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

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{
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
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"display_name": "Python 3.7.0 64-bit ('3.7')"
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"metadata": {
"interpreter": {
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
}
}
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"source": [
"# Nigerian Music scraped from Spotify - an analysis"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 81,
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"metadata": {},
"outputs": [
{
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
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"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"
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]
}
],
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"source": [
"pip install seaborn"
]
},
{
"cell_type": "code",
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"execution_count": 82,
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"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
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" 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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],
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"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>"
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},
"metadata": {},
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"execution_count": 82
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}
],
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"source": [
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"\n",
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"df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
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"df.head()"
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]
},
{
"cell_type": "code",
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"execution_count": 83,
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"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
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"Text(0.5, 1.0, 'Top genres')"
]
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},
"metadata": {},
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"execution_count": 83
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},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 720x504 with 1 Axes>",
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clustering notebooks
4 years ago
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},
"metadata": {
"needs_background": "light"
}
clustering
4 years ago
}
],
"source": [
clustering notebooks
4 years ago
"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",
video callouts and clustering edits
3 years ago
"execution_count": 84,
clustering notebooks
4 years ago
"metadata": {},
video callouts and clustering edits
3 years ago
"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"
]
}
],
clustering notebooks
4 years ago
"source": [
"from sklearn.cluster import KMeans\n",
"wcss = []\n",
"\n",
video callouts and clustering edits
3 years ago
"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",
clustering
4 years ago
"\n",
video callouts and clustering edits
3 years ago
"# X = le.transform(X)\n",
"\n",
"y = le.transform(y)\n",
"\n",
"print(X)\n",
clustering notebooks
4 years ago
"\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_)"
clustering
4 years ago
]
},
{
"cell_type": "code",
video callouts and clustering edits
3 years ago
"execution_count": 85,
clustering
4 years ago
"metadata": {},
"outputs": [
clustering notebooks
4 years ago
{
"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"
]
},
clustering
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{
"output_type": "display_data",
"data": {
clustering notebooks
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video callouts and clustering edits
3 years ago
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clustering
4 years ago
},
clustering notebooks
4 years ago
"metadata": {
"needs_background": "light"
}
clustering
4 years ago
}
],
"source": [
clustering notebooks
4 years ago
"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()"
clustering
4 years ago
]
},
{
"cell_type": "code",
video callouts and clustering edits
3 years ago
"execution_count": 88,
clustering
4 years ago
"metadata": {},
clustering notebooks
4 years ago
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
video callouts and clustering edits
3 years ago
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clustering notebooks
4 years ago
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"\n",
"from sklearn.cluster import KMeans\n",
video callouts and clustering edits
3 years ago
"kmeans = KMeans(n_clusters = 2)\n",
"kmeans.fit(X)\n",
"labels = kmeans.predict(X)\n",
clustering notebooks
4 years ago
"plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
"plt.xlabel('danceability')\n",
"plt.xlabel('popularity')\n",
"plt.show()\n"
]
video callouts and clustering edits
3 years ago
},
{
"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": []
clustering
4 years ago
}
]
}