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 "cells": [
  {
   "source": [
    "# Nigerian Music scraped from Spotify - an analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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: 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: 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: 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",
      "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                       name                         album  \\\n",
       "0                    Sparky            Mandy & The Jungle   \n",
       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
       "2                     LITT!                         LITT!   \n",
       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
       "4                wanted you                         rare.   \n",
       "\n",
       "                artist artist_top_genre  release_date  length  popularity  \\\n",
       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
       "2                 AYLØ        indie r&b          2018  207758          40   \n",
       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
       "\n",
       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \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",
       "\n",
       "   speechiness    tempo  time_signature  \n",
       "0       0.0829  133.015               5  \n",
       "1       0.3600  129.993               3  \n",
       "2       0.0424  130.005               4  \n",
       "3       0.1130  111.087               4  \n",
       "4       0.0447  105.115               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>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
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "metadata": {},
     "execution_count": 83
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
    "df = df[(df['popularity'] > 0)]\n",
    "top = df['artist_top_genre'].value_counts()\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(x=top.index,y=top.values)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Top genres',color = 'blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "\n",
    "X = df[['artist_top_genre','popularity','danceability','acousticness','loudness','energy']]\n",
    "\n",
    "y = df['artist_top_genre']\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
    "\n",
    "# X = le.transform(X)\n",
    "\n",
    "y = le.transform(y)\n",
    "\n",
    "print(X)\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_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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W6MMNr1oN7r4bFi/uf19JktQWGhnglgBLMvOu8v3FFIHu6XL4k/J5abn9CWCXus/vXLb11b5zL+0byMxzM7MzMzsnTZq0WSfVdGq14vmyy6qtQ5IkDZuGBbjMfApYHBF7lU2HA78ArgC67yQ9DZhTvr4C+EB5N+pMYEU51HodcFREbFfevHAUcF257fmImFneffqBuu8aOfbcE/bZx2FUSZJGkNEN/v6PA+dHxFjgUeBDFKHxooj4MLAI+P1y32uAdwALgJfKfcnM5RHxRaB74c8vZOby8vUfA98FtgR+VD5GnloNvvQlWLoUJk+uuhpJktRgkSNsKabOzs6cN29e1WUMrfvvhze+Ec49Fz7ykaqrkSRJQyQi7snMzp7trsTQDvbdF173Oif1lSRphDDAtYOIYlLfG2+E556ruhpJktRgBrh2UavB6tVw1VVVVyJJkhrMANcuDjwQpk51GFWSpBHAANcuOjqKXrhrr4UXX6y6GkmS1EAGuHZSq8Err8CPRuZsKpIkjRQGuHbytrfBpElO6itJUpszwLWTUaNg9uziRoaVK6uuRpIkNYgBrt10dcELL8CPf1x1JZIkqUEMcO1m1izYemuHUSVJamMGuHYzdiyccALMmVPMCydJktqOAa4ddXXBM8/AbbdVXYkkSWoAA1w7OvpoGD/eSX0lSWpTBrh2NH48HHssXHYZrF1bdTWSJGmIGeDaVa0GTz4Jd95ZdSWSJGmIGeDa1fHHFzc0eDeqJEltxwDXrrbeGo44oghwmVVXI0mShpABrp3VarBwIfzsZ1VXIkmShpABrp3Nng0dHQ6jSpLUZvoMcBFxYERMqXv/gYiYExHnRMTExpenzbLDDnDooU4nIklSm+mvB+7fgFUAEfF24MvAecAK4NzGlqYh0dUFDz1UPCRJUlvoL8CNyszl5et3Aedm5iWZ+ZfA7o0tTUPipJOKZ4dRJUlqG/0GuIgYXb4+HLipbtvoXvZXs5k6FQ4+2AAnSVIb6S/AXQDcGhFzgJeB2wEiYneKYVS1gloN7r0XHnus6kokSdIQ6DPAZeb/D3wa+C5wSOZvJxTrAD7e2NI0ZGq14tleOEmS2kJ/d6GOB+7JzMsy88WI2CsizgL2ycx7h6dEbbbddoP99jPASZLUJvobQr0WmA6/HTb9KbAbcEZE/G1jS9OQqtXgJz8p1keVJEktrb8At11mPlK+Pg24IDM/DhwLHN/QyjS0urqKJbUuv7zqSiRJ0mbqL8DVL6I5C7gBIDNXAWsbVZQa4PWvh732clJfSZLaQH8B7v6I+Gp53dvuwPUAEbFtwyvT0IooeuFuuQWeeabqaiRJ0mboL8B9BPg1xXVwR2XmS2X7DOCrDaxLjVCrwZo1cOWVVVciSZI2Q38BbgJwZWZ+IjN/Xte+guIGB7WSAw6AadMcRpUkqcX1F+D+Cdi+l/aJwDeGvhw1VETRC3f99fDCC1VXI0mSNlF/AW73zLytZ2Nm3g78bmNKUkPVarBqFVx9ddWVSJKkTdRfgHtNH9vGDGUhGiZveQtMmeKkvpIktbD+AtyCiHhHz8aIOBZ4tDElqaE6OuCkk+Caa+Dll6uuRpIkbYLR/Wz/JHB1RPw+cE/Z1gkcjBP5tq6uLvjXfy2uhZs9u+pqJEnSIPXXA3cc8D7gDmBa+bgV+N3MfLjBtalRDj0UttvOYVRJklpUfz1wOwNfB14P3E8R5JYC44FXGluaGmbMGDjxRJgzp7ihYezYqiuSJEmD0GcPXGaenZlvAXYEPgssBz4EPBARvxiG+tQoXV3w3HPFygySJKml9DeE2m1LYGtgm/LxK+CuRhWlYXDkkTBhgpP6SpLUgvoMcBFxbkTcAfyQ4saFnwCnZGZnZn5oOApUg2yxBRx3HFx+ebG8liRJahn99cC9FhgHPAU8ASwBnmt0URomtRosXQp33FF1JZIkaRD6uwbuGOBA1i1c/2lgbkRcHxF/3eji1GDveAeMG+fdqJIktZh+r4HLwgPANcCPKO5EfR3wiQbXpkabMAGOProIcJlVVyNJkgaov2vgzoyICyPicYr5344H/geoUSxor1ZXq8HixTBvXtWVSJKkAepvHrjpwH8BZ2Xmk40vR8PuhBNg9OiiF+7AA6uuRpIkDUB/18B9KjMv2ZzwFhGjIuK+iLiqfL9rRNwVEQsi4ocRMbZsH1e+X1Bun173HZ8t238ZEUfXtR9Tti2IiM9sao0j2sSJcNhhxXQiDqNKktQSBjoP3Ob4BPBQ3fuvAF/LzN2BZ4EPl+0fBp4t279W7kdEzABOBd4AHAP8SxkKRwH/DBwLzADeXe6rwerqgkcegQcfrLoSSZI0AA0NcBGxM8V6qt8u3wcwC7i43OV7wEnl69nle8rth5f7zwYuzMyVmbkQWAAcVD4WZOajmbkKuLDcV4M1ezZEOKmvJEktotE9cF8H/gRYW77fHnguM1eX75cAU8vXU4HFAOX2FeX+v23v8ZmNtWuwpkyBQw5xOhFJklpEwwJcRBwPLM3Mexp1jEHUcnpEzIuIecuWLau6nOZUq8H998OCBVVXIkmS+tHIHri3AidGxGMUw5uzgG8A20ZE992vO1Os8ED5vAtAuX0b4Jn69h6f2Vj7BjLz3HL5r85JkyZt/pm1o5NPLp7thZMkqek1LMBl5mczc+fMnE5xE8JNmfle4GbgneVupwFzytdXlO8pt9+UmVm2n1repborsAdwNzAX2KO8q3VseYwrGnU+bW/aNOjsNMBJktQChuMu1J7+FPhURCyguMbtO2X7d4Dty/ZPAZ8ByMwHgYuAXwDXAmdk5pryOrmPAddR3OV6UbmvNlWtBnfdBUuWVF2JJEnqQ+QIm/urs7Mz57nqQO8efhj22gvOOQc+/vGqq5EkacSLiHsys7NnexU9cGpWe+4Jb3iD04lIktTkDHBaX1cX3H47LF1adSWSJGkjDHBaX60Ga9fCFd4PIklSszLAaX2/+7uw224Oo0qS1MQMcFpfRDGMeuON8NxzVVcjSZJ6YYDThmo1ePVVuOqqqiuRJEm9MMBpQwcdBFOnOqmvJElNygCnDXV0FEtrXXstvPhi1dVIkqQeDHDqXVcXvPxyEeIkSVJTMcCpd4ccAjvs4DCqJElNyACn3o0eDbNnFzcyrFxZdTWSJKmOAU4b19UFzz9fTCkiSZKahgFOGzdrFmy9tZP6SpLUZAxw2rhx4+CEE2DOHFi9uupqJElSyQCnvtVq8MwzcNttVVciSZJKBjj17ZhjYMstvRtVkqQmYoBT38aPh2OPhcsug7Vrq65GkiRhgNNA1Grwq1/BXXdVXYkkScIAp4E4/ngYM8ZhVEmSmoQBTv3bZhs44ohiOpHMqquRJGnEM8BpYLq6YOFC+PnPq65EkqQRzwCngTnxROjocFJfSZKagAFOAzNpEhx6qNfBSZLUBEZXXYBayEc/WtzMsGABTJgAkycXvXKSJGlY+a+vBmbtWthjDzjrrOJ55kyYP9+54SRJqoABTgOzdCmcfDIsWlS8X7QIZs8u2iVJ0rAywGlgVq5cF966LVpUtEuSpGFlgNPAjBsH06at3zZtmvPCSZJUAQOcBmbyZJgzZ12ImzYN/uM/4MMf9s5USZKGmQFOA9PRAfvuC3feCY89VjzvvTe88EIxye/ZZ8Orr1ZdpSRJI4IBTgPX0QFTphS9b1OmwE47we23wxlnwD/8A8yaVSx6L0mSGsoAp80zbhx885tw/vlw772w//5w881VVyVJUlszwGlovOc9MHcuTJxYLHz/t3/rHHGSJDWIAU5DZ8YMuPtuOOUU+LM/K+aJe/bZqquSJKntGOA0tF7zGrjgAvinf4LrroM3vakYWpUkSUPGAKehFwEf+xjcdhusXg1veQuce65zxkmSNEQMcGqcmTOL3rdDD4U//EP44AfhpZeqrkqSpJZngFNj7bADXHMNfP7z8P3vF6Hu4YerrkqSpJZmgFPjjRoFf/VX8KMfFfPEdXbCJZdUXZUkSS3LAKfhc/TRxZDqjBnwznfCpz7l6g2SJG0CA5yG12tfW9zc8LGPwde+BocdBk88UXVVkiS1FAOcht/YscU0IxdcAD/7GRxwANx0U9VVSZLUMgxwqs6ppxarN2y/PRx5JPzN37h6gyRJA2CAU7Ve//pi9YZ3vQv+/M/hxBNh+fKqq5IkqakZ4FS9CRPg/PPhm9+E668vVm+YN6/qqiRJaloGODWHCDjjDLj9dlizBt76Vvi3f3P1BkmSemGAU3N585vhvvtg1iz46EfhtNPgxRerrkqSpKbSsAAXEbtExM0R8YuIeDAiPlG2T4yIGyLikfJ5u7I9IuKciFgQEfdHxAF133Vauf8jEXFaXfubImJ++ZlzIiIadT4aRttvD1dfDX/91/CDHxSrN/zyl1VXJUlS02hkD9xq4NOZOQOYCZwRETOAzwA3ZuYewI3le4BjgT3Kx+nAt6AIfMDngTcDBwGf7w595T4fqfvcMQ08Hw2njg743Ofg2mvhySfhwAPh4ourrkqSpKbQsACXmU9m5r3l6xeAh4CpwGzge+Vu3wNOKl/PBs7Lwp3AthHxO8DRwA2ZuTwznwVuAI4pt22dmXdmZgLn1X2X2sVRRxVDqm94A5xyCpx1lqs3SJJGvGG5Bi4ipgP7A3cBO2bmk+Wmp4Ady9dTgcV1H1tStvXVvqSX9t6Of3pEzIuIecuWLdusc1EFdtkFbr0VzjwTvv51+L3fgyVL+v2YJEntquEBLiImAJcAn8zM5+u3lT1nDb/NMDPPzczOzOycNGlSow+nRhg7Fr7xDbjwQrj//mL1hh//uOqqJEmqREMDXESMoQhv52fmpWXz0+XwJ+Xz0rL9CWCXuo/vXLb11b5zL+1qZ+96V7F6w6RJxfDql77k6g2SpBGnkXehBvAd4KHM/Me6TVcA3XeSngbMqWv/QHk36kxgRTnUeh1wVERsV968cBRwXbnt+YiYWR7rA3XfpXa2995w113w7nfDX/4lnHCCqzdIkkaURvbAvRV4PzArIn5WPt4BfBk4MiIeAY4o3wNcAzwKLAD+HfhjgMxcDnwRmFs+vlC2Ue7z7fIz/wv8qIHno2YyYUIxxci//EsxlHrAAa7eIEkaMSJH2Ez3nZ2dOc9/6NvL3LnwznfCU08VNzl89KPFyg6SJLW4iLgnMzt7trsSg1rfgQfCvffC4YfDH/8xvP/9rt4gSWprBji1h+23h6uugi9+Ef7v/y2W5HL1BklSmzLAqX10dMBf/AVcfz08/TR0dsJFF1VdlSRJQ84Ap/ZzxBHF6g377ltMO/KJT8CqVVVXJUnSkDHAqT3tvDPccksR3s45x9UbJEltxQCn9jV2bHFX6kUXwfz5sP/+cMMNVVclSdJmM8Cp/Z1ySjFH3I47wtFHFzc6uHqDJKmFGeA0Muy1V7F6w3vfC5/7HBx3HDzzTNVVSZK0SQxwGjm22grOOw++9S246aZi9Ya77666KkmSBs0Ap5Elolip4Y47iteHHFIsxzXCViSRJLU2A5xGps7OYvWGI4+EM86A970PfvObqquSJGlADHAauSZOhCuvhC99CS68sFi94dFHizVVFy0qnr3ZQZLUhAxwGtk6OuDP/7xYvWGXXeCxx2DmTJg+vXieP98QJ0lqOqOrLkBqCocfDnvsAW9/e9H7BsXz7Nlw441FoBs1qtISJUnqZoCTumWuC2/dFi0qVnB44xvhoIPg4IOLx8yZsMMO1dQpSRrxDHBSt3HjYNq09UPctGmw/fbwwQ/CT38KX/kKrFlTbNtjj3WB7uCDYZ997KWTJA2LyBE2fUJnZ2fOmzev6jLUjNauLa55mz27CHHTpsGcObDvvsW1cgAvvlis6vDTn657LFtWbNtqK3vpJElDKiLuyczODdoNcFKdtWth6VJYubLokZs8eV14600mLFy4fqD7+c/tpZMkDQkDXMkAp4azl06SNEQ2FuC8Bk4aalttBYceWjyg9146r6WTJG0Ge+CkKtT30t15Z/G8dGmxzV46SVLJHjipmWxOL93MmUUv3Wj/85WkkcoeOKlZ2UsnSSOePXBSq2lEL91g77KVJDUle+CkVjaYXrq3vAUWL+57njtJUlNxGpGSAU5tra956S69FKt0/IQAAAyPSURBVM46a8OVJu64A6ZOra5mSdJGOYQqjQQRsNtuxeO97y3aunvpttuu97VeFywo1nrdddd1j912W/d62jQYO3b4z0WStFEGOKnddV9L99RTva/1OmECvPOd8OijcN99cPnl8Oqr6/aJKHroega77sdOOzkEK0nDzAAnjRSTJxfXvPV2Ddy//uu6/dasgV/9qhiKXbiwCHbdr3/842Jb/aUXY8fC9OkbBrvux8SJRQiUJA0Zr4GTRpKhuAt15coiANYHu/rH8uXr77/11r0Hu912K4Lf+PFDdnqS1G68Bk5SEdamTNm87xg3Dvbcs3j0ZsWK3oPdL38J110HL7+8/v477rjx6+922cUJiyWpF/7NKGlobbMN7Ldf8egpE55+ev1g192T99OfwkUXrZvXDoo1YXfZpfdwt+uuRfirH551njtJI4QBTtLwiSh6AKdMKeam6+nVV2HJkg2vvVu4EK6+ugh/9bbcct31d4cdBoccAqeeuu4av8svL67xGzVqWE5PkoaLAU5S8xgzZl3v2qxZG25/6SV47LHer7/bc8914Q2K55NOgq9/HT760eJmisE8tt7a3jtJTcsAJ6l1jB8PM2YUj54WLep9nrvp0+HEE4ubK5Yvh8cfLyY3Xr4cfvObjR+rowO23XbwwW+77bxuT1LD+beMpPYwblzv89xNmQLnntv7Z1atgmefXRfu+nr8+tfw8MPF6+ee67uWrbfetOC3xRb9n6fX+UnCACepXWxsnrvJkzf+mbFjixshdtxxcMdas6YIcX0FvvpguHjxutf1N2n0NH78+oGuZ8jbc8/ipo5TTll3jpdeWoTUUaOKQLfFFsVzq869Z0CVBsQAJ6k9dHQUNyzceWfj//EfNQq23754DEYmvPDCwHr8li9f1+P3zDNFb+Gll64Lb1A812rwta8Vz/W6w1z9o7e2wWwfzHdsyjDy2rUwf37vk00b4qT1OJGvJDW7zGL+vCefhN1333D7Aw/AbbfBK69s+Fi5svf2/rZvrlGjBh8A/+AP4N3v3nAY/PLLYe7cosd07NjiO7pf93y/sW1jxjRHCLSHUYPkRL6S1KoiiuHVrbbq/Tq/7beHP/qjoTteZtHjtzkBcCDbV6xYf/sHP9j7jSgrVsDpp2/+eY0ZM/jgN9h9+9q27bbFndS12roexssuK+Y3HDWq6LXsDpqtOgQO7R9Sm+T8DHCS1Co25Tq/TRFR/MM0btzQfm9/nnqq94C6227F/IArVxbBsvtR/76vbYPZd+XKordzxYq+P7dy5fprAg/EpZfCWWetPwR+8sm9D4GPGbMu0NW/7vk80LZGbOutbautius/60Pq5ZcXv2FHR7Hf6NFFYG3FkNpEw/wOoUpSK2mS//tviCb6x3FAVq8eXEicPr33KXDuv79YZu7VV4vvfPXV9V/3fB7stv72H8oc0DOkQvE79hZSuwNdd+/j5rweiu8YyOv99oPjj9/w/O68c/OXKdwIh1AlqR0MxXq2zWo4b0QZCt3/uI8fP7D9N9bDOGkSnH12Y2ociLVrhy4o7rVX78Pgu+8Of/d3xV3Yq1cXj6F6vWrVpn22/nmgbrml9/NbuXKzf4bBMsBJkppHOwfU4RoCH6yOjqEbMu8rpP6f/7P5398ImUWIHUjwGzWq9/Mb7ssNMMBJkjQ8Wq2HcVM0a0jtS0QRzLrnUuzL2rVNc34GOEmShks79zBC+4fUJjq/lg9wEXEM8A1gFPDtzPxyxSVJkjRyjYSQ2gTn19KROCJGAf8MHAvMAN4dEb3c4iNJktQ+WjrAAQcBCzLz0cxcBVwIzK64JkmSpIZq9QA3FVhc935J2SZJktS2Wj3ADUhEnB4R8yJi3rJly6ouR5IkabO0eoB7Atil7v3OZdt6MvPczOzMzM5JkyYNW3GSJEmN0OoBbi6wR0TsGhFjgVOBKyquSZIkqaFaehqRzFwdER8DrqOYRuQ/MvPBisuSJElqqJYOcACZeQ1wTdV1SJIkDZfIzKprGFYRsQxY1O+O6ssOwK+rLkKbxd+w9fkbtjZ/v9Y3XL/htMzc4AL+ERfgtPkiYl5mdlZdhzadv2Hr8zdsbf5+ra/q37DVb2KQJEkacQxwkiRJLcYAp01xbtUFaLP5G7Y+f8PW5u/X+ir9Db0GTpIkqcXYAydJktRiDHAasIjYJSJujohfRMSDEfGJqmvS4EXEqIi4LyKuqroWDV5EbBsRF0fE/0TEQxFxcNU1aXAi4qzy79AHIuKCiNii6prUt4j4j4hYGhEP1LVNjIgbIuKR8nm74azJAKfBWA18OjNnADOBMyJiRsU1afA+ATxUdRHaZN8Ars3MvYE34m/ZUiJiKnAm0JmZ+1CsInRqtVVpAL4LHNOj7TPAjZm5B3Bj+X7YGOA0YJn5ZGbeW75+geIfjqnVVqXBiIidgeOAb1ddiwYvIrYB3g58ByAzV2Xmc9VWpU0wGtgyIkYD44FfVVyP+pGZtwHLezTPBr5Xvv4ecNJw1mSA0yaJiOnA/sBd1VaiQfo68CfA2qoL0SbZFVgG/Gc5DP7tiNiq6qI0cJn5BPBV4HHgSWBFZl5fbVXaRDtm5pPl66eAHYfz4AY4DVpETAAuAT6Zmc9XXY8GJiKOB5Zm5j1V16JNNho4APhWZu4PvMgwD9to85TXSc2mCOM7AVtFxPuqrUqbK4spPYZ1Wg8DnAYlIsZQhLfzM/PSquvRoLwVODEiHgMuBGZFxA+qLUmDtARYkpndPd8XUwQ6tY4jgIWZuSwzXwUuBd5ScU3aNE9HxO8AlM9Lh/PgBjgNWEQExbU3D2XmP1ZdjwYnMz+bmTtn5nSKi6Zvykz/z7+FZOZTwOKI2KtsOhz4RYUlafAeB2ZGxPjy79TD8UaUVnUFcFr5+jRgznAe3ACnwXgr8H6KnpuflY93VF2UNMJ8HDg/Iu4H9gP+puJ6NAhl7+nFwL3AfIp/h12VoclFxAXAT4G9ImJJRHwY+DJwZEQ8QtGz+uVhrcmVGCRJklqLPXCSJEktxgAnSZLUYgxwkiRJLcYAJ0mS1GIMcJIkSS3GACepaURERsQ/1L0/OyL+aoi++7sR8c6h+K5+jnNKRDwUETc3sq6ImB4R7xl8hZLagQFOUjNZCdQiYoeqC6lXLjo+UB8GPpKZhzWqntJ0YFABbpDnIamJGeAkNZPVFJOantVzQ8+eqoj4Tfn8exFxa0TMiYhHI+LLEfHeiLg7IuZHxOvqvuaIiJgXEQ+Xa8MSEaMi4u8jYm5E3B8Rf1j3vbdHxBX0stpBRLy7/P4HIuIrZdvngEOA70TE3/fymT8tP/PziNhg0s+IeKw7vEZEZ0TcUr4+tG7y7Psi4jUUk4a+rWw7a6DnERFbRcTVZQ0PRMS7BvLDSGou/t+YpGbzz8D9EfF3g/jMG4HXA8uBR4FvZ+ZBEfEJipULPlnuNx04CHgdcHNE7A58AFiRmQdGxDjgjoi4vtz/AGCfzFxYf7CI2An4CvAm4Fng+og4KTO/EBGzgLMzc16PzxxLsYj5mzPzpYiYOIjzOxs4IzPviIgJwCsUi9ifnZndQfT0gZxHRHQBv8rM48rPbTOIOiQ1CXvgJDWVzHweOA84cxAfm5uZT2bmSuB/ge7gMp8itHW7KDPXZuYjFEFvb+Ao4AMR8TPgLmB7YI9y/7t7hrfSgcAt5YLkq4Hzgbf3U+MRwH9m5kvleS4fxPndAfxjRJwJbFses6eBnsd8iuV/vhIRb8vMFYOoQ1KTMMBJakZfp7iWbKu6ttWUf2dFRAcwtm7byrrXa+ver2X9kYaeawcmEMDHM3O/8rFrZnYHwBc36ywG77fnCGzx2yIzvwz8AbAlRc/a3r18dkDnkZkPU/TIzQe+VA77SmoxBjhJTafsnbqIIsR1e4xiyBLgRGDMJnz1KRHRUV4XtxvwS+A64I8iYgxAROwZEVv19SXA3cChEbFDRIwC3g3c2s9nbgA+FBHjy+P0NoT6GOvOsau7MSJel5nzM/MrwFyKnsMXgNfUfXZA51EO/76UmT8A/p4izElqMV4DJ6lZ/QPwsbr3/w7MiYifA9eyab1jj1OEr62Bj2bmKxHxbYph1nsjIoBlwEl9fUlmPhkRnwFupuj5ujoz5/TzmWsjYj9gXkSsAq4B/qzHbn9NcQPEF4Fb6to/GRGHUfQoPgj8qHy9pvzz+C7wjQGex77A30fEWuBV4I/6qltSc4rMniMKkiRJamYOoUqSJLUYA5wkSVKLMcBJkiS1GAOcJElSizHASZIktRgDnCRJUosxwEmSJLUYA5wkSVKL+X89JllQdXJBKAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters = 2)\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Result: 143 out of 286 samples were correctly labeled.\nAccuracy score: 0.50\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}