diff --git a/5-Clustering/2-K-Means/notebook.ipynb b/5-Clustering/2-K-Means/notebook.ipynb
index b25d4f18..fd3613a9 100644
--- a/5-Clustering/2-K-Means/notebook.ipynb
+++ b/5-Clustering/2-K-Means/notebook.ipynb
@@ -1,84 +1,163 @@
 {
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
-  },
-  "metadata": {
-   "interpreter": {
-    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
  "cells": [
   {
-   "source": [
-    "# Nigerian Music scraped from Spotify - an analysis"
-   ],
    "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "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: 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: 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: 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: 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: 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: 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: 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: 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: 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",
-      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
    "source": [
-    "pip install seaborn"
+    "# Nigerian Music scraped from Spotify - an analysis"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Start where we finished in the last lesson, with data imported and filtered."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "<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",
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+       "      <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>"
+      ],
       "text/plain": [
        "                       name                         album  \\\n",
        "0                    Sparky            Mandy & The Jungle   \n",
@@ -107,11 +186,11 @@
        "2       0.0424  130.005               4  \n",
        "3       0.1130  111.087               4  \n",
        "4       0.0447  105.115               4  "
-      ],
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+      ]
      },
+     "execution_count": 11,
      "metadata": {},
-     "execution_count": 6
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -126,37 +205,36 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We will focus only on 3 genres. Maybe we can get 3 clusters built!"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0.5, 1.0, 'Top genres')"
       ]
      },
+     "execution_count": 12,
      "metadata": {},
-     "execution_count": 7
+     "output_type": "execute_result"
     },
     {
-     "output_type": "display_data",
      "data": {
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\n"
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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
      },
-     "metadata": {
-      "needs_background": "light"
-     }
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -171,12 +249,148 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "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>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>"
+      ],
       "text/plain": [
        "                       name                         album  \\\n",
        "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
@@ -205,16 +419,327 @@
        "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>"
+      ]
      },
+     "execution_count": 13,
      "metadata": {},
-     "execution_count": 8
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "df.head()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='release_date'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 4000x4000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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",
+    "y = le.transform(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 1, 2, 0, 0, 1, 0, 1, 1,\n",
+       "       1, 0, 1, 2, 1, 1, 2, 2, 0, 0, 1, 2, 2, 2, 2, 0, 0, 1, 2, 1, 2, 1,\n",
+       "       2, 1, 1, 0, 0, 2, 0, 1, 1, 2, 2, 2, 2, 0, 0, 1, 0, 2, 2, 0, 2, 2,\n",
+       "       0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 2, 2, 1, 2, 0, 0, 0, 2, 2, 2,\n",
+       "       2, 0, 2, 2, 2, 2, 0, 0, 2, 0, 0, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 1,\n",
+       "       0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 1, 0, 2, 0, 2,\n",
+       "       0, 2, 2, 2, 1, 2, 0, 0, 0, 2, 0, 1, 0, 2, 2, 0, 0, 0, 1, 0, 2, 2,\n",
+       "       2, 0, 0, 1, 0, 2, 0, 0, 0, 0, 2, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2,\n",
+       "       1, 0, 1, 1, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 2, 2, 1, 2, 2, 0, 0, 1,\n",
+       "       0, 0, 1, 1, 0, 1, 2, 1, 0, 1, 2, 1, 1, 2, 2, 2, 0, 0, 0, 0, 0, 1,\n",
+       "       2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 1, 0, 0, 0, 1, 2, 2, 2,\n",
+       "       0, 0, 1, 1, 0, 0, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 0, 0, 0, 2, 2, 2,\n",
+       "       0, 2, 0, 2, 0, 0, 0, 1, 2, 2, 2, 0, 2, 0, 1, 0, 2, 0, 0, 0, 2, 0],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5466747351275563"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "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",
+    "    wcss.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(range(1, 11))\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ray/Desktop/ML-For-Beginners/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\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('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result: 112 out of 286 samples were correctly labeled.\n",
+      "Accuracy score: 0.39\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}