunGqohWkQSQ0Ly7FjxwBdh1rSA0vKZkGZjK8n5UYhIhy79yiihdptaABAzVutaLil4wexWAzReLysK0zKWqn2zc6ga2cCFxYikonoHSL6kX27j4heIKLL9s9QLgs2emBVDxiSqflabRuPxx3BqIXUIv2JuEBILqujZX0Z3T19TU86CJOjRw4jVpz25VhKfga7d+9p+P/96NGjAOxgvQ3/vZWEBbD2Ixfuugrgq/k7kGTZt4wwTl9fH+Ts9not7h4TwlI/YVgsXwNwvuT21wG8yBg7COBF+3bgbMRYalgsZhGxmI/C4sViaWJH1VIikQh6+/ohu2xvLmvLGB9v3vCtZnDo0CGguOR6xk81Yto0jhw51PBxJiYmEEskygrLoUONH99P7r33XoAZThpxNdT8Hdxz4B7fLfmhgYGyrjAuNs3uS9fOBCosRDQB4G8C+A8ld38ewHP2788BeCbIPXC8uMISCX8tlmrN7jat3UL9icbHxpxusrWIGMsYH2s8iN1OcAtAzbtz51RC0teA4rIvFoUkSbjnwAFESlxh6tIShkZGfOl95yfHjh0D4OL9YyaixSncd98x3/cwODiISBlXmBCWxgnaYvk/APz3AEorBIcZY9MAYP8s65gnomeJ6BQRnZqbm2t4IxvB+9oWS8LHE3sikXBcXLXgLrNWSKkdHx9DxFiu/URmgopLGBPCUhf89fxE2ygH77kH0eVl53ZsZQWHfKpW95Ph4WF09/TWfP9kbQHQc4G48gYHB514yqY1s1lEotGW+B62K4EJCxF9FsAsY+x0Pa9njH2TMXaCMXbCjzRJt8IiMc1XiyWRSDiWSC2kVrJYxsdBxeWadT+Svgoww5dW5u1EKpXCnr37oOZqDueripq7CUmWfQtM79u3D8jnIeVygGlCXllx+nO1EkSE++87hmhxsurzeOA+CGEZGBgAisVt3085l0N/f39Tu0C3O0FaLB8H8ItEdAPAtwE8RUS/D+AuEY0CgP3Tn5zNGrgVFpkVfZ3BkEwmq3ZRLYV/wFvhSmlsbAxgJmStemYYd5d1msUCAA89eByxwi1rFnudRPM3cfjwEd/ianzWirK6CjmTAQzDalXfghw9ehRSfrZqIa6avwNVjQQijtzVtTWAL2ezGGqRmp92JTBhYYz9D4yxCcbYXgBfBPATxtjfAfADAF+xn/YVAM8HtYdS3MZYJFb0NXieSCSsOhYXAXypWIQkSU1PNwZKqstrxFkU+/FGqsbblePHjwN6rv56FlOHmp/Eg8cf8G1Pu3btAgCoq6tQ19Y23ddqWFlsDEq+cnadWpjCoUOHAun4zT0hW4Ulkst1TOp8UDSjjuW3ATxNRJcBPG3fDhwuLDWHfZlFX11RPGhaqYtqKVKxaI1QbgETfENYqrctkYsLkGQZIyMjYWyrpXjooYcAAJHs9bper+ZvA6a/PbxGR0dBRFDW1qDYwtKqou9U4BcqtPtnJiKFadx779FA1q9ksbTCpNJ2J5TKe8bYSwBesn9fAOBfbwaXSJKESDRWs44FRsFXV1Rpe26zhmBJmoZUScPFZjI0NARFVSEXq1sssraI4eGRps+QaQbj4+MYGBxCLncd6PU+Rz6SswTpwQcf9G1Pqqqib2AAa5kMYJqQZLllT5KDg4Po7ulFNl9eWGRtETDyVm+xAHCEJZdD0a7EJ00DNK1l37N2oWMq7wEgFotXb6zITMDUfHWF1Zr7UIpULKKrRYRFkiSMjo5BqWGxqPoidreoqyVoiAgfffQRxPPX64qzRLPXcM/BQ7530R0dGYGyvg5lfR39AwOQq/TGayZEhCOHDyNaodBUKVj3ByUs6XQaaiQCOZvF8mOPYfmxx0SqsU90lLAkEtWFhQf2/bRYas19KEUuFNDTAoOsOHt274KqV7dYVG0Ruxpo997uPPLII4CW8d7exdQQyd/ERx/1f6Lj8NAQIrkc5GwWIwH1b/OLQ4cOQi7Ols0+VAvTkCQpsKw2IkJff/+mIkn+uxCWxugoYUklk1WzwvycHsnxIixKsdgS80Y4ExMTVoylwlwWMrJgerZlffhhcMIenhXJXvX0ukjuFmDqzuv9ZGBgAFIuh0guh8EWP0EePHgQMHVrQuQWlMJdjE/sCjSZZaC/30rNtpFFnzBf6CxhSaVqWCzWY36mG/NRum6ERSoUWmL0LmdiYgIwipCMTNnH+TySThaWkZERTEzsRiR7xdPrItkrkGTZGdDlJ319fUChACmTafkT5IEDBwCgbGZdVJvF4UP+9gfbyuDAwKbqey4yrf6+tTodJixJKKh8gpcCtFjKzn0oxZ4B0XLCgsopx4q2tOl5ncrjj38Usfx1gLnrsAAAsdwVHDt2LJCaJSdmo+stPwVx7969IJK29wwzNVBhoaEZNW7o6+vb9N2U83lIktRS38N2pKOEJZFIVE03DsIVpigKEqlUTYtFKhQAxlrqRMCr6StlhnHB6bSq+608+uijgFGEmnPX3oWMHJTcJB5/7LFA9lP6GWr1E2Q0GsXwyMg2V5hSbHz4mRv6+voAu0sBYMVY0t3dkKSOOjX6Tke9e8lkEmRWthz4Y35fRfb09FQcgcrhj7fScKHRUatjcSWLRdYW0d3T1xItaJrJww8/DCIJ0exlV8+34jHMEqQAKBWTVrpQqcSB/fsQ0Tb3A1Ts4We8k0BQ9PT0AIw5F35SPu8MARPUT0cJSyqVAozKJ3jJFha/O8EO9PWVbc9dityCwhKLxdDXPwBZWyr7uKwtYmK881q5bKWrqwuHjxxBNLc5gK/FRqHFto8TiGavIBaL47777gtkP/fddx++/OUv41d+5VfwWEBWkZ/s3r17WyEuvx10Oxr+fePCIhcK6Guh72C70lFVbalUCjB165+0/U8nIxhh6e/vh3r7dtXncIul1a6WJsbHMXOlvLBEjWWMjx8IeUetyeOPfRQXLjxnjb6WrCymtaHPlX1uLHcVD594OLCi0lgshq997WuBHDsIeJIImAGQVXMjFxfQ29cfuDXMLTo5n4cOQC0U0NsGVl6r01EWi1OsWMEdJgXkCuvt7a0ZvOcWTasJS8X2+cwEiksdH1/hPPLII1YLkhrtXSRtGVJhHo884n+acbvCkz+IbXTFULRF7AohKcTJ2rS/n1I+3xbuw1anI4WFzPJuKTJziCeSvlcq9/f3bwoQlkPO5UBELfehHh0dBcq0z7fa5Zsd2SOsHA888AAURUUke63q8/jjQcVX2hHeGZtKsuoi5jLGQ3Cz8u8bT55Bi6X8tysdKSxShTiLZOSQTPo/aY/nxFezWqRcDumurpbruTUyMgIw05p0WIKsLQPYCPB3OrFYDPfffz9iueqFkpHsNaTSXbinBYdvNQvnM2TawsIMoLgSymertICZikWAsZYqUm5XOkpYeOykosVi5NHd7f+HireHKDetjiPnchhowRkQ3CLhQsKR9eVNjwuAEycehpyfdmJ15YgXruPEwx8R6awlxGIxpFJdjsUi6RmAmRgOoR1NPB6HrCjWyAo7gC8slsbpqE+3c3VilD/BS2YOPQEIi2OxVMkMU3M5DLVg+w1HWGwh4fABYGF8+dsFq/09QyR3o+zjkrYMKiz62iZ/pzA0PASy3a2ybn22wpiJQkRI2nVmvFGs38k7nUhHCgsZ5U/wCssFYgZXmvuwaW17HGqrwb/ckr666X5JX0E8kWyJaZetwn333QdJlqFWEBYuOHyOi2CD4aFBELjFYrld/RhJ7oZ0Om1ZLLawCFdY43SksFTLCgviQ1XTYmEMlM2G9kXyQjKZRDQag7xFWGR9tSX320xisRgOHz6CaP5m2cfV3E1EozERXynDwMCAY7FwYQmrw3DXFmERFkvjdJSwRCIRRKMxSMZ6+Sfo2UCEJRKJINXVVdFikfJ5wDRbslU3EWFgcHCbxaIYqxgeEsKylYcePA41P1m2b1gsfwv3339fyyVotAL9/f0AM6FFRyEbmVAzJNOpFGQhLL7SUcICAOmuLkjlXGFmETC1wAJ3g4ODFYWF39+qFsDw0CDkLVlhiplp2f02k/vvvx8wte3zWUwNcmEGDzzg33z7nYRVAc+QGfgUJCODVDq8DMlUKgVF04Sw+EjHCUtPTzeoTPCei01Q/tXhwUGoNYSlFS0WwO4cYJa0zmcM0NZaMibUbI4dOwYAUHObOy2ohSmAmc7jgs04rVWMDCR9PdR6rlQqBalEWETcsHE6T1i6uyGXSTfmmWJBfaAHBwehVoixcGEJIwumHgYGBkAlFguZecDUhbCUYXR0FKl0lyUkJaj5SQDAvffe24xttTxOBbyRBRlZ9PX2hLZ2KpUCikWQpiEaj7fsKOd2ovOEpacHCrNO5KVNArmwBOUKGxoaArLZstX3rW6x9PX1AUYBgDVJkg/+EsKyHSLCvUePIFrcLCxKfgrdPb0t+3/cbEqFRWXZ0C0W2BaLn0P+OpmOFBYuImtDn3MaBZIZrLAMDg4CjJWtvpezWXR1d0NV1UDWbhSn47KTtbO++X7BJg4fPgy5cHdTG5xocRpHDh8GETVxZ61LaSlAUNmZleBiImezwg3mEx0pLEzLWk0US+AnyyBdYUD5WhYlm21ZNxiw8Z446aBGsO9Vu3PgwAF7jrvdCp6ZkIuzOHhQpBlXYqMUIAcYWaf9UhhwMZGzWaSEsPhCxwmLZZGwbUWS/GQZqMUCQF7fnuqsZrMYbgNh4WIshKU6vE5FKVqZYbK2AJg69u/f38xttTQ8E0sysoBRDFVYSi0WISz+0HHC4nQzNTdbDpKRRTyRDMwdxS2SshZLLtceFgu4xRJsokO7w6ce8imI/KcQlsooioJINObUS4WZ8utYLIWCcIX5ROcKi77ZcpCM9UCbz/X29oIkabuwmCaQzbZ0UNd5X2yLhYwcVDWCWCzWxF21LvF4HP0DQ46g8HnuQU9DbHcSiYTT7DTME3xpwL7Tx2z7RccJy0a+/FZhyQYajJZlGT29vduEhbd5aeViQ8ctwWMsZg6pEF0V7ci+vbuh2ON1leICUuku0YOqBolEwrFYwszOKl1LZIX5Q8cJi2OxbCmSVFgW/X3BZjkNDQ1tF5YWr7oHLDdFLBYHgcdYckinxUmyGuPj41DtjtBySNMQ251UMun0pAvTYim1vIWw+IMQFhvZDD53fnhwEJEt6catXsPCSaZSG64wM4fuLmGxVGNsbAzQ1gCziIi5jIkJMcK5FslkwmkQG6ZLqnQt4Qrzh44Tllgshkgkur0RpR5sjAWwxKMdLRbAatRHtrDILI90WvRTqsbGHJsVUHFFDERzQbNiHaXririhP3ScsABAV/eWfmFmETCKgRf8DQwMALkcYGwUzsnZLEiSWr7YMN2VBmxXmGwWRKO+GvABaGp+GjA1MRDNBaViEqZLqlnr7mQ6Ulis6vuNpopBt3PhOAO/Stxhci6H7p6elu9PlE6lnBgLmXkhLDXg6eNq/g6A1nd1tgKl1kKYFktpF2VhsfhDRwpLX2/PpkaUXFhCsViwuZZFzuUw2AYnnWQyaXU1BgBD5PvXoq+vDwCgFGYACGFxQ+lJvVkn+Gg02pR1dxodKSy9vb1OI0ogvErycpMklTYRlkQiYVssDDA14TKoQTKZhKpGoBSmAWwIjaAypWLSrBO8EBZ/6Ehh6e7uBpUUSAbdzoXDhUUqERY1n2+LLsGJRMLKCrOtFiEs1SEidHV3Q7ZdrqJLQW34SZ2ImjZlUwiLPwQmLEQUI6KfEdE5IvqAiH7Lvr+PiF4gosv2z9Cj1j09PYCe2yj4C6lFCb9qdSwWxoBsti2uZi0hYeABfJGWWRunFQ5JIiblAn5SlySpaV2ghbD4Q5AWSwHAU4yx4wAeBPBpInoMwNcBvMgYOwjgRft2qHDLhDei5MISdOO7SCSCeDLpBO+pWARMsy2EhQsJTzkWFkttenusz1kylYIkdaRzwBOtcFKPRCLN3sKOILBPO7PgqVeq/Y8B+DyA5+z7nwPwTFB7qETpUCEAICOLRDIVivnd19fnuMK45dJOwsKtPGGx1IZfqITZqbedKXWFNQshLP4Q6GUUEclEdBbALIAXGGNvARhmjE0DgP2zbFtfInqWiE4R0am5uTlf97W1+l4ysujqCja+wunv7XUsFv6z1WtYgFKLRQiLW7j7Ky3cYK5ohUF3rbCHnUCgwsIYMxhjDwKYAPAoEd3n4bXfZIydYIyd8LsqfWOo0IawcLdF0PT19UG1BUVqQ2EBhLC4hbsLUymRmu0Gbi0wntbeBISw+EMojl/G2DKAlwB8GsBdIhoFAPvnbBh7KMURFjvGIrMcent7Qlm7t7cXcqFgrWsLSztkDPFUUG6xiEKy2nBhEfEod/CTejNdYc3KRttpBJkVNkhEPfbvcQCfAnABwA8AfMV+2lcAPB/UHiqxEby3LBbZzIXW0rynpwfI5wHGINkC007CAiEsruHvkbgKdkcrWCxCWPwhyHdxFMBzRCTDErDvMMZ+RERvAPgOEX0VwC0AXwhwD2VJJpMgkhyLxWoDH06Atbu7GzBNkKZByucRjcVaIhumFvwkyaQIYAhhcQP/f23mibKdEBbLziGwd5Ex9i6Ah8rcvwDgk0Gt6wZJkpBMppAxcwAzwfTwLBYnIy2fh1QoIN0mw5/4SdJUugFtsS3EsNkcOnQIsVgcJ06caPZW2gJ+Um+mELd6z752oWPlOZVOYy6TA5kFACxciwXWfG25UEBPwNX+fuGkgtruQyEstXn00Ufx6qsnm72NtoELSzMslmeffRY//NGPhCXuEx1btdXT3QXJzDmDhcKyWJzEgWIRUhsKi2TmQEQi31/gO9wV1gyL5dlnn8UPf/AD4QrziY4Vlq6uNCQz71Tfh2Wx8HWkQgGqprXNHHR+JScZWahqpKl+cMHOpJkWi8BfOlZYUqkUFJZ3LJawejltsliKxbapynZcYUyHKqwVQQCI+MbOoaOFhcwCyAhXWPg6VCwChULbCEtpymwkIuIrAv/hwiKy6NqfjhYWGDlQyBZLLBaDJMtWcaSut03XW0VRINlf/GhUWCwC/+GNOoUrrP3pcGEpQrInSYZ1giciJBIJyOvroa7rB6pqCYqwWARB0ArpxgJ/6Fhh4aN1JX110+1Q1k6lHGFppxG/PBMsJiwWQQAIi2Xn0PHCImsrkGUl1PTZZCIBxZ57307C4lgsQlgEAcBjLCMjI03eiaBROjZpmzcGlIw1xOPhNglMJpOQbWFppwaFkYgVwI+KrDBBAAwODuJLX/oSnnrqqWZvRdAgHS8ssr6GeE+4J/dUMgky22/Eb8SJsQhhEfiPJEn4J//knzR7GwIf6FhXmGOx6BkkE+Ge3EvbRrSVxWK7wES3XoFAUA0hLGYOiWS4J/dSMWkri8V2hQmLRSAQVKNjhWWT1RDyyb107XZqesdjK8JiEQgE1ehYYSm1FMK2GtpVWLigiEZ9AoGgGh0rLM08uZe2nG9HYRGuMIFAUI2OFZZmntxL126nq38uLMIVJhAIqtE+ZzWfaaawfOxjH8Pp06exf//+UNdtFOEKEwgEbujYM4Q1rCqKYrEQumvn6NGj+Pf//t+HuqYfcEERFotAIKhGx7rCADhzRcSYXXdwYREWi0AgqEZHC4vstIEXwuIFMZBJIBBUo6OFhfdQFVlO3hAWi0AgqEZHCwtXFhEz8IYQFoFAUI2OFhaylUVYLN4QwiIQCKrR0cLCEcLiDSEsAoGgGh0tLHxQnThReoNP+hMIBIJyiDMEhLB4RQiLQCCohjhDQATvvSJmkgsEgmp0tLDwrsbCYvEGY6zZWxAIBC1MR59R/+k//ad48cUXcezYsWZvpa0QFotAIKhGRwvLk08+iSeffLLZ22g7hMUiEAiq0dGuMEF9CItFIBBUQwiLQCAQCHxFCItAIBAIfCUwYSGiXUT0UyI6T0QfENHX7Pv7iOgFIrps/+wNag+CYBCuMIFAUI0gLRYdwK8zxo4CeAzAPyKiewF8HcCLjLGDAF60bwsEAoFghxCYsDDGphljZ+zf1wCcBzAO4PMAnrOf9hyAZ4LagyAYRFaYQCCoRigxFiLaC+AhAG8BGGaMTQOW+AAYqvCaZ4noFBGdmpubC2ObApcIV5hAIKhG4MJCRCkAfwzgHzPGVt2+jjH2TcbYCcbYicHBweA2KPCMsFgEAkE1AhUWIlJhicq3GGN/Yt99l4hG7cdHAcwGuQeBf/z8z/88AODw4cNN3olAIGhlgswKIwC/C+A8Y+zflDz0AwBfsX//CoDng9qDwF+efPJJvP766zhy5EiztyIQCFqYIFu6fBzAlwG8R0Rn7fv+OYDfBvAdIvoqgFsAvhDgHgQ+I4aiCQSCWgQmLIyxV+FMld/GJ4NaVyAQCATNRVTeCwQCgcBXhLAIBAKBwFeEsAgEAoHAV4SwCAQCgcBXhLAIBAKBwFeEsAgEAoHAV4SwCAQCgcBXhLAIBAKBwFeEsAgEAoHAV4SwCAQCgcBXqB1aoBPRHICbzd5HGQYAzDd7E22EeL+8Id4vb7Ty+zXPGPt0szcRFm0hLK0KEZ1ijJ1o9j7aBfF+eUO8X94Q71frIFxhAoFAIPAVISwCgUAg8BUhLI3xzWZvoM0Q75c3xPvlDfF+tQgixiIQCAQCXxEWi0AgEAh8RQiLQCAQCHxFCEsdENGniegiEV0hoq83ez+tDhH9P0Q0S0TvN3sv7QAR7SKinxLReSL6gIi+1uw9tTJEFCOinxHROfv9+q1m76nTETEWjxCRDOASgKcB3AHwNoBfZox92NSNtTBE9ASADID/xBi7r9n7aXWIaBTAKGPsDBGlAZwG8Iz4jJWHiAhAkjGWISIVwKsAvsYYe7PJW+tYhMXinUcBXGGMXWOMFQF8G8Dnm7ynloYx9gqAxWbvo11gjE0zxs7Yv68BOA9gvLm7al2YRca+qdr/xBVzExHC4p1xALdLbt+B+NILAoKI9gJ4CMBbTd5KS0NEMhGdBTAL4AXGmHi/mogQFu9QmfvE1ZHAd4goBeCPAfxjxthqs/fTyjDGDMbYgwAmADxKRMLl2kSEsHjnDoBdJbcnAEw1aS+CHYodK/hjAN9ijP1Js/fTLjDGlgG8BKBjGj62IkJYvPM2gINEtI+IIgC+COAHTd6TYAdhB6N/F8B5xti/afZ+Wh0iGiSiHvv3OIBPAbjQ1E11OEJYPMIY0wH81wD+AlZQ9TuMsQ+au6vWhoj+EMAbAA4T0R0i+mqz99TifBzAlwE8RURn7X9/o9mbamFGAfyUiN6FdeH3AmPsR03eU0cj0o0FAoFA4CvCYhEIBAKBrwhhEQgEAoGvCGERCAQCga8IYREIBAKBrwhhEQgEAoGvCGERCAQCga8IYREIBAKBryjN3oBAEDZE9HcB/DNYPd7eBWAAyAM4BmAYwD8VBXYCQf2IAklBR0FExwD8CYCPM8bmiagPwL8BMALgbwA4AOCnAO5hjOWbt1OBoH0RrjBBp/EUgO8yxuYBgDHG58R8hzFmMsYuA7gG4EizNigQtDtCWASdBqH8mIOt9wlTXiCoEyEsgk7jRQB/i4j6AcB2hQHAF4hIIqIDAPYDuNisDQoE7Y4I3gs6CsbYB0T0LwC8TEQGgHfshy4CeBlW8P7XRHxFIKgfEbwXdDxE9B8B/Igx9t1m70Ug2AkIV5hAIBAIfEVYLAKBQCDwFWGxCAQCgcBXhLAIBAKBwFeEsAgEAoHAV4SwCAQCgcBXhLAIBAKBwFf+f/QoQzJZlZ+gAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 414.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "violinplt = sns.catplot(x='cp', y='age', hue='condition', kind='violin', palette='winter', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe0f552",
   "metadata": {},
   "source": [
    "### Encoding Categorical Features\n",
    "We'll encode categorical features within our dataset, specifically 'cp,' 'thal,' and 'slope,' using one-hot encoding. We'll transform these categorical variables into a numerical format, to incorporate them into our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "30bb9993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex          object\n",
       "trestbps      int64\n",
       "chol          int64\n",
       "fbs          object\n",
       "restecg      object\n",
       "thalach       int64\n",
       "exang        object\n",
       "oldpeak       int32\n",
       "ca           object\n",
       "condition     int64\n",
       "cp_0          int32\n",
       "cp_1          int32\n",
       "cp_2          int32\n",
       "cp_3          int32\n",
       "thal_0        int32\n",
       "thal_1        int32\n",
       "thal_2        int32\n",
       "slope_0       int32\n",
       "slope_1       int32\n",
       "slope_2       int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols = ['cp', 'thal', 'slope']\n",
    "df['oldpeak'] = df['oldpeak'].astype(int)\n",
    "# Cast categorical columns to integer data type\n",
    "for col in categorical_cols:\n",
    "    df[col] = df[col].astype(int)\n",
    "\n",
    "df_encoded = pd.get_dummies(df, columns=categorical_cols, prefix_sep='_', dtype=int)\n",
    "df_encoded.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23911ae3",
   "metadata": {},
   "source": [
    "### Preparing Features and Target Variable\n",
    "Prepare the features and the target variable for our analysis. We'll create the variable 'x' by excluding the 'condition' column, which serves as our feature set. The 'y' variable is defined as the 'condition' column, representing our target variable. This separation is fundamental for our project as it sets the stage for further data analysis, modeling, and understanding the relationship between the features and the health condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ed7e0db0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features (x):\n",
      "   age sex  trestbps  chol fbs restecg  thalach exang  oldpeak ca  cp_0  cp_1  \\\n",
      "0   69   1       160   234   1       2      131     0        0  1     1     0   \n",
      "1   69   0       140   239   0       0      151     0        1  2     1     0   \n",
      "2   66   0       150   226   0       0      114     0        2  0     1     0   \n",
      "3   65   1       138   282   1       2      174     0        1  1     1     0   \n",
      "4   64   1       110   211   0       2      144     1        1  0     1     0   \n",
      "\n",
      "   cp_2  cp_3  thal_0  thal_1  thal_2  slope_0  slope_1  slope_2  \n",
      "0     0     0       1       0       0        0        1        0  \n",
      "1     0     0       1       0       0        1        0        0  \n",
      "2     0     0       1       0       0        0        0        1  \n",
      "3     0     0       1       0       0        0        1        0  \n",
      "4     0     0       1       0       0        0        1        0  \n",
      "\n",
      "Target Variable (y):\n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    1\n",
      "4    0\n",
      "Name: condition, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create DataFrame for predictor variables (features)\n",
    "x = df_encoded.drop('condition', axis=1)\n",
    "\n",
    "# Set 'y' as the target variable\n",
    "y = df_encoded['condition']\n",
    "\n",
    "\n",
    "print(\"Features (x):\")\n",
    "print(x.head())\n",
    "\n",
    "print(\"\\nTarget Variable (y):\")\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d3c7b0",
   "metadata": {},
   "source": [
    "### Scaling Features\n",
    "use the MinMaxScaler from the sklearn library to scale the feature set 'x.' Scaling is crucial for ensuring that all the features are on a similar scale, preventing any feature from dominating the analysis due to its magnitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e54a55f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.83333333, 1.        , 0.62264151, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.83333333, 0.        , 0.43396226, ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.77083333, 0.        , 0.52830189, ..., 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       ...,\n",
       "       [0.125     , 1.        , 0.24528302, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.125     , 0.        , 0.41509434, ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.125     , 1.        , 0.30188679, ..., 1.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Fit and transform the 'x' DataFrame\n",
    "x = scaler.fit_transform(x)\n",
    "\n",
    "#--- Inspect data ---\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4226d0cd",
   "metadata": {},
   "source": [
    "### Splitting the Data into Training and Testing Sets\n",
    "We'll split our dataset into training and testing sets using the train_test_split function from the sklearn library. The training set, 'X_train' and 'Y_train,' is designed to train our predictive models, while the testing set, 'X_test' and 'Y_test,' is reserved for evaluating the model's performance. By performing this data split, we ensure that our models are trained and tested on different data subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "fbc78a91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set - Predictor variables (X_train): (237, 20)\n",
      "Testing set - Predictor variables (X_test): (60, 20)\n",
      "Training set - Target variable (Y_train): (237,)\n",
      "Testing set - Target variable (Y_test): (60,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, train_size=0.8, test_size=0.2, random_state=4)\n",
    "\n",
    "\n",
    "print(\"Training set - Predictor variables (X_train):\", X_train.shape)\n",
    "print(\"Testing set - Predictor variables (X_test):\", X_test.shape)\n",
    "print(\"Training set - Target variable (Y_train):\", Y_train.shape)\n",
    "print(\"Testing set - Target variable (Y_test):\", Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8bcf5b",
   "metadata": {},
   "source": [
    "### Building and Evaluating Logistic Regression Model\n",
    "build a logistic regression model for our project. We use the sklearn library to create the 'lr_model' and train it using the training data, 'X_train' and 'Y_train.' Additionally, we assess the model's performance through cross-validation, with 10 folds, to estimate its accuracy. The 'lr_mean_score' represents the mean accuracy across the folds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "730dc22b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy from cross-validation: 0.8268\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Create logistic regression model\n",
    "lr_model = LogisticRegression()\n",
    "\n",
    "# Fit the model to the training data\n",
    "lr_model.fit(X_train, Y_train)\n",
    "\n",
    "# Perform cross-validation with 10 folds\n",
    "lr_cv_results = cross_val_score(lr_model, X_train, Y_train, cv=10)\n",
    "\n",
    "# Calculate mean score from cross-validation\n",
    "lr_mean_score = round(lr_cv_results.mean(), 4)\n",
    "\n",
    "print(\"Mean accuracy from cross-validation:\", lr_mean_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46594d21",
   "metadata": {},
   "source": [
    "### Building and Evaluating Linear Discriminant Analysis Model\n",
    "construct a Linear Discriminant Analysis (LDA) model for our project using the sklearn library. We create the 'ldr_model' and train it with the training data, 'X_train' and 'Y_train.' Subsequently, we assess the model's performance through cross-validation with 10 folds, calculating the mean accuracy. The 'ldr_mean_score' represents this mean accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b665e58f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy from cross-validation: 0.8266\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "# Create LDA model\n",
    "ldr_model = LinearDiscriminantAnalysis()\n",
    "\n",
    "# Fit the model to the training data\n",
    "ldr_model.fit(X_train, Y_train)\n",
    "\n",
    "# Perform cross-validation with 10 folds\n",
    "ldr_cv_results = cross_val_score(ldr_model, X_train, Y_train, cv=10)\n",
    "\n",
    "# Calculate mean score from cross-validation\n",
    "ldr_mean_score = round(ldr_cv_results.mean(), 4)\n",
    "\n",
    "print(\"Mean accuracy from cross-validation:\", ldr_mean_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c56d8bd3",
   "metadata": {},
   "source": [
    "### Building and Evaluating K-Nearest Neighbors (KNN) Model\n",
    "construct a K-Nearest Neighbors (KNN) model for our project using the sklearn library. We create the 'knn_model' and train it with the training data, 'X_train' and 'Y_train.' Subsequently, we assess the model's performance through cross-validation with 10 folds, calculating the mean accuracy. The 'knn_mean_score' represents this mean accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1dd554cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy from cross-validation: 0.8351\n",
      "Standard deviation of scores: 0.0767\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Create KNN model\n",
    "knn_model = KNeighborsClassifier()\n",
    "\n",
    "# Fit the model to the training data\n",
    "knn_model.fit(X_train, Y_train)\n",
    "\n",
    "# Perform cross-validation with 10 folds\n",
    "knn_cv_results = cross_val_score(knn_model, X_train, Y_train, cv=10)\n",
    "\n",
    "# Calculate mean score and standard deviation from cross-validation\n",
    "knn_mean_score = round(knn_cv_results.mean(), 4)\n",
    "knn_std_score = round(knn_cv_results.std(), 4)\n",
    "\n",
    "print(\"Mean accuracy from cross-validation:\", knn_mean_score)\n",
    "print(\"Standard deviation of scores:\", knn_std_score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76146765",
   "metadata": {},
   "source": [
    "### Building and Evaluating Decision Tree Classifier Model\n",
    "build a Decision Tree Classifier model for our project using the sklearn library. We create the 'dt_model' and train it with the training data, 'X_train' and 'Y_train.' Subsequently, we assess the model's performance through cross-validation with 10 folds, calculating the mean accuracy. The 'dt_mean_score' represents this mean accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a6fc2f4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy from cross-validation: 0.7469\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Create Decision Tree Classifier model\n",
    "dt_model = DecisionTreeClassifier()\n",
    "\n",
    "# Fit the model to the training data\n",
    "dt_model.fit(X_train, Y_train)\n",
    "\n",
    "# Perform cross-validation with 10 folds\n",
    "dt_cv_results = cross_val_score(dt_model, X_train, Y_train, cv=10)\n",
    "\n",
    "# Calculate mean score from cross-validation\n",
    "dt_mean_score = round(dt_cv_results.mean(), 4)\n",
    "\n",
    "print(\"Mean accuracy from cross-validation:\", dt_mean_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f4f2a3",
   "metadata": {},
   "source": [
    "### Building and Evaluating Gaussian Naive Bayes Model\n",
    "construct a Gaussian Naive Bayes model for our project using the sklearn library. We create the 'gnb_model' and train it with the training data, 'X_train' and 'Y_train.' Subsequently, we assess the model's performance through cross-validation with 10 folds, calculating the mean accuracy. The 'gnb_mean_score' represents this mean accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "da4b675d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy from cross-validation: 0.852\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# Create Gaussian Naive Bayes model\n",
    "gnb_model = GaussianNB()\n",
    "\n",
    "# Fit the model to the training data\n",
    "gnb_model.fit(X_train, Y_train)\n",
    "\n",
    "# Perform cross-validation with 10 folds\n",
    "gnb_cv_results = cross_val_score(gnb_model, X_train, Y_train, cv=10)\n",
    "\n",
    "# Calculate mean score from cross-validation\n",
    "gnb_mean_score = round(gnb_cv_results.mean(), 4)\n",
    "\n",
    "print(\"Mean accuracy from cross-validation:\", gnb_mean_score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ee8129",
   "metadata": {},
   "source": [
    "### Building and Evaluating Random Forest Classifier Model\n",
    "construct a Random Forest Classifier model for our project using the sklearn library. We create the 'rf_model' with 100 trees and a maximum of 3 features per split. The model is trained using the training data, 'X_train' and 'Y_train.' Subsequently, we assess the model's performance through cross-validation with 10 folds, calculating the mean accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c8ea62f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy from cross-validation: 0.8136\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Define the number of trees and maximum number of features\n",
    "num_trees = 100\n",
    "max_features = 'sqrt'\n",
    "\n",
    "# Create Random Forest Classifier model\n",
    "rf_model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features)\n",
    "\n",
    "# Fit the model to the training data\n",
    "rf_model.fit(X_train, Y_train)\n",
    "\n",
    "# Perform cross-validation with 10 folds\n",
    "rf_cv_results = cross_val_score(rf_model, X_train, Y_train, cv=10)\n",
    "\n",
    "# Calculate mean score from cross-validation\n",
    "rf_mean_score = round(rf_cv_results.mean(), 4)\n",
    "\n",
    "print(\"Mean accuracy from cross-validation:\", rf_mean_score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3185194d",
   "metadata": {},
   "source": [
    "### Building and Evaluating Support Vector Classifier (SVC) Model\n",
    "construct a Support Vector Classifier (SVC) model for our project using the sklearn library. We create the 'sv_model' and train it with the training data, 'X_train' and 'Y_train.' Subsequently, we assess the model's performance through cross-validation with 10 folds, calculating the mean accuracy. The 'sv_mean_score' represents this mean accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e7e6079b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy from cross-validation: 0.8183\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Create Support Vector Classifier (SVC) model\n",
    "sv_model = SVC()\n",
    "\n",
    "# Fit the model to the training data\n",
    "sv_model.fit(X_train, Y_train)\n",
    "\n",
    "# Perform cross-validation with 10 folds\n",
    "sv_cv_results = cross_val_score(sv_model, X_train, Y_train, cv=10)\n",
    "\n",
    "# Calculate mean score from cross-validation\n",
    "sv_mean_score = round(sv_cv_results.mean(), 4)\n",
    "\n",
    "\n",
    "print(\"Mean accuracy from cross-validation:\", sv_mean_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1569ee28",
   "metadata": {},
   "source": [
    "### Evaluating Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "21b8f62a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8\n",
      "\n",
      "Confusion Matrix:\n",
      "[[30  3]\n",
      " [ 9 18]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.91      0.83        33\n",
      "           1       0.86      0.67      0.75        27\n",
      "\n",
      "    accuracy                           0.80        60\n",
      "   macro avg       0.81      0.79      0.79        60\n",
      "weighted avg       0.81      0.80      0.80        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "# Use the selected model to predict on the test data\n",
    "y_pred = lr_model.predict(X_test)  \n",
    "\n",
    "# Calculate accuracy score\n",
    "accuracy = accuracy_score(Y_test, y_pred)\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm = confusion_matrix(Y_test, y_pred)\n",
    "\n",
    "# Create classification report\n",
    "cr = classification_report(Y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(cm)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(cr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61dc6319",
   "metadata": {},
   "source": [
    "### Making Predictions with Gaussian Naive Bayes Model\n",
    "utilize the trained Gaussian Naive Bayes model to make predictions on new data. We provide a set of features in the 'data' variable and use the 'gnb_model' to predict the corresponding health condition outcome. This prediction helps us understand how the model classifies a new instance based on the provided features, which is a crucial aspect of our project's analysis and decision-making."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7e97ae4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: [1]\n"
     ]
    }
   ],
   "source": [
    "data = [[0.254, 1, 0.487, 0.362,  ## age_scaled, sex, trestbps_scaled, chol\n",
    "             1, 0.5, 0.641, 1,  ## fbs, restecg_scaled, thalach_scaled, exang\n",
    "             0.672, 0.863, 0, 0,  ## oldpeak_scaled, ca_scaled, cp_0, cp_1\n",
    "             0, 1, 0, 0,  ## cp_2, cp_3, thal_0, thal_1\n",
    "             0, 1, 0, 1]]  ## thal_2, thal_3, slope_0, slope_1, slope_2\n",
    "\n",
    "# predict the result by passing the sample data available here to your model to make a prediction.\n",
    "prediction = lr_model.predict(data)\n",
    "\n",
    "\n",
    "print(\"Prediction:\", prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7aa13d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lr_model.pkl']"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Save the logistic regression model\n",
    "joblib.dump(lr_model, 'lr_model.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1723dca2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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