From 0dc84275e4d503e17b3775c8594bf65ad924d814 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 4 Jun 2021 16:17:08 -0400
Subject: [PATCH] Cluster detail

---
 .../2-K-Means/solution/notebook.ipynb         | 349 ++++++++++++++----
 README.md                                     |   6 +-
 2 files changed, 286 insertions(+), 69 deletions(-)

diff --git a/5-Clustering/2-K-Means/solution/notebook.ipynb b/5-Clustering/2-K-Means/solution/notebook.ipynb
index 0c68e7cd..f6a6f3d3 100644
--- a/5-Clustering/2-K-Means/solution/notebook.ipynb
+++ b/5-Clustering/2-K-Means/solution/notebook.ipynb
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 817,
    "metadata": {},
    "outputs": [
     {
@@ -43,15 +43,15 @@
      "name": "stdout",
      "text": [
       "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
-      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
       "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
       "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
       "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
       "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
       "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
       "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
       "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
       "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
       "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
       "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
@@ -64,9 +64,16 @@
     "pip install seaborn"
    ]
   },
+  {
+   "source": [
+    "Start where we finished in the last lesson, with data imported and filtered."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 818,
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +111,7 @@
       "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>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "metadata": {},
-     "execution_count": 82
+     "execution_count": 818
     }
    ],
    "source": [
@@ -118,9 +125,16 @@
     "df.head()"
    ]
   },
+  {
+   "source": [
+    "We will focus only on 3 genres. Maybe we can get 3 clusters built!"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 819,
    "metadata": {},
    "outputs": [
     {
@@ -131,13 +145,13 @@
       ]
      },
      "metadata": {},
-     "execution_count": 83
+     "execution_count": 819
     },
     {
      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {
@@ -157,78 +171,269 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 820,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "ipykernel_launcher:12: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "     artist_top_genre  popularity  danceability  acousticness  loudness  \\\n",
-      "1                   1          30         0.710        0.0822    -5.640   \n",
-      "3                   2          14         0.894        0.7980    -4.961   \n",
-      "4                   1          25         0.702        0.1160    -6.044   \n",
-      "5                   2          26         0.803        0.1270   -10.034   \n",
-      "6                   2          29         0.818        0.4520    -9.840   \n",
-      "..                ...         ...           ...           ...       ...   \n",
-      "514                 0          20         0.838        0.0358    -3.723   \n",
-      "515                 0          14         0.786        0.1950    -4.232   \n",
-      "519                 1           2         0.879        0.2240    -4.602   \n",
-      "522                 0          26         0.863        0.0366    -3.130   \n",
-      "525                 0          10         0.735        0.6320    -2.582   \n",
-      "\n",
-      "     energy  \n",
-      "1     0.683  \n",
-      "3     0.611  \n",
-      "4     0.833  \n",
-      "5     0.525  \n",
-      "6     0.587  \n",
-      "..      ...  \n",
-      "514   0.931  \n",
-      "515   0.806  \n",
-      "519   0.916  \n",
-      "522   0.896  \n",
-      "525   0.918  \n",
-      "\n",
-      "[286 rows x 6 columns]\n"
-     ]
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "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>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 820
     }
    ],
    "source": [
-    "from sklearn.cluster import KMeans\n",
-    "wcss = []\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "How clean is this data? Check for outliers using box plots. We will concentrate on columns with fewer outliers (although you could clean out the outliers). Boxplots can show the range of the data and will help choose which columns to use. Note, Boxplots do not show variance, an important element of good clusterable data (https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot)"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 821,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa3d0a46d8>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 821
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 4000x4000 with 12 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
     "\n",
-    "X = df[['artist_top_genre','popularity','danceability','acousticness','loudness','energy']]\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
     "\n",
-    "y = df['artist_top_genre']\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
     "\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
     "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "source": [
+    "Choose several columns with similar ranges. Make sure to include the artist_top_genre column to keep our genres straight. "
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 816,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
     "le = LabelEncoder()\n",
     "\n",
+    "X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "\n",
+    "y = df['artist_top_genre']\n",
+    "\n",
     "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
     "\n",
-    "# X = le.transform(X)\n",
+    "y = le.transform(y)\n"
+   ]
+  },
+  {
+   "source": [
+    "K-Means Clustering has the drawback of needing to tell it how many clusters to build. We know there are three song types, so let's focus on 3."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 806,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 0, 2, 1, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 2, 0, 0, 2, 2, 1, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0,\n",
+       "       2, 0, 0, 1, 1, 2, 1, 0, 0, 2, 2, 2, 2, 1, 1, 0, 1, 2, 2, 1, 2, 2,\n",
+       "       1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 0,\n",
+       "       1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 0, 1, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 0, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1, 0, 1, 2, 2,\n",
+       "       2, 1, 1, 0, 1, 2, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0,\n",
+       "       1, 1, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0,\n",
+       "       2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 1, 1, 0, 2, 2, 2,\n",
+       "       1, 1, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       1, 2, 1, 2, 1, 1, 1, 0, 2, 2, 2, 1, 2, 1, 0, 1, 2, 1, 1, 1, 2, 1],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 806
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
     "\n",
-    "y = le.transform(y)\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "source": [
+    "Those numbers don't mean much to us, so let's get a 'silhouette score' to see the accuracy. \"The value of the Silhouette score varies from -1 to 1. If the score is 1, the cluster is dense and well-separated than other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary of the neighboring clusters. A negative score [-1, 0] indicates that the samples might have got assigned to the wrong clusters.\" - https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam\n",
     "\n",
-    "print(X)\n",
+    "Our score is in the middle"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 807,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0.5466747351275563"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 807
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Import KMeans and build a model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 808,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = []\n",
     "\n",
     "for i in range(1, 11):\n",
     "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
     "    kmeans.fit(X)\n",
-    "    # inertia method returns wcss for that model\n",
     "    wcss.append(kmeans.inertia_)"
    ]
   },
+  {
+   "source": [
+    "Use that model to decide, using the Elbow Method, the best number of clusters to build"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 809,
    "metadata": {},
    "outputs": [
     {
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\n"
      },
      "metadata": {
@@ -253,23 +458,30 @@
    "source": [
     "plt.figure(figsize=(10,5))\n",
     "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
-    "plt.title('The Elbow Method')\n",
+    "plt.title('Elbow')\n",
     "plt.xlabel('Number of clusters')\n",
     "plt.ylabel('WCSS')\n",
     "plt.show()"
    ]
   },
+  {
+   "source": [
+    "Looks like 3 is a good number after all. Create a scatterplot of your clusters. They do group in bunches, but they are pretty close together."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 810,
    "metadata": {},
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -277,32 +489,37 @@
     }
    ],
    "source": [
-    "\n",
     "from sklearn.cluster import KMeans\n",
-    "kmeans = KMeans(n_clusters = 2)\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
     "kmeans.fit(X)\n",
     "labels = kmeans.predict(X)\n",
     "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
     "plt.xlabel('danceability')\n",
     "plt.xlabel('popularity')\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
+  {
+   "source": [
+    "This model's accuracy is not bad, but not great. It may be that the data may not lend itself well to K-Means Clustering. You might try a different method."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 811,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "Result: 143 out of 286 samples were correctly labeled.\nAccuracy score: 0.50\n"
+      "Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\n"
      ]
     }
    ],
    "source": [
-    "\n",
     "labels = kmeans.labels_\n",
     "\n",
     "correct_labels = sum(y == labels)\n",
diff --git a/README.md b/README.md
index d1af6a9e..45b6be97 100644
--- a/README.md
+++ b/README.md
@@ -20,9 +20,9 @@ Travel with us around the world as we apply these classic techniques to data fro
 
  **🙏Special thanks🙏 to our Microsoft Student Ambassador reviewers and content contributors**, notably Rishit Dagli, Rohan Raj, Muhammad Sakib Khan Inan, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
  
-# Getting Started as a Student
+# Getting Started
 
-**Students**, to use this curriculum, fork the entire repo and complete the exercises on your own:
+**Students**, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own:
 
 - Start with a pre-lecture quiz
 - Read the lecture and complete the activities, pausing and reflecting at each knowledge check. 
@@ -30,7 +30,7 @@ Travel with us around the world as we apply these classic techniques to data fro
 - Take the post-lecture quiz
 - Complete the challenge
 - Complete the assignment
-- Consider forming a study group with friends and go through the content together. 
+- Consider forming a study group with friends to go through the content together. 
 - For further study, we recommend following [Microsoft Learn](https://docs.microsoft.com?WT.mc_id=academic-15963-cxa) modules and learning paths.
 
 ---