diff --git a/category_summary.csv b/category_summary.csv
new file mode 100644
index 0000000..fa84c60
--- /dev/null
+++ b/category_summary.csv
@@ -0,0 +1,18 @@
+category,total_videos,avg_views,median_views,avg_likes,avg_comments,avg_engagement,avg_duration_sec,avg_subscriber_count,pct_verified,avg_clickbait
+Autos & Vehicles,295,1935474.76,89323.0,96988.73,12116.45,7.87,747.77,2440766.85,41.0,0.43
+Comedy,602,2999409.32,51703.0,144391.22,18876.76,7.57,807.12,2351725.15,38.0,0.44
+Education,490,1560697.88,49677.5,88373.51,7662.68,7.64,853.53,2241407.83,40.0,0.46
+Entertainment,1218,3777913.81,60079.0,177685.08,20439.61,7.53,748.61,3399595.14,39.0,0.46
+Film & Animation,496,2167875.72,41183.0,121397.01,17449.49,7.54,761.41,2888545.65,35.0,0.46
+Gaming,1000,4277080.74,60338.5,183756.23,17425.5,7.6,1538.87,3022661.92,40.0,0.45
+Howto & Style,420,2035985.54,73331.5,117469.35,11903.5,7.4,786.96,2610956.62,41.0,0.45
+Music,1413,2527652.48,57417.0,151667.09,18002.8,7.7,273.17,2592072.57,39.0,0.46
+News & Politics,674,3475471.41,63311.0,159646.37,31438.52,7.66,731.15,3385566.17,42.0,0.46
+Nonprofits & Activism,205,2471780.43,58147.0,102840.47,13659.3,7.33,712.94,3827599.79,39.0,0.47
+People & Blogs,744,3101332.12,60972.5,180993.38,27293.33,7.42,765.72,3224118.04,41.0,0.46
+Pets & Animals,327,1851227.29,65586.0,109849.53,11237.99,7.69,747.58,1555399.94,38.0,0.44
+Science & Technology,630,2846651.24,57772.5,196156.17,22152.68,7.74,881.59,4027173.69,42.0,0.47
+Shorts,199,4206890.79,64695.0,278094.7,24615.14,7.49,36.99,2990056.56,40.0,0.48
+Shows,207,4633420.31,67257.0,232833.66,21494.22,7.13,742.17,2569678.55,42.0,0.46
+Sports,798,4649919.75,62175.5,260325.56,24826.5,7.59,746.13,2482707.55,41.0,0.46
+Travel & Events,282,2082316.91,62104.5,95230.08,16066.52,7.44,724.29,3901219.56,43.0,0.44
diff --git a/week4_scikit_learn.ipynb b/week4_scikit_learn.ipynb
index d89b8d9..0fa771c 100644
--- a/week4_scikit_learn.ipynb
+++ b/week4_scikit_learn.ipynb
@@ -42,6 +42,1078 @@
     "from sklearn.decomposition import PCA\n",
     "from sklearn.metrics import silhouette_score"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df2910c5",
+   "metadata": {},
+   "source": [
+    "## Часть 1. Применение Gaussian Mixture Model на сгенерированном датасете\n",
+    "\n",
+    "Сначала проверим работу алгоритма на искусственно созданных данных.\n",
+    "Для этого используем функцию `make_blobs`, которая позволяет сгенерировать несколько кластеров."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "74f7d73b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размерность X: (400, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y_true = make_blobs(\n",
+    "    n_samples=400,\n",
+    "    centers=3,\n",
+    "    cluster_std=[1.0, 2.5, 0.8],\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "print(\"Размерность X:\", X.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b046e299",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X[:, 0], X[:, 1], s=30)\n",
+    "plt.title(\"Сгенерированный датасет\")\n",
+    "plt.xlabel(\"Признак 1\")\n",
+    "plt.ylabel(\"Признак 2\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfaa644c",
+   "metadata": {},
+   "source": [
+    "## Препроцессинг данных\n",
+    "\n",
+    "Для корректной работы Gaussian Mixture Model выполним масштабирование признаков.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8895f3a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Пример первых 5 строк после масштабирования:\n",
+      "[[-1.24796648 -1.02418907]\n",
+      " [-0.69906013  1.2704844 ]\n",
+      " [-1.08423598 -1.26725858]\n",
+      " [ 1.71543511  0.0370654 ]\n",
+      " [-0.06794571  1.31748889]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "\n",
+    "print(\"Пример первых 5 строк после масштабирования:\")\n",
+    "print(X_scaled[:5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd2df3d7",
+   "metadata": {},
+   "source": [
+    "## Обучение модели\n",
+    "\n",
+    "Обучим модель GaussianMixture с количеством компонент, равным 3.\n",
+    "Параметр `covariance_type='full'` позволяет каждой компоненте иметь собственную полную ковариационную матрицу."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a686db30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первые 10 предсказанных кластеров:\n",
+      "[1 0 1 2 0 0 0 1 0 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "gmm = GaussianMixture(\n",
+    "    n_components=3,\n",
+    "    covariance_type='full',\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "gmm.fit(X_scaled)\n",
+    "clusters = gmm.predict(X_scaled)\n",
+    "probabilities = gmm.predict_proba(X_scaled)\n",
+    "\n",
+    "print(\"Первые 10 предсказанных кластеров:\")\n",
+    "print(clusters[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5e395701",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=clusters, cmap='viridis', s=30)\n",
+    "plt.title(\"Кластеры, найденные Gaussian Mixture Model\")\n",
+    "plt.xlabel(\"Признак 1 (scaled)\")\n",
+    "plt.ylabel(\"Признак 2 (scaled)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fef3c293",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Silhouette Score для сгенерированного датасета: 0.768\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = silhouette_score(X_scaled, clusters)\n",
+    "print(\"Silhouette Score для сгенерированного датасета:\", round(score, 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4873ee01",
+   "metadata": {},
+   "source": [
+    "## Интерпретация результатов для сгенерированного датасета\n",
+    "\n",
+    "Модель Gaussian Mixture Model успешно выделила 3 кластера.\n",
+    "Алгоритм не просто делит данные на группы, а оценивает вероятность принадлежности каждой точки к каждой компоненте смеси.\n",
+    "\n",
+    "Это делает GMM более гибким методом по сравнению с KMeans,\n",
+    "так как он способен учитывать вытянутую или наклонную форму кластеров.\n",
+    "\n",
+    "Значение Silhouette Score показывает качество разделения объектов на кластеры.\n",
+    "Чем ближе значение к 1, тем лучше разделение."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed7796a",
+   "metadata": {},
+   "source": [
+    "## Часть 2. Применение Gaussian Mixture Model к внешнему датасету"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89f8125f",
+   "metadata": {},
+   "source": [
+    "используемый датасает находится в папке проекта :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5683fe58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>category</th>\n",
+       "      <th>total_videos</th>\n",
+       "      <th>avg_views</th>\n",
+       "      <th>median_views</th>\n",
+       "      <th>avg_likes</th>\n",
+       "      <th>avg_comments</th>\n",
+       "      <th>avg_engagement</th>\n",
+       "      <th>avg_duration_sec</th>\n",
+       "      <th>avg_subscriber_count</th>\n",
+       "      <th>pct_verified</th>\n",
+       "      <th>avg_clickbait</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Autos &amp; Vehicles</td>\n",
+       "      <td>295</td>\n",
+       "      <td>1935474.76</td>\n",
+       "      <td>89323.0</td>\n",
+       "      <td>96988.73</td>\n",
+       "      <td>12116.45</td>\n",
+       "      <td>7.87</td>\n",
+       "      <td>747.77</td>\n",
+       "      <td>2440766.85</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>602</td>\n",
+       "      <td>2999409.32</td>\n",
+       "      <td>51703.0</td>\n",
+       "      <td>144391.22</td>\n",
+       "      <td>18876.76</td>\n",
+       "      <td>7.57</td>\n",
+       "      <td>807.12</td>\n",
+       "      <td>2351725.15</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Education</td>\n",
+       "      <td>490</td>\n",
+       "      <td>1560697.88</td>\n",
+       "      <td>49677.5</td>\n",
+       "      <td>88373.51</td>\n",
+       "      <td>7662.68</td>\n",
+       "      <td>7.64</td>\n",
+       "      <td>853.53</td>\n",
+       "      <td>2241407.83</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Entertainment</td>\n",
+       "      <td>1218</td>\n",
+       "      <td>3777913.81</td>\n",
+       "      <td>60079.0</td>\n",
+       "      <td>177685.08</td>\n",
+       "      <td>20439.61</td>\n",
+       "      <td>7.53</td>\n",
+       "      <td>748.61</td>\n",
+       "      <td>3399595.14</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Film &amp; Animation</td>\n",
+       "      <td>496</td>\n",
+       "      <td>2167875.72</td>\n",
+       "      <td>41183.0</td>\n",
+       "      <td>121397.01</td>\n",
+       "      <td>17449.49</td>\n",
+       "      <td>7.54</td>\n",
+       "      <td>761.41</td>\n",
+       "      <td>2888545.65</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0.46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           category  total_videos   avg_views  median_views  avg_likes  \\\n",
+       "0  Autos & Vehicles           295  1935474.76       89323.0   96988.73   \n",
+       "1            Comedy           602  2999409.32       51703.0  144391.22   \n",
+       "2         Education           490  1560697.88       49677.5   88373.51   \n",
+       "3     Entertainment          1218  3777913.81       60079.0  177685.08   \n",
+       "4  Film & Animation           496  2167875.72       41183.0  121397.01   \n",
+       "\n",
+       "   avg_comments  avg_engagement  avg_duration_sec  avg_subscriber_count  \\\n",
+       "0      12116.45            7.87            747.77            2440766.85   \n",
+       "1      18876.76            7.57            807.12            2351725.15   \n",
+       "2       7662.68            7.64            853.53            2241407.83   \n",
+       "3      20439.61            7.53            748.61            3399595.14   \n",
+       "4      17449.49            7.54            761.41            2888545.65   \n",
+       "\n",
+       "   pct_verified  avg_clickbait  \n",
+       "0          41.0           0.43  \n",
+       "1          38.0           0.44  \n",
+       "2          40.0           0.46  \n",
+       "3          39.0           0.46  \n",
+       "4          35.0           0.46  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"category_summary.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "914a37cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(17, 11)\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 17 entries, 0 to 16\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   category              17 non-null     str    \n",
+      " 1   total_videos          17 non-null     int64  \n",
+      " 2   avg_views             17 non-null     float64\n",
+      " 3   median_views          17 non-null     float64\n",
+      " 4   avg_likes             17 non-null     float64\n",
+      " 5   avg_comments          17 non-null     float64\n",
+      " 6   avg_engagement        17 non-null     float64\n",
+      " 7   avg_duration_sec      17 non-null     float64\n",
+      " 8   avg_subscriber_count  17 non-null     float64\n",
+      " 9   pct_verified          17 non-null     float64\n",
+      " 10  avg_clickbait         17 non-null     float64\n",
+      "dtypes: float64(9), int64(1), str(1)\n",
+      "memory usage: 1.8 KB\n",
+      "None\n",
+      "category                0\n",
+      "total_videos            0\n",
+      "avg_views               0\n",
+      "median_views            0\n",
+      "avg_likes               0\n",
+      "avg_comments            0\n",
+      "avg_engagement          0\n",
+      "avg_duration_sec        0\n",
+      "avg_subscriber_count    0\n",
+      "pct_verified            0\n",
+      "avg_clickbait           0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.shape)\n",
+    "print(df.info())\n",
+    "print(df.isna().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d16587ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_videos</th>\n",
+       "      <th>avg_views</th>\n",
+       "      <th>median_views</th>\n",
+       "      <th>avg_likes</th>\n",
+       "      <th>avg_comments</th>\n",
+       "      <th>avg_engagement</th>\n",
+       "      <th>avg_duration_sec</th>\n",
+       "      <th>avg_subscriber_count</th>\n",
+       "      <th>pct_verified</th>\n",
+       "      <th>avg_clickbait</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>295</td>\n",
+       "      <td>1935474.76</td>\n",
+       "      <td>89323.0</td>\n",
+       "      <td>96988.73</td>\n",
+       "      <td>12116.45</td>\n",
+       "      <td>7.87</td>\n",
+       "      <td>747.77</td>\n",
+       "      <td>2440766.85</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>602</td>\n",
+       "      <td>2999409.32</td>\n",
+       "      <td>51703.0</td>\n",
+       "      <td>144391.22</td>\n",
+       "      <td>18876.76</td>\n",
+       "      <td>7.57</td>\n",
+       "      <td>807.12</td>\n",
+       "      <td>2351725.15</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>490</td>\n",
+       "      <td>1560697.88</td>\n",
+       "      <td>49677.5</td>\n",
+       "      <td>88373.51</td>\n",
+       "      <td>7662.68</td>\n",
+       "      <td>7.64</td>\n",
+       "      <td>853.53</td>\n",
+       "      <td>2241407.83</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1218</td>\n",
+       "      <td>3777913.81</td>\n",
+       "      <td>60079.0</td>\n",
+       "      <td>177685.08</td>\n",
+       "      <td>20439.61</td>\n",
+       "      <td>7.53</td>\n",
+       "      <td>748.61</td>\n",
+       "      <td>3399595.14</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>496</td>\n",
+       "      <td>2167875.72</td>\n",
+       "      <td>41183.0</td>\n",
+       "      <td>121397.01</td>\n",
+       "      <td>17449.49</td>\n",
+       "      <td>7.54</td>\n",
+       "      <td>761.41</td>\n",
+       "      <td>2888545.65</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0.46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_videos   avg_views  median_views  avg_likes  avg_comments  \\\n",
+       "0           295  1935474.76       89323.0   96988.73      12116.45   \n",
+       "1           602  2999409.32       51703.0  144391.22      18876.76   \n",
+       "2           490  1560697.88       49677.5   88373.51       7662.68   \n",
+       "3          1218  3777913.81       60079.0  177685.08      20439.61   \n",
+       "4           496  2167875.72       41183.0  121397.01      17449.49   \n",
+       "\n",
+       "   avg_engagement  avg_duration_sec  avg_subscriber_count  pct_verified  \\\n",
+       "0            7.87            747.77            2440766.85          41.0   \n",
+       "1            7.57            807.12            2351725.15          38.0   \n",
+       "2            7.64            853.53            2241407.83          40.0   \n",
+       "3            7.53            748.61            3399595.14          39.0   \n",
+       "4            7.54            761.41            2888545.65          35.0   \n",
+       "\n",
+       "   avg_clickbait  \n",
+       "0           0.43  \n",
+       "1           0.44  \n",
+       "2           0.46  \n",
+       "3           0.46  \n",
+       "4           0.46  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_real = df.drop(columns=['category'])\n",
+    "X_real.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "393aa581",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X_real)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6eb0c686",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.mixture import GaussianMixture\n",
+    "\n",
+    "gmm = GaussianMixture(\n",
+    "    n_components=3,\n",
+    "    covariance_type='full',\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "gmm.fit(X_scaled)\n",
+    "clusters = gmm.predict(X_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b5e948f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>category</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Autos &amp; Vehicles</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Education</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Entertainment</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Film &amp; Animation</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Gaming</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Howto &amp; Style</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Music</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>News &amp; Politics</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Nonprofits &amp; Activism</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>People &amp; Blogs</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Pets &amp; Animals</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Science &amp; Technology</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Shorts</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Shows</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Sports</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Travel &amp; Events</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 category  cluster\n",
+       "0        Autos & Vehicles        0\n",
+       "1                  Comedy        2\n",
+       "2               Education        2\n",
+       "3           Entertainment        1\n",
+       "4        Film & Animation        2\n",
+       "5                  Gaming        1\n",
+       "6           Howto & Style        0\n",
+       "7                   Music        1\n",
+       "8         News & Politics        1\n",
+       "9   Nonprofits & Activism        0\n",
+       "10         People & Blogs        1\n",
+       "11         Pets & Animals        2\n",
+       "12   Science & Technology        1\n",
+       "13                 Shorts        1\n",
+       "14                  Shows        1\n",
+       "15                 Sports        1\n",
+       "16        Travel & Events        0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['cluster'] = clusters\n",
+    "df[['category', 'cluster']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "7994affe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hz7syRVOmTEl2GmVqFVhrR1ndGDVOSUGPH5T07t3bbVN9/pRt0PecPhM6uHAstonG2igTpYNE4caX+Z0jFaRp3FQwjelJC/8AiIKcZ555xjVeCA7yFdzq4IgOELzyyitu/bVdlI3RGJz0rufRfE6ON52DTMGTvk/0+idHnx8dzNE4JT/4BnI7AiEgnZR1+OCDD9wPr458p0bZkR9++MHtCGqHMPRIcTAdFdXOWqhw5yTSTr5+2HXRD7uO4OkHT8GRP4hbOwXKHumiHzkN5NYAZJU+hDsyqHIlBRRqPhBcOqPgJLOpdEg7MNqZVrCYXso+qMRGpTFaPw161oB4X0oZJm33cOV8aT0XlH8EXzt5yZ0gNj0UaKkpgHYc1XRCR+jD8cvlFHwoCA6m2/z7/b/h3nOaLpiCA+1YK+MYGvClRXrfc2mhcjhlPPTaqkxMGZzQjJ/fHcw/ih5OWt/fftMOHfA42tfTn5d2mtMyL+1g670YvCO6ePFi9ze0o6SCd2WDlOHyS23D0XeF/9wKshU06rvID4TU5EQ7ycrIBGeRwn0XZYRee32m9Z5IjkrM1Bzisssus3fffdd9phT0JncwItzj1QxBy60sqhpm6PX2P/dqjKCDCnovqZTMl54StmP5OQmmz6fmpe+v4KyQX6oZWhYb7nOs94iaWoQGYCrzHD58uNsmyR248umghTJZoZ3+gNyM0jggne6//373g6Ojl+E68IRmFvwfn9DbtaMe7sdV5RbqtOXT+I7QNsfhSj/84EE/9hLamltHPzUeRMuRXMlJuGXVzqx+IDObyvxUPhO8M5ZW2pHTEeXnnnvO7RjqSLZOvOrvQIrf+Srczp22u3Y6gstKVKaX1rIu7eRqjJjKT/R6hQpXrpIaZZe0PXSENjlqTa3n1o6j/7qLsic6Cu+fd0hZTL0/Pv744yQlQMoSBLcXFmVdtPOpMQWh1LkspZ3jtL7n0to+2+ePF9NYKI2X0g586El1tbOn5wvNKGTk/d20aVNX/qjPaOj6pjfbqQyhMlA6UBHucxfuvaGxLsHPp+sKskODP62zvivCdWJLicrOgt8v2i6h66XxPGkNQlKiTJXGMuqzmdLBiNWrV7vXV2NYFMQocNMBirTS+0LroUBcnwdlzIO76IV77fVZCJddTIuj+ZyEUhdEzSv4dRdlqrTNQjtwapsGj93SttM2VvfN0GBH34cK1hQAp0RBmA5Y3HXXXSlmVYHchowQkE4qMdA4FmUwVHqhI5iNGzd2P7A6sqz7dATWb0usnSAdfdbRTu0Iqb2yylnCZVm0A68SHR3h1Q6B32ZXR4eDf/hU1qMfeu3o6mihjuJpZ07P6e8I6kdRP2j6EdQ4Iu0Y64dWjwmtRQ/emdDOh44O6/n1I6ySnvTu/GWE1iMjRyK17mqLrXMZ+YPMtZ460qvzqfzyyy/u9dBRW+2UK2DS9lTLa40B0UVBrbJq2mlVWYjmqZ0pleX4DTJSo2YN2vZqHa4SHR3RVqCsnRYNUldglR56T+mSEu0cq8xJ7XtV8qX3pN8+W9kD7dT4tLOs117LqPVVMO2f8yd4zJvmo0BD06vBgt5Heh4dhdYAcc1b7bHDSet7LqPts/V+VDZWQZ2CRH2mtFz6v5pLaKcvpQYZaX1/6/2iz13nzp3dc2n7KphUAKfxExqUnlZaHs1L7bUVYOkzrqP2CgRV1qptFbwDrKyZygq1jCoZVVCr6TSWJfRov75H9F4LN0bMp0HvKstUsK+gT/PW/IIbMmhskbaDSuL0GdF7VhmK5JoX6H4FUz7/M6KxcqGZci2jsjUpZcOUBdP20feXAqajpc+xsoY6aKXXUK+d3psKlHVd72+95xUo6UBCuIMXqTmaz0koLZO+v5TRV2m1PvfabgpuVK7oZxV9+s7SOga3z5Zw5aeaT1oO6Oj3RZlnbTMgomR12zogp1JL05tvvtmrWbOma12aL18+r27dut5NN93kzZo1K8m0a9asca1N1UZXLXO7d+/urV27NmzLWbU0VhtctcutU6eON2TIkCNaof7www+uVXH58uXddPrbo0cPb/HixYnTDBgwwGvdurVXokQJLy4uzqtRo4Z33333JWkpHa599q+//uqdeuqpbn00X7W8HTduXNiWq+Fa2ybXAjxUai1lg5cvpfbZF154oWsLq/bKwb766is33fPPP59422+//eZa92qbhW57bWe10dV9ajWtdU6uffaLL74YdnmXLVvmXXHFFa4deGxsrGvnrDbFX3zxRZrbZ6cktH2277PPPnMtw/U6q434ZZdd5t5zoUaMGOHVq1fPTacWyl9++WWyr9d7773ntpXeB9q+ag2u94Let8m13U3Ley6j7bODPfnkk15MTIw3c+ZM14Zen5fXX3/9iNbW4dofp/X9Lb/88otrya71L1CggNeoUSOvf//+6WqfHbwsatWsz7++L7RtrrrqqiRtkPVa6Hn0PjrnnHNcS+8yZcq4+cbHxx/xPtQ6/PPPP0meR69lcPvsJ554wn2PaFq1fdd7W9vq4MGDidOoRbjajpcsWdIrWLCgW86FCxceMS//85jSJfh11XW1f54xY0aSZQx93/Tt29e9X2bPnn3U7bN9mzZt8kqVKpWkpfTXX3/tXkNt/6pVq7rvhoEDBx7xHZiW9tnp+Zyk5btObdbvuusu957Ud4da7ut7JvQ97X9P6PtK02i76bMfuoz+Z0e/E6mtk38qBJ2WIdw8gNwsoH+yOhgDACDSKYOpMs9wXSlzAmV0leVLb6YPaacspsZDhpbRAcgYxggBAAAAiDiMEQIAAEdNY5E0rhEAcgoCIQAAcNT8E9MCQE7BGCEAAAAAEYcxQgAAAAAiDoEQAAAAgIiTrccI6SRra9eudSfiS+mM1AAAAAByN8/zbOfOnVa+fHl38utcHQgpCKpUqVJWLwYAAACAbGL16tVWsWLF3B0IKRPkr2zhwoWzenEAAAAAZJEdO3a4JIkfI+TqQMgvh1MQRCAEAAAAIHCMhszQLAEAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiFkep/3UaNGJXv/pEmT3DTbtm1L0/zatGljd9555zFcQgAAAEQiAqEMmjJlikVHR1unTp0y9PgnnnjCmjRpYsfDkCFDrG7dupY3b16rWrWqPf3006k+5oQTTrCbbrop7H2ffPKJxcXF2aZNm4562Vq0aGHr1q2zIkWKHPW8AAAAgLQiEEqH+Pj4xP9/+OGH1rt3b/vpp59s7dq1ll2tXLnSrrjiCuvatastWLDAPv/8c6tWrVqqj7v22mtt+PDhtnfv3iPuGzRokJ1//vlWsmTJo16+PHnyWNmyZY/ZGYIBAACAtCAQSsG+Pfvtu4E/2s3N7reOeXtYh9hL7dz8Pe3Wlg/YsKHD7Lprr3MZoY8++ijJ43S9aNGiSW5TeZi/s6/7n3zySZs9e7a7TRd/HqtWrbIuXbpYwYIFrXDhwnbxxRfbv//+mzgfPaZt27ZWqFAhd3+zZs1s+vTpya6DP/9rrrnGBUCnnHKKXX755am+MTSNgqARI0YkuX3FihWunE2Bknz11VfWtGlTl22qXr26W69Dhw4leYwyRxdccIHlz5/fatWqZV9//XWKpXG//vqrK4HT9MWKFbP27dvb1q1bwy7n/v377d5777UKFSpYgQIFrHnz5m6evr///ts6d+7s5qP7GzRoYGPHjk11/QEAAJC7EQglY+z739vF5a6zl697x5bNXmmHDhzeuT+476BN/v1Hi92X1x4/8xU7sfrJNnDgQPM8L80b/ZJLLrF77rnH7ZSrLEwX3ZaQkOCCoC1bttjkyZNtwoQJtnz5cnef77LLLrOKFSvatGnTbMaMGfbggw9abGxsss+lAOGkk06y2267zfbt25fmZVS2R8uidQumgE3Pf84559jPP//ssk133HGHzZ8/3wYMGODuf/bZZ5M8RsGRArq//vrLzj33XLcOWsdwZs2aZWeddZbVr1/flR/+8ssvLpAJzsYF03ppOmWvNP/u3btbhw4dbMmSJe7+W2+91QVLytzNmTPHnn/+eRdkAgAAILLFZPUCZEdDnv7CPn78s8TrXkLSIGdNwgora5Vtx5adNv6532xjgU0ucFEWIy3y5cvndsZjYmJcWZhPgY921pV1qVSpkrtt8ODBLmBS4HPyySe7jNF9993nxvyIMiwpuf76612QpmxNx44dXQZHmSRRgFGlShV78803wz5WWR89RsujbJLm8/HHH9uVV15pUVFRLsBRIKbroufQ+KP777/fHn/88cT5XHXVVdajRw/3/759+9obb7xhU6dOdQFLqBdeeMEFbm+//XbibVr/cLQtVKanv+XLl3e3KTv03Xffudv1XLqvW7dubsyTv4wAAAAAGaEQPwz9OUkQFGq3t9N22BYra5XMPKXUoqzIrpL24jMvHfW7SWN4FAD5QZAoM6IyO90nd999t1133XXWrl07e+6552zZsmXJzk9ZGmVodHnnnXescuXKLljbsGGDu3/u3Ll2+umnJ/v4s88+22V/FFTIDz/84AKLq6++OrFM76mnnnJBnX9R4KUM1549exLn06hRo8T/qzxNgZi/DMllhNJCQaMyRbVr106yDApK/e1y++232zPPPGMtW7Z0wZmyRgAAAACBUBCVpg18eFiK74q1tsI88+xnG2M/eCPcZbUts/ETx9n27dvdNMqWhJbKHTx48Ji829Rtbt68eW5s0o8//ugCpZEjR4adVjv96u6maTQOR2VuyogoKHj//fdt586drulBcrQeyuYoC6Rto4BI45P8rMquXbtcVkjBi39RcKKyNI0Z8oWW7mlZNL/ksmVppedX5z6VCAYvg4LG119/3U2joFHlhb169XLLpmxT//790/wcAAAAyJ0IhIJMHzfbNqxKviV0gpdg6+xvq2WNrLm1S7ycamdbbEKc9X/xcIlZqVKlXJCxe/fuxMdqBz20W1rouJd69erZ6tWr3SU4q6NGAgpmfMqA3HXXXTZ+/Hi78MILEzM24cYHaXzMH3/84a4raBg2bJjVqFHDbrjhBnv44YdTDTyU/dHyfPnlly7g8pskiJokLFq0yGrWrHnERUFURih7pMxTWpx44oluGyq7FPr8wSWHyrCpFbjWQWOzFAQCAAAgshEIBfn2g+8tOib5TbLJ1tlBO2gVrJoVDBRJcikTVdHef/8DN506l6nj2UMPPeRKtBR8hHaW0/l8NPZGAZK6qilgUbmbxrKomcDMmTPdOBo1IzjjjDNcJkNd3NQcQF3R1A1N3dU0dkgBVDitWrVy5+lRswV1rdOyaPyM2n2rRE3LFVzCFo7GBp155pkucFJ2SYGX77HHHnNjmJQVUpZKmRg1LXjkkUcso/r06ePW6ZZbbnEZrYULF7qyvnDnLFJAqG2lbaQgR9tT26xfv342ZswYN41Ovjpu3Dh3n7bpxIkTk91eAAAAiBwEQkHWLFln8YfCl2zJWltpxa20xQSO7NJWKqGCrdqw0u28Fy9e3J3EVG2aFdh8+umnrqQtmAbwq1mASs2UQdI0KhlTMwO1em7durULjFSG9tlnnyVmdDZv3ux2/BUEqBObmhkoEAlH81Pgc9FFF7mxRcoqKdBQVmfx4sW2fv16F0gkV6bm0/RqX92zZ88kJW9qaz169GiXmVIjh1NPPdVeffVV14Aho7Remp/GH6nV92mnnea2iRpLhKNsmLaHMj116tRx50tSIKXxUKKMkTrHKfjR9tb8gxsxAAAAIDIFvPT0fT7OduzYYUWKFHFjb/xOZ5npylq9be2y9Rl+fOM2DeylH5MGPAAAAACyX2xARihIoeIFMrwhlX0pXLLQUb8gAAAAADIfgVCQk85pYlHRGdsk6iR3YtuGx+p1AQAAAJCJCISCnHtDuyNOnppWcfni7KzLWx+r1wUAAABAJiIQClK6Ukk77fyTLCqFznFhN2J0lLW/qo3lL5T2c+AAAAAAyDoEQiF6v3mtFSlZOM0lcgqaKtQsa1c/0yMzXh8AAAAAmYBAKETJCiXs5YlPWPFyxVINhqKiAlapdnl74fvHrGDRjDdaAAAAAHB8EQiFUalOBXtnxvN2yf1drFCxwwFOdGy0xcRGW3RMtLuuQOmKJy+xN6b0dcETAAAAgJyD8wil4sD+g/bbqKm2Ys4q27NzrxUonN9qn1zDmp/bNDEoAgAAAJCzziNEIAQAAAAg2+OEqgAAAABwlBgjBAAAACDiEAgBAAAAiDgEQgAAAAAiDoEQAAAAgIhDIAQAAAAg4hAIAQAAAIg4BEIAAAAAIg6BEAAAAICIQyAEAAAAIOIQCAEAAACIOARCAAAAACIOgRAAAACAiEMgBAAAACDiEAgBAAAAiDgEQgAAAAAiDoEQAAAAgIhDIAQAAAAg4hAIAQAAAIg4BEIAAAAAIg6BEAAAAICIQyAEAAAAIOIQCAEAAACIOARCAAAAACIOgRAAAACAiEMgBAAAACDiEAgBAAAAiDgEQgAAAAAiDoEQAAAAgIhDIAQAAAAg4hAIAQAAAIg4BEIAAAAAIg6BEAAAAICIk6mBUL9+/ezkk0+2QoUKWenSpa1r1662aNGizHxKAAAAAMjaQGjy5Ml266232u+//24TJkywgwcP2jnnnGO7d+/OzKcFAAAAgBQFPM/z7DjZuHGjywwpQGrdunWq0+/YscOKFCli27dvt8KFCx+XZQQAAACQ/Rzr2OC4jhHSQkvx4sWP59MCAAAAQBIxdpwkJCTYnXfeaS1btrSGDRuGnWb//v3uEhz1AQAAAMCxdtwyQhorNHfuXBs+fHiKzRWU7vIvlSpVOl6LBwAAACCCHJcxQrfddpt99dVX9tNPP1m1atWSnS5cRkjBEGOEAAAAgMi24xiPEcrU0jjFWL1797aRI0fapEmTUgyCJC4uzl0AAAAAIDPFZHY53LBhw1w2SOcSWr9+vbtdkVy+fPky86kBAAAAIGtK4wKBQNjbBw0aZFdddVWqj6d9NgAAAIAcWRoHAAAAANnNcT2PEAAAAABkBwRCAAAAACIOgRAAAACAiEMgBAAAACDiEAgBAAAAiDgEQgAAAAAiDoEQAAAAgIhDIAQAAAAg4hAIAQAAIEcIBAI2atSorF4M5BIEQgAAAMgWNm7caDfffLNVrlzZ4uLirGzZsta+fXv79ddfs3rRkAvFZPUCAACSWrlypVWrVs3+/PNPa9KkCZsHQK5zMD7evl+xzL6YP89W79hmh+ITrFi+fDa734tWNDbWPv74Y6tevbr9+++/9sMPP9jmzZuzepGRC5ERAhDRJRYpXZ544gnLzr777js78cQTLV++fFahQgW75ZZb0vS4q666Kuz6dujQwY4nbV8CPSCyeJ5nH8+eaS0Gvme3jv3GJv+9wpZu2WIrt2+z6cuX27JZs2z9aSfb6IP7rGiZMnbKKadYnz597Pzzz0+cx6ZNm+yCCy6w/PnzW61atezrr79O8hyTJ092j1NGqVy5cvbggw/aoUOH3H2jR4+2okWLWnx8vLs+a9Ys9/2naXzXXXedXX755e7/f//9t3Xu3NmKFStmBQoUsAYNGtjYsWOP09ZCZiMQAhCx1q1bl3h57bXXrHDhwkluu/fee5P8ePs/pNnBvn377MILL7RGjRrZnDlzbMyYMekKKhT0BK+rLp9++mmmLjOAyKbv0ccn/WBPTp5om/fucbcleF7i/VFxeSwQF2c7Z8+xr+bOsQs+H2r/7tp1xHyefPJJu/jii+2vv/6yc8891y677DLbsmWLu++ff/5xt5188sk2e/Zse+edd+zDDz+0Z555xt1/+umn286dO13G3Q+aSpYsaZMmTUqcv25r06aN+/+tt95q+/fvt59++sl91z7//PNWsGDBTN5SOF4IhABEjPUrN9iHfYbaNfXvtO5lr7M7T3rM+l7whs38Zq7ly5vfHRVUPbouCxcutEKFCtm3335rzZo1c0cWf/nlF1u2bJl16dLFypQp434M9WP7/fffJz7HQw89ZM2bNz/iuRs3bmxPPfVU4vUPPvjA6tWrZ3nz5rW6deva22+/ne71iY6OdjsANWvWdEHQDTfckObH+rX3wRcd8ZSePXvaJZdckmT6gwcPup2FwYMHu+sJCQnWr18/V8KnjJTW74svvkicXjsV2p4qaTnppJPckdsWLVrYokWL3P0fffSR25nRjoqfkdJt2lFSpsgfH1C+fHm7/fbb071tAGQ/b0ydYkPmzE72/kB0tJXqeYntmjbdlj/4sE154hlr0bOH/TFjxhFZ7R49erjvvr59+9quXbts6tSp7j59l1aqVMnefPNN993atWtX913z8ssvu++tIkWKuO9LP/DR37vuussFRpqPAqmlS5faGWec4e5ftWqVtWzZ0k444QRXqnfeeedZ69atM3U74fhhjBCAXG/rhu326vXv2pTR0y0qKsoS4hMS79uyfpst+GOJbcyzxg7aQfdDqWl8Kpd46aWX3A+gAoXVq1e7o43PPvus21FXYKCyCe3ga+ddgYkCBAVMNWrUcPOYN2+eO3I5YsQId33o0KH22GOPuR9qlbbpB/j66693ZRdXXnllmtZJAZQGEN9///0u0ChevPgx215ah+7du7udAv/I57hx42zPnj2uHEW0jkOGDLF3333XlaboaKlKSUqVKpW4AyEPP/yw2wHR7TfddJNdc801btCzAq25c+e68j4/kNQOirbRq6++asOHD3clKOvXr3fBEoCcbePu3dZ/6u+pTlegSSPL16Ce7V+2wvb9/bf9M2eOtWje3D784AMXAIky4YnTFyjgsvkbNmxw1xcsWGCnnXaaO7jiUyCj77M1a9a472l9RykAuueee+znn39232eff/65O9ilzJIOwOh7TXQgRs0bxo8fb+3atbNu3boleX7kbGSEAORqG1ZttNtOedD+GDvTzLMkQZB4CYfLMg7uP2QH9h+0Z3u8llg7LsrinH322S6oUbChzMeNN95oDRs2dD+UTz/9tLvPr1HXzrumGTZsWOI8FPgoS6Sjl/L444+74EClbcqo6K+OSA4YMCDN66UjnAqgOnXq5H7U165dm3hf79693VHLlKhOXkFO8EVHVkUBlnYuRo4cmTi91kc1+sqSqUxE0w4cONBNqyBROygKhELXQQGjlq9+/fouqPztt99cWZ+ySHrOmJiYxIyUbtPRV/1fOxzaYVGdv4JEADnbZ/PmpHnaqNhYy1e3thVrf7ZVuKu3lWnZwn1v+mJjY5NMr6BHB7HSSmVvCnp0kEXzUuZItyk4Ullc8MEcjRdavny59erVy5XG6cBT//790/xcyN4IhADkWnt37bUHznnaNq/dckQAlJyfv5hiA+45XP4l+tELpqOKGjuksjYNuNXOvI5Aagc+OKPiB0Iq9dLYG90mu3fvdtmia6+9NkkQovp13Z4WW7dudUcw9WOsQENZGh3xXLJkibtfP9aqg09J27Zt3SDh4IsyNqLgRPX3CuD8Zf7qq68S10FlI8oOKUAMXgdlx0LXIfjIqQYti3/kNhxlovbu3euCKwVACsay09gsAOmn78FP/vozyXigND/WzPYXL2rbd+5M0/T6bp4yZYp7Tp+y0DqIU7FixSTjhJR99oMePxDSxR8f5FOpnb4fv/zyS5dFev/999O9HsieKI0DkGuNGzTJ/lmyPskPYmo06cj+Y63sqT3ddWVGgikImjBhgiuXU4ZHWYyLLrrIDhw4kDiNatcfeOABmzlzptupVzmdP+ZGgZTohzR0LJHG/KSFyvCUlVFZnZ+12rFjh7Vq1co1ffj9998Tg5jkaL38DFU4Cnq0g6CgReur9fS7yvnroAYN6lYXTOWCwYKP3PqlKikdudUOh9ZP5XJ6XnXCe/HFF91R2tCjwAByhh3799vGPYebI6Qkfvdu2zBosBVqforlKV/OAnnj7MCqNbb9h0nW8qwz0/Rc+s7Q96Ay47fddpv7PlE26e67704se1aZsw7S6HtSJcqicT86AKTxkMEZoTvvvNM6duxotWvXdgehJk6c6IIt5A4EQgByJQU/CmgyQj+WU76eHvY+HVlUGZg/VkZBgc77E0xHHfVDqh9ZBULKnJQuXdrdpyYLqj9XqYWfYUkvP/jQuBw/wNKRTR3hVKMD1bSHBijppcYGCko+++wz1zBCmRo/EFGZmwIeZcGCdxjSK0+ePEnKEH0KujTuShd1bFLZirJcTZs2Pap1ApA1dh/8/wNFKYmKi7O4KlVs+6Sf7NDmzebFx1tM0aJWuEVz63bP3Wmah7771N76vvvuc2XKKmlWBv6RRx5JMp2+u5QJ97M/mk7fbTpvUZ06dRKn03eUvoc0vkhjkXRASN+3yB0IhADkSnN+XmBrl67P0GNVRjflm6RdinwaF6TyCO2kK8Px6KOPhs1wKMjRUUhlikJ/NDW+R8GKmgPoR1XZnenTp7ujjTpqmRoFKJdeemliW1eVxa1YscKVpSnTo/FKalLgB1/h6HFqRBBMJXHqDOdTUKVmCIsXL3ZHQX0qMVFmTOOatO7KRG3fvt0FidpRSGvDh6pVq7rl1s6IgkfNV2WE2vFQtkyd5tSQQYFRlSpV0jRPANlPwTxJM8XJCcTEWPHO55rpEqJ44cLub7gM/7Zt244IcvwucslR1kiXYPouCsV4oNyNMUIAcqVVC/456vFF4bzyyiuurEIZEwVDahYQLlOhcjmdCV1jadS+NZgG36p99qBBg1xLVv1oq3W0Gieklc66rqBJY4TUoEENHDQfnfxPAZYaGygblRx1a9OYneCLAprQYG7+/PnuCKuCrWBqEqEgUGOVVCaigE6lculZB3Vf0uM0Xkld5RQEadyVygb1fCpdUYncN998YyVKlEjzfAFkL4Xy5LGKhQrb//dxS79GZcoewyUCDgt46SmeP85U864fdB1p1FFGAEirL175xt5/YEiamySEM3jpm1auehk2OgAcpQ9mTrd+v0x2zQ/SI8oCVrdUSRvd4wpeA9ixjg3ICAHIlQoUyX9UQZA/DwDA0etev6HFprEhTLAE8+zKxowPROYgEAKQK9U/rXbGHxwwlwkqVPzwyUQBAEenSN689vDpSdtSpyY6ELBTyle0LnXo0obMQSAEIFeqUr+SNWxV16Ki0/81F7CAde3dMcmZyQEAR6dXoyZ2z2mHxxum9u0aFQjYCaXL2Hudu1ieDGSSgLQgEAKQa3W9rWOGyuNi42LsnCvTd+QSAJC6W08+1d46t7PVKnG4Q2VMICpJ8COF8sTZDU1PtmHdLrbCcXnZrMg0tM8GkGu16tbcTjv/JPtj9AxLSEj7EN3eb11vBYsmPZEqAODY6FiztnWoUcv+XL/ORiyYZ2t37rQD8YeseL581rZqdetUq47FxbCLisxH1zgAudq+PfvtyW4v2Yzxs8Oef8KnEjplj25+5Sq78M5Ox3UZAQBA6ugaBwDpkDd/nD3zzYN23XOXWckKxd1t0THRbvyPgp/omMNlGfVOrWV9xz5EEAQAQIQgIwQgYsTHx9u0b2fZ76Nn2I4tOy0mNtpKVShh7a44w6o1rJzViwcAAI5jRohACAAAAEC2R2kcAAAAABwl2mcDAAAAiDgEQgAAAAAiDoEQABxHTzzxhDVp0oRtDgBAFiMQAhDWVVddZV27dj3i9kmTJrnW09u2bcsRgcLLL79sVatWtXz58lmdOnXsvffeS9PjZs+ebeeff76VLl3a8ubN6+ZxySWX2IYNG47LdgAAAJmL0/YCSGwtveKvVbZt4w6LigrYrm27c/yW+emnn+zee++1N954wzp37mxr1qyxjRs3pvo4TXPWWWfZeeedZ+PGjbOiRYvaypUr7euvv7bdu3P+dgEAAGSEgIi3fdMO++yFr6xX9Vvt5mb3W58Oz9gD5zxtv301zf78ca6NeW+C7d21N8XtNGLECGvQoIHFxcW5zImyML4333zTGjZsmHh91KhRLpPy7rvvJt7Wrl07e+SRR+yjjz6yJ5980mVjNI0uuk1WrVplXbp0sYIFC7pzB1x88cX277//prhcUVFRFh0dbddee61brlatWtkFF1yQ6mv+66+/unMUfPDBB3biiSdatWrVrG3btvbqq6+6/yso0nUpVqyYW05l0AYPHmwlSpSw/fv3J5mfMmu9evVK9vn0PPXq1XOZp7p169rbb7+d6jICAICjQ2kcEMGmjZtll1e9xT58aKhtXL35iPv37txrr938nl1Rs7ctnbUi7DxmzJjhgpJLL73U5syZ40rbHn300cQA5owzzrD58+cnZmImT55sJUuWdKVlcvDgQZsyZYq1adPGlZ7dc889Lqhat26du+i2hIQEFwRt2bLFPX7ChAm2fPlyd19KVGJXoUIFu+WWW9w80qps2bJ26NAhGzlypHmed8T9lSpVcsGfLFq0yC3n66+/bt27d3eZNWWOfCqlGzNmjF1zzTVhn2vo0KH22GOP2bPPPmsLFiywvn37uu338ccfp3l5AQBA+hEIARFq6rd/2iPn9bP9+w6Yl3Dkzr5ssnU2MWGkjfz3Y6t7Ym0rkL+AdezYMck0r7zyiisj08577dq1XWbktttusxdffNHdr2xQ8eLFXQAjCoAU7PjXp06d6oKhFi1auHE8yvjExMS4YEQX3fbDDz+4IGvYsGHWrFkza968ucu+aB7Tpk0Lu+wKfJSJady4sRvH07NnTztw4EDi/SeccIK99NJLYR976qmn2kMPPeQeo6BN66z18TNQyjJpnURjiLScOtO1llWPGTRoUOK8hgwZYpUrV3aBXjiPP/64y6BdeOGFLtukv3fddZcNGDAghVcPAAAcLQIhIAJtWrvFnrroJRcAJRcESTErZc3tbGtu7ey06LPtjELn2bvv/H9JmyiL0bJlyyS36fqSJUtcdkRlY61bt3YBkAISZYeUoVH52MKFC10wc/LJJ1v+/PmTXQ49h7Iwuvjq16/vxu7ovnC+++47V+KmzNRnn31mmzdvduOENMZn3759tnTpUjv99NOTfU5laNavX+9K+JSh0l+VrSkgS8n1119v48ePt3/++cdd1/MrONR2CKVlWbZsmSvdUwDoX5555hl3OwAAyDwEQkAEGjNggh08cChs2VewaIux/IGC7pI3oYAd3Jhgm5akv0uasiEKhH7++Wc35kZjfPzgSIGQyueOtb/++stlYpS50dgljU3atWuXy1699tprVr16dZdZSonG+6jcTZkjBVzly5dPNovk0/opC6WMlcoG582b5wKhcLQ88v7779usWbMSL3PnzrXff//9KNYeAACkhkAIiDCHDh6yb94ZZwnxaR8z41M3uV++/CPJbRrkr8xLMF1XmZxKyILHCf3vf/9LLBHT3++//95NG1w2lidPHpdJCn2O1atXu4tP81OGSZmhcDQ2aMWKFa5TnBQoUMDGjh3ryuP69Onjsi7poeWqUaNGYtc4XZfQZZXrrrvOZYJUIqdGEMGZrGBlypRxwZXGO9WsWTPJRWVyAAAg8xAIARFm7i8LbfumnRl6bEKCZ6sWHi758mm8j8bwPP3007Z48WI3yF+d4tS22teoUSPXXU1jfIIDIWVpVCIXXFqn7m4KYJQZ2bRpk7tfwYTG9Fx22WU2c+ZMN67oiiuucAHWSSedFHZZu3Xr5jJCnTp1cgGXSuG+/fZb13BBQZGClOQaKIwePdouv/xy91frpIYIygQpkFLTBqlSpYord9M0agThZ3dE44QUgCnTk1yTBJ+65PXr18+1+NZzqfROy6axVwAAIPMQCAERZuu/24/p/Jo2bWqff/65DR8+3DVGUAe0p556Kkk5mAIGjcfRX7Ww9oMjlcgpkFFgEhzAdOjQwbWnLlWqlH366afucV999ZULplRSp8BIpW0a+5McjTn67bff3Pijq6++2i2bGh4oYFODBZXl3XnnnWEfqyyTHq8gT53n1DxB66g2134bbGWcFMQ8+OCDLrOjBhE+NU7Qemi8T7iT0oZmjzRfBT8K9hTcKZtERggAgMwV8FIbJJCFduzY4XYodD4P7TABOHoTh/9qfXu+dlTzeGvac1a7WQ1ejhRoLJKaLCjTAwAAsl9sEHMMlglADlKsTJGjnkfR0kc/j9xq69atLtukCydGBQAg+yIQAiJMw1Z1rUjJQhkaJ6RmCbWa1bDSlUpmyrLlBuoap2Do+eeftzp16mT14gAAgGQQCAERJiY2xjrf3N6G9f0y3Z3j1Cyha++kJ1RFUitXrmSTAACQA9AsAYhAnW5oZzF5YsKe5DM5UdFRVrxsUWt90amZumwAAADHA4EQEIFKVihhj/3vHrOAWSAqkKYgKDYu1vp++7DlyXv4/DkAAAA5GYEQEKGan9vUnh3dx/LkjU02GHIJo4BZ4eIF7bWfn7Yajase9+UEAADIDARCQAQ7ucOJNmTF23bNsz2tZMUSR9xfqW4Fu+PtG2zwsjet5onVsmQZAQAAMgPnEQLgxMfH27JZK103OXWHK16umFVtUCld44gAAAAyC+cRApApoqOjOUkqAACIGJTGAQAAAIg4BEIAAAAAIg6BEAAAAICIQyAEAAAAIOIQCAEAAACIOARCAAAAACIOgRAAAACAiEMgBAAAACDiZGog9NNPP1nnzp2tfPny7uz0o0aNysynAwAAAICsD4R2795tjRs3trfeeisznwYAAAAA0iXGMlHHjh3dBQAAAACyE8YIAQAAAIg4mZoRSq/9+/e7i2/Hjh1ZujwAAAAAcqdslRHq16+fFSlSJPFSqVKlrF4kAAAAALlQtgqE+vTpY9u3b0+8rF69OqsXCQAAAEAulK1K4+Li4twFAAAAAHJsILRr1y5bunRp4vUVK1bYrFmzrHjx4la5cuXMfGoAAAAAyJpAaPr06da2bdvE63fffbf7e+WVV9pHH32UmU8NAAAAAFkTCLVp08Y8z8vMpwAAAACAnN0sAQAAAACOBwIhAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAABAtvXEE09YkyZNEq9fddVV1rVr1xQfM2nSJAsEArZt27bjsITIqQiEAAAAsoh26rXD/txzzyW5fdSoUe727GTIkCFWt25dy5s3r1WtWtWefvrpND2uTZs2bl100WPr169vb7/9doaX4/XXX7ePPvooyfzvvPPOJNO0aNHC1q1bZ0WKFMnw8yD3IxACAAA4Tjbu3m0jF8y3QbNm2uDZf9ranTtccPD888/b1q1bs+3rsHLlSrviiitcJmbBggX2+eefW7Vq1dL8+Ouvv94FJvPnz7eLL77Ybr31Vvv0008ztCwKbooWLZriNHny5LGyZctmu2AS2QuBEAAAQCabvvYf6/3tN3bawAF2z4Rv7dmfJ9mTk3+0KWtWW55aNS1PkSL2yJNPpjiPX375xU4//XTLly+fVapUyW6//XbbvXu3u+/NN9+0hg0bHpFRevfddxNva9eunT3yyCPu/7Nnz7a2bdtaoUKFrHDhwtasWTObPn16ss/tZ3SuueYaFwCdcsopdvnll6d5/fPnz+8Ck+rVq7tSt1q1atnXX3/t7lu1apV16dLFChYs6JZFgdK///6b7LyCS+P0/8mTJ7sskb+MCtrClcb9+uuvLnukZSlWrJi1b98+Mfj84osv7IQTTnDbtkSJEm5b+dsWuReBEAAAQCbxPM/e+GOKXfzFcPtu6RJL8Dx3u/4e/p/ZIfMs0K6tvfP22/bNtGlh57Ns2TLr0KGDdevWzf766y/77LPPXGB02223ufvPOOMMl23ZuHGju67goGTJki4gkIMHD9qUKVNcICCXXXaZVaxY0aZNm2YzZsywBx980GJjY5NdjwoVKthJJ53knm/fvn1HvV0UcBw4cMASEhJcELRlyxa3zBMmTLDly5fbJZdckqb5KAA67bTTEjNOuihIDDVr1iw766yzXFmetoO2XefOnS0+Pt49pkePHi7IU7ZL2+zCCy90rx1yt5isXgAAAIDc6o2pU+z1P6a4/8ensGOdv1FDy1OhvPW6o7dN/nLkEff369fPBS/+WBhlVN544w0XAL3zzjsuG1S8eHEXTFx00UVuZ/6ee+5xgYJMnTrVBUMaO+NnYe677z435sefX0oUaCgwUEanY8eO9tVXX7nsjSigqFKlistKpUaBh0riFMzdcMMN9sMPP9icOXNsxYoViQHM4MGDrUGDBi5IO/nkk1Mtk1MZnJ9xSs4LL7zgArngsUl6Dpk5c6YdOnTIBT9aD1F2CLkfGSEAAIBMMG3tmsQgKC2KdT7Xtv8xzXq9/64dSkhIcp9K2dQgQOVj/kWlXcqoKIhQGVjr1q1dAKRyMGWHbrnlFtu/f78tXLjQBUgKKhQwyN13323XXXedKwFTowZlnJKjeem5dVHQVblyZZdZ2rBhg7t/7ty5rmQvJQpAtMzKBCmouuuuu+zmm292GRgFQMFZHGVtNAZI9x0rfkYonMaNG7v7FPx0797d3n///Ww9XgvHDoEQAABAJvho1p8WnY7B+vlq1rB8dWvbsv+NsNnr1yW5b9euXXbjjTe6HXr/ouBoyZIlVqNGDTeNghMFQj///LOdeOKJLmPjB0cKhJQ98mmczrx586xTp072448/uuBj5MgjM1Gi7E1cXJybRgHXwIEDXWaoZcuWLmjYuXOnnX/++Smum7JZWmYFbRp788orr1hU1PHbDVUAlpzo6GhXkvftt9+6dezfv7/VqVPHLStyNwIhAACAY2zD7l02btmSFMvhwineuZPtmTvf/jd+XJLbmzZt6jIzNWvWPOKi0rDgcUL/+9//EscC6e/333+f2CggWO3atV1mZvz48a4sbNCgQcmOD1Jm6Y8//kgMHIYNG+YCMJW3PfzwwykGGn4Jm5ZV8woOgOrVq2erV692F5/WQVktBSVpofVXyV1KGjVq5MrwkqMAT4Hdk08+aX/++aebZ3KBIXIPAiEAAIBj7OdVfyc2RkiPPOXLWcGTmtrisd8muf2BBx6w3377zTUrUGZFmSCN0/GbJfg7++qGpiAlOBBSBzkFMtrRl71797rHKVP0999/uyBJ43EUlITTqlUrN7ZIDQw0L5XRfffdd7Z27VorUKCAe749e/ZYRqg0TyVpyhhprI7GMqlNt4I6jelJC53TSEGausVt2rTJlQuG6tOnj1tHlQsqw6VyQZX5aXo9tm/fvq5rnsZOffnll67pRHLbA7kHgRAAAMAxtm3fPovK4DlsinZsb15C0iBKQY7K2xYvXuzG46j07bHHHrPy5csnyWroPv1V8OI/TiVyCioUtPgZnc2bN7uAQ1khtatWAwRlQ8LR/BT4qAmDxhYpU6PA4tprr3XLs379ehfIhAtAUqN5K6BTAKcyPgVGKrtTV7y0uvfee906ablKlSrlgplQWk9lvlROqNbf6jSn542JiXHb56effrJzzz3XTacW4y+//LLbJsjdAl427g24Y8cOl0rdvn17YmcSAACA7O7j2TPtqckTE1tkZ8Tv195opQsUPIZLBeRsO45xbEBGCAAA4BgrW7DQUQVBsVFRVjRvyuNuABwdAiEAAIBjrE2ValY4Li5Dj1WnuS516lme6GheFyATEQgBAAAcY3ExMdajYaN0tc/2qdPc5Y2a8JoAmYxACAAAIBP0atTEBUTpaZqgwKlFpcrWqExZXhMgkxEIAQAAZILyhQrbB50vcMFNWoIhTVe1aDF7q2NnXg/gOCAQAgAAyCSnVqxkn3a7xIr91/ggXEDkl8+dUqGifdG9hxXJm5fXAzgOYo7HkwAAAESqpuXK26/X3GDjli2xwbP/tBnr1ibpDnd+nXpuTFBjyuGA44pACAAAIJOpA1zn2nXdZef+/bZ9/z6LiYpymSKNIwJw/PHJAwAAOI4KxcW5C4CsxRghAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAABAtrFx40a7+eabrXLlyhYXF2dly5a19u3b26+//prpz121alV77bXXMv15cqtAIGCjRo2ynCImqxcAAAAAkelgfLz9sGK5zdmw3nYdOGD5Y2Nt8B13W8HoaPv444+tevXq9u+//9oPP/xgmzdvzrTlOHDggOXJkyfT5p+bgtTHHnvMxowZ416XYsWKWePGjd1tLVu2zPQga+jQocd0ngRCAAAAOK527t9vA2fNsCF/zbbNe/dYTNThIqX4PXtt+YwZVv2u2+2PPNF2QqlSdkqVKnbKKack2SF+++237euvv7ZJkyZZuXLl7IUXXrCLLroocZo5c+bYHXfcYVOmTLH8+fNbt27d7JVXXrGCBQu6+6+66irbtm2bnXzyyfbWW2+5zFOVKlXs77//trvuustdxPM8d9ttt91mv/zyiwuYlDV68cUX7dxzz831r9FPf6+0TXt3u+sl8xWwvtdeZ/EHD+aaIJXSOAAAgFwgJ5QlPfHEE9agUSO78PNh1n/q7y4IkkMJCe6SEBtjgbg42zTzT/vgj9/t/E8/saVbjtzJfvTRR11wM3v2bLvsssvs0ksvtQULFrj7du/e7UrplK2YNm2a/e9//7Pvv//eBTPBtAO/aNEimzBhgo0ePdq+/PJLq1ixoj311FO2bt06d5Fbb73V9iso+OknF2A9//zziQFVbrRk82Z7dOL31vzDd633d6PtqckT3eWWL/9nv/3yi9W5tLtVatzIBY4KUPv06WPnn39+4uM3bdpkF1xwgQtAa9Wq5QLWYJMnT3aPU/CpIPbBBx+0Q4cOJd7fpk0b91rdeeedVrJkSfdaKvgUvdZywgknuL96/du2bWuFChWywoULW7NmzWz69OlpXlcCIQAAgKOg7IKCEF105LpmzZpuZzp45y6nSEhIsAceeMDKly9v+fLls0aNGtlXX32Vpsf620CXmJgYN8bn7rvvdkGEb9+hQ/b3tm22cttWS/C8I+cRHW2lel5iu6ZNt+UPPmx/Pvuctb78MvthypQk03Xv3t2uu+46q127tj399NN20kknWf/+/d19w4YNs3379tngwYOtYcOGduaZZ9qbb75pn3zyictg+AoUKGAffPCBNWjQwF2KFy9u0dHRbqda45J0kVWrVrmyL+18Kwty3nnnWevWrS03GrVwvp077GMbPvcv91qJ998lKi6PC1K/+upr6/DRh/bVosOBZ6gnn3zSLr74Yvvrr79c1kzBy5YtW9x9//zzj7tNmTgFMe+88459+OGH9swzzySZhzJO+ixpXNi7777rAlpRJlAmTpzo/mreCl51/4wZM1xQFRsbm+b1pTQOAAAgHRZv3mTD581xmYo9Bw7a7NV/W/3TTrXPhwx1Y1vGjh3rsgjaIdPR8pxkyJAh9uqrr7og4tRTT7WlS5em6/GDBg2yDh062MGDB92O7tVXX+0CDgUrMmX1KjuYEG/xYYIgX4EmjSxfg3q2f9kK2/f337Z1/kI7u1UrG/jhhy7olNNOOy3JY3R91qxZ7v/KDGncip7Xp0BGQZ4yQGXKlHG3KbBJS8nV7bff7po3jB8/3tq1a+cyUQoQc5tvFi+0u8d/m+z9fpC66bP/2Y5ff7NL3nzHupxzjj18y61Jtodeox49erj/9+3b19544w2bOnWqe18okKlUqZILTBUw161b19auXeuCb40zivqvRFKZJJU7hipSpIj7q0yRH6Ted999bj7+49KDjBAAAEAaTF/7j138v+HWYejH9snsP+2XVX/bzPVrbf2uXbZy107rOmaUvbJgrl3Q63K3w+yXBCkjcu+991qFChXcznnz5s3d2JZgI0aMcFkJlQupDOjll19Ocr9uUzChHUzNQ/PS2JaUrF692h2ZL1q0qMt2dOnSxVauXJnyjmFUlJUqVcqVmuk5tR66pJWeS5kU7ewqc6LnnDlzZuKYk/mbNiSZ3ktIsK3fjbdVjz1tK+5+wP554RXbs2ChRcXGWr66ta1Y+7OtWNfOFsib1wVVyvzIlVdemRj4bN261b799luXPVAWa+DAgbZ+/fpUlzU4UEqJMk/Lly+3Xr16udK44OxTbrF+1067d/y3FkhlOgWplZ56zMpcd43lr1fHvh4/3po2bWofffRR4jTBQZG2sUrWNmzYkBikKmhVEBQcpO7atcvWrFmTeJtK3NJCGUe9PnqPPvfcc7Zs2bJ0rDWBEAAAQKrGLllkPb/83AU+Ei6jods0XZfhQywhOtoN8haNd9Cg/eHDh7tyIZV16ej4kiVL3P0q6VHAouBDO9oaR6MxMME7l6IB+sp0/Pnnn64ESM0ANL4lHGVkNLZCZV4///yzCxI0rkXP6y9XOGeddZZt377dPf/RWrx4sf34448u8JORC+e7cUDBdkz+xbZP/MmKdz3PKjxwjwt+/n1/kB3csNHdn7Bvn/373kCLKV7c8hUsmJhZCqZlVdCncSra0b7mmmvcTrXGCvm0/gry6tSpk+IyK0MUHx9/xO0K7G666SY3juiee+6x999/33KT4XPnuPdv8nm6/+cHqUXbn20V7uptJ3c61x5//PHE+0NL0xT0KBuXHmkNUvVZmTdvnnXq1Mm91+rXr28jR45M8/OQEQIAAEjBb6tX2e3fjTk8mD+Fki7RNGtmzbYfv//eTj29lSvdUbmYBuyffvrpVqNGDZcdatWqlbtd1M1MAYh26DXmRaVFCp4U+ATTkXMFQJqmd+/erkuaytjC+eyzz9zOp8bAqASsXr167vm0PKHZKN+ePXvs7LPPtp49e7oAS8uprmk+Hdn/4osvUlx/ZawUcOXNm9cFHcpy+eWBv69ZfcT02ydOsqJntbWCTU+0PGVKW5Gz2logJsY2/e9LO/DPWts++Wfz4g/Zoe3brNRJTa1jx46Jj1VjCAVbWta9e/e6cVnKYmm8iQJAZY3mzp3rxpNoeymj45fFJUePV1MEjWXRoH/RoP1x48bZihUrXHZL89P2zE0tzIfMmZXqezscPeafuDxJgs6UaLvpoEDw+0pBql4vjfVJiQKscEGqPg/q8qfSxQsvvDDxc5UWBEIAAADJ0A7bQz9OSLLjFs6eeQts5X0P2cp7HrS177xvBZs2sQLntHMZHu28aWdNAYJ/Uecsv4xHWYzQc7DoujJGwTt+4cbF+J3SQml8jsb3aAfTf06Vx6mJQHLlQ8pAqaW0Su5UaqYAQ+VoavqgkjqVL6V2rhgFZipZ0/OrE5sCFQUgsm3fviTTKtsTv32HxVU/3BFMouLiLLZ0Kdv/99+2rv/btu27CdrbtsKnnWpVel6a5PEKTlSGpQyWmjMogLv//vvd8+s+DdDXoHwFjAo0NS4lNQqmtK4KWFUiKHoNNOZLO/HKqOm19Aft5wbzN26wLXv3pjpd/O7dtu7Nd2zXtBkuSD24ebPt/nO2rf1uvJ1+zjlpeq5bbrnFZe8UmC5cuNA14lA2SSVu/viglIJUfW78ckgFvzpgoMBeLc4VUKlpQnqCVJolAAAAJGPKmtW2avu21HeoSpWy+G3brNIjD1p0kcJuYPnolcutfuESrhOZyt/0N1hmtmBW0KJxFuFOQOnv4IdS2Z4yODryrtbTCoSUxVIrZA1CVxCgdscp0fggdc0TZYR27tzpskTK0uSNCb/bqTFBGz4YZFWee8Zlg/JWr2ZReeOs3G032+Yvv7IDa9dasU4drED+/Ekep45jTZo0STzRp5pUaJkV9ChwUalUaKCn7I6CvdCyQ58aRCiIC5bbxgOF2hoSoCZHQWpclSq2fdJPdmjzZvPi4y2maFErdFpzu+Xxx9I0D41t0+ukBgcq81Rwfu2119ojjzyS6mM1bk6vn6hrnw4U6PxFV1xxhesGqAYKygipa11aEQgBAICIpx1pda0aM2aM26lSIKAdtfztzrTovHks3kt5jENsyeJWvvdNFl2oUOJt+w8dso2FC7mMggaLK6gIR0ewdTQ7mK4r8xAcPP3+++9JptH15I5+awC7yuNKly7tStqC6ai5diiVOdFYDO1UavySdlI1vkLBizJJeqzOv6PlVnZHgge5B1MZWjj+8uvofdWiRS0QNBw/Km9eFzQe2nC4BM23b8VKi6tSyf1f2aFd02da1KF4q16smCVHwZ2WQRctr3a0X3rppWSnx//zT2abGgWpxTufa6ZLiIL/BanhMqcKPIOdccYZrotccpIr3ezcubN7rDrHKdOq8VyffvrpUb2UlMYBAICIp5bIakKg85eonEsd33Rix4WrV6UaBEkgKipJECTRUVG2pUA+d64THbXWQHuNM9FOYL9+/VzQJRp8r5N7qhGAnlvLoDIujdEJDY7UUljTqHxN447UMCEcPaeOkKtrm5ol6Hm1g6lW0CoVU6CjE09qvItOSCk6Mq+gTU0HfvvtN9dqWiVmyi6pG5vGLukko+pwJ7rfP/Ho66+/nrjTq45taomsMiaVmimgU8DWvcEJFjocv8iZbWzP/AXmHYq3A/9usC1fj3FlV0VaHw4aC57UVHvX9u/wz+3UuPxueRK3+X9BmQJYlVipFFAD5xW05aYxPJmt3DHITJYNee/nFAEvtaLXLLRjxw4X9an2M/RoBgAAQEYs3LTRhs6Z7Zog7Dyw36L3H7Dfb7vTXhw6xO68tEeSI+RNBrxpO/47IWj8nr229Zsxtvuvuebt22cxJUtY8c6dbPfsv1z51qHNW1x5l2/vnHkWP3Gy7VjzjwskREGFshcqwVL2Rd3HFBCpXMg9R3y8y8xoDIUCIe3Y6xwrul8la9of0jzUplpNCPLnz+9KhhToqE33DTfcYAMGDHDzUkDiP1ZZHs1XZWPK8qjpgqYNpTbReoyCJ+2HqVW0ptXzqJHC888/77JNCp40TkPLIQpEunbtekSZnI7g61wyKoHSfD/8+GOL37/fYsuWcdsuX/269u+HH9veufPUu9tdFN7krVnDSva8xGKKFHYZoo3vDbT4vXvdiV41MF8Bl8YEqcRNy6STqGobqEGEdm2ViVIgq3PY+OeWCS6NCy6vU+ZI41aqVavmSrT8MU3uvbJwoWvPrKBRJ1PV/LQd9NppfXWyVnUqCx5/pOyitrXGWWl75wSdP/3E5m/ceESgmpqoQMAaliptoy693HJibEBGCAAARIRlWzbbRZ9/aucOG2zD5/5lK7ZttU179tj6A/stEBdnT783wE4b8KaNXDA/8TH5/2sFrPPd/DvgA9u3fIWV7tXTKvS5z+3IW1TASl12qRX+L4Ph27dsuW0Y8qmdfOEFNn/+fPv8889d0KAdbWVLlB0SjWdQ62wFPGrPrIBJmSkFQepcprI1nVtI3c6UOVJGSCVtysKUKFHCZUOeffZZ1zRBmRp1dVNGyQ9E9H/tmPtNEt577z035kdjdsKdU0g7+8o0KYhSFzl1UDv33HNdUKGW3BrUHkpBkzJeCjT0HOrepYHtWh+V3On/6vamjNZLH35olR97KHHbKZNWoFFD1dC54Kf8HbdauTtvs0Nbt9qWr75x889braq1Puccl8VSedRDDz3kAh11jdNzaptqG59zzjmuyYGyayobVECkZddyh6NgRhk1bVd1l7vxxhtdcwhlyfygVMGOgsA//vjDbbuHH344yTwUJCkIUxAafFJaBUIKknKKKxufmO4gyO8ad2XjppZTEQgBAIBcb9b6dXbB58Ns9r/rjjgPkBoblOp5ie2aNt1m3HGP9ejUydpffeXh5gGlSlt0IGB7Fy+x/X+vsjLXXuXOoRJbsoTlb1jf8tcPX4K19bsJVqRdW7uoZ08XXCiLoNI3P1vjU7mZmgmowYAyJ8r2+OMnVP6mo98KJhQMacyOdtT9c+Go25ayQRogrmyG/qqNcOhzBFNgpOBB3buUrVEA4dO8GjZsmO5tq2BOWSONzwm3rspAaZ0U/N152WX24sWXWoGG9a1A8LaLj7eSl3SzuMqVLK5SRSt8ekvbt3ipu+v0LTssz759LhDSiTNVhqdzLuncMQp6RAPnVc6oduEaI6TxXWoUoWBSAVM4ygRp+2tbqHxPQZ62oT+2SI0XFNgNHjzYzU8tzxV0BtP0fkbMp+2r+SY3nio7Oq92HatQqLB7r6eVpq1UuIidW6u25VQEQgAAIFdbs2O7XfXVCNtz8GDYE6FKgSaNrNJTj1mZ666xfPXq2ORJk+3Epk2txKKl7jEatxJdtIgbvJ8Wmn7buO+td6vWie2rr7/+epfJUabFp/bPictQoIAr91FjBVHmRzv1oSeoFJWHaSdd43qC23Ir05Nce2yVjSlgUZCiv8omKePkN2HQAPTkGjqkRF3WNBYoeDmC11XroXPEKNiQixucYO926mLF/isX1L53IE+sxZYsmVhuFV24sMXv2mXPnXWONchfwGWdND9lynRCWmVn1MHO31bKiKmFtn/yVlHGTEFjci3Gk2tb7k+vMVA6kaoya75TTjklyfQ6X5JK6QYOHOiu6zxDyi4pEMpJ8sbE2sddu1nBPHFpCoY0TaG4OPeYuGS6AeYEOXfJAQAA0uCd6VNt94EDqZ4wMio21mV7dCnW/mzb8fkIG9b/Tav8xMO2I0wwkhLvwH4748or7f0HHjziPu08+0KDHGURFLCIP65IQsvYlDkSjTEK3vmX0DbdPgUNKnk78cQT3XUFURo7pCyLMikqrfMzLOmhZVFWyM+OhK5r8Hr4zq5R09pUrWYTli+zJxcvtc3/LbNabNcuUdLqNGlqL3gfu6DJGpzgMlca2xOc3QneVllJ5XFq471mzRp3Mk+VxFWpUsVymurFituoSy6za7/+0pZv2+qCndADB/5tVYsWsw/Pv8AqFzk8RiynIhACAAC5lhodjFgwL9lMUEq8UiVt27wF9lGbdnb5n7Msftt2O7hhY6pZIWU08leubGX27U88p05GKFukUjaNcQkNmDRmSI0D1NxAHeLSQi3BFZRo3I9/clY1D/DP9aNucaEZj7RQ8wRlT5JbV62HggR1u/OzQhIbHe3Kqjac1NzuHPKpLet9d2I5WXLlbMlRlzid+FVjeVq0aOFu0zlmtFxqZpDcYzRuKbj1t6770yubpCYKaqeu7e23Hg91wgknuKYSCko1XigtJ27NrqoULWrje11tP/+90gb/NcsmrVyeOHJIr4yC1ysanWgtK1dx7/OcjkAIAADkWl8tWmAH4+NTnCZ+927bMGiwFWp+iuUpX84CeePswKo1tv2HSVbppGZ2ZrXq9vpNt9gV3463fwd+bCW6nm8xpUrYwX83uLqu/PXqJjlirvKi+5591m67vJc9WbOma1cdFRXlSshUNqXytbS47bbb3Mk8NR5GHeI0XkhlbApWtJOuLIzaYet2nexUA/bV3Uzd3MI1NdA4IzUH0OPUAECPUYbIP5+Qys8UOPhjkNJKJXbnnXeeVa5cOey6aiySSvDUovyVV15xAZO6sSno0TL4jmZMjTrDqVW4SvI0NknjiVT+p6YFuj0cnWtIjSqUIVNW7JtvvnHjmDSmSTTWSc0XFCipSYUCRv/En6HLqqyQXi9tRzWjyMmiAgE7o2o1d1E56dZ9e93txfLmS2weklswRggAAORaS7dsdufzSUlUXJzFVali2yf9ZOv6v23/PPeSbR37nRU6rbkVvuB8N033+g1dR7bStWrZhsFD7J9+L7pz3th/pVlR/+0Xn165qisvuvGSS935bNRB7eSTT3btsl999dV0lUxpjMuPP/7oSs8UTDRr1sxlHfzskHa+VdKmcixlJTSNBuqrcUJyNNhfy6ExNsrUqAW1xsGo9E4BlhoQbNqU9ASnqWnfvn2q66qyO92nzJMyLvfff7/rynYsaTtoGykoU8ZLXeP8tuPhqCOcGi+oOUKDBg1cAKV5qEOe+J3ptP217Nrefte44PJG0XppjJL+ht6Xk+WPjXVNFHTJbUGQcB4hAACQa9034TsbtXB+hkrjJC462hbcemfide1cqwPdp3P/ssWbN9nugwetSFxeO6VCRet5QiOrWLjIMVx6ZDcqnVP3OJ28VdkinwJJXVfpnEoFkTPOI3RcSuPU/vHFF1906Ve1H1SaNyM1qMg9lFL2T0aWG54HAJA9qbOVK2PKYCCUPzZPkuua14nlyrsLcj/tQ6gLnkrvFPyotFCd5fwgSOO3NBZJJXPKhBEE5SyZXhr32WefuTpV9bpXS0EFQkqh+u0OI4nfU16XPHnyuBpZtZvU4L60ULrbP4Pz0di7d69L7+rM1vpwKyj97bff0jUPvYZKGYcbNJgWaoGpk6sBAJCZTixbzg5lsLOYxvucXL7CMV8m5BwaF3Trrbda3bp13X6cSuSCzxmkDJHaeGt/6N13383SZUU2zAhpUJwGrukEYKI3yZgxY1y/dQ1iy828hF1mB6aaedsOb+qETdahQ3sbNOgjN6BRdav6cKl2VYMgjxdl51TnrCBV3Vs0mFF1rWm1atUqFzhpUKBeR30ppFdwT34AADJL+xq1rGjevLZt3750P1bldL0aN8mU5ULOcMUVV7hLcjSeSOWSyJkyNSN04MABmzFjhuvEkfiEUVHu+pQpU46YXsGBav+CLzmRd3CxJWx/wrwNLczbdpN52x80b/u95u3/yfLYdCtT8GurXKmQ3XzzzW5b6EzI/vrrRGHqcKKuIzovwKRJk9x9+qtgUjWRflbpiSeecPe9/fbbLmWrwXlq76iOLSnRa6CBisrqaEBl586d01WqqIGEGoio5f/0009dhin0S0FdbDQQsnjx4i7o8ZfVp+X3W2OqrlbXP//8c3ciN7X2VHClNps6wqKWlMpcKYO0cePGxHnoPnV0KVmypKsX1SBRZR1Tej8qeNORG20rDeLs169fmtcbAJDz5ImOtstOaJzuVr/aQapcpIi1qFg505YNQC4OhNR1RB1B/N7rPl3XeKFQ2inVDq1/UReTnMbbPcS8zZ3N9n5mZmGOPnn7zNv1snkbO5p3cI7b6dcOumgnXQHi8OHD7a+//nJnTlZbySVLlrie+K+99pobGKayMv/symqTqaBDJXZqefndd9+5FpUpUeCjPvsffvhh+tfP81wgdPnll7s0scr7lF0KpfMeKJjT86jlpJZvwoQJKc5b5ZOqsVUwowyVOtkomFJHF7X0VG2uWnQGp6vV0vKXX35x7UQVDJ577rnu9nDeeOMNF3Qq4NK2Gjp0qFWtWjXd2wAAkLNc3/Rkq1a0mCt1S4vAfwcNX2jX4ahaOgPI3rLVeYRUHhbc914ZoZwUDHm7B5u30z83QEotIT3zErbZ9yO72Lhx/1jv3re7cjMFGPqrE6SJAh0FNrq9b9++LjjUF3JwWZmmV8ChDI165ivL4Z8xOhydFEzB1QMPPGDPP/+8awmpgX+iwX7KrvhZmHDUW3/Pnj0umyQKiBRQ9erVK8l0asmpwEYUoOjkYjpbtTI4ydH6+vPVMqkFpR6jQYn+GbA1TsqnMzcHUytQjaGaPHmy2x6htK20LOr2ou2YE8/6DABIv8JxcTa460XWa9T/7O9t21LsIKdgSdmjt87t7DrBAci9MjUjpJ1qDajXzncwXQ83RkQn+lLGI/iSU3gH55m389lUpxvz/W4rXGOp5a+6xDpdtsIu7lLUHn/8YZszZ47LnmnMjsrA/It26pctW5bs/BRYaIe+evXqLhhRlkOBSnJefvlld8IzBVbK0Oi6f3IwLYOCKTW0SI7GBF1yySWJY4oUrGigYOgyKhAKpnK01BpkBD/GzyLqvAjBtwXPQ+8jjT9TcKMgUe8XBXYKeMLRIEedNE4nilMWTec7AICcTpltVQwgZeUKFbIvL+5pVzZuagX+Ox+KnyFS4KP/61rrKtXsi4t7WrvqNdmkQC6XqRkhdUbTia10VN9vX5yQkOCuqwwsN/F2f/JfXJnyycHatMxnbz9X2vLEBqx82RiLiQlYIOYPtwOvoFFjqvQ3mAKi5ChwUSmZxhBpx16lYxqPo6xOuA5zKrlr0uTwwE8FUMrwaFyOxt4oA6cMT3InHtuyZYtrI6lWke+8807i7QrgFCDpJG2+0HkoA6PXPiXBj/FLEUJvC56HyuKUxVLpnNZFgbROoOaXGoZSS8sVK1bYt99+69ZbZ5PWGK1wpX0AkB4q91Z5t5oBrVmzxh2cUemwvlP1XZU/f/5M26D6vldlAFJXOC6vPdK6jd1zWkv7evFCm772H9u5f7/FxcRY1aJF3UlTOQ8QEDkyvTROpW76EVCplQbk66jV7t27E7vI5QZewlazfd+kGgRJgXxRVrNa8DkJoszbM8ROPPFRF1Ao46HAJLnAMtxZmJWd0Q69LipHUwCkM1FfeOGFR0yrRgzq+Kb5KOBSBkoBlBocqOmBAoXkKNtUsWLFxCYHPj1emSWNAwoN4jKTMlFqFKFxQbJ69epUz4atrJEyWrqoqYTKBBXgqakDAKTFPzt32NqdO+xAfLwVjctrMdt3WJvWrd13r7LtymTrwIyy7CrZ1ffu+eefn2kbV6dCQPrki421Sxqc4C4AIlemn0dIO5wvvfSSy1QoE6HSJI17CW2gkKPt/1Wn1MrggxPMDvxhtWqWt8suu8y1aPzyyy9dQDJ16tTEI4x++YMyR8qoaYdfJXCjR492TQC0Xf/++28bPHiwy5qo/CsclYSp6cCll17qMknz5s1z2RH/XEaffKLMVngaC6TgoWHDhkkuGruj5dHrejypJE7Lu2DBAteUQdtPzSdSauWuLncLFy50Hen+97//uRLNY3FuJgC5m85D893SJdZzxOd2+qD37ZIvPrNeI7+wzsOH2CkXdLF9CQk2ZtJEl2muV6+eK1fu0qWL+/5Wgxr/O0hBkrI3Gv96yy23uO/00HPF6Xtd3+HKIuk7V9/1akCj34BixYq57/Hgg2KhpXHKnn/wwQd2wQUXuHnou9LvTurTdb/baNu2bd389bht23S6BwCIDJkeCInK4LSTrvbQ2mFVW+hcJWHLfz1mjmYeW11TBAVC99xzj/sRVDmhSh40pkfUOe6mm25ywaWOAKobm340FTipcYB+fHWeJu3sN2jQIOzTaPyPOtPpx1fji3QWZAVCGi+kH041rBgxYsQRj1PJ3uzZs61bt25H3KcSkLPOOitDXeiOhp5v69atruRN46O0c1C6dOkUywi1zZSdVHtute3WuZzUGQgAkvPvrl3W+dNP7JaxX9vUtWuS3Be/e7ftmL/QopqfZO0/H2YjFsw74vF+qa++a3TgSgegFHgoc6/OmMEU9GgadQ/VwSWVPSug0XeVLjr4M2DAgFRLep988kkXlKkcWllzHShS9lt0oE0Bln5j9L1+44032sMPP8wbAEDECXjZ+CxQGrOinWydOyc7N07w9gw1b8dTrhtcRgVKTbJA9OFucQCA7GHj7t3W9bOhtmH3rrCdxvat/NvWvdrfSl9zpRVofLjMqu+ZZ9ttbc60ff+dwFMnzlaXzlAKZnRwyy/pVUZIZePK2teoUcPdpvsV/Kg5jD9eVCW9ygL5Z7HX/++880538QMvNcF5+umn3XWVo+uxGh+px+pk5spUqXTPp+k1zlMHl8iSA4iU2IBD4cdClLIQRxNPRplFFTsmiwIAOHZuHft1skFQch6Z+L0NHjvGlSwrO69qCFH2XdlzjRlShlqZbDV8Ce70qVI2PwgSlZEr0AlumhPaQTO1LpwqxdMOg/8YnUdNWfFg6TmpNgDkFgRCx0Lc6WaB5Du7pSzaLO4cCwSSH9sCADj+Zv+73qavW5tiEBRbqqRSMHZww8bE21QIN2HrZtc1zh+3qFJcnd9MAYrKj1Vu/NZbb7n7gjtdhuu4ebRdONP6GACINARCx0AgkNcs3yWHg5p0i7dAgcuPxWIAAI6hIX/NSjzPTHKiCxSwfHVq2Y6ff7WE/zI/CpzGLFlkm4IyPQp8FIiow6bGZqpj59q1a7Pk9dIY1OnTpye5TeNRASDSEAgdI4H8l+kYXDqbJkSbxZxgFpu0RAEAkLU0fHb04oVpKokr0f1CnSTP1r78uu2aOcsOrP/X9q5bb4+//prrUqnTCig7pHOw9e/f35YvX+7G/fhjfI43NUfQcj3wwAOug+bnn3/uxicFN3YAgEhAIHSMBGIqWqBY//8CobT8kESbRZWyQLF3+eEBgGxm14EDtj/MedvCiS1Z0srfd5flq13Lto4ea/+88Ir98/LrNmboULv33ntd0wJ17FT7bDVN0GkHdF42nR4hK1SrVs01alDHUZXq6QTZftc4nf8IACIFXeOOMW//FPO23Wrm7fovIAo9mqjyuXizmHoWKPa+BaKTb/cMAMgaO/fvt8YD3szw42Oiouy2k0+125ufZjmBOsYpQ6UTUwNApHSNizkmS4VEgbjTzEr9bLbvG/N2DzaLXxq0dQJmca0tkP9yszwtLRAgIQcA2VHBPHksNiraDiakLSsUKj4hwUrkz2/Z1dtvv+06x5UoUcJ+/fVXe/HFF905/wAgkhAIZYJAVAGz/JcebqCQsM4sQWfqjjWLLmkB2mQDQLansTIda9WyMYsXpat1dvDjz67+/22ws5slS5bYM888406yqpN260TeOqE2AEQSSuMAAAhj5rq1dtH/Pk33tlGnuQ41a1n/jp3ZrgBwDHFCVQAAjoMTy5azE0qXSbWFdqgEz7OrmzTLtOUCABwbDFIBACCZ8rZ3O3WxYvnypSsYeqR1W2tarjzbFACyOQIhAACSUa5QIfvy4p5WqUgRd33/ipW25rmXbMVd99u/HwyyvUuW2oo77jXbu8/1CX2yzVl2dZOmuXJ7Vq1a1V577bU0T9+mTRu78847M3WZAOBoEAgBADLVVVdd5bIrzz33XJLbR40alSPOo1axcBH7tueV9vLZHW3PN2MtT4XyVunxh6xkz0stb7WqVve5Z+y6Fi3txyuutfiZs6xo0aJH/Zx79+616667zkqVKmUFCxa0U045xX777bd0zaN9+/buZK7Tpk1L1+N0ctVw66D53HDDDWmej85TpHMoAUB2Rdc4AMAxtfyvv23+lMW2e/sey1sgzras22p58+Z1JxO98cYbrVixYtlqi3ueZ/Hx8RYTk/xPYlxMjF1Qr75dv227PXjHnXba+Z3tQPwhKxyX1xqVKWN5Y2KP6TKpnbVOevrZZ59Z7dq1be7cuSkuX6hVq1a5wEktsQcOHOhaZR8tBWXpUbx48aN+TgDITGSEAABHTYHEj8N+tttPe8hubHKvvX7zezbw4WH21u0Dbfr42VYiqowVyFPQnnriqRTnM2LECGvQoIHFxcW5UqyXX345yf26rW/fvnbNNddYoUKFXOvn9957L/H+lStXuizT8OHDrUWLFi4Aa9iwoU2ePDlxmkmTJrlpvv32W2vWrJl7rl9++cX2799vt99+u5UuXdo9rlWrVonZFH++mzdvtgdvv93OqFrN1vz0i+1ZstTyxeaxbdu2ufleffXV7kR/mlaXJ554IvG8PbVq1XLzLVOmjF100UUpboeoqCirX7++y+pUq1bNOnfu7LJCaTVo0CA777zz7Oabb7ZPP/3UZZiCaXkVlGpZ/G00evToFNchuDSuZ8+edskllySZ58GDB61kyZI2ePDgsKVxKW0DTdu7d283vQJl3f/+++/b7t273fLota5Zs6Z7zQDgmPGyse3bt+vkDe4vACB72rNrr/dQp2e9doGLvHOiu7u/wZdyVsUrZeW9xoEWXnQg2pvx25/ucSNHjnTf8b7p06d7UVFR3lNPPeUtWrTIGzRokJcvXz7311elShWvePHi3ltvveUtWbLE69evn3vMwoUL3f0rVqxw86xYsaL3xRdfePPnz/euu+46r1ChQt6mTZvcNBMnTnTTNGrUyBs/fry3dOlSb/Pmzd7tt9/ulS9f3hs7dqw3b94878orr/SKFSvm7jt06JC3bt06r3Dhwt5rr73m/r9nz57EeW3dutXbv3+/u0/T6H5ddu7c6U2bNs2Ljo72hg0b5q1cudKbOXOm9/rrr6e4TWfNmuXW64MPPkj365GQkOC20+jRo931Zs2aeYMHD068Pz4+3jv11FO9Bg0auPVftmyZ980337j1Tm4d/G3/6quvuv9r3npt/PtE89BtO3bscNfPOOMM74477nD/T20baFq9Rk8//bS3ePFi91fTd+zY0XvvvffcbTfffLNXokQJb/fu3eneJgByh+3HODYgEAIAZNjBAwe9+9o96Z0Tc/ERAVBoIKT/FwmU8KoXqONtWb/1iECoZ8+e3tlnn51k/vfdd59Xv379xOvaGb/88suT7PSXLl3ae+edd5IEQs8999z/L+PBgy4wev755911P3gZNWpU4jS7du3yYmNjvaFDhybeduDAARcYvfDCC4m3FSlSJElgFhwIie7TNMFGjBjhAgs/QEjN+vXrvbJly3p9+vTxatWq5QITn4I5PZ8Ci+QouClVqpRbb1HwokDDN27cOBdkKdgMJ9w6hAZCmnfJkiWTBFg9evTwLrnkksTrwYFQattA07Zq1SrxugLPAgUKeL169Uq8TUGZ1n3KlCnJrjuA3G37MQ6EKI0DAGTYFy9/Y7MmzrWE+IQ0TV/Ta2grdi+2hy49skRuwYIF1rJlyyS36fqSJUtc6Z2vUaNGif9X6VbZsmVtw4YNSR532mmnJf5fY2tOOukkN/9gus23bNkyV9oV/PyxsbGuHC30cel19tlnW5UqVax69erWq1cvGzp0qO3ZsyfZ6VUOqJI/lQBOmDDBXX/kkUfcfXPmzHFlYo0bN0728RoTpLI1f0xRjx497Ndff3XrKLNmzbKKFSu6sUcZpXlffPHFbl1EJWxfffWVXXbZZRneBsGvq5o8lChRwk444YTE21QuJ6GvNQBkFIEQACBD4g/F28j+35qXoAN0aVMsUMqKWxkbMekz27phW4aeVwFKMAVDCQlpC8SCFShQwI4HBS4zZ850Y3XKlStnjz32mAtkNE4nnL/++suaNGni/q/g4fvvv3fjZTSmZ8CAAXb55ZcfsQ18W7ZssZEjR7rxOApWdKlQoYIdOnTIBUiSL1++Y7JeCnp++OEHF5ioA6Dm26FDhwxvg3Cva/BtfofBjLzWABAOgRAAIEP+GDPTdYRLr1p2gm20dfb5wBFJbq9Xr57LXATTdWUulCFIj99//z3x/woCZsyY4eafnBo1aliePHmSPL8yRGqWoKYFaaV5BGevfApI2rVrZy+88IILdNR84ccffww7DwUu6vjmz0frP378ePv8889dkPPoo48m+/zKtCjbM3v2bJf58S/KKqkttuapzMuaNWts8eLF6VqHUGpGUalSJdfZTs/bvXv3ZAO09G4DADgeaJ8NAMiQqWNnWnRMtMsMpUfBQBEr61WyCVOTdgC75557XJtnnXtGpV1TpkyxN99802U30uutt95yHcoU/Lz66qu2detW12kupeyQOqzdd999ru2zStO0w67yrWuvvTbNz6vOart27XKZEmU88ufP73b2ly9fbq1bt3Yd0caOHeuyGnXq1Ak7D3WuO/XUU+3SSy+1Pn36uK52ygopoJNPPvnE7r///rCP/fDDD103NnWBC6aARfP67rvvrFOnTm5ZunXrZq+88orrxrZw4UKXcVFGJ9w66BKOuse9++67LqiaOHFisttFHenSsw0A4HggIwQAyJAdW3eleWxQqBrWwJ2/J1jTpk1d1kOtr7Ujr/Kpp556yp2QNb108lZdtCOv1thff/21a+2c2mMUHGgMi5Zl6dKlNm7cuHSd90hZkptuuskFcjrvjoIpnZxUJxc988wzXWCmwEElYmoTHo6WWUGgghGNrVFQpEBI44U++OADF9CozXgoZb2UCdI6hCpSpIidddZZLlASPV5Bp8YPKeOlwMrPAoVbh5TK4+bPn++yWKHju4KldxsAwPEQUMcEy6Z27Njhvrx1PoPChQtn9eIAAIL0vew1m/zZb5aQjjFCwUpWKG6frh5wTLepyq103p0///wzcZwNACB32HGMYwMyQgCADClVoYQFog4PYE8vPa505ZQzNAAAZCYCIQBAhrS74gyLP5Sx0jh1mut47VlseQBAlqFZAgAgQ6o1rGwNWtaxBVOWpLulcb5Cea3NpcmPKckoDfTPxhXfAIBshIwQACDDLn+0uyV46c8KXXJfV8ubP44tDwDIMgRCAIAMO+mcxnbLq1en/QEBs7Y9WlmPhy5gqwMAshSBEADgqFxw+7l236BbLU++PC7QcZfQH5voKHeemu53d7YHBt9mUVH8/AAAshbtswEAx8TuHXvs+09+spH9x9o/i9cl3l64RCHrfNM5du4N7ax0JTrFAQCyR/tsAiEAwDGlZgV7duyx3dv3WN6Cea1g0QJkgAAA2S4QomscAOCYUglcgSIF3AUAgOyKIm0AAAAAEYdACAAAAEDEIRACAAAAEHEIhAAAAABEHAIhAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAAAAEHEIhAAAAABEHAIhAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAyEGeeOIJa9KkSVYvBgAAOR6BEICIt379euvdu7dVr17d4uLirFKlSta5c2f74YcfIn7bAMhcH330kRUtWpTNDGQBAiEAEW3lypXWrFkz+/HHH+3FF1+0OXPm2HfffWdt27a1W2+9NasXD0AyrrrqKgsEAkdcOnTokKZtNmnSJDf9tm3bsjRbe8kll9jixYstp9A2GzVqVFYvBnBMEAgBiAie55l3YJolbL3TEjacbgn/NrWEDS3s5utbWcAO2B9//GbdunWz2rVrW4MGDezuu++233//3T121apV1qVLFytYsKAVLlzYLr74Yvv333+P2AEaOHCgVa5c2U13yy23WHx8vL3wwgtWtmxZK126tD377LNJlkk7YNddd52VKlXKzffMM8+02bNnJ5nmueeeszJlylihQoXs2muvtX379iXe99NPP1lsbKzLaAW788477fTTT8+kLQlkjQ27d9mXC+bZwD9n2ODZf9ranTvsnPbtbd26dUkun3766XH/bjl06FCGH58vXz73/QDg+CMQQq6TXY5W6Whl165ds3oxoB2VAzPN29TRvC2Xme0fZ5bwr5m3y7Zs/tfG/fCP3XKlZ/n3dDRv75gk20vlKgkJCS4I2rJli02ePNkmTJhgy5cvd0dxgy1btsy+/fZbl03SjtiHH35onTp1sjVr1rjHPf/88/bII4/YH3/8kfiY7t2724YNG9zjZsyYYU2bNrWzzjrLPZd8/vnnLsjq27evTZ8+3cqVK2dvv/124uNbt27tyvk++eSTxNsOHjxoQ4cOtWuuuYbXHrnC1H/W2K1jv7EWA9+zeyd8Z31/mWxPTv7RpqxZbX+sX2dDli81K1jAHXDQpVixYom/BR988IFdcMEFlj9/fqtVq5Z9/fXXiZlgZX1F02tafWeLPvP9+vWzatWquSClcePG9sUXXxyRSdLnVtlkldMOGTLEnnzySXcgw89MqeRNXnnlFTvhhBOsQIECruxWB0l27dqVbGmcf2BFn+uqVatakSJF7NJLL7WdO3cmTtOmTRtXzquDHlp+HSx5//33bffu3Xb11Ve7Ayc1a9Z0yxhs7ty51rFjR3ewRo/p1auXbdq0Kcl8b7/9drv//vutePHibntqeXxaHtE21Tr614Ecy8vGtm/f7mkR9ReR6corr3TvgdBL+/btk32M7h85cuRxW8YVK1a45/zzzz+T3L5t2zZv69atx205EF7C3h+9+HX1vPh1dbz4dbWSXKaMreReuy8+LJd4W8KugUkeP378eC86OtpbtWpV4m3z5s1zj5s6daq7/vjjj3v58+f3duzYkTiN3qNVq1b14uPjE2+rU6eO169fP/f/n3/+2StcuLC3b9++JM9Xo0YNb8CAAe7/p512mnfLLbckub958+Ze48aNE68///zzXr169RKvjxgxwitYsKC3a9cu3hLI0RISErxXpvziVXv9Ja/mGy+7v8GXgqec5OU/oYFX442XvUbvvOFN/Wd1ksfrM1qxYkVv2LBh3pIlS7zbb7/dfTY2b97sHTp0yH1WNM2iRYu8devWue9seeaZZ7y6det63333nbds2TJv0KBBXlxcnDdp0iR3/8SJE93jGjVq5L4fli5d6q1Zs8a75557vAYNGrh56bJnzx43/auvvur9+OOP7rfihx9+cN8DN998c+Jyav5FihRJvK7vEy3nhRde6M2ZM8f76aefvLJly3oPPfRQ4jRnnHGGV6hQIe/pp5/2Fi9e7P7qe6pjx47ee++9527Tc5QoUcLbvXu3e4x+j0qVKuX16dPHW7BggTdz5kzv7LPP9tq2bZtkvvpeeuKJJ9w8Pv74Yy8QCLj1lA0bNrh11zJrHXUdyMmxQaZlhFQC0qJFC3cUhkGASD0gP2TevnGWsPVmS9h0viVs6mQJW64wO7TMOrRvl+WlDxmho3i897OWd3C+edt6m1m8jvMeeb++TkNv29nPvH3/fxR1wYIF7iiuLr769eu711b3+XRkVEdhfTraqumioqKS3KYMkOjIsY4KlyhRwh2d9S8rVqxw2SX/uZs3b55k+U477bQk13UUe+nSpYllfDq6rNI9HX0GcrLX/vjN+k89/L6OD/dhNbM98xbY8nv72Jw77rXm1WpY/gIFXAY1+PPRo0cPlx3R7frMTZ061aKjo13GQ1SWpsyHvrP379/vplOZa/v27V3GVfO4/PLLbcCAAUme+6mnnrKzzz7batSoYRUqVHCf35iYmMTMlLJJoqyNsk/6jlD56zPPPOOyvSlRVkqf5YYNG7oyV2VuQpu3KFOlLLMyXX369LG8efNayZIl7frrr3e3PfbYY7Z582b766+/3PRvvvmmnXjiiW796tat6/6v9Zw4cWKSMUqNGjWyxx9/3M3jiiuusJNOOinxuVXGK/r+0zr614GcKtMCoQMHDriyj5tvvjmzngK5ZdzG7sHmbTz98A7r/olmhxaaHVpiduAP8w7+ZXnsDyud710rU7rQEaUPS5YsceVB+gHQTqfKllIbDDtr1ix3m0ojfL/++qsrCVDgrnnrB3Dr1q3uPpU6tWrVyn3xa6f1vPPOS9xRFZVPiH5UNF/NJ1xpnH5gVXKgH10tr+Y5bdq0I5ZVPzj64dGy6GDCokWLMmHLRwZv1+v/BUHhd6JqVYu1QMBs0dIDSR+34znzvCMDp5RorE4wvZbhbtMOjmiHTKVuej8GX/R633fffWl+Xr2f1OFu0KBBbtySSmEoi0NuKIfzg6CU5K1Zwyrcf7e7VHrgHqv98AN27fXXJ9mp9+nggMbi+QcjwtFBhT179rgAJ/gAxeDBg5N874u+p9Pi+++/dyWvCpZ0sERBjQIUPU9yQg+s6LsidLmD102BnX6fVIIXfOBFgg++KOgJXi8FRBK8bsHzTe65gdwiJrNmrFpZ8WtkgVDa0fR2PGq2939BtwbvfPqVcAlme4ebd3CmWfGPLRB1OAjSDuWFF17ovuw17mL79u3uyFt6aedTP1LaeXz99dfdET39WGigu6jmWgPn9eOgnVcdZVN9tB6no/06unjKKae4HzsNss+TJ0/Y51HN9YgRI+zjjz+2KlWquEH0Crj0w+sfmZSHH37YXn75ZXek7aabbnLLpUAN6ePF/2O2f1KyQZAULxZt57TJb29/tN16X1fUCuT/79hQwjqzA7/Ytr2NrF69erZ69Wp38bNC8+fPd8G1gu+M0nggNTnQ+y25Ons9t97bOirr8zM/wdRwQUe9K1as6I5Ot2zZMsPLBWQHg2bNsOhAINlMkC8qLo/FliqZeH2Hmc3cvtU6/pepSOlgRDj+2J0xY8a4wCWYxgIFS0vWVQfcdPBMB4VVKaPv+l9++cU1PtEBYx3wCicty53awRddl+CDLzpoovGKoRTspOe5gdwi0wKhjNARc118O3boKw25lbfrtZAgKLwx3++2wjWUtl9sFtCPW1576KGH3NG4hQsX2rhx46x8+fJuWqX8NRA0PRSQaF7Bg9AV0PjUSSyYSgkUpGhnWGULfmmAjsYpWxWOgql33nnHHRjwl08DW5XB0qD64AyAfizPOOMM9/8HH3zQDbhXpzBlkZB23h4NbtaOQMo7Um/2K22nn7/aTu242p64r7g1qh9nhw5F2fe/PWTvfrzZvc46ynrZZZfZa6+95rpDabCzXqO0HhEOp127dq7MTVlDvQfVrW7t2rVuB0yBtuZ9xx13uMyi/q/gRk0Q5s2b58p1gimg1pFuldyoXAfIydbv2mkTli+zhFSCoHAUPKmjXMeatVOd1j9o5R/0Eh3cUMCjTpH+93BaaX7B8xI1QVEQoYNbfplsamVxmUUHX3QwTgdedAAmoxQoha4nkFNlq65x6tKiGl3/ElyTj1x4tH530nrr5LRpmc9mfl/58GVCRfvz9xdcpsQfu+EHQeHGT6QnI5Qcld/paLt2PrWz6R+91w9lWqnsQN28go/U68dEmaTgcSahZQn+UTrKEjIgfnmaJqteJdamj6/s3mf3PbnJGrVdZe0vXW0/TF7kglcdDf3qq69cyaTKMBXA6L3w2Wef2dHQfMeOHevmqS5PCoTUGervv/9OLGlRZ7pHH33UZRPVnUr3hSs31g6WAibtnARnj4Cc6OdVf6c5CPIOHbJDO3YkXvZv326/LVpkew8eTPWxyszrczh69GjbuHGjy5ioHO3ee++1u+66y2Xv9d09c+ZM69+/v7ueEv02aIyfflPUiU0HdjU2Sd/9ery6TaoT3LvvvmtZQedFU0dK/Z6pLFvrpgOJ+v5JT2Cj9VQJtzLafgk5kFOl65CAjk6HS6kG006dX3OaXhrspxKk4IwQwVDu5O35LE1H66VAviirWc0vN4syi/neoorfkqbn8Y/AHW4gdJh+lIL5A1qTo1IC/WAqg6OgS0f3lAlSWUNmSKm0Aeng6Xw7adtu5crEWP++pa3//4+xNosqbVGlD4/30rmBFAwlR+1lg1vMJlcWrHFgwbTT9cYbb7hLcpT91CVYuO/hf/75x84999wkJS5ATrRt316LUjlWGoKhvQsW2epHk2ZBY0uXsm2970r1sSp9Uxm/9m0UDOgggj63Tz/9tMv06+CsgheND1U2JfRzGErVA19++aVrjKDSWY3b0wEKtc/WZ1b7ODrwoflmxQEL/X6pzPqBBx6wc845xwVq+m3TCWiDm7qkRtkt7avpN1HbMHi8LZCrA6F77rknsc9+ckJLNtJD6ejQGlzkzg5xtkdd3zKyc59gdmiu6wbmj91QFzl/5y90/IRftqZp/AYLOloXmoHR0S1/XFswDWjV4HV94fsnqFR9d2rlFaE0bkPT6UdIPzx+QKajchkZ14Q0CGigsX7cE47i8dmfxsbNmTPHhg0blniOFCAni4mKTtN0pS671F3CiY2OTnIAzBfcOEeUcdUlmA5AqSxVl3DUECfcvLX/Eny+IZ+yS7oEU8MEn/argvetwh1Y0e9E8G9F6EEVCReQhC6nOsEpWEtOuPmGnpdPBwd1ASIuENJOJa0ScdQSNqkDfJon33/As/Ubkp61O/bgVGvX7gpXTnTllVfaiy++6DKIajQQTGUJyirqR0Vjb9QiVEezgukoncaAaNyHSu4UsKhZgroeamCrxv689957LthSOZyOHoZ27VJWSd3lNFhdY3lU2hk6qFYlTRoLpHkqw6BxIeoapEGzOPYCeZqZty+jgUG0WZ6kbauzK53sVQ079N5VpysgpytXsFCGxgf58kRHWxEOqgLIyjFC2mHUkXf91ZFyvzVs8NmUEaG83emafNzEPVah8Yokl9PPesil8keOHGl79+51Y23UOUvBTmiZmc45pKYKyvyoPEEDyoMpmBo/frxrLar5aJyRyqA0mFTPMXz4cDfgVeVwOqqnoCuYplNpk84xodID7ZiG89xzz7nSCR0JVJmFusWpPtvPVOEYy6sjlhltMBFvgfw9LSfQEVwF1K+++mpWLwpwTLStWs0KJdN9My3NErrWqecyQgCQmoDOqmqZQGnecAMLdaTdP89KanSEX0fWVfqhQerIHbz49eZtbH1U8wgUed4C+S44ZsuE3Clhx5Nme4b/dy6htIo2i21sUSX0OABZ4blfJtuHf85ItX12OF9ferk1LH244QiA3GXHMY4NMi0jpAGH7mSZIZe0BkHIxaJKmUWVOLp5xPx/e2sgOYGCvc2iNX4srUeHo8wCcRYo/DQbFchCvRqdaHHKyv/XMCat2aBWlaoQBAHIme2zERkCgej/yo4y8vaLMos90QKxqZ8jAghEFbdAscFm0eXTEAxFmwXyW6DYQAvE1mLjAVmoQuHC9t55XV1wk5ZgSNNVK1bc3jz3vOOyfAByBwIhZI18F2fwgQkWyP//3XaA1ARiKlqgxBdmet8ECgT1iYn67692smLM8p5vgRIjLZCnKRsVyAZaVKpsw7pdbMX+O5l0uIBIAZCcWrGSfdH9Uiscx4mnAWSDMULHAmOEcjdv1zvm7UrPAO8o18krUOxDCwQyflZsRC7P22u2d6x5B2fpDXg4AxRT0yxfVwtE0bQCyI4OxMfbuGVL7ONZf9rM9WsTb4+NirIuderZ5Y2aWKMyZbN0GQHkzNiAQAhZxo0b2/m82Z6BaSyJa3w4CIoqeByWDgCQ3ezcv9+2799nsVHRVjRvXjeOCEDk2HGMAyG+QZBldNK6QOEHzYupbd7ut8ziV/83jsPv8KWSB08j3s3y97BAwdstEOCEuwAQqQrFxbkLABwLBELIcoH8F5qpFfaBKebtHWWWsM7MO+Q6ywXynmmW91wLBKj7BgAAwLFDIIRskx2yuBYWiGuR1YsCAACACEDXOAAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAAAAEHEIhAAAAABEHAIhAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAAAAEHEIhAAAAABEHAIhAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAQsapWrWqvvfZaVi8GACALEAgBALKVq666ygKBgN10001H3Hfrrbe6+zTNsTBt2jS74YYbjsm8AAA5S0xWLwAAILIt3LTRvlu6xLbs3eOCnAWbNlq5ChVs+PDh9uqrr1q+fPncdPv27bNhw4ZZ5cqVj9lzlypV6pjNCwCQs5ARAgBkiXHLlthF//vUzh022N6a9rsNnzfHPp37ly3YuMG2Fy9qVqSw9X1vQOL0X375pQuCTjzxxBRL25o0aWJPPPGE+7/nee7/elxcXJyVL1/ebr/99mQfv23bNrvxxhutTJkyljdvXmvYsKGNHj06k7cEACArkBECABxXCk6e//Une2/mdIsKBNxt8Z6nOw7f/990sc1OtJfeftsqtGxhN510ig0cONCuvvpqmzRpUpqfa8SIES6rpOxSgwYNbP369TZ79uyw0yYkJFjHjh1t586dNmTIEKtRo4bNnz/foqOjj8FaAwCyGwIhAMBx9dofv7kgSBL+C37CyX9SU9v8zVh7dvRXtmvDRvv1119dQJOeQGjVqlVWtmxZa9euncXGxrrM0CmnnBJ22u+//96mTp1qCxYssNq1a7vbqlevnu71AwDkDJTGAQCOG5W99Z/6e5qmjS5Y0PLVr2e7/phuffu/YWe2P8dKliyZrufr3r277d271wU0119/vY0cOdIOHToUdtpZs2ZZxYoVE4MgAEDuRiAEADhuhsyZbdH/lcOlRaFTT7ZdU6fZzqnTrdzprY64PyoqypXaBTt48GDi/ytVqmSLFi2yt99+2zVduOWWW6x169ZJpvH5TRkAAJGBQAgAcFzs2L/fRiyYd3g8UBrlq1fXvPh4d5lRIK8diI8/ouvbunXr/v85duywFStWJJ1HvnzWuXNne+ONN1xZ3ZQpU2zOnDlHPFejRo1szZo1tnjx4gytHwAgZ2GMEADguJizYf0RgUxqAlFRVvGh+93/tx84YMu2bE5y/5lnnmkfffSRC3SKFi1qjz32WJLmBrovPj7emjdvbvnz53dNEBQYValS5YjnOuOMM1y2qFu3bvbKK69YzZo1beHCha6ld4cOHTK83gCA7ImMEADguGWEMiIqb153CTePPn36uADmvPPOs06dOlnXrl1dtzefgqP333/fWrZs6TI+aojwzTffWIkSJZLtMnfyySdbjx49rH79+nb//fe7QAoAkPsEvNDi6mxEJQ5FihSx7du3W+HChbN6cQAAR+HHFcvtum9GHtU2HHlxT2tcthyvAwBEoB3HODYgIwQAOC6qFi16VI/XOYcqFC5yzJYHABDZCIQAAMdF9WLFrWnZ8oknUU0PdZprV62GlcyfP1OWDQAQeQiEAADHzZVNTkzxJKrJUae5Xo2bZMoyAQAiE4EQAOC4aV+jltUrWSpd5xLStKdWqGQtKlbO1GUDAEQWAiEAwHGTJzraPurSzcoXKpymYEhldDWLl7B3zzvftbEGAOBYIRACABxXpQoUsJGX9LTTKx8+l0+4gEgBkG7tULOW/a97Dyscd7h9NgAAxwonVAUAHHfF8+W3gV262fKtW2zYnL/sm8ULbdu+vS7rUyJfPrugbgPr0bCRVeDUCQCATMJ5hAAAAABke5xHCAAAAACOEmOEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAAAAEHEIhAAAAABEHAIhAAAAABEn0wKhlStX2rXXXmvVqlWzfPnyWY0aNezxxx+3AwcOZNZTAgAAAECaxFgmWbhwoSUkJNiAAQOsZs2aNnfuXLv++utt9+7d9tJLL2XW0wIAAABAqgKe53l2nLz44ov2zjvv2PLly7Pk7LEAAAAAcqZjHRtkWkYoHC108eLFk71///797hK8sgAAAACQY5slLF261Pr372833nhjstP069fPRXn+pVKlSsdr8QAAAABEkHQHQg8++KAFAoEULxofFOyff/6xDh06WPfu3d04oeT06dPHZY38y+rVqzO2VgAAAABwLMcIbdy40TZv3pziNNWrV7c8efK4/69du9batGljp556qn300UcWFZX22IsxQgAAAACyxRihUqVKuUtaKBPUtm1ba9asmQ0aNChdQRAAAAAAZJZMa5agIEiZoCpVqrh22cok+cqWLZtZTwsAAAAAWRcITZgwwTVI0KVixYpJ7juOHbsBAAAA4AiZVqt21VVXuYAn3AUAAAAAshKDdgAAAABEHAIhAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAAAAEHEIhAAAAABEHAIhAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAAAAEHEIhAAAAABEHAIhAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAAAAEHEIhAAAAABEHAIhAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAAAAEHEIhAAAAABEHAIhAAAAABGHQAgAAABAxCEQAgAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACACCtGnTxu68887E61WrVrXXXnstItb1eJo0aZIFAgHbtm1bljw/AAAEQgAizlVXXeV2wkMvS5cutS+//NKefvrp47IcL7/8sgu08uXLZ3Xq1LH33nsvXY/v16+fRUdH24svvpih5z9e6xou4GrRooWtW7fOihQpkunPDwBAOARCACKG53nmefHu/x06dHA74sGXatWqWfHixa1QoUKZviw//fST3XvvvXbPPffYggUL7MMPP7RSpUqlax4DBw60+++/3/3NiOO1ruHkyZPHypYt6wJQAACyAoEQgFzNi99s3q53LWHDmeb9W9+8f+uZt/dryxO1yMoUW2llypRxO+S6KLuSWrmYdtwHDBhg5513nuXPn9/q1atnU6ZMcdkkPbZAgQIu27Fs2bIUlysqKso937XXXuuyQq1atbILLrggzes1efJk27t3rz311FO2Y8cO++2335Lc/8QTT1iTJk3sk08+cfNX5uXSSy+1nTt3plgG+Mwzz9gVV1xhBQsWtCpVqtjXX39tGzdutC5durjbGjVqZNOnT098zObNm61Hjx5WoUIFtz1OOOEE+/TTT5Nk37Ssr7/+emLmbeXKlWFL40aMGGENGjSwuLg4tyzKmAXTbX379rVrrrnGBXCVK1dOdxYNAAAfgRCAXMnzDlnCjr7mbWxl3q7XzBLWmNnhbJDZIbP4jeZtvcK8Te3NO7ggXfNWOZmChVmzZlndunWtZ8+eduONN1qfPn1ckKDM02233ZbiPBSkKHi45ZZbLCEhId3rpwySApDY2Fj3V9dDKRgbNWqUjR492l0UkDz33HMpzvfVV1+1li1b2p9//mmdOnWyXr16uXW9/PLLbebMmVajRg13Xeso+/bts2bNmtmYMWNs7ty5dsMNN7jHTJ061d2vAOi0006z66+/PjHzVqlSpSOed8aMGXbxxRe7YG3OnDkukHv00Ufto48+SjKdgqOTTjrJLZ+23c0332yLFi1K9/YDACBTA6Hzzz/fHbHLmzevlStXzv04rl27lq0OINODIG/bbWZ7Pv4v+Dky0Bjz/S4rXGOpFa76vRUq2tC6d2uX5vlfffXVbqe9du3a9sADD7gMx2WXXWbt27d3GaI77rjDZTySo8Cna9eu1rhxY5cRUSB14MCBxPuVVXnppZeSfbwyQF988YULTkR/P//8c9u1a9cRz6NAomHDhnb66ae77+AffvghxXU799xzXVBXq1Yte+yxx9xznXzyyda9e/fE9VUp37///uumVzCnEj8FdtWrV7fevXu7skMtjygTpTI4ZYuCM2+hXnnlFTvrrLNc8KPnUSZJwWTo+CctnwKgmjVrumUpWbKkTZw4McV1AgDguAdCbdu2dT+GOlqnkgcdnbzooosy8ykBwLwd/cz2a+f4cNYinDYt89nM7yv/d6lirz2+wbz4tB2oUXmYT6V1fvASfJsyJQoiwvnuu+/s119/dUHKZ5995srLOnfubLt373aPU5mdApfkqPRMmRkFUqIgRGVsmldoKVnwGCAdkNqwYcNRr5v484mPj3cZMk2jMUcqnxs3bpytWrXK0kPBlTJRwXR9yZIl7jnCLZ9K6xRYpbZOAACEE2OZ6K677kr8v36kH3zwQXcU9ODBg66cAwCONS/+X7O9Q1MMgqRAviirWS1P0C0HzdutDFLqgr+//MH+4W5LruTtr7/+ctlyBQ6i8rVzzjnHZUT0HanMSvPmzZN9fpXBzZs3z2Ji/v8rXM+lpgkacxRuOf3lSq0ML73rpoyNyt/UYlzBkMZIadxRcIbrWMrIOgEAcNwDoWBbtmyxoUOHukHEyQVB+/fvdxdfckdTASBZew+XZKVf/H+PzfwuaionW7Fiha1Zs8YqVqzogoexY8e6LLrGGamtdXI0fkbjkFR65wdS/nesmh8sXLjQjVs6XpTZUiMFv0xPQcnixYutfv36idOoNC44qxOOSgo1r9B5q0wuXCkdAADZvlmCarj1I1+iRAlXKvHVV1+leE4M1ZP7l3ADagEgJd6e4WHHBKWJt9ssYWumb+Bu3bq5jJCaEXz//feuFO7bb791wYy+LwcNGpRslkPZoFNOOcVat27txv74F13XWJ5wTRMyk8YSTZgwwXWtU3mbxhf544eCS/T++OMPN5Zq06ZNYddNbcQ1fklldgqkPv74Y3vzzTfd+CMAALJFIKTytnAnIgy+6Iik77777nPdfcaPH++O6gV3GwqlI6Hbt29PvKxevfro1g5ARPG8g2YJG49iDjFm3j7LbGocoMBBgYsaLyiQUYmZgoBp06a5bE+4Ft4qNxsyZIgLpMLR7YMHD3blx8fLI488Yk2bNnWNIpSR0pgdlfcFUzCj739liXSupHDjhzQPjSkdPny42x5q1KDW4GqaAABAZgh4yUUlydD5JDSwNyWqb1cpRCiVgSjLox0AtVNNjUrjlBlSUFS4cOH0LCaACOQl7DZvw4lHMYcYswLXWlShe47hUgEAgGPhWMcG6R4jpKN56T37uc8vhwgeBwQAx0wgv5lpPEnK41GSl2CBqCK8IAAARIBMa5agenCVeOhs6cWKFXOts3V+CLV8TUs2CADSS6W5Xp7mZgf+yGAwlGCW51Q2PAAAESAqM2vg1flI7WDr1KnjWrrq/A86s3lcXFxmPS2ACBfIf3kGg6Aos5iGFohtmAlLBQAAIiYjpPNJ/Pjjj5k1ewAIL66NWVRps4RN6ewel2CBAr3YqgAARIhMb58NAMdTIBBjgaKv/ff1dvjkn6mLMos72yxvl0xeOgAAkF0QCAHIdQJ5TrJAsXd1Ks//mickO+XhP3FnWqDoKxYI8JUIAECk4FcfQK4UiGttgZKjzPJe8F9AFPivGjjm/4OjmFoWKPysBYr2t0CAsYsAAESSTBsjBABZLRBTwwJF+5qX8IDZvrHmxa8x8w5aIKqwWZ6WZrFNXKc5AAAQeQiEAOR67txA+XukecQQAADI/SiNAwAAABBxCIQAAAAARBwCIQAAAAARh0AIAAAAQMQhEAIAAAAQcQiEAAAAAEQcAiEAAAAAEYdACAAAAEDEIRACAAAAEHEIhAAAAABEnBjLxjzPc3937NiR1YsCAAAAIAv5MYEfI+TqQGjnzp3ub6VKlbJ6UQAAAABkkxihSJEiRz2fgHesQqpMkJCQYGvXrrVChQpZIBCw3BDFKqhbvXq1FS5cOKsXBxnAa5jz8RrmbLx+OR+vYc7Ha5jz5dTX0PM8FwSVL1/eoqKicndGSCtYsWJFy230hstJbzocidcw5+M1zNl4/XI+XsOcj9cw5yucA/dJj0UmyEezBAAAAAARh0AIAAAAQMQhEDqO4uLi7PHHH3d/kTPxGuZ8vIY5G69fzsdrmPPxGuZ8vIY5oFkCAAAAAGQGMkIAAAAAIg6BEAAAAICIQyAEAAAAIOIQCAEAAACIOARCWej888+3ypUrW968ea1cuXLWq1cvW7t2bVYuEtJo5cqVdu2111q1atUsX758VqNGDdcR8MCBA2zDHOTZZ5+1Fi1aWP78+a1o0aJZvThIg7feesuqVq3qvjebN29uU6dOZbvlED/99JN17tzZnRE+EAjYqFGjsnqRkA79+vWzk08+2QoVKmSlS5e2rl272qJFi9iGOcg777xjjRo1SjyJ6mmnnWbffvutRTICoSzUtm1b+/zzz90XyYgRI2zZsmV20UUXZeUiIY0WLlxoCQkJNmDAAJs3b569+uqr9u6779pDDz3ENsxBFLh2797dbr755qxeFKTBZ599Znfffbc76DBz5kxr3LixtW/f3jZs2MD2ywF2797tXjMFs8h5Jk+ebLfeeqv9/vvvNmHCBDt48KCdc8457nVFzlCxYkV77rnnbMaMGTZ9+nQ788wzrUuXLm4/JlLRPjsb+frrr90Rlv3791tsbGxWLw7S6cUXX3RHW5YvX862y2E++ugju/POO23btm1ZvShIgTJAOiL95ptvuus6GFGpUiXr3bu3Pfjgg2y7HEQZoZEjR7rfPORMGzdudJkhBUitW7fO6sVBBhUvXtztv6jKJRKREcomtmzZYkOHDnVlOgRBOdP27dvdFwqAzMne6Shmu3btEm+Liopy16dMmcImB7LgN0/43cuZ4uPjbfjw4S6jpxK5SEUglMUeeOABK1CggJUoUcJWrVplX331VVYvEjJg6dKl1r9/f7vxxhvZfkAm2LRpk/vhLlOmTJLbdX39+vVsc+A4UjZWWfSWLVtaw4YN2fY5yJw5c6xgwYIWFxdnN910k8vM1q9f3yIVgdAxpvIMpfxTumh8ie++++6zP//808aPH2/R0dF2xRVXmOd5x3qxkEmvn/zzzz/WoUMHN9bk+uuvZ1vnwNcQAJB2Gis0d+5cl1FAzlKnTh2bNWuW/fHHH2587JVXXmnz58+3SMUYoUyomd28eXOK01SvXt3y5MlzxO1r1qxx9e6//fZbRKcpc9Lrpy5/bdq0sVNPPdWNM1GpDnLeZ5AxQjmjNE7d/b744osk40r0I66xXWTTcxbGCOVct912m/u8qQugOqciZ2vXrp3rfKvmT5EoJqsXILcpVaqUu2Q01SxqloDs//opE6TOf82aNbNBgwYRBOWCzyCyLwWu+qz98MMPiYGQvjN1XTtmADKXqlXUmESlVJMmTSIIyiUSEhIier+TQCiLKCU5bdo0a9WqlRUrVsy1zn700UddVE42KPtTEKRMUJUqVeyll15yWQhf2bJls3TZkHYal6dGJfqr8ScqF5CaNWu6GmpkL2qdrQzQSSedZKeccoq99tprbqDv1VdfndWLhjTYtWuXG0/pW7FihfvMabC9zqmH7F8ON2zYMJcN0rmE/LF5RYoUcefTQ/bXp08f69ixo/u87dy5072eCmrHjRtnkYrSuCwcrHbHHXfY7Nmz3Q+5TqiqcSaPPPKIVahQIasWC2mkUqrkdr4Y45VzXHXVVfbxxx8fcfvEiRNdoIvsR62z1epVO2FNmjSxN954w7XVRvanHS5l0UMpuNV3KrJ/OWM4qojQdymyP7XIVhZ93bp1LoBt1KiRa9p19tlnW6QiEAIAAAAQcRjZDQAAACDiEAgBAAAAiDgEQgAAAAAiDoEQAAAAgIhDIAQAAAAg4hAIAQAAAIg4BEIAAAAAIg6BEAAAAICIQyAEAAAAIOIQCAEAAACIOARCAAAAACIOgRAAAAAAizT/B9a4nDpvOktGAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "pca = PCA(n_components=2)\n",
+    "X_pca = pca.fit_transform(X_scaled)\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "plt.scatter(X_pca[:,0], X_pca[:,1], c=clusters, s=120)\n",
+    "\n",
+    "for i, txt in enumerate(df['category']):\n",
+    "    plt.annotate(txt, (X_pca[i,0], X_pca[i,1]))\n",
+    "\n",
+    "plt.title(\"Gaussian Mixture Models: кластеризация категорий\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "840f2aac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_videos</th>\n",
+       "      <th>avg_views</th>\n",
+       "      <th>median_views</th>\n",
+       "      <th>avg_likes</th>\n",
+       "      <th>avg_comments</th>\n",
+       "      <th>avg_engagement</th>\n",
+       "      <th>avg_duration_sec</th>\n",
+       "      <th>avg_subscriber_count</th>\n",
+       "      <th>pct_verified</th>\n",
+       "      <th>avg_clickbait</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cluster</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>300.500000</td>\n",
+       "      <td>2.131389e+06</td>\n",
+       "      <td>70726.500000</td>\n",
+       "      <td>103132.157500</td>\n",
+       "      <td>13436.442500</td>\n",
+       "      <td>7.51</td>\n",
+       "      <td>742.990000</td>\n",
+       "      <td>3.195136e+06</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>0.447500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>764.777778</td>\n",
+       "      <td>3.721815e+06</td>\n",
+       "      <td>61557.555556</td>\n",
+       "      <td>202350.915556</td>\n",
+       "      <td>23076.477778</td>\n",
+       "      <td>7.54</td>\n",
+       "      <td>718.266667</td>\n",
+       "      <td>3.077070e+06</td>\n",
+       "      <td>40.666667</td>\n",
+       "      <td>0.462222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>478.750000</td>\n",
+       "      <td>2.144803e+06</td>\n",
+       "      <td>52037.375000</td>\n",
+       "      <td>116002.817500</td>\n",
+       "      <td>13806.730000</td>\n",
+       "      <td>7.61</td>\n",
+       "      <td>792.410000</td>\n",
+       "      <td>2.259270e+06</td>\n",
+       "      <td>37.750000</td>\n",
+       "      <td>0.450000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         total_videos     avg_views  median_views      avg_likes  \\\n",
+       "cluster                                                            \n",
+       "0          300.500000  2.131389e+06  70726.500000  103132.157500   \n",
+       "1          764.777778  3.721815e+06  61557.555556  202350.915556   \n",
+       "2          478.750000  2.144803e+06  52037.375000  116002.817500   \n",
+       "\n",
+       "         avg_comments  avg_engagement  avg_duration_sec  avg_subscriber_count  \\\n",
+       "cluster                                                                         \n",
+       "0        13436.442500            7.51        742.990000          3.195136e+06   \n",
+       "1        23076.477778            7.54        718.266667          3.077070e+06   \n",
+       "2        13806.730000            7.61        792.410000          2.259270e+06   \n",
+       "\n",
+       "         pct_verified  avg_clickbait  \n",
+       "cluster                               \n",
+       "0           41.000000       0.447500  \n",
+       "1           40.666667       0.462222  \n",
+       "2           37.750000       0.450000  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('cluster').mean(numeric_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "152be1ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Кластер 0:\n",
+      "['Autos & Vehicles', 'Howto & Style', 'Nonprofits & Activism', 'Travel & Events']\n",
+      "\n",
+      "Кластер 1:\n",
+      "['Entertainment', 'Gaming', 'Music', 'News & Politics', 'People & Blogs', 'Science & Technology', 'Shorts', 'Shows', 'Sports']\n",
+      "\n",
+      "Кластер 2:\n",
+      "['Comedy', 'Education', 'Film & Animation', 'Pets & Animals']\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in sorted(df['cluster'].unique()):\n",
+    "    print(f\"\\nКластер {c}:\")\n",
+    "    print(df[df['cluster']==c]['category'].tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "3513213e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.mixture import GaussianMixture\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "n_components_range = range(1, 7)\n",
+    "\n",
+    "bic_scores = []\n",
+    "aic_scores = []\n",
+    "\n",
+    "for n in n_components_range:\n",
+    "    model = GaussianMixture(\n",
+    "        n_components=n,\n",
+    "        random_state=42\n",
+    "    )\n",
+    "    model.fit(X_scaled)   # или X_real_scaled для CSV\n",
+    "    bic_scores.append(model.bic(X_scaled))\n",
+    "    aic_scores.append(model.aic(X_scaled))\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "plt.plot(n_components_range, bic_scores, marker='o', label='BIC')\n",
+    "plt.plot(n_components_range, aic_scores, marker='o', label='AIC')\n",
+    "\n",
+    "plt.xlabel(\"Количество компонент\")\n",
+    "plt.ylabel(\"Значение критерия\")\n",
+    "plt.title(\"Выбор числа компонент для GMM\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "4c353425",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "cluster_counts = df['cluster'].value_counts().sort_index()\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "cluster_counts.plot(kind='bar')\n",
+    "\n",
+    "plt.title(\"Количество объектов в каждом кластере\")\n",
+    "plt.xlabel(\"Номер кластера\")\n",
+    "plt.ylabel(\"Количество категорий\")\n",
+    "plt.grid(axis='y')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7ea207d",
+   "metadata": {},
+   "source": [
+    "## Результаты исследования\n",
+    "\n",
+    "После обучения модели Gaussian Mixture Model все категории были распределены по нескольким кластерам.\n",
+    "\n",
+    "Для визуального анализа использовались:\n",
+    "\n",
+    "1. PCA-проекция данных на двумерную плоскость;\n",
+    "2. график количества объектов в кластерах;\n",
+    "3. график средних просмотров по кластерам.\n",
+    "\n",
+    "Результаты показали, что категории действительно разделяются на группы\n",
+    "с различными статистическими характеристиками."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dc723ee",
+   "metadata": {},
+   "source": [
+    "## Интерпретация результатов\n",
+    "\n",
+    "Полученные кластеры можно трактовать как различные типы категорий контента.\n",
+    "\n",
+    "Одни кластеры объединяют категории с высоким количеством просмотров\n",
+    "и большим числом подписчиков.\n",
+    "\n",
+    "Другие кластеры содержат категории со средней активностью аудитории.\n",
+    "\n",
+    "Также могут выделяться более нишевые категории\n",
+    "с меньшим количеством просмотров, но высоким уровнем вовлечённости.\n",
+    "\n",
+    "Это показывает, что алгоритм Gaussian Mixture Model способен выявлять скрытую структуру данных."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8699566b",
+   "metadata": {},
+   "source": [
+    "## Вывод\n",
+    "\n",
+    "В ходе выполнения работы был изучен алгоритм Gaussian Mixture Models\n",
+    "из библиотеки scikit-learn.\n",
+    "\n",
+    "Модель была применена:\n",
+    "\n",
+    "1. к сгенерированному набору данных;\n",
+    "2. к внешнему датасету category_summary.csv.\n",
+    "\n",
+    "На обоих типах данных алгоритм успешно выполнил кластеризацию объектов.\n",
+    "\n",
+    "Было установлено, что Gaussian Mixture Model позволяет находить скрытые группы данных,\n",
+    "учитывая вероятностную природу распределений.\n",
+    "\n",
+    "Метод может использоваться для анализа категорий контента,\n",
+    "сегментации объектов и исследовательского анализа данных."
+   ]
   }
  ],
  "metadata": {