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

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clustering
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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"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
"display_name": "Python 3.7.0 64-bit ('3.7')"
},
"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": 10,
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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",
"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",
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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",
"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",
video callouts and clustering edits
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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: 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",
Cluster detail
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"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",
tinkering with clustering lessons
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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",
"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"
]
},
Cluster detail
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{
"source": [
"Start where we finished in the last lesson, with data imported and filtered."
],
"cell_type": "markdown",
"metadata": {}
},
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{
"cell_type": "code",
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"execution_count": 11,
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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": 11
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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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]
},
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{
"source": [
"We will focus only on 3 genres. Maybe we can get 3 clusters built!"
],
"cell_type": "markdown",
"metadata": {}
},
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{
"cell_type": "code",
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"execution_count": 12,
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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": 12
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},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 720x504 with 1 Axes>",
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clustering notebooks
3 years ago
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},
"metadata": {
"needs_background": "light"
}
clustering
3 years ago
}
],
"source": [
clustering notebooks
3 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",
missed facetgrid
3 years ago
"execution_count": 13,
clustering notebooks
3 years ago
"metadata": {},
video callouts and clustering edits
3 years ago
"outputs": [
{
Cluster detail
3 years ago
"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": {},
missed facetgrid
3 years ago
"execution_count": 13
video callouts and clustering edits
3 years ago
}
],
clustering notebooks
3 years ago
"source": [
Cluster detail
3 years ago
"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",
missed facetgrid
3 years ago
"execution_count": 14,
Cluster detail
3 years ago
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
missed facetgrid
3 years ago
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbc18790a20>"
Cluster detail
3 years ago
]
},
"metadata": {},
missed facetgrid
3 years ago
"execution_count": 14
Cluster detail
3 years ago
},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 4000x4000 with 12 Axes>",
missed facetgrid
3 years ago
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Cluster detail
3 years ago
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"plt.figure(figsize=(20,20), dpi=200)\n",
clustering notebooks
3 years ago
"\n",
Cluster detail
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"plt.subplot(4,3,1)\n",
"sns.boxplot(x = 'popularity', data = df)\n",
video callouts and clustering edits
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"\n",
Cluster detail
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"plt.subplot(4,3,2)\n",
"sns.boxplot(x = 'acousticness', data = df)\n",
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"\n",
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"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",
video callouts and clustering edits
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"\n",
Cluster detail
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"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",
missed facetgrid
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"execution_count": 15,
Cluster detail
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"metadata": {},
"outputs": [],
"source": [
adding a note about scaling
3 years ago
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
video callouts and clustering edits
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"le = LabelEncoder()\n",
"\n",
adding a note about scaling
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"# scaler = StandardScaler()\n",
"\n",
Cluster detail
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"X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
"\n",
"y = df['artist_top_genre']\n",
"\n",
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"X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
clustering
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"\n",
adding a note about scaling
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"# X = scaler.fit_transform(X)\n",
"\n",
"y = le.transform(y)\n",
"\n"
Cluster detail
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]
},
{
"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",
missed facetgrid
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"execution_count": 16,
Cluster detail
3 years ago
"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": {},
missed facetgrid
3 years ago
"execution_count": 16
Cluster detail
3 years ago
}
],
"source": [
"\n",
"from sklearn.cluster import KMeans\n",
"\n",
"nclusters = 3 \n",
"seed = 0\n",
video callouts and clustering edits
3 years ago
"\n",
Cluster detail
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"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": [
Assignment callout made more clear
3 years ago
"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."
Cluster detail
3 years ago
],
missed facetgrid
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"cell_type": "markdown",
"metadata": {}
Cluster detail
3 years ago
},
{
"cell_type": "code",
missed facetgrid
3 years ago
"execution_count": 17,
Cluster detail
3 years ago
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.5466747351275563"
]
},
"metadata": {},
missed facetgrid
3 years ago
"execution_count": 17
Cluster detail
3 years ago
}
],
"source": [
"from sklearn import metrics\n",
"score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
"score"
]
},
{
"source": [
"Import KMeans and build a model"
missed facetgrid
3 years ago
],
"cell_type": "markdown",
"metadata": {}
Cluster detail
3 years ago
},
{
"cell_type": "code",
missed facetgrid
3 years ago
"execution_count": 19,
Cluster detail
3 years ago
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import KMeans\n",
"wcss = []\n",
clustering notebooks
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"\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_)"
clustering
3 years ago
]
},
Cluster detail
3 years ago
{
"source": [
"Use that model to decide, using the Elbow Method, the best number of clusters to build"
],
"cell_type": "markdown",
"metadata": {}
},
clustering
3 years ago
{
"cell_type": "code",
missed facetgrid
3 years ago
"execution_count": 20,
clustering
3 years ago
"metadata": {},
"outputs": [
clustering notebooks
3 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"
]
},
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{
"output_type": "display_data",
"data": {
clustering notebooks
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missed facetgrid
3 years ago
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clustering
3 years ago
},
clustering notebooks
3 years ago
"metadata": {
"needs_background": "light"
}
clustering
3 years ago
}
],
"source": [
clustering notebooks
3 years ago
"plt.figure(figsize=(10,5))\n",
"sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
Cluster detail
3 years ago
"plt.title('Elbow')\n",
clustering notebooks
3 years ago
"plt.xlabel('Number of clusters')\n",
"plt.ylabel('WCSS')\n",
"plt.show()"
clustering
3 years ago
]
},
Cluster detail
3 years ago
{
"source": [
clustering
3 years ago
"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."
Cluster detail
3 years ago
],
clustering
3 years ago
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": []
Cluster detail
3 years ago
},
clustering
3 years ago
{
"cell_type": "code",
missed facetgrid
3 years ago
"execution_count": 21,
clustering
3 years ago
"metadata": {},
clustering notebooks
3 years ago
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
missed facetgrid
3 years ago
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clustering notebooks
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"from sklearn.cluster import KMeans\n",
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"kmeans = KMeans(n_clusters = 3)\n",
video callouts and clustering edits
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"kmeans.fit(X)\n",
"labels = kmeans.predict(X)\n",
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"plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
"plt.xlabel('popularity')\n",
missed facetgrid
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"plt.ylabel('danceability')\n",
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"plt.show()"
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]
video callouts and clustering edits
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},
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{
"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": {}
},
video callouts and clustering edits
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{
"cell_type": "code",
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"execution_count": 811,
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"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\n"
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]
}
],
"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": []
clustering
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}
]
}