diff --git a/module-3/unsupervised-learning/your-code/main.ipynb b/module-3/unsupervised-learning/your-code/main.ipynb
index 908ebc0c..a537b8c3 100644
--- a/module-3/unsupervised-learning/your-code/main.ipynb
+++ b/module-3/unsupervised-learning/your-code/main.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,11 +38,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "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"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# loading the data:\n"
+    "# loading the data:\n",
+    "customers = pd.read_csv(\"../data/Wholesale customers data.csv\")\n",
+    "customers.head()"
    ]
   },
   {
@@ -66,20 +173,191 @@
   },
   {
    "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>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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "customers.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Channel               2\n",
+       "Region                3\n",
+       "Fresh               433\n",
+       "Milk                421\n",
+       "Grocery             430\n",
+       "Frozen              426\n",
+       "Detergents_Paper    417\n",
+       "Delicassen          403\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your observations here"
+    "customers.nunique()"
    ]
   },
   {
@@ -93,16 +371,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "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"
+    "# Your code here\n",
+    "customers.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,15 +427,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.44865163,  0.59066829,  0.05293319, ..., -0.58936716,\n",
+       "        -0.04356873, -0.06633906],\n",
+       "       [ 1.44865163,  0.59066829, -0.39130197, ..., -0.27013618,\n",
+       "         0.08640684,  0.08915105],\n",
+       "       [ 1.44865163,  0.59066829, -0.44702926, ..., -0.13753572,\n",
+       "         0.13323164,  2.24329255],\n",
+       "       ...,\n",
+       "       [ 1.44865163,  0.59066829,  0.20032554, ..., -0.54337975,\n",
+       "         2.51121768,  0.12145607],\n",
+       "       [-0.69029709,  0.59066829, -0.13538389, ..., -0.41944059,\n",
+       "        -0.56977032,  0.21304614],\n",
+       "       [-0.69029709,  0.59066829, -0.72930698, ..., -0.62009417,\n",
+       "        -0.50488752, -0.52286938]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your import here:\n",
     "\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "scaler = StandardScaler()\n",
+    "customers_scale = scaler.fit_transform(customers)\n",
+    "customers_scale"
    ]
   },
   {
@@ -148,13 +475,226 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
-   "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",
+       "      <th>labels</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",
+       "      <td>1</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",
+       "      <td>1</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",
+       "      <td>1</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",
+       "      <td>2</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",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>29703</td>\n",
+       "      <td>12051</td>\n",
+       "      <td>16027</td>\n",
+       "      <td>13135</td>\n",
+       "      <td>182</td>\n",
+       "      <td>2204</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>39228</td>\n",
+       "      <td>1431</td>\n",
+       "      <td>764</td>\n",
+       "      <td>4510</td>\n",
+       "      <td>93</td>\n",
+       "      <td>2346</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>14531</td>\n",
+       "      <td>15488</td>\n",
+       "      <td>30243</td>\n",
+       "      <td>437</td>\n",
+       "      <td>14841</td>\n",
+       "      <td>1867</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10290</td>\n",
+       "      <td>1981</td>\n",
+       "      <td>2232</td>\n",
+       "      <td>1038</td>\n",
+       "      <td>168</td>\n",
+       "      <td>2125</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2787</td>\n",
+       "      <td>1698</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>65</td>\n",
+       "      <td>477</td>\n",
+       "      <td>52</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>440 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n",
+       "0          2       3  12669   9656     7561     214              2674   \n",
+       "1          2       3   7057   9810     9568    1762              3293   \n",
+       "2          2       3   6353   8808     7684    2405              3516   \n",
+       "3          1       3  13265   1196     4221    6404               507   \n",
+       "4          2       3  22615   5410     7198    3915              1777   \n",
+       "..       ...     ...    ...    ...      ...     ...               ...   \n",
+       "435        1       3  29703  12051    16027   13135               182   \n",
+       "436        1       3  39228   1431      764    4510                93   \n",
+       "437        2       3  14531  15488    30243     437             14841   \n",
+       "438        1       3  10290   1981     2232    1038               168   \n",
+       "439        1       3   2787   1698     2510      65               477   \n",
+       "\n",
+       "     Delicassen  labels  \n",
+       "0          1338       1  \n",
+       "1          1776       1  \n",
+       "2          7844       1  \n",
+       "3          1788       2  \n",
+       "4          5185       1  \n",
+       "..          ...     ...  \n",
+       "435        2204       6  \n",
+       "436        2346       6  \n",
+       "437        1867       4  \n",
+       "438        2125       2  \n",
+       "439          52       2  \n",
+       "\n",
+       "[440 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "# Your code here:\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "kmeans = KMeans()\n",
+    "kmeans.fit(customers_scale)\n",
+    "\n",
+    "customers['labels'] = kmeans.labels_\n",
+    "customers"
    ]
   },
   {
@@ -166,11 +706,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    176\n",
+       "1     96\n",
+       "0     87\n",
+       "6     39\n",
+       "4     35\n",
+       "3      4\n",
+       "7      2\n",
+       "5      1\n",
+       "Name: labels, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "customers['labels'].value_counts()"
    ]
   },
   {
@@ -184,13 +744,144 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "scrolled": true
    },
-   "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",
+       "      <th>labels</th>\n",
+       "      <th>labels_DBSCAN</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",
+       "      <td>1</td>\n",
+       "      <td>-1</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",
+       "      <td>1</td>\n",
+       "      <td>-1</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",
+       "      <td>1</td>\n",
+       "      <td>-1</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",
+       "      <td>2</td>\n",
+       "      <td>1</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",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  \\\n",
+       "0        2       3  12669  9656     7561     214              2674   \n",
+       "1        2       3   7057  9810     9568    1762              3293   \n",
+       "2        2       3   6353  8808     7684    2405              3516   \n",
+       "3        1       3  13265  1196     4221    6404               507   \n",
+       "4        2       3  22615  5410     7198    3915              1777   \n",
+       "\n",
+       "   Delicassen  labels  labels_DBSCAN  \n",
+       "0        1338       1             -1  \n",
+       "1        1776       1             -1  \n",
+       "2        7844       1             -1  \n",
+       "3        1788       2              1  \n",
+       "4        5185       1             -1  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "dbscan = DBSCAN(eps=0.5)\n",
+    "dbscan.fit(customers_scale)\n",
+    "\n",
+    "customers['labels_DBSCAN'] = dbscan.labels_\n",
+    "customers.head()"
    ]
   },
   {
@@ -202,11 +893,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1    255\n",
+       " 1    130\n",
+       " 5     22\n",
+       " 4      7\n",
+       " 6      6\n",
+       " 2      5\n",
+       " 7      5\n",
+       " 3      5\n",
+       " 0      5\n",
+       "Name: labels_DBSCAN, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "customers['labels_DBSCAN'].value_counts()"
    ]
   },
   {
@@ -233,11 +945,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 7.5))\n",
+    "axes[0].scatter(x=customers['Detergents_Paper'], \n",
+    "                y=customers['Milk'], \n",
+    "                c=customers['labels'])\n",
+    "axes[1].scatter(x=customers['Detergents_Paper'], \n",
+    "                y=customers['Milk'], \n",
+    "                c=customers['labels_DBSCAN'])\n",
+    "fig.show()"
    ]
   },
   {
@@ -249,11 +982,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 7.5))\n",
+    "axes[0].scatter(x=customers['Grocery'], \n",
+    "                y=customers['Fresh'], \n",
+    "                c=customers['labels'])\n",
+    "axes[1].scatter(x=customers['Grocery'], \n",
+    "                y=customers['Fresh'], \n",
+    "                c=customers['labels_DBSCAN'])\n",
+    "fig.show()"
    ]
   },
   {
@@ -265,11 +1019,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4PTNPQUREpFNSfhQRkc4s1IZj+gF3Z1YGc4BHrLXPGWM+AB4xxlwALAfOBLDWzjbGPALMAVLAD621XuZa3wfuArKBFzJfAHcA9xpjFpK+UzllR3RORERkJ1J+FBGRTsvsqjcGi4uL7bRp0zo6DBERaQfGmOnW2uKOjmNXoRwpItI1tJYft2mOnoiIiIiIiHR+KvREREREREQCRoWeiIiIiIhIwKjQExERERERCRgVeiIiIiIiIgGjQk9ERERERCRgVOiJiIiIiIgEjAo9ERERERGRgFGhJyIiIiIiEjAq9ERERERERAJGhZ6IiIiIiEjAqNATEREREREJGBV6IiIiIiIiAaNCT0REREREJGBU6ImIiIiIiASMCj0REREREZGAUaEnIiIiIiISMCr0REREREREAkaFnoiIiIiISMCo0BMREREREQkYFXoiIiIiIiIBo0JPREREREQkYFToiYiIiIiIBIwKPRERERERkYBRoSciIiIiIhIwKvREREREREQCRoWeiIiIiIhIwKjQExERERERCRgVeiIiIiIiIgGjQk9ERERERCRgVOiJiIiIiIgEjAo9ERERERGRgFGhJyIiIiIiEjAq9ERERERERAJGhZ6IiIiIiEjAqNATEREREREJGBV6IiIiIiIiAaNCT0REREREJGBU6ImIiIiIiASMCj0REREREZGAUaEnIiIiIiISMCr0REREREREAkaFnoiIiIiISMCo0BMREREREQkYFXoiIiIiIiIBo0JPREREREQkYFToiYiIiIiIBIwKPRERERERkYBRoSciIiIiIhIwKvREREREREQCRoWeiIiIiIhIwKjQExERERERCRgVeiIiIiIiIgGjQk9ERERERCRgVOiJiIiIiIgEjAo9ERERERGRgFGhJyIiIiIiEjAq9ERERERERAJGhZ6IiIiIiEjAqNATEREREREJGBV6IiIiIiIiAaNCT0REREREJGBU6ImIiIiIiASMCj0REREREZGAUaEnIiIiIiISMCr0REREREREAkaFnoiIiIiISMCo0BMREREREQmYrRZ6xphBxpg3jDFzjTGzjTE/ybQXGmNeMcYsyHwvaHDO5caYhcaYecaYYxu072OMmZV572ZjjMm0R40xD2faPzLGDN0JfRUREdlhlB9FRKQza8sTvRTwM2vt7sBk4IfGmLHAZcBr1tpRwGuZP5N5bwowDjgOuMUY42au9S/gQmBU5uu4TPsFQIW1diTwN+C6HdA3ERGRnUn5UUREOq2tFnrW2lJr7SeZ11XAXGAAcDJwd+awu4FTMq9PBh6y1sattUuAhcAkY0w/IN9a+4G11gL3bHHOpms9Bhy56W6miIhIZ6T8KCIindk2zdHLDBnZG/gI6GOtLYV0sgN6Zw4bAKxocNrKTNuAzOst2xudY61NAZVAz22JTUREpKMoP4qISGfT5kLPGJMHPA5cbK3d2NqhzbTZVtpbO2fLGC40xkwzxkwrKyvbWsgiIiI7XWfIj5k4lCNFRKRemwo9Y0yYdBK731r7RKZ5TWa4CZnvazPtK4FBDU4fCJRk2gc2097oHGNMCOgOlG8Zh7X2VmttsbW2uKioqC2hi4iI7DSdJT+CcqSIiDTWllU3DXAHMNdae0ODt54Bzs28Phd4ukH7lMxKYcNITyqfmhm+UmWMmZy55jlbnLPpWmcAr2fmKYiIiHRKyo8iItKZhdpwzIHAt4BZxpgZmbYrgD8BjxhjLgCWA2cCWGtnG2MeAeaQXpHsh9ZaL3Pe94G7gGzghcwXpBPlvcaYhaTvVE75ct0SERHZ6ZQfRUSk0zK76o3B4uJiO23atI4OQ0RE2oExZrq1trij49hVKEeKiHQNreXHbVp1U0RERERERDo/FXoiIiIiIiIBo0JPREREREQkYFToiYiIiIiIBIwKPRERERERkYBRoSciIiIiIhIwKvREREREREQCRoWeiIiIiIhIwKjQExERERERCRgVeiIiIiIiIgGjQk9ERERERCRgVOiJiIiIiIgEjAo9ERERERGRgFGhJyIiIiIiEjAq9ERERERERAJGhZ6IiIiIiEjAqNATEREREREJGBV6IiIiIiIiARPq6AC6itdXLuS6T95kadUGBuTm88u9D+W4IaM7OiwREZEOVVddxx1XPMBr972Dl/LY/6Ri/u8v51DYt6CjQxMR2aWp0GsHr61cyA/feoqYlwJg8cZyLnn3WeJeipOHj+vg6ERERDqGtZZfHPk7Fn+2nGQ8CcBbj3zAZ2/P4c4vbiYrJ9rBEYqI7Lo0dLMd/GHaG/VF3iZ1XorrPn2rgyISERHpeLPemcvyuavqizwAL+VRXVHDmw+/34GRiYjs+lTotYNlVRXNtpfUbMTz/XaORkREpHNYMms5ntc0D8Zq4iz4ZFEHRCQiEhwq9NpB39xuzbb3ysrBdfRXICIiXdOAUf1wQ03zYFZOlCG7D+qAiEREgkNVRjv46V4Hk+02ng6Z7Ya5aM8DOygiERGRjjfxqD3o2a8AN+TWtxnHEMkOc+TZB3VgZCIiuz4Veu3gtBHjuWrS0fTKysExhoJoNpdOPJRvjZ7Y0aGJiIh0GMdxuOHta5j81X1wwy6O67DHwbtz0/t/ILd7bkeHJyKyS9Oqm+3k66P24msj9yTupYi6IYwxHR2SiIhIhyvo3Z2rHv8FXsrD933CkXBHhyQiEggq9NqRMYaskBKYiIjIltyQi4u79QNFRKRNNHRTREREREQkYFToiYiIiIiIBIwKPRERERERkYBRoSciIiIiIhIwKvREREREREQCRoWeiIiIiIhIwKjQa2fWWmJeJSk/3tGhiIiIdCqJeJLKdRux1nZ0KCIiuzzto9eOSmo/5oM1f6I2VQbGMCT3MPbvcylhJ4fa1Do8myAv1E+bqYuISJeSiCe55eL/8srdb2GtJb+wGz/8+wUcfNp+JBNJ1ixbR4+ifPJ65HZ0qCIiuwwVeu2kIr6I10suxbOxdIOFZTVvUrOqDN8mKE8sxGDIcgs4uO9v6ZO9V8cGLCIi0k7+9t1/887jH5KIJQFYX1rBdefczJz3j+H521/D9y1e0uPg0/fjp7d9j2h2tIMjFhHp/DR0s53MrngQ3yYatfk2ydrYDNbHv8C3CTwbpya1mldXXUJNqqyDIhUREWk/G8ureOvRD4jXNc6R8doET9z4P2o31hGrjpGMJ3n3iY/424X/6aBIRUR2LSr02kllchkWv9n3tmz3rceCymfbIywREZEOtb6kgnCk+QFGvt94rl4iluTtxz6kprKmPUITEdmlqdBrJ72z9sQh3KZjfZJUJ1ft5IhEREQ6Xr/hffC85m+ENscNOVSuq9qJEYmIBIMKvXYytmAKISeKafAjd4hgcJscGzJZ9M3Zpz3DExER6RBZOVGmXHYK0ZzN8+6MAdd1ME7TxcnckEvvwb3aM0QRkV2SCr12khsq4sRB/2Vw3qFEnHzyQgMoLvoRg/MOxTVZ9cc5JkxOqIhheUd2YLQiIiLt5+wrT+eif36HQaP7k1eQy6QTJnLVk78kKzcLp0GxF82JcOH13yIU1lpyIiJbY3bVvWqKi4vttGnTOjqML823HvMqn2R+5ZOk/DhDux3JHgXfJOJ26+jQREQ6DWPMdGttcUfHsasISo5cOb+Ee69+lM/f/YKigT35xhWnsd8JEzs6LBGRTqO1/KhbYh3MMS679ziD3Xuc0dGhiIiIdCoDd+vP5ff9pKPDEBHZJWnopoiIiIiISMCo0BMREREREQkYFXoiIiIiIiIBo0JPREREREQkYFToiYiIiIiIBIwKPRERERERkYBRoSciIiIiIhIwKvREREREREQCRoWeiIiIiIhIwKjQExERERERCRgVeiIiIiIiIgGjQk9ERERERCRgVOiJiIiIiIgEjAo9ERERERGRgFGhJyIiIiIiEjAq9ERERERERAJGhZ6IiIiIiEjAqNATEREREREJGBV6IiIiIiIiAaNCT0REREREJGBU6ImIiIiIiASMCj0REREREZGAUaEnIiIiIiISMCr0REREREREAkaFnoiIiIiISMBstdAzxvzXGLPWGPN5g7ZCY8wrxpgFme8FDd673Biz0BgzzxhzbIP2fYwxszLv3WyMMZn2qDHm4Uz7R8aYoTu4jyIiIjuFcqSIiHRWbXmidxdw3BZtlwGvWWtHAa9l/owxZiwwBRiXOecWY4ybOedfwIXAqMzXpmteAFRYa0cCfwOu297OiIiItLO7UI4UEZFOaKuFnrX2baB8i+aTgbszr+8GTmnQ/pC1Nm6tXQIsBCYZY/oB+dbaD6y1Frhni3M2Xesx4MhNdzJFREQ6M+VIERHprLZ3jl4fa20pQOZ770z7AGBFg+NWZtoGZF5v2d7oHGttCqgEem5nXCIiIh1NOVJERDrcjl6Mpbm7jLaV9tbOaXpxYy40xkwzxkwrKyvbzhBFREQ6hHKkiIi0m+0t9NZkhpqQ+b42074SGNTguIFASaZ9YDPtjc4xxoSA7jQdBgOAtfZWa22xtba4qKhoO0MXERHZqZQjRUSkw21vofcMcG7m9bnA0w3ap2RWCRtGekL51MzQlSpjzOTM3IJztjhn07XOAF7PzFEQERHZFSlHiohIhwtt7QBjzIPAYUAvY8xK4LfAn4BHjDEXAMuBMwGstbONMY8Ac4AU8ENrrZe51PdJr06WDbyQ+QK4A7jXGLOQ9F3KKTukZyIiIjuZcqSIiHRWZle9MVhcXGynTZvW0WGIiEg7MMZMt9YWd3QcuwrlSBGRrqG1/LijF2MRERERERGRDqZCT0REREREJGC2OkdPZEeriC+mOllKQXQEeeG+HR2OiIhIpxCvi/P5u1/guA7jDxpDOBLu6JBEZBemQk/aTcKr5rWSX7A+/gWOCeHbJEPyjuDAPlfiGLejwxMREekwHzw7jT9+8ybSC6+C4zpc9cQv2OvQcR0cmYjsqjR0U9rNB2v/xLrYHDwbJ+nX4NkEy6rfZE7FQx0dmoiISIcpW7mea7/xN+qqYtRurKN2Yx3VFTX8+qt/omZjbUeHJyK7KBV60i5Sfpzl1e/gk2zU7tkYX1Q+1kFRiYiIdLzXH3wX3/ObtFss7z7xUQdEJCJBoEJP2oVvE0DzW3kkfd2tFBGRrquqvJpkPNWk3Uv61FQqR4rI9lGhJ+0i4nYjL9y/mXcc+ufs1+7xiIiIdBb7HjuBrNxok3bHMUw8as8OiEhEgkCFXhtVbazjthtf4ltfuYELTruZx+9/Hy/ldXRYu5QD+lxOyGRhMmsAuSZC1Mljn17f7+DIRETky5j55mx+dvhvmTLw//jVV//Igk8Wd3RIu5Q9Dx3LPsfs1ajYy8qNctQ5hzJ03KAOjExEdmXG2uaH03V2xcXFdtq0ae3yWfFYku9NuYWy1ZUkk+niLpoVpviAkfzm+intEkNQbEysZO6GR6lMLKV39h6M6X46WaGCjg5LRDo5Y8x0a21xR8exq2jPHPneU1P54zdvIl6bAMAYiGRH+POrv2Xs5N3aJYYg8H2fdx7/iFfvews35HLceUew34kT61fhFBFpTmv5UdsrtMFbL39O+bqq+iIP0sXfx+8tYOnCNQwd2acDo9u15EcGsl/vSzo6DBER2QGstdxy8Z31RV66DeK1CW79xb3c+M41HRjdrsVxHA49c38OPXP/jg5FRAJCQzfb4LPpS4nVJZu0O8Ywb05JB0QkIiLS8WK1cdatKm/2vYWfLmnnaEREpCEVem3Qb2AB4UjTh5/GMfTqnd8BEYmIiHS8aHaEaHak2fcK+nRv52hERKQhFXptcNwpE3HdxmPkHceQ3yOHCfsO66CoREREOpbjOJz84+OJ5jQu9rJyopx1xWkdFJWIiIAKvTbpWZTPH285l74DCohEQ4TDLmPGD+Qvt52P6+pHKCIiXde3r/46J3z3KCLZEbJyo2TlZXHWladx3PlHdHRoIiJdmlbd3AbWWsrWbCQScelRmLfN565aUIrvWwaN7q9VtEREtoFW3dw2HZEj62pibFhTSc8BhUSi4W06t7aqjpJFq+k9qBf5PbvtpAhFRIJHq27uIMYYevfd9jkHC2cs4eoz/kr56goMhu5F+fz6kZ8yet+ROyFKERGR9pedm0X28KxtOsday3+vfIAnbnyeUMQllUhx6NcO4JJb/49wZNuKRRERaUzjDneyuuo6fnHE7yhdvIZ4bYJYbZw1y8r45VFXU72hpqPDExER6TDP/uslnrr5BRKxBLUb60jEkrz96Afc9sv7Ojo0EZFdngq9neydxz8ilfKatHuezxsPvdfm62ysizFvdRnV8cTWDxYREdkFPHz9M8Rq443a4nUJnr/9VbxmcmdzrLWsXFDKyvkl7KrTUUREdgYN3dyC5/lM/2AhSxetZeCQXux30CjckLvd1ytfvYFkrOkefPHaOOWrK7Z6fsrzufr513l65hxCrovn+Zw9aS9+dtTBOI7m+YmISPspX13Bu09MJZVIsd9XJjJgZL8vdb2N66uabU8lUiRiCbLzsls9f+GMJVxz5l9ZX7oBSG/p8JtHf8aoicO/VFwiIkGgQg+YXb6GV1cswK/1mHn9DDaUVZOIp4hEQ3QvyOXGO79DQc9tW3xlk/EHjiYcDTW5M5mVl8X4A8ds9fwbX3+PZz6bSzzlEc9c4/6PZ1LULZdv77/PdsUkIiLSFvG6OG8/+iEr5q2idmMdL9zxGsYYfN9yxxX3M+XyU/nWr8/c7uvvvt8oPn1tVpP23oOLyMptfb5fbVV6akTDaRCrl6zlF0f8jvuX3UJu99ztjktEJAi6/NDNa6e9zukv3MtNM9/jkVvepmRFOXW1CTzPp642QdnqSm76w7Pbff1xB45h/IFjiOZE69uiORFGTRzO3kfu0eq51loemDqTWDLVqD2WTPHf96dvd0wiIiJbs3bFOs4Z+WP+/qPbefCPT/L0P18kEUsSr0uQjCdJxJI8fN1TLPx0yXZ/xoXXf4us3ChOZqsiY9I58sf/uGCrq1O//diHpLbIjwCe5/HWIx9sd0wiIkHRpZ/ofVpWwn3zPiXmpRNF3oIUxm98jOf5TH1nPr7v4zjbXhcbY7jm2ct47j+v8OIdr2Ot5ZhvH8ZXv3/sVq+X8n3qkk2HfQJsqI1tcywiIiJtdeP3bmXD2kp8z2/xmGQsyRsPvsvIvYdt12eMnDCMf358Hff//nHmT1vIoDEDOOuK0xgzadRWzy0vrSDRzNSIWG2c9aVbnxohIhJ0XbrQe37ZXGJegyTRwhxuay1fZn53KBzilB8dzyk/On6bzgu7LkN7FrBkfdOENa5/nyZtsVQFi6peoi61nr45E+ifMxnHbP/8QhER6Zp832f6yzNbLfIArE3fEP0yBo8ZwOX3XbTN5407cDSRrDCxmsaLuWTlZjFui6kR1lo+e3sOH784g9z8bI48+2B6Dy76UnGLiHR2XbrQc4yDwWAzFV7VCJf8BV6jp3qOY5g4eQSuu/np29rVldz9r9eY9v5CcvOyOPWsyZx4evF2PfHbml+fcDg/ePAZ4qkUFnCMIRpyufzYQxsdt6ZuJq+u+ikWH8/GmVf5BIXRURwz4GZcJ7LD4xIRkWDb2tBJgEh2hEO/dkCjtrce/YAH/vA45aUV7D55N86/9iyGjhu0w+Pb85Cx7D55N+Z8MI94bXpF6mh2hN0mDmfvI8bXH+f7PtdOuZGpL3xCrCZOKBLivt8/zmX3/JiDT5+8w+MSEeksuvQcvZOGjSXibn7iVXZImGSewc/s0ZqVE6F7YS4/ufKk+mM2VNTwo7P/zesvfMaG8hpWLV/PbTe+zD+u+99OifGAEUO497wzOWL0CAYX9uCYsaN4+DvfYM+BfeuPsdbnrdJfkbJ1eDZ9ZzNl61gfn8e8yid3SlwiIq2xXgl+9T/xK6/Gxl7H2rYtlS+dg+M47PeVic2uOm0cg+M6RLMjfOV7R7P7fpuHWT5x03Ncf94/WTxzGRvWbuTDZ6fz4/2vYNnclTs8RmMM1/7vcs6/9iyG7TGYoeMHcd7vp/DHl37VqEh9/+mPmfrip/VP/lKJFIm6BH/+9j+oq9E0CBFpX77v89H/pvOPi+7g3mseZc2ysp32WWZX3XOmuLjYTps27Utf5++fvcc/Zn0A1mKMwaYsFzp7ULgxxKChRRx85FiiWeH64+/59+s8cve7JBON/9ESjrjc/cwl9Czq9qVj2lYV8UU8v+JCUrauyXuF0d346uC72j0mEem6bPxNbMVFgAckgRwIj8cU/hdjtm+EgTFmurW2eEfGGWQ7IkdWrNnARQdcSeW6jSTqkkSywxT06cER3zgIx3XY/6RiRk7YPDcvEU9yRtH51FU3Lp6MYzj49Mn8+uGffql4ttfVZ/6Fdx7/qEl7Tn42V9z/E/Y7UStYi0j7SCVTXHHCH5j70QJi1TFCkRCu63DFgxdzwEn7btc1W8uPXXroJsCP9zyQk4aN5bWVCwk7LscO3o3e2S1vpTDrk2VNijyASCTE4vmrO6TQM8alpQmGBs3RE5H2Y20Cu+FnQMN/7NdCahbUPQk5X++o0GQbFfTpwV3zbuaj/33CyvklDBk3iOJj98J1m88ra5evazYTWd8y98P5OzfYVrjhlv+p43yJfXJFRLbVa/e/w9wP5zcaYZACrjvn7zy65g4i0XDrF9hGXb7QAxjSrYDzd09X0fM3lPHc0rl0C0c5dvBu5Eca7+MzYHBPPp+xDN9rnM5SSY8+/XtQsqKcVSvWM2hoL/r2L2iX+LuHh5DlFlKdKmnU7posdut+crvEICICQPJzoJnFOWwdtu4ZjAq9XYobcjng5HR+TMSTvPvEVMpWrGPMfqMYd8DoRkMkC3rnN9kzdpM+Q3uTSqaY/f48rG8Zd+BowpEd+w+alhxz7mF8+Oy0Jou2GGPY67Bx7RKDiAjAq/e93eR30SZzP5zPXofu2N9JKvQyrLVc+eFLPLH4c3xrCTkOv536CnceeSb79RnMp/NW8s+H3mHB8jKSw/KIrqnDj3qsOyBMrJdDjxVhvnnN/XhVSbrVeLgVSfY/eDcuvfZ0wq3cTWxoUeV6/vHZ+8xcX8qw/EJ+tMcB7F3Uf6vnGWM4vP+feGnlj/BtCt8mMcZlQM5+jMw/8cv+aERE2s6EaHEJ4+0ctikdb+WCUi45+NfE6+Ik4ylC4RBjJo3k2uevAOCBax/n+dtfw/oW4xrsFjdDl36+nBOyz8JxHMLREK7rcuVDF7PvcXu36fN93+fVe9/mmVteIlYb47CvHcBpF3+FnG7ZWz23+Ji9OO78I3j+9tfAWtyQi7WWq574xQ6/ey4i0poWf+dYCEd2fFnW5efobfLayoX8+O2nqU013pPHNYbRXk8q36omldp8l9pYS+WYFHX9fQo/DhOqNRg/c2fTt4QrkxSUJznlG5O54MdHb/Xz51as5fQX7iXupfCsxQBRN8S/Dj2VwweOaFMfUn6c5TVvEUtV0Dt7T3pl7d7m/ouI7AjW+tiyQ8Bfu8U72Zge12Gyjtuu62qO3rbZ0Tny+8W/ZNGnS2n4bwbHdSjs2wPP86iuqCEZz2xebmix1m8omhPlnoV/p7Dv1ke//PU7/+LNh9+rvxMeyQrTb3gfbpl2HZGstt1AWP7FKqa/PJOc/GwOOnUSud1z23SeiMiO8t5TU/nTt25u8lSvoE93Hlz5nxaHxremtfzYpVfdbOi+GdNhgUfefJdwualPUp61lE7b0KjIA7DGkLs4RNZaF7euQZEH4BiS3cPUWZ/nn2hbov3DtNepTSXxMknUAjEvxa8/epm2FuMhJ8rwbscwtuDrKvJEpEMY42AK/gMmH0wukJX+yv4qRI/t6PBkO6wrWc+SWcub5CLf81m3qpyK1ZWbizxoU5EHYH2fNx96f6vHrVpYyusPvNPoH0aJWJI1y8p48+Gtn7/J4DEDOPWiEzj224eryBORDnHAyfty9DmHEsmOEM2JkN0ti9zuOVzz7OXbVeRtjYZuAu/OWMzcx0rIsy74kL3SIVHgUznBAwOh6ub3EnJSEFlncLxm3rfgZbvU1STaFMMn60qabV9dW0VVMt5krqCISGdlwuOg97sQfxP8CohMwoTaNjJBOpdUMsW1U27ES+747TES8SQby6u2etycD+ZntnloPOImVhNn+qufccy5h+3w2EREdgZjDBf987ucetEJzHhjNvmFeUz+6j5Es6M75fO6fKGXSKb4zS3PYz2LIV2wGQ8iFQ5Zqy2xfj5elsWpaVrMWQe8bLDGYmzT903KssfeQ9oUR2E0m5pk06Iw5DhkhzSHQER2LcZkwXYO05TO46U732DBJ0t2yrWzcqIUH7PXVo8r7NsDmtm8PRRx6T2o106ITERk5xo0egCDRg/Y6Z/TJQu9lO/zVslilmwsx1kPzY2MdDxDVqkh1g9qRnjkf25wGgzPDIddqoYkqevrkbvcaTxUxVoc39INh+///Pg2xfR/4yZz7bTXqfM237HMckN8beSehB0t/ywiIu1jXUk5Hzz9MdbCS3e9Qby2+RXiWmMcgzEG32tmBVYgFHbZ55i9GHfgmK1ea8IR48nrkUOsJob1NydbNxTihO8euc2xiYh0FV2u0Curq+GMF+9lXV0tCd8ja4NDdpL6p3kNRdY7FL3hEOvrUzXKo9sSl7BniIbDnHX8PoR2D3PDjHepneiRPRNC1sF4EDUOJ04Yzjk3Hkrvvt3bFNfZu02gtGYjd8z9mJDjkPQ8jh8yml8Vty2Jxb2NzKt8klU1H5Ib6sPYgq/RK2vsNv1sRESka3v+9lf550X/xTjpuerxWNumH2zihlyGjh/EhX/+Fg9d9xSz3p6D7/n41hKOhEklU/QZUsT5136DQ87cv9H2DC1e03X56xu/46rTrmfVglIc1yGaE+XSe35Mv2F92hTXnA/m8eTNz7O+pILJX92HEy88mtz8nG3qm4jIrqbLrbp54RuP8/rKRaRs5i6jD0VvhXFSWyYbC5nizxqLl+dz5Lc+4MT+h3DQoItwnfQ6Nknfo6yuhvxwlNVrNhKNhBjUZ/v3z6tOxlletYF+OfkUZG192WiAmLeBZ5d/m7hXiWfjgME1EQ7ofTnD84/Z7lhERDoLrbq5bbYnR65dsY7zRl9EIpbc+sHNcCMud869qVHxVVVRTbwuQSjsUrZiPf1H9v1SBVbp4jXEauMMGTsQx2nbenLP3/4qt1x8F4m6ONZCNDtCYb8C/jX9Oi3KIiK7vNbyY5d6ouf5fuMiD8CBDRNS9Pg01GBRlc1FHoCxhuy4T2FViH49htcXeQBhx6V/bj4AIwcVbVM81bVx/vP4+7zy4TwMcNyBu/Pd0/ZnbGHb7lBuMrviQWKpCvz6ieoWz8b5sOwvDOl2OK7RHD8REWndu49/1OxUhraKRMMUDezZqK1bQR7dMvc+exS1bYTLJjPe+Jw7f/Ugy+auZMCIvnz7milt3ndvk1htnH9dclej4afxugTrS8p5+p8vctYVp2/T9UREdiVdansFC9hm1n1OFljWHdDwDmbToSS+51BRVsiwblvfE68tUp7Pd69+iCdfn0nFxlrKN9by6Csz+N61j+D725ZpV9S806DI28xan8rE0h0Sr4iIBJvn+W3ezqc5X//FyYTCO+b+8bSXZ/Krr/yROR/Mp2ZDLfOnL+Z3p/+Fd5/8aJuus2jGUhy36T91ErEk7z398Q6JVUSks+pShV7IcTig7xCcLecE+BAtb32egOsajh79HcJO24ZTbs17ny6mdN1Gkg3250umPFasrmDq7GXbdK0sp0ez7ZYUEafblwlTRES6iANOLsZxtj5nrjm53XM468od93Ts1l/cQ7yu8fzAeF2C//zsnm26TrfCPLxU81tD9CjK3+74RER2BV2q0AP4w/7HURjNJiezZUFOKEzv7DzGVPYk6jhk1fk40GiCuOsYevco4PCJE7d6/XhdnM/fncuSWctavTP6xdI11MWbPoWLJ1LMX7Z2m/q0e8HXCZnG++wZXLr5uzH3tVI+efUzkontm3MhIiJdw4CR/fjmb84kmh3BcZ30oifZEfY4ZHeimc19jWOaPCGL5kT5v7+e06aFVUoXr2HmW7O3un/e8i9WNdu+eunaFgu35gweM4ABI/s1G/NBp03m/ac/ZuX85vexFRHZ1XWpOXoAg/J68Pap3+O5ZV+wcMN6xhb25vA+w7l/2es89/o0QmEXP+JS1TNMItvFOIYD9xrGZecf1WhuXnNevudN/v6jO3Cc9JLSRQN7cu3/rqDf8KZz7gb07kF2NNyk2ItGQvTfxnkMQ/IOpaLgbGZV3ItLGB+Pstf78ejPanHcvwHgOA5XPfkL9jp0XP151lrWLCsjFHbpNaBnS5cXEZEu4huXncoBJxXz1qMfYK3lkDP2JxFL8oezbmTtinVk52YRr4sTjobwkh7denbj29dM4fjzW18humZjLVeddj1zPphPOBIiGU9yykUn8J0/nt1sgVjYt4CyFeuatOf3zMtsnt521zx7GVccfy1rlpXhui6JeIKiQT35x4/vIBR2SSU9Jhw+jt8+9nMiWZH68+pqYqxfVU6vgT3Jytk5mxmLiOxMXW7VzS19+PY8/nD5o8SbWWUsr3s29z3/U7Ib/OJvyYJPFnPJIb8mXrt5qIlxDH2GFHHPwn80SWR1sSSn/PR2Kqvr6ie/O8ZQkJ/DU3+7gMh2zHOIextZH59HzSqHiyfcQGKLYS/ZeVk8uPI/5Obn8MXUBfzh7JtYX1KB9S1Dxg3k1w//lP4j+m7z54qI7GxadXPb7IgcmYgn+f3XbuCj/01vMnc8kh3mG5edxtm/Or1NT/KuOv16pj7/Ccl4qr4tKzfKj26+gGPPO7zJ8S/c8Rr//MmdjRZRieZEOffqr3HmT0/a5r5Ya1n82TIq11Xx1qPv8+o9bzVaXTSSFebE/zuaH/ztPHzf57Zf3ssz/3oZ13XwfcvpF5/It6+Z0qa+ioi0p9byY5cburlJWUU1/33sfa7+xUPNFnkAfspn1rSlbbreM/98keQW17G+ZcOaSv79s7t58c43qKqorn8vOyvMbb+ZwviR/Qi5Bsex9Btcztcu+IBltS9u14T4qJtP/5x9mfb48hY3qX3/qY/ZUFbJL4+6mtJFa0jUJUjGkyyasZRLDvkNqWSq2fNERKRr8H2f6a/M5NKjr2bqi582u0BYoi7J87e92qbCp6ayho/+17jIA4jVxLnnd4/wxI3/Y+ZbsxvlvePOP4Lzr51Cbvcc3FB6dA3AnA/ms3JB6Tb3yRjDiL2GMvHIPXj9/nebbCGRiCV58b+vA3D/7x/n2X+/QqIuQV11jHhtnMdv/B9P3Pi/bf5cEZGO1OWGbgK8/MEX/P7WFwkvrcZN+c2ssZkWI8Xdr3/Ae/4q6j6pZPE7K+hekMPp3zyQAw4bA6Sfos2peIS5S95qNhnGauM8888XCUVC/OPHd/Dbx3/OvsdOAKBfr3wu/79i3l53EZ6tIxxNP4GbWnYDVcmVTOz1ve3qX3VFNalE04LNS3lUb6jhlXvewtuiELS+JVYdY+rzn3LAyftu1+eKiMiuLV4X55dHXc3CmUtJ1La+WfqGskr+8eM7yO6WxZwP5lO+egN7HzGeb1x+Wv02C7Pemcs9v3sEL9n8vLq1K9Zx+2X34UZCDBs/iD+/+luycqIYYzjqm4dSungtL9z+GvE6j3htnPeenMqnr87iPzP+Qp8h27al0SaJFjaB3zQi5/Ebn2v0JDH9XpxHrn+a0y/5ynZ9pohIR+hyhV5VTYzf3/YSoUVVuDWpFos8C3g1HgueXsyCpxdTOc4h2c9Q9ME6Fs4t5evnH8xJ35jAzQ/9lKWfhonnOLhRDy/e9IqppEcqk+SuOfOvPLL6dp56Zza3Pv4++x7xMaMnbCTsbi4SUzbGnA0PMb7gbCLutq+aOen4iTxzy0vEahonKoxhn2P24plbXmwyrDMdZ4qyleu3+fNERCQYHv3rs8yfvrjZm4VbSsZTPP3PFxu1lS5aw5sPv89/ZvyFNx9+j9svvx8/1fwIEwAsJBMpkokUi2Ys5b6rH+W0i0/kj2ffzOfvzq3PnfWH+5ZYTZyHrnuSn9xy4Xb1cdwBo5n1ztwm7eMPGoPv+9RsqG32vMr1rS8gIyLS2XS5Qu/9z5YSqvMI1bZe5G35XvfZPusOMfQ9YQ0D+5QzrWQhr//fIEoX9SMVd4Ge+M5SnEgcP9HyUBbjGP77jxd4eN5yYokUvQesxXWbPgl0TJjKxDKKssdvcx/3OGR3dj+iH5+/tpRkJl9l5UY59tuHM3jMAPY4aHdevutN6qpjW8TmsPvkUdv8eTuLtZa1sZmUx+eTF+rPgNzJOKbL/ScrItJuXrn7rTYVeS3xUh5VFdX85pTrWPjJkm06NxFL8so9b/H+M9MoWViK10KB6KU8Pn/ni+2O8Vu/OYMrTvxjeqqChVDYJZId4cf/+A6O4zBwdH9Wzmu6EufwPYds92fuDNUbanj/6Y+J1cQpPnYvzbEXkSa65L+aTU2SZJ5h/b4h8pb65C7zcDI3DVuaGRfOSnHOSVPJ7RMjEvZIJRyst4g3/jCRdQt6ACHMqOE41WX07LaG5NoebCitbnKdhOfxygdzieWlV/CqLM+jsHclWy7o6dskOeGmq3U2uV4yRVlFNT2755IVTW8Z8UHZdex5/XTyXoIlz+Tghh32npLH9755DgAHnjqJ+699nFULSuvnKURzIkw4fBy77TNiq5/ZHlJ+jJdX/YSK+EJ8m8I1YSJuPscP+g+5oe0briNtY20SbALj5HZ0KCKyK7K0WuS5YTe9RUIzCTdWE6euOtZikQdgDAwY1baipmJtJcl4kqKBPTHGULpkDVd/7Qas9es/37eW7//t2wwdNwiAH950Pled+uf6ffyMgUh2hO/f8O02fWZ7mPbyTK467fr6Vb7/83PL6Zd8hfOvPaujQwu8uuo6wtEwoe1YNE+kvXW5xVj233Motd0tS78ZZeO4ECVfCVNyfISNIxxihS7WNH2aB7D7V5fSrXcdkXC6IgxFfMLZHgf8eBabsoVxHZw+Pdn/eo/v/PMU/GjTH28ikWLtwM3LNH/6/mi8VOOloh0ToV/2vq0WNNZa7nzmI475/r84+4p7OeYH/+LG+9+kPLaUJVUv45kYQ46Lcdgt5Rx80zp6HLCcVbXvp2MPh7jx3d9zxs++Sr/hfRg0ZgDfvnoKVz3xi238ae48M8vvZH1sHilbh0+SpK2lJrWW91Zf09GhBZa1dfiVV2DXTMSuLcYvOxabmNrRYYlIOzrqnEN2+meMnDC0xfl1vu+nK6tWRLIifP3SU1s9Zu3yMi468ErOHvI9zhvzE87d7cfM+XA+d/3mYWora/GSmwtJP+Vz568eTH82UHzMXvz51d+wz9F7UjSoJ/seP5G/vnk1exy8+zb2dOeI1ca5+oy/EK9NF8XxugSJWJInbnqez99tOiRVdoxZ78zl/LEXc2rheZyUfw7Xn/9P6mpiWz9RpAN1udsR+blZxCeGiJb5hGoMqTxLzVBDzQiX0EYY8FSKaEWySbE39MDVhCJN7zBmdU+QWxSjpiwbAN+D0hfzePXexxl8cA29941Tu9pl8XM5xDaEWHveYHqMjcL76flza0sKeeWJ/Tj0hE+I5iSJhB2G5B7G/n0ubbUfT78xi7ue/ohYgyE2T77+GTV2MUMmNzNP0NaxqmYqg/MOpbaqjlt+8l9ef/BdUkmPMZNGMfGoPTv87pS1PiuqprIu/hnzKh/HZ8t5hD6r62aQ9OsIO9kdEmOQ2Q0XQ/x9IDO301uCLf8O9HoCExrZkaGJSDsZf9Du+N2y8foXYg2ESitwKpufs7ZdDPi+Zc3SsmbfTiU9DM0v3OKGXLoXdeOif36X3fdreZqB53lccuhvWLeyvH4F6tJFa7jsmGuIZEWaXTitekMt60sqKBrYk09fn8U/fnwHK75YRVZuFsP2GMzICUO3va872IaySl574F3mfjC/vihtKFEX56W732T8QZ2jIA2SFfNWcfnx19Yv0uOlPN546D0qVm/gD89f2cHRibSsyxV6a8qrcD43dPNcHM/gu5a8hS7lk5KkulkqRxl6f+hjHYPJlHsW8FLNP/w0BvxUprAyPpHacmbfZTn0tmXkD0sSzgUvDnteXMX9H+9LVagnE4t6sXTaGuKJFBZYOr8/JUsGctrRu3HSoXszol+/rfbjkbdeZf/jPqbfoHVUbchl+ntjKFnam5ff3Mh392saq0OYnFB6FbQrT/wD8z5eWL/U9dwP53PJIb/mv3Nvome/gm3/oe4Asxas4Ne330dpSQQ35DFmr9048JjPCIW3TGY2PeRGdijrrWpc5NVLYmvuwHT/Y0eEJSLt7PGXPiV+2DhwDGDwRvXDXbSayJyVO+T60ewICz9teVhnKOyy12HjmPHG7Pp/VLshh7weeXz3z9/k4DMmk5PX+o2+6a98RsWayibbDHkpr8VxTNbzye2ew4JPFvPrk/5UvwJnXXWMp/7+PFXrq7jk1u1bCfvL8jyPv5x/C6/e+3arx1nLl5pfKS177IbnSMYbb8mRjCWZ+eZsShevod/wrU+1EekIXW7o5vV3vYaTMDheujhzPIMTh27zXDCGyr0hsedaeg9cRVb3WkxWikRWgrnP5ZKqM1gL6xZ0Z+X0XtRuCLOxJA/i+WTlhCjslYdZWcuwM8vpsVu6yANwoxDJsnx98iecOmImJ+w2net/tTsH7j2Mwu455OdG8X149o3FnPerR7n4+ieIxZvf2w9gY2IFh5/5FKP3XEaPnjUMGrGWE6e8x8hxy0kkLXjRJucY4zAi/wQWzVzKgk+WNNnPKBlP8ey/X25ynmeTlNV9Tnl8wXbt7dcWK9ds4Id/eozSkihg8FIhvpg5jJcem7xlL+gZ3Y2Iq7ljO1xqBZhIM294kFrY7uGISPtbvbaSdxethZALjpMu9kIu3oi++Pk5WKj/aqtIdoSs3CihSIiJR+2BMQbbzBO1TXzPZ+Q+w/nG5acwcHR/uvfqhhtyScQS3HLxnXy9/4W8/dgHrX7m4zc812RfW0gv9jJot/5k5TbOkeFoiP1PKianWzb3X/t4k1Wp47UJXr3v7UZ74W5StnI9n787lw1lla3G9GX865K7tlrkQXrRtcOnHLTT4ujKls1Z0ez+xOFomNLFazogIpG26VJP9Ky1vP/Z0ibDMg2GrDUO3e+to9vYGN3HZDN09CqGjPoCx8D0P+az8OZcevXOZ+6b44lVRsBY/JTL8afvwUWXj6Fbj2ymLS3hiVemM/LMWtxm/s2cHUqxV9EyYv4yFtjpnHb2UYx46yAeevFTUp5PKpNcps9dwU0PvMWl5x3V5Bqrl67ludmXERqRarSASzjicfBxM6hZO5bjh97IG6WXEvcqMRiMcTm471Xkhfsyff4HuG7T+j4ZT7J45tJGbSuq3+XdNVdjrY/FkuUWcGT/6+kRHbatP/pWPfjidJIpj4b3HbyUy8olfdhYkUN+QS0hk4VjwhzY99c79LMlIzQS7JZP8wBCEN673cMRkfb33rRFOK6TnoOwiW8xFTXUHNuXxJBsct9P4FTGCC1Zi1Pb3O+MzaLZEb5/03lkZUfJH1bEjb96qOm2P1tIxJI8+uenCYVD/OGFK7nihGszi4ZtLtz+fO4/GLXPcPoNa/wUxVrLC3e8xqevzWr22m7Y5fgLjmTFF6t4/Kb/EYmGScaT7HnoWH52xw8AWDZ7Jc3d0wxFQqxZVka3grxMnAn+cPZNTH3hUyLRMIlYkuPOP5wf/f0CnC1XV/sSaqvqeP62V1s+IPMPmqycKAecMol9j5uwwz5bNtt98m7M+3hRkyemiXiSwWMHdlBUIlvXpQo9SN+gbH70P8T75BIvz2VdeS+WLehH/yHrOGHKe+xz+UZWvpHF1Lv3xGRFwGz+Jf7CE3N5ft06qvMNyZI4eRGXcG7zQwsbzi9P2TqWVL3C46/lEk9u8Ysj6fG/d+bwy28fiWlw0uLPlnHxwb/ihOeXEW68fgsAoYjH987YnXWzDAf3/g/h/hvwbJKe0d3qtyUYOm4gqVTTn0AkK8yYSZvnYW1MrOSt1b/Ga/CP/+pUjJdW/Ygzhz29Q7c5WLC8DN9vmhhd16OyvAd7Dz6UXlnjGN7tGCJu3g77XNnMuL2w2adA3bNA3aZWMFmY3PM6MDIRaS+u6+A0yDnOuioiH83HugZ78FhC88LYPnl4vXy84X2IfDgft2xji9eLxxL888f/pXBAAWtCLvGcKBGaX/CsoUQsSSKW5I/fvKnZkSSe5/HKPW9xzm+/1qj9xu/fyit3v9ni6JNQOMSY/UYyZOxATrnoBEoXr6FoYM9GC8OM3HsoqxaWNnnqmEqk6Desd/2fb7nkTj5+4VOSsWT908OX736L/iP6csZPv7qVHrbdulXl6eK7Bb0H9eLQrx3A/l8tZvxBYxr9m0F2nNMvPpEXbn8NL+nV//cVzYlw2JQD6dW/sIOjE2lZlyr0jDEcXjyKlz+aBxasY6nr55Ps7pM/N4Sxm39BppJhli/oy0OXH0X/ojK6719HbNHmIs8C8cIIiYII/spaYj0d4oVg9h1EdUkJWYUtD73cxLNx6uLNj6dPpFL41uI2+KV9y8V3UlcVo67MJbt302Jy0QO5PHXjv3FDLqlEit0n78ZvHvsZTtbmv+YhYwex16Fjmfnm7PqtFYxjiOZEOeG7m58gLtz4LL7dsiC0JP06Smo/YmDugc3GPX/ZWu585iMWr1zP6KG9+fZJ+zF8QM9Wfw7jRvTl80UleFt8nOe5DOgb4cA+v1Lyagcm/3dYdxjU3g22CiKTMN0uxbhbnzMqIru+gyeN4qb/vp7+QzJF5P25GM+SHDsQsiKwqeDIfI8dNpqavSqIflZJ3gvluFVb/BK36dEiaxavxQKRsAsmPZesLb/R1y5fRzizbVBDqYTHxvWNh1Eu/2IVr97zVpNpCQ1l50X5/j6XEgq7WN/yvRvOZfyBYxodc/avzuDDZ6cTa/C0MpoT5SsXHkVu9/S0AS/l8crdb9Xn0E3itXEe/9tzLRZ6qWSK5297jRf/+xrWwjHnHspXvncM4UjTPm5SNKhni2NlHdfh21dP4ehzDm3xfNkxeg3oyT8++iP/+cW9zHxzNrn52Zzy4+M542c7rqgX2Rm63By9n51zBKksH9/4rN8vSdVuXvpuZXM/CWOoSeawalpv1i4f1OinVTMoh1jvLPyoC65DtBLySi0Vh/Vhxhsj8NswH9ohxOiRzc2LgrHD+uJmhn/UVse45YZnmPn2HAA+v7UbydrGabL07Txm/i2feG2C2o11JGJJZr/3Bdd87YYm177qiV9w8o+OJ68gl3A0zH4nTOQfH/2RHkXd64+pTa3H0rQTno1RFpvTbMzT567gu1c/xBsfL2BpSTmvfDCP8357P3MWr2715zDl2H3IikRomM1CIY+RY9Zw8u5XqchrJ8a4OHkX4PR+G6fPpzgF/8GEhnd0WCLSTnoW5HL4qD7g+YRmLQMv/TvZH9Bzc5HXgLGG1IBsqk7tzZqbRpHq2XLBYgCSHl5h2+dYO67T7NO5aE6U/U6cWP/npUtncPc/riZa2PLqoMYYqsqrSdSlc2RddYxbLr6Tz95unM+GjhvEX964inEHjCYUCVHYtwfnXvU1LvzLOfXHJGIJUsnmxwaVl1Y0G7O1lt+c/Gdu/eW9LPhkCQs/XcIdVzzA5cdd2+r89+zcLE67+EQiWU1/tnseOpYjztacvPYycLf+XPP0pTxTeQ8PrvgPX//lKbhuM8OrRDqRLlfo1WyoJW9xNcmCOF42EALrtvBL1rcY36YTlO+w6R5kKsshlRvKrEqWZiwYD0yOxyeFo1m1rAjfa71AMcbh5EMnNPvesAHpoQBvPvwep/Y6jycuf6B+KMnyF7P57O/pYi9RbfDihkV39SVZt8VQk6THnPfnsW7V+kbtkawIF/75Wzy5/i6er3uAa565jP4jGm8+2z9nUotxl9RMa7b9L3e/TiyRqp/f4FtLLJ7ib/e92eK1APr07Mbtv5nCpPFDCIcc8nINJx3Th/9cchWF0d1aPVdERHacJUMqCH08i9CydZufum053KK+3WJDQMTgd3PZeHbv5o/LMIC7vqZNcYTCLpNP3Iduew7FNigyresQ757DoInDSPl1vLrwfKbWnM/wb0/jtMdXcMRfVoPTOBeGs8KZTdobj4SJ1yZ4/G/PNfns0fuO5MZ3f88LsQd5uOQ2zvz5SY3m3WXnZdOjKL/ZuC0w+70vmrTPfn8es96ZU7+S6KbPnzdtUYtzCjc57/ff4Dt/OpvCfgW4IYfeg3vx09u+x/Wv/laFhoi0qksN3QS48eqniVZYaocYyPx+TBS2PI7ESfn4jsHxLdYBY31S2c0/hXM8Q3iDoWq0zyNv7M+Pz/kfEbf5BFm7thsjzHd58OVFuG4Kz2v8V/HqR/M5+8Bx/Pm8f+InUk3Cm/vfbsy/P4+CUXDQ8Ufgl88HVjX5nFA4xIayjfTayvDJLfXJ3ov0D2VzwkwmXZYv6MMiL8nxAy1Og0I35fks2aKgrI91SetP9ACGD+zF3y89Y5tiFBGRHacmleDjVCl9ymKNco67aA2pPYekV+PcxPcxdQncckhlA44htnc3bMhgUltZl9MYrLUtDt90Qw69hxaxz9f35+2HPyDljCS0vAw8izeoJ86Q3jz54kwmHfs65fGZfPKvAj67qwdewpBV4DH0iBqWvpqezx3OCnPZ3T/ir9/9T7NbD6xb2Xze2prdJ4/ivac+bvqGtUx7eWaTvew+f/eLJsvzA8SqY3z+7hdMPGrPFj/LGMOpF53IqReduF2xikjX1eUKvdmzVhIviDS6Q4gLG/ZO0ePTEFhwGuSCWM8sYr2yyC6pI1SXZP1Yl+zS5tOT71i8HAsu1AwB12ma7OJVYd7+656UzSvECc/AAAd/bSFF+1Ty5nP7UFa6eVLvM7e+QrKZxLTpqtYPMWa3Yr579Xe5PXEfJQtXNxlOkvJjvB/+Hu98HmLhzN2pW70/Zx2/L8VjB7f6c8pyC3CIkPLilJflE6uL8vm0ESyeO4Bw2Cda/gpXfueYzT9Cx5CVFaaumSWtu+VktfpZIiLS8d4onYezMYlJpfPMpkwXWlaGLczDG9QLfJsexmkMRMPkv+PgdbNsPLoOp9ojOaIf4UWrMakWFiUDbON7iE34vqVk0Rpu+sZNOEDEMaSG9ia1x2BwHDzfZ+Hy1fSrfoFpf+/BrHt7kKrLTHUoC7Hi3RwAcvKzueL+n7DnoWPxkk1zqRtyWb10LSflf4tIVphuhXkccsb+nHbxiXTv1fwTu032PnIPpr44o8k2DtbCo395hkPO2J/hew6pby/o051IVoS66lij46M5EQr69mj1s0REtlfXG7rZwyXWJ4vslS4Np6Ale1jKDk5SPXxThjPpoZlu+ntd/2xS2YZ1+4dI5KWfANotM5WBuv7p5OZHoGRpT/wtct07f9uTsnkFYMFPuHgJl1mPjiC5OsTJ57xNXn56joHjGFLVsXRS3YIhHd9hl5/Cbx/7OZFomCmXnkJej1xCkc21eyjbsvelFdhIFU60gmETPiI88HF+9teneOK1ma3+nFwnwu4FZ7BozgieuPNwnrn3UBbPHQgYkkmXl96fy4o1FZtjMoYzj9qL3v1q6F5YxaYsnhUJ8Y3j92n1s0REpOOVlpTR60/LgcaDXAwQ+XQJkddmYdZWZlZTSe+xZzyDu9GQ+2GE3P+tx62uwTtkSOObqVvyW36aZyE9TSFzjAGMbwktWUv4k/RG65Gwy9iRRaRiqUZF3iZeLP3n+5b+i/1O3IfsvGy+fc0Uojmb989zXAfPSy/qUlcdo3JdFSvnl/LI9c9w4V4/p3Jdy6uJAhx59iHNzpuD9Kqht/7inkZth5wxGeM07bXjOhz29QNa/SwRke3VpQq92niCWM8scAxZqx2yS5z0XgspMClwEpC73G1xGGd1P58B/yohWhojUlKDW5PEYrGOxYv61OVXkrMwSdYqh+zFST75RZhUjcHLDMmvWh1l3YLumduZm3nxEHOfG4LjeowrXkRWJMS5X53E/l8tbnlZZdcwf9FqvvqTW/nu1Q8xd3UFt372V0758fEMGz+Y0Uf25Ih/b2DU1zfPhwhHPHbbYzlOpIqbH3ybWKLp07dVC0v547du5qwh3+Oek1ZSOnssqWTTZOY4Dp/NL6n/89q6WfTe9wZOO/9Vvnbhq3zzxy/Rp38VJx+2B988obj1vxgREelwC/76Ic5Gr1EKtKTnxVnH4PcrwPbr0WRhFuMbIitC5Ly8nqxcn6zyGqJFLk5o8xbr+T2TjDrMgxE98Yryt2nTdQBjLe6q9ZhEkkgkxKnHTiJUOxzb/INDnLDLD3/3CGd871ZuuP1Vjjj/CH77+M/Z5+g9GTp+ULroaiaIVDJFVXkVj93QdO6etZZX7n2L7038Bf834edMOHx8i/HO+WB+/WvP87j98vtJxpL1i4u5IYdeAwu57uXf1O/NJyKyo3WpoZsl6zfUF3EGQ/68ELnLLMnuPk7CEK4wGEzzhZ4BN5lHavfujdaizF5ZS8qrI9U7n9y67rA6hbtqPd6gnqyL9+bZE3xGn11Nzz2SrJzanXg4TN2gHPyIg5PwyV4bI1ydom5DlFDIMnzgCso/6sH705YROnMyRYN6smZpWdN4LHyxsQYfn7Xl1Vzx9+f46bcO58I/f4uSq0bz7ppriHlNVyDzPIeefSopL+nOohXrGDdi89L5JYtW84PiS4nVxPE9n7IV60kmh+Lu3m/T4mubfxwGemaWmo55G3hl1SWkbC2OC44L4Ug1U777Pl8bfln9XD5rLevic4l7lRRljSPqtj40RkRE2s+SdxZjtvhdnxw3CLIjeL26QXa0+RNJL0iWOG4iCccBx4GBgzHVtXSb+QWjzunJzFVDKPMNY/YuI2xSfPbZUKLvzME02A7BhhySuw/EG1yUzrklFYQ/X47ZNIXBwFEHzWHfM6uYW/sFY4edhht5rP5maqO4u+eyeHl6/t0zL3/G2x8u4N6bzuOK4hE89KcnWT636Zz2+nPjKaY+/wkX/OGsRu23XXovz/7r5fpN39etKm/xGvk9u9W/fvCPT/LSnW82morhhlyOO/9Idt9vVH1bVUU1cz9cQLfCPMZMGqkVp0XkS+tShV6v3FzsFnWcGzO4sTasWmUMRNzGu54Dsf45QM7mFTijYbzBvXBLNxAvHkbN/6qY8bf09RODCokdlFd/rJ/lUjMwh9zSavrvtY5kwmHJUyHWzFkJwMoFpXg5EWzIgZSPAfzsCF5hLjl5dYzaZzbhgRFWLe3DyiW9+fuDb9FzzLMsq36VlG08D2ATx7VUbcgllfIp6JbT6L37rnmsvsir//l8UYo3snejSfjGQE5WhH3Hp+f5Lal6GdvMNvQWj+U1bzG82zFUJUt4ZdXF1KXWY4yDb5PsVXgBexR+a+s/exER2emi2ZFGc/Ms4A0pIrS8jMj0xdjcKKkRfbH5Oc1fIBLenCNdsPm5bDxgAtOXuuniD5i7tIjc7CQ9+yconziC6Afz6j8rftDu6Wtnnhh6A3vi9+pG9NXPML7FdXxGHjWHmPWJ1S1jTdXH9J3Qh5UfRvGTBgv4PfOwrotJpAh/uiQd8+BeVLkO9979Fq/+5iHidY3zXHMK+xc0+vOGskqe+vuLjRZU8T0f4xgcxzRa0TMrJ8qZP9+8v9qTN/2v0WqbkB7e+dTNz3PuVelN3x/5yzPc/ZuHCEVCWN/SvSifP730KwaM1D6mIrL9ulSh1z0/BxtJYuLhxgXbpvkGm143zHQNj2vu7ppppj3k4vXrAY5DzWGjyZ62lNDqSuITBjfakgFIr1TWJ5sxX11GrDLEjBnjiJ3RGxyH0KoKop8tJ7nHQJzSSrzBPUkOK2LQ8NUcd9ZcjPFxQ5Y9Ji2idHkRn30wjiVV7+LTzO1NIJVyWLuqkOoN3Rk7ojf9e3dv9P6sd+Y2SX5OTZweU5fgHzWOpOfjW8uAou78+ZKTNu/zl1qHZ5t+pm+T1KXKsdby6qqfUpVcRXqn+vT7n5XfSc/oaPrntryVg4iItI/jzjuCu377cKMUGH39c0wyhfF87Dpwl68jUTwCb0BhegTMJs3lR9dJ57wG71kc4kmHcMjB75NHYtwgwgtL8bvlYPOyGw8LdR1sJIzXv5BQaTmDv2F56r6jWFtSQG5ejElHfM6Rf1vCnZOG4XXLJnHgaHBcIh8vwKmqgw014BhCX6wisd8oXrnxOWo21NRvVdSSaE6UMy75SqO2xZ8tJ5IVbrJypvUteT27UbuxjnA0RCqR4qQfHcdJPziu/pjqDc3v71dTWYu1ls/emsM9Vz1CIpas34Q9VhvnihP+wF3zbtaTPRHZbl2q0DPGsHe4B1+sXE+iMIp1N43j3CJZ+ZboujhexCHVPdw0gVlLuCqFk/DSx3Rr5pjMqmS2ew61h4wm+vlKbLfmV5/0Qi4li3rwxoN7kRycV//0LDW4J16ffKKfLiU1uJDUsF44ETj66x8Tjmx+ghaJevQfUob15+KTaHJ9a8H6hhULBvLO8/sydngf/vyTk5oc13twL1YvWduk3S3ZwEPXfpN1tQmyoiEG9Wl8p7NP9l58seFxUrauUbufggUv1pF78ExqU2VsOSEiZWPMrXxUhZ6ISCcw5fJTuPO3D2++zwkQT9QP5zQWsJbI1IUkR/YhNXoAJtJ0DrepieOUpoc1+v0KsLmNc18yFaaiKgTG4I3om35quHhN0xuhAGEXr1c3Rn9lPV+UF5Nanv5ny8YNebz57D5UV2SRP7iKNWPGQFYYd/k6nPJqzKabln56jmDk44XU+LbVIi+SHcFxDBf84Sz2OXqvRu/1GlDY7PYMjmPY97gJ/N9fzmHdynL6De9NbvfGm8KPmDCUBdMXNzm3sF8Ppr/yGc/95+UmT/ysbylfvYGFny5h1MThLcYsItKaLlXoARyyzxiWTHuD7HUJ4vlh6vpnN52T5xr8qENuSR01rkkXchkm6dNtSXVmM3XAAT8cp3po3ubCERoXfmGX+B6DoC4BOc3NcTC8eu9E/PzsxvsUOQYbckn2K8DvmS4Aew9Yh7PlJArSC60MGFSLQwifxnccvVSIcXkXUrz7EXxnvxwG9unR7M9mymWnMu/jRY0STiQrzP4nFVPQqzsFzZ4F/XMmUxAdQXl8Qf2TvVSdYc1HIR695DWsfZkDr48y4Oi6JufGvcoWrioiIu0pFAoRyg7j16VziDUGY5tf+Tm8cA2hleXEj9u7Ub5zF5YSnr1i88GzV5AcPxhvRN9mrkLmpij4hXmZrRuaxuUP68O6iEsq2fjNVDLM9PfGkZ01N507jcFdvm5zkdeQTW/CnmzmPTfkcNvnN1C1vpphew4hO7fpTdnBYwYwYsJQ5k9fRCqx+UZrOCvM6Zd8hR5F3elR1L3JeQA/vOl8Lj3mGhKxRKNCc8PaSq4+4y/YZn7GkC4iqze0bYN5EZHmdKlVNwEWlq7FWIgVRJov8gCsxXgWYyF3RS25S6sxsfQvdrfOy2ygvmnZZ3DiPllrmp8TV8/3CS0vh+SWc9kseH6z2ygAEHYxvg8hJxNaC4vFAP0LBjfZziF9juWBR3z2GNW/xSIPYN9jJ/DDm84jt3sOWblRwtEwB5wyiZ//94etds0xLscO+DsTen6XHG8QFXMjTP9TPm/+oIB4bZxEXYq3f55DYmPjwF0TZXDeYa1eW0RE2o9fl8RGQsQOHYvfPbvF4wzgJDyyP19DaH1dOoelPExmnzjj2/qv8OfLMTWbbiA2k+scB79bNiaRpFESsxaspbBoA+Vl+bSU/MZ9N7b5ui3kx2gkxH4nTGz2PWuhYnUlY/cf3WyRt8k1z17G3ofvQTgaIpoTpUfv7lzxwMWMnDCsxXMAxh0wmpvfv5aDT9uPrNxofYxeyqeuOkYilmx2hW0v6TFm0shWry0i0pouV+jNWbCKVG56L70t5w7UsxCtyAyBdKCuXzY2nP5RpbqFqB6eR6Lb5oehBohsTNQnpWYZg1tRAylv83GZ+YCmJk5kbknz56U83A21hFZVgOezdlVhk7uaANaLMCD8VWa+fQippEsiFiIeC5FMuLz8+GQ+mV1Bxcbm5wk0dPwFR/LY2jv496fX80jpbVz5wMVkNfsUsjHXiTK+4CzW3Xo8z5/WmwUP5WG9zT/bUChM6Vvd2JThqsoLWbt0N3rao7d6bRER2fkWfroECyQmjcJ2z8Ub2a/1/fAALy9MqiAr/as95JIaP5j44ePTi4g14JaU03gCfGMmkcJZU5nOyQ1yqXEtY/dZQq++Fc2eZ61h/CErMaH0db2hvZuNORx2OfHCo3Dcpp/vez7P3PJiq/0EyC/sxh9euJKHVt7KbZ/9lYdLbuWAk/bd6nkAw/ccwhUPXEwynmpS6/qej7WWaE5kc7zRECf98Dgi2RFERLZXlyr0aqpjlM0po64oq8UnefiW7NI63Hj6rmKsMIofdtIbp0P9Rup1/bOb3pc0LRSOvsXEk8SLh0F2ZPNxmS+bGyU1qBCnJpZ+utfgPDxLZPE6op+txCRS2KTPCw8fQCIWIhEPkUo5JBMu8z/vx09+vYi5MwZw1w1f4fVni3n96X25869fZdmCfriOoTbWdP5ec0LhEANG9iOvR+7WD96Cl/Kav2FLiHH559A/fCwv3nciD//nSJ57ZA++9rMH+NN/X8HfyuR4ERHZud598iP8blnpYZSugzewJ4k9h+BnphHYLebQWSAxuKDxTdOQi82JkhrWp5lPaOFxW8qDugTe4F5N86Pv8OFre7DHpEWEwo3nyIXCKfacPJ9uPeo46PhPMdbD61eA17+wfu8/6zpY1yGRSHHrz+8l0sIWEVXl1W3+OeX37Ea/4X1wnG37J5S1tsVhmlk5US788zmMnDAUN+zihFz+959X+Hr/C5nz4fxmzxER2ZpOU+gZY44zxswzxiw0xly2Mz7DdR1iBSH87KbbJADgW3KX1xCt3DzHLZkfbnaCuMXgZWWGUwKJ/KYT0tNvWkwiRWhlRfOfCeA6pAb3JPuVOYRWltcP5XTXVZH78qz0nc5YkrznZhCdu5I1C/K4+28n8M4Le/Pha3vwxJ2H89rT+1IXT1G+sZZEPMziuQNZMm8AqWT6yWO33Cj9ejU/f2BHOuxrBxBt5g6kl/I48uQTeet/E1mxPI9E0lJTlyCR8njhvbk89uqMnR6biMiuqj1yZG1NDG9gz805zxj8ob2Jf2Uf4sUjSBSPaHS8HdafZou3kIs3oLBRk9evhVne1uIsXoMtym88R73huZ5D5fo8TjjrXfILqjHGJxJNUHzIHA45cQYA+x4xj6//8FVG7rGC5MRhxA8ZS3LcIJIThhI7Zi/ijmH5vJXEqptOs4jmRDn49P1b/dnsCKFwiD0PHVu/t+wmbsjlwFMnsf9JxayYX4KX9IjXxKmtqqOybCOXH/d76qqbznEXEdmaTrEYizHGBf4JHA2sBD42xjxjrZ2zIz8nKzuC32AT02YCIVTXeA6d2XKn8Po3AB+sAT/kECtqeXhjdOpiUkN61s+za+mznUSKnHcXYDNbNpgtnnKZhEfW3BISQ/uQTEaZ99mQlq/X6NKGX33n2CbJZWcYd+AYjj3vcF688w0SsQSu6+C4Dj/6+wVk5efw5rRFJFONf8axRIqHXvqErx2z906PT0RkV9NeOTLcuzveqP7NrCLtYnt3x1m0enObY3Ai4RYf0pHy6nNZcuxAbF4Lc9+SHuEFpcSbLNaymbXpYm/kuFWMGLsKL+Xguj5mi5Tab/A69ihewMI5Q7A9cvE2jUrxfEzKw0s2v3dez/4FHHPuoS1+/o7009u+x0X7X0msNk6sOkZ2Xhb5vbpx4fXn8NKdrze7v5/1Le8/PY0jzz64XWIUkeDoFIUeMAlYaK1dDGCMeQg4GdihSQyAsNPCvDxLdE2sfhnpTTMJouVxarPdRk/1rLUYa0nlh4lnuSS7tfBjtDadxFZWQE6EVP8CCDe3pJglVFKxeUnrzBLWDeMAsK4hPDqnxW0aWmKwTBgzYJvO2V7GGH709ws4+tzDeP/pqUSyIhw+5UD6j+jLhqqW70hW1TS/95+IiLRPjly0sS69GEpz8/KsJbygtMGfweBikj42skVezYxkSe0+CG9AITYnmr7ulkMdrSX0RXpagkl5Lc4HDIV8dtszvZKnMRAK+5tO37wFrg+pmOHdlyY0Ptn309stxJtujbDJHgePIZLVPnPh+g3rwz2L/sFbj3zAii9WMmLCMA46bT8i0TCV66rSc/i24KW8bRpaKiKySWcp9AYADdZjZiWw35YHGWMuBC4EGDx48PZ9UstzwcnakKg/BCc9RS5UnSJSEU/vu8emlTbTc/liRdHMPDqf0II1pEb1qd8/L30L0if3xVlk50TonfJZnPTScxwaJjPfx8RTRD9ZuvmzN3ENXrdsnLpEeijM7kWcdcVJPPjiXGKJJIkmK3g2zxgHp503XB1dPILRWwzz6Z6XRe/CPFatbbylgmMM++3RtqeTIiJdULvkSLelhbesxVlVjsnsI2ddB3oX4rguWaU1xAbkNl58xbf4fXrgD3Ah6WGq67CeD/nZ4LqbF1opKSe6tIxQVpi9hvlML0vVTzfYlA3dkMce+86nd//y+rXOjIFkrcF6UL06RE4vj7Wzonz6z74c8J0jeOHN2STrEumcXRsn8vHCFvvsOIasVlba3Bmyc7M47rzDm7QXH7MXz/37FWI1jYeXGmOYcMT49gpPRAKksxR6zS6N0qTB2luBWwGKi4u3a/WOrKwQsXiq6d3HpI8fdjC+JZkXYt2kEHUD09sseFmWvBll9PxfBSaWxJRVYXvnk+rfAxIpIkvX4dQmoGQ9dcP6EO3dnWMPH8+EPoWUjR/O4DED2P+kYq7/4e288PlS4v17AOBU1hFevg6A5Ki+pGJJ/OwwobokxjHEBvXEL9y8IEpeTpizTziYc75yCNPnruD2Jz/ki6VrcB0Hk+lPMpUimdo89MN1DJP3HEIk3PF/1cYYrjj/aH52w1MkUx6ebwmHHLKjEX5w5kEdHZ6ISGfVLjnyu+cexlsfLWj6hufjrN+IzQpjIyFSA3ulV9FcvAyTSBL91EtPi4iGcNZXg/FJDeqJjUZxy2swpRUYA97gIvxBPek3oJDzzjmUmi9KSJ0+mQNO3pfeI3L5x4vf5r2XR7NhfTeysuMMHL6WPYoXMOvGMJ+u7kGy1uAnXIrGOKyeCV88lUeyOl1gumGXs648jXO+dwznnDGZN16eyf1XPogpr4awQ4oQvm/xtrhBGs4Kc/S32mfY5tbsfeQe7HHwGGa9M5dYZpRLVm6Uw6ccxNBxgzo4OhHZFZmWVoBq1yCM2R+4ylp7bObPlwNYa//Y0jnFxcV22rRp2/xZny8o4YLfPdj0DT89BiSnew0lkwwJx8VicPEx5SkGXTuf0Pr0E7/u/aPkjtpI6fsRbMqQVeRz6LUeP/r23URMtxZX4qqrruPKE//I/OmLcVwHP+XhF3Wj+tDRxDPz8bKiIU45bE/2GNWP39/2Mo5jsNaSFQlzw89PZfctVjJbsmo9sxetpndhHmNH9OWi6x5n8cr1+L6P6zoU5udw66+n0HM7VtDcWZasWs+DL05nWWkFe+7WnynHTOxU8YlI52OMmW6tLe7oODpCe+bIb134b5asriQ9NjO98qVZsyG9WIrjYMvWk/3h4kajY4wlvb1B2GIcQ7ejs6l8urb+CZzjOnz9Fydx3u+/gTGm/sbkllbXvsfU1b+sv7DnJXn90iLmPZ2DtRY35BLNifDXN67ijise4LO35xKOhEjEkxx06n5cevePcBss6JJMJJn20kyqyquZcPg4ls5eydVn/hXHMfi+j/UtZ11xGmf/6oxt/jntLF7K4/UH3+WVe98iHA5x3AVHctCpk1r8mYmItJYfO0uhFwLmA0cCq4CPgbOstbNbOmd7kxjAolXr+OEfHqViQy1ORQ3ZSyogGqKgqJw9T1xN0e5RyvKHUekXMKHXeM4YchyzXp9DxZpKxh84moG79aekZiqzNzxInVfOwJwDGFswhSy3bataLpyxhBVflDBk7ED67dafx16dwasfzSMnK8IZR0/giH1HYYwhlkjy2fwSopEQ40f2w23DUs7WWmbMX8XC5WUM7NODSeOHtOk8EZHOrIsXeu2aI2+57RUeevRDIEHe4PWEvRSxRVCTyIGRRRT2yCN3bTVFubmc+73jGTygF5++Nouc/GwmHb83iViSp/7+PO8+OZXuvfI57ScnsN+J+7Tps1N+HWvrPsJan945+7F0xhoe+tOTrFxQytjJu/H1S0+h79DeAKxcUErJwtUMHTeQ3oOL2nT96g01fPDMNGK1cSYdvzd9hrTtPBGRzqrTF3oAxpgTgBsBF/ivtfba1o7/MklMRER2LV250APlSBERaV5r+bHjJ25lWGufB57v6DhEREQ6G+VIERHZVhrTJyIiIiIiEjAq9ERERERERAJGhZ6IiIiIiEjAqNATEREREREJGBV6IiIiIiIiAaNCT0REREREJGBU6ImIiIiIiASMCj0REREREZGAUaEnIiIiIiISMCr0REREREREAkaFnoiIiIiISMCo0BMREREREQkYFXoiIiIiIiIBY6y1HR3DdjHGlAHLvuRlegHrdkA4nZn6GAzq464v6P2DndvHIdbaop107cBRjmyzoPcx6P0D9TEo1Mft12J+3GULvR3BGDPNWlvc0XHsTOpjMKiPu76g9w+6Rh+7kq7w9xn0Pga9f6A+BoX6uHNo6KaIiIiIiEjAqNATEREREREJmK5e6N3a0QG0A/UxGNTHXV/Q+wddo49dSVf4+wx6H4PeP1Afg0J93Am69Bw9ERERERGRIOrqT/REREREREQCp8sWesaY44wx84wxC40xl3V0PK0xxvzXGLPWGPN5g7ZCY8wrxpgFme8FDd67PNOvecaYYxu072OMmZV572ZjjMm0R40xD2faPzLGDG3XDqZjGGSMecMYM9cYM9sY85Og9dMYk2WMmWqMmZnp4++C1sdMDK4x5lNjzHMB7d/STGwzjDHTAtrHHsaYx4wxX2T+n9w/aH2UlpldKD9C8HOkUX4MRB8bxKccuev3cdfJkdbaLvcFuMAiYDgQAWYCYzs6rlbiPQSYCHzeoO3PwGWZ15cB12Vej830JwoMy/TTzbw3FdgfMMALwPGZ9h8A/868ngI83AF97AdMzLzuBszP9CUw/czEk5d5HQY+AiYHqY+Zz/0p8ADwXED/W10K9NqiLWh9vBv4TuZ1BOgRtD7qq8W/+10qP2ZiDnSORPkxEH1s0FflyF2/j7tMjmz3/8A7w1fmh/pSgz9fDlze0XFtJeahNE5i84B+mdf9gHnN9QV4KdPffsAXDdq/Afyn4TGZ1yHSmzmaDu7v08DRQe0nkAN8AuwXpD4CA4HXgCPYnMQC07/M5y6laRILTB+BfGDJlp8ZpD7qq9W//10uP2biHEoXyZEoP+6yfUQ5cpfvI7tYjuyqQzcHACsa/Hllpm1X0sdaWwqQ+d47095S3wZkXm/Z3ugca20KqAR67rTItyLziHpv0nf0AtXPzJCNGcBa4BVrbdD6eCPwS8Bv0Bak/gFY4GVjzHRjzIWZtiD1cThQBtyZGV50uzEml2D1UVoWhPwIAf3vVflx1+4jypFB6OMulSO7aqFnmmmz7R7FztFS31rrc6f5eRhj8oDHgYuttRtbO7SZtk7fT2utZ62dQPqu3iRjzPhWDt+l+miM+Qqw1lo7va2nNNPWafvXwIHW2onA8cAPjTGHtHLsrtjHEOlhcP+y1u4N1JAehtKSXbGP0rKg/93ssv+9Kj82ssv1UTmyWbtiH3epHNlVC72VwKAGfx4IlHRQLNtrjTGmH0Dm+9pMe0t9W5l5vWV7o3OMMSGgO1C+0yJvgTEmTDqJ3W+tfSLTHLh+AlhrNwBvAscRnD4eCJxkjFkKPAQcYYy5j+D0DwBrbUnm+1rgSWASwerjSmBl5m46wGOkk1qQ+igtC0J+hID996r8GIg+KkcSiD7uUjmyqxZ6HwOjjDHDjDER0hMdn+ngmLbVM8C5mdfnkh6zv6l9SmbFnmHAKGBq5jFylTFmcmZVn3O2OGfTtc4AXreZgcHtJRPTHcBca+0NDd4KTD+NMUXGmB6Z19nAUcAXBKSP1trLrbUDrbVDSf8/9bq19psEpH8AxphcY0y3Ta+BY4DPCVAfrbWrgRXGmNGZpiOBOQSoj9KqIORHCNB/r8qPweijcmQw+rjL5cjtmdgXhC/gBNIrVy0CruzoeLYS64NAKZAkXeVfQHqs7mvAgsz3wgbHX5np1zwyK/hk2otJ/w+3CPgHmYmdQBbwKLCQ9ApAwzugjweRfiz9GTAj83VCkPoJ7Al8munj58BvMu2B6WOD+A5j80TzwPSP9Nj8mZmv2Zt+dwSpj5kYJgDTMv+tPgUUBK2P+mr173+XyY+ZeAOdI1F+DEQft+jvYShH7pJ9zMQwgV0kR266oIiIiIiIiAREVx26KSIiIiIiElgq9ERERERERAJGhZ6IiIiIiEjAqNATEREREREJGBV6IiIiIiIiAaNCT0REREREJGBU6ImIiIiIiASMCj0REREREZGA+X9y7sLN2EVgJAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1080x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:"
+    "# Your code here:\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 7.5))\n",
+    "axes[0].scatter(x=customers['Frozen'], \n",
+    "                y=customers['Delicassen'], \n",
+    "                c=customers['labels'])\n",
+    "axes[1].scatter(x=customers['Frozen'], \n",
+    "                y=customers['Delicassen'], \n",
+    "                c=customers['labels_DBSCAN'])\n",
+    "fig.show()"
    ]
   },
   {
@@ -281,11 +1056,186 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "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",
+       "      <th>labels_DBSCAN</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>labels</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>1.057471</td>\n",
+       "      <td>1.321839</td>\n",
+       "      <td>10590.045977</td>\n",
+       "      <td>3182.540230</td>\n",
+       "      <td>4112.390805</td>\n",
+       "      <td>3233.482759</td>\n",
+       "      <td>862.022989</td>\n",
+       "      <td>1096.275862</td>\n",
+       "      <td>1.459770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.812500</td>\n",
+       "      <td>8286.322917</td>\n",
+       "      <td>7542.781250</td>\n",
+       "      <td>11633.843750</td>\n",
+       "      <td>1346.031250</td>\n",
+       "      <td>4788.822917</td>\n",
+       "      <td>1471.145833</td>\n",
+       "      <td>-0.375000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8926.835227</td>\n",
+       "      <td>2940.676136</td>\n",
+       "      <td>3419.875000</td>\n",
+       "      <td>2687.670455</td>\n",
+       "      <td>773.278409</td>\n",
+       "      <td>1002.562500</td>\n",
+       "      <td>0.465909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>29862.500000</td>\n",
+       "      <td>53080.750000</td>\n",
+       "      <td>60015.750000</td>\n",
+       "      <td>3262.250000</td>\n",
+       "      <td>27942.250000</td>\n",
+       "      <td>3082.250000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>6144.828571</td>\n",
+       "      <td>15924.314286</td>\n",
+       "      <td>26319.371429</td>\n",
+       "      <td>1723.142857</td>\n",
+       "      <td>12791.571429</td>\n",
+       "      <td>2378.857143</td>\n",
+       "      <td>-1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>36847.000000</td>\n",
+       "      <td>43950.000000</td>\n",
+       "      <td>20170.000000</td>\n",
+       "      <td>36534.000000</td>\n",
+       "      <td>239.000000</td>\n",
+       "      <td>47943.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.051282</td>\n",
+       "      <td>2.794872</td>\n",
+       "      <td>40430.512821</td>\n",
+       "      <td>5084.923077</td>\n",
+       "      <td>5767.641026</td>\n",
+       "      <td>6726.076923</td>\n",
+       "      <td>922.589744</td>\n",
+       "      <td>2719.153846</td>\n",
+       "      <td>-0.564103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>22015.500000</td>\n",
+       "      <td>9937.000000</td>\n",
+       "      <td>7844.000000</td>\n",
+       "      <td>47939.000000</td>\n",
+       "      <td>671.500000</td>\n",
+       "      <td>4153.500000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Channel    Region         Fresh          Milk       Grocery  \\\n",
+       "labels                                                                 \n",
+       "0       1.057471  1.321839  10590.045977   3182.540230   4112.390805   \n",
+       "1       2.000000  2.812500   8286.322917   7542.781250  11633.843750   \n",
+       "2       1.000000  3.000000   8926.835227   2940.676136   3419.875000   \n",
+       "3       2.000000  3.000000  29862.500000  53080.750000  60015.750000   \n",
+       "4       2.000000  2.200000   6144.828571  15924.314286  26319.371429   \n",
+       "5       1.000000  3.000000  36847.000000  43950.000000  20170.000000   \n",
+       "6       1.051282  2.794872  40430.512821   5084.923077   5767.641026   \n",
+       "7       1.000000  2.500000  22015.500000   9937.000000   7844.000000   \n",
+       "\n",
+       "              Frozen  Detergents_Paper    Delicassen  labels_DBSCAN  \n",
+       "labels                                                               \n",
+       "0        3233.482759        862.022989   1096.275862       1.459770  \n",
+       "1        1346.031250       4788.822917   1471.145833      -0.375000  \n",
+       "2        2687.670455        773.278409   1002.562500       0.465909  \n",
+       "3        3262.250000      27942.250000   3082.250000      -1.000000  \n",
+       "4        1723.142857      12791.571429   2378.857143      -1.000000  \n",
+       "5       36534.000000        239.000000  47943.000000      -1.000000  \n",
+       "6        6726.076923        922.589744   2719.153846      -0.564103  \n",
+       "7       47939.000000        671.500000   4153.500000      -1.000000  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "customers.groupby(['labels']).mean()"
    ]
   },
   {
@@ -297,11 +1247,200 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "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",
+       "      <th>labels</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>labels_DBSCAN</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>-1</th>\n",
+       "      <td>1.490196</td>\n",
+       "      <td>2.45098</td>\n",
+       "      <td>13729.564706</td>\n",
+       "      <td>8359.886275</td>\n",
+       "      <td>11484.364706</td>\n",
+       "      <td>3795.003922</td>\n",
+       "      <td>4356.800000</td>\n",
+       "      <td>2080.470588</td>\n",
+       "      <td>2.125490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.00000</td>\n",
+       "      <td>11401.000000</td>\n",
+       "      <td>3536.200000</td>\n",
+       "      <td>6729.000000</td>\n",
+       "      <td>720.200000</td>\n",
+       "      <td>3561.000000</td>\n",
+       "      <td>258.200000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.00000</td>\n",
+       "      <td>9128.823077</td>\n",
+       "      <td>1862.438462</td>\n",
+       "      <td>2423.530769</td>\n",
+       "      <td>2131.430769</td>\n",
+       "      <td>496.576923</td>\n",
+       "      <td>741.946154</td>\n",
+       "      <td>2.030769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.00000</td>\n",
+       "      <td>41446.600000</td>\n",
+       "      <td>1421.200000</td>\n",
+       "      <td>2167.600000</td>\n",
+       "      <td>1276.400000</td>\n",
+       "      <td>416.200000</td>\n",
+       "      <td>784.800000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.00000</td>\n",
+       "      <td>2357.200000</td>\n",
+       "      <td>10224.800000</td>\n",
+       "      <td>11862.800000</td>\n",
+       "      <td>609.600000</td>\n",
+       "      <td>5780.000000</td>\n",
+       "      <td>572.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.00000</td>\n",
+       "      <td>3068.142857</td>\n",
+       "      <td>6095.857143</td>\n",
+       "      <td>10355.000000</td>\n",
+       "      <td>994.000000</td>\n",
+       "      <td>4675.571429</td>\n",
+       "      <td>1826.428571</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>9837.863636</td>\n",
+       "      <td>1751.863636</td>\n",
+       "      <td>2161.681818</td>\n",
+       "      <td>1513.181818</td>\n",
+       "      <td>333.954545</td>\n",
+       "      <td>669.045455</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>6823.000000</td>\n",
+       "      <td>1943.000000</td>\n",
+       "      <td>2372.666667</td>\n",
+       "      <td>6780.333333</td>\n",
+       "      <td>386.500000</td>\n",
+       "      <td>613.666667</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>7494.600000</td>\n",
+       "      <td>1537.600000</td>\n",
+       "      <td>3383.000000</td>\n",
+       "      <td>2575.400000</td>\n",
+       "      <td>227.400000</td>\n",
+       "      <td>941.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Channel   Region         Fresh          Milk       Grocery  \\\n",
+       "labels_DBSCAN                                                                \n",
+       "-1             1.490196  2.45098  13729.564706   8359.886275  11484.364706   \n",
+       " 0             2.000000  3.00000  11401.000000   3536.200000   6729.000000   \n",
+       " 1             1.000000  3.00000   9128.823077   1862.438462   2423.530769   \n",
+       " 2             1.000000  3.00000  41446.600000   1421.200000   2167.600000   \n",
+       " 3             2.000000  3.00000   2357.200000  10224.800000  11862.800000   \n",
+       " 4             2.000000  3.00000   3068.142857   6095.857143  10355.000000   \n",
+       " 5             1.000000  1.00000   9837.863636   1751.863636   2161.681818   \n",
+       " 6             1.000000  1.00000   6823.000000   1943.000000   2372.666667   \n",
+       " 7             1.000000  2.00000   7494.600000   1537.600000   3383.000000   \n",
+       "\n",
+       "                    Frozen  Detergents_Paper   Delicassen    labels  \n",
+       "labels_DBSCAN                                                        \n",
+       "-1             3795.003922       4356.800000  2080.470588  2.125490  \n",
+       " 0              720.200000       3561.000000   258.200000  1.000000  \n",
+       " 1             2131.430769        496.576923   741.946154  2.030769  \n",
+       " 2             1276.400000        416.200000   784.800000  6.000000  \n",
+       " 3              609.600000       5780.000000   572.800000  1.000000  \n",
+       " 4              994.000000       4675.571429  1826.428571  1.000000  \n",
+       " 5             1513.181818        333.954545   669.045455  0.000000  \n",
+       " 6             6780.333333        386.500000   613.666667  0.000000  \n",
+       " 7             2575.400000        227.400000   941.000000  0.000000  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your observations here"
+    "# Your observations here\n",
+    "customers.groupby(['labels_DBSCAN']).mean()"
    ]
   },
   {
@@ -363,9 +1502,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [conda env:ironhack_env]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-ironhack_env-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -377,9 +1516,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }