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ML-For-Beginners/5-Clustering/2-K-Means/solution/notebook.ipynb

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"cells": [
{
"source": [
"# Nigerian Music scraped from Spotify - an analysis"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 10,
"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: 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: 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: 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: 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: 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: 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: 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"
]
},
{
"source": [
"Start where we finished in the last lesson, with data imported and filtered."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 11,
"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 "
],
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},
"metadata": {},
"execution_count": 11
}
],
"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()"
]
},
{
"source": [
"We will focus only on 3 genres. Maybe we can get 3 clusters built!"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Top genres')"
]
},
"metadata": {},
"execution_count": 12
},
{
"output_type": "display_data",
"data": {
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\n"
},
"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": 13,
"metadata": {},
"outputs": [
{
"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": 13
}
],
"source": [
"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": 14,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbc18790a20>"
]
},
"metadata": {},
"execution_count": 14
},
{
"output_type": "display_data",
"data": {
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LjoZkfWCdut6vc3Br/0MoU9potJZvrHeaBcGynCci4tHAE+vmRW37lgO2qJtn9an0t8pxeWCzgWZS9xERL6RM7XU9U9Gi/fh3uXBYluNlt/qYlOkfAYiINSLikRGxRp/jLc/54aWN9WM67LccJQ3DKNpC/PzSOBqndsB+7R/SoMzFff9JYGXgC5l5xhKeS5qtod/7tY2zFUD6/cy8vT6/dEQ8NCLWi4hOA81IwzKqz/xWQPT63RJExKrAmm3pJUkLl/VtTQrr2Jok1qs1KaxLS4O1OVMBQV1/I679RVsD8m0+oFm9RmIhB1Q8tj7+vs+ov83OwI/tmkrjaqPG+kVdU913v2U9ets21n/TYb9lOcYiYqXayfctlH+Iy9RdH21L+ihg6bpuOY6JiLg/cGjd3K9OrzUd/l2Op90i4tcRcVtE3BwRv4uIoyOiV6S8ZTlenlIfL83MmyPiRRFxAXAdcDFwXUT8NiLeGhHLdzje8hxzEXE/piLtLwPO6JDMcpQ0DKNoC/HzS+NonNoB+7V/SIMy0vs+IvYAdgBuAP59tueRBmAU9/4TmJqZ94KIWDUiPkoZSfBPlBEHb4qI70fEohmeW5qNUX3mf7o+PiAiXtslzQEd0kuSFi7r25oU1rE1SaxXa1JYl5YGaza/ES8DPHI42Rm8BRlQUSMYWxFd3aYVASAzb6BEoQE8dJj50lCs21jvWdbA5Y11y3qEImIp4O2Np77aIZllOWYiYu+IyIhIyufkxcAhwINqkvcDX2o7zHIcTx8E1gbOBI6YwXGW53jaiFKJWxG4H/AIykj4P4iIEyNitQ7HWJZjov5PfEzdvDYiDgW+CGzclvRRwIco5Xr/tn2W5/j7F8qoQgDHZmZ2SGM5ShqoEbaF+PmlsTJO7YAR8QRgx7p5QWbawUNDMer7PiJWZ2pQjbdn5jWzOY+0pEZ47zd/HFwKOBd4I9Csny8HPINSb99vhueXpm3En/mfZ2qWzcMi4nMRsVNEbBYRu0bEiUzNfvyezDx1FteQJM0T1rc1Kaxja5JYr9aksC4tDcWC/414QQZUAKs01m+ZRvrWB+L9hpAXDddMyvrWxrplPVpvBrao6ydkZqdptCzL+eN8YIvM3L9DB1HLccxExDbAq4C7gNd26dTbjeU5Xm4DvgK8GtgG2BR4FvAeyswGAM8HTuowXZplOT5WY+o7+CbAvwFXAnsBawArUUZZak1/txWlAt5keY6/lzbWj+mSxnKUNGijagvx80vjZizaAevMYoczNWvjOwZ5fqnNqO/7D1EG2DgL+NwszyENwqju/TUa6/tRRlH7DqWNewXggcDrgJuAAN4fEc+b4TWk6RrZZ35m3p2ZLwN2A35BaVf+JnAO8HVK2+PpwDMz850zPb8kad6xvq1JYR1bk8R6tSaFdWlp8Bb8b8QLNaBihcb64mmkv6M+rjiEvGi4ZlLWdzTWLesRiYhtKTMZAFxN+ULciWU5fr5B6ey7CaVSsydwIvBE4MsR8dwOx1iOYyQilgM+S6mAfiQzfzXDU1ie4+UhmblnZh6emT/JzPMz8/u1wvU44Lyablvu+1lrWY6PlRvrK1ACZbbLzC9m5g2Z+ffM/BHwdEplG2CXiPintuNaLM8xExHrAovq5v9m5sVdklqOkgZtVG0hfn5p3IxLO+AngM3q+tGZ+a0Bn19qGtl9HxFPA17B7AZqkAZtVPd+e939+8BzM/OczLwjM6/JzE8DzwXuqeneFxExw+tI0zHS7zoR8VjKQBGbdEmyJfDKiHjIbM4vSZpXrG9rUljH1iSxXq1JYV1aGrwF/xvxQg2ouL2xvtw00i9fH/8+hLxouGZS1ss31i3rEYiIx1E64C9DKavdMvPqLsktyzGTmTdm5q/qck5mfiUzd6V8AdyAMgr+3m2HWY7j5T+BxwB/Ag6exfGW5xjJzBt77Psr8ALgzvrUvm1JLMvxcXvb9uGZ+dv2RJn5d+49ytLuXc5heY6fvZiqZx3dI53lKGnQRtUW4ueXxs2ctwNGxP6UUaegjDr1r4M6t9TFSO77OhJsa6CGQzPzguXcewAAIABJREFUlzM5XhqCufi+A7BfZt7dnigzfwKcUDcfS/cfzaUlMbLvOnW247OAnYA/Ay8B1q7XfSjlO85twB7A2fU3IEnSwmV9W5PCOrYmifVqTQrr0tLgLfjfiBdqQMXNjfXpTBfSioqczvQ+Gi8zKetm9KtlPWQRsT7wPWB14G5gjzridjeW5TyRmV8Avkb5H/KJiGhO1Wc5jomIeAywf93cNzNv7ZW+C8tzHsnMP1JGdgB4RESs09htWY6Pm9u2v9cj7WmUEWoANu9yDstz/LykPt4BHNcjneUoadBG1Rbi55fGzZy2A0bEa4D31s2LgB1mWf+SZmJU9/07gEcDlwMHzvBYaRjm4vvONZl5XteU8N3G+uZdU0mzN5L7vnbw+zKwGnAV8JTMPDYz/5qZd2bmFZn5SeBplB/Q16H3QBKSpPnP+rYmhXVsTRLr1ZoU1qWlwVvwvxEvM9cZGIbMvD0irgMeAKzbK21ErM5U4V0+7Lxp4K5orPcsa0rEX4tlPUS1E++plC8BCbwiM0/qc5hlOb+cBLyQ8vn5HOBL9XnLcXy8mRIN+kdgpYjYo0OajRvrT4+Itev6t2qDpOU5//wa2KGuPwT4S123LMdEZt4REdcAa9Wnur7H9TvttZTRC9Zq7LI8x1REbAZsVDdPzswbeiS3HCUN1AjbQvz80liZy3bAiNgT+GTdvAx4ZmZeu6TnlfoZ4X2/X308FdgpIjqlaZ175Ubbw9WZ+YMZXkvqa4T3fjP9FV1T3TftWl1TSbM0wvv+OZT2RICPZ+ZVXfJzYUQcSxkt/MkR8YTM/MUMryVJmgesb2tSWMfWJLFerUlhXVoaivbfiM/tkXZe/ka8IAMqql8D21BGaF4mM+/qku4xjfXfDD9bGrBfN9Yf0zXVffdb1kMSEWtSRkjfoD61b2YeM41DLcv55ZrG+sMb6xdTZiRZGstxrrWmztqAEg3dzwGN9fWBW/Hvcj7KLs9bluPlQmBRXV+6T9rW/uZ3WctzfL20sd5vZAnLUdIwjKItxM8vjaORtwNGxM7AMZTZG68Ets/Mfj8OSoM0ivu+NW33y+vSy5pMtT/8ELCzh4ZlFPf+hY316dbb4d51d2mQRnHfP7ax/vM+aX9G6QTSuqadQCRp4bK+rUlhHVuTxHq1JoV1aWmwZvMb8V3A74aTncFbaq4zMEQ/qY8rA0/ukW7bxvqZw8uOhuQSpkbf3rZXQsrUSQB/Bi4dVoYmWUSsRpmKrTUy89sz87BpHm5Zzi8Paaz/Y1qmzFwMnF03t4yI5eiuVc530DtiUXPHv8v5Z6PG+l8a65blePlRY32DbokiYlVKgymU8mixPMdQRCwLtEYLugb4dp9DLEdJwzCKthA/vzSORtoOGBHbA1+lDFZzHWWkzD/M9nzSLNn+rUk19Hs/My8D/lQ314suQ8dWGzbW/9w1lbRkRvGZ3+xY0m9AvmW7HCdJWnisb2tSWMfWJLFerUlhXVoarHOAxXW962/Etb/oU1rHZOadw87YoCzkgIpvNNY7RvZGxFJMjSJ7I3D6sDOlwcrMBE6qm4+JiKd0Slefb0U9nVSP0wBFxErAKcCT6lPvycwPTPd4y3Le2a2xfkHbvtbn76rArp0Ojoh1gWfUzdMy8+bBZk+ZuXdmRq8FOLhxyHaNfZfWc/h3OY9ExPrAM+vmHzLzH40NluXY+XpjfZce6XYBWo1LP249aXmOrX9mahraL/UY4QKwHCUNzdDbQvz80pgaWTtgRGxF+RtYHrgJeHZmXtj7KGkoRvGZ37NdobYtXFaTX9Z4ftEMX4s0E6P6zG/V3VcFtu+Rrtn++ZOuqaQlM4r7/pLG+jZ90jZ/NL+kaypJ0kJgfVuTwjq2Jon1ak0K69LSANX+nafVzWfU/p+d7Er57Ac4cegZG6AFG1CRmWcz1fHslRGxZYdk/87UtDuHzqdIGN3LR4G76/rHI2LF5s66/fG6eVdNrwGqUWUnAlvXpw7NzHfO4lSW5RyLiL0jYoU+ad4M7FA3L6HRybc6nNLIBfD+iHhA2/FLA59katq+Dy1RpjVs/l2OgYjYKSK6RrNHxIMoDRKtWWE+2SGZZTkmMvOXTM1esGcdceleImJt4N11czFwZFsSy3P8vLSxfsw0j7EcJQ3UINpCImJRRGRdjupyKT+/NFZGde9HxBMpg0msDNwK7JiZPxvEa5BmaoSf+dJYGfH3ndvr+v/UWSTvJSL2AhbVzVMy8/LpvxJp+kZ0358G3FbXXxcRm3TKS0T8M1MDhPwZOH/6r0SSNN9Y39aksI6tSWK9WpPCurQ0M7XfaOt+P6hLsg/Xx2WAw2o/0OY51gRag7DfSOlHOm/0m2ZmvnsjZRqeFYHvRcR7KVFkKwJ7APvUdBcDh8xJDidcRDwVeETjqTUb64+IiL2b6TPzqPZzZObFEfEh4O3AZsCZEfEB4A+UacH2AzatyT+Umb8b2AtQy5eBZ9X1HwBHRMTGPdIvzsyL25+0LMfCQcAhEfF1SuT3H4BbgFWATYAXMxU4sxjYJzPvbp4gM6+PiP2ATwMPB/4vIt5DmcliHeBNwHY1+Zcz84xhviAtGf8ux8bHgWXr3+ZZwKXA3yn/NxcBr2Hqf+hPgMPaT2BZjp03AVsC9wdOjoiPAv+PUq5bAPsDrWjuA5ozjoDlOW4iYnXguXXzV5n58+kcZzlKGpKht4X4+aUxNdR7PyI2BL5L+f4G8E7gpj7tH1dn5tUzvZY0A7Z/a1KN4vvOnyLiv4APUtpFz67fd37J1My8r6vJ/wa8eXYvRZq2od73mXljRLwfeBfl94CfRsTHge8DNwAPAp4HvJqpAfvenpn3zPoVSZLmC+vbmhTWsTVJrFdrUliX1kQYRD/s6cjMH0TEVyh/PzsD36/9nf5C+ax/B/Cwmny/zLxhNteZK5GZc52HoYqInYBjmZpCpN3FlOj2348uV2qpkXsvm276OsVdp/MsBXwOeEWPw4+gdP72H9KARcRMP0guy8z1upzLspxDEXEpJQiinyuAV2Tm93uc62DgAKDj3y2l4/C/ZObtXfZryGo06YF1c7tuwS3+Xc69Gfxtfh14VWbe2OU8luUYqRWa4ymV6E4SeE9mHtDleMtzTETEa4FP1c3/yMxpz75kOUoahiVpC4mIRUxNaXx0Zu7d5Rp+fmnsDPPer43N7bOG9XNwZh40w2OkGRnFZ36f619Kqa92be+ThmFU935EvI8SLNqtjfNq4PmZedb0ci7N3rDv+4gI4H8oHU663fMAdwL/mZkf7pFGkrSAWN/WpLCOrUlivVqTwrq0JsEg+mG3fS/v+n07Ilak9HXaocvp7wH+ez5+X1+qf5L5LTO/BTwe+Ajlw+82ylQi51JHTDSYYv7LzHsy85XAjsBJlIinxfXxJGCHzHyVnTjGn2U5555Nmc7sBEpU+F+Bu4CbKaPNfh14OfDoXsEUAJl5IPBU4EvA5ZRyvJoShfuizNzRYIr5wb/LsfAySvDLdyjfZ66n/G3eSJn95TPAVpn5gm7BFGBZjpvM/AnwOOBg4BeUkTduBy6hVFKe3C2Yoh5veY6Pl9THu4EvzuRAy1HSMIyiLcTPL40j2wE1ibzvNalGde9n5v6UGXu/QJkx9A7gJuAcymAyj7LTh0Zl2Pd9Fm8GNqfMQP0rym8Dd1Pu+59ROolsbAcQSZos1js0KbzXNUmsV2tSWJeWBisz/56ZOwIvpvQDvZryG/HllH6iT52PwRQwATNUSJIkSZIkSZIkSZIkSZIkSZIktVvwM1RIkiRJkiRJkiRJkiRJkiRJkiS1M6BCkiRJkiRJkiRJkiRJkiRJkiRNHAMqJEmSJEmSJEmSJEmSJEmSJEnSxDGgQpIkSZIkSZIkSZIkSZIkSZIkTRwDKiRJkiRJkiRJkiRJkiRJkiRJ0sQxoEKSJEmSJEmSJEmSJEmSJEmSJE0cAyokSZIkSZIkSZIkSZIkSZIkSdLEMaBCkiRJkiRJkiRJkiRJkiRJkiRNHAMqJEmSJEmSJEmSJEmSJEmSJEnSxDGgQpIkSZIkSZIkSZIkSZIkSZIkTRwDKiRJkiRJkiRJkiRJkiRJkiRJ0sQxoEKSJEmSJEmSJEmSJEmSJEmSJE0cAyokSZIkSZIkSZIkSZIkSZIkSdLEMaBCkiRJkiRJkiRJkiRJkiRJkiRNHAMqJEmSJEmSJEmSJEmSJEmSJEnSxDGgQpIkSZIkSZIkSZIkSZIkSZIkTRwDKiRJGjMRcVREZERcOtd5AYiIvWt+MiLWm+v8SJIkSZKkyRARixptEovmOj+SJEmSJEmSJGnhMaBCkiRJkiRJkiRJkiRJkiRJkiRNHAMqJEnSEhm3GTUkSZIkSdL4ioj1GrNO7D3X+ZEkSZIkSZIkSZNtmbnOgCRJGm+ZeRRw1BxnQ5IkSZIkTZjMPAOIuc6HJEmSJEmSJElauJyhQpIkSZIkSZIkSZIkSZIkSZIkTRwDKiRJkiRJkiRJkiRJkiRJkiRJ0sQxoEKSNKci4qCIyIjIun3/iDg4Ii6MiFsi4vqIOD0i9pzGudaLiI/UY2+OiNsi4ncR8ZmI2KTPsVmXg+r2MyLimxFxZUTcHhF/jIhPRMRDpvtaeqRb1Ljeon6vq8PxK0fE7hFxeEScHxE3RcSdEXFNRPwwIt4aEfeb4et9ekR8LSIur+e6tJF270b69dpfL/Cy+tTDG+n+sdS0Ozee22Mar/GQmvauiFhnpu+RJEmSJEnDFBEbR8Q7I+K7EXFFRNxR2zF+FxFHR8RTpnmerWv9/rcR8beIWFzPd3JE/GtE3L/HsTtFxPGN618XEWdFxNt7tQsMsv0iIp4cEUdExMURcWttQ7k8In4WEYfV9oBopE/gksYpjuzQlnDQTPLQSLtDRBxb23BaebkkIr5e2zZWakt/r/aOiFgqIvaJiJ9GxA31HL+MiHe0H9vl+ktHxMtq2f2lUSY/iYi3RMSKfY6f0XvZOG6FiPi3iDgjStvQnVHa034bEd+u116vX/4lSZIkSVqIImK72lbzxyh9SP4WERdExIe69UVobzupde+3RcTPo/RFuTkizo6IN0TEMtPIwwo17WkRcVVt/7k6Ik6NiFf2OkdEXFrzclTdfnJEHFXbPO7o1L4TEStFxAG1XePWRvvEK6Lo2N4SEY9vPP/2abyufRvpt+iXXpKkcdb3H7okSaMSEesD3wc2bDy9MrAIWBQRzwdenJl3dTj2pcBngeXbdj2iLq+MiAMy833TyMeBwEFtT68P/CuwV0TslJk/ntaLGo5TgG07PL8m8LS6vD4idsjMi/qdLCLeA/znYLN4H6cAVwIPBvYGvtIjP8sAe9XN72TmX4acN0mSJEmSpq3+0Hx6h13LMdUO8dKIeH9m7t/lHCsCRwCdBpB4SF12BNairY0iIlYAvgTs0nbcGsBT6rJvROyYmedP71XNXES8Gfgw9x24ad26PAl4PbAKcMsQ8/EA4Dhg+w6716vLrnX7qC6nWQn4XodzbFKXnSPi6Zl5a5c8PAz4JvCEtl1rAFvX5XW1TC7ucPys3suIeDBwKrBR23Gr1+VRwHOAdYC3dsq7JEmSJEkLUW0/ORLoNODjxnV5XUTsmZnf6nGeBwHfAZ7YtmvzujwrIp6fmfd0Of4JwEnAw9t2rUVph9geeE3th/LXPq/ptcDH6dHnMyLWBX4APLLx9EpMtU/sAnys07GZ+cuIOKe+rpcB7++VH+Dl9fHCzDy7T1pJksaaARWSpHFyHCVw4dPA8cBNwOOB/Sg/AL8Q+Avw5uZBEbEj5QfxoPyofAjlx+S7gK2A/SnBBu+NiBsz81M98rAjsBnwW+CDwC+B1YDdgFfX9ZMjYuPMvHyJX/HsLANcQPmh/lzKexKUCvgulPdpfeAbEfHEzLy9x7l2pXQMuAD4CPArYEXu2xjQyScp5fRu4Hk1H8/ulDAz764jJuwPPDMi1s3MK7qcd0fggXX989PIhyRJkiRJo7QMcCtl8IAfABcBf6PUZR8H/Buljv72iLg4M49sHhwRS1F+SH9mfep3lDr2ucBtlMEItqLU7zs5mqlgil9Q2kF+Q+m8vwdlIIN1gNMi4vGZ+ecle7n3FRGPZyoA4BLgE8D5wPWUTv+PBrajtBc0bVLz9t26/U7Ke9F09QzysRIluKU1M+nPKANu/Aq4A3goZeCJ3fuc6nOUQJSjga8CVwEPA/4D2BLYoub1PgEyNaDjJ/Vad9Rz/RC4FLgf8CzgjZRAm29HxJMy86bG8bN9L6F0omgFUxwLnEBpn7mbch9t1uU4SZIkSZIWrDrD4/GUvgcA36LU9/8I3EOp5/87pe5/fERsnZnndjndCZS698fqea6n1NUPAB4L7ETpS/KZDvl4BKWNYDVK29FhwNnA5cADgJ2B11ACGE6KiG0y884u+dicMjDl5ZR2hHMpbVTbNK63LKW9qhVMcQqlneIKyoAN+wDPpQRzdHN4vdZjImLLzDyrU6IaKLJp3bRfhyRp3ovMnrN6S5I0VBFxEHBg46kXZeaX29KsAvyYMsrfPcATMvNXdd+ylB+o16EEU2zTPvpiRDwcOIvyQ/JtwMMz89q2NM1/iD8Hts3MW9rSvAQ4pm5+LTNf2Lb/H68lM6PHa17E1EiW22XmGW37j6JE+1+Wmet1OP6Rmfm7Hud/BqVjwlLAqzLziA5pmq/3NGDHzLyjy/n2pozcALB+Zl46k/w20m1I6SQSwDsy871d0p1EaTi4BnhIjwYDSZIkSZJGLiLWBO7KzBu77F8OOJkSMHEZsGFm3t3Y/2/AoXXzRGDPTnXyGnjx4GZARB1U4uS6eRqwQ2Yubjvu1ZSgAoCvZububfsPYgnbLyLiXZSOA7fW19dxBMWIWA24uTlKY0SsRwkcAHh5Zh41mzzU/f/D1MAbhwH7ZocfPWqZrN7MZ1t7B8BLMvPYtuOWp3RQ2Bi4Dli7febUiPgi8CJKWW+XmZfQJiI2pbRtrQy8NzPf0dg3q/eyjrT5N2BZ4JDM7DoDRUSskZnXd9svSZIkSdJC0mgbuRPYOTO/0yHN6pS6+uOAMzPzqY19BzHVj+VO4Fkd2iTWAH4NPAj4ZWa2z1pJRJxJGTTjvHqOazukeQ4l8GEpYJ/M/Fzb/kuZmt3iAuBpPdqk3gh8tG5+NDPf3CHNx4E3NJ5qb/NZBbiS0obxuczcp8u1DqUMKnInpV/HNZ3SSZI0X7RPHy1J0lw6uT2YAiAzb6ZEykP53/Xaxu5dKMEUAO9uD6aox18GvK1ursTUtIPd7NMeTFHP8wXg263rRsTafc4zFL2CKer+UymzVwA8v8/p7qEEXXQMphikzPwDcEbd3LtTmjpd5g5181iDKSRJkiRJ4yYzr+32w3Xdv5ipdoiH05gFsgZJtPZdAby0W508M+/pMLvEv9bHOynBCIvb9lN/eD+1bu4aEQ/u85Jmo9UmcnG3AICal5uawRSDFBH3p4ziCGVmijd2Cqao+VjcK5/ACe3BFPW4OygzRkAZOXKj5v4aHNIKWHlDp2CKep7zKAEfcN82kdm+l2tQgikAftTtuHqswRSSJEmSpIlQZ6fYr25+rFMwBUBm3sBUG83WEfHITumAj7cHU9Tjr2dqoIZN6kAIzXxsQwmmAHhZp2CKep7vUGbTgC79KBr+tVebFFN9aa4A3t4lzX9QZrfsqPbP+Wrd3D0iVmxPUweueHHdPNlgCknSQmBAhSRpnBzZbUdmng1cWDef0djVWk96TyP4NeCmDse3uyAzf9Zjf+saywCLeqQbmYhYKyIeGREbtxbK7A5QZvXo5cz2GSeG7PD6+MiIeGqH/XtR3ltwWkhJkiRJ0jwQEctHxMMiYqNGvbw580Ozbv5EYN26/rlOAzr0uM4ywLZ183uZeXmP5K3RDIfVfnFlfdwoIrYYwvmn4+mUgTOgdJC4u1fiPr7YY1+znWiDtn07AktTZkT9Nr2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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"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)"
]
},
{
"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": 15,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
"le = LabelEncoder()\n",
"\n",
"# scaler = StandardScaler()\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 = scaler.fit_transform(X)\n",
"\n",
"y = le.transform(y)\n",
"\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": 16,
"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": 16
}
],
"source": [
"\n",
"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"
]
},
{
"source": [
"Those numbers don't mean much to us, so let's get a 'silhouette score' to see the accuracy. Our score is in the middle."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.5466747351275563"
]
},
"metadata": {},
"execution_count": 17
}
],
"source": [
"from sklearn import metrics\n",
"score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
"score"
]
},
{
"source": [
"Import KMeans and build a model"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 19,
"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",
" 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": 20,
"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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\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('Elbow')\n",
"plt.xlabel('Number of clusters')\n",
"plt.ylabel('WCSS')\n",
"plt.show()"
]
},
{
"source": [
"Looks like 3 is a good number after all. Fit the model again and create a scatterplot of your clusters. They do group in bunches, but they are pretty close together."
],
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 21,
"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": [
"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()"
]
},
{
"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": 811,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\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)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
]
}
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