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

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nigerian Music scraped from Spotify - an analysis"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: seaborn in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (0.12.1)\n",
"Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from seaborn) (3.6.2)\n",
"Requirement already satisfied: numpy>=1.17 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from seaborn) (1.23.5)\n",
"Requirement already satisfied: pandas>=0.25 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from seaborn) (1.5.2)\n",
"Requirement already satisfied: pyparsing>=2.2.1 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
"Requirement already satisfied: pillow>=6.2.0 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.3.0)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.6)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
"Requirement already satisfied: cycler>=0.10 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.38.0)\n",
"Requirement already satisfied: packaging>=20.0 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (21.3)\n",
"Requirement already satisfied: pytz>=2020.1 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from pandas>=0.25->seaborn) (2022.6)\n",
"Requirement already satisfied: six>=1.5 in /Users/jimbennett/GitHub/ML-For-Beginners/.venv/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install seaborn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Start where we finished in the last lesson, with data imported and filtered."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"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>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>"
],
"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 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"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": "markdown",
"metadata": {},
"source": [
"We will focus only on 3 genres. Maybe we can get 3 clusters built!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Top genres')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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": 4,
"metadata": {},
"outputs": [
{
"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",
"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 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot: xlabel='release_date'>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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6qsJ3jz76aGb9so3/+OGHHzJv+ejXr19auHBh1mXURD7G+qSUn/E+1Y0vKVumfIzLAYCltapbOAYAhW7BggVx1VVXRcSSSPsTTjihQpq2bdvGVVddVSHKv6xVVlml0jc+lBoyZEjsv//+ERFx9913V1uuq6++Orp27Vrh+7322ivzZMJJkyZVmH7eeefF4sWLo6ioKMaPHx8rrrhi1mUs/UaH8ePHx8yZM6N9+/Zx3XXXRceOHSud74ADDoj1118/IiKuueaaCtNXW221rMuMiNh8880zTxzIZVtcccUV0blz5wrf77333rH11ltHRMRdd90Vn332WbV5NYbf/va3UVRUFIsXL45rr7220jQ33HBDLFy4MJMeAAAAqN7MmTPjlltuiYiIiy++uNK3N0RErLPOOnHYYYdFROV9GWVddNFFscIKK1T4fuONN46f//znEVF5n8wVV1wRKaWIiLjyyiujQ4cOFdLsuuuuMWrUqCqXX1tnnHFGpW/NWHXVVWOnnXaKiIjJkyfXy7IBAACguRozZkyMHDmywvffffddXHLJJRGx5Jp7vfXWq3T+fv36xZ/+9KeIiLjtttti3rx5OS970qRJ8eyzz0ZExLXXXhu9evWqNN1WW20Vu+66a0RU3q9R1/EqpWMt1l133UrHZpTq0aNHhe/OPvvsiIjYZZddYr/99qt0vvbt28fFF18cERHvv/9+PPXUU1mXkat8jfWJyP94n6rka1wOACxNQAUAdfLiiy/GN998ExERo0ePznqRtOKKK1Z6EZ3NN998E//973/jtddeixkzZsSMGTOie/fuERHx+uuvZwbPV2bIkCGx1lprVTqtqKgo1llnnYiIePfdd8tNW7x4cTz00EMRETFixIhMulzde++9ERExfPjw6N27d5Vpf/nLX0ZExJQpU6rNd9asWfH2229ntsOMGTMy+b/yyitVzjtkyJCsHRMR/ws+KCkpiQkTJlRblsbQv3//2HzzzSMi+4Vu6Ssf11tvvax1DwAAAJT3wAMPxKJFi6Jjx46Zhy5kU9qX8cknn8QHH3xQaZru3bvHtttumzWP0j6KpftkIiIef/zxiFgSvFFZYEOpffbZp8py1kZRUVHstddeWaeXlvvrr7+O2bNn5335AAAA0Fz9+te/rvT7p59+OubMmRMRkQlmyKa0z2HhwoXx4osv5rzs0jEaq6++egwZMiSnZbzwwgtRUlJSZdqajldZfvnlIyJi4sSJ8d///jfn8s+dOzczTqO6bbTmmmtmAkZyGWdSnfoa6xNRt/E+1amvcTkA0KaxCwBA8/bqq69mPg8bNqzKtOuvv3488MADVeZ1wQUXxEMPPVTl2xIWL14c33zzTfTp06fS6WussUaV5SiN+v/222/Lff/ee+9lbopvsskmVeZRmWnTpkVExCOPPFJl9H1Z2dbzmWeeiX/84x/x+OOPx9dff511/i+//LLK/HOpk1Kvvvpq7LHHHlWmbyz7779/PPbYY/HOO+/EpEmTytXPtGnTYsaMGRHh7RQAAABQE6V9Gd9//320aZP77YLPPvssVl555Qrfr7baatGqVfbnOGXrk/nxxx/jnXfeiYio8sEQERFDhw7NuZy56tWrV/Ts2TPr9LJPkPz2228zgwAAAACg0GV74GFpn0PE/wIOclHVWJFsy3jzzTdzHqOxcOHC+PrrryuMN6nLeJV99tknrrvuuvjqq69i8ODBseOOO8aWW24Zm2yySay66qpZ85o+fXosXrw4IiL23HPP2HPPPXNah5pso2zyOdanNL98jPepTj7H5QBAWQIqAKiTsoP9q7vgWXbZZbNOu+qqq+Lggw+u9kkApX744Yes07K90q9U6Y39RYsWlfu+bHBCTS7oS33xxRc1nqey9Rg7dmycdtpptZ6/rJrUSVWBG41tp512il69esWXX34Z48aNKxdQcfXVV0fEktdcVvU0SQAAAKC82vRlRCwJwKhMrn0ypYMFSpV960N1Txesbnpt5FruiIr9SQAAAFDIlllmmUq/z3efQ30uo67jVX71q1/FxRdfHH/4wx/ihx9+iFtuuSVuueWWiIhYYYUVYrvttotDDjkkfvazn9VL+WsjX2N9IvI73qc6+RqXAwBLE1ABQN7kGv29tP/85z+Zi6s+ffrEH/7wh9hss82if//+0aVLl2jbtm1ELBk4v99++0VEREopb+XOl9Ib6ltvvXWce+65tcrjiSeeyARTDBw4MI499tjYeOONY+WVV45OnTplnhZ5yimnxBlnnFFtfrWtk6amuLg49t5777jgggvitttui4suuig6deoUP/74Y9x8880RETFq1ChPiAQAAIAaKO3L6NWrVzz11FM5zzdgwID6KhIAAADQjLRu3brS78s+kOCll17KjPuozoorrpjzskuX8bOf/SxuuOGGnOdbYYUVMp/zNV7lsMMOi9122y1uuummeOyxx+KZZ56JOXPmxMcffxyXX355XHHFFfHHP/4x/vznP1cof0TE5ZdfHhtttFFO5c8WxFJbdRlX0tDjffIxLgcAKiOgAoA6KXuh9vnnn8egQYOypv38888r/f6aa66JkpKSaN26dTz99NOxxhprVJquvt+g0KtXr8znTz/9tMbz9+zZMz755JNYsGBBDB48uFZluPLKKyNiyXZ97rnnsj51MddtkW2bVza9R48eOZaycey///5xwQUXxHfffRe33XZbjBkzJu6+++7MUyx/+9vfNm4BAQAAoJnp2bNnRER8++23seaaa2YdBFHfyj4gYdasWVWmrW46AAAA0PhK+xwilrxtsiaBEjVdxnfffVfrMRr5HK/Sp0+fOOqoo+Koo46KxYsXx8svvxx33XVXXHzxxTF79uw488wzY9iwYbHjjjuWK3/Ekrdn1nYdaiMfY30iGn68Tz7G5QBAZVpVnwQAshsyZEjm8wsvvFBl2mzTX3vttYhY8tSAbBdXERHTpk2rRQlzN2DAgMwN/IkTJ9Z4/nXWWScilpRzwYIFtSpD6bbYdNNNswZTlC4jFzWpk6Z+sfmTn/wkNtxww4iIGDduXEQseYpBRES/fv3iV7/6VaOVDQAAAJqj0r6M+fPn13u/S1Xat28fq6yySkREvPjii1Wmra6cLeVtnQAAANCclfY5REQ888wz9bqMd999Nz777LNa5VFf41VatWoV6667bpxxxhnxxBNPZL6/9dZbM5/XXnvtTD9GfW2jbPIx1icif9sv1/6cfIzLAYDKCKgAoE7WW2+9TOT69ddfn/XVfB9//HE8+uijlU4rKSmJiIh58+ZlXc6nn34a9957bx1LW7VWrVrFtttuGxERTz/9dEyfPr1G8++www4RETFnzpzMgP+aymVbTJ8+PZ5//vmc8nv11VerXI/SgITWrVvHiBEjci9oHrRv3z4ilgzayNX+++8fERGTJk2Kp556KtPxMGbMGAMmAAAAoIa23377zPX0hRde2KhlKX1QwvTp0zM34ytz3XXXVZlPaX9DRM36HAAAAID82XzzzaNjx44REfGPf/wj61iSuigdo5FSir///e+1yqMhxqusu+66mXE1X375Zeb73r17xwYbbBARETfddFODvpUzH2N9IvK3/XIdP5KPcTkAUBkBFQDUSbt27WLfffeNiIiXX345zjvvvAppSkpK4oADDsgaHb7aaqtFRMTbb78dzz77bIXp33//fey1117xww8/5LHklTv22GOjVatWkVKKPfbYIz766KOsaZeeNnr06FhppZUy+VT3lovJkyfH008/Xe670m0xefLkeOeddyrMM2vWrNh7771zWpdSBx54YKUXrzfddFM8+OCDERGx0047xfLLL1+jfOuqdHn//e9/c55n9913jy5dukRKKfbaa69YvHhxFBUVZdogAAAAkLvVV189dtttt4iIGD9+fJx//vlVpn/vvffi5ptvrpeyHHjggZngjgMOOKDSfqA77rgj7rrrrirzKdu/UZM+BwAAACB/unfvHocffnhERDz77LNx9NFHx+LFi7Om//zzz+Nf//pXjZYxcuTIWH/99SMi4rzzziv39ofKvPrqq3HfffeV+y4f41VuueWWKqdPmzYtvvnmm4iIGDBgQLlpJ598ckREzJ07N3bdddeYPXt21nzmz58fl1xySfz4449Z0+QqH2N9IvI33qe0P+fdd9+tMvgmH+NyAKAyAioAqLNTTjklVlxxxYiIOP7442OvvfaKhx9+OF566aUYP358bLTRRvHQQw/F0KFDK52/NEBg8eLFse2228ZZZ50VEydOjKlTp8all14aa6+9dkyYMCF+8Ytf1Pu6rL322nHaaadFRMRbb70VQ4YMiZNPPjmeeOKJePnll2PChAlx4YUXxi9/+csKgQ3t2rWLW2+9Ndq1axffffddbLbZZvGb3/wmbr/99njxxRfjhRdeiHvvvTdOPfXUWGuttWKTTTaJV199tVwe++yzT0Qsid4fPnx4XHTRRfHss8/Gs88+G3/961/jZz/7Wbz++uux4YYb5rQ+Q4cOjWnTpsXQoUPjmmuuiRdffDGefPLJOPTQQzPl79KlS/z1r3+t66arsY022igiIu699964/PLLY8aMGfHOO+/EO++8E1988UWl83Tq1Cn22GOPiIjMKzs322yz6NevX8MUGgAAAFqYSy+9NAYOHBgREcccc0wMHz48rrrqqnjuuedi+vTp8fjjj8ff/va32GKLLWLVVVeNO+64o17Ksd5668UBBxwQERFTpkyJYcOGxbXXXhsvvvhiPPXUU3HEEUfE7rvvnhkoERGVvq1ynXXWyTzV8E9/+lM89thj8dZbb2X6HBrigR0AAABAxOmnnx4///nPIyLi73//e6y77rpxySWXxDPPPBMvv/xyPPXUU3HxxRfHTjvtFCuvvHJcdtllNV7GTTfdFD169IhFixbF7rvvHjvssEPceOONMXXq1HjxxRfjoYceirPOOis23HDDWGuttSoMrs/HeJXjjz8++vbtG2PGjImrr746Jk+enOlTGTt2bGy55ZYREdG6devYf//9y827zTbbxJFHHhkRERMnTow111wzTjvttMwYlWeeeSauvfba2H///WP55ZePww8/PPNWiLqq61ifiPyN9ykdP/LFF1/E73//+3jxxRczfTnvv/9+Jl0+xuUAQGXaNHYBAGj+unXrFg8//HBsvvnm8dlnn8XNN99c4WmFY8aMieHDh1f6JoFhw4bFaaedFqeeemrMnj07TjrppAppjjnmmBg8eHA888wz9bYepU4++eRo1apVpjxnnnlmnHnmmRXSDR8+vMJ3G2ywQUyYMCH+7//+Lz788MO48cYb48Ybb8y6rK5du5b7f9ddd4199903xo0bF5988kn87ne/Kze9devWccEFF8Q333wTU6ZMqXZdtt1229h2223jtNNOq3Tbd+3aNe69997o379/tXnl27HHHhu33357zJ8/Pw4++OBy00aPHh3XXHNNpfPtv//+ceWVV2b+/+1vf1ufxQQAAIAWrUePHvHMM8/E//3f/8WkSZNi4sSJVT7db+m+jHy66KKL4pNPPon7778/XnvttRgzZky56QMGDIibbropVl111YiITOBEWV26dInf/e53ce6558ZLL70UI0eOLDf9qaeeihEjRtTXKgAAAAD/X7t27eKxxx6LMWPGxJ133hmvvPJK5q0VlalNn8Mqq6wSU6ZMiV122SVmzJgR9913X4W3UFS1jHyNV5k9e3Zce+21ce2111Y6vV27dnHZZZdVGpxwwQUXRI8ePeKMM86Izz77LMaOHZt1OZ06dYrWrVtnnV4TdR3rE5G/7bfHHnvEX/7yl3j33XfjwgsvjAsvvDAzrV+/fjFz5szM/3UdlwMAlfGGCgDy4qc//Wm89tprcdxxx8Vqq60W7dq1i169esWmm24aN910U4wbN67K+U855ZR44IEHYuTIkbHMMstEcXFxrLjiirHzzjvHo48+2uBvUPjjH/8Yr7/+ehx11FExePDg6Nq1a7Rp0yZ69+4dw4cPjz//+c9x/fXXVzrvBhtsEG+//XZcdtllse2220bfvn2juLg42rdvHyuttFKMHDkyzjzzzPjPf/6TeSNFWVdffXVcf/31sckmm0SXLl2iXbt20a9fv9h7773j2WefzTydIFdjx46Nhx9+OLbddttYdtllo7i4OPr37x+HHnpovPbaa5UGhjSEtddeO6ZMmRJ77rlnrLzyytGuXbuc5lt//fVj0KBBEbHkNaE777xzfRYTAAAAWrzlllsuJk6cGPfff3/8+te/joEDB0bHjh2jbdu20bt379hoo43imGOOiaeffjquvvrqeitHcXFx3HvvvTFu3LjYeOONo1u3btGxY8dYc801449//GO8+OKL0bNnz0z6bt26VZrP2WefHVdeeWVssskm0aNHj7wNNAAAAABqpkuXLnHHHXfEpEmTYv/994/VV189unTpEm3atIkePXrEsGHD4rDDDosHH3wwHnvssVotY9CgQfHyyy/HTTfdFLvsskusvPLK0aFDhyguLo7ll18+RowYESeffHK8+OKLccopp1SYv67jVZ566qn4+9//HrvssksMGTIkevfuHW3atImuXbvGOuusE8cee2y8/vrrFR4cUaqoqChOOeWUeOutt+K4446LoUOHZvozunTpEj/5yU/i17/+dVx77bXx6aefRocOHWq1nSpT17E+EfkZ79O5c+fMeJg111wzOnbsWGX6uo7LAYClFaWUUmMXAgAgV3Pnzo3lllsufvjhhzjkkEPin//8Z2MXCQAAAGggkydPjk022SQiIh5//PH41a9+1cglAgAAAAAAmjNvqAAAmpWbb745fvjhh4iI2G+//Rq5NAAAAEBDuvnmmyMiom3btrHeeus1cmkAAAAAAIDmTkAFANBslJSUxPnnnx8REUOHDjVwAgAAAFqQL7/8MmbPnp11+iOPPBKXX355RETssMMO0b1794YpGAAAAAAA0GK1aewCAABU5euvv46vv/46vvrqq/jb3/4Wb731VkRE/PGPf2zkkgEAAAD5NGPGjNhxxx1jt912i8033zxWWWWVaNWqVbz//vtx7733xg033BCLFi2KDh06xFlnndXYxQUAAAAAAFqAopRSauxCAABkM3bs2DjttNPKfbfddtvFfffd10glAgAAAOrDhAkTYtNNN60yTdeuXeO2226LkSNHNlCpAAAAAJqWhQsXxptvvlmreQcMGBCdOnXKc4kAoHnzhgoAoFlo06ZN9OvXL/bcc8848cQTG7s4AAAAQJ4NHTo0rrnmmnj44YfjlVdeiVmzZsXs2bOja9euseqqq8ZWW20Vhx9+ePTu3buxiwoAAADQaD7++OMYMmRIreZ96qmnYsSIEfktEAA0c95QAQAAAAAAAAAAANAMzJw5MwYMGFCreQVUAEBFAioAAAAAAAAAAAAAAICC06qxCwAAAAAAAAAAAAAAANDQBFQAAAAAAAAAAAAAAAAFR0AFAAAAAAAAAAAAAABQcARUAAAAAAAAAAAAAAAABUdABQAAAAAAAAAAAAAAUHAEVAAAAAAAAAAAAAAAAAVHQAUAAAAAAAAAAAAAAFBwBFQAAAAAAAAAAAAAAAAFR0AFAAAAAAAAAAAAAABQcARUAAAAAAAAAAAAAAAABUdABQAAAAAAAAAAAAAAUHAEVAAAAAAAAAAAAAAAAAVHQAUAAAAAAAAAAAAAAFBwBFQAAAAAAAAAAAAAAAAFR0AFAAAAAAAAAAAAAABQcARUAAAAAAAAAAAAAAAABUdABQAAAAAAAAAAAAAAUHAEVAAAAAAAAAAAAAAAAAXn/wFj1guZ0P5jywAAAABJRU5ErkJggg==",
"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": 6,
"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": 7,
"metadata": {},
"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],\n",
" dtype=int32)"
]
},
"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",
"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": 8,
"metadata": {},
"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": "code",
"execution_count": 9,
"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_)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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), wcss) ,marker='o',color='red')\n",
"plt.title('Elbow')\n",
"plt.xlabel('Number of clusters')\n",
"plt.ylabel('WCSS')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.8 ('.venv': 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": "3.10.8"
},
"metadata": {
"interpreter": {
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
}
},
"orig_nbformat": 2,
"vscode": {
"interpreter": {
"hash": "b59fb33dab871bf69887c451a50f1630bf8b80f7a0c0187fd7f9758fa12a0c70"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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