diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 9a2d1ae..1026eba 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": [
@@ -67,11 +67,93 @@
   },
   {
    "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",
+       "  </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"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "customers.head(3)"
    ]
   },
   {
@@ -80,7 +162,436 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your observations here"
+    "# Your observations here\n",
+    "\n",
+    "# 1) FRESH: annual spending (m.u.) on fresh products (Continuous);\n",
+    "# 2) MILK: annual spending (m.u.) on milk products (Continuous);\n",
+    "# 3) GROCERY: annual spending (m.u.)on grocery products (Continuous);\n",
+    "# 4) FROZEN: annual spending (m.u.)on frozen products (Continuous)\n",
+    "# 5) DETERGENTS_PAPER: annual spending (m.u.) on detergents and paper products (Continuous)\n",
+    "# 6) DELICATESSEN: annual spending (m.u.)on and delicatessen products (Continuous);\n",
+    "# 7) CHANNEL: customers Channel - Horeca (Hotel/Restaurant/Coffee) or Retail channel (Nominal)\n",
+    "# 8) REGION: customers Region Lisbon, Oporto or Other (Nominal)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Channel             int64\n",
+       "Region              int64\n",
+       "Fresh               int64\n",
+       "Milk                int64\n",
+       "Grocery             int64\n",
+       "Frozen              int64\n",
+       "Detergents_Paper    int64\n",
+       "Delicassen          int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers.dtypes\n",
+    "\n",
+    "#no categorical data to convert"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Channel             0\n",
+       "Region              0\n",
+       "Fresh               0\n",
+       "Milk                0\n",
+       "Grocery             0\n",
+       "Frozen              0\n",
+       "Detergents_Paper    0\n",
+       "Delicassen          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers.isnull().sum()\n",
+    "\n",
+    "#no missing data to remove"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>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>Channel</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.062028</td>\n",
+       "      <td>-0.169172</td>\n",
+       "      <td>0.460720</td>\n",
+       "      <td>0.608792</td>\n",
+       "      <td>-0.202046</td>\n",
+       "      <td>0.636026</td>\n",
+       "      <td>0.056011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>0.062028</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.055287</td>\n",
+       "      <td>0.032288</td>\n",
+       "      <td>0.007696</td>\n",
+       "      <td>-0.021044</td>\n",
+       "      <td>-0.001483</td>\n",
+       "      <td>0.045212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fresh</th>\n",
+       "      <td>-0.169172</td>\n",
+       "      <td>0.055287</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.100510</td>\n",
+       "      <td>-0.011854</td>\n",
+       "      <td>0.345881</td>\n",
+       "      <td>-0.101953</td>\n",
+       "      <td>0.244690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Milk</th>\n",
+       "      <td>0.460720</td>\n",
+       "      <td>0.032288</td>\n",
+       "      <td>0.100510</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.728335</td>\n",
+       "      <td>0.123994</td>\n",
+       "      <td>0.661816</td>\n",
+       "      <td>0.406368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Grocery</th>\n",
+       "      <td>0.608792</td>\n",
+       "      <td>0.007696</td>\n",
+       "      <td>-0.011854</td>\n",
+       "      <td>0.728335</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.040193</td>\n",
+       "      <td>0.924641</td>\n",
+       "      <td>0.205497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Frozen</th>\n",
+       "      <td>-0.202046</td>\n",
+       "      <td>-0.021044</td>\n",
+       "      <td>0.345881</td>\n",
+       "      <td>0.123994</td>\n",
+       "      <td>-0.040193</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.131525</td>\n",
+       "      <td>0.390947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <td>0.636026</td>\n",
+       "      <td>-0.001483</td>\n",
+       "      <td>-0.101953</td>\n",
+       "      <td>0.661816</td>\n",
+       "      <td>0.924641</td>\n",
+       "      <td>-0.131525</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.069291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delicassen</th>\n",
+       "      <td>0.056011</td>\n",
+       "      <td>0.045212</td>\n",
+       "      <td>0.244690</td>\n",
+       "      <td>0.406368</td>\n",
+       "      <td>0.205497</td>\n",
+       "      <td>0.390947</td>\n",
+       "      <td>0.069291</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Channel    Region     Fresh      Milk   Grocery    Frozen  \\\n",
+       "Channel           1.000000  0.062028 -0.169172  0.460720  0.608792 -0.202046   \n",
+       "Region            0.062028  1.000000  0.055287  0.032288  0.007696 -0.021044   \n",
+       "Fresh            -0.169172  0.055287  1.000000  0.100510 -0.011854  0.345881   \n",
+       "Milk              0.460720  0.032288  0.100510  1.000000  0.728335  0.123994   \n",
+       "Grocery           0.608792  0.007696 -0.011854  0.728335  1.000000 -0.040193   \n",
+       "Frozen           -0.202046 -0.021044  0.345881  0.123994 -0.040193  1.000000   \n",
+       "Detergents_Paper  0.636026 -0.001483 -0.101953  0.661816  0.924641 -0.131525   \n",
+       "Delicassen        0.056011  0.045212  0.244690  0.406368  0.205497  0.390947   \n",
+       "\n",
+       "                  Detergents_Paper  Delicassen  \n",
+       "Channel                   0.636026    0.056011  \n",
+       "Region                   -0.001483    0.045212  \n",
+       "Fresh                    -0.101953    0.244690  \n",
+       "Milk                      0.661816    0.406368  \n",
+       "Grocery                   0.924641    0.205497  \n",
+       "Frozen                   -0.131525    0.390947  \n",
+       "Detergents_Paper          1.000000    0.069291  \n",
+       "Delicassen                0.069291    1.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers.corr()\n",
+    "\n",
+    "# \"channel\" is correlated with \"grocery\" and \"detergent_paper\", and \"milk\"\n",
+    "# \"fresh\" is somewhat correlated with \"frozen\"\n",
+    "# \"milk\" is highly correlated with \"grocery\", \"detergent_paper\"\n",
+    "# \"grocery\" is highly correlated with \"detergent_paper\", \"milk\" and \"channel\"\n",
+    "# \"delicassen\" is somewhat correlated with \"frozen\" and \"milk\" \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(12,10))\n",
+    "\n",
+    "# plot to different axes\n",
+    "\n",
+    "customers.Fresh.plot.hist(bins=100, alpha=0.8, ax=ax[0,0])\n",
+    "customers.Milk.plot.hist(bins=100, alpha=0.8, ax=ax[0, 1])\n",
+    "customers.Grocery.plot.hist(bins=100, alpha=0.8, ax=ax[1, 0])\n",
+    "customers.Frozen.plot.hist(bins=100, alpha=0.8, ax=ax[1, 1])\n",
+    "customers.Detergents_Paper.plot.hist(bins=100, alpha=0.8, ax=ax[2, 0])\n",
+    "customers.Delicassen.plot.hist(bins=100, alpha=0.8, ax=ax[2, 1])\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -107,7 +618,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your comment here"
+    "# Your comment here\n",
+    "\n",
+    "#The scales of the variables differ a lot from one another, and the standard deviation of \n",
+    "# each variable as well. This should probably be corrected with a normalization of the data\n",
+    "# before proceeding with the analysis."
    ]
   },
   {
@@ -127,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +650,16 @@
     "\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "\n",
+    "#create object:\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "#fit:\n",
+    "scaler.fit(customers) #data without the target\n",
+    "\n",
+    "#transform\n",
+    "customers_scale = scaler.transform(customers)"
    ]
   },
   {
@@ -149,13 +673,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "from sklearn import cluster, datasets\n",
+    "\n",
+    "kmeans = cluster.KMeans(n_clusters=3)\n",
+    "kmeans.fit(customers_scale)\n",
+    "\n",
+    "customers['labels'] = kmeans.fit(customers_scale).labels_\n",
+    "\n",
+    "pred= kmeans.predict(customers_scale)\n"
    ]
   },
   {
@@ -167,11 +699,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    297\n",
+       "1    130\n",
+       "2     13\n",
+       "Name: labels, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "customers['labels'].value_counts()"
    ]
   },
   {
@@ -185,13 +732,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "dbscan = cluster.DBSCAN(eps=0.5)\n",
+    "\n",
+    "dbscan.fit_predict(customers_scale)\n",
+    "\n",
+    "customers['labels_DBSCAN'] = dbscan.fit(customers_scale).labels_"
    ]
   },
   {
@@ -203,11 +755,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1    255\n",
+       " 1    130\n",
+       " 5     22\n",
+       " 4      7\n",
+       " 6      6\n",
+       " 0      5\n",
+       " 3      5\n",
+       " 2      5\n",
+       " 7      5\n",
+       "Name: labels_DBSCAN, dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "customers['labels_DBSCAN'].value_counts()"
    ]
   },
   {
@@ -234,11 +807,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 92,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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FpZRSk8KUSggna403BsKR8WuYUUqpC9GUSgi186vILYrQ3zVwpGyob5hgjp9Zy2ZMXGBKKTUJTKmE4A/4ueubnyKSn0P7wU46GruwfRZ3fvNT5OSFJzo8pZSaUFOqURmgpLqY3/7rdXS39GIch+LqImzbPv2KSin1MTflEgKAZVmU1py7lnqllLoQTakqI6WUUienCUEppRRwBglBRGwR+UBEnvZ+LxKRF0Vkj/daOGLZ+0WkQUTqReTmEeWXiMg277PvifdosIgEReQRr3y9iMwYx2NUSik1Bmdyh/DHwK4Rv98HvGyMmQu87P2OiCwE1gGLgLXA90XkcKvtD4B7gbnez1qv/B6g1xgzB/g74DtndTRKKaXO2pgSgojUAJ8E/mVE8e3Ag977B4E7RpT/3BiTNMbsBxqA1SJSCUSNMe8aYwzw0HHrHN7WY8D1ogMLKaXUeTXWO4S/B/4EGDk1T7kxphXAey3zyquBphHLNXtl1d7748uPWccYkwH6gRO6AYnIvSJSJyJ1nZ2dYwxdKaXUWJy226mI3AZ0GGM2isi1Y9jmaN/szSnKT7XOsQXGPAA8ALBy5coTPldKnZ3e9j62v72bvvZ+pi2sZv7queM6zr66MIzlOYQrgE+LyK1ACIiKyL8C7SJSaYxp9aqDOrzlm4HaEevXAC1eec0o5SPXaRYRH5AP9JzlMSmlzkDzhy08+rdP4TgOwVCA3ev3sPHFbXzxvjsI5+oT/FPJaauMjDH3G2NqjDEzcBuLXzHGfBl4CrjbW+xu4Env/VPAOq/n0EzcxuMNXrXSoIis8doHvnrcOoe3dae3D70DUOocM8bw/P97lVAkSFltCfmlUcpnlNHV3M3m13ZMdHjqPPsozyF8G7hRRPYAN3q/Y4zZATwK7ASeA75hjMl663wdt2G6AdgLPOuV/xAoFpEG4Jt4PZaUUufWYO8QPe19RPKPnWwlWpzHh3V7JygqNVHOaOgKY8xrwGve+27g+pMs9y3gW6OU1wGLRylPAHedSSxKqY/OH/QjIhjHIPbRprx0MkNx9fjPyKUmN31S+TwyxtC6v53tb+/m4M4mstns6VdS6hwKR0IsvGwenc3dHK6lTacyxAZirLh+yQRHp863KTm43UTIpDP85oEXqX9/75G5m0tri7nzm58ityAywdGpqey6L11FMp6iYdN+dwZBS/jEl65k1tLpEx2aOs80IZwnW17bwa71DVTOLDuSEDqbu3n5p29y+++vPc3aSp07oZwgn/nDW+nt6Cc2EKOospBwJDTRYakJoAnhPNn6+g4KSqOMfAC7uKqQPXV7SSVSBEKjT++p1PlSWJZPYVn+RIehJpC2IZwnjmM4cTQOAQTtYauUmgw0IZwni69cQF9H/zEX/962XmYtm65PhCqlJgWtMjpPLr5uMQd2NHFwRxOCYDAUlOVz/W9dNdGhKaUUoAnhvAmEAtz1Hz5FU30L3Yd6yCvKZfqiWgJB/0SHppRSgCaE88qyLKYvqGH6gprTL6yUUueZtiEopZQCNCEopZTyaEJQSikFaEJQSinl0YSglFIK0ISglFLKowlBKaUUoAlBKaWURxOCUkopYAwJQURCIrJBRLaIyA4R+a9eeZGIvCgie7zXwhHr3C8iDSJSLyI3jyi/RES2eZ99T7zhP0UkKCKPeOXrRWTGOTjWSSs2GKe/a0BHPT3POho7eeoHz/PP//FBnvjfz9C6r32iQ1JqQo1l6IokcJ0xZkhE/MBbIvIs8FngZWPMt0XkPuA+4E9FZCGwDlgEVAEvicg8Y0wW+AFwL/Ae8AywFngWuAfoNcbMEZF1wHeAL4zrkU5CwwMxXvrJG+zZuA8DFJXnc/PXrqNmbuVEh/ax17q/nZ9+63Fs2yJSEKFx5yEaPtjP5//T7Tq0iJqyTnuHYFxD3q9+78cAtwMPeuUPAnd4728Hfm6MSRpj9gMNwGoRqQSixph3jftV+KHj1jm8rceA6w/fPXxcGWP49Q9eYM+mfZTUFFNWW0wiluSx//UU/V0DEx3ex95bT2zAH/BRVFlIMBygsDyfcG6YN37xzkSHptSEGVMbgojYIrIZ6ABeNMasB8qNMa0A3muZt3g10DRi9WavrNp7f3z5MesYYzJAP1A8Shz3ikidiNR1dnaO6QAnq65DPTTuPkRpTbE7j60IeYW5pFMZdq3fM9Hhfew117eQV5R7TFleYYTWfR04jjNBUSk1scY02qlX3bNcRAqAJ0Rk8SkWH+2bvTlF+anWOT6OB4AHAFauXHlBV7jHBmJHEsFI/oBf7xDOg6KKAob7Y+QWRI6UJYaT5JfkjTKzncsYQ1N9C3s27gURLlo5m+q5lSddXqkLzRn1MjLG9AGv4db9t3vVQHivHd5izUDtiNVqgBavvGaU8mPWEREfkA/0nElsF5qS6iIAspnsMeXJeJJp87UO+1xb86lLGOgZIjGcBCAZT9Hb3sflt68a9QJvjOH1X7zLz/7mcba+vpMtr+7g4W/9kreffP98h67UOTOWXkal3p0BIhIGbgB2A08Bd3uL3Q086b1/Cljn9RyaCcwFNnjVSoMissZrH/jqcesc3tadwCvmY97lJpIf4fLbV9He2EV/1wDD/TFa97dTPbeS2ctnTHR4H3vzLpnNbb97I5lMho7GLpLxJGu/dh2Lr1xwzHLZbJbmPa1semkr7zy5gbJppRRXFVFSXURZbQnvPLmBnrbeCToKpcbXWKqMKoEHRcTGTSCPGmOeFpF3gUdF5B6gEbgLwBizQ0QeBXYCGeAbXpUTwNeBHwNh3N5Fz3rlPwR+IiINuHcG68bj4Ca7yz61krJpJXzwynaSsSSrblnOkqsW6Cxq54GIsPiK+SxYM5dkLEUwJ4Bt28cs09vRz+N//zQ9bX30tPbR0tCGWBZVs8sRBNtngxFa9rZTVFF4kj0pdeE4bUIwxmwFLh6lvBu4/iTrfAv41ijldcAJ7Q/GmAReQplKRIQ5y2cyZ/nMiQ5lyrJtm5y88Anlxhie+v5zDPUOUz6tFMuy6D7UzYFtjeQVRIgW5wEgAn5N4OpjQp9UVmoU3S09dBzsoqAsH4CC0ii+gB8n69DR3AXAUN8wwZwg0xdqm4/6eNCEMEVlM1l9MvoUMuksMqIXmD/oZ+Fl80CEvvZ+2hu7QODOb95GKCc4wdEqNT7G1O1UfXwcamjltUfe4VBDG5FomEs/uYIVNyzFsvS7wUgl1UWEc0PEBuNHqpTyinKZubSWqz+7hplLp1Mxo8xtR1DqY0ITwhTS2dzNz7/9K4LhAOXTSkgl0rz0kzdIJdJc/ulVEx3epOLz+7j1397AE//wDIO9Q9i2TSaVZuGaeVz26VWaCNTHkiaEKWTTS1uxLOtIg2gwHKC0toT1v9nEypuWEQgFxnV/Az2DbHjmA/Zs2kckGuaSm5ax8LKLLpgHuWYunsY9f/MlPqzbS2wwzrQFNUxbUH1CbySlPi40IUwh7Qc7CeeFjinzB3w42Syxwfi4JoTYYJyf/X+PM9gzTEFZlOH+OL/+wQv0dgxw5R2rx20/51p+SZRVa0/oZKfUx5JWHE8hVbMrGO6PHVOWSqTxBfxE8nPGdV873qlnoHuIsmklBEIBIvk5lE0rZf3TG4kNxsd1X0qp8aEJYQq55Mal2H6b7tZeMukMw/0xug51c+VnVuMPjG9f+kN7WghFjr0b8fndqpb+Th2rSanJSKuMcJ9I3fr6Djoau6iaXcGSqxcQLcqb6LDGXWF5AV/+z5/j7Sff58D2JvJL8rh23eXMXz133PdVUl1MwwcHgKPn0XEcnKxDpGB870aUUuNjyieE9oOd/OxvHiebcQhFghzc2cwHL2/jS3/+2Y/lcAQl1cXc/vtrz/l+Fl85n7rnt9DfNUC0OI9MOkvXoW6WXr1w0ifbbDbLge1NfLhxL4Ggn/mXzqV6jk5apD7+pnyV0WuPvI1lWZTWFJNXmEtZbQnpZJp3n6qb6NAuaAWl+ay77w6KKgvpaOxisGeQNbddwg1fuXqiQzslx3F47oev8Iv/9WvqN+xly2s7efi//5L3X9g80aEpdc5N6TuEbDbLwV3NlE8rPaY8vzRKw+YDExPUx0jFjDK+9GefJZ1MY/vsC6LvfvOHrex4u57KmWVHusdm0hlef+QdFl46l0h+5DRbUOrCNWUTwkDPIEN9MfwBP+lk+pgul6lEmtwLqJ7bGMOB7Y1sfXMXmVSWBZfOYd7K2fj8E//nFZFxf77hXGrc1YTts495VsLn92GMoXVfB3Mu1oEI1cfXxF8xzrNUIsWLP3mdne9+iIjQdaiHloY2FqyZi8/vI5PO0tcxwCfvHXUg10np7V9t4O0nNhDKDWHbFns27eWiVXP49O/ffEE8RGWMIZ3K4A/4JvyhtXBu+KRTaAbCF05iU+psTLmE8MZj77L9rXrKp5diWUJBaZQd7+zmwI4mosV5WJZw9Z2XsuiK+RMd6pj0dw3w7q/rKJtWcqRKJq8olz11+2iub2H6wtrTbGFi1dc18MYv3qOvs5+8wghXfnYNiy6fuKeZ566YxeuPvkN8KEE41+0229c5QH5JlOo5FRMSk1Lny5RKCKlkmq2v76Ss1p3YHiAQCjB7+Syixbms/dp1RIvzLpjRK40xtB1wZy4dWT8v4k7e0vxh66ROCHu3HOBX33uW/NIo5dNKiQ8nePr/vIAILLp8YhJytDiPO/7oVp554CUGe4cwxlBUUcDtf3DLpGsDSSXTbHtjJzvfrcf22Sy7dhHzL517QdwVqslpSiWEdDJNNuNg2cd2rvIHfaSTacpqSyYosjPjOA4fvLyN9c9sov1gJx2NXeTkhckrzB2xTPaE/v7JeJJ9Wxvp6+ynrLaEGYtqJ/Qi9+5TdeQWRo6MJhqOhKC8gLef2DChYx7NWjKd3/vu3XQ0duHz25TUFE+60WCz2SxPfO8ZDmxrJFqSh5M1/PoHL3BoTys33f2JiQ5PXaCmVELIyQtTUlPEUN/wMRfPga5BLv3kigmM7My89+s63njsPYorC5mxuJaOg51sfHErq9YuJxLNYahvGH8owNwRDaB9nf088p1fMdA9hFiCk3WomlPBnd/81ITdEXUd6ia/JHpMWSgSpP1gJ47jTOg3XZ/fR9XsyVtF1LizmYM7mqgY0Rsqkh9m86s7uOSm5RRXfvyeoVHn3mm/9ohIrYi8KiK7RGSHiPyxV14kIi+KyB7vtXDEOveLSIOI1IvIzSPKLxGRbd5n3xPvX7KIBEXkEa98vYjMOAfHiohw41euIRlL0dnczUD3IG0HO8kvjbLihqXnYpfjLpVIsf6ZTZTWlhDMCWJbNhdfv4RgToD6ugbaG7vwBX3c9R8+dUwXyVd/9haxgQTl00spqy2hYkYZLQ1tbHxhy4QdS9WcSgZ7h48pG+6Pue0hWu1xSq0HOrFs65i7KMuysCyhq7l7AiNTF7Kx3CFkgP9gjNkkInnARhF5Efht4GVjzLdF5D7gPuBPRWQhsA5YBFQBL4nIPGNMFvgBcC/wHvAMsBZ4FrgH6DXGzBGRdcB3gC+M54EeVjOvit/+6y+w7c1d9LT2UTu/ikWXX0Q498R5dSej4f4YTsbBHzj6pwvlhFh69UJ8AR+f+aNbKaooOKaKI5VMs3fzAUpqjq0SKyzLZ/vbu7ligkYfvfIzq3n4W7+kt6Of3IIIsYE48aE4a++5bkLiuZDkFUYwzokz3hkgJ3ph/FtWk89pE4IxphVo9d4PisguoBq4HbjWW+xB4DXgT73ynxtjksB+EWkAVovIASBqjHkXQEQeAu7ATQi3A3/lbesx4B9FRMw5muOxqKKQa+66/Fxs+pyLFESw/TbpZPqYyd1jA3GWX7eYkqqiE9axLEEs8abMPPqN0nEMvsDEfROvml3Bl//znbzz5AZa93dQPr2YNZ9axbT51RMW04VizsUzyYmG6e3op6A0ijHQ09pLWW0J1XN1mA11ds6oDcGryrkYWA+Ue8kCY0yriJR5i1Xj3gEc1uyVpb33x5cfXqfJ21ZGRPqBYqDruP3fi3uHwbRp084k9FH1tvex890PGeodZvqiGuZcPHNSPMx1KoGgn8vvWM0rD79FQWmUYE6A/s4BbL/NiuuXjLqOz+9j4WUXseOd+iMN58YYejv6ueHLV53P8E9QOaucz/37T01oDBeicCTEF/7kdp7/8au07G1HEGYtm86NX71m0jWAqwvHmK9+IpIL/BL4d8aYgVP0ABntA3OK8lOtc2yBMQ8ADwCsXLnyI9097N/eyON//xuMMfj8Pja/tp3pC2r47L+/jUBwfIeCHm+rbl5OTl6Y9b/ZSH/XADOW1HLFHZdSWF5w0nWu+fzldLf20rq3HQSMgUWXzWP5Jxafv8DVuCqpLuZLf/Y5YgMxxLKO9NZS6myNKSGIiB83GTxsjHncK24XkUrv7qAS6PDKm4GRnd9rgBavvGaU8pHrNIuID8gHes7ieMYkm8ny3A9fIZKfc+Q/kTGGgzubqd+whyVXLSQRS7L9rV3Uv7+XnLwwy69bzIxFtRP+JC24jeOLr5jP4jN4eC4nL8yX/uyztOxtZ7hvmMLyfEprSybF8aizJyI6vpIaN2PpZSTAD4FdxpjvjvjoKeBu7/3dwJMjytd5PYdmAnOBDV710qCIrPG2+dXj1jm8rTuBV85V+wG4VUXD/cPHfKMSESIFEerf30sqkeLRv32Slx9+i/7OAZp2H+KR//EkdS9OXI+c8WBZFjVzK7lo1RzKppVqMlBKHWMsdwhXAF8BtonIZq/sz4BvA4+KyD1AI3AXgDFmh4g8CuzE7aH0Da+HEcDXgR8DYdzG5Ge98h8CP/EaoHtweymdM/6gH2Pcu4KRF8VMKkM4N0R93V7a9nVQObPsyGeRgghv/uJdFl8x332ASimlPmbG0svoLUav4wcYdQQ4Y8y3gG+NUl4HnFBpbYxJ4CWU8yG/JMr0RTXs3XyQnLwQwZwgiViC9oOdXPbplWx8YQs9bb04jkNxVRHBcMCdjN6B7pZearQXx7jIZrMkYymCOQF97kCpSWByd6k5R7KZLMFwgIM7m4gNxhnqG0ZEmLmklof+66MMdA0ittDb0c/BXc0sXDOPaHEexnGODHimzp4xhi2v7+CtxzcQH4yTkx/m6s+tYfGVC7QaS6kJNCX7p21+dTu73tvDpbetoHZeFdHiPAorCsAI6UQaf8iPk3EIhAL4/D7q6/bSfrCTGYtrKaoomOjwL3g7363nuR++SjAcoHx6Kf6An2f+78vs3rBnokNTakqbkncIG1/YSmFFAbZlew/25APQ0tBGQXk+OdEcBnuHsCwhGUsRH05QNaeCT95746T5Bnu4V9S2N3aSjKe4aPUc5q+egz8wubvMArzzZB0FZVGC3vwCli0MDcT40Z//jLVfu45l1y4a9QG7+FAcy7YIhi+M0WiVutBMyYSQjCfJiXojgRqDiNvLyADGcX8PhPxcfMMSEsNJetv7uPXfXD+p+nmv/81GXv/Fu4RyQtg+i72bD7B7/R4++8efnHTDNI9kjDky2iq4f4utb+wiGU/iZBy2vLqdza9u5wv/6XZq5lUB0N3ay4sPvUbj7hYsS7ho5Wyu+9KV2t1SqXE2JauM5l86h972PgDKppUSG4wTH05QMaOUbNY5MsCaJRaJoSQ186pO+dDXqbQd6OD5B1/lse/+mk0vbSURS37k+If6hnnriQ2U1pRQWJ5PtDiPipll7NtykP3bG896u32d/Wx/eze7N+whPhT/yHGORkSonlPBYM8QAIca2txquoCf4spCSqqLCYWDvPzwmxhjiA8neOQ7v6Jtfyfl00ooqSriw7p9PP4Pz5x0ZjOl1NmZkncIa25byYHtTbQf6CC3MAf2QTKWomp2Bb6Aj4HuQUKREO0HOymqLOC2373prKqK6usaeOqfnscX8BEI+tm39SBbXt/Juvvu+EhdVzub3BE9fP5jJ8UJhPw07jrEnOVnPu/v+89v5rVH3sY4IOLOEXHHH97KjEVnN8HOoYZW6p7fQm97H9MX1rDihqVHhrq++q7L+dnfPE4mnaWzuRuDwclkmbbAfW4xtzBC6/52Wve309HYyXDfMOUz3C7AYgultcW07e+gdV871XO0x5dS42VKJoS8wly+8pef58O6vbQf6ODaL1xBODdEfDhBYVkBRZUFdLf0EoqEqJxVdlZdIrOZLC8+9Dr5JVFCEbfOO68ol7YDHex4ezcrb1p+1vGHIiGy2SyDPe7cBpH8HESEbMYh97hJccaio7GTV3/2NiXVhUfGcooNxnnyn57j69+9m0DozOYS3rNpH0987xmC4QChSIhNL21jxzv1fOW/3EV+SZSauZV8+S/uZMMzmzi4s5FQTpDZy2aQWxDBcRz2bWukafchHv7rx+hp70NEKJtRiozs/SzCUF/sjI9VKXVyUzIhAIRygiy9eiFcvXDUzwvLCj7S9ns7+kkMJ4kW5R1THsmP0LD5wEdKCLGhOI27DjHcHyMYDhDODTFtfg2232L+6rlnvL2GzQewbDlmYL+cvDBDvUMcamhj5uKxDyToOA6v/PRNosV5R2dCyw3R0dRF3QtbuP5L7mB6lTPLuf0bt7BgzTwe//tnCHqT9DTtPsT+rQeZvXwG5dPLQIStr++ktKaY4soibx8GjKG4SieBUWo8Tbk2hMP10tlM9vQLfwShnCAY7+I1QjqRIlqUe5K1Tm+gZ5An//E5Llo5m7LaYrese5CGzfv5zB/dSrQ47zRbgOGBGK372hnu9yanGWWUEIMhm3U40xFEYgNxBnuGT2iAzyvK5cCOphOWn7tiFtd/5SpvsqIO9m09yPSFNUxf6FYfldYUU1ieT33dXuJDCYb7Y7Ttb2fJVQtG7YmklDp7U+oOYd/Wg7zys7foa+/DH/Kz+pYVrL714nPylGxuQYR5q2ZRv2Ev+aVRMqkMWEIqmWbZtYvOersNm/aTTWcpqSoivzRKMpbCyToMdA8iloUxhnQqg89vH2n3OPyazWZ585fvUff8FtyHzw3LP7GYBWvm8tYTG8iks/j8Np2Hutm3+QCxwTjP/svLXPP5y1l0+djmOA6EA9h+m0w6c8wdRzKWoqT6xAu4iLDqpuUsvWoB7Y1dPPzXj1E1u+LIvizLYsGai2g/0I4/6MMX8HH5HatYfOXYB/ZTSo3NlEkILXvbeOzvniavIELZtFJSyTSv/+IdHMfhitvPzYxhn1h3BTve+ZC3f7UBEbB8Njf/9ic+0ly9ieEEYnkXegR/wEdf5wD9nQNsfnU7Lz/8Bh0Hu+jvGsD22RRVFLDkqgVcfsdq6jc08N7TmyifXoptWzhZh40vbCG3MMI1d13GG4+9R3/3II07mwnlBFj+icX4/D6e/ucXsWyLhWvmnTY+f8DHnItn8Noj7xAtziMQ9NPfPUAyluKKO1addL1gOEjtvCoqZpQx3B8jt+Bol9LYQIzLb1/NTXdfe9bnTSl1elMmIdQ9v5lgyE8k3210DQT9lNaU8P6zH7Bq7cXnZA6ED17eTjadZfayGaRTaQrK8tm35QC71u855uKayKRpGRwkYNtU5UWxTvFNvHZ+NdnHN+A4hvhQnJ3v1JOMJxnuj3Fw1yGq5pRhsobejgEwhlBuiC2v7eRQQxvxoQSF5fnYtltTaNkWRZWF1D2/mW/8w9eYe8ksfnj/w8y9ZBaVs8qPnBNjDO/8asOYEsKGZzax850PAdj21i5S8RTRojxmL5vOyw+/ie2zTzoHg4hw3Zeu5LH/9WuS8RShSJBYf5xgJMCqWy4e83lXSp2dKZMQult7CeWGiA8laNnbRn/nAOHcEKHcEMlYctwSwkDPIDverqf9YCevP/o2Q70xLJ87GXrHwS6ixbm8/9wHRy6uH7S28NiuHWSyWQxQlpvL3csupjRn9IeuqudWsuSq+Wx9YycHdzSTSqTw+X3kl+aDMXQ2dmOAovICnKxD6952Vq1dTkdjF/GhxAnTU/qDfvo6+wF3alHb76NmXsmRpAFeo3Bj1wmjwx6vp62X1x97l9LaEkK5Ifq7hghFAqQSKarmVuLz+3j54TeZt3L2SR/ym76wlq/85V1sfGkr3S29LLxsHhdft2RMbSNKqY9myiSE2ouq2fDsBxza00o2k0Usi/7uARzHcKihlfmrzrx3zvE6m7v5+befIBFLYvts9m1rIhj2U1ZbQiqZIZvJ0n6wi8KKVgBaBwf52fatFIVzCPncP0VXbJgHt3zAN9dcMeqdgmVZrP3adVTMLOOhv3qUsukllNYUs2fTPjBCbCjOYPcQAvhDfjBuF1gRobi6kL6OgSO9cwyGA9sPkhhO8r1v/AvTF1YTLc5jqHeY/JKjF+ChvmEqZp5+/oTmD1vBcZ+P6O/oxx+wCYYCpBNpBnuGKKstwTiG9oOdp+y5VDatlFu+NupAukqpc2jK9DK65KZl9Lb1MdQ3zFDvEB0HO+jrGCCbzvLQXz5KMvHRnyB+7ZG3cbIO5dNKCYYDBMN+0skMTR+20NvWx2D3IH2dA7TsbSOdSrO5rRVL5EgyACjJidAxNETL4ADg9ghqqj9Ed6s7HHfXoW4adx2iuKKQmrlVzFoynWhRHtGiPIYHYvR3DJCIJRkeiNHT0ktfZz/pVAZjDFfcvppAyE97YycD3YPUb9jDwR3NlE0rJbcwQv2GBvZvO0hPWy99He56/Z0DDPfHuOpzl532+H1+232qDfAFfJjDPawELK/dw2AIhCb/eEtKTUVT5g6hsCyf6Qur6e3oY7hvmHBemNyCCPGhBNvf2s1f3vG3XHPXZVx912VEvHGOjDHeIHcWvoCP5g/dGT+r51ae8KRxNpNl//ZGympLAfcCmFuQS0djJ07WEMoJYRyHQNCHMcLu9XuIFRssOTEniwiJdJoXf/I6b/7yPWyfRSAcJDGUIBQJEcsTPvQl6AkkKeuOsbKwHLGEtv0dpJMpbL9NbDBBMOQ+GLbznXouuXEZS65awNwVs9j+9m5aGlo51NDKihuWkJOfw56Ne+lt7ycVS5FTECZakkd8KE75zDIu//TKI+MKnUwqmSavOA8RiA8lKK4qonH3IeLDCWzbIlqSR19HP4XlBVSMmHhIKTV5TJmEAFA2vRQn61C7oAbBHWconUgTCPlJDCfZ8U49HU1dfPkv7qSntY/nfvQKbQc6GOgeore9j8pZZUeGxP7U1286ZogIsYRgOEgmnSEQ9JMTzSESDWP7bQJhnzvJTsiPJcK0+VXsfPdDlvzO5bzTdPCYuvlkJkMyk+Zfv/042x/dSG5emKDfR2I4SSaVQeYU0pAXwBlMYycdBnOS7E0OUN3Qg+23COXkYvksUvEU/oCPSDQHn9/H5755Gz6/j9wCH8s/sZiOxk5a93XQ09ZHKpHGsi3yS/IIhoOI5XYTveMPbjntQ2nGGDa9tJU3H19PNp1leCBGZ3MXJdXFlNWW0Lqvjaq5lfS29VNSU8zt37hZJ8NRapKaUgnh4uuX8vQ/v0gmnSGbypKKpxHLHcoCYyirLaHtQAd7Nx/kxYdeI5vOUlCWz97NBzBZh86mHpZ/YhGpeJqnvv88v/u3Xzky4qZlWay8aRlvPb6e8hmlWJbF7OUzaKxvIZIfdhtRBaYvrCGcG8If9HNRcQnLKirZ0tZKyOcnaxzah4fI7O9j7xMbkZDQa2fI99kM9QySTmfpyMvF9BsCGXCyDqGAkArbDM7PZ6Yv6F3QBcS9qC//xCLSyQy5XpzGGH79f15g7wcHCOeGsAM+Opu68Qf95BVGSCfTFFcXkZMXZvMr20+bED7cuI8XH3qDkpoiAkE/RRUFtO7vYO6KmSy/bgll00roaeul+1AvPr9NbCBOQVk+ljVlaiuVumCcNiGIyI+A24AOY8xir6wIeASYARwAPm+M6fU+ux+4B8gCf2SMed4rv4Sj8yk/A/yxMcaISBB4CLgE6Aa+YIw5MG5HOMKc5TNYeeNSdrzzIfHhBI7jUFRWgOWzKKkpOny81L+/h+H+OBUzSt3B14whtzCXof4YA91DFJRG6evsZ9/Wg+QV5ZGMp6icWcbSaxey5fUdvPfrOnwBH0UVhay6eTlO1iGvKJfcwggG2LNxL8GcIC/++FVqcoPED8To92epumQGsXQK52CMmN+H5bMwBnp6BiGVIZ3Nko76CcUdLNsGgdRwipxwhHhRgLwiH/u3NWHZ3kNdPpu2A53H9N/vOtTD/m2NVM4qwzjOkWErstks/Z0DBHOCVM0qR0SIDZx+rKANz24iryj3SC8t22dTPq2Ext2H+PQ31pJKpHnzl+tprm9xhxg3MG1+FXf80a3u09xKqUljLHcIPwb+Efeifdh9wMvGmG+LyH3e738qIguBdcAioAp4SUTmGWOywA+Ae4H3cBPCWuBZ3OTRa4yZIyLrgO8AXxiPgzueiLDu/s/y828/waGGNprqD+EYQzQ/QuWscowxGGOwffbhtlF36IYjbaOGdDINQDKW5Ml/ev7IJC9O1nGfzg34WHjZRQz2DjE8EGPlNcvoOtRLd0sP3Yd63CRSGCE2EOdX//s5YoNx8kvz6G3v531exffZeRSlLQKFYYYP9JBNZsgag0m5vZSspIPxWWDcuRtsv42VE8A+1EdfZ4KcaJihvmFS8RSO43BgeyO23z5SLTXkTfwjIlTNqUAsYdPL20glUohlsfiK+eQWRGjb387KtctPe04He4aOnIPDfAEfqXiaTCrDu0/V0VzfQvn0Ui8hGBp3H+K9pzdy7ecvH7e/rVLqozvtfbsx5g2g57ji24EHvfcPAneMKP+5MSZpjNkPNACrRaQSiBpj3jXu4DgPHbfO4W09Blwv53BasuLKQmYsqsUYg2UJsf4YvoBNOpmm7UAHFTPLKJ9RhuO44/hEi3NBIJNxnxOIRHNIJ9M01bcQigQpn15K+fRSEKh/v4GcvDDD/TGadrfQ2djFL7/7NJH8MJ/7d59k3srZTFtQw9JrF9HfN0jWyRIfjtOw+QCJoQTJwQRdP9lM24dtIJBNZsC47RO238JCyN3dTzJskTYOlm1hRfyYiJ+SpiTppNse4jiGQE6A6Qtq8AV8vPDj19jy2g4AiioLMY7BcRw3Kcyu4Io7VhEtzqNyVhmOY2jd30H5jDKWXLXgtOdz9rIZDHQNHFM22DvsTo0Z9LP1jZ0UVxUdM4xGcWUhW1/fMe5/W6XUR3O2bQjlxphWAGNMq4gc7jZSjXsHcFizV5b23h9ffnidJm9bGRHpB4qBruN3KiL34t5lMG3a2EfgHGnTS1vZ+e6HLFwzj/mr5nJoTyv7tzdi+30UluXTsreN9oOdtO3vYLBniKrZFZTPKGX/1kaKq4sY7BsiPhAjHAmRSWXoaeuloCyfod4YvoCf/dsb6WjsJpVIgTFk0hneffJ9CkrziQ3GCRbn8G5TIy1b95HqjZHtS+KzBH/QT8C2SKbiDDX3UlCQhx32YwScZIb84nz8fptMb4aC5jQdpX5STgZsh2jcoeqGiwh92E/HB80UlEbJL43i9/sYHoiRVxThnSffZ9m1i8gvibL8usVsfGELBaX52D6LVDzNmk9ewoI1cxnujzF9YQ0XrZozpmGvV9+6gj2b9tHR2EUkP4f4UAIwXPelte4dQdY58fkFkRMG/VNKTbzxblQe7Zu9OUX5qdY5sdCYB4AHAFauXHnGV5RMOsNzP3qF7tZeult6KZ9eQu38aipmlrLppW0UlkWpnOnWn+fmR2iuP4TtsymuLKR8Wim5RREqZ5ax6aXtHNrbRmP9IQQhvyxK7fwqjHFoP9BJfChOKCeEWIKTdejvGuLNx9cz/8qL2NzcQjaWwumKYWIpyDpkshwZqiFibGI+m3TUh39mIbbPYlZZCXNmVREbiLPltR3kdTlY21oZzKSxbQu7NJem6jySCwoo68ilKG1j2RaZTBbbZ5FXlEvXoR4y6Qz+gJ/rvnQlZdNK+ODlbSRjSS697RJW3rTsrKYILSzL56t/9Xm2vLaDpvoW5lw8g4uvX0JJtTsS64LL5vHyw28SH4yTzWQpqS4mnBvi0k+uOON9KaXOrbNNCO0iUundHVQCHV55MzByiq0aoMUrrxmlfOQ6zSLiA/I5sYrqI3Mch6e+/zx7Nx8gHM2hv2uQ/TsaCQR92D6bvs5BHMchNphgxsJaIvk5RMvy6WjqdCd1D0PHwS62vbaTrpYejDEEwwEs26Kvox/Hccimswz2DYP3/ILjOIRzQ4TzQrQ3dVJ60VK6tyTwNfThL46QSbjtEfgtkskUIkK0KJfchMPypRfR3dTN9JkVhAIBBnuHaPjgALGhOGYgTibrECnLI+D3kRlKEehNkdrSScvMPPx1AwQCPlKpDJWzymja3UL5jNIjo4/ats2yaxax7JrRR111HIdkPEUg5B9TF9FoUR5XfXbNqJ8N98fo7xw40obRuPsQBaVRll+/5Oz+kEqpc+ZsE8JTwN3At73XJ0eU/1REvovbqDwX2GCMyYrIoIisAdYDXwX+93Hbehe4E3jFnOkg/GNwaE8rDR/sp2pOBXs3HyCbdefj7WsfwPLb2D6bcG6I1r1tiAilNcW07W8nOZxk7orZOFmHwvICtr25i1h/zOuB1EM2m8GyLTqbuqmcWeY25npDY9h+i/hQgqa9rcQ/VUrfhztJJZKkTAqrPESgP0A2kYGsQ9YIts/CH/Bh2RZf/L1b2bvlAO8+VUdHPEnDBwfw+W2ihbkMDyZI9gzAUIK4bSEBm2zWoeiiEpKzS7G2DZN1DLH+OFtf24njGPJLciksy+fz/+n2Uw5BsWfTPl79+dsMdA0SyAlw2adWcsmNS8fUTXR4IMbBHU1k0lmq51YiAvu2HOSyT69koGuQxHCSUCRIIpakub6F4gqd4EapyWQs3U5/BlwLlIhIM/CXuIngURG5B2gE7gIwxuwQkUeBnUAG+IbXwwjg6xztdvqs9wPwQ+AnItKAe2ewblyO7DhtBzpo3ddGy94Oejv7ERFsn+3O5+s4pAZT7Nm0n9z8MN2tfRRVFNC2vx1wG0lzojlYlpBMJIkNxWnZ147gTmcptpCKp5g2v5qB7iGGB4bJprM4GYMvYBHzZTHvd+M0Otg9MaQthelIksmALxIgk8pg0g7pVIacaJh193+GylnlVM4qZ/6lc3nhx6+SGE4yc8k06l7YQmIojpPMuJVtYT8k0sTjaQJVucycVckfP/pZ/ubzf09sIEZuUYSCkihiCU8/8BKVs8q5+s7Rh6Fo3H2IJ/7hN0RLopRNKyGVSPHyv74BwKqbl5/y/O7f3sivvvcM6VTGLRBh7opZIO4dSWF5wZFle1p76Wzq/oh/UbcKcLB3mJy8kHsXp5T6SE6bEIwxXzzJR6OOPmaM+RbwrVHK64ATxj02xiTwEsq5tOOdeho+OOBesIxbLeJk3LsEy3Z7HGUzGXo7BtwJ671xiMQSult73eqckjwGO4dIpzKIuF03k7E0liWEoyFsn01eUYRUwp20BnBHOjUZ7P4Mg63tIAYzmMFkwdhgLAvBHaY6vySPtV+77piqnOLKQsJ5YcpqS/D53buHoZ5hcHATQtYgttu/v69rgOuWL8AyQiKeZNr8anyBo3/iUNjPS//6xkkTwvrfbCScFz7SlhAIBSipLuLdX9ex4oYlJ60+SiVSPPX958mJ5hDOdYf0yGaybH9rJ8YxJ4ySmk5lKPVmezsbxhi2vbmL1x59h1Q8hWW7DwVeccdqbJ8+Ba3U2ZoSTypns1k2PPuBO22mMUcHXTvCIJZFKBIiPhQnm3YY6B7CF/RjHPfC3tPWS3/XgNtQ67e8WSe9u4tUlsKqAnKLcvEH/IRy3QblTDKNP+hHehI4eF1IbTCOt1cHxDgY40496WQdHv3bp9izaR9f+rPPkhhO8spP32Lne/W0H+yipKaYwZ4hjOB2GHaAZAbjtyE3QH5ehBXRUpKDCdLJDHbRsRdHX8DHYM8QbQfcJp/S2uJjLvI9rb1HLuiHBUIB+jr6SSXShCOjX2xb9raTTqQoLMs/Umb7bHJyc/AFbNoOdFBcWYjts+lp6yNanMu8lbPP8K941N4tB3jmX16muLKQgpIomXSWd5+qw+e3ufwcTXak1FQwJRJCfChBb2uf27c/47hdmCxxR+bMOmSzBicSIJYTIOsYGIyTSaYxuIPUZdJerZeXR/w5AXILI/S19ZP17jK6D/Wy7Y2d5OTn4PiFwdkBkhURTE+C4C4f1kAGbDDZo9sR79XkCBJ3p77s6+jj4M5DPPlPz9HX4T45PPfiWQz1DbOnrsGdScwWjIAJCmILEvFRUBVlel4BkdwwBcVR8ovziA3EjgytYXAbusWy+NGf/5SOg10MD8SYs3wmV372UlbfejE1F1XxYd0+iiuPdjeNDcaJlkRPePjseGaUzmIGw8U3LMEf8LPxxa2kEikWrJnLFXesPmFwwDOx4dkPyC2IHInJ57cpqS7i/ec2c+knL9G7BKXO0pRICKGcIMYYsuks5ARwAj7M4T72WQdTGMHkhgCBCiCegj2tWFmHjHfB9wd9RxJDOpmht83tWYQFYtwLc1dLL3mSpfOWKEPZFHbcIVObS2J5HrlPduHvTGHM0X62Au4Tx1lDptLPwE0lDFs+kjvbGX4+xpzlM4gW5QKw7JrF9LT20d89iJNnQ1qwcnxuVok59IcyzL59wZGRWtfd/xn+75/8K4lYL/6gj0QsRTqRoXZ+Fa37Okh5vYgO7Ggim83S09rLZZ9eyYd1++hu6SGvKJf4UILYYILP/NEtp2xUrppTQTAcIDYYP1LdlElnMFmHi1bOoXx6KZd9auW4/T0HuwdHfzo6mSaVTBPWhKDUWZkSCcHn91E5q5yu9j4ytSVY7f1IJgsCTnkuTkk+0j+EpAxkjFsNE/JjBhNHtpFOZtwx/W1xn2L26u4RwQRs0pZFGhgsyWAahijvdogPJ8kYh+RFYeLXFeJ7uP2E2AxAFjIlfgI+H/gteq+OYjbHuchns+eDfXQ1dZP2hq7IOg6+mVFMa5xsd8JNKIV+gjdUUvLJo9Uwl396FeXTSnjhodfpbOoivzSf/q4BfH4fyVjyyGB3Q/3DBHOC7FrfwOV3rOYr/+VO1j+ziabdLVTMLGP1rSuYvqDmhLhHCgT93P6Nm3nifz/LQPcQAGLBNeuucJ/iHmczFk9jx1u7CdYcbYcY7o9RVFmo4yMp9RFMiYQAsPTqBXx4sJN00A/GQcRglfpwysPIUBIxBkIgXUmkN4bJCWIGE8dUhITzQmSzDolhdzIdYwC/jS8cJJt2u4+mMlnspgQJn+CPBEinUoR2xohdlQ95NjLgVT+J+2N8YPwWdhLEWGRjWYJZYfjiCNueq8fvWAz3D5MYTpKMp8gkM5jN3WBZiLjtED5jEWhNn1BVMnv5TL7uDdG99Y2dPP+jV4kNxLBGTI+JASfjYNsW/Z0DzFo6nU/+2xvP+PxOX1jL7/7tV2jcdYh0KkPNvEoKSvNPv+JZWH3rCvZs3EdHU9eROS0y6Qy3/tsbTjurm1LnS/OeVja+sJne9n5mLJ7GihuWEC2a3FPBTpmEsObTq/jlj17Bd6gbnCzSn8YZsnB8+W57grEgabCGU+7gcaNcWBzHEAwHScZS2AGbbMhPIBhwB7wTMJksvoYYknUwjiHTncZvQzrXxnco6fYwsgA/WH734m3C4j7KHXNIDieOXOgtO0DMlyXUn3an5PTbBAiQiCdxb20MVn4QK+IjE0+T3txNVf/Jvx1XzioHgVBu6EgPKOO49Ve5BREGe4fJL41+pHMczg1z0ao5H2kbY1FYls9X/vIuPnhlG027DzF9QQ0rblx6Tu5GlDob9XUNPPmPzxEMBwlFgtQ9v5md79Tz5b+4c1LPDz5lEkLjrmYCBmJOFiwL/D5IZbEau3DmVWMSKXceAcdAwI+k0sduQCAZd7+liwiRaJiBeJpUPOleWDMO+G38w1kyxT5IOiCClXGwY1loTeLMzMVXPwRpA94skpIRiFhIvp9IQQTbZ+OIIWMJOfgQvzsWkC/gI7cgQnI4ScZxyAbA5NpkbCBok58J0Ln+ELFZM0cdgqKkuoiVa5fx7lMbMcbQ09aLL+CjYmY5Az2DLFwzj+LKC+dBsfySKNd+/oqJDkOpE2SzWV5++E3yS6JHeu2Fc0N0NHWx6eVtk3qU3ymREJLxJB+8vB1/bgi600g6694V2D6seBpp7CJbXYSxBScvjAzGkKEE+GyckM/9Nm7AyWQJ5AbICQXJxN1xiEwgANEc93mDTJps7yC+YUM27LYvOD4Lf3+WaKePnEiYfhPHsRzsrFttEy2J0hVOkVmch89v44sE8FXlUH7IItnbR/mMMncGtrwwyXgKy28Ttt0Gbn/WRzAcwkmmSLUO89Yv36N+/R5WrV3OlZ+99JgupSLCtZ+/gpmLp/PBK9to+GA/sYEYRRWFrLhhKatvvfiYc2aMoaOxi9hgnOKqwkl/qzsWfZ39NH/YimVbTF9Yc6QBXqnxNNwXIzYQp6y25JjyvMJcDmxvBE0IEysZTxEfTtATTyHxFNgWBgvjB2wf0jeML57C5AQhncYaiGMCNk4khMTTSCZLJieICCQGEqRiGcAgoRBgYDiGYwkkUpDKQMLg6wMr5CObzhKO5FCzcCYDvX04oQipoIOvO43fsomU5JITsuipzMG/IB+/Y1HWJNjv99IzEGewd4hMKkMqmSKVSJFfksdQzxC5BblUziyjr2OAnoEBiquLmLVkOoGQn3efqiOvKJcV1y895jyICDMW1TJjUe0oZ+mo4YEYT/7Tcxz6sBWxBOMYLv3kCq763JoLto6+7oXNvPrzt71nQAz+oJ/b/2Ats5ZMn+jQ1MdMKBI80l3d5z/6pSwxnKRi5uSu1pwS8xjmFkTo7hjAeHMaGNsiW1VIdl4V2TmVZGuKMRis7kGs4SQm7CdbVeS2GjsOTkEOBGxMOOB2CzIGbBuTSEImi8QTbkOtWO7DYo47yQ4pB1IOyUSKru5h2uYHSIfAnxuiYEUVNTfNIbU8j+L8KHO3WNzSWMnCV7LEnjxIvDdGbkEEYyCVShMbiJNfGmXeilnUzK8mmBOgu6WXntZeoqVRFqy5yJ0S02dTWFFA3XNbzvp8vfSvb9DS0EbZtBLKaksoqSnmnSff58O6veP1JzmvOpu7efVnb1NcWUjFjFIqZpSREw3z1PefJxlPTnR46mMmEApw8Q1L6WzqOtJVPT6UIBlPcsmNyyY4ulObEncIIkI24idrDJJxyM4rA9uCeMp9yjc/h2xuCBrakFQGkxfElOVhpRI4Jug29GadI5PVkM2CbbvJIeHdccRTkMkcaYzOeGMNid/GZB36m1pJ5ufg89k4sSy+Ch8Bv5/EQJJkxGblygU07W5m/9ZGgjlBMqkMM5dMY8aiWtoOdHDLPddRUl1MMCdIQVmUtn0dNNUf4ukHXmTGwmkEQv4jxxsI+unrHDjJ2Ti1+FCcPRv3UVJ9dFIb27bILcxly2s7zkuj8Xjbt+UACEdGewUIR0IMdA9yaE8bs5bqXYIaX1d99lJEYNNL23CyDpFoDrd/Yy0186omOrRTmhIJAaAvHMD4LEx+DsZvI7EUIiAZ95kCpyyKmVeBJNI4RblINIjT6gOfe5E3gnvhF6/hGbeHjjhez6FUBoyDFfJjOzbpRMpdz/uGYDD4dqTIXFKMLw5Dzf2EMiAhi1lfWcoXb7yFV376Jof2tBEtyqVseinFlYWICMFwkPYDnSy9+ugYR9VzK6meW+lOBbq7haKKgqPH2jHAnBUzz+o8ZdJZjGNOqBqyfRaJ2Mfs27TxpkhVapz5/D6u/fwVXP7pVSSGk0QKcsY0lPxEmxJVRiJCOp3FGkzg5AZxcoI4xXk4uWEcEfDb4BhM1sEpioLfhxEf2YpiNwkEbEwk5D7NfCQpOO5dg215g8w5YNl489sjluUlDsC2ELEQwN47QO7N04l8cha1X15CxR8t4/LVS/EH/Fx83RJmLqllwZp5lIyYdjKdShMtGb1L6DV3XYZlW3Q0dTHQPUhHYxeBkJ8rP3N2Y/rkFkQorXXHTDrMGMNA9xAL1sw7q21OtFnLZrjdgNOZI2WJ4ST+oI/quZUTGJn6uAuEAkSL8y6IZABT6A4hakNXJIQzpxIC3qB1+W4vE5NKu91Q80rdJ5XjScg6OEV5SH8Mqz+GMVn37iEcxBT6sDv6EQfIzXGrikwWn21hi5DKZDFeX393BwYxBjstOH6LRP8Q1pwS+kvhovwSlha5s4mW1pZQPa+K1oZ2iquLEIGhvhj+gI8Fl84d9bhKqov57f/2Bba/vZv2g51UzCxj8RXzySvMPavzJCLc/Duf4NH/8STtjZ34/D7SiTTVcytYevXp51iejEprivnEF6/ktUfexnEMguAL2NzxB7fok81KjSAX6i3zypUrTV1d3ZiX/zd3/U92t/S5TyRnHExB2G0EDniZ+/BTvgZwHEhnIRyATBZ6hvG19yKAyQnghAL4mzoJDCfx+X0YW0j2xvHnBIgW5tLX2ks2dfTbqNuuYBC/DwlbBK6uonrtRXztzutZVlRL0D6al4cHYrzw4Gvs/WA/IBSW53Pz166jZsQ32a6WHuqe30xzfQultcWsWnsxVbMrzvpcjmawd4j69xvo7xqgZm4Vs5ZNxx/wn37FSay3o5/m+hZsn8X0RbXa7VRNSSKy0Rgz6uBiU+YOoTUTRzCQyri9hSzLreZJGvfCf7jO3OtBhG157y3IDWFlo9i2Rdi2iAH+4nzseBeZWArHGPy5Qfy2TV9rL5YIwWiY+FACgxkxQ7TBSjgU73OYszHN3M8VHZMMACLRHD7zh7cyPBAjnUyTXxI9pj6/s7mbh//7YxjHECmIcGB7Mx/W7ePO//ApZi6eNm7nK68wl5U3LR+37U0GhWX5xwzRrZQ61pRICMYYTLoPyfrdYSnsEfX7ZuQFG/eBNcAdX9q4iSJgg99CEhmytoUvEsJnCf5oDrGuAfy5IaYtm0lVSR6bXtpKKp6itKaYRDxJb2ufO6EOgt9nM2uZOwl9a0MbD/ynh/AH/eTkhVi19mKWX7f4SF3jyb69vvfrOoyB4qoiwB3JdahvmNd+/jYz/rr2gn1OQCk18aZEo3J3/xDZeBYZSEAo4FYJHZ4PwWe73UiP5w0+R8b9LBMK4Av4SIoQxlAR9OMk09h+H4XFUZZdPJNocR4lVUUEw0Fsn0V5bak7a5nfJic/h2vuupyVNy4jk8zQVN/C3i0HKSzLx7JtXnzodd74xbunPZbG3YeODIl9WCQ/h65DPUenr1RKqbMwaRKCiKwVkXoRaRCR+8Zz2/3JQeJ2CBlOHBmSAttyexclUm4V0UjGuD+ZDFYyjWQdfH4fgVCASCxBzsEOkn1DhAM+SkqjrLhmIeGcIHmFEQJhd/Icx7jtAZlMlkAowCUjBl/rbOoiGU9RWJ6P7bMJ5QQpn1HGxhe3MjwQO+WxFJbnHxlt9bBUIk04L3TMU5FKKXWmJkWVkYjYwD8BNwLNwPsi8pQxZue47CCRxfFFoNRgd/YjPUOYoM8d4rokCr6Rw0F7yQAHSaawxIdYFuUDMabnBInMr+H637rSHYbawNu/2kChN8yzbdvMWjKNXe/tYdaSaSRj7sikV9y+mrYDnWTSGXx+H73t/RhjqJ5ztCHYti1AGOgaPGVj5+pbV/DL7z6NP+gnFAmSSqbpbunhhq9cfcpJbJRS6nQmRUIAVgMNxph9ACLyc+B2YFwSwtCAW0Xk1BRh8nOweofc6qLCXMjxZt7yhoImkwFLENynkY1tk+s4BHuGsCJB1tx6MZd/ahXgtk1k0hk2vrDl6FO9fpv/+KPfxxdwB6CrnlNBODfE20++z8YXNpPNOAQjAarnVBwzX0A264AxRItP3V10zvKZ3PZ7N/L6L96lo3GQQNjPJ754JStuWHrK9ZRS6nQmS0KoBppG/N4MXHr8QiJyL3AvwLRpY+9RU1VVBJYD6TQmL0Q2Gj52vgNjMALCILM/nSTZ6qd7PeQE8sgZSmA19VBYns8n772BNbcd7a0lIlz3xStZeNk8mutb8Af9zFo2fdSRQa/+3BrW3HYJiaEETtbhJ3/9GN2tvRSW55NOZuhu6WXV2mVH5kA+lUWXz2f+pXNJDCUI5gSPGZJBKaXO1mS5kozWNeaEBySMMQ8AD4D7HMJYN15anEdFRRFtrX3eHYDl/gDGZ/DlGMqmC9MXliE+iEaL+MSNy7C7E8QGYkxbUMPMxdNGvfCKCJUzy6mcWX7aOAJBP4Gg25f/i/d/hjd/+R4NH+wnJy/Mdb91JZecwbd827bHlDyUUmqsJktCaAZGjslcA7SM5w7+z9/czb+9/0E6u3vdYSeChuLFab765VWsKF3G9JxppNJZEsk0uTlBr07/3CmpKuIzf3grxpw4bpBSSk2EyZIQ3gfmishM4BCwDvjSeO6gpDiPx37w++za20Zjcw/zZpcz57gpF4MBH8HA+T0lmgyUUpPFpEgIxpiMiPwB8DxgAz8yxuwY7/3YtsXieVUsnuRD0Cql1ESYFAkBwBjzDPDMRMehlFJTlXZcV0opBWhCUEop5dGEoJRSCtCEoJRSynPBTpAjIp3AwbNcvQToGsdwxstkjEtjGpvJGBNMzrg0prE5VzFNN8aUjvbBBZsQPgoRqTvZjEETaTLGpTGNzWSMCSZnXBrT2ExETFplpJRSCtCEoJRSyjNVE8IDEx3ASUzGuDSmsZmMMcHkjEtjGpvzHtOUbENQSil1oql6h6CUUuo4mhCUUkoBUzAhiMhaEakXkQYRue887O+AiGwTkc0iUueVFYnIiyKyx3stHLH8/V5s9SJy84jyS7ztNIjI9+QMxs0WkR+JSIeIbB9RNm4xiEhQRB7xyteLyIyzjOmvROSQd642i8it5zmmWhF5VUR2icgOEfnjiT5Xp4hpos9VSEQ2iMgWL67/OgnO1climtBz5a1ni8gHIvL0RJ+nUzLGTJkf3KG19wKzgACwBVh4jvd5ACg5rux/APd57+8DvuO9X+jFFARmerHa3mcbgMtwZ5d7FrjlDGK4GlgBbD8XMQC/D/wf7/064JGzjOmvgP84yrLnK6ZKYIX3Pg/40Nv3hJ2rU8Q00edKgFzvvR9YD6yZ4HN1spgm9Fx5y34T+Cnw9GT4/3fSOM92xQvxxzuZz4/4/X7g/nO8zwOcmBDqgUrvfSVQP1o8uPNDXOYts3tE+ReBfz7DOGZw7MV33GI4vIz33of7dKWcRUwn+4973mI6br9PAjdOhnM1SkyT5lwBOcAm3HnQJ8W5Oi6mCT1XuDNAvgxcx9GEMCnO0/E/U63KqBpoGvF7s1d2LhngBRHZKCL3emXlxphWAO+17DTxVXvvjy//KMYzhiPrGGMyQD9QfJZx/YGIbBW3SunwbfR5j8m77b4Y91vmpDhXx8UEE3yuvGqQzUAH8KIxZsLP1Uligok9V38P/AngjCibFP+mjjfVEsJo9e7nut/tFcaYFcAtwDdE5OpTLHuy+M5n3GcTw3jF9wNgNrAcaAX+10TEJCK5wC+Bf2eMGTjVoucrrlFimvBzZYzJGmOW434DXi0ii091COcjrpPENGHnSkRuAzqMMRtPFffIVc51TKcy1RJCM1A74vcaoOVc7tAY0+K9dgBPAKuBdhGpBPBeO04TX7P3fjzjHs8YjqwjIj4gH+g504CMMe3ef2gH+L+45+q8xiQiftwL78PGmMe94gk9V6PFNBnO1WHGmD7gNWAtk+Tf1ciYJvhcXQF8WkQOAD8HrhORf2WSnKfjTbWE8D4wV0RmikgAtwHmqXO1MxGJiEje4ffATcB2b593e4vdjVsvjFe+zus1MBOYC2zwbikHRWSN17PgqyPWOVvjGcPIbd0JvGK8Cs0zcfg/iOczuOfqvMXkbeOHwC5jzHdHfDRh5+pkMU2Cc1UqIgXe+zBwA7B7gs/VqDFN5LkyxtxvjKkxxszAvd68Yoz58kSep1M6m4aHC/kHuBW3p8Ze4M/P8b5m4fYY2ALsOLw/3Pq9l4E93mvRiHX+3IutnhE9iYCVuP+Q9wL/yJk1+v0M91Y5jftt4p7xjAEIAb8AGnB7Qsw6y5h+AmwDtnr/yCvPc0xX4t5qbwU2ez+3TuS5OkVME32ulgIfePvfDvyX8f63fRbn6mQxTei5GrHNaznaqDyh//9O9qNDVyillAKmXpWRUkqpk9CEoJRSCtCEoJRSyqMJQSmlFKAJQSmllEcTglJKKUATglJKKc//DyehUoBfMvP0AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "\n",
+    "x = customers[\"Detergents_Paper\"]\n",
+    "y = customers[\"Milk\"]\n",
+    "\n",
+    "plt.scatter(x,y, c=customers['labels'], alpha= 0.5)\n",
+    "plt.title(\"KMeans\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(x,y, c=customers['labels_DBSCAN'], alpha= 0.5)\n",
+    "plt.title(\"DBSCAN\")\n",
+    "plt.show()\n",
+    "\n",
+    "# KMeans seems to be more reliable in creating clusters for detergents_paper vs milk"
    ]
   },
   {
@@ -250,11 +861,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 93,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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 code here:\n"
+    "# Your code here:\n",
+    "\n",
+    "x = customers[\"Grocery\"]\n",
+    "y = customers[\"Fresh\"]\n",
+    "\n",
+    "plt.scatter(x,y, c=customers['labels'], alpha= 0.5)\n",
+    "plt.title(\"KMeans\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(x,y, c=customers['labels_DBSCAN'], alpha= 0.5)\n",
+    "plt.title(\"DBSCAN\")\n",
+    "plt.show()\n",
+    "\n",
+    "# KMeans is more reliable in creating clusters for grocery vs fresh"
    ]
   },
   {
@@ -266,11 +915,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 95,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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 code here:"
+    "# Your code here:\n",
+    "\n",
+    "x = customers[\"Frozen\"]\n",
+    "y = customers[\"Delicassen\"]\n",
+    "\n",
+    "plt.scatter(x,y, c=customers['labels'], alpha= 0.5)\n",
+    "plt.title(\"KMeans\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(x,y, c=customers['labels_DBSCAN'], alpha= 0.5)\n",
+    "plt.title(\"DBSCAN\")\n",
+    "plt.show()\n",
+    "\n",
+    "# frozen and delicassen are correlated so neither one of the algorithms performs well"
    ]
   },
   {
@@ -282,11 +969,314 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 99,
    "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>2.000000</td>\n",
+       "      <td>2.623077</td>\n",
+       "      <td>8165.915385</td>\n",
+       "      <td>8748.515385</td>\n",
+       "      <td>13905.492308</td>\n",
+       "      <td>1441.630769</td>\n",
+       "      <td>5991.146154</td>\n",
+       "      <td>1535.007692</td>\n",
+       "      <td>-0.538462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.003367</td>\n",
+       "      <td>2.505051</td>\n",
+       "      <td>13075.898990</td>\n",
+       "      <td>3218.737374</td>\n",
+       "      <td>3855.696970</td>\n",
+       "      <td>3620.400673</td>\n",
+       "      <td>776.875421</td>\n",
+       "      <td>1242.249158</td>\n",
+       "      <td>0.646465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.846154</td>\n",
+       "      <td>2.615385</td>\n",
+       "      <td>25770.769231</td>\n",
+       "      <td>35160.384615</td>\n",
+       "      <td>41977.384615</td>\n",
+       "      <td>6844.538462</td>\n",
+       "      <td>19867.384615</td>\n",
+       "      <td>7880.307692</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       2.000000  2.623077   8165.915385   8748.515385  13905.492308   \n",
+       "1       1.003367  2.505051  13075.898990   3218.737374   3855.696970   \n",
+       "2       1.846154  2.615385  25770.769231  35160.384615  41977.384615   \n",
+       "\n",
+       "             Frozen  Detergents_Paper   Delicassen  labels_DBSCAN  \n",
+       "labels                                                             \n",
+       "0       1441.630769       5991.146154  1535.007692      -0.538462  \n",
+       "1       3620.400673        776.875421  1242.249158       0.646465  \n",
+       "2       6844.538462      19867.384615  7880.307692      -1.000000  "
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "\n",
+    "customers.groupby(by = [\"labels\"]).agg(\"mean\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "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>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>0.607843</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>0.000000</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>1.000000</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>1.000000</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>0.000000</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>0.000000</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>1.000000</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>1.000000</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>1.000000</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  0.607843  \n",
+       " 0              720.200000       3561.000000   258.200000  0.000000  \n",
+       " 1             2131.430769        496.576923   741.946154  1.000000  \n",
+       " 2             1276.400000        416.200000   784.800000  1.000000  \n",
+       " 3              609.600000       5780.000000   572.800000  0.000000  \n",
+       " 4              994.000000       4675.571429  1826.428571  0.000000  \n",
+       " 5             1513.181818        333.954545   669.045455  1.000000  \n",
+       " 6             6780.333333        386.500000   613.666667  1.000000  \n",
+       " 7             2575.400000        227.400000   941.000000  1.000000  "
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "customers.groupby(by = [\"labels_DBSCAN\"]).agg(\"mean\")"
    ]
   },
   {
@@ -302,7 +1292,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your observations here"
+    "# Your observations here\n",
+    "\n",
+    "#KMeans appears to perform better, as there are less clusters thus their means are more different between one another.\n",
+    "# For DBSCAN a lot of labels were created where the variance between each variable is not big enough to make the clusters relevant\n"
    ]
   },
   {
@@ -364,7 +1357,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -378,7 +1371,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,