From 0edd81861afa806c0cc53a5fd140434122b2c2e6 Mon Sep 17 00:00:00 2001
From: mahehsma <maxi.hehl@gmail.com>
Date: Wed, 12 Jun 2024 09:23:15 +0200
Subject: [PATCH] removed

---
 codeprojekt_nicole.ipynb | 989 ---------------------------------------
 1 file changed, 989 deletions(-)
 delete mode 100644 codeprojekt_nicole.ipynb

diff --git a/codeprojekt_nicole.ipynb b/codeprojekt_nicole.ipynb
deleted file mode 100644
index 8892a9c..0000000
--- a/codeprojekt_nicole.ipynb
+++ /dev/null
@@ -1,989 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ffe86c63-5718-4c71-a3a3-94672d34e676",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# import libraries\n",
-    "from ucimlrepo import fetch_ucirepo\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1b8dc0f-8e8b-485a-9147-cf694f832357",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "### Explorative Datenanalyse\n",
-    "Deskriptive Statistik: Berechnung von Mittelwerten, Mediane, Standardabweichungen und anderen Kenngrößen für die verschiedenen Merkmale.  \n",
-    "Visualisierung: Nutzung von Histogrammen, Boxplots und Scatterplots, um die Verteilung und Beziehungen der Merkmale zu analysieren.  \n",
-    "Korrelation: Untersuchung der Korrelation zwischen den Merkmalen und dem Outcome."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "282a1c63-e28c-4d81-b1dc-1d971e9a4c12",
-   "metadata": {},
-   "source": [
-    "### Dataset preparation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "130b7fb5-7f85-4393-a741-05246920ce63",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 303 entries, 0 to 302\n",
-      "Data columns (total 14 columns):\n",
-      " #   Column    Non-Null Count  Dtype  \n",
-      "---  ------    --------------  -----  \n",
-      " 0   age       303 non-null    int64  \n",
-      " 1   sex       303 non-null    int64  \n",
-      " 2   cp        303 non-null    int64  \n",
-      " 3   trestbps  303 non-null    int64  \n",
-      " 4   chol      303 non-null    int64  \n",
-      " 5   fbs       303 non-null    int64  \n",
-      " 6   restecg   303 non-null    int64  \n",
-      " 7   thalach   303 non-null    int64  \n",
-      " 8   exang     303 non-null    int64  \n",
-      " 9   oldpeak   303 non-null    float64\n",
-      " 10  slope     303 non-null    int64  \n",
-      " 11  ca        299 non-null    float64\n",
-      " 12  thal      301 non-null    float64\n",
-      " 13  healthy   303 non-null    int64  \n",
-      "dtypes: float64(3), int64(11)\n",
-      "memory usage: 33.3 KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "# fetch dataset \n",
-    "heart_disease = fetch_ucirepo(id=45)\n",
-    "\n",
-    "# features\n",
-    "x = heart_disease.data.features \n",
-    "\n",
-    "# target variable\n",
-    "y = heart_disease.data.targets \n",
-    "\n",
-    "# complete dataframe\n",
-    "heart_df = pd.concat([x, y], axis=1).rename(columns={'num':'healthy'})\n",
-    "\n",
-    "# replace values for target variable: 0=sick, 1=healthy\n",
-    "heart_df['healthy'] = heart_df['healthy'].replace({0: 1, 1: 0, 2: 0, 3: 0, 4: 0})\n",
-    "\n",
-    "# view summary of dataset\n",
-    "heart_df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "0780f01d-771b-4443-86b2-0e2aa8ef464f",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of rows with NaN values to be removed: 6\n",
-      "Total number of rows: 303\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>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>healthy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
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-       "      <td>2.6</td>\n",
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-       "      <td>0</td>\n",
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-       "    <tr>\n",
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-       "      <td>3</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
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-       "      <td>0</td>\n",
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-       "      <td>130</td>\n",
-       "      <td>204</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>172</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>57</td>\n",
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-       "      <td>140</td>\n",
-       "      <td>241</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>123</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>298</th>\n",
-       "      <td>45</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>110</td>\n",
-       "      <td>264</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>132</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
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-       "      <td>193</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>141</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>131</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>115</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>301</th>\n",
-       "      <td>57</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>130</td>\n",
-       "      <td>236</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>174</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>297 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
-       "0     63    1   1       145   233    1        2      150      0      2.3   \n",
-       "1     67    1   4       160   286    0        2      108      1      1.5   \n",
-       "2     67    1   4       120   229    0        2      129      1      2.6   \n",
-       "3     37    1   3       130   250    0        0      187      0      3.5   \n",
-       "4     41    0   2       130   204    0        2      172      0      1.4   \n",
-       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
-       "297   57    0   4       140   241    0        0      123      1      0.2   \n",
-       "298   45    1   1       110   264    0        0      132      0      1.2   \n",
-       "299   68    1   4       144   193    1        0      141      0      3.4   \n",
-       "300   57    1   4       130   131    0        0      115      1      1.2   \n",
-       "301   57    0   2       130   236    0        2      174      0      0.0   \n",
-       "\n",
-       "     slope   ca  thal  healthy  \n",
-       "0        3  0.0   6.0        1  \n",
-       "1        2  3.0   3.0        0  \n",
-       "2        2  2.0   7.0        0  \n",
-       "3        3  0.0   3.0        1  \n",
-       "4        1  0.0   3.0        1  \n",
-       "..     ...  ...   ...      ...  \n",
-       "297      2  0.0   7.0        0  \n",
-       "298      2  0.0   7.0        0  \n",
-       "299      2  2.0   7.0        0  \n",
-       "300      2  1.0   7.0        0  \n",
-       "301      2  1.0   3.0        0  \n",
-       "\n",
-       "[297 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# data cleaning\n",
-    "rows_with_nan = heart_df.isna().any(axis=1).sum()\n",
-    "print(f\"Number of rows with NaN values to be removed: {rows_with_nan}\")\n",
-    "total_rows = heart_df.shape[0]\n",
-    "print(f\"Total number of rows: {total_rows}\")\n",
-    "\n",
-    "# remove rows with NaN values\n",
-    "heart_df = heart_df.dropna()\n",
-    "\n",
-    "heart_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "15d04b9c-2803-43e8-98a0-118dda07536f",
-   "metadata": {},
-   "source": [
-    "### Clustering and PCA\n",
-    "Um zu analysieren, ob ähnliche Merkmale auch zur gleichen Diagnose führen, wird zuerst ein k-Means Clustering angewandt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "76dc9ce9-8e81-4a50-ad4d-557576a58a92",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# prepare data for clustering\n",
-    "from sklearn.decomposition import PCA\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "# split data into featrues (x) and targets (y)\n",
-    "X = heart_df.iloc[:, :-1]\n",
-    "y = heart_df.iloc[:, -1]\n",
-    "\n",
-    "# scale data (MinMax probieren)\n",
-    "scaler = StandardScaler() \n",
-    "X_scaled = scaler.fit_transform (X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "b56af950-7a58-43b5-8a83-1fd95b0c7220",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2, n_init=&#x27;auto&#x27;, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2, n_init=&#x27;auto&#x27;, random_state=42)</pre></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "KMeans(n_clusters=2, n_init='auto', random_state=42)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# k-means clustering\n",
-    "from sklearn.cluster import KMeans\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "\n",
-    "# prepare model\n",
-    "kmeans = KMeans(n_clusters=2, random_state=42, n_init='auto')\n",
-    "kmeans.fit(X_scaled)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "4dfc00f7-a2b6-4408-b48d-c24a9c32f74d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "79.12% der Datensätze wurden mithilfe von KMeans richtig einem Cluster zugeordnet\n"
-     ]
-    }
-   ],
-   "source": [
-    "# calculate percentage of data points correctly assigned to each cluster\n",
-    "cluster1 = kmeans.labels_ == 0\n",
-    "cluster2 = kmeans.labels_ == 1\n",
-    "\n",
-    "perc_cluster1 = np.round(np.mean(cluster1 == y) * 100, decimals=2)\n",
-    "perc_cluster2 = np.round(np.mean(cluster2 == y) * 100, decimals=2)\n",
-    "\n",
-    "# choose cluster with higher correspondence\n",
-    "if perc_cluster1 > perc_cluster2:\n",
-    "    km_healthy = cluster1\n",
-    "    max_perc = perc_cluster1\n",
-    "else:\n",
-    "    km_healthy = cluster2\n",
-    "    max_perc = perc_cluster2\n",
-    "\n",
-    "print(f\"{max_perc}% der Datensätze wurden mithilfe von KMeans richtig einem Cluster zugeordnet\")\n",
-    "\n",
-    "# hier vlt noch irgendwie diskutieren ob das ein smart way ist um das auszuwerten, anscheinend gibt's dafür andere Metriken"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "d5edbc93-22e2-4086-aa77-ec7a76765048",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Proportion of variance explained by each principal component:\n",
-      "[0.23695056 0.12349486 0.0960383  0.0851614  0.07638123 0.06720242\n",
-      " 0.06493755 0.05994447 0.05268635 0.04368496 0.03486402 0.03140469\n",
-      " 0.02724919]\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# apply PCA\n",
-    "pca = PCA()\n",
-    "pca.fit(X_scaled)\n",
-    "\n",
-    "print(f\"Proportion of variance explained by each principal component:\\n{pca.explained_variance_ratio_}\")\n",
-    "\n",
-    "# Plot the proportion of variance explained\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.bar(range(1, len(pca.explained_variance_ratio_) + 1), pca.explained_variance_ratio_, color='skyblue')\n",
-    "plt.xlabel('Principal Component')\n",
-    "plt.ylabel('Proportion of Variance Explained')\n",
-    "plt.title('Proportion of Variance Explained by Principal Components')\n",
-    "plt.xticks(range(1, len(pca.explained_variance_ratio_) + 1))\n",
-    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "c3d40f19-8299-44ab-bb8a-326fdd2008c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Contributions of features to the first principal component:\n",
-      "     Feature  Contribution\n",
-      "9    oldpeak      0.397006\n",
-      "10     slope      0.352024\n",
-      "12      thal      0.346208\n",
-      "8      exang      0.333140\n",
-      "11        ca      0.306420\n",
-      "2         cp      0.286212\n",
-      "0        age      0.285868\n",
-      "3   trestbps      0.167841\n",
-      "6    restecg      0.145938\n",
-      "1        sex      0.116802\n",
-      "4       chol      0.083504\n",
-      "5        fbs      0.076094\n",
-      "7    thalach     -0.392706\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get the loadings or weights of features in the first principal component\n",
-    "first_pc_loadings = pca.components_[0]\n",
-    "\n",
-    "# create a DataFrame to display the contributions of features to the first principal component\n",
-    "pc_loadings_df = pd.DataFrame({\"Feature\": X.columns, \"Contribution\": first_pc_loadings})\n",
-    "pc_loadings_df = pc_loadings_df.sort_values(by=\"Contribution\", ascending=False)\n",
-    "\n",
-    "print(\"Contributions of features to the first principal component:\")\n",
-    "print(pc_loadings_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b68452ef-aa60-4b2f-aec4-4762f9136360",
-   "metadata": {},
-   "source": [
-    "#### Interpretation:\n",
-    "Die \"Proportion of Variance Explained by Principal Components\" gibt an, wie viel der gesamten Varianz in den Daten durch jede einzelne Hauptkomponente erklärt wird.\n",
-    "Hauptkomponenten mit einer höheren Proportion der erklärten Varianz tragen mehr zur Erklärung der Variation in den Daten bei und sind daher wichtiger.\n",
-    "Eine höhere Proportion der erklärten Varianz in einer bestimmten Hauptkomponente kann darauf hinweisen, dass diese Hauptkomponente wichtige Informationen über die Variation in den Merkmalen oder Beobachtungen enthält, die mit den zugrunde liegenden Mustern oder Gruppierungen in den Herzdaten zusammenhängen könnten. Dabei ist wichtig zu erwähnen, dass die Ergebnisse nicht in direktem Zusammenhang mit der goal-Variable stehen, sondern lediglich auf Muster in den Daten hinweisen.\n",
-    "\n",
-    "Merkmale, die hohe Contributions zu der ersten Hauptkomponente haben, tragen wesentlich zur Variation in den Daten bei. Diese Merkmale haben möglicherweise eine starke Korrelation mit der ersten Hauptkomponente und könnten daher wichtigere Informationen über die zugrunde liegenden Muster oder Strukturen in den Daten liefern, als jene mit einer geringen Contribution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e8dcc395-b548-4dde-bd46-67a962af6f03",
-   "metadata": {},
-   "source": [
-    "### Cholesterinwerte im Vergleich Frauen/Männer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "ed81d87f-a0b0-4dbb-88f6-471423ce3151",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/nicole/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "/Users/nicole/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "/Users/nicole/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "heart_df['sex'] = heart_df['sex'].replace({0: 'female', 1: 'male'})\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.boxplot(x='sex', y='chol', data=heart_df)\n",
-    "plt.title('Cholesterinwerte nach Geschlecht')\n",
-    "plt.xlabel('Geschlecht')\n",
-    "plt.ylabel('Cholesterin in mg/dl')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "3617a148-3ae4-4eb9-99d1-c7a6aa78bd17",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Geschlecht  Untere_Grenze  Obere_Grenze\n",
-      "0     Männer     234.288517    246.199046\n",
-      "1     Frauen     249.044612    275.413721\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Konfidenzintervall (95%) für Cholesterin Level jeweils für Männer und Frauen\n",
-    "from scipy import stats\n",
-    "\n",
-    "# Filtern nach Geschlecht und Berechnen des Konfidenzintervalls\n",
-    "conf_level = 0.95\n",
-    "chol_men = heart_df.loc[heart_df['sex'] == 'male', 'chol']\n",
-    "chol_women = heart_df.loc[heart_df['sex'] == 'female', 'chol']\n",
-    "conf_int_men = stats.t.interval(conf_level, len(chol_men) - 1, loc=chol_men.mean(), scale=stats.sem(chol_men))\n",
-    "conf_int_women = stats.t.interval(conf_level, len(chol_women) - 1, loc=chol_women.mean(), scale=stats.sem(chol_women))\n",
-    "\n",
-    "result_table_men_vs_women = pd.DataFrame({\n",
-    "    'Geschlecht': ['Männer', 'Frauen'],\n",
-    "    'Untere_Grenze': [conf_int_men[0], conf_int_women[0]],\n",
-    "    'Obere_Grenze': [conf_int_men[1], conf_int_women[1]]\n",
-    "})\n",
-    "\n",
-    "print(result_table_men_vs_women)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b4ad40f-dd3b-4309-a5f3-a085d042c1eb",
-   "metadata": {},
-   "source": [
-    "### Cholesterin im Vergleich zur Erkrankung"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "60f42093-3616-4e2b-8557-56794af3f25b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/nicole/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "/Users/nicole/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "/Users/nicole/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Boxplot gruppiert nach Diagnose\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.boxplot(x='healthy', y='chol', data=heart_df)\n",
-    "plt.title('Cholesterin im Vergleich zur Erkrankung')\n",
-    "plt.xlabel('Diagnose')\n",
-    "plt.ylabel('Cholesterin in mg/dl')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "7bef4e62-968e-4a2f-9948-063fac1e7a88",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Diagnose  Untere_Grenze  Obere_Grenze\n",
-      "0   Gesund     234.397652    250.882836\n",
-      "1    Krank     243.175250    259.774391\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Filtern nach healthy und Berechnen des Konfidenzintervalls\n",
-    "conf_level = 0.95\n",
-    "chol_healthy = heart_df.loc[heart_df['healthy'] == 1, 'chol']\n",
-    "chol_sick = heart_df.loc[heart_df['healthy'] == 0, 'chol']\n",
-    "conf_int_healthy = stats.t.interval(conf_level, len(chol_healthy) - 1, loc=chol_healthy.mean(), scale=stats.sem(chol_healthy))\n",
-    "conf_int_sick = stats.t.interval(conf_level, len(chol_sick) - 1, loc=chol_sick.mean(), scale=stats.sem(chol_sick))\n",
-    "\n",
-    "result_table_healthy_vs_sick = pd.DataFrame({\n",
-    "    'Diagnose': ['Gesund', 'Krank'],\n",
-    "    'Untere_Grenze': [conf_int_healthy[0], conf_int_sick[0]],\n",
-    "    'Obere_Grenze': [conf_int_healthy[1], conf_int_sick[1]]\n",
-    "})\n",
-    "\n",
-    "print(result_table_healthy_vs_sick)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "2aa2b03a-1da6-4846-be5d-1070fa1ae43e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t-Statistik: 1.4829209163941466\n",
-      "p-Wert: 0.06957083510218263\n",
-      "Der p-Wert ist größer als oder gleich 0.05, daher wird die Nullhypothese nicht abgelehnt.\n",
-      "Es gibt keine signifikanten Hinweise darauf, dass der Cholesterinwert bei kranken Personen höher ist als bei gesunden Personen.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# t-Test\n",
-    "# Nullhypothese (HO) = Cholesterinwert bei Kranken ist gleich oder kleiner wie bei Gesunden\n",
-    "# Alternativhypothese (H1) Cholesterinwert bei Kranken ist höher als bei Gesunden\n",
-    "\n",
-    "from scipy.stats import ttest_ind\n",
-    "\n",
-    "# Daten für gesunde und kranke Personen\n",
-    "chol_healthy = heart_df.loc[heart_df['healthy'] == 1, 'chol']\n",
-    "chol_sick = heart_df.loc[heart_df['healthy'] == 0, 'chol']\n",
-    "\n",
-    "# Durchführung des t-Tests\n",
-    "t_statistic, p_value = ttest_ind(chol_sick, chol_healthy, alternative='greater')\n",
-    "\n",
-    "# Ausgabe der Ergebnisse\n",
-    "print(\"t-Statistik:\", t_statistic)\n",
-    "print(\"p-Wert:\", p_value)\n",
-    "\n",
-    "# Überprüfung der Nullhypothese\n",
-    "if p_value < 0.05:\n",
-    "    print(\"Der p-Wert ist kleiner als 0.05, daher wird die Nullhypothese abgelehnt.\")\n",
-    "    print(\"Es gibt signifikante Hinweise darauf, dass der Cholesterinwert bei kranken Personen höher ist als bei gesunden Personen.\")\n",
-    "else:\n",
-    "    print(\"Der p-Wert ist größer als oder gleich 0.05, daher wird die Nullhypothese nicht abgelehnt.\")\n",
-    "    print(\"Es gibt keine signifikanten Hinweise darauf, dass der Cholesterinwert bei kranken Personen höher ist als bei gesunden Personen.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3852db8-3787-4ddf-aed7-80396f7a89ac",
-   "metadata": {},
-   "source": [
-    "### Systolischer Ruheblutdruck"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "f063878c-5866-4a19-b065-a41d3e26e290",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/nicole/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "/Users/nicole/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "/Users/nicole/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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+lsTEROP//u//jKCgICMwMNB48sknjZ07dxqSjNmzZ9sdu3XrVqN9+/ZGYGCg4ePjY1SqVMkYPHiwbf/19/7jjz9sbRcuXDAefPBBIygoKM+/qwCcw2IYf/pe8Ab++OMP2zgjR5g9HgAAAHAGh4pbAAAA4HZgesK9OXPmaOnSpbbt4cOHq1SpUrYVbwAAAAB3MV3cvvXWW7ZlGrdu3arp06dr4sSJCgkJ0eDBg50eEAAAAHCU6WEJxYsX18GDB1WhQgW9/PLLSkxM1Ny5c/Xrr7+qWbNmptbnBgAAAJzJdM9tyZIlbaverFq1yjZ/pa+vb675BwEAAIBbyfQKZa1bt9bTTz+tunXr6vDhw+rYsaMk6ddff7VNgg0AAAC4g+ni9j//+Y9ee+01nTp1Sl999ZVtecddu3bddGLywsxqteq3336Tv7+/U5adBAAAgHMZhqGLFy8qIiJCHh43HnzAVGC6tkZ4ZGSku2MAAADgb5w6dUrly5e/4X7TPbcbN2686f7ra5nfTq6vT3/q1KlcSz4CAADA/dLS0hQZGWmr227EdHHbrFmzXG1//io/JyfH7Cnd7nr+gIAAilsAAIBC7O+GkJqeLSElJcXukZycrBUrVqh+/fpatWpVvoMCAAAABWW65zYwMDBXW+vWreXj46PBgwdr165dTgkGAAAAmGW65/ZGQkNDdejQIWedDgAAADDNdM/t3r177bYNw1BiYqImTJig2rVrOy0YAAAAYJbp4rZOnTqyWCz66wxi999/v2bNmuW0YAAAAIBZpovb48eP2217eHgoNDRUvr6+TgsFAAAA5Ifp4jYqKsoVOQAAAIACM13cSlJ6ero2bNighIQEZWdn2+178cUXnRIMAAAAMMt0cfvTTz+pQ4cOunz5stLT0xUUFKSzZ8+qePHiKlOmDMUtAAAA3Mb0VGCDBw9W586ddf78efn5+Wnbtm06efKk6tWrp3feeccVGQEAAACHmC5u9+zZo6FDh8rT01Oenp7KyspSZGSkJk6cqJEjR7oiIwAAAOAQ08Wtl5eXbU3fsLAwJSQkSLq2ctn15wAAAIA7mB5zW7duXe3cuVNVq1ZV8+bNNXr0aJ09e1bz5s1TdHS0KzICAAAADjHdc/vWW2+pbNmykqQ33nhDwcHBeu6555ScnKwPP/zQ6QEBAEDhtWXLFnXv3l1btmxxdxRAUj6K25iYGDVv3lySFBoaqmXLliktLU27d+82vfzu+PHjVb9+ffn7+6tMmTJ65JFHdOjQIbtjDMPQ2LFjFRERIT8/PzVr1ky//vqr3TFZWVkaMGCAQkJCVKJECXXp0kWnT582+9EAAIAJmZmZmjRpkn7//XdNmjRJmZmZ7o4EmC9unWnDhg16/vnntW3bNq1evVpXr15VmzZtlJ6ebjtm4sSJmjRpkqZPn64dO3YoPDxcrVu31sWLF23HDBo0SIsXL9bChQu1efNmXbp0SZ06dVJOTo47PhYAAHeETz75ROfOnZMknTt3TgsWLHBzIkCyGIZhmHnB77//rmHDhmnt2rVKTk7WX19ekILyjz/+UJkyZbRhwwY1adJEhmEoIiJCgwYN0ssvvyzpWi9tWFiY3n77bT3zzDNKTU1VaGio5s2bp+7du0uSfvvtN0VGRmrZsmVq27bt375vWlqaAgMDlZqaqoCAgHznBwDgTnH69Gn16tXL7v/7xYoVU3x8vMqXL+/GZCiqHK3XTN9Q1rt3byUkJGjUqFEqW7asbeYEZ0hNTZUkBQUFSZKOHz+upKQktWnTxnaMj4+PmjZtqi1btuiZZ57Rrl27dOXKFbtjIiIiVLNmTW3ZsiXP4jYrK0tZWVm27bS0NKd9BgAAijrDMDR16tQbtk+cONGp9QFghunidvPmzdq0aZPq1Knj1CCGYWjIkCFq3LixatasKUlKSkqSdG3KsT8LCwvTyZMnbcd4e3urdOnSuY65/vq/Gj9+vMaNG+fU/AAA3CkSEhK0Y8eOXO05OTnasWOHEhISFBUV5YZkQD7G3EZGRuYaiuAML7zwgvbu3atPP/00176//vZnGMbf/kZ4s2NGjBih1NRU2+PUqVP5Dw4AwB2mQoUKql+/vjw9Pe3aPT091aBBA1WoUMFNyYB8FLdTpkzRK6+8ohMnTjgtxIABA/TNN99o3bp1duN0wsPDJSlXD2xycrKtNzc8PFzZ2dlKSUm54TF/5ePjo4CAALsHAABwjMVi0cCBA2/YzpAEuJNDxW3p0qUVFBSkoKAgPf7441q/fr0qVaokf39/W/v1hxmGYeiFF17QokWL9P333+uuu+6y23/XXXcpPDxcq1evtrVlZ2drw4YNatSokSSpXr168vLysjsmMTFR+/btsx0DAACcq3z58oqLi7MVshaLRXFxcSpXrpybk+FO59CY2ylTprjkzZ9//nktWLBAX3/9tfz9/W09tIGBgfLz85PFYtGgQYP01ltvqUqVKqpSpYreeustFS9eXHFxcbZj+/Xrp6FDhyo4OFhBQUEaNmyYoqOj1apVK5fkBgAAUo8ePbR8+XKdPXtWISEhtv83A+5keiowp775Db62mD17tnr37i3pWu/uuHHj9MEHHyglJUUNGzbUf/7zH9tNZ9K1SaRfeuklLViwQBkZGWrZsqX++9//KjIy0qEcTAUGAED+bNmyRVOnTtXAgQP5xhQu5Wi9lq/i1mq16ujRo0pOTpbVarXb16RJE/Np3YziFgAAoHBz2Ty327ZtU1xcnE6ePJlr1gSLxcKqYAAAAHAb08Xts88+q5iYGC1dutTpizgAAAAABWG6uD1y5Ii+/PJLVa5c2RV5AAAAgHwzPc9tw4YNdfToUVdkAQAAAArEdM/tgAEDNHToUCUlJSk6OlpeXl52+2vVquW0cAAAAIAZpmdL8PDI3dlrsVhsy93ejjeUMVsCAABA4eay2RKOHz9eoGAAAACAq5gubqOiolyRAwAAACgw0zeUSdK8efP04IMPKiIiQidPnpR0bYner7/+2qnhAAAAADNMF7fvv/++hgwZog4dOujChQu2MbalSpXSlClTnJ0PAAAAcJjp4nbatGn66KOP9Oqrr8rT09PWHhMTo19++cWp4QAAAAAzTBe3x48fV926dXO1+/j4KD093SmhAAAAgPwwXdzedddd2rNnT6725cuXq0aNGs7IBAAAAOSL6dkSXnrpJT3//PPKzMyUYRj68ccf9emnn2r8+PH6+OOPXZERAAAAcIjp4rZPnz66evWqhg8frsuXLysuLk7lypXT1KlT9fjjj7siIwAAAOAQ0yuUXbhwQaVKlZIknT17VlarVWXKlJEkHT16VJUrV3Z6SFdjhTIAAIDCzdF6zfSY2w4dOigzM1OSFBISYitsDx06pGbNmuUvLQAAAOAEpovb0qVL65FHHtHVq1dtbQcOHFCzZs306KOPOjUcAAAAYIbp4varr75Senq64uLiZBiG9u3bp2bNmumJJ57Q1KlTXZERAAAAcIjp4tbX11ffffedjhw5oscee0wtW7ZUz549NWnSJFfkA4qULVu2qHv37tqyZYu7owAAUCQ5dENZWlparrakpCS1atVKnTp10oQJE2ztt+MNWdxQhlshMzNTTz75pM6ePauQkBDNnz9fvr6+7o4FAMBtwdF6zaHi1sPDQxaLJVf79ZdaLBYZhiGLxaKcnJwCxHYPilvcCjNnztT8+fNt/1aeeuop9e3b192xAAC4LTharzk0z+26deucFgy4E50+fVoLFiyw/UJoGIYWLFigNm3aqHz58m5OBwBA0eFQcdu0aVNX5wCKLMMw8rzZ8nr7xIkT8/xmBAAAmGd6hTLp2kIOP/74o5KTk2W1Wu329ezZ0ynBgKIiISFBO3bsyNWek5OjHTt2KCEhQVFRUW5IBgBA0WO6uP3222/Vo0cPpaeny9/f367HyWKxUNwCf1GhQgXVr19fu3fvthuT7unpqXr16qlChQpuTAcAQNFieiqwoUOHqm/fvrp48aIuXLiglJQU2+P8+fOuyAjc1iwWiwYOHHjDdoYkAADgPKaL2zNnzujFF19U8eLFXZEHKJLKly+vuLg4WyFrsVgUFxencuXKuTkZAABFi+nitm3bttq5c6crsgBFWo8ePRQcHCxJCgkJUVxcnJsTAQBQ9Jgec9uxY0e99NJL2r9/v6Kjo+Xl5WW3v0uXLk4LBxQlvr6+GjJkiKZOnaqBAweygAMAAC7g0CIOf+bhcePOXhZxAAAAgCs4dRGHP/vr1F8AAABAYWF6zC0AAABQWOVrEYf09HRt2LBBCQkJys7Ottv34osvOiUYAAAAYJbp4vann35Shw4ddPnyZaWnpysoKEhnz55V8eLFVaZMGYpbAAAAuI3pYQmDBw9W586ddf78efn5+Wnbtm06efKk6tWrp3feeccVGQEAAACHmC5u9+zZo6FDh8rT01Oenp7KyspSZGSkJk6cqJEjR7oiIwAAAOAQ08Wtl5eXbZWlsLAwJSQkSJICAwNtzwEAAAB3MD3mtm7dutq5c6eqVq2q5s2ba/To0Tp79qzmzZun6OhoV2QEAAAAHGK65/att95S2bJlJUlvvPGGgoOD9dxzzyk5OVkffvih0wMCAAAAjjLVc2sYhkJDQ3XvvfdKkkJDQ7Vs2TKXBAMAAADMMtVzaxiGqlSpotOnT7sqDwAAAJBvpopbDw8PValSRefOnXNVHgAAACDfTI+5nThxol566SXt27fPFXkAAACAfLMYhmGYeUHp0qV1+fJlXb16Vd7e3vLz87Pbf/78eacGvBXS0tIUGBio1NRUBQQEuDsOAAAA/sLRes30VGBTpkwpSC4AAADAZRwubq9cuSIvLy/16tXrhscwVAEAAADu5PCY2yeeeEI3G8Gwb98+tWzZ0imhAAAAgPxwuLjdvn27nnnmmTz3/frrr2rZsqWaNGnitGAAAACAWQ4PS1i1apWaNGmioKAgTZgwwdZ+4MABtWzZUg8++KAWLlzokpAAAACAIxwubqtXr65ly5apZcuWCg4O1ksvvaSDBw+qRYsWatiwob744gt5enq6MisAAABwU6ZmS6hfv76WLFmiTp06KT09XR999JFiYmL05ZdfUtgCAADA7UxPBdaiRQstWLBAjz32mNq0aaNFixbJy8vLFdkAAAAAUxwubkuXLi2LxWLXtmnTJoWFhdm13Y6LOAAAAKBocLi4ZfEGAAAAFHYOF7c3W7wBAAAAKAwcnucWAAAAKOwobgEAAFBkUNwCAACgyKC4BQAAQJFR4OI2JydHe/bsUUpKijPyAAAAAPlmurgdNGiQZs6cKelaYdu0aVPdd999ioyM1Pr1652dDwAAAHCY6eL2yy+/VO3atSVJ3377rY4fP66DBw9q0KBBevXVV50eEAAAAHCU6eL27NmzCg8PlyQtW7ZMjz32mKpWrap+/frpl19+cXpAAAAAwFGmi9uwsDDt379fOTk5WrFihVq1aiVJunz5sjw9PZ0eEAAAAHCUwyuUXdenTx/FxsaqbNmyslgsat26tSRp+/btuueee5weEAAAAHCU6eJ27Nixqlmzpk6dOqXHHntMPj4+kiRPT0+98sorTg8IAAAAOMpiGIbh7hDulpaWpsDAQKWmpiogIMDdcVCEbdmyRVOnTtXAgQPVqFEjd8cBgAKbOXOmPvnkE/Xo0UP9+vVzdxwUYY7Wa/ma53bt2rUaOXKknn76afXt29fuYcbGjRvVuXNnRUREyGKxaMmSJXb7L126pBdeeEHly5eXn5+fqlevrvfff9/umKysLA0YMEAhISEqUaKEunTpotOnT+fnYwEulZmZqUmTJun333/XpEmTlJmZ6e5IAFAgFy5c0CeffCKr1apPPvlEFy5ccHckwHxxO27cOLVp00Zr167V2bNnlZKSYvcwIz09XbVr19b06dPz3D948GCtWLFC8+fP14EDBzR48GANGDBAX3/9te2YQYMGafHixVq4cKE2b96sS5cuqVOnTsrJyTH70QCX+uSTT3Tu3DlJ0rlz57RgwQI3JwKAghk1apSsVqskyWq1avTo0W5OBORjWELZsmU1ceJEPfXUU84NYrFo8eLFeuSRR2xtNWvWVPfu3TVq1ChbW7169dShQwe98cYbSk1NVWhoqObNm6fu3btLkn777TdFRkZq2bJlatu2rUPvzbAEuNrp06fVq1cvu1+6ihUrpvj4eJUvX96NyQAgf3bu3Klhw4blan/nnXcUExPjhkQo6lw2LCE7O/uWjRVs3LixvvnmG505c0aGYWjdunU6fPiwrWjdtWuXrly5ojZt2theExERoZo1a2rLli03PG9WVpbS0tLsHoCrGIahqVOn3rCdYe8AbjdWq1Wvv/56nvtef/11W28u4A6mi9unn376ln2d+t5776lGjRoqX768vL291a5dO/33v/9V48aNJUlJSUny9vZW6dKl7V4XFhampKSkG553/PjxCgwMtD0iIyNd+jlwZ0tISNCOHTtyDZXJycnRjh07lJCQ4KZkAJA/27dvv2HHUFpamrZv336LEwH/j+mpwDIzM/Xhhx9qzZo1qlWrlry8vOz2T5o0yWnh3nvvPW3btk3ffPONoqKitHHjRvXv319ly5a1LR6RF8MwZLFYbrh/xIgRGjJkiG07LS2NAhcuU6FCBdWvX1+7d++2K3A9PT1Vr149VahQwY3pAMC8hg0bKiAgIM8CNzAwUA0bNnRDKuAa08Xt3r17VadOHUnSvn377PbdrKA0KyMjQyNHjtTixYvVsWNHSVKtWrW0Z88evfPOO2rVqpXCw8OVnZ2tlJQUu97b5OTkmw6d8PHxsc3PC7iaxWLRwIED1atXrzzbnfnvBgBuBQ8PD40ePTrPMbdjxoyRh0e+JmMCnMJ0cbtu3TpX5MjlypUrunLlSq5/IJ6enraxPPXq1ZOXl5dWr16t2NhYSVJiYqL27duniRMn3pKcgCPKly+vuLg4zZs3z9YWFxencuXKuTEVAORfTEyMoqOj9csvv9jaatWqpfvuu8+NqYB8znPrLJcuXdKePXu0Z88eSdLx48e1Z88eJSQkKCAgQE2bNtVLL72k9evX6/jx44qPj9fcuXPVtWtXSde++ujXr5+GDh2qtWvX6qefftKTTz6p6Ojomw5bANzh0Ucftf2y5uHhoW7durk5EQAUzGuvvWa3/eqrr7opCfD/ONRz261bN8XHxysgIOBv/4e8aNEih998586dat68uW37+jjYXr16KT4+XgsXLtSIESPUo0cPnT9/XlFRUfrXv/6lZ5991vaayZMnq1ixYoqNjVVGRoZatmyp+Ph4eXp6OpwDuBW++uor28wIhmFo0aJFphc+AYDC5LvvvrPbXrp0Kdc1uJ1D89z26dNH7733nvz9/dWnT5+bHjt79mynhbtVmOcWrsY8twCKGq5ruNUcrddML+JQFFHcwpUMw9Dw4cPznC3hvvvu08SJE7mpDMBthesa3MFlizgAMId5bgEUNVzXUJhR3AIudn2e27+OA/f09FSDBg2Y5xbAbYfrGgozilvAxa7PZ3ujdr66A3C74bqGwoziFrgFrs9ze/2Cb7FYmOcWwG2N6xoKqwIVt5mZmc7KARR5PXr0UHBwsCQpJCREcXFxbk4EAAXDdQ2Fkeni1mq16o033lC5cuVUsmRJ/e9//5MkjRo1SjNnznR6QKCo8PX11ZAhQxQWFqbBgwfL19fX3ZEAoEC4rqEwMl3cvvnmm4qPj9fEiRPl7e1ta4+OjtbHH3/s1HBAUdOoUSN99tlnatSokbujAIBTcF1DYWO6uJ07d64+/PBD9ejRw+4uyVq1aungwYNODQcAAACYYbq4PXPmjCpXrpyr3Wq16sqVK04JBQAAAOSH6eL23nvv1aZNm3K1f/HFF6pbt65TQgEAAAD5UczsC8aMGaOnnnpKZ86ckdVq1aJFi3To0CHNnTtX3333nSsyAgAAAA4x3XPbuXNnffbZZ1q2bJksFotGjx6tAwcO6Ntvv1Xr1q1dkREAABRSW7ZsUffu3bVlyxZ3RwEkSRbDMAx3h3C3tLQ0BQYGKjU1VQEBAe6OAwDAbSEzM1NPPvmkzp49q5CQEM2fP5/pwOAyjtZrBVrE4dKlS0pLS7N7AACAO8Mnn3yic+fOSZLOnTunBQsWuDkRkI/i9vjx4+rYsaNKlCihwMBAlS5dWqVLl1apUqVUunRpV2QEAACFzOnTp7VgwQJd/wLYMAwtWLBAp0+fdnMy3OlM31DWo0cPSdKsWbMUFhZmW1MaAADcGQzD0NSpU2/YPnHiROoDuI3p4nbv3r3atWuXqlWr5oo8AACgkEtISNCOHTtytefk5GjHjh1KSEhQVFSUG5IB+RiWUL9+fZ06dcoVWQAAwG2gQoUKql+/vt1KpZLk6empBg0aqEKFCm5KBuSj5/bjjz/Ws88+qzNnzqhmzZry8vKy21+rVi2nhQMAAIWPxWLRwIED1atXrzzbGZIAdzJd3P7xxx86duyY+vTpY2uzWCwyDEMWi0U5OTlODQgUJVu2bNHUqVM1cOBANWrUyN1xACDfypcvr7i4OM2fP99WA8TFxalcuXLujoY7nOl5bmvUqKHq1atr+PDhed5QdjuOsWGeW9wKzAcJoKj583UtNDRU8+bN47oGl3G0XjPdc3vy5El98803qly5coECAneavOaD7Nu3r5tTAUD++fr6asiQIbZvpChsURiYvqGsRYsW+vnnn12RBSiymA8SQFHVqFEjffbZZwy1QqFhuue2c+fOGjx4sH755RdFR0fnuqGsS5cuTgsHFAXMBwkAwK1jesyth8eNO3tv1xvKGHMLVzp58mSuO4r/bM6cObflWHUAAG4lR+s108MSrFbrDR+3Y2ELuBrzQQIAcOuYLm4BmHN93scbtTMkAQAA5zE95laS1q5dq7Vr1yo5OVlWq9Vu36xZs5wSDChKmA8SAIBbw3TP7bhx49SmTRutXbtWZ8+eVUpKit0DQN569Oih4OBgSVJISIji4uLcnAgAgKLHdM/tjBkzFB8fr6eeesoVeYAii/kgAQBwPdPFbXZ2NnPZoUAMw1BmZqa7Y9xyhmGoevXqmjFjhnx8fJSRkeHuSLecr68vY4xRJN3J17WsrCxJko+Pzx3575vrWuFjurh9+umntWDBAo0aNcoVeXAHyMzMVPv27d0dA26wfPly+fn5uTsG4HRc1+5cXNcKH4eK2yFDhtieW61Wffjhh1qzZo1q1aqVaxGHSZMmOTchAAAA4CCHFnFo3ry5YyezWPT9998XONStxiIOt9ad+vVdZmamunbtKklavHjxHTnmlq/vUFRxXeO6BtdztF5zqOd23bp1TgsGWCyWO/4rHF9f3zv+ZwAUJVzXuK6h8GARBwAAABQZ+VrEYceOHfriiy+UkJCg7Oxsu32LFi1ySjAAAADALNM9twsXLtSDDz6o/fv3a/Hixbpy5Yr279+v77//XoGBga7ICAAAADjEdHH71ltvafLkyfruu+/k7e2tqVOn6sCBA4qNjVWFChVckREAAABwiOni9tixY+rYsaOkaxM2p6eny2KxaPDgwfrwww+dHhAAAABwlOniNigoSBcvXpQklStXTvv27ZMkXbhwQZcvX3ZuOgAAAMAE0zeUPfTQQ1q9erWio6MVGxurgQMH6vvvv9fq1avVsmVLV2QEAAAAHGK6uJ0+fbptouoRI0bIy8tLmzdvVrdu3ViSFwAAAG5lurgNCgqyPffw8NDw4cM1fPhwp4YCAAAA8iNf89xarVYdPXpUycnJslqtdvuaNGnilGAAAACAWaaL223btikuLk4nT56UYRh2+ywWi3JycpwWDgAAADDDdHH77LPPKiYmRkuXLlXZsmVlsVhckQsAAAAwzXRxe+TIEX355ZeqXLmyK/IAAAAA+WZ6ntuGDRvq6NGjrsgCAAAAFIjpntsBAwZo6NChSkpKUnR0tLy8vOz216pVy2nhAAAAADNMF7ePPvqoJKlv3762NovFIsMwuKEMAAAAbmW6uD1+/LgrcgAAAAAF5lBxe99992nt2rUqXbq05syZo2HDhql48eKuzgYAAACY4tANZQcOHFB6erokady4cbp06ZJLQwEAAAD54VDPbZ06ddSnTx81btxYhmHonXfeUcmSJfM8dvTo0U4NCAAAADjKoeI2Pj5eY8aM0XfffSeLxaLly5erWLHcL7VYLBS3AAAAcBuHittq1app4cKFkiQPDw+tXbtWZcqUcWkwAAAAwCzTsyVYrVZX5AAAAAAKzHRxO3fu3Jvu79mzZ77DAAAAAAVhurgdOHCg3faVK1d0+fJleXt7q3jx4hS3AAAAcBuHpgL7s5SUFLvHpUuXdOjQITVu3FiffvqpKzICAAAADjFd3OalSpUqmjBhQq5eXQAAAOBWckpxK0menp767bffnHU6AAAAwDTTY26/+eYbu23DMJSYmKjp06frwQcfdFowAAAAwCzTxe0jjzxit22xWBQaGqoWLVro3XffdVYuAAAAwDTmuQUAAECR4bQxt7/88osGDRrkrNMBAAAAphWouE1LS9MHH3ygBg0aqHbt2lq/fr2p12/cuFGdO3dWRESELBaLlixZkuuYAwcOqEuXLgoMDJS/v7/uv/9+JSQk2PZnZWVpwIABCgkJUYkSJdSlSxedPn26IB8LAAAAt6l8FbcbNmxQz549VbZsWfXv318tWrTQ4cOHtWfPHlPnSU9PV+3atTV9+vQ89x87dkyNGzfWPffco/Xr1+vnn3/WqFGj5Ovraztm0KBBWrx4sRYuXKjNmzfr0qVL6tSpk3JycvLz0QAAAHAbc3jMbWJiombPnq1Zs2YpPT1dTzzxhDZs2KAHHnhAPXv2VOXKlU2/efv27dW+ffsb7n/11VfVoUMHTZw40dZ29913256npqZq5syZmjdvnlq1aiVJmj9/viIjI7VmzRq1bdvWdCYAAADcvhzuub3rrrt04MAB/ec//9GZM2c0adIkxcTEuCyY1WrV0qVLVbVqVbVt21ZlypRRw4YN7YYu7Nq1S1euXFGbNm1sbREREapZs6a2bNlyw3NnZWUpLS3N7gEAAIDbn8PFbVRUlDZv3qyNGzfq8OHDrswkSUpOTtalS5c0YcIEtWvXTqtWrVLXrl3VrVs3bdiwQZKUlJQkb29vlS5d2u61YWFhSkpKuuG5x48fr8DAQNsjMjLSpZ8FAAAAt4bDxe2hQ4c0f/58JSYmqn79+qpXr54mT54s6dpct852fcqxhx9+WIMHD1adOnX0yiuvqFOnTpoxY8ZNX2sYxk0zjRgxQqmpqbbHqVOnnJodAAAA7mHqhrIHH3xQs2bNUmJiop599ll9/vnnysnJUf/+/fXRRx/pjz/+cFqwkJAQFStWTDVq1LBrr169um22hPDwcGVnZyslJcXumOTkZIWFhd3w3D4+PgoICLB7AAAA4PaXr9kSSpYsqX/84x/aunWrfv31V9WrV0+vvfaaIiIinBbM29tb9evX16FDh+zaDx8+rKioKElSvXr15OXlpdWrV9v2JyYmat++fWrUqJHTsgAAAOD2YHqFsr+qXr263nnnHU2YMEHffPONqddeunRJR48etW0fP35ce/bsUVBQkCpUqKCXXnpJ3bt3V5MmTdS8eXOtWLFC3377rW0+3cDAQPXr109Dhw5VcHCwgoKCNGzYMEVHR9tmTwAAAMCdo8DFre1ExYqpW7dupl6zc+dONW/e3LY9ZMgQSVKvXr0UHx+vrl27asaMGRo/frxefPFFVatWTV999ZUaN25se83kyZNVrFgxxcbGKiMjQy1btlR8fLw8PT2d88EAAABw27AYhmG4O4S7paWlKTAwUKmpqYy/hctkZGTY5nVevny5/Pz83JwIAAqG6xpuJUfrtQItvwsAAAAUJhS3AAAAKDIobgEAAFBkOHRDWbdu3RQfH6+AgIC/vWls0aJFTgkGAAAAmOVQcRsYGGhb8SswMNClgQAAAID8cqi4nT17dp7PAQAAgMIkX2Nur169qjVr1uiDDz7QxYsXJUm//fabLl265NRwAAAAgBmmF3E4efKk2rVrp4SEBGVlZal169by9/fXxIkTlZmZqRkzZrgiJwAAAPC3TPfcDhw4UDExMUpJSbGbrLlr165au3atU8MBAAAAZpjuud28ebN++OEHeXt727VHRUXpzJkzTgsGAAAAmGW659ZqtSonJydX++nTp+Xv7++UUAAAAEB+mC5uW7durSlTpti2LRaLLl26pDFjxqhDhw7OzAYAAACYYnpYwuTJk9W8eXPVqFFDmZmZiouL05EjRxQSEqJPP/3UFRkBAAAAh5gubiMiIrRnzx59+umn2r17t6xWq/r166cePXrY3WAGAAAA3Gqmi1tJ8vPzU9++fdW3b19n5wEAAADyLV/F7aFDhzRt2jQdOHBAFotF99xzj1544QXdc889zs4HAAAAOMz0DWVffvmlatasqV27dql27dqqVauWdu/erejoaH3xxReuyAgAAAA4xHTP7fDhwzVixAi9/vrrdu1jxozRyy+/rMcee8xp4QAAAAAzTPfcJiUlqWfPnrnan3zySSUlJTklFAAAAJAfpovbZs2aadOmTbnaN2/erIceesgpoQAAAID8cGhYwjfffGN73qVLF7388svatWuX7r//fknStm3b9MUXX2jcuHGuSQkAAAA4wGIYhvF3B3l4ONbBa7FY8lyat7BLS0tTYGCgUlNTFRAQ4O44KKIyMjLUvn17SdLy5cuZFxrAbY/rGm4lR+s1h3purVar04IBAAAArmJ6zC0AAABQWFHcAgAAoMiguAUAAECRQXELAACAIoPiFgAAAEWG6eV309LS8my3WCzy8fGRt7d3gUMBAAAA+WG6uC1VqpQsFssN95cvX169e/fWmDFjHJ4fFwAAAHAG08VtfHy8Xn31VfXu3VsNGjSQYRjasWOH5syZo9dee01//PGH3nnnHfn4+GjkyJGuyAwAAADkyXRxO2fOHL377ruKjY21tXXp0kXR0dH64IMPtHbtWlWoUEH/+te/KG4BAABwS5keN7B161bVrVs3V3vdunW1detWSVLjxo2VkJBQ8HQAAACACaaL2/Lly2vmzJm52mfOnKnIyEhJ0rlz51S6dOmCpwMAAABMMD0s4Z133tFjjz2m5cuXq379+rJYLNqxY4cOHjyoL7/8UpK0Y8cOde/e3elhAQAAgJsxXdx26dJFhw4d0owZM3T48GEZhqH27dtryZIlqlixoiTpueeec3ZOAAAA4G+ZLm4lqWLFipowYYKzswAAAAAF4nBxu3fvXoeOq1WrVr7DAAAAAAXhcHFbp04dWSwWGYYhSbaFHK5vX2/LyclxckQAAADAMQ4Xt8ePH7c9NwxDNWvW1LJlyxQVFeWSYAAAAIBZDhe3fy1iLRaLypcvT3ELAACAQsP0PLcAAABAYUVxCwAAgCKjQMXt9ZvKAAAAgMLA4TG3devWtStmMzIy1LlzZ3l7e9sdt3v3buelAwAAAExwuLh95JFH7LYffvhhZ2cBAAAACsTh4nbMmDGuzAEAAAAUGDeUAQAAoMhwuOf2unPnzmn06NFat26dkpOTZbVa7fafP3/eaeEAAAAAM0wXt08++aSOHTumfv36KSwsjBkTAAAAUGiYLm43b96szZs3q3bt2q7IAwAAAOSb6TG399xzjzIyMlyRBQAAACgQ08Xtf//7X7366qvasGGDzp07p7S0NLsHAAAA4C6mhyWUKlVKqampatGihV27YRiyWCzKyclxWjgAAADADNPFbY8ePeTt7a0FCxZwQxkAAAAKFdPF7b59+/TTTz+pWrVqrsgDAAAA5JvpMbcxMTE6deqUK7IAAAAABWK653bAgAEaOHCgXnrpJUVHR8vLy8tuf61atZwWDgAAADDDdHHbvXt3SVLfvn1tbRaLhRvKAAAA4Hami9vjx4+7IgcAAABQYKaL26ioKFfkAAAAAArMdHErSWfOnNEPP/yg5ORkWa1Wu30vvviiU4IBAAAAZpkubmfPnq1nn31W3t7eCg4Otpvn1mKxUNwCAADAbUwXt6NHj9bo0aM1YsQIeXiYnkkMAAAAcBnT1enly5f1+OOPU9gCAACg0DFdofbr109ffPGFK7IAAAAABWJ6WML48ePVqVMnrVixIs9FHCZNmuS0cAAAAIAZpovbt956SytXrlS1atUkKdcNZQAAAIC7mC5uJ02apFmzZql3794uiAMAAADkn+kxtz4+PnrwwQed8uYbN25U586dFRERIYvFoiVLltzw2GeeeUYWi0VTpkyxa8/KytKAAQMUEhKiEiVKqEuXLjp9+rRT8gEAAOD2YrrnduDAgZo2bZree++9Ar95enq6ateurT59+ujRRx+94XFLlizR9u3bFRERkWvfoEGD9O2332rhwoUKDg7W0KFD1alTJ+3atUuenp4FzuhKhmEoMzPT3TFwi/z5z5o/9zuLr6/vHTNsi+vanYXr2p2rMF/XLIZhGGZe0LVrV33//fcKDg7Wvffem+uGskWLFuUviMWixYsX65FHHrFrP3PmjBo2bKiVK1eqY8eOGjRokAYNGiRJSk1NVWhoqObNm6fu3btLkn777TdFRkZq2bJlatu2bZ7vlZWVpaysLNt2WlqaIiMjlZqaqoCAgHzlz4+MjAy1b9/+lr0fAPdYvny5/Pz83B3jluC6BtwZ3HFdS0tLU2Bg4N/Wa6Z7bkuVKqVu3boVKJyjrFarnnrqKb300ku69957c+3ftWuXrly5ojZt2tjaIiIiVLNmTW3ZsuWGxe348eM1btw4l+UGAACAe+Rr+d1b5e2331axYsVuuKRvUlKSvL29Vbp0abv2sLAwJSUl3fC8I0aM0JAhQ2zb13tu3elSnSdkeJj+48DtxDAk69Vrzz2KSYX06xw4h8V6VSX3fOruGG41vfF5+Xia+nIQtxnDkLKt1557e3BZK+qycix6YXOQu2P8rUJbTe3atUtTp07V7t27TY/pMAzjpq/x8fGRj49PQSM6leFRTPL0+vsDcZvzdncA3CKUdJKPpyGfwn3rA5zA190BcAvdHle2QruG7qZNm5ScnKwKFSqoWLFiKlasmE6ePKmhQ4eqYsWKkqTw8HBlZ2crJSXF7rXJyckKCwtzQ2oAAAC4U6Etbp966int3btXe/bssT0iIiL00ksvaeXKlZKkevXqycvLS6tXr7a9LjExUfv27VOjRo3cFR0AAABu4tZhCZcuXdLRo0dt28ePH9eePXsUFBSkChUqKDg42O54Ly8vhYeH21ZHCwwMVL9+/TR06FAFBwcrKChIw4YNU3R0tFq1anVLPwsAAADcz63F7c6dO9W8eXPb9vWbvHr16qX4+HiHzjF58mQVK1ZMsbGxysjIUMuWLRUfH1/o57gFAACA8+WruF27dq3Wrl2r5ORkWa1Wu32zZs1y+DzNmjWTmWl2T5w4kavN19dX06ZN07Rp0xw+DwAAAIom08XtuHHj9PrrrysmJkZly5YttKtTAAAA4M5juridMWOG4uPj9dRTT7kiDwAAAJBvpmdLyM7OZiYCAAAAFEqmi9unn35aCxYscEUWAAAAoEBMD0vIzMzUhx9+qDVr1qhWrVry8rJfVWvSpElOCwcAAACYYbq43bt3r+rUqSNJ2rdvn90+bi4DAACAO5kubtetW+eKHAAAAECBFdrldwEAAACzHOq57datm+Lj4xUQEKBu3brd9NhFixY5JRgAAABglkPFbWBgoG08bWBgoEsDAQAAAPnlUHE7e/bsPJ8DAAAAhQljbgEAAFBkOFTctmvXTlu2bPnb4y5evKi3335b//nPfwocDAAAADDLoWEJjz32mGJjY+Xv768uXbooJiZGERER8vX1VUpKivbv36/Nmzdr2bJl6tSpk/7973+7OjcAAACQi0PFbb9+/fTUU0/pyy+/1GeffaaPPvpIFy5ckHRt4YYaNWqobdu22rVrl6pVq+bKvAAAAMANObyIg7e3t+Li4hQXFydJSk1NVUZGhoKDg3MtwQsAAAC4g+kVyq4LDAxkWjAAAAAUKsyWAAAAgCKD4hYAAABFBsUtAAAAigynFreGYTjzdAAAAIAppovb8ePH59mek5Njm0kBAAAAcAfTsyVMmTJFwcHB+uc//2lry8nJ0eOPP659+/Y5NRwA4PaVlePuBACc6Xb5N226uF22bJlatWqlUqVKKTY2VleuXFH37t118OBBrVu3zhUZAQC3oRc2B7s7AoA7kOnitl69elq8eLEefvhh+fj4aObMmTp27JjWrVunsLAwV2QEAAAAHJKvRRyaNWumefPm6dFHH1X16tW1YcMGhYSEODsbAOA2Nr3xOfl4ujsFAGfJyrk9vpFxqLjt1q1bnu2hoaEqVaqU3fjbRYsWOScZAOC25uMpilsAt5xDxe2Nltlt27atU8MAAAAABeFQcTt79mxX5wAAAAAKzPQ8t8ePH9eRI0dytR85ckQnTpxwRiYAAAAgX0zfUNa7d2/17dtXVapUsWvfvn27Pv74Y61fv95Z2e4sOVfcnQCAM/FvGgDcwnRx+9NPP+nBBx/M1X7//ffrhRdecEqoO5H/zwvdHQEAAOC2Z3pYgsVi0cWLF3O1p6amKifnNlm6AgAAAEWS6Z7bhx56SOPHj9enn34qT89rc7zk5ORo/Pjxaty4sdMD3iku1n5c8vRydwwAzpJzhW9kAMANTBe3EydOVJMmTVStWjU99NBDkqRNmzYpLS1N33//vdMD3jE8vShuAQAACsj0sIQaNWpo7969io2NVXJysi5evKiePXvq4MGDqlmzpisyAgAAAA7J1/K7EREReuutt5ydBQAAACgQ08Xtxo0bb7q/SZMm+Q4DAAAAFITp4rZZs2a52iwWi+05MyYAAADAXUyPuU1JSbF7JCcna8WKFapfv75WrVrliowAAACAQ0z33AYGBuZqa926tXx8fDR48GDt2rXLKcEAAAAAs0z33N5IaGioDh065KzTAQAAAKaZ7rndu3ev3bZhGEpMTNSECRNUu3ZtpwUDAAAAzDJd3NapU0cWi0WGYdi133///Zo1a5bTggEAAABmmS5ujx8/brft4eGh0NBQ+fr6Oi0UAAAAkB+mxtxeuXJFvXv3VlZWlqKiohQVFaXIyEgKWwAAABQKpopbLy8v7du3z25eWwAAAKCwMD1bQs+ePTVz5kxXZAEAAAAKxPSY2+zsbH388cdavXq1YmJiVKJECbv9kyZNclo4AAAAwAzTxe2+fft03333SZIOHz5st4/hCgAAAHAn08XtunXrXJEDAAAAKDCnrVAGAAAAuJtDPbfdunVz+ISLFi3KdxgAAACgIBzquQ0MDLQ9AgICtHbtWu3cudO2f9euXVq7dq0CAwNdFhQAAAD4Ow713M6ePdv2/OWXX1ZsbKxmzJghT09PSVJOTo769++vgIAA16QEAAAAHGB6zO2sWbM0bNgwW2ErSZ6enhoyZIhmzZrl1HAAAACAGaaL26tXr+rAgQO52g8cOCCr1eqUUAAAAEB+mJ4KrE+fPurbt6+OHj2q+++/X5K0bds2TZgwQX369HF6QAAAAMBRpovbd955R+Hh4Zo8ebISExMlSWXLltXw4cM1dOhQpwcEAAAAHGW6uPXw8NDw4cM1fPhwpaWlSRI3kgEAAKBQMD3mtkWLFrpw4YKka0Xt9cI2LS1NLVq0cGo4AAAAwAzTxe369euVnZ2dqz0zM1ObNm1ySigAAAAgPxwelrB3717b8/379yspKcm2nZOToxUrVqhcuXLOTQcAAACY4HBxW6dOHVksFlksljyHH/j5+WnatGlODQcAAACY4XBxe/z4cRmGobvvvls//vijQkNDbfu8vb1VpkwZu4UdAAAAgFvN4eI2KipKklioAQAAAIWW6anA5s6de9P9PXv2zHcYAAAAoCBMF7cDBw60275y5YouX74sb29vFS9enOIWAAAAbmN6KrCUlBS7x6VLl3To0CE1btxYn376qalzbdy4UZ07d1ZERIQsFouWLFli23flyhW9/PLLio6OVokSJRQREaGePXvqt99+sztHVlaWBgwYoJCQEJUoUUJdunTR6dOnzX4sAAAAFAGmi9u8VKlSRRMmTMjVq/t30tPTVbt2bU2fPj3XvsuXL2v37t0aNWqUdu/erUWLFunw4cPq0qWL3XGDBg3S4sWLtXDhQm3evFmXLl1Sp06dlJOTU6DPBAAAgNuP6WEJN+Lp6ZmrV/XvtG/fXu3bt89zX2BgoFavXm3XNm3aNDVo0EAJCQmqUKGCUlNTNXPmTM2bN0+tWrWSJM2fP1+RkZFas2aN2rZtm78P4wYW61UZ7g4B1zIMyXr12nOPYpLF4t48cCnL9T/rO1hWjkXiylakGYaU/f/fZ+7twWWtqLv2b7rwM13cfvPNN3bbhmEoMTFR06dP14MPPui0YHlJTU2VxWJRqVKlJEm7du3SlStX1KZNG9sxERERqlmzprZs2XLD4jYrK0tZWVm27bS0NJfmdkTJPeaGdABAYffC5iB3RwBwBzJd3D7yyCN22xaLRaGhoWrRooXeffddZ+XKJTMzU6+88ori4uIUEBAgSUpKSpK3t7dKly5td2xYWJjdCmp/NX78eI0bN85lWQEAAOAepotbd8xze+XKFT3++OOyWq3673//+7fHG4Yhy02+GxkxYoSGDBli205LS1NkZKRTsprh6+ur5cuX3/L3hXtkZmaqa9eukqTFixfL19fXzYlwq9xJf9Zc1+4sXNfuXIX5z9ppY25d5cqVK4qNjdXx48f1/fff23ptJSk8PFzZ2dlKSUmx671NTk5Wo0aNbnhOHx8f+fj4uDS3IywWi/z8/NwdA27g6+vLnz2KJK5rdy6uaygsHCpu/9zL+XcmTZqU7zB/db2wPXLkiNatW6fg4GC7/fXq1ZOXl5dWr16t2NhYSVJiYqL27duniRMnOi0HAAAAbg8OFbc//fSTQye72VCAvFy6dElHjx61bR8/flx79uxRUFCQIiIi9H//93/avXu3vvvuO+Xk5NjG0QYFBcnb21uBgYHq16+fhg4dquDgYAUFBWnYsGGKjo62zZ4AAACAO4dDxe26detc8uY7d+5U8+bNbdvXe4h79eqlsWPH2mZmqFOnTq48zZo1kyRNnjxZxYoVU2xsrDIyMtSyZUvFx8fL09PTJZkBAABQeBVozO2pU6dksVhUvnz5fL2+WbNmMowbz4F4s33X+fr6atq0aZo2bVq+MgAAAKDoML1C2dWrVzVq1CgFBgaqYsWKioqKUmBgoF577TVduXLFFRkBAAAAh5juuX3hhRe0ePFiTZw4UQ888IAkaevWrRo7dqzOnj2rGTNmOD0kAAAA4AjTxe2nn36qhQsX2i2bW6tWLVWoUEGPP/44xS0AAADcxvSwBF9fX1WsWDFXe8WKFeXt7e2MTAAAAEC+mC5un3/+eb3xxhvKysqytWVlZelf//qXXnjhBaeGAwAAAMxwaFhCt27d7LbXrFmj8uXLq3bt2pKkn3/+WdnZ2WrZsqXzEwIAAAAOcqi4DQwMtNt+9NFH7bYjIyOdlwgAAADIJ4eK29mzZ7s6BwAAAFBgpsfcStfmul2zZo0++OADXbx4UZL022+/6dKlS04NBwAAAJhheiqwkydPql27dkpISFBWVpZat24tf39/TZw4UZmZmUwFBgAAALcx3XM7cOBAxcTEKCUlRX5+frb2rl27au3atU4NBwAAAJhhuud28+bN+uGHH3LNaRsVFaUzZ844LRgAAABglumeW6vVqpycnFztp0+flr+/v1NCAQAAAPlhurht3bq1pkyZYtu2WCy6dOmSxowZow4dOjgzGwAAAGCK6WEJkydPVvPmzVWjRg1lZmYqLi5OR44cUUhIiD799FNXZAQAAAAcYrq4jYiI0J49e/Tpp59q9+7dslqt6tevn3r06GF3gxkAAABwq5kubiXJz89Pffv2Vd++fZ2dBwAAAMi3fBW3hw8f1vr165WcnCyr1Wq3b/To0U4JBgAAAJhlurj96KOP9NxzzykkJETh4eGyWCy2fRaLheIWAAAAbmO6uH3zzTf1r3/9Sy+//LIr8gAAAAD5ZnoqsJSUFD322GOuyAIAAAAUiOni9rHHHtOqVatckQUAAAAoEIeGJbz33nu255UrV9aoUaO0bds2RUdHy8vLy+7YF1980bkJAQAAAAc5VNxOnjzZbrtkyZLasGGDNmzYYNdusVgobgEAAOA2DhW3x48fd3UOAAAAoMBMj7kFAAAACivTU4H93apks2bNyncYAAAAoCBMF7cpKSl221euXNG+fft04cIFtWjRwmnBAAAAALNMF7eLFy/O1Wa1WtW/f3/dfffdTgkFAAAA5IdTxtx6eHho8ODBuWZVAAAAAG4lp91QduzYMV29etVZpwMAAABMMz0sYciQIXbbhmEoMTFRS5cuVa9evZwWDAAAADDLdHH7008/2W17eHgoNDRU77777t/OpAAAAAC4kunidt26dTfcd+bMGZUrV65AgQAAAID8csqY26SkJA0YMECVK1d2xukAAACAfHG4uL1w4YJ69Oih0NBQRURE6L333pPVatXo0aN19913a9u2bSzgAAAAALdyeFjCyJEjtXHjRvXq1UsrVqzQ4MGDtWLFCmVmZmr58uVq2rSpK3MCAAAAf8vh4nbp0qWaPXu2WrVqpf79+6ty5cqqWrWqpkyZ4sJ4AAAAgOMcHpbw22+/qUaNGpKku+++W76+vnr66addFgwAAAAwy+Hi1mq1ysvLy7bt6empEiVKuCQUAAAAkB8OD0swDEO9e/eWj4+PJCkzM1PPPvtsrgJ30aJFzk0IAAAAOMjh4vavq489+eSTTg8DAAAAFITDxe3s2bNdmQMAAAAoMKcs4gAAAAAUBhS3AAAAKDIobgEAAFBkUNwCAACgyDBV3F65ckV9+vTR//73P1flAQAAAPLNVHHr5eWlxYsXuyoLAAAAUCCmhyV07dpVS5YscUEUAAAAoGAcnuf2usqVK+uNN97Qli1bVK9evVwrlL344otOCwcAAACYYbq4/fjjj1WqVCnt2rVLu3btsttnsVgobgEAAOA2povb48ePuyIHAAAAUGD5ngosOztbhw4d0tWrV52ZBwAAAMg308Xt5cuX1a9fPxUvXlz33nuvEhISJF0bazthwgSnBwQAAAAcZbq4HTFihH7++WetX79evr6+tvZWrVrps88+c2o4AAAAwAzTY26XLFmizz77TPfff78sFoutvUaNGjp27JhTwwEAAABmmO65/eOPP1SmTJlc7enp6XbFLgAAAHCrmS5u69evr6VLl9q2rxe0H330kR544AHnJQMAAABMMj0sYfz48WrXrp3279+vq1evaurUqfr111+1detWbdiwwRUZAQAAAIeY7rlt1KiRfvjhB12+fFmVKlXSqlWrFBYWpq1bt6pevXquyAgAAAA4xHTPrSRFR0drzpw5zs4CAAAAFEi+ilur1aqjR48qOTlZVqvVbl+TJk2cEgwAAAAwy3Rxu23bNsXFxenkyZMyDMNun8ViUU5OjtPCAQAAAGaYLm6fffZZxcTEaOnSpSpbtizTfwEAAKDQMF3cHjlyRF9++aUqV67sijwAAABAvpmeLaFhw4Y6evSoK7IAAAAABeJQz+3evXttzwcMGKChQ4cqKSlJ0dHR8vLysju2Vq1azk0IAAAAOMihnts6deqobt26qlOnjh599FEdOHBAffv2Vf369e321a1b19Sbb9y4UZ07d1ZERIQsFouWLFlit98wDI0dO1YRERHy8/NTs2bN9Ouvv9odk5WVpQEDBigkJEQlSpRQly5ddPr0aVM5AAAAUDQ41HN7/Phxl7x5enq6ateurT59+ujRRx/NtX/ixImaNGmS4uPjVbVqVb355ptq3bq1Dh06JH9/f0nSoEGD9O2332rhwoUKDg7W0KFD1alTJ+3atUuenp4uyQ0AAIDCyaHiNioqyiVv3r59e7Vv3z7PfYZhaMqUKXr11VfVrVs3SdKcOXMUFhamBQsW6JlnnlFqaqpmzpypefPmqVWrVpKk+fPnKzIyUmvWrFHbtm1dkhsAAACFk+kbysaPH69Zs2blap81a5befvttp4SSrvUWJyUlqU2bNrY2Hx8fNW3aVFu2bJEk7dq1S1euXLE7JiIiQjVr1rQdk5esrCylpaXZPQAAAHD7M13cfvDBB7rnnntytd97772aMWOGU0JJUlJSkiQpLCzMrj0sLMy2LykpSd7e3ipduvQNj8nL+PHjFRgYaHtERkY6LTcAAADcx3Rxm5SUpLJly+ZqDw0NVWJiolNC/dlfF4kwDONvF474u2NGjBih1NRU2+PUqVNOyQoAAAD3Ml3cRkZG6ocffsjV/sMPPygiIsIpoSQpPDxcknL1wCYnJ9t6c8PDw5Wdna2UlJQbHpMXHx8fBQQE2D0AAABw+zNd3D799NMaNGiQZs+erZMnT+rkyZOaNWuWBg8erH/84x9OC3bXXXcpPDxcq1evtrVlZ2drw4YNatSokSSpXr168vLysjsmMTFR+/btsx0DAACAO4fp5XeHDx+u8+fPq3///srOzpYk+fr66uWXX9aIESNMnevSpUt2q50dP35ce/bsUVBQkCpUqKBBgwbprbfeUpUqVVSlShW99dZbKl68uOLi4iRJgYGB6tevn4YOHarg4GAFBQVp2LBhio6Ots2eAAAAgDuH6eLWYrHo7bff1qhRo3TgwAH5+fmpSpUq8vHxMf3mO3fuVPPmzW3bQ4YMkST16tVL8fHxGj58uDIyMtS/f3+lpKSoYcOGWrVqlW2OW0maPHmyihUrptjYWGVkZKhly5aKj49njlsAAIA7kMUwDKMgJ0hLS9P333+vatWqqXr16s7KdUulpaUpMDBQqampjL+Fy2RkZNjmdV6+fLn8/PzcnAgACobrGm4lR+s102NuY2NjNX36dEnX/lLHxMQoNjZWtWrV0ldffZX/xAAAAEABmS5uN27cqIceekiStHjxYhmGoQsXLui9997Tm2++6fSAAAAAgKNMF7epqakKCgqSJK1YsUKPPvqoihcvro4dO+rIkSNODwgAAAA4Kl/z3G7dulXp6elasWKFbenblJQU+fr6Oj0gAAAA4CjTsyUMGjRIPXr0UMmSJRUVFaVmzZpJujZcITo62tn5AAAAAIeZLm779++vBg0a6NSpU2rdurU8PK51/t59992MuQUAAIBbmS5uJSkmJkYxMTF2bR07dnRKIAAAACC/HCpuhwwZojfeeEMlSpSwLbRwI5MmTXJKMAAAAMAsh4rbn376SVeuXLE9vxGLxeKcVAAAAEA+OFTcrlu3Ls/nAAAAQGFieiowAAAAoLByqOe2W7duDp9w0aJF+Q4DAAAAFIRDxW1gYKCrcwAAAAAF5lBxO3v2bFfnAAAAAAosX/PcStIff/yhQ4cOyWKxqGrVqgoNDXVmLgAAAMA008Vtenq6BgwYoLlz58pqtUqSPD091bNnT02bNk3Fixd3ekgULYZhKDMz090xbrk/f+Y78fNLkq+vL1MGokjiusZ1DYWHxTAMw8wLnnnmGa1Zs0bTp0/Xgw8+KEnavHmzXnzxRbVu3Vrvv/++S4K6UlpamgIDA5WamqqAgAB3xynyMjIy1L59e3fHgBssX75cfn5+7o4BOB3XtTsX17Vbx9F6zXTP7VdffaUvv/xSzZo1s7V16NBBfn5+io2NvS2LWwAAABQNpovby5cvKywsLFd7mTJldPnyZaeEQtHm6+ur5cuXuzvGLWcYhrKysiRJPj4+d+TXWL6+vu6OALgE1zWuayg8TA9LaNmypYKDgzV37lzbH2hGRoZ69eql8+fPa82aNS4J6koMSwAAACjcXDYsYcqUKWrfvr3Kly+v2rVry2KxaM+ePfL19dXKlSsLFBoAAAAoCNM9t9K1ntr58+fr4MGDMgxDNWrUUI8ePW7bAdX03AIAABRuLuu53bhxoxo1aqR//OMfdu1Xr17Vxo0b1aRJE/NpAQAAACfwMPuC5s2b6/z587naU1NT1bx5c6eEAgAAAPLDdHFrGEaed0OeO3dOJUqUcEooAAAAID8cHpbQrVs3SZLFYlHv3r3l4+Nj25eTk6O9e/eqUaNGzk8IAAAAOMjh4jYwMFDStZ5bf39/u5vHvL29df/99+cahwsAAADcSg4Xt7Nnz5YkVaxYUcOGDWMIAgAAAAod02Nuhw8fbjfm9uTJk5oyZYpWrVrl1GAAAACAWaaL24cfflhz586VJF24cEENGjTQu+++q4cffljvv/++0wMCAAAAjjJd3O7evVsPPfSQJOnLL79UeHi4Tp48qblz5+q9995zekAAAADAUaaL28uXL8vf31+StGrVKnXr1k0eHh66//77dfLkSacHBAAAABxluritXLmylixZolOnTmnlypVq06aNJCk5OZmlawEAAOBWpovb0aNHa9iwYapYsaIaNmyoBx54QNK1Xty6des6PSAAAADgKIthGIbZFyUlJSkxMVG1a9eWh8e1+vjHH39UQECA7rnnHqeHdLW0tDQFBgYqNTWV3mcAAIBCyNF6zXTPbXx8vAIDA1W3bl1bYStJDRo0uC0LWwAAABQdpovbESNGKCwsTP369dOWLVtckQkAAADIF4dXKLvu9OnTWrp0qeLj49W8eXPddddd6tOnj3r16qXw8HBXZHS56yMz0tLS3JwEAAAAeblep/3diNp8jbm9Ljk5WfPnz1d8fLwOHjyodu3aqV+/furcubPdkIXC7vTp04qMjHR3DAAAAPyNU6dOqXz58jfcX6DiVpK2b9+uWbNmac6cOSpbtqwuXLigUqVKafbs2WrWrFlBTn3LWK1W/fbbb/L397dbWhhwtrS0NEVGRurUqVPcvAigSOC6hlvFMAxdvHhRERERN+1ENT0sQZJ+//13zZs3T7Nnz9b//vc/PfLII/ruu+/UqlUrZWRk6LXXXlOvXr1um0UdPDw8bvobAOBsAQEB/E8AQJHCdQ23QmBg4N8eY7rntnPnzlq5cqWqVq2qp59+Wj179lRQUJDdMb/99pvKly8vq9VqLjFQxDHtHICihusaChvTPbdlypTRhg0bbIs35KVs2bI6fvx4gYIBAAAAZpkubmfOnPm3x1gsFkVFReUrEFCU+fj4aMyYMfLx8XF3FABwCq5rKGwcHpawfft2nT9/Xu3bt7e1zZ07V2PGjFF6eroeeeQRTZs2jb/cAAAAcBuH5+saO3as9u7da9v+5Zdf1K9fP7Vq1UqvvPKKvv32W40fP94lIQEAAABHONxzW7ZsWX377beKiYmRJL366qvasGGDNm/eLEn64osvNGbMGO3fv991aQEAAICbcLjnNiUlRWFhYbbtDRs2qF27drbt+vXr69SpU85NBwAAAJjgcHEbFhZmmwEhOztbu3fvtpsx4eLFi/Ly8nJ+QuAO0Lt3bz3yyCPujgGgCDMMQ//85z8VFBQki8WiPXv2uCXHiRMn3Pr+KPocni2hXbt2euWVV/T2229ryZIlKl68uB566CHb/r1796pSpUouCQkAAApmxYoVio+P1/r163X33XcrJCTE3ZEAl3C4uH3zzTfVrVs3NW3aVCVLltScOXPk7e1t2z9r1iy1adPGJSEBAEDBHDt2TGXLllWjRo3cHQVwKYeHJYSGhmrTpk1KSUlRSkqKunbtarf/+g1lQFHXrFkzDRgwQIMGDVLp0qUVFhamDz/8UOnp6erTp4/8/f1VqVIlLV++XJKUk5Ojfv366a677pKfn5+qVaumqVOn3vQ9DMPQxIkTdffdd8vPz0+1a9fWl19+eSs+HoAiqHfv3howYIASEhJksVhUsWLFv73OrF+/XhaLRStXrlTdunXl5+enFi1aKDk5WcuXL1f16tUVEBCgJ554QpcvX7a9bsWKFWrcuLFKlSql4OBgderUSceOHbtpvv3796tDhw4qWbKkwsLC9NRTT+ns2bMu+3mgaHO4uL0uMDBQnp6eudqDgoLsenKBomzOnDkKCQnRjz/+qAEDBui5557TY489pkaNGmn37t1q27atnnrqKV2+fFlWq1Xly5fX559/rv3792v06NEaOXKkPv/88xue/7XXXtPs2bP1/vvv69dff9XgwYP15JNPasOGDbfwUwIoKqZOnarXX39d5cuXV2Jionbs2OHwdWbs2LGaPn26tmzZolOnTik2NlZTpkzRggULtHTpUq1evVrTpk2zHZ+enq4hQ4Zox44dWrt2rTw8PNS1a1dZrdY8syUmJqpp06aqU6eOdu7cqRUrVuj3339XbGysS38mKMIMAKY0bdrUaNy4sW376tWrRokSJYynnnrK1paYmGhIMrZu3ZrnOfr37288+uijtu1evXoZDz/8sGEYhnHp0iXD19fX2LJli91r+vXrZzzxxBNO/CQA7iSTJ082oqKiDMNw7Dqzbt06Q5KxZs0a2/7x48cbkoxjx47Z2p555hmjbdu2N3zf5ORkQ5Lxyy+/GIZhGMePHzckGT/99JNhGIYxatQoo02bNnavOXXqlCHJOHToUL4/L+5cppffBSDVqlXL9tzT01PBwcGKjo62tV2fNi85OVmSNGPGDH388cc6efKkMjIylJ2drTp16uR57v379yszM1OtW7e2a8/OzlbdunWd/EkA3InMXGf+fL0LCwtT8eLFdffdd9u1/fjjj7btY8eOadSoUdq2bZvOnj1r67FNSEhQzZo1c2XZtWuX1q1bp5IlS+bad+zYMVWtWjV/HxJ3LIpbIB/+Ou2dxWKxa7NYLJIkq9Wqzz//XIMHD9a7776rBx54QP7+/vr3v/+t7du353nu6/8jWLp0qcqVK2e3j+WtATiDmevMX69teV3//jzkoHPnzoqMjNRHH32kiIgIWa1W1axZU9nZ2TfM0rlzZ7399tu59pUtW9bcBwNEcQu43KZNm9SoUSP179/f1nazmytq1KghHx8fJSQkqGnTprciIoA7jKuuM+fOndOBAwf0wQcf2KYLvb6S6Y3cd999+uqrr1SxYkUVK0ZZgoLjbxHgYpUrV9bcuXO1cuVK3XXXXZo3b5527Nihu+66K8/j/f39NWzYMA0ePFhWq1WNGzdWWlqatmzZopIlS6pXr163+BMAKGpcdZ0pXbq0goOD9eGHH6ps2bJKSEjQK6+8ctPXPP/88/roo4/0xBNP6KWXXlJISIiOHj2qhQsX6qOPPsrzJnbgZihuARd79tlntWfPHnXv3l0Wi0VPPPGE+vfvb5sqLC9vvPGGypQpo/Hjx+t///ufSpUqpfvuu08jR468hckBFGWuuM54eHho4cKFevHFF1WzZk1Vq1ZN7733npo1a3bD10REROiHH37Qyy+/rLZt2yorK0tRUVFq166dPDxMT+oEyGIYhuHuEAAAAIAz8CsRAAAAigyKWwAAABQZFLcAAAAoMihuAQAAUGRQ3AIAAKDIoLgFAABAkUFxCwAAgCKD4hYAAABFBsUtABQRzZo106BBg256jMVi0ZIlS5z2ns4+HwAUFMUtADhJUlKSBg4cqMqVK8vX11dhYWFq3LixZsyYocuXL7s7nlusX79eFotFFy5ccHcUAHeIYu4OAABFwf/+9z89+OCDKlWqlN566y1FR0fr6tWrOnz4sGbNmqWIiAh16dLF3TEBoMij5xYAnKB///4qVqyYdu7cqdjYWFWvXl3R0dF69NFHtXTpUnXu3FmSlJqaqn/+858qU6aMAgIC1KJFC/3888+28/z8889q3ry5/P39FRAQoHr16mnnzp22/T/88IOaNm2q4sWLq3Tp0mrbtq1SUlJs+61Wq4YPH66goCCFh4dr7NixN8195swZde/eXaVLl1ZwcLAefvhhnThxwu6YWbNm6d5775WPj4/Kli2rF154wW7/2bNn1bVrVxUvXlxVqlTRN998I0k6ceKEmjdvLkkqXbq0LBaLevfubfZHCwCmUNwCQAGdO3dOq1at0vPPP68SJUrkeYzFYpFhGOrYsaOSkpK0bNky7dq1S/fdd59atmyp8+fPS5J69Oih8uXLa8eOHdq1a5deeeUVeXl5SZL27Nmjli1b6t5779XWrVu1efNmde7cWTk5Obb3mTNnjkqUKKHt27dr4sSJev3117V69eo8M12+fFnNmzdXyZIltXHjRm3evFklS5ZUu3btlJ2dLUl6//339fzzz+uf//ynfvnlF33zzTeqXLmy3XnGjRun2NhY7d27Vx06dFCPHj10/vx5RUZG6quvvpIkHTp0SImJiZo6dWrBftgA8HcMAECBbNu2zZBkLFq0yK49ODjYKFGihFGiRAlj+PDhxtq1a42AgAAjMzPT7rhKlSoZH3zwgWEYhuHv72/Ex8fn+T5PPPGE8eCDD94wR9OmTY3GjRvbtdWvX994+eWXbduSjMWLFxuGYRgzZ840qlWrZlitVtv+rKwsw8/Pz1i5cqVhGIYRERFhvPrqqzd8T0nGa6+9Ztu+dOmSYbFYjOXLlxuGYRjr1q0zJBkpKSk3PAcAOBNjbgHASSwWi932jz/+KKvVqh49eigrK0u7du3SpUuXFBwcbHdcRkaGjh07JkkaMmSInn76ac2bN0+tWrXSY489pkqVKkm61nP72GOP3TRDrVq17LbLli2r5OTkPI/dtWuXjh49Kn9/f7v2zMxMHTt2TMnJyfrtt9/UsmVLh9+zRIkS8vf3v+F7AoCrUdwCQAFVrlxZFotFBw8etGu/++67JUl+fn6Sro2HLVu2rNavX5/rHKVKlZIkjR07VnFxcVq6dKmWL1+uMWPGaOHCheratavtPDdzfQjDdRaLRVarNc9jrVar6tWrp08++STXvtDQUHl4ODZyzcx7AoCrMeYWAAooODhYrVu31vTp05Wenn7D4+677z4lJSWpWLFiqly5st0jJCTEdlzVqlU1ePBgrVq1St26ddPs2bMlXeshXbt2rdNy33fffTpy5IjKlCmTK09gYKD8/f1VsWLFAr2nt7e3JNmNCwYAV6K4BQAn+O9//6urV68qJiZGn332mQ4cOKBDhw5p/vz5OnjwoDw9PdWqVSs98MADeuSRR7Ry5UqdOHFCW7Zs0WuvvaadO3cqIyNDL7zwgtavX6+TJ0/qhx9+0I4dO1S9enVJ0ogRI7Rjxw71799fe/fu1cGDB/X+++/r7Nmz+crco0cPhYSE6OGHH9amTZt0/PhxbdiwQQMHDtTp06clXetJfvfdd/Xee+/pyJEj2r17t6ZNm+bwe0RFRclisei7777TH3/8oUuXLuUrKwA4iuIWAJygUqVK+umnn9SqVSuNGDFCtWvXVkxMjKZNm6Zhw4bpjTfekMVi0bJly9SkSRP17dtXVatW1eOPP64TJ04oLCxMnp6eOnfunHr27KmqVasqNjZW7du317hx4yRd69FdtWqVfv75ZzVo0EAPPPCAvv76axUrlr8RZsWLF9fGjRtVoUIFdevWTdWrV1ffvn2VkZGhgIAASVKvXr00ZcoU/fe//9W9996rTp066ciRIw6/R7ly5TRu3Di98sorCgsLyzWNGAA4m8UwDMPdIQAAAABnoOcWAAAARQbFLQAAAIoMilsAAAAUGRS3AAAAKDIobgEAAFBkUNwCAACgyKC4BQAAQJFBcQsAAIAig+IWAAAARQbFLQAAAIoMilsAAAAUGf8fJ8JGhzCxllsAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.boxplot(x='sex', y='trestbps', data=heart_df)\n",
-    "plt.title('Überblick über Blutdruck')\n",
-    "plt.xlabel('Geschlecht')\n",
-    "plt.ylabel('Systolischer Ruheblutdruck (in mmHg bei Aufnahme ins Krankenhaus)')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "f20c6b80-bac5-489e-98e1-b5f2d9d90615",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Diagnose  Untere_Grenze  Obere_Grenze\n",
-      "0   Gesund     126.751354    131.748646\n",
-      "1    Krank     131.420542    137.716148\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Filtern nach goal und Berechnen des Konfidenzintervalls\n",
-    "conf_level = 0.95\n",
-    "blutdruck_gesund = heart_df.loc[heart_df['healthy'] == 1, 'trestbps']\n",
-    "blutdruck_krank = heart_df.loc[heart_df['healthy'] == 0, 'trestbps']\n",
-    "conf_int_gesund = stats.t.interval(conf_level, len(blutdruck_gesund) - 1, loc=blutdruck_gesund.mean(), scale=stats.sem(blutdruck_gesund))\n",
-    "conf_int_krank = stats.t.interval(conf_level, len(blutdruck_krank) - 1, loc=blutdruck_krank.mean(), scale=stats.sem(blutdruck_krank))\n",
-    "\n",
-    "# Erstellen der Tabelle\n",
-    "result_table_blutdruck = pd.DataFrame({\n",
-    "    'Diagnose': ['Gesund', 'Krank'],\n",
-    "    'Untere_Grenze': [conf_int_gesund[0], conf_int_krank[0]],\n",
-    "    'Obere_Grenze': [conf_int_gesund[1], conf_int_krank[1]]\n",
-    "})\n",
-    "\n",
-    "# Anzeige der Tabelle\n",
-    "print(result_table_blutdruck)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "bc919e7f-2b7b-41c1-a5b3-a97d61baa6ca",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t-Statistik: 2.647004396805282\n",
-      "p-Wert: 0.004274134464297464\n",
-      "Der p-Wert ist kleiner als 0.05, daher wird die Nullhypothese abgelehnt.\n",
-      "Es gibt signifikante Hinweise darauf, dass der Blutdruck bei kranken Personen höher ist als bei gesunden Personen.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# t-Test\n",
-    "# H0 Kranke haben einen niedrigeren oder gleichen Blutdruck wie Gesunde\n",
-    "# H1 Kranke haben einen höheren BLutdruck als Gesunde\n",
-    "\n",
-    "# Daten für gesunde und kranke Personen\n",
-    "blutdruck_healthy = heart_df.loc[heart_df['healthy'] == 1, 'trestbps']\n",
-    "blutdruck_sick = heart_df.loc[heart_df['healthy'] == 0, 'trestbps']\n",
-    "\n",
-    "# Durchführung des t-Tests\n",
-    "t_statistic, p_value = ttest_ind(blutdruck_sick, blutdruck_healthy, alternative='greater')\n",
-    "\n",
-    "print(\"t-Statistik:\", t_statistic)\n",
-    "print(\"p-Wert:\", p_value)\n",
-    "\n",
-    "# Überprüfung Nullhypothese\n",
-    "if p_value < 0.05:\n",
-    "    print(\"Der p-Wert ist kleiner als 0.05, daher wird die Nullhypothese abgelehnt.\")\n",
-    "    print(\"Es gibt signifikante Hinweise darauf, dass der Blutdruck bei kranken Personen höher ist als bei gesunden Personen.\")\n",
-    "else:\n",
-    "    print(\"Der p-Wert ist größer als oder gleich 0.05, daher wird die Nullhypothese nicht abgelehnt.\")\n",
-    "    print(\"Es gibt keine signifikanten Hinweise darauf, dass der Blutdruck bei kranken Personen höher ist als bei gesunden Personen.\")"
-   ]
-  },
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-   "source": [
-    "### Weitere Dinge die wir machen könnten\n",
-    "- Korrelationsanalyse\n",
-    "- logistische Regression, KNN, Entscheidungsbäume\n",
-    "- ROC/AUC Kurve plotten für Modelle\n",
-    "\n",
-    "\n",
-    "\n",
-    "- Classification:\n",
-    "- SVMs\n",
-    "- Decision Trees\n",
-    "- K-Nearest Neighbor\n",
-    "- Random Forest\n",
-    "\n",
-    "Dimensionality Reduction\n",
-    "- PCA\n",
-    "\n",
-    "Regression:\n",
-    "- lineare \n",
-    "- logistische\n",
-    "\n",
-    "Clustering:\n",
-    "- hierarchisch -> bäume\n",
-    "- K-means clustering\n",
-    "\n",
-    "Statistik:\n",
-    "- Hypothesen"
-   ]
-  }
- ],
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