From f24c0a2d769638111e73592183e5a595cff08adf Mon Sep 17 00:00:00 2001
From: Ricard0V <rjr.veloso@gmail.com>
Date: Sun, 22 May 2022 11:54:34 +0100
Subject: [PATCH] Update main.ipynb

---
 your-code/main.ipynb | 868 +++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 838 insertions(+), 30 deletions(-)

diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 9a2d1ae..fd95239 100755
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,20 +67,440 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "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>Channel</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12669</td>\n",
+       "      <td>9656</td>\n",
+       "      <td>7561</td>\n",
+       "      <td>214</td>\n",
+       "      <td>2674</td>\n",
+       "      <td>1338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7057</td>\n",
+       "      <td>9810</td>\n",
+       "      <td>9568</td>\n",
+       "      <td>1762</td>\n",
+       "      <td>3293</td>\n",
+       "      <td>1776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6353</td>\n",
+       "      <td>8808</td>\n",
+       "      <td>7684</td>\n",
+       "      <td>2405</td>\n",
+       "      <td>3516</td>\n",
+       "      <td>7844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13265</td>\n",
+       "      <td>1196</td>\n",
+       "      <td>4221</td>\n",
+       "      <td>6404</td>\n",
+       "      <td>507</td>\n",
+       "      <td>1788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22615</td>\n",
+       "      <td>5410</td>\n",
+       "      <td>7198</td>\n",
+       "      <td>3915</td>\n",
+       "      <td>1777</td>\n",
+       "      <td>5185</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
+       "0        2       3  12669  9656     7561     214              2674        1338\n",
+       "1        2       3   7057  9810     9568    1762              3293        1776\n",
+       "2        2       3   6353  8808     7684    2405              3516        7844\n",
+       "3        1       3  13265  1196     4221    6404               507        1788\n",
+       "4        2       3  22615  5410     7198    3915              1777        5185"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 440 entries, 0 to 439\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column            Non-Null Count  Dtype\n",
+      "---  ------            --------------  -----\n",
+      " 0   Channel           440 non-null    int64\n",
+      " 1   Region            440 non-null    int64\n",
+      " 2   Fresh             440 non-null    int64\n",
+      " 3   Milk              440 non-null    int64\n",
+      " 4   Grocery           440 non-null    int64\n",
+      " 5   Frozen            440 non-null    int64\n",
+      " 6   Detergents_Paper  440 non-null    int64\n",
+      " 7   Delicassen        440 non-null    int64\n",
+      "dtypes: int64(8)\n",
+      "memory usage: 27.6 KB\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "display(customers.head(5))\n",
+    "customers.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "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>Channel</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.322727</td>\n",
+       "      <td>2.543182</td>\n",
+       "      <td>12000.297727</td>\n",
+       "      <td>5796.265909</td>\n",
+       "      <td>7951.277273</td>\n",
+       "      <td>3071.931818</td>\n",
+       "      <td>2881.493182</td>\n",
+       "      <td>1524.870455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.468052</td>\n",
+       "      <td>0.774272</td>\n",
+       "      <td>12647.328865</td>\n",
+       "      <td>7380.377175</td>\n",
+       "      <td>9503.162829</td>\n",
+       "      <td>4854.673333</td>\n",
+       "      <td>4767.854448</td>\n",
+       "      <td>2820.105937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>55.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3127.750000</td>\n",
+       "      <td>1533.000000</td>\n",
+       "      <td>2153.000000</td>\n",
+       "      <td>742.250000</td>\n",
+       "      <td>256.750000</td>\n",
+       "      <td>408.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8504.000000</td>\n",
+       "      <td>3627.000000</td>\n",
+       "      <td>4755.500000</td>\n",
+       "      <td>1526.000000</td>\n",
+       "      <td>816.500000</td>\n",
+       "      <td>965.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>16933.750000</td>\n",
+       "      <td>7190.250000</td>\n",
+       "      <td>10655.750000</td>\n",
+       "      <td>3554.250000</td>\n",
+       "      <td>3922.000000</td>\n",
+       "      <td>1820.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>112151.000000</td>\n",
+       "      <td>73498.000000</td>\n",
+       "      <td>92780.000000</td>\n",
+       "      <td>60869.000000</td>\n",
+       "      <td>40827.000000</td>\n",
+       "      <td>47943.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
+       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
+       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
+       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
+       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
+       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
+       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
+       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
+       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
+       "\n",
+       "             Frozen  Detergents_Paper    Delicassen  \n",
+       "count    440.000000        440.000000    440.000000  \n",
+       "mean    3071.931818       2881.493182   1524.870455  \n",
+       "std     4854.673333       4767.854448   2820.105937  \n",
+       "min       25.000000          3.000000      3.000000  \n",
+       "25%      742.250000        256.750000    408.250000  \n",
+       "50%     1526.000000        816.500000    965.500000  \n",
+       "75%     3554.250000       3922.000000   1820.250000  \n",
+       "max    60869.000000      40827.000000  47943.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your observations here"
+    "customers.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.boxplot(customers.values)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Channel             False\n",
+       "Region              False\n",
+       "Fresh               False\n",
+       "Milk                False\n",
+       "Grocery              True\n",
+       "Frozen              False\n",
+       "Detergents_Paper     True\n",
+       "Delicassen          False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cc = customers.corr()\n",
+    "cc[(cc > 0.8) & (cc < 1)].any()\n",
+    "# Grocery x Detergents_Paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Grocery', ylabel='Detergents_Paper'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your observations here\n",
+    "customers.plot.scatter('Grocery','Detergents_Paper')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Deco\\AppData\\Local\\Temp\\ipykernel_17160\\2084659139.py:4: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  mask = np.triu(np.ones_like(customers.corr(), dtype=np.bool))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Triangle Correlation Heatmap')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Bu08HxgCVzexDM/vJzN4OE1PM7E4zm2xms8xsQET5KDN70MwmmdkvZtYlLD/PzD42sy/N7Fcze2jLvszsSDObYGbfm9kHZla5RFsvIiIiIiKxl5BQtEcZVDaj3jmtgKkFLNsPuAZoCTQFOoXlz7h7e3dvBVQEekSsk+TuHcL1+kaUtwV6Aa2BXmbW0MxqAbcDh7v7/sAU4LpiaJOIiIiIiJQlSjbjziR3n+/u2cA0oHFYfoiZfWdmM4FDgX0i1vk4/HdqRH2AEe6+yt03AHOARsBBBInsODObBpwblhcovJd0iplNGTBgQFHaJiIiIiIipYUlFO1RBsXDbLSzgVMKWLYx4nkWkGRmycBzQDt3n2dm/YDkfNbJIvfx22ZbgAHD3f30wgbr7gOALVmmF3Y9EREREREpxcpo72RRxEOLRwIVzKzPlgIzawN0KaD+lsRyaXh/ZUGJamFMBDqZ2R7hfiuZ2V5F2J6IiIiIiEiZsMsnm+7uwInA4eFPn8wG+gOLC6i/EngJmAUMAyYXYd8ZwHnAu2Y2A5gANP+32xMRERERkTIqwYr2KIMsyMWklNKbIyIiIiKyrTKXfSWcdXKRru2z3/qozLU5Hu7ZFBERERERia04vGdTyaaIiIiIiEi0xWGyGX8tFhERERERkahTz6aIiIiIiEi0ldHfyiwKJZsiIiIiIiLRVkZnlC0KJZsiIiIiIiLRpns2RURERERERIpOPZsiIiIiIiLRpns2RUREREREpLhZHA6jVbIpIiIiIiISZQlKNkVERERERKS4xWOyGX8tFhERERERkahTz6aIiIiIiEiUlUTPppkdDTwJJAIvu/sDeZY/DhwSvtwNSHP3auGyLGBmuOxvdz++qPEo2RQREREREYmyaCebZpYIPAscAcwHJpvZYHefs6WOu18bUf9KYL+ITax397bFGZOSTRERERERkSgrgZ7NDsBv7v47gJm9B/QE5hRQ/3SgbzQD0j2bIiIiIiIiZV99YF7E6/lh2TbMrBHQBBgZUZxsZlPMbKKZnVAcAalnsxRbvGZVrEPY5dWpkhLrEEREREQkDiSYFWl9M+sD9IkoGuDuA/7l5noDH7p7VkRZI3dfYGZNgZFmNtPd5/7beEHJpoiIiIiISNQVdRhtmFhuL7lcADSMeN0gLMtPb+DyPNtfEP77u5mNIrifs0jJpobRioiIiIiIRFlCQkKRHoUwGdjTzJqYWXmChHJw3kpm1hyoDkyIKKtuZhXC57WAThR8r2ehqWdTREREREQkyqI9QZC7Z5rZFcAwgp8+edXdZ5vZ3cAUd9+SePYG3nN3j1i9BfCimWUTdEg+EDmL7b+lZFNERERERGQX4O5DgaF5yu7M87pfPuuNB1oXdzxKNkVERERERKKsBH76pNRRsikiIiIiIhJlSjZFRERERESk2CnZFBERERERkWKXGIfJZvy1WERERERERKJOPZsiIiIiIiJRpmG0IiIiIiIiUuyUbIqIiIiIiEixi8dkM/5aLCIiIiIiIlGnnk0REREREZEoi8eezbhONs0sC5hJcBz+AM5295X/Yjv1gKfc/ZTijTC2vhs/gacfeZTs7Gz+c0JPzjzv3FzLN23axP19+/HLjz9RNSWFvv3vo269egDM/fVXHrm/P/+sW4dZAi8OfB13p+/Nt7Jw/nwSEhPo2KULF195RSyaJiIiIiJSouIx2Yy/Fue23t3bunsrYDlw+b/ZiLsv3NUSzaysLJ548CEeeupJ3vhgECOGDePP33/PVeeLzwZTpUoV3vn0Y04943RefPoZADIzM7n3jr5cf+stvPH+IJ588XmSkoLvNXqdfSZvfvQBL7/9FjOnT2fiuPEl3jYRERERkZKWYFakR1kU78lmpAlAfQAza2ZmX5rZVDMbY2bNI8onmtlMM7vXzNaG5Y3NbFb4PNnMXgvr/GBmh4Tl55nZx+F2fzWzh2LUzkL5cfZs6jdsQL0G9SlXrhyHHnkkY78dnavOuG+/5age/wGg22GH8v2kybg7UyZ+R7M992CPvfYCIKVaNRITE0lOTmb/du0AKFeuHHs1b05GenrJNkxEREREJAYSEhKK9CiLymbUxczMEoHDgMFh0QDgSnc/ALgBeC4sfxJ40t1bA/ML2NzlgId1TgfeMLPkcFlboBfQGuhlZg2Luy3FZWl6Bmm1a+e8Tk1LY2l6RoF1kpKSqFS5MqtWrWLe338Dxg1XXMmFZ57NO28M3Gb7a9asYfyYMRzQvn1U2yEiIiIiIrER1/dsAhXNbBpBj+aPwHAzqwx0BD6wrd3VFcJ/DwZOCJ+/AzySzzY7A08DuPtPZvYXsFe4bIS7rwIwszlAI2BeMbanVMjKymLm9Gm8OPANkpOTufbSy9i7RXMO6NABCIbZ3n3b7Zzcqxf1GtSPcbQiIiIiItFXVnsniyL+WpzbendvS5D0GUGvZAKwMryXc8ujRTHtb2PE8yzySfbNrI+ZTTGzKW++9nox7Xbn1UpLJX3JkpzXGenp1EpLLbBOZmYm69auJSUlhdS0NPbdbz+qVatGcnIyB3XqxC8//Zyz3iP39adBw4acesbpJdMYEREREZEY0zDaOOXu/wBXAdcD/wB/mNmpABbYN6w6ETg5fN67gM2NAc4M190L2B34uYC6+cUywN3buXu7s88/b2ebUmyat2zJ/HnzWLRgAZs3b2bkV1/RqWuXXHU6de3KsCFfAPDtiJHs174dZkaHgw/i99/msmHDBjIzM5n+/fc0btoEgJefe551a9dy5fXXlXibRERERERiJR6TzXgfRpvD3X8wsxkE91meCTxvZrcD5YD3gOnANcBbZnYb8CWwKp9NPReuOxPIBM5z941WxmaQSkpK4pobb+SGK68iOyubY48/jibNmvHKCy/SvEULOnXryrE9j+e+O/tyxgknUaVqVfrefx8AVapW5bQzz+Dic87FMA7s1JGDO3cmfckS3nz1NXZv3JiLzjobgBNPO5UeJ5wQw5aKiIiIiERfWU0Yi8LcPdYxlBlmthvB0Fs3s97A6e7eM1r7W7xmld6cKKtTJSXWIYiIiIjIzitbPTlAu+ceK9K1/ZTLritzbVbP5s45AHjGgm7KlcB/YxuOiIiIiIiUBfHYs6lkcye4+xhg3x1WFBERERERiZCoZFNERERERESKWzwmm/HXYhEREREREYk69WyKiIiIiIhEWTz2bCrZFBERERERiTIlmyIiIiIiIlLsEhOVbIqIiIiIiEgxi8eezfhrsYiIiIiIiESdejZFRERERESiLB57NpVsioiIiIiIRJmSTRERERERESl2CUo2RUREREREpLjFY89m/LVYRERERERkF2RmR5vZz2b2m5ndks/y88wsw8ymhY8LI5ada2a/ho9ziyMe9WyKiIiIiIhEWbR7Ns0sEXgWOAKYD0w2s8HuPidP1UHufkWedWsAfYF2gANTw3VXFCUm9WyKiIiIiIhEWWJCQpEehdAB+M3df3f3TcB7QM9ChncUMNzdl4cJ5nDg6H/V0Ajq2SzFUjb8GusQdnmL2TPWIcSFOlVSYh2CiIiISEwlJlqR1jezPkCfiKIB7j4g4nV9YF7E6/nAgfls6mQz6wr8Alzr7vMKWLd+kQJGyaaIiIiIiEipFyaWA3ZYcfs+B951941mdjHwBnBokYMrgIbRioiIiIiIRFkJDKNdADSMeN0gLMvh7svcfWP48mXggMKu+28o2RQREREREYmyEkg2JwN7mlkTMysP9AYGR1Yws7oRL48HfgyfDwOONLPqZlYdODIsKxINoxUREREREYmyaM9G6+6ZZnYFQZKYCLzq7rPN7G5girsPBq4ys+OBTGA5cF647nIzu4cgYQW4292XFzUmc/eibkOiZH3GFL05UbYqWRMElQRNECQiIiLFrGiz7cTAJV9/VqRr+xcO71nm2qxhtCIiIiIiIlLsNIxWREREREQkyqI9jLY0UrIpIiIiIiISZUo2RUREREREpNgp2RQREREREZFiF4/JZvy1WERERERERKJOPZsiIiIiIiJRlpgYf/18SjZFRERERESiLB6H0SrZFBERERERibJ4TDbjr8UiIiIiIiISderZ3A4zywJmRhSd4O5/FmF7fwLt3H1pEUMrce7OQ08OZOyE6SQnl+fu/7uYFns32abe0y++z5BhY1i9Zh0Thr+aU/7wU28y+fs5AGzYsInlK1cz9suXSiz+0uq78RN4+pFHyc7O5j8n9OTM887NtXzTpk3c37cfv/z4E1VTUujb/z7q1qvHooULOefUXuzeaHcAWrZqxfX/dysAV/e5hGVLl1IhuQIAjzzzNNVr1CjZhomIiIhILvHYs6lkc/vWu3vb/BaYmQHm7tklG1JsjJ04nb/nLWbwe48yc/Zv3PfIa7z10t3b1OvWaT96n3wEx59+fa7yG686O+f5ux8O46df/op6zKVdVlYWTzz4EI8++wyptdO4+Jxz6dS1C42bNs2p88Vng6lSpQrvfPoxI4Z9xYtPP0O//vcDUL9+fV555+18t337vXfTvGXLEmmHiIiIiOxYQhwmm/HX4iIws8Zm9rOZDQRmAQ3N7EYzm2xmM8zsrrBeJTP7wsymm9ksM+sVsZkrzex7M5tpZs1j0pB/YdSYqfQ4ugtmRptWe7Jm7T9kLF2xTb02rfYktVb17W7rf19P4OgjDo5WqGXGj7NnU79hA+o1qE+5cuU49MgjGfvt6Fx1xn37LUf1+A8A3Q47lO8nTcbdYxGuiIiIiBRBolmRHmWReja3r6KZTQuf/wFcC+wJnOvuE83syPB1B8CAwWbWFUgFFrr7fwDMLCVim0vdfX8zuwy4AbiwZJpSNOlLl1MnrWbO69ppNUhfumKHiWVeCxdnsHBRBh3236e4QyxzlqZnkFa7ds7r1LQ0fpw1u8A6SUlJVKpcmVWrVgGwaOFCLjjjLCpVrsQFl17Cvvvtl7PeA3fdQ2JiAl0PPZRzLvgvVkZPUCIiIiK7ikSLv34+JZvbl2sYrZk1Bv5y94lh0ZHh44fwdWWC5HMM8KiZPQgMcfcxEdv8OPx3KnBS9EIvnYZ9PZHDu3eIy98ZKk41a9Xi/SGDSalWjZ9//JHbbriRNwa9R6XKlbn93rtJTUvjn3XruOOmWxj2xVCODntHRURERERKiq74d966iOcG9Hf3tuFjD3d/xd1/AfYnmFzoXjO7M2KdjeG/WeST7JtZHzObYmZTXhn4cd7FJeq9j77itPNu5bTzbqVWzWosTl+Ws2xJ+nLSdrJXE+DLERM4+nANoQWolZZK+pIlOa8z0tOplZZaYJ3MzEzWrV1LSkoK5cuXJ6VaNQD2btGC+vUbMO/vv4GghxRgt0qVOPzoo/hp9pwSaI2IiIiIbE88DqNVslk0w4D/mlllADOrb2ZpZlYP+Mfd3wIeJkg8C8XdB7h7O3dvd8E5se347H3ykbz/en/ef70/h3Rpx5Avx+DuzJj1K5UrV9zpIbR//LWQ1WvWsW+rPaMUcdnSvGVL5s+bx6IFC9i8eTMjv/qKTl275KrTqWtXhg35AoBvR4xkv/btMDNWrlhBVlYWAAvnL2D+vHnUq1+fzMxMVq5cCQTJ6YQxY2nSrCkiIiIiElvxmGxqGG0RuPtXZtYCmBDeE7cWOAvYA3jYzLKBzcClsYuyeHQ5uC1jJ0zjuF7XkZxcnrv+7+KcZaeddyvvv94fgMefe4f/DR/Phg2bOPLEKzixxyFcesHJAHz59QSOPuxg3T8YSkpK4pobb+SGK68iOyubY48/jibNmvHKCy/SvEULOnXryrE9j+e+O/tyxgknUaVqVfrefx8A07//gVdffJGkpCTMErju1luompLC+vXrufGKq8jMzCQ7O4sDOnSgx4knxLahIiIiIkJCHN6zaZrZsvRanzFFb06UrUpWL2tJqFMlZceVRERERAqvzPVePD17cpGu7a/cp32Za3P8pdciIiIiIiISdRpGKyIiIiIiEmWJCfHXz6dkU0REREREJMrK6iQ/RaFkU0REREREJMoS4jDZjL++XBEREREREYk69WyKiIiIiIhEWWIc/vSJkk0REREREZEo0z2bIiIiIiIiUuzUsykiIiIiIiLFLh57NuMvvRYREREREZGoU8+miIiIiIhIlMXjT58o2RQREREREYmyxIT4G1SqZFNERERERCTKdM+miIiIiIiIFLtESyjSozDM7Ggz+9nMfjOzW/JZfp2ZzTGzGWY2wswaRSzLMrNp4WNwcbRZPZsiIiIiIiJlnJklAs8CRwDzgclmNtjd50RU+wFo5+7/mNmlwENAr3DZendvW5wxqWdTREREREQkyhLNivQohA7Ab+7+u7tvAt4DekZWcPdv3P2f8OVEoEGxNjIP9WyWYjfMXBTrEHZ593TYO9YhxIU/Vi2PdQi7vCYpNWIdgoiIiGxHQiGHwhbEzPoAfSKKBrj7gIjX9YF5Ea/nAwduZ5MXAP+LeJ1sZlOATOABd/+0SAGjZFNERERERCTqijpBUJhYDthhxUIws7OAdkC3iOJG7r7AzJoCI81sprvPLcp+NIxWRERERESk7FsANIx43SAsy8XMDgduA453941byt19Qfjv78AoYL+iBqRkU0REREREJMpK4J7NycCeZtbEzMoDvYFcs8qa2X7AiwSJZnpEeXUzqxA+rwV0AiInFvpXNIxWREREREQkyhITotvP5+6ZZnYFMAxIBF5199lmdjcwxd0HAw8DlYEPLEhg/3b344EWwItmlk3QIflAnlls/xUlmyIiIiIiIlGWUMR7NgvD3YcCQ/OU3Rnx/PAC1hsPtC7ueJRsioiIiIiIRFliEWejLYvir8UiIiIiIiISderZFBERERERibKi/vRJWaRkU0REREREJMoSULIpIiIiIiIixSwh/nJN3bMpIiIiIiIixU89myIiIiIiIlFmGkYrIiIiIiIixa0kfmeztFGyKSIiIiIiEmXxeP+ikk0REREREZEoi8eezXhMsIvMzNzM3op4nWRmGWY2JHx9vJndEj7vZ2Y3hM9HmVm72ERdNEtn/8T4vg8y7s7+/DlsZIH1lnw/g68vvYHVf83LKVszfyGTH3qaCXc/zIR7HiFr8+aSCLnMmDB+PL1OOolTep7AwNde32b5pk2buP2WWzml5wlccM65LFq4EIDZs2ZxzulncM7pZ3B279MZNfIbAJYsXszlfS7m9FNO5YxTT2PQO++WZHPKlCkTJnDBKb04/6RTGPTGwG2Wz/z+By4/+1yOPbgzY0YU/LkXERERkW2pZ/PfWQe0MrOK7r4eOAJYsGWhuw8GBscquOLm2dn8/N4n7HdVH5KrpzDpgSep1aYllevWyVUvc8MG5n0zhqqNd88py87KYvbr77LPeadTpUE9Nq1dR0JiYkk3odTKysri0Qce5MnnniWtdm3+e/Y5dOnWlSZNm+bU+fzTz6hStQoffvYpw4cN49mnnubeB/rTrNkevPrmQJKSkliasZRzTj+dzl27kJiYxFXXXsveLZqzbt06zj/rbDocdGCubUpw7J996FHuf+ZJaqWlcdW5/+WgLl1o1LRJTp3UOnW4/s47+Oitt2MYqYiIiOwK4rGXLx7bXFyGAv8Jn58O5HQfmdl5ZvZMQSuaWYKZvW5m90Y5xmKx6s+/qZhak91Sa5KQlETtdm3JmD57m3pzBw+j8ZGHkFBu63cYy3/8hcr161KlQT0AyleuhCXoY7fFnNmzadCwIfUbNKBcuXIcfuSRjB71ba46Y779lmN79ADgkMMOY8qkSbg7yRWTSUoKjvWmTRshHJpRK7UWe7doDkClSpVo3KQxGenpJdiqsuHn2XOo26ABdevXp1y5cnQ78nAmjB6dq06denVpuuce+syKiIhIkVkR/yuLdAX1770H9DazZKAN8F0h10sC3gZ+dffboxVccdq4chXJ1avlvE6uXo2NK1flqrP67/lsWLGSWq1b5ir/Z0kGAN8/NYDv7n+cP7/6JurxliUZ6emk1a6d8zqtdhoZGbkTw4yMdGqHdZKSkqhcuTKrwuM/e+Yszjj1NM7q1Zubbr01J/ncYtHChfzy08/s06pVlFtS9izLyCC1dlrO61ppaSzLyIhhRCIiIrIrSzAr0qMsUrL5L7n7DKAxQa/m0J1Y9UVglrvfl99CM+tjZlPMbMrsIV8WPdAS4NnZ/PLhYPY65bh8l62c+wet/nsm7W64nIxps1j+068xiHLXtE/rVrzzwfu8+uZABr7+Ghs3bsxZ9s8//3DrjTdxzQ3XU6ly5RhGKSIiIiIJRXyURWU17tJiMPAIEUNoC2E8cEjYI7oNdx/g7u3cvd0+PY4ujhiLrEK1FDasWJnzesOKlVSolpLzOmvjRtYtXMzUx55n7G33sfqPv5n2/Gus/mseFaqlUH2PppSvXInE8uWp2ao5q/+eH4NWlE6paWmkL1mS8zp9STqpqWm566SmsSSsk5mZydq1a0mJOP4AjZs0YbeKu/H73LlBvc2Z/N+NN3HUMUfT/dBDo9yKsqlmaioZS7b2Ii9NT6dmamoMIxIRERHZtSjZLJpXgbvcfeZOrPMKQU/o+2ZWJiZoqtqoIevTl7J+6TKyMzNZMmUaqW32yVmeVLEi3R65m8733Ubn+26japPdaXvp+VRt1JCaLfdm7cLFZG3aRHZWFit/+Z3KdWtvZ2/xpUXLlsybN4+FCxawefNmvv7qK7p065qrTuduXRk6ZAgA34wYwQHt22NmLFywgMzMTAAWLVrEX3/+Sd269XB37rvnbho1acLpZ51V4m0qK/Zu2YKF8+axeMFCNm/ezLdffc1BXbrEOiwRERHZRcXjMNoykeyUVu4+H3jqX6z3mJmlAG+a2Znunl380RWfhMRE9u59Ij88/RKe7dTr2J7K9eow9/Mvqbp7Q1L33afAdctV2o3dD+vKpAeeBKBWqxbb3NcZz5KSkrj+phu55ooryc7KokfP42narBkDnn+BFi1b0KVbN47r2ZO77riTU3qeQNWUqtxz//0ATJ82jTdff4OkpCTMjBtuuYVq1asx/YdpfPnFUJrtsQfnnH4GAJdcfhkdO3eOZVNLncSkJC678Xpuu+oasrOzOfK4HjRu1pSBLw5gzxYtOLhrF36eM4d7brqFNavX8N2Ysbw54GUGDHon1qGLiIhIGZRQRif5KQpz91jHIAW4fOTnenOi7J4O3WMdQlxYlaXfVo22Jik1Yh2CiIhISSpzmdsfq5YX6dq+SUqNMtdmDaMVERERERGRYqdhtCIiIiIiIlEWj8NolWyKiIiIiIhEWTwOKVWyKSIiIiIiEmVldUbZolCyKSIiIiIiEmXxOIw2HntzRUREREREJMrUsykiIiIiIhJlcTiKVsmmiIiIiIhItMXjMFolmyIiIiIiIlGmCYJERERERESk2MXjZDnx2GYRERERERGJMvVsioiIiIiIRFk83rOpnk0REREREZEoMyvao3D7sKPN7Gcz+83MbslneQUzGxQu/87MGkcsuzUs/9nMjiqONivZFBERERERibIErEiPHTGzROBZ4BigJXC6mbXMU+0CYIW77wE8DjwYrtsS6A3sAxwNPBdur4htFhERERERkbKuA/Cbu//u7puA94Ceeer0BN4In38IHGZmFpa/5+4b3f0P4Ldwe0WiezZFRERERESirAR++qQ+MC/i9XzgwILquHumma0CaoblE/OsW7+oASnZLMW+++nXWIewy6tw0GGxDiEu1I6/++FLXM2br451CLu8ZQ8+GesQRESkDDO8aOub9QH6RBQNcPcBRdpolCnZFBERERERiTbPLtrqQWK5veRyAdAw4nWDsCy/OvPNLAlIAZYVct2dpns2RUREREREoi67iI8dmgzsaWZNzKw8wYQ/g/PUGQycGz4/BRjp7h6W9w5nq20C7AlM+nft3Eo9myIiIiIiImVceA/mFcAwIBF41d1nm9ndwBR3Hwy8ArxpZr8BywkSUsJ67wNzgEzgcnfPKmpMSjZFRERERESirYjDaAu1C/ehwNA8ZXdGPN8AnFrAuvcB9xVnPEo2RUREREREoi76yWZpo2RTREREREQk2kqgZ7O0UbIpIiIiIiISdfGXbGo2WhERERERESl26tkUERERERGJNg2jFRERERERkeKnZFNERERERESKWxz2bOqeTRERERERESl26tkUERERERGJuvjr2VSyKSIiIiIiEm1xOIxWyaaIiIiIiEjUKdncZZlZbeBx4CBgBbAJeMjdP4lpYGXEwQ0bc0Pn7iQkJPDpnJm88cPkbeoc3mwv+rQ/GMf5delSbv96KABP9TiJ1rXrMG3RQq4d+mkJR176jBs3jkcefpis7GxOPOEEzv/vf3Mt37RpE3fccQc//vgj1VJSeODBB6lXrx4Ar77yCp9+9hmJCQnceNNNdOzYkT///JNbbr45Z/0FCxZwyaWXcuaZZ3LzzTfz159/ArBmzRqqVKnCe4MGlVhbSwt356GHH2Hc2HEkJydz1139aNGi+Tb15sz5kb79+rFxw0Y6de7ETTfegJkxfPjXvPDiAP744w/efPMN9mnZEoCJEyfy1FPPsDlzM+WSynHNNVfToUP7km5eqXPoXs3pf/xJJFgCb02eyJOjvs61/N4eJ9K52R4AVCxXntTKlWna71YaVKvOwHMuIMGMcomJvDRuDK9/Ny4WTRARESl25h7rEEpcXCSbZmbAp8Ab7n5GWNYIOD5PvSR3z4zC/hPdPau4t1tSEsy4ueuhXP75RyxZu4aBp5zJ6D/n8seK5Tl1GqZU4/z9O3DBJ++xZuNGqlesmLPszR8mk5xUjpP2aROL8EuVrKwsHnzgAZ57/nlq167NWWeeSbdu3WjarFlOnU8//ZSqVaowePBghn35JU8++SQPPvggv8+dy7Bhw/jwww/JyMjg0ksu4ZNPP6Vx48Y5CWRWVhZHH3UUhxxyCAAPPvhgznYfe/RRKleuXLINLiXGjhvH33/P47PPPmHmzFnc378/bw58Y5t69/fvzx23307r1q244sqrGTd+PJ07daJZs2Y8+shD3Hvf/bnqV6tWjSeefJy01FR+++03Lrv8Sr4a9r+SalaplGDGQyecyskvP8fCVSv5+orr+XLOTH5OX5JT5/YhW7/ju6hjF1rXawDAkjWrOfrZx9mUlUWl8uUZe+0tfDlnJovXrC7xdoiIiEjRxctstIcCm9z9hS0F7v6Xuz9tZueZ2WAzGwmMMLMaZvapmc0ws4lm1gbAzCqb2WtmNjNcdnJYfqSZTTCz783sAzOrHJb/aWYPmtn3wC3hv4TL9ox8Xdrtk1aHeatWsmD1KjKzs/nqt5/o1qRZrjontmzN+7OmsWbjRgBWrF+fs2zygnn8s3lTicZcWs2aNYsGDRvSoEEDypUrx1FHHcWoUaNy1Rk1ahQ9jjsOgMMOP5zJkybh7owaNYqjjjqK8uXLU79+fRo0bMisWbNyrTtp0iQaNGiQ0xO6hbszfPhwjj766Ki2r7T6dtS39OhxLGZGmzatWbNmDRkZS3PVychYyrp162jTpjVmRo8exzLqm1EANG3ahMaNG2+z3ebNm5OWmgpAs2bN2LhxI5s2xfdnff+GjfhjWQZ/LV/G5qwsPpn+Pce0bF1g/ZPaHsDH04PT4easLDZlBd/LlU9KIiEhXv5EiYhIfMgu4qPsiYueTWAfYHvJ3f5AG3dfbmZPAz+4+wlmdigwEGgL3AGscvfWAGZW3cxqAbcDh7v7OjO7GbgOuDvc7jJ33z+sf7iZtXX3acD5wGvF3sooSatUmSVr1+S8Tl+7lla16+aqs3tKdQBeObEXCZbAgMkTmDDvz5IMs0zISE+nTu3aOa/TatfeJmHMSE+nTp06ACQlJVG5cmVWrlxJekYGrVtvvWivnZZGRnp6rnWHDRvGUfkklN9//z01atRg90aNirM5ZUZ6egZ1atfJeV07rTbpGemkptbaWicjnbS02rnrpGcUeh9fjxhB8+bNKV++fPEEXUbVTUlhwcqVOa8XrlrJAbvn/7lrUK06u1evwejffskpq5dSjffOv5gmNWvRb+hn6tUUEZFdRxxOEBSXXxub2bNmNt3Mttx4ONzdt4wJ7Qy8CeDuI4GaZlYVOBx4dss23H0Fwf2fLYFxZjYNOBeIvKqKvDnuZeB8M0sEegHvFBBbHzObYmZTMsZOKGJLS05iQgINU6rT57MPuG34F9x2yBFULl8h1mHFlc2bNzP622854ogjtlk27Msv47ZXsyTMnTuXp556mttv+79Yh1KmnLTv/nw+czrZEfewLFy1kq5PPEj7h+6h9wEdSK1cJYYRioiIFKf469mMl2RzNkHvJQDufjlwGJAaFq37l9s1gkS1bfho6e4XRCyP3O5HwDFAD2Cquy/Lb4PuPsDd27l7u9TOB//LsIpX+rq11I644EurXJn0dWvy1FnD6D/nkpWdzcI1q/l75Qp2r1athCMt/VLT0li8ZOu9a+lLluQMw8xVZ/FiADIzM1m7di3VqlUjLTWVJWE5wJL0dFLT0nJejxs7lubNm1OzZs1c28vMzGTkyJEcedRR0WhSqTVo0Pv06n0GvXqfQa3UWixeEnnslpCWmparflpqGukR9xUuSV9CWlru9yY/S5Ys4brrb+Seu++iYcMGxdeAMmrRqlXUj/h/v15KNRatWpVv3RP33Z+Ppk/Nd9niNav5cfEiDmrSNBphioiIlDzPLtqjDIqXZHMkkGxml0aU7VZA3THAmQBm1h1Y6u6rgeHA5VsqmVl1YCLQycz2CMsqmdle+W3U3TcAw4DnKUNDaAHmpC+mYUo16lWpSlJCAkfu0ZzRf/yeq86o3+dyQDjJR0pyMrtXq86CAi4w49k+++zDvL//ZsGCBWzevJlhw4bRrXv3XHW6devGkM8/B2DE11/Tvn17zIxu3bszbNgwNm3axIIFC5j399+0atUqZ70vv/wy3yG03333HY0bN6Z2xPDdeNCr12kMeu8dBr33Dod0786QIUNxd2bMmEnlypVzDaEFSE2tRaVKlZgxYybuzpAhQ+nWvdt297FmzRquvOoarrryCtq2bRvF1pQdP8z/m6Y1U9m9eg3KJSZy4r77878fZ21Tb8/UNKpVrMjkv/7MKauXkkJyUjkAUipW5MDGTfktI32bdUVERKRsiIt7Nt3dzewE4HEzuwnIIOh1vBmomKd6P+BVM5sB/EMwNBbgXuBZM5sFZAF3ufvHZnYe8K6ZbRkzejvwC/l7GzgR+Ko42lVSstx5eMw3PH3cySSaMfinWfy+YhkXt+/IjxmLGf3n70yY9ycHNWzE+73PJdudp8aPZtXGDQC8dMJpNK5eg4rlyvPFORdxzzdfMXHeXzFuVWwkJSVx8803c/lll5Gdnc3xPXvSrFkznn/uOVq2bEm37t054YQTuOP22zn++ONJqVqV/g88AAQT0Bxx5JGccvLJJCYmcsstt5CYmAjA+vXr+e6777jt9tu32edXw4bF/RDazp07MXbsOI7veQLJycn069c3Z1mv3mcw6L1gVPutt95C37792LhxI506dqRzp04AjBz5DQ8+9DArVqzgqquuYe+99uK5557hvUGDmDdvHgNeepkBL70MwPPPPUONGjVKvpGlRFZ2Njd/9hEfXHApiQkJvDN5Ij8vWcwtRxzDtPnz+DJMPE/cd38+mf5DrnX3SqvD3f85AXfHzHh29Eh+XLwoFs0QERGJgrLZO1kU5nH4ey+xYmY3ACnufkdh6rd77jG9OVH27XmXxDqEuGBl95d/yoyGd2/7RYMUr2UPPhnrEEREZCuLdQA7a/3SaUW6tq9Yq22Za3Nc9GyWBmb2CdCM4GdYREREREQkrsRfz6aSzRLi7ifGOgYREREREZGSomRTREREREQk2srojLJFoWRTREREREQk6pRsioiIiIiISHFTz6aIiIiIiIgUv/hLNhNiHYCIiIiIiIjsetSzKSIiIiIiEm0aRisiIiIiIiLFzT0r1iGUOCWbIiIiIiIiUebZ6tkUERERERGRYhaPPZuaIEhERERERESKnZJNERERERGRKPPsrCI9isLMapjZcDP7Nfy3ej512prZBDObbWYzzKxXxLLXzewPM5sWPtoWZr9KNkVERERERKLMPatIjyK6BRjh7nsCI8LXef0DnOPu+wBHA0+YWbWI5Te6e9vwMa0wO9U9myIiIiIiItEW2wmCegLdw+dvAKOAmyMruPsvEc8Xmlk6kAqs/Lc7Vc+miIiIiIjIrq22uy8Kny8Gam+vspl1AMoDcyOK7wuH1z5uZhUKs1P1bJZiY07uEOsQdnnum2MdQlx45c9fdlxJimTCLXfEOoRd3j9rMmIdQlzYrUpqrEMQEYmKog6FNbM+QJ+IogHuPiBi+ddAnXxWvS13HO5m5tvZT13gTeBcd9/SHXsrQZJaHhhA0Ct6945iVrIpIiIiIiISZUWd5CdMLAdsZ/nhBS0zsyVmVtfdF4XJZHoB9aoCXwC3ufvEiG1v6RXdaGavATcUJmYNoxUREREREYmyGE8QNBg4N3x+LvBZ3gpmVh74BBjo7h/mWVY3/NeAE4BZhdmpejZFRERERESizGM7QdADwPtmdgHwF3AagJm1Ay5x9wvDsq5ATTM7L1zvvHDm2bfNLBUwYBpwSWF2qmRTRERERERkF+buy4DD8imfAlwYPn8LeKuA9Q/9N/tVsikiIiIiIhJlxTAUtsxRsikiIiIiIhJlRZ0gqCxSsikiIiIiIhJl8dizqdloRUREREREpNipZ1NERERERCTKYjwbbUwo2RQREREREYmyeBxGq2RTREREREQk2jRBkIiIiIiIiBS3eOzZ1ARBIiIiIiIiUuzUsykiIiIiIhJlmiAoTplZFjAzougEd/8zRuGUCe7OQ0+9y9iJM0muUJ67b/0vLfZulKvO+g0bufHO55m/MIOEhAS6ddyXqy85JUYRl17uzkMPP8q4seNJTk7mrrvupEWL5tvUmzPnR/r2u5uNGzbSqXNHbrrxesyMxx9/itFjxlAuqRwNGtbnrn53UqVKFVauXMmNN93K7NlzOP64Htxyy40xaF3p89f30xnz6pt4djYtD+/OAScdn2v5rGEjmPG/4SQkJFAuOZlDLr2AGg3rszo9g7evuonq9eoCUHuvPTjkkv/Gogml3tQJE3np8SfIzs7miOOP49Rzzs61fNYP03jp8Sf5c+5cbrrnLjodeggAv//yC8899Aj/rFtHYkIip513Dl2OODwWTSgT3J2HHnmSceMmBOeOfv9Hi+Z7b1Nvzo8/0bff/WzcuJFOnQ7mphuuxsxylg98610ef+JZRn49hOrVqpVgC0RE4ks8DqNVshlY7+5t81tgwV9kc/f4+ypiO8ZOnMnf85cw+J37mTnnd+577E3eevH2beqd2/to2u/fnM2bM+lz7SOMnTiTzge1jkHEpdfYceP5++95fPbZR8ycOYv7+z/ImwNf26be/f0f5I7b/4/WrVtxxZXXMG78BDp36shBB3XgyisvIykpiSeffJpXX32dq6++kgoVKnDZpRfz29y5zP3t9xi0rPTJzsrm25feoGffW6hcswbv33QnTdofQI2G9XPq7NXlYFoddRgAf0yaytjX3uL4O28GIKV2bXo/dn9MYi8rsrKyeOGRR7nnqSeomZbGdedfyIFdOrN7kyY5dVJr1+aaO27jk3fezbVuheRkrrvzDurt3pBlGRlce94F7HfQgVSuUqWkm1EmjB03kb/nzeOzT95j5qzZ3N//Ed5846Vt6t3f/1HuuP0mWrfahyuuvoFx4yfSudPBACxevISJEydTp07tkg5fRCTueBxOEKR7NvNhZo3N7GczGwjMAhqa2cNmNsvMZppZr7De3WY2LXwsMLPXwvKzzGxSWP6imSWG5WvN7D4zm25mE82szP51HzV2Gj2O6oiZ0WafZqxZ+w8ZS1fmqlMxuQLt9w966MqVS6L5nruzJGN5DKIt3b4dNZoePY4NjmWb1qxZs4aMjKW56mRkLGXdunW0adMaM6NHj2MZ9c23ABx88EEkJQXfG7Vu3Yol6ekAVKxYkf32a0uF8hVKtkGl2JLf5pJStzYpddJILJfEnp0P4vdJU3PVKb/bbjnPN2/cCBE9QLJjv875kboNGlCnfn3KlStH1yMO47vRY3LVqV2vLk323CNX7xpA/d13p97uDQGomZpKSvXqrF6xsqRCL3O+/XYMPY49Ojh3tG7FmjVryVia59yxNDx3tG4VnDuOPZpRo7a+H4889jRXX3XpNu+FiIgUP/esIj3KIiWbgYoRSeMnYdmewHPuvg/QDmgL7AscDjxsZnXd/c6wR7Q7sBx4xsxaAL2ATuGyLODMcJuVgInuvi8wGrioJBoXDelLV1AnrUbO69qp1UnPk2xGWr3mH0aPn86BB7QsgejKlvT0dOrU3vq9Q+20NNIz0nPXyUgnLS0td5303HUAPvvsczp17Bi9YMu4dctWUKXm1s9t5Zo1WLd8xTb1ZvxvOAMvvY7xA9+j6wXn5JSvTs/gvetv4+Pb72XhnJ9KJOayZllGBrUiPqs109JYlpGx09v5ZfYcMjdvpk6D+juuHKfSM5ZSp07EeaF2GunpuZPN9PSlpNVOzV0n/DLrm1FjSEurxd577VkyAYuISNzRMNpArmG0ZtYY+MvdJ4ZFnYF3PfhKYYmZfQu0BwaHw2zfAh5z96lmdgVwADA5/Ka4IrAlK9gEDAmfTwWOiGqrSonMzCxuvftFTj/5cBrUS93xCvKvvPzyqyQmJXLssUfHOpQyr80xR9DmmCP4efR4Jn/4KUdcdQmVqlfj3AFPULFKFdLn/sHQBx7njCcfyNUTKsVj+dKlPHbX3Vxz5+0kJOg70WhYv2EDr742kOeefTzWoYiIxA1NECSR1hWyXj9gvrtvucnOgDfc/dZ86m52dw+fZ5HP8TezPkAfgKcfvpELzj4+b5WYee/jkXw8ZDQA+zRvzOL0rUNil2SsIK1WtXzXu+eRN9i9QW3OOi0ucutCGTToAz7+5FMA9tmnJYuXLMlZtiQ9nbTUtFz101Jz92QuSc/d0zl48BBGjxnLiy88p+Fw21GpZnXWLNv6uV27bDmValQvsP5enQ/i2wHB/9qJ5cpRsVw5ANKaNaFqnTRWLFxM7T2aRjfoMqZmaipLIz6ry9LTqZla+C+Z/lm3jruuu5GzL7mY5q1aRSPEMm3Q+x/x8aefA7BPyxYsXhxxXliSTlparVz109Jqkb4kI3ed1FrMn7+ABQsX0ev08wBIT8/gjDP/y5tvvEStWjWj3xARkThUVofCFoW+Mi6cMUAvM0s0s1SgKzDJzI4jGFZ7VUTdEcApZpYGYGY1zKzRNlssgLsPcPd27t6uNCWaAL1POpT3X+3H+6/245Au+zFk2HjcnRmz51K50m6k5pNsPvPSx6xdu54br+xd8gGXYr16ncqg995m0Htvc0j3bgwZMjQ4ljNmUrlyZVJTc18wpqbWolKlSsyYMRN3Z8iQoXTr3hWAceMm8Pobb/LEE49SsWJyLJpTZtTeoymrFi1m9ZJ0sjZn8uvYiTRpv3+uOisXLs55/ufUaaTUrQPA+lWryc4KvpFctTidVYuWkFI795cCAnu2aM7CefNZvHAhmzdvZvTwEXTo0rlQ627evJn7br6VQ489OmeGWsmt12knM+id1xn0zusc0r0LQ4Z+GZw7Zs4Kzh218pw7aoXnjpmzgnPH0C/p1q0Le+7RjJHDhzD08w8Z+vmHpKWl8s7bryrRFBGJIs/OKtKjLFLPZuF8AhwMTAccuMndF5vZdUB9gsQTYLC732lmtwNfmVkCsBm4HPgrNqFHR5eD2jB2wkyOO/1WkiuU565bt/4ExGn/DRLSJenLefnNL2iye116X3g3ECSsJ/XoGqOoS6fOnTsxdux4ju95EsnJyfTrd0fOsl69z2TQe28DcOutN9G3793Bzxd07EjnTsG9mQ8++DCbNm/i0kuvAIJJgm6/LehYP/Y/PVm3bh2bN2/mm1Hf8txzT9Gsafz2xCUkJtL1wnP57O6Hgp8+OawbNXdvwHfvfkhasyY06XAAM/73FfNnzCYhMZEKlStx+JUXA7Bgzk9Meu8jEhITMTO6X3w+yVUqx7hFpU9iUhKX3HAtfa++juzsLA7v0YNGTZvy1oCX2LN5cw7s2oVf5vzI/Tffyto1a5g8dhxvv/Qyz737NmO/HsnsH6axZtUqRnwxFIBr7riNpnvtFeNWlU6dOx3M2HETOP6EXsG5o+//5SzrdcZ5DHrndQBuveV6+va7Lzx3HETnTgfFKGIREYk3tnVUp5Q265eM1ZsTZV5ZP8NSEl7585dYh7DLO6pekx1XkiJpkKRTcknYrYru7ReRQilz9w39ObZvkf6QNO58V5lrs3o2RUREREREoqysDoUtCiWbIiIiIiIiUeZZSjZFRERERESkmMVjz6ZmoxUREREREZFip55NERERERGRKNMwWhERERERESl22XE4jFbJpoiIiIiISJSpZ1NERERERESKXTwmm5ogSERERERERIqdejZFRERERESizLMzYx1CiVOyKSIiIiIiEmXZcTiMVsmmiIiIiIhIlHkczkarezZFRERERESk2KlnU0REREREJMpiORutmdUABgGNgT+B09x9RT71soCZ4cu/3f34sLwJ8B5QE5gKnO3um3a0X/VsioiIiIiIRJlnZxXpUUS3ACPcfU9gRPg6P+vdvW34OD6i/EHgcXffA1gBXFCYnSrZFBERERERibLsrKwiPYqoJ/BG+PwN4ITCrmhmBhwKfLiz62sYbSnW/u3RsQ5hlzfpkv1iHUJcuLBe9ViHsMvbd8CzsQ5hl/frmFGxDiEu/DNQn+Voq1ijZaxDEIlLRe2dNLM+QJ+IogHuPqCQq9d290Xh88VA7QLqJZvZFCATeMDdPyUYOrvS3bf8dst8oH5hdqpkU0REREREpJQLE8sCk0sz+xqok8+i2/Jsx83MC9hMI3dfYGZNgZFmNhNY9W9jVrIpIiIiIiISZdGeIMjdDy9omZktMbO67r7IzOoC6QVsY0H47+9mNgrYD/gIqGZmSWHvZgNgQWFi0j2bIiIiIiIiUeZZmUV6FNFg4Nzw+bnAZ3krmFl1M6sQPq8FdALmuLsD3wCnbG/9/CjZFBERERERibLs7KwiPYroAeAIM/sVODx8jZm1M7OXwzotgClmNp0guXzA3eeEy24GrjOz3wju4XylMDvVMFoREREREZEoi+XvbLr7MuCwfMqnABeGz8cDrQtY/3egw87uVz2bIiIiIiIiUuzUsykiIiIiIhJlRf3pk7JIyaaIiIiIiEiUxXIYbawo2RQREREREYmyYpjkp8zRPZsiIiIiIiJS7NSzKSIiIiIiEmUaRisiIiIiIiLFzrMzYx1CiVOyKSIiIiIiEmXq2RQREREREZFipwmCRERERERERIqBejalUDo1bsot3Y8gMcH4aOZ0Xpk8YZs6R+3VgssO7oK78/PSdG4e+lnOskrly/PZuX0YOfcX7h/5VUmGXuq4Ow899DDjxo0lOTmZu+66ixYtWmxTb86cOfTt24+NGzfQqVNnbrrpRsyMVatWcfPNt7Bw4ULq1avHQw89SNWqVfnjjz/o27cfP/30E1dccTnnnHNOzraOPfY/VKpUiYSEBBITE3nnnbdLssmllrvz0GMvMnbCFJIrVODuO66lRfM9tqn39PNvMOR/I1m9Zi0TvvkoBpGWLV2aNOO2w44mMSGBD6Z/z4Dvxm1T55jmLbmyU3cc56f0JVz/+ccA1K1SlfuOOZ66VaviDhd98DYLVq8q6SaUekft354n+1xBYkIiL3/1BQ9++G6u5bun1ubVa24itWoKy9eu4axH7mPBsqXs26QZz19+LVUrViIrO4v73n+b98d8E6NWlH7uzkOPv8LY8VNJTq7A3XdcSYu9m21T7+kX3mLI/0axes06Jozc+l588PGXDProfyQkJrBbxWTuuOUymjVpWJJNEJFSRMNo82FmWcBMoByQCQwEHnf37O2s0xjo6O7vFFOcO83M2gL13H3oTq7XGPgR+BkoD4wGLttee3d1CWbcfuhRXPTRuyxes5pBZ57PN3N/5fflS3Pq7F6tOhd2OJiz3xvI6o0bqFFxt1zbuLJjN6YumFfSoZdKY8eO4++//+azzz5j5syZ3H9/f958c+A29e6/vz933HE7rVu35oorrmTcuPF07tyJ1157jQ4dOvDf/57Pq6++xmuvvcbVV19NSkoKN998E998k/+F44ABL1K9evVoN69MGTthCn/PW8jgD15i5uyfue+hZ3nr1ce3qdety4H0PvU4jj/1ohhEWbYkmNH3iGM5f9CbLF6zmo/OvYgRv/3M3GVbzxeNqtfg4oM60/utV4PzxW5bzxcP9TiR5yeMYfyfv7NbuXJku8eiGaVaQkICz156NUfcfiPzl2Uw+fEXGPzdeH6c91dOnUcuuISBI75i4MhhHNJmP/qfexHnPNaffzZu5JzH+vPbwgXUrVGTqU+8yLDvJ7Fq3boYtqj0Gjvh+/Ac8RwzZ//CfQ+9yFuvPLRNvW6d29P7lGM5/rTLc5Ufc1RXTj3paABGjZnEo0++xnNP3FkisYtI6ZOdHX9/0wozjHa9u7d1932AI4BjgL47WKcxcMbOBGJmxd3L2hY49l+uO9fd2wJtgJbACcUTUm5RaHNUtK5Tj79XrmD+qpVkZmfzv5/mcGizPXPVOaV1W96bNpXVGzcAsHz9PznLWqbVoeZulRj/5+8lGndp9e23o+jRowdmRps2bVizZg0ZGRm56mRkZLBu3TratGmDmdGjRw9GjQqSyFGjvuW443oAcNxxPfjmm1EA1KhRg3322YekpDLxsSoVRo2eSI9jDw3ei1bNWbN2HRlLl29Tr02r5qTWqhGDCMueNnXr89fK5cxbtZLN2dl88eNsDt+zea46p+27P29/P3nr+eKf4HzRrGYtkhIScs4V/2zezIbM+Ju5b0c67NWc3xYt5I8li9icmcl7o0fS86BOueq0bNiYkTO+B+CbGT/kLP914Xx+W7gAgEXLl5G+aiWpKdVKNP6yZNToSfQ45pDwHLH3ds4Re+d7jqhcaesXKevXb8QsquGKSCmXnZ1dpEdZtFP3bLp7OtAHuMICiWb2sJlNNrMZZnZxWPUBoIuZTTOzawuqZ2bdzWyMmQ0G5phZgpk9Z2Y/mdlwMxtqZqeEdQ8ws2/NbKqZDTOzumH5KDN70MwmmdkvZtbFzMoDdwO9whh6mVm38Pk0M/vBzKoUor2ZwHhgDzO7KIx/upl9ZGa7hft/3cxeMLMp4f57hOWFavPOHP9YSatchcVrVue8XrJ2DWlVch++RtVr0Kh6Dd7sdTZvn34unRo3BcCAG7sdxiOjR5RkyKVaeno6derUznldu3Ya6ekZeepkkJaWlqdOOgDLli0jNTUVgFq1arFs2bId7tPMuOyyyznjjDP46CMNA90iPWMZddJSc17XTqtFesaOj6cUrHaVKixevfV8sXjNampXzn2+aFK9Jo1r1OTdM8/n/bMvoEuTYFhikxo1Wb1hA8+ccBqfnteHm7ofQYKuzrdRv2Yt5mWk57yevzSD+jVr5aoz/Y+5nNSxKwAnHtyFqrtVokaVqrnqtN+rOeWTkpi7aGH0gy6j0jOWUad2zZzXtVNrkp6xbbK5Pe99OJQep1zCE8++wU3XXVjcIYpIGZKd7UV6lEU7PUGQu/8OJAJpwAXAKndvD7QHLjKzJsAtwJiwR/Tx7dQD2B+42t33Ak4i6BVtCZwNHAxgZuWAp4FT3P0A4FXgvoiwkty9A3AN0NfdNwF3AoPCGAYBNwCXhz2WXYD1O2prmFAeRjCM+GN3b+/u+xIMs70gompjoAPwH+AFM0veiTbvEpISEmhUvQbnf/A2N33xKXcdcSxVKlSgd9sDGP3HXJasXRPrEHdJZoYV4mL8tdde5d133+GZZ55h0KD3mTp1aglEJ5K/xIQEGlevwdnvvsF1gz/i3qOPo0qFCiQmJNCu4e48+M1XnPzGSzSsVo2TWreNdbhl0g2vPk+3Vm34/skBdGu9L/OXZpAVMQtineo1ePO6Wzn/iQdxDVWOqt6nHMuQD1/g6svO4aXXPoh1OCIiJaqo4+2OBNps6X0EUoA9gU07UW+Su/8RlncGPgjvj1xsZltuPtsbaAUMDy+sE4FFEdv/OPx3KkHil59xwGNm9jZB4jh/O+1qZmbTAAc+c/f/hT2j9wLVgMrAsIj674cx/2pmvwPNd6LNuZhZH4LeY+qe0pMaB3fYTpglI33tGupEfCNeu3IV0tfkTh6XrFnDjMULyczOZsHqVfy5YjmNqtVg37r1OaB+Q3rvuz+7lS9PuYRE/tm0iSfGjirhVsTWoEGD+PjjTwDYZ599WLx4Sc6yJUvSSYvoXQNIS0vN6cncWifo6axZsyYZGRmkpqaSkZFBjRo7Ht65Zd0aNWpw6KGHMHv2bA444IAit6sseu/DIXz82ZcA7NNiLxZH9CovSV9KWmrNglaVQliyZg11qm49X9SpUnWbL5sWr1nN9EULyMzOZv6qlfy5fBmNq9dk8ZrV/LhkMfNWrQTg619/pm29BnzIDyXZhFJvwbKlNEzdOvKhQa1UFkTcEwvBENmT7w/ueKmUnMzJHbvm3JdZpeJufNG3P7e9+Qrf/fxjyQVeRrz34VA+HjwcgH1a7MHiJVtHOyzJWEZa6r8bUn/0EZ25/+EXiyVGESmbymrvZFHsdM+mmTUFsoB0glGSV4a9h23dvYm75zfV6PbqFWZWAgNmR6zf2t2PjFi+Mfw3iwISaHd/ALgQqAiMM7Pm+dULzQ33s5+79wvLXgeucPfWwF1AcuTm8+6Of9lmdx/g7u3cvV1pSDQBZi1eyO7VqlO/agpJCQkc07wl3/z+a646I+b+QvsGuwNQLbkijavXYN6qldzyv8Ec8fKzHPXKczzy7QgG/zgz7hJNgF69ejFo0HsMGvQehxzSnSFDhuDuzJgxg8qVK+cMi90iNTWVSpUqMWPGDNydIUOG0K1bdwC6devK558PAeDzz4fQvXu37e57/fr1rAsvMtevX8+ECRNp1mzb2RTjRe9TevD+m8/w/pvPcEi3gxgydGTwXsz6icqVK+nezCKauWgBjavXpEFKNcolJPCfFvsw4refc9X5+tefOLBhIwCqV6xI4xo1mbdyBTMXLaRqcjLVwwnGDmrUmN+WZmyzj3g3+Zef2LNefRrXrkO5pCR6dz2Uwd+Nz1WnZtWqOaMebj31TF4d/j8AyiUl8cnt9zBw5Fd8NG50icdeFvQ+5VjeH/g47w98nEO6HsiQ/30TniN+pnKl3XbqHPHXvK1DlMeMm8ruDetGI2QRKSOyPbtIj7Jop3o2zSwVeAF4xt3dzIYBl5rZSHffbGZ7AQuANUDkTToF1ctrHHCumb0BpALdgXcIZoZNNbOD3X1COKx2L3efvZ1wc8VgZs3cfSYw08zaE/Q+/rQTza8CLAr3fWae+E8NY24CNA3jLWybS70sd+7/5itePLk3iZbAJ7OmM3fZUi7v2JXZixcx6vdfGffn73Rs1ITPzu1Dlmfz6OiRrNqww5HKcalz586MHTuW44/vSXJyMv369ctZ1qtXbwYNeg+AW2+9lb59+7Jx40Y6depI587BBB/nn38+N998M59++il169bloYceBGDp0qWceeZZrFu3DjPj7bff4aOPPmTlypVcd931AGRlZXHMMUfTqVPuyUTiVZeO7Rk7fgrHnXIhyckVuOv2a3OWnXb2Fbz/5jMAPP70q/zvq1Fs2LCRI487hxOPP4pLLzozVmGXalnu3D18KK+cdhaJZnw4cxq/Lc3gqs7dmbV4ISN/+4Uxf8ylc5NmDL3gMrI8m4dGDWdleL544JvhvNH7HMxg9uJFvD9dQ77zysrO5ooXnmLY3Q+RmJDAq8P/x5y//+SuM89nyq8/8/mk8XRv3Zb+516EuzN61gwuf/5JAE7r3J2u+7ShZpWqnHd4MEvqeY8/wPQ/5saySaVWl44HMHb8VI479VKSK1TgrtuvzFl22jnX8v7AYPbqx595g/99NSY4Rxx/IScefziXXtib9z4cyneTZ5CUlEjVKpW5+46rYtUUESkF4rFn03Z0r0Y+P33yJvCYu2ebWQJwL3AcQU9eBsHMrf8QJFs1CXoEnyyg3n7ADe6+ZVKdBOA5giRzXlj3QXcfHv6UyVMEw1GTgCfc/SUzGxVuY4qZ1QKmuHtjM6sRxlAO6E8wRPcQIBuYDZzn7lt6RCPb2xgY4u6t8pRfCtwUxv4dUMXdzzOz14ENQDugKnCduw/ZzrHJ1ebtafXY/fH3iSxhky65OtYhxAXbuGjHlaRI9h2g306Ntl/HjIp1CHHhn4HPxjqEXV7FGi1jHYJIcShzM8gNuXmvIl3b93jwlzLX5h0mmyXNzCq7+1ozqwlMAjq5++JYx1WQMNkc4u4fFve2lWxGn5LNkqFkM/qUbEafks2SoWQz+pRsyi6izCVeg2/co0jX9sc//FuZa3Np/EG+IWZWDSgP3FOaE00REREREZHCiMdhtKUu2XT37iWxHzNrTTAkONJGdz9wZ7bj7ucVW1AiIiIiIrJLUrIZR8LJgtrGOg4REREREdn1ZWeXzRlli2Knf/pEREREREREZEfitmdTRERERESkpGgYrYiIiIiIiBQ7JZsiIiIiIiJS7LI9/u7ZVLIpIiIiIiISZfHYs6kJgkRERERERKTYqWdTREREREQkyuLxp0+UbIqIiIiIiERZPA6jVbIpIiIiIiISZfGYbOqeTRERERERkV2YmdUws+Fm9mv4b/V86hxiZtMiHhvM7IRw2etm9kfEsraF2a+STRERERERkSjLzs4u0qOIbgFGuPuewIjwdS7u/o27t3X3tsChwD/AVxFVbtyy3N2nFWanGkYrIiIiIiISZTEeRtsT6B4+fwMYBdy8nfqnAP9z93+KslNzj7+xw2XF4jWr9OZEWZKpc78kTF6ZEesQdnnjF/0V6xB2eUc32ivWIcSFEQt+j3UIu7yLmrWJdQi7vLpVU2IdQjywWAews144q2aRru0vfXv5xUCfiKIB7j6gMOua2Up3rxY+N2DFltcF1B8JPObuQ8LXrwMHAxsJe0bdfeOO9queTRERERERkSgr6lDYMLEsMLk0s6+BOvksui3PdtzMCkx8zawu0BoYFlF8K7AYKB/GcDNw945iVrIpIiIiIiJSxrn74QUtM7MlZlbX3ReFyWT6djZ1GvCJu2+O2Pai8OlGM3sNuKEwMWkMoYiIiIiISJRluxfpUUSDgXPD5+cCn22n7unAu5EFYYK6ZQjuCcCswuxUPZsiIiIiIiJRVgwzyhbFA8D7ZnYB8BdB7yVm1g64xN0vDF83BhoC3+ZZ/20zSyW4V3YacElhdqpkU0REREREJMpiORutuy8DDsunfApwYcTrP4H6+dQ79N/sV8mmiIiIiIhIlMX4p09iQvdsioiIiIiISLFTz6aIiIiIiEiUxWPPppJNERERERGRKIvDXFPJpoiIiIiISLTFY7KpezZFRERERESk2KlnU0REREREJMqy4rBrU8mmiIiIiIhIlMVhrqlkU0REREREJNqUbIqIiIiIiEixU7JZyplZFjATKAdkAgOBx909ezvrNAaGuHsrM2sHnOPuV5VEvGXdd+Mn8PQjj5Kdnc1/TujJmeedm2v5pk2buL9vP3758SeqpqTQt/991K1Xj0ULF3LOqb3YvdHuALRs1Yrr/+9WADZv3swTDz3MtKlTSbAELrzsUroddmiJt600mTh+PE888gjZWdkcd8IJnH3+ebmWb9q0iXvu7MvPP/5ISkoKdz/Qn7r16uUsX7xoMWedeir/7dOHM845G4CTexzHbrvtRkJiIomJibz61psl2aRS7cdJU/j42Rfw7GwOOvZoDj/9tFzLv/ngYyYO/ZKExEQqV0vh9BuvpUbt2jnLN6xbR///XkzrTh055arLSjr8MmHR9NlMe/N9PDubJt070eL4o/OtN3/S94x/agCH330rNZo2YuOatYx/agArfv+Lxl0PYv9zTy/hyMuOmd9N4p0nn8Gzs+nS41j+c9YZuZYPe+8DRg8ZSmJiIlWqpXD+rTdSq04dAB67/mbmzpnDnq1bc81D98ci/DJj4bRZTHnjXTw7mz0O7cI+PY/NtfyX4aP45atvSEhIICm5AgdedA4pDeqRlZnJpJcGsuz3vzAz2p3bm9r7NI9RK0qf78ZP4JlHHyUrO5v/9Mz/+qJ/3378/NNPpKSkcOf99+X83Zv766882r8//6xdhyUk8MIbr1OhQgVGfjWct157jeysLA7u0pmLr7wyFk0TkTzKVLIJrHf3tgBmlga8A1QF+hZmZXefAkyJWnS7kKysLJ548CEeffYZUmuncfE559KpaxcaN22aU+eLzwZTpUoV3vn0Y0YM+4oXn36Gfv2DC5f69evzyjtvb7PdN199jerVq/P2xx+RnZ3N6tWrS6xNpVFWVhaPPvAgTzz3LGm1a3Ph2efQuVtXmkQc5yGffkaVqlV4/7NP+XrYMJ576mnueaB/zvKnH3+Mgzp23GbbT7/4ItWqVyuJZpQZ2VlZfPjUs1z60P1US63FY5ddTauDD6RO40Y5dRrs0Yzrn3+K8snJjB08hMEDXuW8O27NWT70tTdp1qZ1LMIvE7Kzs/n+jXfpdsvVVKxRna/v7E+9A9qQUr9ernqb12/gl2EjqdGsSU5ZYrlytDrleFbNX8jq+QtKOvQyIzsri7cee5LrH3+YGqmp3H3RpbTt1JH6TRrn1Nl9rz248+XnqZCczDeffMYHzw/g0rvuBODo03uxaeMGRn02JEYtKBuys7OZ/OrbHHrbdexWszpf/t+9NDigLSkNtn6Wm3Q6kL2O6A7A/CnTmPrmIA699Vp+GzEagB4P38WGVav55oEnOPq+27EE/QhAVlYWTz70EI88E1xfXHLuttcXQz8bTOWqVXjnk48Z8dVXDHj6Gfr2v5/MzEzuu7Mv/3dXP/bYay9WrVxJUlISq1au5IWnnmLAmwOpVr06/fv1Y+qkSRzQoUMMWyqyrewCu8d2XWX2rOfu6UAf4AoLJJrZw2Y22cxmmNnFedcxs+5mNiR8XtnMXjOzmWH9k8Py581sipnNNrO7ItZ9wMzmhHUfCctONbNZZjbdzEaHZfnGEe57lJl9aGY/mdnbZmbRP1L/zo+zZ1O/YQPqNahPuXLlOPTIIxn77ehcdcZ9+y1H9fgPAN0OO5TvJ03GffvjA4YOHsyZYc9dQkIC1apVi0b4ZcaPs2fToGFD6jdoQLly5TjsyCMZM+rbXHXGfPstx/boAUD3ww5j6qRJOcd59DejqFuvPk2aNd1m27Ktv376hVr161GrXl2SypVjv0O6MXP8xFx19txvX8onJwPQuEVzVmUszVk275dfWbNiBXsfsH+Jxl2WLJ/7J5Vrp1E5LZXEpCR2P6g9C6fO2KberA8H07zHUSSW2/qdZ1JyBVL33iNXmWzr9x9/Iq1+fdLq1SOpXDkOPOxQpo0dn6tOi/33o0L4OW66T0tWpGfkLGvZbn+Sd9utRGMui5b99gdV6qRRpXbwWW7UsQPzpkzLVafcbhVznmdu3AgEf9ZXLVhE7X1aAJCcUpVyu+3Gst//LKHIS7ef8l5fHHEk4/JeX4z+lqP/E15fHHooUycH1xdTvvuOpnvswR577QVASrVqJCYmsmjBQho0bEi16tUBOKBDB0aP/KZkGyZSCFnuRXqURWU22QRw99+BRCANuABY5e7tgfbARWbWZDur3xHWb+3ubYCRYflt7t4OaAN0M7M2ZlYTOBHYJ6x7b1j3TuAod98XOD4s214c+wHXAC2BpkCnoh2B6FmankFaxNDB1LQ0lkZcrOStk5SURKXKlVm1ahUAixYu5IIzzuKqPhcz/YcfAFizZg0Arzz/AheeeTZ33nwLy5ctK4nmlFoZ6em5jnNa7TQyMtJz18lI3/Y4r1zFP//8w1tvvMF/+1y0zXbNjGsvv5z/nnkWn338cXQbUYasWrqU6qmpOa+rpdZi1dKCP4MT//cVLTq0A4Jejk9feImel1wY9TjLsvUrVrBbjeo5ryvWqMb6FSty1Vnxx9/8s3wF9fZTD/G/sTJjKTXS0nJeV0+txYqlGQXWH/PFUFofpB6enbV++Qp2q7n1s7xbjeqsX75im3o/DxvJZ1fdyg9vf0i784Kh39V3b8CCqdPIzspibXoGy//4i3+WbbtuPMrIyCA18vqidhoZGbk/vxnpW+skJSVROby+mPfX35gZN155JReddTbvDhwIQP2GDfj7779ZtHAhmZmZjB31LelLlpRco0QKKduL9iiLdqWvj48E2pjZKeHrFGBP4JcC6h8O9N7ywt23/BU4zcz6EBybugSJ4RxgA/BK2DO6ZezROOB1M3sf2HJFX1Acm4BJ7j4fwMymAY2Bsf+yvaVWzVq1eH/IYFKqVePnH3/kthtu5I1B75GVlUXGknRatWnDFdddy6C33ua5J57i9nvu2vFGZRuvvjiAXmecwW759FA8/8rLpKalsWL5cq657HIaNW5M2/3VG7czpgwfybxffuHKxx4CYNzgIbTs0J5qEcmq7DzPzmba2x/Q4eJzd1xZimzCsOH8+dMv3Pz047EOZZe191GHsvdRh/LH2O+Y9ckQOl52Ac0O6czqBYv48v/upVKtmqTu1UxDaItBVlYWM6dP44U33iA5OZnrLruMvZo354AOHbju5pu5+/9uwxKMVq3bsGDB/FiHKyKU8WTTzJoCWUA6wdiVK919WJ46jXdie02AG4D27r7CzF4Hkt0908w6AIcBpwBXAIe6+yVmdiDwH2CqmR2wnTi6AxsjirLI5/iHiW4fgIeefGKbyWJKSq201FzfCmakp1MrLTXfOmm1a5OZmcm6tWtJSUnBzChfvjwAe7doQf36DZj399/s3aIFycnJdD30EAAOOfxwhg4eXHKNKoVS09JyHef0JemkpqblrpOatu1xrpbC7Fmz+GbECJ576inWrlmDJSRQvkJ5TunVi9Sw16N6jRp0PaQ7c2bNVrIJpNSqxYqIb9BXZiwlpVbNber9PPUHvnrnPa587CGSws/yn3N+ZO7M2YwdPIRN6zeQmbmZChWTOe6i/5ZY/GVBxerV+Sei92f98pVUrL61d2jzho2smr+Qb+57DIANq1Yz9rHn6HzdZdRo2mib7cm2qqXWYnn61hEQKzKWUr3Wtl+CzJ4ylSFvvs3NTz9OufBzLIVXsUb1XL2R/yxfQcWIXvu8Gndsz+RX3gIgITGRA87N+T6bYXf0p2rd2gWtGldSU1PJiLy+WJJOap4v8VLTgjpb/u6tDa8vUmunse9+++XcgnNQx078+vPPHNChAx27dqFj1y4AfP7xJyQkKrmX0kf3bJYhZpYKvAA848ENbMOAS82sXLh8LzOrtJ1NDAcuj9hedYLJhtYBq8ysNnBMuKwykOLuQ4FrgX3D8mbu/p273wlkAA3/RRy5uPsAd2/n7u1ilWgCNG/Zkvnz5rFowQI2b97MyK++olN4Et+iU9euDBvyBQDfjhjJfu3bYWasXLGCrKwsABbOX8D8efOoV78+ZkbHLl2YNnUqAFMnT6ZRk+2NdN71bTnOC8PjPOKrr+jcrWuuOp27dWXokKAzfdSIERzQvj1mxvOvvMxHQz7noyGfc9oZp3PO+edzSq9erF+/nnXr1gGwfv16Jk38jqZ7NCvxtpVGuzffi6ULFrJs0WIyN2/mh2++pVXHg3LVmf/rb7z/+FNcdE9fqkRMsHT2/91Mv3cH0vedNzj+4gtpf8ThSjTzUaNpI9YuTmdt+lKyMjP5e+Jk6u3fJmd5+d0qcsILj9Ljifvp8cT91GzWRInmTmrSvDlL5i8gY+EiMjdv5rsRI2nb+eBcdf765VcGPvwYV/W/l6rVC06QpGA1mzVmzeIlrE3PICszk7/GT6LBAfvmqrN60dakacEPM6hSN/iiL3PjRjI3BN8vL5oxG0tMyDWxUDzbu2VL5v8dcX0x/KucJHGLjl268uUX4fXFyJHsH15fdDjoIH7/bS4bNmwgMzOTad9/n3MdsWL5cgDWrF7Npx9+yH969izZhokUgobRln4Vw+GnW3765E3gsXDZywTDUr8PJ97JAE7YzrbuBZ41s1kEvYx3ufvHZvYD8BMwj2CYLEAV4DMzSyboubwuLH/YzPYMy0YA04EZOxlHqZSUlMQ1N97IDVdeRXZWNscefxxNmjXjlRdepHmLFnTq1pVjex7PfXf25YwTTqJK1ar0vf8+AKZ//wOvvvgiSUlJmCVw3a23UDUlBYCLr7qC++7sy9OPPk616tW4pe+dsWxmzCUlJXHtTTdy3RVXkpWVRY+ex9O0WTNeev4FmrdsQZdu3ejRsyf33HEnp/U8gaopVbnr/u3/VMHyZcv4vxtuBCAzK4sjjz4q39lq41FiYiInX3kpL9x8O9nZWRx4zJHUbdyIoa8NZPe996JVx4MYPOAVNq7fwGt3B8e5eloqF93bL7aBlyEJiYnsf24vRj/0VPDTJ906ktKgHrM+HEz1Jo2on+diPa8h1/wfmes3kJ2ZxYIp0+l6y1XbzGQb7xKTEjnr2it57Pqbyc7OovN/jqF+kyZ88vJrNG6+F/t17sT7z73IxvUbeO7O4DaFmrXTuOqB4Bzd//KrWfTX32xcv57rTzqN82++kVYHto9lk0qlhMRE2p1/BiPvfwLPzqbZIZ2o1rA+09//lJpNG9OgXVt+GTaSxbN+JCExkfKVduPgS4MvoDasWsPI/o9jZuxWozodL9e93lskJSVx9U03cuNVwfXFMeH1xasvvMjeEdcX9/ftyxknnkTVqlW5877gs1ulalVOPeMMLjnnXDDjoE4dObhzZwCefvQx5v76KwDnXHgBDRvpCywpfcpqwlgUtqPZQyV2Fq9ZpTcnypKszHbulymTVxY8eYkUj/GL/op1CLu8oxvtFesQ4sKIBb/HOoRd3kXN2uy4khRJ3aopsQ4hHpTaX3UoyNWHJBfp2v7JbzaUuTbrSltERERERESKXVkbRisiIiIiIlLmxOMEQUo2RUREREREoiwe79lUsikiIiIiIhJlSjZFRERERESk2MXjMFpNECQiIiIiIiLFTj2bIiIiIiIiUZYVhz85qWRTREREREQkynTPpoiIiIiIiBQ73bMpIiIiIiIiUgzUsykiIiIiIhJlGkYrIiIiIiIixS4ek00NoxUREREREYmyLPciPYrCzE41s9lmlm1m7bZT72gz+9nMfjOzWyLKm5jZd2H5IDMrX5j9KtkUERERERGJsuzsoj2KaBZwEjC6oApmlgg8CxwDtARON7OW4eIHgcfdfQ9gBXBBYXaqZFNERERERGQX5u4/uvvPO6jWAfjN3X93903Ae0BPMzPgUODDsN4bwAmF2a/u2RQREREREYmyMnDPZn1gXsTr+cCBQE1gpbtnRpTXL8wGlWyWYnWqpFisY9hZZtbH3QfEOo5dWVk8xsdUrhLrEHZKmTzGDZrGOoSdVhaPc1lTFo9xp7RCXb+UGmXxGJdFOs7Rp2McfR/8sKlI1/Zm1gfoE1E0IPI9M7OvgTr5rHqbu39WlH3/WxpGK8Wtz46rSBHpGEefjnHJ0HGOPh3j6NMxLhk6ztGnY1zKufsAd28X8RiQZ/nh7t4qn0dhE80FQMOI1w3CsmVANTNLylO+Q0o2RUREREREZDKwZzjzbHmgNzDY3R34BjglrHcuUKgEVsmmiIiIiIjILszMTjSz+cDBwBdmNiwsr2dmQwHCezKvAIYBPwLvu/vscBM3A9eZ2W8E93C+Upj96p5NKW4a6x99OsbRp2NcMnSco0/HOPp0jEuGjnP06Rjvwtz9E+CTfMoXAsdGvB4KDM2n3u8Es9XuFPMi/kCoiIiIiIiISF4aRisiIiIiIiLFTsmmYGZ1zOw9M5trZlPNbKiZ9TGzITGMaZSZtYvV/qPJzLLMbJqZzTKzz82s2r/cTj0z+3DHNSXimG95NC7i9v40s1rFFN4uyczczN6KeJ1kZhlbzitmdryZ3RI+72dmN4TPd9n/93eGmdU2s3fM7PfwvDzBzE6MdVy7quI+R4gUp4jP52wzm25m15vZdq/hzayxmc0Kn7czs6dKJlqR3HTPZpwzMyMYv/2Gu/cOy/YFjo9pYLu29e7eFsDM3gAuB+7b2Y2EY+xP2WFFgYhjnlf4/4C5e3bJhrTLWwe0MrOK7r4eOIKIadLdfTAwOFbBlWbhZ/JTgvPyGWFZI/Kcl80sKeIHtotz/4nunlXc2y3l4u4cYWZZwEygHJAJDAQe3147wyS8o7u/UyJB5h9DW6BeeF/ZzqzXmGDCk5+B8sBo4LIy8r5GXjekAe8AVYG+hVnZ3acAU6IWnch2qGdTDgE2u/sLWwrcfTowBqhsZh+a2U9m9nb4Bxczu9PMJoc9cwMiykeZ2YNmNsnMfjGzLmH5eWb2sZl9aWa/mtlDW/ZlZkeG39h/b2YfmFnlEm197E0A6gOYWbPwGE01szFm1jyifKKZzTSze81sbVge+a1lspm9Ftb5wcwOCcsLPPbxLDx2P5vZQGAW0NDMbgw/1zPM7K6wXiUz+yL8JnmWmfWK2MyV4ed25pb3SrYxFPhP+Px04N0tC8LP5jMFrWhmCWb2upndG+UYS6NDgU15zst/ufvT4XEbbGYjgRFmVsPMPg0/txPNrA2AmVWOOCfMMLOTw/J8z7kW9NY/aGbfA7eE/xIu2zPydTwo4BzxcHgemLnlXGBmd9vW3tAFZvZaWH5W+Ldwmpm9aGaJYflaM7svPKdMNLPasWtlkMC4+z4EXwYdw46Tl8bAGTuzE9v6u3zFpS0Rk5nspLlh0tYGaAmcUDwh5RaFNudw93SC38O8wgKJ4Wdzy9+vi/OJp7ttHVVS0LnheTObYkHv6V0R6z5gZnPCuo+EZaeG/y9MN7PRYVm+cYT7HmX5XE9KfFCyKa2AqQUs2w+4huCE3BToFJY/4+7t3b0VUBHoEbFOkrt3CNeL/KPVFugFtAZ6mVlDC4Yh3g4c7u77E3zrdl0xtKlMCC8+DmNr784A4Ep3PwC4AXguLH8SeNLdWwPzC9jc5YCHdU4H3jCz5HBZW/Ic++JuSxlQMeKCcMtMbHsCz4UXWnuHrzsQHK8DzKwrcDSw0N33DT/vX0Zsc2n4uX2e4P2Sbb0H9A4/i22A7wq5XhLwNvCru98ereBKsX2A7SV3+wOnuHs34C7gB3dvA/wfQe8UwB3AKndvHS4bWYhz7jJ339/d7wNWWdCDBHA+8Foxta202tE5oh3BuWFf4HDgYTOr6+53hslLd2A58IyZtSA453YKl2UBZ4bbrARMdPd9CXrWLiqJxu3ITiQwDwBdwuN07Q4SjDFmNhiYY8GXR8+FycZwC27XOSWse4CZfWvBF63DzKxuWL7NF9gW/O7f3QR/y6aZWS8z6xbx3v1gZlUK0d5MYDywh5ldFMY/3cw+MrPdwv2/bmYvhAnYL2bWIywvVJuL793JN/7fgUQgDbiA4P/19kB74CIza7Kd1bc5N4Tlt7l7O4JzdTcza2NmNYETgX3Culu+/LsTOCr8HG8ZcbG9OAq6npQ4oGG0sj2T3H0+gJlNI/hGcyxwiJndBOwG1ABmA5+H63wc/js1rL/FCHdfFW5rDtAIqEZw4hkXfslVnqCnb1dXMTye9QmG9Ay3oHehI/BBxBd+FcJ/D2brt6/vAI/ks83OwNMA7v6Tmf0F7BUuy+/YzyvG9pQFuYbIWTCc6i93nxgWHRk+fghfVya40BwDPGpmDwJD3H1MxDYjP+snRS/0ssvdZ4TH+nTymUZ9O14k+G2vnR5evisys2cJ/h/fBDwLDHf35eHizsDJAO4+0sxqmllVgoSo95ZtuPuK8GJ5e+fcQRHPXwbON7PrCBKnnZ7uvozZ0TmiM/BuOLx4iZl9S3BBPTjspXkLeMzdp5rZFcABwOTwOFcE0sPtbAK2zIcwlaBHsVRw99/DL0HTgJ6EiYOZVSD4zHwF3ALc4O5bEq8+BdSD4EuRVu7+R5hYNib4/KUR/O171czKEfzt6unuGRb0GN8H/DfcRpK7dzCzY4G+7n64md0JtHP3K8IYPgcud/dx4d/SDTtqa5hQHkaQNE1y95fC8nsJkqanw6qNCT77zYBvzGwP4JzCtLlwR71YHAm02ZK8AykEf79+KaD+NueG8Olp4fuZBNQleK/mEBzPVyzoGd3y2R0HvG5m77P1b2FBcWyi4OtJiQNKNmU2Bd/3tzHieRaQFPZQPEdwop9nZv2A5HzWySL352ubbQFGcNF0+r8Pv0xa7+5twz92wwh6JV8HVhZ0z1AR5XfsJbincAsD+rv7i3krmdn+BEO27jWzEe5+d7iooM+65DaY4AuS7gQ/Al0Y4wm+1HrU3Xd44bgLmk2YQAK4++UW9EpuuedqXb5r7diOzrmR2/2IYHTKSGCquy/7l/ssywp7nPsB8919S++vEdxve2s+dTf71t+cK83nju0lDoWtNyki6eoMfBDeH7nYzL4Jy/cmGGE1PEzME4FFEdsv6AvsSOOAx8zsbeDjLUlNAZqFyY4Dn7n7/8Ke0XsJvgCvTPB3eYv3w5h/NbPfgeY70eaoMbOmBJ+fdILP25XuPixPncY7sb0mBCN02odfTL0OJLt7ppl1IEjMTwGuAA5190vM7ECC2ySmmtkB24mjO7oOiWsaRisjgQrht1kAWHDPT5cC6m9JLJeG3yAWZYKaiUCn8JvCLffH7bWDdXYZ7v4PcBVwPfAP8IeZnQrBhBQWTNQEwXHacuHZe5sNBcYQDtMKj+HuBJMgSOEMA/5rW+9fq29maWZWD/jH3d8CHib41lp2zqvAXe4+cyfWeYWgJ/R9i+K9T6XYSCDZzC6NKNutgLqR/+93JxjevRoYTvBFFuGy6uzEOTdM8ocRDBPf1YfQFsYYgqGbiWaWCnQFJpnZcQQ9RVdF1B0BnGLBRC5YcF9toxKPeCcVkMC0DR9N3P2r/FbbTr3CJOsGzI5Yv7W7HxmxfIdf6rn7A8CFBD3I42z799DPDfezn7v3C8teB64Ib0O5i9xfoOf9MXqn6G0ukvDz9wLBLU1O8P/ppWEvMWa2l5lV2s4m8js3VCWIfZUF9xEfEy6rDKR4MBnTtQTDyDGzZu7+nbvfCWQADf9FHBInlGzGufBEdSJwuAU/fTIb6A8sLqD+SuAlggkThgGTi7DvDOA84F0zm0EwnCuuJlpx9x+AGQTDDM8ELjCz6QQ9Gz3DatcA14XHaA9gVT6beg5IMLOZBEPhznP3jfnUk3yEFwrvABPCY/ghUIXgPtdJ4Tfhfdl6v4oUkrvPd/ednnLf3R8jGNb8pu1giv9dTXhePoHgvqk/zGwS8AZwcz7V+xHcYzyD4H66c8Pye4HqFk7iARzyL865bwPZQH5JRrz5hOBcPZ3gy4Cb3H0xwT2v9QnPE2Z2t7vPIbg39qvwOA8nGJZYau1EArOG4Ny4RWETjHHAyRbcu1mbYKQDBF+KpprZweH65cxsnx2EmyuGMPGZ6e4PElyT7Ox1RBVgUdiGM/MsOzWMuRnBvYY/E5ukass9xbOBrwn+n9wyic/LBMNdv7dg0sAX2X7PYX7nhukE59ufCP4WjgvrVgGGhJ/jsWy9x/thCyYYmkUwEmX6v4hD4oRtHc0hIqVRONx2vbu7mfUGTnf3njtaT0SkKCz47dMUd78j1rFI8bNtf/rkTYL7TrPDL3juBY4j6MnLIPgC5B+CZKsmQY/gkwXU24/c93YmEHwp2p1gzgADHnT34RZMRPUUwXDUJOAJd3/JzEaF25iyZRi5uzc2sxphDOUIvhzvTDCzfjbBF7X5ftkaDisd4sFkb5HllwI3hbF/B1Rx9/PCoaQbCCaHqgpc5+5DtnNscrVZRAJKNkVKOQt+QuYZgj9qK4H/uvtvMQ1KRHZpFszI2ozg/qylsY5Hyj4zq+zuay2Y4XQSwWy9+Y6iKg3CZHOIu38Y61hEyjJ1b4uUcuEMqPvusKKISDFx9xNjHYPscoaYWTWCWZDvKc2JpogUH/VsioiIiMguwcxaEwwJjrTR3Q+MRTwi8U7JpoiIiIiIiBS7uJrhT0REREREREqGkk0REREREREpdko2RUREREREpNgp2RQREREREZFip2RTREREREREit3/A/fsLAllTFQBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(16, 6))\n",
+    "mask = np.triu(np.ones_like(customers.corr(), dtype=np.bool))\n",
+    "heatmap = sns.heatmap(customers.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
+    "heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16)"
    ]
   },
   {
@@ -94,20 +514,319 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "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>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "      <th>Channel_1</th>\n",
+       "      <th>Channel_2</th>\n",
+       "      <th>Region_1</th>\n",
+       "      <th>Region_2</th>\n",
+       "      <th>Region_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>12669</td>\n",
+       "      <td>9656</td>\n",
+       "      <td>7561</td>\n",
+       "      <td>214</td>\n",
+       "      <td>2674</td>\n",
+       "      <td>1338</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7057</td>\n",
+       "      <td>9810</td>\n",
+       "      <td>9568</td>\n",
+       "      <td>1762</td>\n",
+       "      <td>3293</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6353</td>\n",
+       "      <td>8808</td>\n",
+       "      <td>7684</td>\n",
+       "      <td>2405</td>\n",
+       "      <td>3516</td>\n",
+       "      <td>7844</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13265</td>\n",
+       "      <td>1196</td>\n",
+       "      <td>4221</td>\n",
+       "      <td>6404</td>\n",
+       "      <td>507</td>\n",
+       "      <td>1788</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>22615</td>\n",
+       "      <td>5410</td>\n",
+       "      <td>7198</td>\n",
+       "      <td>3915</td>\n",
+       "      <td>1777</td>\n",
+       "      <td>5185</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen  Channel_1  \\\n",
+       "0  12669  9656     7561     214              2674        1338          0   \n",
+       "1   7057  9810     9568    1762              3293        1776          0   \n",
+       "2   6353  8808     7684    2405              3516        7844          0   \n",
+       "3  13265  1196     4221    6404               507        1788          1   \n",
+       "4  22615  5410     7198    3915              1777        5185          0   \n",
+       "\n",
+       "   Channel_2  Region_1  Region_2  Region_3  \n",
+       "0          1         0         0         1  \n",
+       "1          1         0         0         1  \n",
+       "2          1         0         0         1  \n",
+       "3          0         0         0         1  \n",
+       "4          1         0         0         1  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here# Your code here\n",
+    "categorical_features = ['Channel', 'Region']\n",
+    "continuous_features = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']\n",
+    "\n",
+    "for col in categorical_features:\n",
+    "    dummies = pd.get_dummies(customers[col], prefix=col)\n",
+    "    customers = pd.concat([customers, dummies], axis=1)\n",
+    "    customers.drop(col, axis=1, inplace=True)\n",
+    "    \n",
+    "customers.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Deco\\AppData\\Local\\Temp\\ipykernel_17160\\2084659139.py:4: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  mask = np.triu(np.ones_like(customers.corr(), dtype=np.bool))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Triangle Correlation Heatmap')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your comment here"
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(16, 6))\n",
+    "mask = np.triu(np.ones_like(customers.corr(), dtype=np.bool))\n",
+    "heatmap = sns.heatmap(customers.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
+    "heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "      <th>Channel_1</th>\n",
+       "      <th>Channel_2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>12669</td>\n",
+       "      <td>9656</td>\n",
+       "      <td>7561</td>\n",
+       "      <td>214</td>\n",
+       "      <td>2674</td>\n",
+       "      <td>1338</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7057</td>\n",
+       "      <td>9810</td>\n",
+       "      <td>9568</td>\n",
+       "      <td>1762</td>\n",
+       "      <td>3293</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6353</td>\n",
+       "      <td>8808</td>\n",
+       "      <td>7684</td>\n",
+       "      <td>2405</td>\n",
+       "      <td>3516</td>\n",
+       "      <td>7844</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13265</td>\n",
+       "      <td>1196</td>\n",
+       "      <td>4221</td>\n",
+       "      <td>6404</td>\n",
+       "      <td>507</td>\n",
+       "      <td>1788</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>22615</td>\n",
+       "      <td>5410</td>\n",
+       "      <td>7198</td>\n",
+       "      <td>3915</td>\n",
+       "      <td>1777</td>\n",
+       "      <td>5185</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen  Channel_1  \\\n",
+       "0  12669  9656     7561     214              2674        1338          0   \n",
+       "1   7057  9810     9568    1762              3293        1776          0   \n",
+       "2   6353  8808     7684    2405              3516        7844          0   \n",
+       "3  13265  1196     4221    6404               507        1788          1   \n",
+       "4  22615  5410     7198    3915              1777        5185          0   \n",
+       "\n",
+       "   Channel_2  \n",
+       "0          1  \n",
+       "1          1  \n",
+       "2          1  \n",
+       "3          0  \n",
+       "4          1  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# LÇooking at the new coorelation matrix we can see that the Region doesn't afect our features in a considerable way\n",
+    "customers.drop(columns=['Region_1','Region_2','Region_3'], axis=1, inplace=True)\n",
+    "customers.head()"
    ]
   },
   {
@@ -127,7 +846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +854,10 @@
     "\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(customers)\n",
+    "customers_scale = scaler.transform(customers)"
    ]
   },
   {
@@ -149,13 +871,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "kmeans = KMeans()\n",
+    "kmeans.fit(customers_scale)\n",
+    "customers['labels'] = kmeans.labels_"
    ]
   },
   {
@@ -167,11 +894,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    233\n",
+       "0     96\n",
+       "6     60\n",
+       "3     38\n",
+       "2      7\n",
+       "4      4\n",
+       "5      1\n",
+       "7      1\n",
+       "Name: labels, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "customers['labels'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.21266537608201763"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import silhouette_score\n",
+    "\n",
+    "data = customers.drop(columns=['labels'], axis=1)\n",
+    "target = customers['labels']\n",
+    "\n",
+    "score = silhouette_score(data, target, metric='euclidean')\n",
+    "score"
    ]
   },
   {
@@ -191,7 +964,12 @@
    },
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "dbscan_model = DBSCAN(eps=0.5)\n",
+    "dbscan_model.fit(customers_scale)\n",
+    "customers['labels_DBSCAN'] = dbscan_model.labels_"
    ]
   },
   {
@@ -207,7 +985,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here:\n",
+    "customers['labels_DBSCAN'].value_counts()"
    ]
   },
   {
@@ -238,7 +1017,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "target_kmeans = customers['labels']\n",
+    "score_kmeans = silhouette_score(data, target_kmeans, metric='euclidean')\n",
+    "print(score_kmeans)\n",
+    "\n",
+    "target_dbscan = customers['labels_DBSCAN']\n",
+    "score_dbscan = silhouette_score(data, target_dbscan, metric='euclidean')\n",
+    "print(score_dbscan)"
    ]
   },
   {
@@ -254,7 +1039,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "fig, ax = plt.subplots(1,2, figsize=(14,4))\n",
+    "sns.scatterplot(customers['Detergents_Paper'], customers['Milk'], hue=customers['labels'], data=customers, ax=ax[0])\n",
+    "sns.scatterplot(customers['Detergents_Paper'], customers['Milk'], hue=customers['labels_DBSCAN'], data=customers, ax=ax[1])"
    ]
   },
   {
@@ -270,7 +1058,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:"
+    "# Your code here:\n",
+    "fig, ax = plt.subplots(1,2, figsize=(14,4))\n",
+    "sns.scatterplot(customers['Frozen'], customers['Delicassen'], hue=customers['labels'], data=customers, ax=ax[0])\n",
+    "sns.scatterplot(customers['Frozen'], customers['Delicassen'], hue=customers['labels_DBSCAN'], data=customers, ax=ax[1])"
    ]
   },
   {
@@ -286,7 +1077,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "customers.groupby(['labels','labels_DBSCAN']).mean()"
    ]
   },
   {
@@ -302,9 +1094,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your observations here"
+    "customers.groupby('labels').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "customers.groupby('labels_DBSCAN').mean()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -364,7 +1172,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -378,7 +1186,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,