{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "813222c9-dfa6-490f-ba57-5d1aca11e5c3",
   "metadata": {
    "id": "813222c9-dfa6-490f-ba57-5d1aca11e5c3",
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "---\n",
    "license:\n",
    "    code: MIT\n",
    "    content: CC-BY-4.0\n",
    "github: https://github.com/ocademy-ai/machine-learning\n",
    "venue: By Ocademy\n",
    "open_access: true\n",
    "bibliography:\n",
    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753b1c0e",
   "metadata": {
    "id": "753b1c0e",
    "tags": []
   },
   "source": [
    "# K-Means clustering\n",
    "\n",
    "In this section, you will learn how to create clusters using Scikit-learn and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier section, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!\n",
    "\n",
    "Terms you will learn about:\n",
    "\n",
    "- Silhouette scoring\n",
    "- Elbow method\n",
    "- Inertia\n",
    "- Variance\n",
    "\n",
    "## Introduction\n",
    "\n",
    "K-Means Clustering is a method derived from the domain of signal processing. It is used to divide and partition groups of data into 'k' clusters using a series of observations. Each observation works to group a given datapoint closest to its nearest 'mean', or the center point of a cluster.\n",
    "\n",
    "The clusters can be visualized as Voronoi diagrams, which include a point (or 'seed') and its corresponding region.\n",
    "\n",
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/voronoi.png\n",
    "---\n",
    "name: voronoi diagram\n",
    "---\n",
    "voronoi diagram infographic by Jen Looper\n",
    ":::\n",
    "\n",
    "The K-Means clustering process executes in a three-step process):\n",
    "\n",
    "- The algorithm selects k-number of center points by sampling from the dataset. After this, it loops:\n",
    "\n",
    "1. It assigns each sample to the nearest centroid.\n",
    "2. It creates new centroids by taking the mean value of all of the samples assigned to the previous centroids.\n",
    "3. Then, it calculates the difference between the new and old centroids and repeats until the centroids are stabilized.\n",
    "\n",
    "One drawback of using K-Means includes the fact that you will need to establish 'k', that is the number of centroids. Fortunately the  'elbow method' helps to estimate a good starting value for 'k'. You'll try it in a minute.\n",
    "\n",
    "## Prerequisite\n",
    "\n",
    "You will work in this section's _notebook.ipynb_ file that includes the data import and preliminary cleaning you did in the last section.\n",
    "\n",
    "## Exercise - preparation\n",
    "\n",
    "Start by taking another look at the songs data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83752800-50f3-480c-af1d-f9bc675b6db3",
   "metadata": {
    "id": "83752800-50f3-480c-af1d-f9bc675b6db3",
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Install the necessary dependencies\n",
    "\n",
    "import os\n",
    "import sys\n",
    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython threadpoolctl seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8bf636c3",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<p style=\"text-align: center;\">\n",
       "  <iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/clustering-vis/clustering.html\" width=\"105%\" height=\"800px\"\n",
       "  style=\"border:none;\" scrolling=\"auto\"></iframe>\n",
       "  A demo of linear-regression. <a href=\"https://www.naftaliharris.com/blog/visualizing-k-means-clustering/\"> [source]</a>\n",
       "</p>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "display(HTML(\"\"\"\n",
    "<p style=\"text-align: center;\">\n",
    "  <iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/clustering-vis/clustering.html\" width=\"105%\" height=\"800px\"\n",
    "  style=\"border:none;\" scrolling=\"auto\"></iframe>\n",
    "  A demo of linear-regression. <a href=\"https://www.naftaliharris.com/blog/visualizing-k-means-clustering/\"> [source]</a>\n",
    "</p>\n",
    "\"\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ae8e949",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 706
    },
    "id": "2ae8e949",
    "outputId": "f6329d7f-1760-45c0-a1a6-e10bc296b5e7",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/nigerian-songs.csv\")\n",
    "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",
    "\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": "markdown",
   "id": "0138665b",
   "metadata": {
    "id": "0138665b"
   },
   "source": [
    "1. Create a boxplot, calling `boxplot()` for each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52cd7b0a",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "52cd7b0a",
    "outputId": "6cf2d712-cc5a-4bf6-f6e3-020fd0466a14"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='release_date'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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yE/zbb7+tzaoA1Nopp5yS7rvvvvTZZ5+llMq/FSvXQfNvvvlmZvDP0KFDK/wGmTdvXho6dGjmmJvtaSNXXXVVZtmHHnpohelvv/125kkmq666atbfsaW/hSMi3XrrrRWml+1YzbaOCxYsSAMHDkwRS54w/c4771RIc+ihh2byyfZ0tXxtm8aQj7aRr+vOP/3pT5l8zj333ArTn3nmmcx2ruqpUfk493/11VepW7duKWJJoMnST/4tKSkpFwybbYDfE088kUmz/fbbVxjsNmvWrLTyyiuniEjdu3dPX3/9ddb1akjbbrttuuWWW7IOzps1a1a5QcZPP/10pekcN1recSNfbcNxo+UdN6oazFvqrrvuyqzbqFGjKkxvatuwJbavxpCPtlE2oKIu6+Z80nTOJ1W54IILUkSkNdZYI5144onV1r3fG4XVPqClaonXYGPGjEmXX355+vHHHytdxrx588oNJr/22msrTZeLxrgf3hLrLKX/BVTU5AEstdEY56iWWmfV+ec//5lZzhlnnFGrPFKyn6WU0vjx49O4cePSjBkzMkFgZZ+onquGGm/QWL8FW2K95WudclF2oHdDaYl1llLKy36Ui8boy2updVad4447LpPn9ddfX+t8Cn0/O//889Pxxx+fvvjii6zlLTtuad99982arqWf05YmoKIZeP755zON7qCDDqo0zaJFi9Kaa66ZOTAvWLCgXsoyZcqUTFmOPfbYellGoWms+v3b3/6WIiKtvvrqaf78+QIq6kFD1a2AiobXUHU7adKkzHLOO++8uhabHDSVc+7DDz+c0w9XctdQdbvMMsukiEg9e/bMmmb27NmZsqy77ro1XgblNUTd3nbbbZllnHTSSVnTPfbYY5l0F110UY2WAZBvtRk0f8ghh2TmmTJlSqVpyvYLVHYjMKWUOeb26NEjzZs3r9I0f/nLXzL5VDYI6dNPP808WW3LLbfMWuYtt9wyRSx5ktunn35aYXrZwUx/+ctfKs1j3rx5mXP4T37yk0rT5GvbNAWNFVCxYMGCzEDRNddcM+uTKQ866KDMsqZOnVpher7O/eecc04mn5tvvrnSfD788MPME7W32WabStNsvfXWmQ7cbE/xKfumq8oG7DZV9913X6bcRxxxRKVpHDcK47ixtFzahuNGYR43UkqZJxH36tWrwrSmtA1bavtqyqpqG/kKqHA+afrnk/fffz/z9pcJEyaUG+SQre793iic9gGFrrldg+Xi1VdfzeSx/fbb1yqPlJru/fDmWGcNFVDRVM9RzbHOqrPBBhukiCVvUJ85c2at8kjJfpZNTQcMN+R4g6a6n6XU/OotF7msUy4aY6B3LppjnZWmr++Aiqbal9cc66wqixYtyrzFoXPnzlnPnbmwn1Vv/vz5afnll08Rkbp161Zp32ghntOaVouhUmWfzvLcc89lTVf2h/YjjzxSL2WZMWNGZhmHH354vSyj0DRG/c6cOTN16tQpRfyvg1pARf41VN021Qvblqyh6nb33XfP/HD54Ycf6lJkctRUzrl77bVXJv8JEybkPf9C1FB1W3p+HTp0aJXpevXqlSIiDR48uMbLoLyGqNvjjz8+M+/kyZOzplu4cGFq3759iqj66aUADaGmg+YXL16ceUPWGmusUWXa0sFwK6ywQlq8eHG5aW+++WZmuQcffHDWPD799NNMuspebXv55Zdnpo8fPz5rPmU7qSt7lfKee+6ZmV7ZQKZSZQdLvvnmm+Wm5WvbNBWNFVDxyCOPZPI4++yzs6Yr2yFa2Wt783Xu33DDDVPEkicCz58/P2s+pYPh2rVrV+F1xXPnzk3FxcUpItJWW22VNY/58+dnnji44YYbZk3X1Hz33XeZbVjZwF3HjcI5biyturaRkuNGoR43UkqZJ4V17ty5wrSmtA1bYvtq6qpqG/kIqHA+aR7nk+22267c79DqAir83iis9gGFrrldg+Wq9H5ItuCwXDTV++HNsc4aIqCiKZ+jmmOdVeWtt97KzD9ixIgaz19Woe9n2dR0wHBD3ZNuyvtZSs2v3nKRS39YLprqQO/mWGel6eszoKIp9+U1xzqryqOPPprJb8yYMXXKy36Wm1133TVTnsreZlGI57RWQZM3efLkiIjo1KlTrLfeelnTDR8+PPP5mWeeqZeyjB8/PvN5jTXWqJdlFJrGqN9DDz005s2bF3vvvXeMGDGiTnmRXVPad8mvhqjbBQsWxD333BMREVtssUW0b98+IiIWLVoUH374YcycOTN+/PHHmhadajSF/fbbb7+Nu+++OyIi+vfvH7/85S/zmn+haqi6XX311SMi4r333suaZu7cufHll1+WS0/tNUTdfvXVV5nPyy67bNZ0bdq0iR49ekRExJQpU6KkpKRGywFoTO+991588sknEVH+mFmZ0ukff/xxzJw5s9y00uNydfkst9xyMWjQoIio/Licaz7VHd9L81l99dVjueWWq1U++do2hS7XOh06dGh07NgxIqqu07qc+xcsWBBTp06NiIgNN9wwiouLq81n/vz5MW3atHLTXnjhhViwYEG161RcXBwbbLBBZp6FCxdmTduUzJ8/P/O5devWFaY7bhTucaO6tpEvjhvN77jx5ptvxssvvxwRFe8fNLVt2BLbV1NWVdvIF+eTpn8+ufXWW+P++++PHj16xF//+tec5vF7o3DaB9D8rsFyVfrbrT6vHRpLS62zumrK56iWVmfXXXdd5vM+++xT4/mbg4aqs3xpqHvSTW29l9bc6i0XDdUf1lhaYp3lQ1Puy2tpdeac1vB1Vl15CvGcJqCiGXjjjTciImLVVVeNNm3aZE1XthO6dJ58+PLLL2PKlCmx3377xZlnnhkREb169Ypf//rXeVtGIWvo+h0/fnw8+OCDscwyy8Tf/va3WudD9Rpj3913332jb9++UVxcHL169YoNNtggTj755Pj444/rlC/lNUTdvvLKK5mAiSFDhsTcuXPjqKOOil69esXKK68cAwYMiG7dusUWW2wREyZMqPlKUKnGPudGRNx+++3x/fffR0TE3nvvHUVFRXnNv1A1VN0efPDBEbFkAP5ll11WaZozzjijQnpqryHqtnPnzpnPc+bMyZoupRRz586NiCU3id55550aLQegMb3++uuZz9UNcqvqmFqbfD788MOYN29epfl069atygFGyy+/fHTt2rXSsnz33Xfx4Ycf1qgsleWTr23TUtT2ujPX7dimTZtYddVVI6LybZiPc/9bb70VixYtqrYs1eVTm7ZRUlISb7/9dpVpm4qnn34683nNNdesMN1xo3CPG9W1jaU5bvxPSzxufP/99/H222/H+eefH8OHD88Elh911FHl0jW1bdgS21dTk2vbWNpJJ50U/fr1i3bt2sUyyywT66yzThx99NHx1ltvVTmf80nTbhuzZ8+OI488MiIizjnnnOjVq1dO8/m9URjtA1iiuV2D5WL69OmZPvNcrh1y0ZTuhzfnOps4cWKsvfba0aVLl+jYsWMMGDAgdt9997j77rsjpVTlMqrTlM9RzbnOlpZSihtuuCEiIjp27Bi77rprzvNWpxD3s3xpqHvSTW29l9bc6i0XNe0Py8XIkSOjT58+UVxcHH369IkRI0bE2WefHd98801e8q+J5lxnt912W/zkJz+Jjh07RpcuXWK11VaL0aNHx1NPPVXnvJtyX15zrrOlfffdd3HXXXdFRES/fv3y+oBw+1nlFi5cGFOmTImIJQ8VLX1waFmFeE4TUNHE/fjjj5knGK+44opVpl1mmWWiU6dOERGZjrjaGjFiRBQVFUVRUVH07t07Ntpoo7j66qsjpRS9evWKu+66K7p3716nZdDw9fvNN99kblacffbZ0bt371rlQ/Uaa9+dMGFCfPrpp7Fw4cL46quv4vnnn48zzzwzVl111bj88svrlDdLNFTdlv2xsHjx4hg6dGj8/e9/j9mzZ2e+X7BgQTz++OOx2WabxTnnnFOj/KmosfbbpRVC1HVDa8i6/e1vf5upt8MOOywOOOCAuO+++2LatGlx5513xqhRozJP4DvppJNi8803r/Ey+J+GqtuyF7NlL3KXNn369Pjuu+8y/3/wwQc1Wg5AY/roo48yn6s7pq600kqZz0sfU2uTT0qp3Hxl86kuj7L55KMs9ZlPS1Hb687S7dipU6dq+5RKt+OsWbPKPSEnX+d+baN6ixcvjrPPPjvz///93/9VSOO4oW1EVN42lua4kf98Gts111yTuX/QqVOnGDRoUBxzzDHx+eefR0TECSecEHvttVe5eZraNmyJ7aspqE3bWNqzzz4bH3zwQSxYsCBmz54dL7/8clx44YWx5pprxtixY7MO8HM+adpt47jjjovPPvssfvGLX8R+++2X83x+bxRG+wCa5zVYLs4666zM51yuHXLRVO6HN/c6e++99+KVV16J7777Ln744YeYOXNm3HrrrTFq1KjYZJNN6jRwvqmeo5p7nS1t4sSJmSczjxo1Krp06ZLzvNUpxP0sHxrynnRTWu+lNbd6y0Vt+sNy8dhjj8WsWbNi4cKFMWvWrHj66afjxBNPjIEDB8Y999yTl2XkornX2euvvx5vvPFG/PDDD/Hdd9/FO++8E9ddd11sttlmMWrUqCofmFidprrezb3OlnbHHXdkggp/85vf5PXBs/azyl1xxRWZc9Zuu+1WYXqhntOyh43QJHz77beZz2WfjJtNp06dYt68eeUGcuXT7373u/jTn/6U85NjqFpD1+8f/vCH+Pzzz2PDDTeMAw44oFZ5kJuGrtuBAwfGzjvvHBtuuGHmxPHuu+/GHXfcEbfffnv8+OOPcfDBB0dRUVEceOCBtVoGSzRU3X799deZz+ecc078+OOPsdVWW8Xpp58ea621VsydOzfuuOOOOOGEE2LOnDlxwgknxBprrBE77rhjjZbD/zSFc+4HH3yQGay90UYbZZ6ASN00ZFJqReEAAEL1SURBVN22bt06rr322th+++3jrLPOin/961/xr3/9q1yaTTfdNP74xz8KpsiDhqrbrbfeOtq0aRMlJSVx/vnnxz777FPh9/DixYvjpJNOylo+gKauJsfU0k6xiKhwTM13Prke3xuiLHXNp7mr63Vnbeo0Ysl2bNeuXbk8apJPZed+baN6F1xwQUydOjUiInbeeedKX+fc1Laj40bDyKVtlHLcKKy2ERGx9tprxxVXXBHDhg2rMK2pbcOW2L6asqraRqnll18+dt5559h4441j4MCB0aZNm/jggw/i/vvvj+uuuy4WLlwYp512WixYsKDc4MxSzidNt21MmjQp/vWvf0WbNm3isssuq9HAiKa2LbUPoL40x2uw6pT+7o+IWG+99WLnnXeu0fxLa2r3w5trnRUXF8cOO+wQI0eOjMGDB0e3bt1i9uzZMWXKlLj00kvjww8/jGeeeSa22GKLmDJlSnTr1q3K5VWmqZ6jmmudZVP2IX2jR4/Oeb6qFPJ+lg8NeU+6Ka330ppbveWiJv1huRgyZEjstNNOsf7660ffvn1j4cKF8eabb8aNN94Yjz76aMyePTt22WWXuO+++2LrrbfOxypUqbnWWceOHWOHHXaIX/3qV7HGGmtE586dMwPmL7vssvjqq6/i7rvvjh133DEee+yxaNu2bY2X0RTXO6L51lk29fHgWftZdu+++25mbEvnzp3jxBNPrFNZSsvTEs5pAiqauB9//DHzubi4uNr0pR35P/zwQ52WO27cuJg3b16klGL27Nkxbdq0uPTSS+Piiy+Od999N/71r3/FsssuW6dl0LD1O3HixLj66qtr1WFNzTVk3Y4aNSpGjx5doU6HDRsWu+++e9x///2x8847x8KFC+Poo4+OHXbYocpXUVO1hqrbsq/z/PHHH2OLLbaI+++/P1q3bh0REb17946DDz44Bg8eHMOHD4/FixfHiSeeGDvssIP9u5Ya65xb1g033JB5yp63U+RPQ9ftG2+8Edddd128+uqrlU6fMmVKXHXVVbHmmmvGCiusUKtlsERD1e1KK60UBx98cFx88cXx8ccfxy9+8Ys499xzY9NNN43i4uJ4+eWXY+zYsfHII49EcXFxLFiwoFbLAWhMNTmmlh5PIyoe6/KdT12O701tnZqzfFx31qZOI8pvx3yd+7WNqj399NNxwgknREREnz594tJLL600XVPbjo4b9S/XthHhuFFZWfKZT2PbaaedYujQoRGxpGz//e9/49Zbb4277ror9txzz7jwwgtju+22KzdPU9uGLbF9NQW1aRsRS44N77//foXBDeuuu27stNNOceCBB8bIkSNjzpw5cfbZZ8fuu+8eP/vZz8qldT5pmm1jwYIFceCBB0ZKKY4++ugYPHhwjeZvattS+wDqQ3O9BqvKG2+8Efvuu29ERHTo0CGuv/76Ot2/bGr3w5tznU2dOrXSN7SNGDEiDj/88Nh1113j0UcfjTfeeCNOO+20OP/886tcXmWa4jmqOddZtnKWBiytsMIK8atf/Sqn+apS6PtZPjTkPemmtN5lNcd6q05N+sNycdRRR8XYsWMrfP/zn/889tlnn7j88svj4IMPjkWLFsX+++8f//3vf6N9+/Z1WmZVmnOdffzxx5We07bYYos44ogjYuutt47p06fH008/HZdeemn87ne/q/EymuJ6N+c6q8xHH30UEyZMiIiIDTbYIAYNGlTnPO1n2X3//fex8847Z97cctFFF0Xfvn3rVJay5Wnu57RWec+RvCq7o5YOzKpK6eumO3ToUKflDhgwIAYPHhxDhgyJTTbZJI4++uj497//Hdtss03cf//9MWzYsFq90pHyGqp+58+fn+mwPvLII2OttdaqWUGpsYbcd7t161ZlB9R2220Xp5xySkQsOSleddVVNV4G/9NQdbv0D7VzzjknE0xR1sYbb5x5ossbb7yRdQA31Wusc25Z119/fUQs+QG4++675y3fQteQdTtp0qTYcMMN47777osVVlghrr/++vjss89iwYIF8eGHH8Yll1wSHTt2jPHjx8f6668fr732Wo2Xwf80ZN3+9a9/jW222SYiIt56663Yaaedolu3btGhQ4fYcMMN45FHHomhQ4fGfvvtl5knn69XBqhvNTmmlh5PIyoeU/OdT12O701tnZqzfFx31qZOI8pvx3yd+7WN7F577bUYNWpUlJSURPv27eO2226LPn36VJq2qW1Hx436VZO2EeG4UVlZ8plPY+vevXsMHjw4Bg8eHMOGDYs99tgj7rzzzrjuuuvi3XffjR133DGuueaacvM0tW3YEttXU1CbthGx5OlyVT0pcv3114+LL744IiJSSpnPZTmfNM22cdZZZ8V//vOfWHnllePUU0+t8fxNbVtqH0C+NedrsGw++eST2GabbeLbb7+NoqKiuPrqq2PNNdfMad5smtL98OZeZ5UNPC3VpUuXuPXWW6NHjx4REXHFFVfkdM5bWlM7RzX3OqvM3XffHXPnzo2IiN/85jfRqlXdh/8V+n6WDw1537IprXep5lpvValpf1guqjoOR0QcdNBBmfvNn3zySdxxxx11Wl5VmnudVbUtl1122bj99tszfQ0XXXRRrZbR1Na7uddZZW644YZYvHhxROTvjUv2s8qVlJTEbrvtFq+88kpERBxyyCExZsyYOpelbHma+zlNQEUTV3YQVi6vKCl9onkur1mpqfbt28e4ceOiY8eO8eGHH8Zxxx2X92UUmoaq3zPPPDPefPPNWGmlleK0006rWSGplaa070ZEHHjggZmL36effrpellEoGqpuyy6nd+/esc4662RNu+WWW2Y+v/DCCzVaDv/T2Pvt1KlT4z//+U9EROywww7V/sAndw1Vt/Pnz48999wz5syZE8stt1w899xz8Zvf/CaWXXbZaNu2bay44opx6KGHxsSJE6N9+/bxySef5O2CsFA15H7brl27uO++++LKK6+Mtddeu1yncp8+feKkk06KSZMmZd4yExGxzDLL1Hg5AI2lJsfUsm9TW/qYmu986nJ8b2rr1NJVd91ZmzqNKL8d83Xu1zYq995778XIkSPjm2++idatW8f48ePjl7/8Zdb0TW07Om7Un5q2jVw5bjT/trG0vffeO3bbbbdYvHhxHH744fH1119npjW1bdgS21dTVlXbyNUee+wRXbt2jYiqjxkRzidNpW385z//ib/85S8RsWTwSqdOnWqcR1PbltoHkE/N/RqsMl9//XWMHDkyZs6cGRFLjv977LFHtfPlQ0PcD2+Jdba0bt26Zeps3rx5MW3atBrn0ZTOUS21zq677rrM53322SenefKhJe9n+dCQ9y2b0npHNO96y6a++sNycdBBB2U+29dqb+DAgbHFFltERMQ777wTn3zySY3zaErr3VLrrLEePFto+1lKKcaMGRMPPvhgRET83//9X6UPLKlNWcqWp7mf0wRUNHHt27ePnj17RkRU+0aIb775JtNgVlpppXopT69eveIXv/hFRETcc889sXDhwnpZTqFoqPo955xzIiJi8803j/vuuy/Gjx9f4a8073nz5mW+e/LJJ2u6Svx/TW3f7dOnT6Y8H3/8cb0so1A0VN2WTb/iiivmnHbWrFk1Wg7/09j7bWN1fBWChqrbhx9+OHOMPeKII7K+5vanP/1p/OY3v4mIiBdffDET/U3NNfR+26pVq9h///1j+vTpMWfOnHj77bfj448/jk8//TT+/Oc/R/v27ePtt9/OpP/JT35Sq+UANIayvzmrO6Z++OGHmc9LH1Nrk09RUVGF37yl/+fydszSfJYuyworrFDjslSWT762TUtX3XVn6XacN29ezJ49u8q8Srdj7969y72+N1/n/sZs75Xl0xR88sknsfnmm8cnn3ySeYLojjvuWOU8jhvaRl05bjTvtpFNafuYN29ePPzww5nvm9o2bIntq6nL1jZy1aZNmxg0aFBEVH3MiHA+aSpt44ILLogFCxbEwIED4/vvv6/03tSMGTMy6Z988skK96z83mi57QMKXUu4Blvat99+G1tttVXmzdxnnHFGHHbYYVXOk0/1fT+8JdZZNmXvbdRmWzaVc1RLrbPPP/88Hn300YiIWG+99Rr0XlRL3s/yoSHvWzal9W7u9VaZ+uwPy0Vdj8PVaYl1lo1z2hJNtc6mTZsWr7/+ekQseRNSQz6wstD2s8MOOyxuvPHGiIjYeuut44YbbqjyDVeFek4TUNEMlO6877zzTpSUlGRNV/pU64io8ysTq9K7d++IWPIKty+//LLellMoGqJ+S1+FM27cuNhzzz0r/Sutyy+//DLz3emnn17T1aGMprbvVvV6RmqmIer2pz/9aebzokWLqkxbdnqbNm1qtBzKa6z9duHChTF+/PiIWNIRtdVWW9U5T8priLp94403Mp/XXXfdKtOut956lS6Tmmus/bZLly6x6qqrRt++fTMXmosWLYqXX345IpY88aJXr151Xg5AQynbcVjduamqY2pt8llppZUqPK22NJ85c+bEZ599ljWPTz/9NPN6+6XL0qVLl0xnWkOvU2X5FIKqrjtz3Y4lJSXx3//+NyIq34b5OPcPGjQoWrduXW1ZqsunNm2jTZs2sdpqq1WZtqF9+eWXscUWW8S7774bEUueIJpLkLfjRss/btS2bdSE40b1+TTF40ZVSu8fRES8//77mc9NbRu2xPbV1GVrGzWRj2NG2enOJ/Vr/vz5ERHx7rvvZr03dccdd2TSn3HGGZnvSx8c5PdGy20fUMhayjVYWT/88ENsv/328cILL0RExB/+8Ic4+eSTq8y7PtTX/fCWWGdVqet2bArnqJZcZzfeeGNmfMDo0aOrTZ9vLXU/y5eGum/ZVNa7pdRbWQ3RH1ad+hzf1RLrrCqNcU7Ld19eS66zsg+ebehzWiHtZ8cff3xceumlERHxy1/+Mu64445o27ZtzuUplHNahICKZmHjjTeOiCVP7XnxxRezpiv76pnSt0jUh7IRWV4LW3dNrX7Jn6ZUt7NmzcoEzfTt27dellFIGqJu+/XrFyuvvHJERMycOTNSSlnTlt7UjSj/dCtqrrH22wceeCC++uqriIjYa6+9BMbUg4ao27L1VtXFRESUe8uX+q6bpnS+feqppzL7ckO+jhIgHwYMGJC5Vqju1bYTJ06MiCW/Pfv3719uWulxubp8Pvvss3jrrbciovLjcq75VHd8L83nzTffrHLAU1X55GvbtHTVXXfmWqfTpk3LPMmmqjqty7m/uLg41l9//YiImDJlSuZBFFXl065duxg6dGi5acOGDYvi4uJq12nBggXx3HPPZebJpaO6ocyZMye23HLLzFOgzj777JyfIOq40bKPG3VpG7ly3Giex43qZLt/0NS2YUtsX01dXe8tlZSUZM4DdTlmOJ80L35vaB/Q0rSka7BSCxcujF122SWT18EHHxznnntuleWrD/V1P7wl1ll1Stc1onbbsrHPUS29zkoHn7Zt2zb23HPPnObJl5a8n+VLQ923bArr3ZLqrVRD9Ifloq7H4WxaYp1Vp67bsrH78lpynZV98Gzv3r1j6623bpDlliqU/ezPf/5z5rf5sGHD4v77748OHTrkVJ5COqdlJJq8559/PkVEioh00EEHVZpm0aJFac0110wRkbp3754WLFhQL2X58MMPU3FxcYqI1K9fv3pZRqFpKvXbr18/9ZpnTaVuU0rpz3/+c6YsZ5xxRr0so5A0VN0effTRmeU89thjWdONGDEik+6DDz6o8XL4n8bab0eNGpVZ7vTp0+ucHxU1RN3efvvtmWUcd9xxVabdZZddMmlffPHFGi2H8prK+Xbx4sXpF7/4RYqI1LZt2/Tuu+/mfRkANfHee+9ljo+jR4/OaZ5DDjkkM8+UKVMqTTNlypRMmkMPPbTSNKXH3B49eqR58+ZVmuYvf/lLJp9bb721wvRPP/00tWrVKkVE2nLLLbOWecstt0wRkVq1apU+/fTTCtNvueWWzHL+8pe/VJrHvHnz0jLLLJMiIv3kJz+pNE2+tk1TUJu2kYvqrjvnz5+funXrliIirbnmmmnx4sWV5nPQQQdl8pk6dWqF6fk6959zzjmZfG6++eZK8/nwww9T69atU0SkbbbZptI0W2+9dYqI1KZNm/Thhx9Wmubmm2/OLOvcc8+tNE1jmDdvXub3S0Skk046qcZ5OG60zONGPtpGLhw3mt9xIxfbbLNNpuxPPfVUuWlNaRu21PbVlFXVNnJxww03ZObfb7/9Kk3jfNK0zie5OPXUU6ttF35vFG77gJampV2DpZRSSUlJ2nXXXTPp9t5776y/q+pbfdwPb4l1Vp3Zs2ennj17pohIHTt2TD/++GOt8mmsc1RLr7N///vfmfQ77rhjzuuULy19P6vM8OHDM/PloiHvWzbmb8GWVm8pNVx/WC7233//TDmuv/76vOTZEuusOu+++25mnOsqq6xS63waqy+vpdfZPffck5n/yCOPrFUedVEI+9mFF16YSTdkyJD01Vdf1agshXJOK0tARTOxySabZA7Mzz77bIXp5557bqaxnHrqqRWmP/XUU5npld0sf/PNN9MTTzxRZRlmz56dKUdEpD/96U+1XR2WUt/1mwsBFfWjvuv2vffeSy+99FKVZbjvvvsyPxA7dOiQPvroo9quDmU0xH77/vvvp/bt22d+2MyZM6dCmuuvvz6Tz7bbblvX1SI1/DH5q6++yuyjQ4YMycMakE191+0333yTOnbsmCIidenSJf373/+utBwPPvhg5sbrCiuskBYtWlTXVSt4DbHffvnll1lvHJSUlKRDDz00k8cpp5xSl9UByIvaDJp/8803M4P0hg4dmr7//vty07///vs0dOjQzDH3rbfeqjSfq666KrPsww47rML0d955J3Xt2jVFRFp11VXTwoULK81n7733zuRz2223VZh+6623VruOCxYsSAMHDkwRkbp27ZreeeedCmnKHsPHjRtXaT752jZNQU3bRj6vO//0pz9VeVPh2WefTW3atEkRkYYPH551eXU996e05Hd46UDafv36pS+//LLc9JKSkrT99ttn8sk2wO+JJ57IpNlhhx1SSUlJuemzZs1KK6+8cqYz+euvv866Xg1p/vz5aeTIkXW+YeG40fKOG/loG44bLfO4MW7cuPTDDz9Umeb888/PrNuAAQMqrFtT24YtsX01hrq2ja+//rra9Xn++edT9+7dU0SkoqKiNG3atErTOZ80nfNJrnIJqPB7o3DbB7QkLfEabPHixWnffffN5LPLLrtU+F2Wi6Z6P7wl1tlDDz1UYfllffvtt+XW+Ygjjqg0XS73VBrjHNUS62xpxxxzTCbPO+64I+d1sp/Vvr3VZsBwPq4Rm+p+llLLrLd8rdO4ceOqrNt///vf6e23364yj8svvzyTx3LLLZe+++67WpWlrJZYZ/fee2+Vx83PPvssrbPOOpk8//a3v1Warro6S6lx+vJaYp0trbYPIbWf5VZnV199dSoqKkoRkQYNGpQ+++yzWpWnpZ/Tliagopl46aWXUocOHVJEpM6dO6ezzjorTZkyJT355JPpwAMPzDS4QYMGpblz51aYv7pGWTr9Zz/7WTr11FPTvffem6ZOnZpeeuml9OCDD6aTTjopLbfccpk8Bg8enDWCmpqr7/rNhYCK+tFQ++6GG26YzjrrrPTAAw+kF154Ib3wwgvplltuSbvttlvm5BgR6ZJLLmmAtS4MDbXflv3hsfrqq6err746TZs2LT355JPp8MMPz/yY6Nq1q5shedLQx+RLLrkkk/6vf/1rPawRpRqibk8//fRMms6dO6cTTzwxPfnkk2n69Onp4YcfToccckhmMEZE/iLdC11D1O1tt92Wll122XTkkUemO++8M02bNi1Nnjw5/fOf/0xrr712Zv6tt946zZ8/v57XGKCiSZMmpXHjxmX+zjvvvMyx6Re/+EW5adkG46SU0gknnJCZb5111knjx49PL7zwQho/fny5zucTTzwxax4lJSXlnsCyyy67pIcffjg9//zz6aKLLkp9+vRJEUue1vrggw9mzeeDDz5IvXv3znSOHX/88WnSpElp0qRJ6fjjj8+cU3v37p31qUAppfTAAw9kghmXXXbZdNFFF6Xnn38+Pfzww+U6bDfeeOMqb/znY9s0hrq2jXxed86dOzcNGjQok/bAAw9MTz75ZJoyZUo666yzUufOnVPEkpuyVb25ra7n/lKXXXZZJu0qq6ySrr766vTCCy+ke+65J2266aaZaXvuuWeV23iPPfbIpN10003TPffck1544YV09dVXp1VWWSUz7fLLL68yn4a08847Z8q12WabpX//+9/p1Vdfzfr35ptvZs3LcaNlHTfy0TYcN1rmcaNfv36pR48e6YADDkjXXnttmjx5cnr55ZfTpEmT0j//+c9y+3BxcXHWt602pW3YUttXQ6tr2ygN9lxrrbXSKaecku655540derU9OKLL6a77ror7bfffpmBWxGR/vCHP2Qti/NJ0zmf5CqXgIqU/N4o1PYBLUlLvAb7/e9/n5l/8ODB6cUXX6xynV599dVK82mq98NbYp0NHz489ejRI+2///7pmmuuSZMmTUrTp09PEyZMSGeddVZmMGjEkvvS2Z5inOu90IY+R7XEOls6v+WXXz5FLHnrRU3uRdnPcquzTz/9tEJf6eqrr56Zd+lp2Qbs5uMasanuZym1zHrL1zpVN9B73LhxqXXr1mnzzTdPf/vb39Kjjz6aXnzxxfT888+na6+9ttxg7NatW6d77723+grJQUuss379+qW+ffumI444It10003p2WefTdOnT0+PPfZYOumkk1KvXr3KXVNle3BiLgEVKTV8X15LrLOyvv7669SuXbvM78iasJ9VX2d33XVXufGEDz30ULW/07MFlbT0c9rSBFQ0I/fee28mOrmyv0GDBmU9GOX64ziXv2233TZ98cUX9by2hac+6zcXAirqT1PYdzt27NikbsC2FA21355wwgnlOiiW/uvTp0+lUaDUXkMek3/+859nfqhX9vp38qu+63bx4sXpqKOOqnKfjYjUtm3bdN5559Xjmhae+q7b2267rco6LSoqSr/97W9r/fprgLoaPXp0ztf1Edm7gxYtWpR++9vfVjnvfvvtV+0blmbNmpWGDRuWNY927dqlK6+8str1eu6558o94GHpv+WWWy4999xz1eZzxRVXlBuIt/Tf+uuvn2bNmlVlHvnaNg2trm0j39edb7/9dlpttdWy5tO1a9d03333VZtPXc79ZZ1yyilV/nbbZpttqn3q9vfff5+22WabrHm0atWqypsxjaEmbSKi6v4ix43smuNxIx9tw3GjZR43SvuPq/tbccUV06OPPlplXk1pG7bE9tXQ6to2yr49q6q/1q1bp7Fjx6bFixdXWR7nk+Yl14AKvzeya8ntA1qSXM51Zf+awzVYrr8Byv5VpqneD2+JdVb2SdJV/Q0fPrzKtw/kei+0oc9RLbHOynrooYcy8x166KE12TT2sxzrrCZj5yKyv0UspbpfIzbV/Syllllv+VqnXAZ655J/z5490913311NTeSuJdZZrr9Ddtlll/TNN99kLUuuARUN3ZfXEuusrEsvvTSTtrK3xlbFflZ9ndX0vmBE1X0yLfmctjQBFc3MzJkz09FHH50GDRqUOnbsmLp3756GDh2azjnnnCrfGFFdo1ywYEF65JFH0h/+8Ie06aabptVWWy117do1tWnTJvXo0SOtt9566bDDDkuTJ0+ux7Wjvuo3FwIq6ld91e3cuXPTDTfckA477LD085//PK288sqpY8eOqbi4OC277LJps802S2eeeWb6/PPP63HtCltD7bfPPvts2nvvvVP//v1Tu3btUrdu3dKwYcPSGWeckWbPnp3HNaJUQ9TtW2+9lUm71VZb5XkNyKYh6nbatGnp4IMPToMHD05dunRJrVu3Tt26dUvrrbde+v3vf19ltDm1V591+9lnn6Xzzjsvbb311mnAgAGpY8eOqXPnzmnQoEHpoIMOyuk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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": "markdown",
   "id": "6b1f2872",
   "metadata": {
    "id": "6b1f2872"
   },
   "source": [
    "This data is a little noisy: by observing each column as a boxplot, you can see outliers.\n",
    "\n",
    "You could go through the dataset and remove these outliers, but that would make the data pretty minimal.\n",
    "\n",
    "2. For now, choose which columns you will use for your clustering exercise. Pick ones with similar ranges and encode the `artist_top_genre` column as numeric data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9cdd6a5d",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "id": "9cdd6a5d"
   },
   "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": "markdown",
   "id": "7a7059ff",
   "metadata": {
    "id": "7a7059ff"
   },
   "source": [
    "3. Now you need to pick how many clusters to target. You know there are 3 song genres that we carved out of the dataset, so let's try 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ea15cfb",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4ea15cfb",
    "outputId": "700d8be7-c6ac-4ba7-91ea-daea1e6bf589"
   },
   "outputs": [
    {
     "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])"
      ]
     },
     "execution_count": 7,
     "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_init='auto')\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": "markdown",
   "id": "11af26e8",
   "metadata": {
    "id": "11af26e8"
   },
   "source": [
    "You see an array printed out with predicted clusters (0, 1,or 2) for each row of the dataframe.\n",
    "\n",
    "4. Use this array to calculate a 'silhouette score':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f35f6d5d",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f35f6d5d",
    "outputId": "d17fe6ef-b099-476d-a247-5eb593d61c47"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5466747351275563"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fafb9e2",
   "metadata": {
    "id": "9fafb9e2"
   },
   "source": [
    "## Silhouette score\n",
    "\n",
    "Look for a silhouette score closer to 1. This score varies from -1 to 1, and if the score is 1, the cluster is dense and well-separated from other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary of the neighboring clusters.\n",
    "\n",
    "Our score is **.53**, so right in the middle. This indicates that our data is not particularly well-suited to this type of clustering, but let's continue.\n",
    "\n",
    "### Exercise - build a model\n",
    "\n",
    "1. Import `KMeans` and start the clustering process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7db20a94",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7db20a94",
    "outputId": "35823b7c-0f94-45b0-ec5a-a576b7496bbc"
   },
   "outputs": [],
   "source": [
    "wcss = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42, n_init = 'auto')\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d02c3553",
   "metadata": {
    "id": "d02c3553"
   },
   "source": [
    "There are a few parts here that warrant explaining.\n",
    "\n",
    ":::{seealso}\n",
    "🎓 range: These are the iterations of the clustering process\n",
    "\n",
    "🎓 random_state: \"Determines random number generation for centroid initialization.\"\n",
    "\n",
    "🎓 WCSS: \"within-cluster sums of squares\" measures the squared average distance of all the points within a cluster to the cluster centroid.\n",
    "\n",
    "🎓 Inertia: K-Means algorithms attempt to choose centroids to minimize 'inertia', \"a measure of how internally coherent clusters are.\" The value is appended to the wcss variable on each iteration.\n",
    "\n",
    "🎓 k-means++: In Scikit-learn you can use the 'k-means++' optimization, which \"initializes the centroids to be (generally) distant from each other, leading to probably better results than random initialization.\n",
    ":::\n",
    "\n",
    "### Elbow method\n",
    "\n",
    "Previously, you surmised that, because you have targeted 3 song genres, you should choose 3 clusters. But is that the case?\n",
    "\n",
    "1. Use the 'elbow method' to make sure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b70dfd7",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 487
    },
    "id": "9b70dfd7",
    "outputId": "84929290-f7ef-4d8e-844d-24275d9ae489"
   },
   "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(wcss)\n",
    "plt.title('Elbow')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889ceb5c",
   "metadata": {
    "id": "889ceb5c"
   },
   "source": [
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/elbow.png\n",
    "---\n",
    "name: elbow\n",
    "---\n",
    ":::\n",
    "\n",
    "Use the `wcss` variable that you built in the previous step to create a chart showing where the 'bend' in the elbow is, which indicates the optimum number of clusters. Maybe it **is** 3!\n",
    "\n",
    "## Exercise - display the clusters\n",
    "\n",
    "1. Try the process again, this time setting three clusters, and display the clusters as a scatterplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "99778db3",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "99778db3",
    "outputId": "5b6debe8-ef6e-450b-ad7f-1b88299b5f53"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 3,n_init='auto')\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": "markdown",
   "id": "f1a1fc8f",
   "metadata": {
    "id": "f1a1fc8f"
   },
   "source": [
    "2. Check the model's accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22155deb",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "22155deb",
    "outputId": "62858ab9-9518-43f7-e124-43e234b745c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result: 111 out of 286 samples were correctly labeled.\n",
      "Accuracy score: 0.39\n"
     ]
    }
   ],
   "source": [
    "labels = kmeans.labels_\n",
    "correct_labels = sum(y == labels)\n",
    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfabf71a",
   "metadata": {
    "id": "cfabf71a"
   },
   "source": [
    "This model's accuracy is not very good, and the shape of the clusters gives you a hint why.\n",
    "\n",
    "This data is too imbalanced, too little correlated and there is too much variance between the column values to cluster well. In fact, the clusters that form are probably heavily influenced or skewed by the three genre categories we defined above. That was a learning process!\n",
    "\n",
    "In Scikit-learn's documentation, you can see that a model like this one, with clusters not very well demarcated, has a 'variance' problem:\n",
    "\n",
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/problems.png\n",
    "---\n",
    "name: problem models\n",
    "---\n",
    "roblem models by Scikit-learn\n",
    ":::\n",
    "\n",
    "## Variance\n",
    "\n",
    "Variance is defined as \"the average of the squared differences from the Mean\" . In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean.\n",
    "\n",
    ":::{seealso}\n",
    "This is a great moment to think about all the ways you could correct this issue. Tweak the data a bit more? Use different columns? Use a different algorithm? Hint: Try scaling your data to normalize it and test other columns.\n",
    ":::\n",
    "\n",
    "## Your turn! 🚀\n",
    "\n",
    "TBD.\n",
    "\n",
    "## Self study\n",
    "\n",
    "You can use this tool to visualize sample data points and determine their centroids.\n",
    "\n",
    "- [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/)\n",
    "- [this handout on K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html)\n",
    "\n",
    "## Acknowledgments\n",
    "\n",
    "Thanks to Microsoft for creating the open-source course [Machine Learning for Beginners](https://github.com/microsoft/ML-For-Beginners). It inspires the majority of the content in this chapter."
   ]
  }
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