From 629c19edb5933221601bfad0baf668b5c64a857d Mon Sep 17 00:00:00 2001
From: Fran Tamarit <fcodpaula.tamarit@gmail.com>
Date: Thu, 19 Mar 2020 18:45:09 +0100
Subject: [PATCH 1/2] solving first challenges

---
 .../your-code/main.ipynb                      | 469 ++++++++++++++++--
 1 file changed, 437 insertions(+), 32 deletions(-)

diff --git a/module-3/lab-unsupervised-learning/your-code/main.ipynb b/module-3/lab-unsupervised-learning/your-code/main.ipynb
index 9d986f93..da7ec31e 100644
--- a/module-3/lab-unsupervised-learning/your-code/main.ipynb
+++ b/module-3/lab-unsupervised-learning/your-code/main.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,20 +67,342 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "execution_count": 22,
+   "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  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Channel              0.760951\n",
+       "Region              -1.283627\n",
+       "Fresh                2.561323\n",
+       "Milk                 4.053755\n",
+       "Grocery              3.587429\n",
+       "Frozen               5.907986\n",
+       "Detergents_Paper     3.631851\n",
+       "Delicassen          11.151586\n",
+       "dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "display(customers.describe())\n",
+    "display(customers.skew())\n",
+    "customers.corr()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your observations here"
+    "# Your observations here\n",
+    "#The dataset contains all numerical data, without nulls values. There are no extreme correlations besides between Grocery\n",
+    "#and Detergents_Paper"
    ]
   },
   {
@@ -127,15 +449,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.44865163,  0.59066829,  0.05293319, ..., -0.58936716,\n",
+       "        -0.04356873, -0.06633906],\n",
+       "       [ 1.44865163,  0.59066829, -0.39130197, ..., -0.27013618,\n",
+       "         0.08640684,  0.08915105],\n",
+       "       [ 1.44865163,  0.59066829, -0.44702926, ..., -0.13753572,\n",
+       "         0.13323164,  2.24329255],\n",
+       "       ...,\n",
+       "       [ 1.44865163,  0.59066829,  0.20032554, ..., -0.54337975,\n",
+       "         2.51121768,  0.12145607],\n",
+       "       [-0.69029709,  0.59066829, -0.13538389, ..., -0.41944059,\n",
+       "        -0.56977032,  0.21304614],\n",
+       "       [-0.69029709,  0.59066829, -0.72930698, ..., -0.62009417,\n",
+       "        -0.50488752, -0.52286938]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your import here:\n",
     "\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "customers_scale = scaler.fit_transform(customers)"
    ]
   },
   {
@@ -149,13 +497,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "kmeans = KMeans(n_clusters=10, random_state=42).fit(customers_scale)\n",
+    "\n",
+    "customers['labels'] = kmeans.labels_"
    ]
   },
   {
@@ -167,11 +521,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    158\n",
+       "1     90\n",
+       "7     79\n",
+       "4     33\n",
+       "2     30\n",
+       "5     23\n",
+       "8     19\n",
+       "3      5\n",
+       "9      2\n",
+       "6      1\n",
+       "Name: labels, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "\n",
+    "customers['labels'].value_counts()"
    ]
   },
   {
@@ -185,13 +562,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "\n",
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "clustering = DBSCAN().fit(customers_scale)\n",
+    "\n",
+    "customers['labels_DBSCAN'] = clustering.labels_"
    ]
   },
   {
@@ -203,11 +586,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1    255\n",
+       " 1    130\n",
+       " 5     22\n",
+       " 4      7\n",
+       " 6      6\n",
+       " 2      5\n",
+       " 7      5\n",
+       " 3      5\n",
+       " 0      5\n",
+       "Name: labels_DBSCAN, dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "customers['labels_DBSCAN'].value_counts()"
    ]
   },
   {
@@ -238,7 +642,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "\n"
    ]
   },
   {
@@ -364,9 +769,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [conda env:data_env]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-data_env-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -378,9 +783,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From eeba9b7e366418534146557b4ca543237e25273f Mon Sep 17 00:00:00 2001
From: Fran Tamarit <fcodpaula.tamarit@gmail.com>
Date: Fri, 24 Apr 2020 17:36:46 +0200
Subject: [PATCH 2/2] lab finished until bonus

---
 .../your-code/main.ipynb                      | 655 ++++++++++++++++--
 1 file changed, 601 insertions(+), 54 deletions(-)

diff --git a/module-3/lab-unsupervised-learning/your-code/main.ipynb b/module-3/lab-unsupervised-learning/your-code/main.ipynb
index da7ec31e..2dafed8a 100644
--- a/module-3/lab-unsupervised-learning/your-code/main.ipynb
+++ b/module-3/lab-unsupervised-learning/your-code/main.ipynb
@@ -67,9 +67,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "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>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12669</td>\n",
+       "      <td>9656</td>\n",
+       "      <td>7561</td>\n",
+       "      <td>214</td>\n",
+       "      <td>2674</td>\n",
+       "      <td>1338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7057</td>\n",
+       "      <td>9810</td>\n",
+       "      <td>9568</td>\n",
+       "      <td>1762</td>\n",
+       "      <td>3293</td>\n",
+       "      <td>1776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6353</td>\n",
+       "      <td>8808</td>\n",
+       "      <td>7684</td>\n",
+       "      <td>2405</td>\n",
+       "      <td>3516</td>\n",
+       "      <td>7844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13265</td>\n",
+       "      <td>1196</td>\n",
+       "      <td>4221</td>\n",
+       "      <td>6404</td>\n",
+       "      <td>507</td>\n",
+       "      <td>1788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22615</td>\n",
+       "      <td>5410</td>\n",
+       "      <td>7198</td>\n",
+       "      <td>3915</td>\n",
+       "      <td>1777</td>\n",
+       "      <td>5185</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
+       "0        2       3  12669  9656     7561     214              2674        1338\n",
+       "1        2       3   7057  9810     9568    1762              3293        1776\n",
+       "2        2       3   6353  8808     7684    2405              3516        7844\n",
+       "3        1       3  13265  1196     4221    6404               507        1788\n",
+       "4        2       3  22615  5410     7198    3915              1777        5185"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
@@ -382,13 +485,14 @@
        "Delicassen                0.069291    1.000000  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Your code here:\n",
+    "display(customers.head())\n",
     "display(customers.describe())\n",
     "display(customers.skew())\n",
     "customers.corr()"
@@ -396,13 +500,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe dataset contains all numerical data, without nulls values. There are no extreme correlations besides between Grocery\\nand Detergents_Paper. There is relatively high skewness in Delicassen, probably due to extreme high values typical in this category\\n'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your observations here\n",
-    "#The dataset contains all numerical data, without nulls values. There are no extreme correlations besides between Grocery\n",
-    "#and Detergents_Paper"
+    "\"\"\"\n",
+    "The dataset contains all numerical data, without nulls values. There are no extreme correlations besides between Grocery\n",
+    "and Detergents_Paper. There is relatively high skewness in Delicassen, probably due to extreme high values typical in this category\n",
+    "\"\"\""
    ]
   },
   {
@@ -416,11 +533,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "\n",
+    "#customers['Delicassen_log'] = np.log(customers['Delicassen'])\n",
+    "\n",
+    "#customers[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']] = \\\n",
+    "#np.log(customers[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']])"
    ]
   },
   {
@@ -429,7 +551,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your comment here"
+    "# Your comment here\n",
+    "\n",
+    "\"\"\"\n",
+    "I have tried reducing the skewness of numberical features and only the Delicassen feature but the results have wor\n",
+    "\"\"\""
    ]
   },
   {
@@ -449,32 +575,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.44865163,  0.59066829,  0.05293319, ..., -0.58936716,\n",
-       "        -0.04356873, -0.06633906],\n",
-       "       [ 1.44865163,  0.59066829, -0.39130197, ..., -0.27013618,\n",
-       "         0.08640684,  0.08915105],\n",
-       "       [ 1.44865163,  0.59066829, -0.44702926, ..., -0.13753572,\n",
-       "         0.13323164,  2.24329255],\n",
-       "       ...,\n",
-       "       [ 1.44865163,  0.59066829,  0.20032554, ..., -0.54337975,\n",
-       "         2.51121768,  0.12145607],\n",
-       "       [-0.69029709,  0.59066829, -0.13538389, ..., -0.41944059,\n",
-       "        -0.56977032,  0.21304614],\n",
-       "       [-0.69029709,  0.59066829, -0.72930698, ..., -0.62009417,\n",
-       "        -0.50488752, -0.52286938]])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Your import here:\n",
     "\n",
@@ -497,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -521,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -540,7 +643,7 @@
        "Name: labels, dtype: int64"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -562,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
@@ -572,7 +675,7 @@
     "\n",
     "from sklearn.cluster import DBSCAN\n",
     "\n",
-    "clustering = DBSCAN().fit(customers_scale)\n",
+    "clustering = DBSCAN(eps=0.5).fit(customers_scale)\n",
     "\n",
     "customers['labels_DBSCAN'] = clustering.labels_"
    ]
@@ -586,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -604,7 +707,7 @@
        "Name: labels_DBSCAN, dtype: int64"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -638,12 +741,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1368x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(19, 6))\n",
+    "\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.scatter(customers['Detergents_Paper'], customers['Milk'], c=kmeans.labels_, cmap='Spectral', s=5)\n",
+    "plt.gca().set_aspect('equal', 'datalim')\n",
+    "plt.colorbar(boundaries=np.arange(kmeans.n_clusters+1)-0.5).set_ticks(np.arange(kmeans.n_clusters))\n",
+    "plt.title('K-Means over Detergent & Milk', fontsize=24);\n",
+    "\n",
+    "\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.scatter(customers['Detergents_Paper'], customers['Milk'], c=clustering.labels_, cmap='Spectral', s=5)\n",
+    "plt.gca().set_aspect('equal', 'datalim')\n",
+    "plt.colorbar(boundaries=np.arange(len(np.unique(clustering.labels_))+1)-0.5).set_ticks(np.arange(len(np.unique(clustering.labels_))))\n",
+    "plt.title('DBSCAN over Detergent & Milk', fontsize=24);"
    ]
   },
   {
@@ -655,11 +784,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1368x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(19, 6))\n",
+    "\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.scatter(customers['Grocery'], customers['Fresh'], c=kmeans.labels_, cmap='Spectral', s=5)\n",
+    "plt.gca().set_aspect('equal', 'datalim')\n",
+    "plt.colorbar(boundaries=np.arange(kmeans.n_clusters+1)-0.5).set_ticks(np.arange(kmeans.n_clusters))\n",
+    "plt.title('K-Means over Grocery & Fresh', fontsize=24);\n",
+    "\n",
+    "\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.scatter(customers['Grocery'], customers['Fresh'], c=clustering.labels_, cmap='Spectral', s=5)\n",
+    "plt.gca().set_aspect('equal', 'datalim')\n",
+    "plt.colorbar(boundaries=np.arange(len(np.unique(clustering.labels_))+1)-0.5).set_ticks(np.arange(len(np.unique(clustering.labels_))))\n",
+    "plt.title('DBSCAN over Grocery & Fresh', fontsize=24);"
    ]
   },
   {
@@ -671,11 +827,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1368x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:"
+    "# Your code here:\n",
+    "\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(19, 6))\n",
+    "\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.scatter(customers['Frozen'], customers['Delicassen'], c=kmeans.labels_, cmap='Spectral', s=5)\n",
+    "plt.gca().set_aspect('equal', 'datalim')\n",
+    "plt.colorbar(boundaries=np.arange(kmeans.n_clusters+1)-0.5).set_ticks(np.arange(kmeans.n_clusters))\n",
+    "plt.title('K-Means over Frozen/Delicassen', fontsize=24);\n",
+    "\n",
+    "\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.scatter(customers['Frozen'], customers['Delicassen'], c=clustering.labels_, cmap='Spectral', s=5)\n",
+    "plt.gca().set_aspect('equal', 'datalim')\n",
+    "plt.colorbar(boundaries=np.arange(len(np.unique(clustering.labels_))+1)-0.5).set_ticks(np.arange(len(np.unique(clustering.labels_))))\n",
+    "plt.title('DBSCAN over Frozen/Delicassen', fontsize=24);"
    ]
   },
   {
@@ -687,11 +871,367 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "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></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",
+       "    <tr>\n",
+       "      <th>labels_DBSCAN</th>\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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"10\" valign=\"top\">-1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8151.916667</td>\n",
+       "      <td>6479.111111</td>\n",
+       "      <td>7083.361111</td>\n",
+       "      <td>1859.222222</td>\n",
+       "      <td>1804.972222</td>\n",
+       "      <td>1935.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.904110</td>\n",
+       "      <td>9478.013699</td>\n",
+       "      <td>7714.000000</td>\n",
+       "      <td>12120.219178</td>\n",
+       "      <td>1519.958904</td>\n",
+       "      <td>4778.931507</td>\n",
+       "      <td>1509.013699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.083333</td>\n",
+       "      <td>2.750000</td>\n",
+       "      <td>45660.208333</td>\n",
+       "      <td>5312.666667</td>\n",
+       "      <td>6791.666667</td>\n",
+       "      <td>6387.583333</td>\n",
+       "      <td>1205.041667</td>\n",
+       "      <td>3347.208333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>25603.000000</td>\n",
+       "      <td>43460.600000</td>\n",
+       "      <td>61472.200000</td>\n",
+       "      <td>2636.000000</td>\n",
+       "      <td>29974.200000</td>\n",
+       "      <td>2708.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.038462</td>\n",
+       "      <td>2.692308</td>\n",
+       "      <td>16053.076923</td>\n",
+       "      <td>4361.269231</td>\n",
+       "      <td>4093.807692</td>\n",
+       "      <td>11422.192308</td>\n",
+       "      <td>515.884615</td>\n",
+       "      <td>1600.730769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.391304</td>\n",
+       "      <td>4163.000000</td>\n",
+       "      <td>10296.782609</td>\n",
+       "      <td>17951.173913</td>\n",
+       "      <td>1364.956522</td>\n",
+       "      <td>8255.869565</td>\n",
+       "      <td>1421.304348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>36847.000000</td>\n",
+       "      <td>43950.000000</td>\n",
+       "      <td>20170.000000</td>\n",
+       "      <td>36534.000000</td>\n",
+       "      <td>239.000000</td>\n",
+       "      <td>47943.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.043478</td>\n",
+       "      <td>1.369565</td>\n",
+       "      <td>11965.347826</td>\n",
+       "      <td>4366.065217</td>\n",
+       "      <td>5223.217391</td>\n",
+       "      <td>2704.934783</td>\n",
+       "      <td>1183.434783</td>\n",
+       "      <td>1328.260870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.736842</td>\n",
+       "      <td>7758.000000</td>\n",
+       "      <td>19774.315789</td>\n",
+       "      <td>27522.789474</td>\n",
+       "      <td>2007.631579</td>\n",
+       "      <td>13633.315789</td>\n",
+       "      <td>3429.473684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>22015.500000</td>\n",
+       "      <td>9937.000000</td>\n",
+       "      <td>7844.000000</td>\n",
+       "      <td>47939.000000</td>\n",
+       "      <td>671.500000</td>\n",
+       "      <td>4153.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8625.393443</td>\n",
+       "      <td>1865.860656</td>\n",
+       "      <td>2440.229508</td>\n",
+       "      <td>1813.122951</td>\n",
+       "      <td>503.229508</td>\n",
+       "      <td>727.434426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>31812.000000</td>\n",
+       "      <td>1433.000000</td>\n",
+       "      <td>1651.000000</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>113.000000</td>\n",
+       "      <td>1440.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>14662.428571</td>\n",
+       "      <td>1864.142857</td>\n",
+       "      <td>2242.857143</td>\n",
+       "      <td>7869.285714</td>\n",
+       "      <td>435.428571</td>\n",
+       "      <td>895.142857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <th>2</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <th>1</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <th>1</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <th>7</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <th>7</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Channel    Region         Fresh          Milk  \\\n",
+       "labels_DBSCAN labels                                                   \n",
+       "-1            0       1.000000  3.000000   8151.916667   6479.111111   \n",
+       "              1       2.000000  2.904110   9478.013699   7714.000000   \n",
+       "              2       1.083333  2.750000  45660.208333   5312.666667   \n",
+       "              3       2.000000  2.800000  25603.000000  43460.600000   \n",
+       "              4       1.038462  2.692308  16053.076923   4361.269231   \n",
+       "              5       2.000000  1.391304   4163.000000  10296.782609   \n",
+       "              6       1.000000  3.000000  36847.000000  43950.000000   \n",
+       "              7       1.043478  1.369565  11965.347826   4366.065217   \n",
+       "              8       2.000000  2.736842   7758.000000  19774.315789   \n",
+       "              9       1.000000  2.500000  22015.500000   9937.000000   \n",
+       " 0            1       2.000000  3.000000  11401.000000   3536.200000   \n",
+       " 1            0       1.000000  3.000000   8625.393443   1865.860656   \n",
+       "              2       1.000000  3.000000  31812.000000   1433.000000   \n",
+       "              4       1.000000  3.000000  14662.428571   1864.142857   \n",
+       " 2            2       1.000000  3.000000  41446.600000   1421.200000   \n",
+       " 3            1       2.000000  3.000000   2357.200000  10224.800000   \n",
+       " 4            1       2.000000  3.000000   3068.142857   6095.857143   \n",
+       " 5            7       1.000000  1.000000   9837.863636   1751.863636   \n",
+       " 6            7       1.000000  1.000000   6823.000000   1943.000000   \n",
+       " 7            7       1.000000  2.000000   7494.600000   1537.600000   \n",
+       "\n",
+       "                           Grocery        Frozen  Detergents_Paper  \\\n",
+       "labels_DBSCAN labels                                                 \n",
+       "-1            0        7083.361111   1859.222222       1804.972222   \n",
+       "              1       12120.219178   1519.958904       4778.931507   \n",
+       "              2        6791.666667   6387.583333       1205.041667   \n",
+       "              3       61472.200000   2636.000000      29974.200000   \n",
+       "              4        4093.807692  11422.192308        515.884615   \n",
+       "              5       17951.173913   1364.956522       8255.869565   \n",
+       "              6       20170.000000  36534.000000        239.000000   \n",
+       "              7        5223.217391   2704.934783       1183.434783   \n",
+       "              8       27522.789474   2007.631579      13633.315789   \n",
+       "              9        7844.000000  47939.000000        671.500000   \n",
+       " 0            1        6729.000000    720.200000       3561.000000   \n",
+       " 1            0        2440.229508   1813.122951        503.229508   \n",
+       "              2        1651.000000    800.000000        113.000000   \n",
+       "              4        2242.857143   7869.285714        435.428571   \n",
+       " 2            2        2167.600000   1276.400000        416.200000   \n",
+       " 3            1       11862.800000    609.600000       5780.000000   \n",
+       " 4            1       10355.000000    994.000000       4675.571429   \n",
+       " 5            7        2161.681818   1513.181818        333.954545   \n",
+       " 6            7        2372.666667   6780.333333        386.500000   \n",
+       " 7            7        3383.000000   2575.400000        227.400000   \n",
+       "\n",
+       "                        Delicassen  \n",
+       "labels_DBSCAN labels                \n",
+       "-1            0        1935.166667  \n",
+       "              1        1509.013699  \n",
+       "              2        3347.208333  \n",
+       "              3        2708.800000  \n",
+       "              4        1600.730769  \n",
+       "              5        1421.304348  \n",
+       "              6       47943.000000  \n",
+       "              7        1328.260870  \n",
+       "              8        3429.473684  \n",
+       "              9        4153.500000  \n",
+       " 0            1         258.200000  \n",
+       " 1            0         727.434426  \n",
+       "              2        1440.000000  \n",
+       "              4         895.142857  \n",
+       " 2            2         784.800000  \n",
+       " 3            1         572.800000  \n",
+       " 4            1        1826.428571  \n",
+       " 5            7         669.045455  \n",
+       " 6            7         613.666667  \n",
+       " 7            7         941.000000  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "\n",
+    "customers.groupby(['labels_DBSCAN', 'labels']).mean()\n"
    ]
   },
   {
@@ -707,7 +1247,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your observations here"
+    "# Your observations here\n",
+    "\n",
+    "\"\"\"\n",
+    "The algorithm that seems to perform better is DBSCAN. In the table above, DBSCAN labels are assigned to 1 subgroup of K-Means labels, besides\n",
+    "on -1 DBSCAN label, where K-Means has several labels. This means DBSCAN is grouping better.\n",
+    "Same can be seen in the charts, were DBSCAN groups together higher value records, and K-Means adds more granuality into that segment but without\n",
+    "much logic apparently.\n",
+    "\"\"\""
    ]
   },
   {