diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 9a2d1ae..c5a1ea4 100755
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,8 @@
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import seaborn as sn"
    ]
   },
   {
@@ -38,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,11 +68,404 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "customers.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(440, 8)\n",
+      "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\n",
+      "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\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(customers.shape)\n",
+    "print(customers.dtypes)\n",
+    "print(customers.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\pedro\\AppData\\Local\\Temp\\ipykernel_32744\\2006132591.py:2: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  mask = np.zeros_like(corr, dtype=np.bool)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr=np.abs(customers.corr())\n",
+    "mask = np.zeros_like(corr, dtype=np.bool)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "f, ax = plt.subplots(figsize=(6, 6))\n",
+    "cmap = sn.diverging_palette(220, 10, as_cmap=True)\n",
+    "sn.heatmap(corr, mask=mask,  vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>440.0</td>\n",
+       "      <td>440.0</td>\n",
+       "      <td>440.0</td>\n",
+       "      <td>440.0</td>\n",
+       "      <td>440.0</td>\n",
+       "      <td>440.0</td>\n",
+       "      <td>440.0</td>\n",
+       "      <td>440.0</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>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3127.75</td>\n",
+       "      <td>1533.0</td>\n",
+       "      <td>2153.0</td>\n",
+       "      <td>742.25</td>\n",
+       "      <td>256.75</td>\n",
+       "      <td>408.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8504.0</td>\n",
+       "      <td>3627.0</td>\n",
+       "      <td>4755.5</td>\n",
+       "      <td>1526.0</td>\n",
+       "      <td>816.5</td>\n",
+       "      <td>965.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>16933.75</td>\n",
+       "      <td>7190.25</td>\n",
+       "      <td>10655.75</td>\n",
+       "      <td>3554.25</td>\n",
+       "      <td>3922.0</td>\n",
+       "      <td>1820.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>112151.0</td>\n",
+       "      <td>73498.0</td>\n",
+       "      <td>92780.0</td>\n",
+       "      <td>60869.0</td>\n",
+       "      <td>40827.0</td>\n",
+       "      <td>47943.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IQR</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>13806.0</td>\n",
+       "      <td>5657.25</td>\n",
+       "      <td>8502.75</td>\n",
+       "      <td>2812.0</td>\n",
+       "      <td>3665.25</td>\n",
+       "      <td>1412.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>outliers_low</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>outliers_up</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Channel    Region         Fresh         Milk      Grocery  \\\n",
+       "count            440.0     440.0         440.0        440.0        440.0   \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                1.0       1.0           3.0         55.0          3.0   \n",
+       "25%                1.0       2.0       3127.75       1533.0       2153.0   \n",
+       "50%                1.0       3.0        8504.0       3627.0       4755.5   \n",
+       "75%                2.0       3.0      16933.75      7190.25     10655.75   \n",
+       "max                2.0       3.0      112151.0      73498.0      92780.0   \n",
+       "IQR                1.0       1.0       13806.0      5657.25      8502.75   \n",
+       "outliers_low     False     False         False        False        False   \n",
+       "outliers_up      False     False          True         True         True   \n",
+       "\n",
+       "                   Frozen Detergents_Paper   Delicassen  \n",
+       "count               440.0            440.0        440.0  \n",
+       "mean          3071.931818      2881.493182  1524.870455  \n",
+       "std           4854.673333      4767.854448  2820.105937  \n",
+       "min                  25.0              3.0          3.0  \n",
+       "25%                742.25           256.75       408.25  \n",
+       "50%                1526.0            816.5        965.5  \n",
+       "75%               3554.25           3922.0      1820.25  \n",
+       "max               60869.0          40827.0      47943.0  \n",
+       "IQR                2812.0          3665.25       1412.0  \n",
+       "outliers_low        False            False        False  \n",
+       "outliers_up          True             True         True  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "stats = customers.describe().transpose()\n",
+    "stats['IQR'] = stats['75%'] - stats['25%']\n",
+    "stats['outliers_low'] = (stats['25%'] - 1.5*stats['IQR'])>stats['min']\n",
+    "stats['outliers_up'] = (stats['75%'] + 1.5*stats['IQR'])<stats['max']\n",
+    "stats.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Channel                 1.322727\n",
+      "Region                  2.543182\n",
+      "Fresh               12000.297727\n",
+      "Milk                 5796.265909\n",
+      "Grocery              7951.277273\n",
+      "Frozen               3071.931818\n",
+      "Detergents_Paper     2881.493182\n",
+      "Delicassen           1524.870455\n",
+      "dtype: float64\n",
+      "Channel                1.0\n",
+      "Region                 3.0\n",
+      "Fresh               8504.0\n",
+      "Milk                3627.0\n",
+      "Grocery             4755.5\n",
+      "Frozen              1526.0\n",
+      "Detergents_Paper     816.5\n",
+      "Delicassen           965.5\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(customers.mean())\n",
+    "print(customers.median())"
    ]
   },
   {
@@ -94,20 +488,229 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler=StandardScaler()\n",
+    "scaler.fit(customers)\n",
+    "\n",
+    "x_customers = scaler.transform(customers)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your comment here"
+    "# Your comment here\n",
+    "customers_scaled = pd.DataFrame(x_customers, columns = customers.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>0</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.052933</td>\n",
+       "      <td>0.523568</td>\n",
+       "      <td>-0.041115</td>\n",
+       "      <td>-0.589367</td>\n",
+       "      <td>-0.043569</td>\n",
+       "      <td>-0.066339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.391302</td>\n",
+       "      <td>0.544458</td>\n",
+       "      <td>0.170318</td>\n",
+       "      <td>-0.270136</td>\n",
+       "      <td>0.086407</td>\n",
+       "      <td>0.089151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.447029</td>\n",
+       "      <td>0.408538</td>\n",
+       "      <td>-0.028157</td>\n",
+       "      <td>-0.137536</td>\n",
+       "      <td>0.133232</td>\n",
+       "      <td>2.243293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.100111</td>\n",
+       "      <td>-0.624020</td>\n",
+       "      <td>-0.392977</td>\n",
+       "      <td>0.687144</td>\n",
+       "      <td>-0.498588</td>\n",
+       "      <td>0.093411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.840239</td>\n",
+       "      <td>-0.052396</td>\n",
+       "      <td>-0.079356</td>\n",
+       "      <td>0.173859</td>\n",
+       "      <td>-0.231918</td>\n",
+       "      <td>1.299347</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>1.401312</td>\n",
+       "      <td>0.848446</td>\n",
+       "      <td>0.850760</td>\n",
+       "      <td>2.075222</td>\n",
+       "      <td>-0.566831</td>\n",
+       "      <td>0.241091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>2.155293</td>\n",
+       "      <td>-0.592142</td>\n",
+       "      <td>-0.757165</td>\n",
+       "      <td>0.296561</td>\n",
+       "      <td>-0.585519</td>\n",
+       "      <td>0.291501</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.200326</td>\n",
+       "      <td>1.314671</td>\n",
+       "      <td>2.348386</td>\n",
+       "      <td>-0.543380</td>\n",
+       "      <td>2.511218</td>\n",
+       "      <td>0.121456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.135384</td>\n",
+       "      <td>-0.517536</td>\n",
+       "      <td>-0.602514</td>\n",
+       "      <td>-0.419441</td>\n",
+       "      <td>-0.569770</td>\n",
+       "      <td>0.213046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.729307</td>\n",
+       "      <td>-0.555924</td>\n",
+       "      <td>-0.573227</td>\n",
+       "      <td>-0.620094</td>\n",
+       "      <td>-0.504888</td>\n",
+       "      <td>-0.522869</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>440 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Channel    Region     Fresh      Milk   Grocery    Frozen  \\\n",
+       "0    1.448652  0.590668  0.052933  0.523568 -0.041115 -0.589367   \n",
+       "1    1.448652  0.590668 -0.391302  0.544458  0.170318 -0.270136   \n",
+       "2    1.448652  0.590668 -0.447029  0.408538 -0.028157 -0.137536   \n",
+       "3   -0.690297  0.590668  0.100111 -0.624020 -0.392977  0.687144   \n",
+       "4    1.448652  0.590668  0.840239 -0.052396 -0.079356  0.173859   \n",
+       "..        ...       ...       ...       ...       ...       ...   \n",
+       "435 -0.690297  0.590668  1.401312  0.848446  0.850760  2.075222   \n",
+       "436 -0.690297  0.590668  2.155293 -0.592142 -0.757165  0.296561   \n",
+       "437  1.448652  0.590668  0.200326  1.314671  2.348386 -0.543380   \n",
+       "438 -0.690297  0.590668 -0.135384 -0.517536 -0.602514 -0.419441   \n",
+       "439 -0.690297  0.590668 -0.729307 -0.555924 -0.573227 -0.620094   \n",
+       "\n",
+       "     Detergents_Paper  Delicassen  \n",
+       "0           -0.043569   -0.066339  \n",
+       "1            0.086407    0.089151  \n",
+       "2            0.133232    2.243293  \n",
+       "3           -0.498588    0.093411  \n",
+       "4           -0.231918    1.299347  \n",
+       "..                ...         ...  \n",
+       "435         -0.566831    0.241091  \n",
+       "436         -0.585519    0.291501  \n",
+       "437          2.511218    0.121456  \n",
+       "438         -0.569770    0.213046  \n",
+       "439         -0.504888   -0.522869  \n",
+       "\n",
+       "[440 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers_scaled"
    ]
   },
   {
@@ -135,7 +738,8 @@
     "\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "\"\"\" already done in the previous challenge \"\"\""
    ]
   },
   {
@@ -149,13 +753,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KMeans(n_clusters=5)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "from sklearn import cluster, datasets\n",
+    "kmeans = cluster.KMeans(n_clusters=5)\n",
+    "kmeans.fit(customers_scaled)"
    ]
   },
   {
@@ -167,11 +785,263 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "customers[\"labels\"] = kmeans.labels_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    211\n",
+       "0    125\n",
+       "1     91\n",
+       "3     11\n",
+       "4      2\n",
+       "Name: labels, dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers[\"labels\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "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",
+       "      <th>labels_DBSCAN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.052933</td>\n",
+       "      <td>0.523568</td>\n",
+       "      <td>-0.041115</td>\n",
+       "      <td>-0.589367</td>\n",
+       "      <td>-0.043569</td>\n",
+       "      <td>-0.066339</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.391302</td>\n",
+       "      <td>0.544458</td>\n",
+       "      <td>0.170318</td>\n",
+       "      <td>-0.270136</td>\n",
+       "      <td>0.086407</td>\n",
+       "      <td>0.089151</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.447029</td>\n",
+       "      <td>0.408538</td>\n",
+       "      <td>-0.028157</td>\n",
+       "      <td>-0.137536</td>\n",
+       "      <td>0.133232</td>\n",
+       "      <td>2.243293</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.100111</td>\n",
+       "      <td>-0.624020</td>\n",
+       "      <td>-0.392977</td>\n",
+       "      <td>0.687144</td>\n",
+       "      <td>-0.498588</td>\n",
+       "      <td>0.093411</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.840239</td>\n",
+       "      <td>-0.052396</td>\n",
+       "      <td>-0.079356</td>\n",
+       "      <td>0.173859</td>\n",
+       "      <td>-0.231918</td>\n",
+       "      <td>1.299347</td>\n",
+       "      <td>0</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",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>1.401312</td>\n",
+       "      <td>0.848446</td>\n",
+       "      <td>0.850760</td>\n",
+       "      <td>2.075222</td>\n",
+       "      <td>-0.566831</td>\n",
+       "      <td>0.241091</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>2.155293</td>\n",
+       "      <td>-0.592142</td>\n",
+       "      <td>-0.757165</td>\n",
+       "      <td>0.296561</td>\n",
+       "      <td>-0.585519</td>\n",
+       "      <td>0.291501</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.200326</td>\n",
+       "      <td>1.314671</td>\n",
+       "      <td>2.348386</td>\n",
+       "      <td>-0.543380</td>\n",
+       "      <td>2.511218</td>\n",
+       "      <td>0.121456</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.135384</td>\n",
+       "      <td>-0.517536</td>\n",
+       "      <td>-0.602514</td>\n",
+       "      <td>-0.419441</td>\n",
+       "      <td>-0.569770</td>\n",
+       "      <td>0.213046</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.729307</td>\n",
+       "      <td>-0.555924</td>\n",
+       "      <td>-0.573227</td>\n",
+       "      <td>-0.620094</td>\n",
+       "      <td>-0.504888</td>\n",
+       "      <td>-0.522869</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>440 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Channel    Region     Fresh      Milk   Grocery    Frozen  \\\n",
+       "0    1.448652  0.590668  0.052933  0.523568 -0.041115 -0.589367   \n",
+       "1    1.448652  0.590668 -0.391302  0.544458  0.170318 -0.270136   \n",
+       "2    1.448652  0.590668 -0.447029  0.408538 -0.028157 -0.137536   \n",
+       "3   -0.690297  0.590668  0.100111 -0.624020 -0.392977  0.687144   \n",
+       "4    1.448652  0.590668  0.840239 -0.052396 -0.079356  0.173859   \n",
+       "..        ...       ...       ...       ...       ...       ...   \n",
+       "435 -0.690297  0.590668  1.401312  0.848446  0.850760  2.075222   \n",
+       "436 -0.690297  0.590668  2.155293 -0.592142 -0.757165  0.296561   \n",
+       "437  1.448652  0.590668  0.200326  1.314671  2.348386 -0.543380   \n",
+       "438 -0.690297  0.590668 -0.135384 -0.517536 -0.602514 -0.419441   \n",
+       "439 -0.690297  0.590668 -0.729307 -0.555924 -0.573227 -0.620094   \n",
+       "\n",
+       "     Detergents_Paper  Delicassen  labels  labels_DBSCAN  \n",
+       "0           -0.043569   -0.066339       0             -1  \n",
+       "1            0.086407    0.089151       0             -1  \n",
+       "2            0.133232    2.243293       0             -1  \n",
+       "3           -0.498588    0.093411       2              1  \n",
+       "4           -0.231918    1.299347       0             -1  \n",
+       "..                ...         ...     ...            ...  \n",
+       "435         -0.566831    0.241091       2             -1  \n",
+       "436         -0.585519    0.291501       2             -1  \n",
+       "437          2.511218    0.121456       0             -1  \n",
+       "438         -0.569770    0.213046       2              1  \n",
+       "439         -0.504888   -0.522869       2              1  \n",
+       "\n",
+       "[440 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers_scaled"
    ]
   },
   {
@@ -185,13 +1055,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "metadata": {
     "scrolled": true
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DBSCAN(min_samples=6)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "dbscan = cluster.DBSCAN(eps=0.5, min_samples=6)\n",
+    "dbscan.fit(customers_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "customers[\"labels_DBSCAN\"] = dbscan.labels_"
    ]
   },
   {
@@ -203,11 +1095,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1    294\n",
+       " 0    124\n",
+       " 1     17\n",
+       " 2      5\n",
+       "Name: labels_DBSCAN, dtype: int64"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "customers[\"labels_DBSCAN\"].value_counts()"
    ]
   },
   {
@@ -234,11 +1142,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "x = 'Detergents_Paper'\n",
+    "y = 'Milk'\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.scatter(customers_scaled[x], customers_scaled[y], c=customers_scaled['labels'], cmap='viridis')\n",
+    "plt.xlabel(x)\n",
+    "plt.ylabel(y)\n",
+    "plt.title('K-Means Clustering')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.scatter(customers_scaled[x], customers_scaled[y], c=customers_scaled['labels_DBSCAN'], cmap='viridis')\n",
+    "plt.xlabel(x)\n",
+    "plt.ylabel(y)\n",
+    "plt.title('DBSCAN Clustering')\n",
+    "plt.show()"
    ]
   },
   {
@@ -250,11 +1184,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "x = 'Grocery'\n",
+    "y = 'Fresh'\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.scatter(customers_scaled[x], customers_scaled[y], c=customers_scaled['labels'], cmap='viridis')\n",
+    "plt.xlabel(x)\n",
+    "plt.ylabel(y)\n",
+    "plt.title('K-Means Clustering')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.scatter(customers_scaled[x], customers_scaled[y], c=customers_scaled['labels_DBSCAN'], cmap='viridis')\n",
+    "plt.xlabel(x)\n",
+    "plt.ylabel(y)\n",
+    "plt.title('DBSCAN Clustering')\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -266,11 +1227,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:"
+    "# Your code here:\n",
+    "x = 'Frozen'\n",
+    "y = 'Delicassen'\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.scatter(customers_scaled[x], customers_scaled[y], c=customers_scaled['labels'], cmap='viridis')\n",
+    "plt.xlabel(x)\n",
+    "plt.ylabel(y)\n",
+    "plt.title('K-Means Clustering')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.scatter(customers_scaled[x], customers_scaled[y], c=customers_scaled['labels_DBSCAN'], cmap='viridis')\n",
+    "plt.xlabel(x)\n",
+    "plt.ylabel(y)\n",
+    "plt.title('DBSCAN Clustering')\n",
+    "plt.show()"
    ]
   },
   {
@@ -282,11 +1269,232 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "    </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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7877.640000</td>\n",
+       "      <td>8913.512000</td>\n",
+       "      <td>14212.624000</td>\n",
+       "      <td>1339.280000</td>\n",
+       "      <td>6149.592000</td>\n",
+       "      <td>1537.168000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12183.945055</td>\n",
+       "      <td>3254.714286</td>\n",
+       "      <td>4130.923077</td>\n",
+       "      <td>3458.252747</td>\n",
+       "      <td>860.263736</td>\n",
+       "      <td>1149.934066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13891.473934</td>\n",
+       "      <td>3297.379147</td>\n",
+       "      <td>3814.625592</td>\n",
+       "      <td>3490.218009</td>\n",
+       "      <td>788.028436</td>\n",
+       "      <td>1301.658768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16911.090909</td>\n",
+       "      <td>34864.363636</td>\n",
+       "      <td>46126.181818</td>\n",
+       "      <td>3245.454545</td>\n",
+       "      <td>23008.090909</td>\n",
+       "      <td>4177.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>34782.000000</td>\n",
+       "      <td>30367.000000</td>\n",
+       "      <td>16898.000000</td>\n",
+       "      <td>48701.500000</td>\n",
+       "      <td>755.500000</td>\n",
+       "      <td>26776.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Fresh          Milk       Grocery        Frozen  \\\n",
+       "labels                                                           \n",
+       "0        7877.640000   8913.512000  14212.624000   1339.280000   \n",
+       "1       12183.945055   3254.714286   4130.923077   3458.252747   \n",
+       "2       13891.473934   3297.379147   3814.625592   3490.218009   \n",
+       "3       16911.090909  34864.363636  46126.181818   3245.454545   \n",
+       "4       34782.000000  30367.000000  16898.000000  48701.500000   \n",
+       "\n",
+       "        Detergents_Paper    Delicassen  \n",
+       "labels                                  \n",
+       "0            6149.592000   1537.168000  \n",
+       "1             860.263736   1149.934066  \n",
+       "2             788.028436   1301.658768  \n",
+       "3           23008.090909   4177.363636  \n",
+       "4             755.500000  26776.000000  "
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "customers.groupby(\"labels\").agg({'Fresh':\"mean\",'Milk':\"mean\",'Grocery':\"mean\",'Frozen':\"mean\",'Detergents_Paper':\"mean\",'Delicassen':\"mean\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "    </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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>-1</th>\n",
+       "      <td>13659.894558</td>\n",
+       "      <td>7749.578231</td>\n",
+       "      <td>10720.180272</td>\n",
+       "      <td>3637.918367</td>\n",
+       "      <td>4078.870748</td>\n",
+       "      <td>1924.353741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8449.104839</td>\n",
+       "      <td>1882.580645</td>\n",
+       "      <td>2413.483871</td>\n",
+       "      <td>2006.548387</td>\n",
+       "      <td>494.532258</td>\n",
+       "      <td>729.548387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7742.529412</td>\n",
+       "      <td>1962.058824</td>\n",
+       "      <td>2275.529412</td>\n",
+       "      <td>1230.823529</td>\n",
+       "      <td>352.823529</td>\n",
+       "      <td>584.117647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16962.000000</td>\n",
+       "      <td>1037.200000</td>\n",
+       "      <td>1774.600000</td>\n",
+       "      <td>2473.200000</td>\n",
+       "      <td>269.800000</td>\n",
+       "      <td>957.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Fresh         Milk       Grocery       Frozen  \\\n",
+       "labels_DBSCAN                                                         \n",
+       "-1             13659.894558  7749.578231  10720.180272  3637.918367   \n",
+       " 0              8449.104839  1882.580645   2413.483871  2006.548387   \n",
+       " 1              7742.529412  1962.058824   2275.529412  1230.823529   \n",
+       " 2             16962.000000  1037.200000   1774.600000  2473.200000   \n",
+       "\n",
+       "               Detergents_Paper   Delicassen  \n",
+       "labels_DBSCAN                                 \n",
+       "-1                  4078.870748  1924.353741  \n",
+       " 0                   494.532258   729.548387  \n",
+       " 1                   352.823529   584.117647  \n",
+       " 2                   269.800000   957.800000  "
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "customers.groupby(\"labels_DBSCAN\").agg({'Fresh':\"mean\",'Milk':\"mean\",'Grocery':\"mean\",'Frozen':\"mean\",'Detergents_Paper':\"mean\",'Delicassen':\"mean\"})"
    ]
   },
   {
@@ -302,7 +1510,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your observations here"
+    "# Your observations here\n",
+    "\"\"\" K-Means\"\"\""
    ]
   },
   {
@@ -364,7 +1573,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -378,7 +1587,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,