{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loving you is complicated: Quantifying musical sentiment and success through data science"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Spotify](https://en.wikipedia.org/wiki/Spotify) has quickly become the most popular music streaming service in the world with over 271 million active users every month. A close collaborator of Spotify is [Genius](https://en.wikipedia.org/wiki/Genius_(website)), a website with 26.5 million monthly users that allows members of their community to upload, annotate, and interpret lyrics from music artists. Inspired by the [blog of Thompson Analytics](https://www.rcharlie.com/blog/fitter-happier/) I will use the Spotify API, the Genius API and the [NRC Emotion](https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm) Lexicon to quantify both the _musical sentiment_ and _lyrical sentiment_ of one of the most prominentes artists of our time: Kendrick Lamar. A visualization of his discography will accompany this process of data collection. In addition to this, I will track the data of the [top 100 most streamed songs of all time](https://en.wikipedia.org/wiki/List_of_most-streamed_songs_on_Spotify#100_most-streamed_songs) and will use different regression techniques to determine the usefulness of these features when it comes to the prediction of musical success."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PART I: Collecting Kendrick Lamar's Discography.\n",
"\n",
"### Spotify features\n",
"\n",
"The Spotify API assigns [musical features](https://developer.spotify.com/documentation/web-api/reference/tracks/get-audio-features/) to every track on their platform. These features are defined in the following way:\n",
"\n",
"- **Acousticness**: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.\n",
"\n",
"\n",
"- **Danceability**: Describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.\n",
"\n",
"\n",
"- **Energy**: A measure from 0.0 to 1.0 that represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. \n",
"\n",
"\n",
"- **Instrumentalness**: Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.\n",
"\n",
"\n",
"- **Liveness**: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live. \n",
"\n",
"\n",
"- **Loudness**: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.\n",
"\n",
"\n",
"- **Speechiness**: Detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.\n",
"\n",
"\n",
"- **Valence**: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).\n",
"\n",
"I will be using these features for visualization and analytical purposes in the first and second part respectively. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting the Data from Spotify\n",
"\n",
"In order to get the data from spotify I will be using [Spotipy](https://spotipy.readthedocs.io/en/2.11.1/) , a lightweight Python library for the Spotify Web API:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already up-to-date: spotipy in /opt/conda/lib/python3.7/site-packages (2.11.2)\n",
"Requirement already satisfied, skipping upgrade: six>=1.10.0 in /opt/conda/lib/python3.7/site-packages (from spotipy) (1.14.0)\n",
"Requirement already satisfied, skipping upgrade: requests>=2.20.0 in /opt/conda/lib/python3.7/site-packages (from spotipy) (2.23.0)\n",
"Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.20.0->spotipy) (2019.11.28)\n",
"Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.20.0->spotipy) (1.25.7)\n",
"Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.20.0->spotipy) (2.9)\n",
"Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.20.0->spotipy) (3.0.4)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install spotipy --upgrade"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I will be using _Plotly_ for most visualization tasks since I love how crisp it looks. For the second part of this project I will be relying on _Scikit-learn_ for the computation of different prediction model. In addition to this I need to set the credentials obtained from my Spotify API application to access their data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# import libraries \n",
"import spotipy\n",
"from spotipy.oauth2 import SpotifyClientCredentials\n",
"import pandas as pd\n",
"import time\n",
"from datetime import date\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from math import pi\n",
"import plotly.express as px\n",
"from plotly.subplots import make_subplots\n",
"import plotly.graph_objects as go\n",
"import numpy as np\n",
"from sklearn import (linear_model, metrics, neural_network, pipeline, model_selection, preprocessing, pipeline)\n",
"import statsmodels.api as sm\n",
"from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
"from statsmodels.tools.tools import add_constant\n",
"from scipy import stats\n",
"\n",
"# authenticate and connect to the Spotify API\n",
"client_id = '547191f3201147df8e76a2aa96607aa3'\n",
"client_secret = '43cb18993cf04a53866a3690d1796600'\n",
"client_credentials_manager = SpotifyClientCredentials(client_id, client_secret)\n",
"sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, I define a series of functions that will allow me to gather data from the Spotufy API and put it into a dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Definining functions \n",
"\n",
"def get_artist(name):\n",
" # Returns the artist object with respective data\n",
" results = sp.search(q='artist:' + name, type='artist')\n",
" items = results['artists']['items']\n",
" if len(items) > 0:\n",
" return items[0]\n",
" else:\n",
" return None\n",
"\n",
"def get_artist_albums_ids(artist):\n",
" # Given an artist object, returns a list of with each of the album's ids\n",
" albums_ids = []\n",
" for i in range (len(sp.artist_albums(artist['id'], album_type='album',limit=50)['items'])):\n",
" albums_ids.append(sp.artist_albums(artist['id'], album_type='album',limit=50)['items'][i]['id'])\n",
" return albums_ids\n",
"\n",
"def filter_albums_ids(albums_ids):\n",
" # Spotify has many versions of the same album. \n",
" # This function filters a list of albums ids and returns the most popular ones\n",
" album_names = [] \n",
" for album_id in albums_ids:\n",
" album_names.append(sp.album(album_id)['name']) \n",
" album_pop = [] \n",
" for album_id in albums_ids:\n",
" album_pop.append(sp.album(album_id)['popularity']) \n",
" d = {'id': albums_ids,'name': album_names, 'popularity': album_pop}\n",
" df = pd.DataFrame(data=d) \n",
" df = df.sort_values('popularity', ascending=False).drop_duplicates('name').sort_index()\n",
" df = df.reset_index(drop = True) \n",
" return (df['id'])\n",
"\n",
"def get_artist_albums_tracks_ids(albums_ids):\n",
" # Given a list of albums ids, returns a list of ids of the albums' tracks\n",
" tracks_ids = []\n",
" for album_id in albums_ids:\n",
" for i in range(len(sp.album_tracks(album_id)['items'])):\n",
" tracks_ids.append(sp.album_tracks(album_id)['items'][i]['id'])\n",
" return(tracks_ids)\n",
"\n",
"def get_track_features(id):\n",
" # Given a track id, returns a nested list with its musical features as described in the introduction\n",
" meta = sp.track(id)\n",
" features = sp.audio_features(id)\n",
"\n",
" # Meta\n",
" name = meta['name']\n",
" album = meta['album']['name']\n",
" artist = meta['album']['artists'][0]['name']\n",
" release_date = meta['album']['release_date']\n",
" length = meta['duration_ms']\n",
" popularity = meta['popularity']\n",
"\n",
" # Features\n",
" acousticness = features[0]['acousticness']\n",
" danceability = features[0]['danceability']\n",
" energy = features[0]['energy']\n",
" instrumentalness = features[0]['instrumentalness']\n",
" liveness = features[0]['liveness']\n",
" loudness = features[0]['loudness']\n",
" speechiness = features[0]['speechiness']\n",
" valence = features[0]['valence']\n",
" tempo = features[0]['tempo']\n",
" time_signature = features[0]['time_signature']\n",
"\n",
" track = [name, album, artist, release_date, length, popularity, danceability, acousticness, energy, instrumentalness, liveness, loudness, speechiness, valence, tempo, time_signature]\n",
" return track\n",
"\n",
"def get_tracks_features(tracks_ids):\n",
" # given a list of ids of tracks, returns a list with their features\n",
" tracks = []\n",
" for i in range(0, len(tracks_ids)):\n",
" track = get_track_features(tracks_ids[i])\n",
" tracks.append(track)\n",
" return(tracks)\n",
"\n",
"def tracks_features_to_csv(tracks_features, csv_title):\n",
" # transforms the list of track features into a csv file\n",
" df = pd.DataFrame(tracks_features, columns = ['name', 'album', 'artist', 'release_date', 'length', 'popularity', 'danceability', 'acousticness', 'energy', 'instrumentalness', 'liveness', 'loudness', 'speechiness', 'valence','tempo', 'time_signature'])\n",
" df.to_csv(csv_title + \".csv\", sep = ',')\n",
" \n",
"def get_discography(artist_name):\n",
" # a function that returns the discography of an artist given its name (string)\n",
" # do not use, it is quite unreliable since the discography is not filtered\n",
" tracks_features = get_tracks_features(get_artist_albums_tracks_ids(filter_albums_ids(get_artist_albums_ids(get_artist(artist_name)))))\n",
" return tracks_features_to_csv(tracks_features, artist_name)\n",
"\n",
"def get_playlist_track_ids(user, playlist_id):\n",
" # initially I wanted to use a playlist to get an artist's discography\n",
" # however, finding a good playlist proved to be quite difficult\n",
" ids = []\n",
" playlist = sp.user_playlist(user, playlist_id)\n",
" for item in playlist['tracks']['items']:\n",
" track = item['track']\n",
" ids.append(track['id'])\n",
" return ids"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Done! Let's see the functions in action."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# first, we find the artist object\n",
"kendrick = get_artist(\"Kendrick Lamar\")\n",
"# we look for his albums\n",
"albums_ids = get_artist_albums_ids(kendrick)\n",
"# we filter this list since spotify has many versions of the same album. We keep the most popular one\n",
"filtered_albums_ids = filter_albums_ids(albums_ids)\n",
"# we get the id of every track in his discography\n",
"tracks_ids = get_artist_albums_tracks_ids(filtered_albums_ids)\n",
"# we get the features of every track\n",
"tracks_features = get_tracks_features(tracks_ids)\n",
"# we export the data to csv format\n",
"tracks_features_to_csv(tracks_features, \"Kendrick Lamar\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The API tends to be quite unreliable. At times the process might yield an error so the previous step might require more than one attempo. Anyways, I got the data in the second try, let's see how it looks"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Black Panther The Album Music From And Inspired By'\n",
" 'DAMN. COLLECTORS EDITION.' 'DAMN.' 'untitled unmastered.'\n",
" 'To Pimp A Butterfly' 'good kid, m.A.A.d city (Deluxe)'\n",
" 'good kid, m.A.A.d city' 'Section.80' 'Overly Dedicated']\n"
]
},
{
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" name \\\n",
"0 Black Panther \n",
"1 All The Stars (with SZA) \n",
"2 X (with 2 Chainz & Saudi) \n",
"3 The Ways (with Swae Lee) \n",
"4 Opps (with Yugen Blakrok) \n",
".. ... \n",
"118 Barbed Wire \n",
"119 Average Joe \n",
"120 H.O.C \n",
"121 Cut You Off (To Grow Closer) \n",
"122 She Needs Me (Remix) \n",
"\n",
" album artist \\\n",
"0 Black Panther The Album Music From And Inspire... Kendrick Lamar \n",
"1 Black Panther The Album Music From And Inspire... Kendrick Lamar \n",
"2 Black Panther The Album Music From And Inspire... Kendrick Lamar \n",
"3 Black Panther The Album Music From And Inspire... Kendrick Lamar \n",
"4 Black Panther The Album Music From And Inspire... Kendrick Lamar \n",
".. ... ... \n",
"118 Overly Dedicated Kendrick Lamar \n",
"119 Overly Dedicated Kendrick Lamar \n",
"120 Overly Dedicated Kendrick Lamar \n",
"121 Overly Dedicated Kendrick Lamar \n",
"122 Overly Dedicated Kendrick Lamar \n",
"\n",
" release_date length popularity danceability acousticness energy \\\n",
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"4 2018-02-09 180893 60 0.706 0.1520 0.775 \n",
".. ... ... ... ... ... ... \n",
"118 2010-09-14 265678 44 0.613 0.0152 0.843 \n",
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"120 2010-09-14 316975 44 0.613 0.1050 0.591 \n",
"121 2010-09-14 364103 47 0.685 0.0770 0.681 \n",
"122 2010-09-14 195790 53 0.606 0.4170 0.835 \n",
"\n",
" instrumentalness liveness loudness speechiness valence tempo \\\n",
"0 0.000004 0.2650 -9.454 0.2970 0.480 90.035 \n",
"1 0.000194 0.0926 -4.946 0.0597 0.552 96.924 \n",
"2 0.000000 0.2680 -8.406 0.2590 0.405 131.023 \n",
"3 0.000001 0.1760 -5.856 0.0488 0.589 140.080 \n",
"4 0.000033 0.4160 -6.819 0.3350 0.847 127.929 \n",
".. ... ... ... ... ... ... \n",
"118 0.000000 0.0917 -8.343 0.1860 0.325 102.968 \n",
"119 0.000000 0.1840 -3.343 0.2480 0.218 91.603 \n",
"120 0.000000 0.1910 -8.580 0.3910 0.371 77.124 \n",
"121 0.000000 0.1290 -7.176 0.4810 0.614 82.982 \n",
"122 0.000001 0.1030 -6.107 0.2650 0.313 100.145 \n",
"\n",
" time_signature \n",
"0 4 \n",
"1 4 \n",
"2 4 \n",
"3 4 \n",
"4 4 \n",
".. ... \n",
"118 4 \n",
"119 4 \n",
"120 4 \n",
"121 4 \n",
"122 4 \n",
"\n",
"[123 rows x 16 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kendrick = pd.read_csv(\"Kendrick Lamar.csv\",index_col=[0])\n",
"print(df_kendrick['album'].unique())\n",
"df_kendrick"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking good. However, notice that we still got some repeated observations because of the deluxe edition of _good kid, m.A.A.d city_ and the collector's edition of _DAMN._. I will keep the former and drop the latter because of popularity reasons. Moreover, I will be dropping the _Black Panther's soundtrack_ since I believe it does not stand as a singular effort by Kendrick, but rather as a collective effort that seeks to accompany the movie. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" album | \n",
" artist | \n",
" release_date | \n",
" length | \n",
" popularity | \n",
" danceability | \n",
" acousticness | \n",
" energy | \n",
" instrumentalness | \n",
" liveness | \n",
" loudness | \n",
" speechiness | \n",
" valence | \n",
" tempo | \n",
" time_signature | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" BLOOD. | \n",
" DAMN. | \n",
" Kendrick Lamar | \n",
" 2017-04-14 | \n",
" 118066 | \n",
" 61 | \n",
" 0.357 | \n",
" 0.14200 | \n",
" 0.238 | \n",
" 0.085900 | \n",
" 0.5500 | \n",
" -16.780 | \n",
" 0.265 | \n",
" 0.494 | \n",
" 156.907 | \n",
" 4 | \n",
"
\n",
" \n",
" 1 | \n",
" DNA. | \n",
" DAMN. | \n",
" Kendrick Lamar | \n",
" 2017-04-14 | \n",
" 185946 | \n",
" 78 | \n",
" 0.638 | \n",
" 0.00454 | \n",
" 0.523 | \n",
" 0.000000 | \n",
" 0.0842 | \n",
" -6.664 | \n",
" 0.357 | \n",
" 0.422 | \n",
" 139.913 | \n",
" 4 | \n",
"
\n",
" \n",
" 2 | \n",
" YAH. | \n",
" DAMN. | \n",
" Kendrick Lamar | \n",
" 2017-04-14 | \n",
" 160293 | \n",
" 64 | \n",
" 0.670 | \n",
" 0.57600 | \n",
" 0.700 | \n",
" 0.000005 | \n",
" 0.2260 | \n",
" -7.893 | \n",
" 0.196 | \n",
" 0.648 | \n",
" 69.986 | \n",
" 4 | \n",
"
\n",
" \n",
" 3 | \n",
" ELEMENT. | \n",
" DAMN. | \n",
" Kendrick Lamar | \n",
" 2017-04-14 | \n",
" 208733 | \n",
" 71 | \n",
" 0.748 | \n",
" 0.20400 | \n",
" 0.705 | \n",
" 0.000000 | \n",
" 0.2460 | \n",
" -4.547 | \n",
" 0.485 | \n",
" 0.483 | \n",
" 189.891 | \n",
" 4 | \n",
"
\n",
" \n",
" 4 | \n",
" FEEL. | \n",
" DAMN. | \n",
" Kendrick Lamar | \n",
" 2017-04-14 | \n",
" 214826 | \n",
" 64 | \n",
" 0.746 | \n",
" 0.13700 | \n",
" 0.798 | \n",
" 0.000000 | \n",
" 0.1390 | \n",
" -8.382 | \n",
" 0.349 | \n",
" 0.553 | \n",
" 109.968 | \n",
" 4 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" name album artist release_date length popularity \\\n",
"0 BLOOD. DAMN. Kendrick Lamar 2017-04-14 118066 61 \n",
"1 DNA. DAMN. Kendrick Lamar 2017-04-14 185946 78 \n",
"2 YAH. DAMN. Kendrick Lamar 2017-04-14 160293 64 \n",
"3 ELEMENT. DAMN. Kendrick Lamar 2017-04-14 208733 71 \n",
"4 FEEL. DAMN. Kendrick Lamar 2017-04-14 214826 64 \n",
"\n",
" danceability acousticness energy instrumentalness liveness loudness \\\n",
"0 0.357 0.14200 0.238 0.085900 0.5500 -16.780 \n",
"1 0.638 0.00454 0.523 0.000000 0.0842 -6.664 \n",
"2 0.670 0.57600 0.700 0.000005 0.2260 -7.893 \n",
"3 0.748 0.20400 0.705 0.000000 0.2460 -4.547 \n",
"4 0.746 0.13700 0.798 0.000000 0.1390 -8.382 \n",
"\n",
" speechiness valence tempo time_signature \n",
"0 0.265 0.494 156.907 4 \n",
"1 0.357 0.422 139.913 4 \n",
"2 0.196 0.648 69.986 4 \n",
"3 0.485 0.483 189.891 4 \n",
"4 0.349 0.553 109.968 4 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kendrick = df_kendrick[df_kendrick['album'] != 'good kid, m.A.A.d city']\n",
"df_kendrick = df_kendrick[df_kendrick['album'] != 'Black Panther The Album Music From And Inspired By']\n",
"df_kendrick = df_kendrick[df_kendrick['album'] != 'DAMN. COLLECTORS EDITION.']\n",
"df_kendrick = df_kendrick.reset_index(drop=True)\n",
"df_kendrick.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting the data from Genius\n",
"\n",
"I will be using the LyricsGenius package that provides a simple interface to the song, artist, and lyrics data stored on Genius."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting git+https://github.com/johnwmillr/LyricsGenius.git\n",
" Cloning https://github.com/johnwmillr/LyricsGenius.git to /tmp/pip-req-build-_w3rl_eu\n",
" Running command git clone -q https://github.com/johnwmillr/LyricsGenius.git /tmp/pip-req-build-_w3rl_eu\n",
"Requirement already satisfied (use --upgrade to upgrade): lyricsgenius==1.8.2 from git+https://github.com/johnwmillr/LyricsGenius.git in /opt/conda/lib/python3.7/site-packages\n",
"Requirement already satisfied: beautifulsoup4==4.6.0 in /opt/conda/lib/python3.7/site-packages (from lyricsgenius==1.8.2) (4.6.0)\n",
"Requirement already satisfied: requests>=2.20.0 in /opt/conda/lib/python3.7/site-packages (from lyricsgenius==1.8.2) (2.23.0)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.20.0->lyricsgenius==1.8.2) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.20.0->lyricsgenius==1.8.2) (1.25.7)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.20.0->lyricsgenius==1.8.2) (2019.11.28)\n",
"Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.20.0->lyricsgenius==1.8.2) (2.9)\n",
"Building wheels for collected packages: lyricsgenius\n",
" Building wheel for lyricsgenius (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for lyricsgenius: filename=lyricsgenius-1.8.2-py3-none-any.whl size=15038 sha256=8cd1776e3a526e58217a9324415b1e95849fa37880a194dc8f2d22f4fd7ab40f\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-60w3__2c/wheels/12/d5/2b/6b771ebb067bceb8816ec5eef0dd0d36bf069b18f03ac8ca20\n",
"Successfully built lyricsgenius\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install git+https://github.com/johnwmillr/LyricsGenius.git"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like before, we set the credentials obtained from my Genius app."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import lyricsgenius\n",
"\n",
"# Genius API\n",
"genius = lyricsgenius.Genius(\"BCf3jrIhyBKfxJSB7t8ELCDvW9ASizVNtK8VCaka8TuM3Igd3Tma5eDumwizSicV\")\n",
"genius.remove_section_headers = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"My plan is to iterate through my dataframe looking for every song name and creating a list with the lyrics. I decided against applying a function to the dataframe since the Genius API is very unreliable, (it like to throw out errors at random: at times it works, at times is just does not!). Albeit this approach is more lengthy, it is much more reliable. Before doing any of this, I will need to do some name standarization between my dataframe and the Genius API (titles that include the word featuring are problematic). Please forgive the profanity in the following cell."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df_kendrick.loc[df_kendrick['name'] == \"Swimming Pools (Drank) - Extended Version\", 'name'] = \"Swimming Pools (Drank)\"\n",
"df_kendrick.loc[df_kendrick['name'] == \"F*ck Your Ethnicity\", 'name'] = \"Fuck Your Ethnicity\"\n",
"df_kendrick.loc[df_kendrick['name'] == \"LOYALTY. FEAT. RIHANNA.\", 'name'] = \"LOYALTY.\"\n",
"df_kendrick.loc[df_kendrick['name'] == \"LOVE. FEAT. ZACARI.\", 'name'] = \"LOVE.\"\n",
"df_kendrick.loc[df_kendrick['name'] == \"XXX. FEAT. U2.\", 'name'] = \"XXX.\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are good to go! Let's see how the loop performs."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Searching for \"BLOOD.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"DNA.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"YAH.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"ELEMENT.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"FEEL.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"LOYALTY.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"PRIDE.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"HUMBLE.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"LUST.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"LOVE.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"XXX.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"FEAR.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"GOD.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"DUCKWORTH.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"untitled 01 | 08.19.2014.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"untitled 02 | 06.23.2014.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"untitled 03 | 05.28.2013.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"untitled 04 | 08.14.2014.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"untitled 05 | 09.21.2014.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"untitled 06 | 06.30.2014.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"untitled 07 | 2014 - 2016\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"untitled 08 | 09.06.2014.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Wesley's Theory\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"For Free? - Interlude\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"King Kunta\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Institutionalized\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"These Walls\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"u\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Alright\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"For Sale? - Interlude\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Momma\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Hood Politics\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"How Much A Dollar Cost\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Complexion (A Zulu Love)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"The Blacker The Berry\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"You Ain't Gotta Lie (Momma Said)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"i\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Mortal Man\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Sherane a.k.a Master Splinter’s Daughter\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Bitch, Don’t Kill My Vibe\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Backseat Freestyle\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"The Art of Peer Pressure\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Money Trees\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Poetic Justice\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"good kid\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"m.A.A.d city\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Swimming Pools (Drank)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Sing About Me, I'm Dying Of Thirst\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Real\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Compton\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"The Recipe - Bonus Track\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Black Boy Fly - Bonus Track\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Now Or Never - Bonus Track\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"The Recipe (Black Hippy Remix) - Bonus Track\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Bitch, Don’t Kill My Vibe - Remix\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Fuck Your Ethnicity\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Hol' Up\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"A.D.H.D\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"No Make-Up (Her Vice) (feat. Colin Munroe)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Tammy's Song (Her Evils)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Chapter Six\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Ronald Reagan Era\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Poe Mans Dreams (His Vice) (feat. GLC)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Chapter Ten\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Keisha's Song (Her Pain) (feat. Ashtro Bot)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Rigamortus\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Kush & Corinthians (feat. BJ The Chicago Kid)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Blow My High (Members Only)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Ab-Souls Outro (feat. Ab-Soul)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"HiiiPower\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Growing Apart (To Get Closer)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Ignorance Is Bliss\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"P&P 1.5\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Alien Girl (Today W/ Her)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Opposites Attract (Tomorrow W/O Her)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Michael Jordan\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"R.O.T.C (Interlude)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Barbed Wire\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Average Joe\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"H.O.C\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Cut You Off (To Grow Closer)\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"She Needs Me (Remix)\" by Kendrick Lamar...\n",
"Done.\n"
]
}
],
"source": [
"lyrics = []\n",
"for name in df_kendrick['name']:\n",
" lyrics.append(genius.search_song(name, \"Kendrick Lamar\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A quck inspection of the list of lyrics reveals that we got a few `None` values. Let's fix those:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"while None in lyrics:\n",
" none_index = [i for i in range(len(lyrics)) if lyrics[i] == None] \n",
" for i in none_index:\n",
" lyrics[i] = genius.search_song(df_kendrick['name'][i], \"Kendrick Lamar\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are ready to extract the lyrics and append them into the dataframe."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" album | \n",
" artist | \n",
" release_date | \n",
" length | \n",
" popularity | \n",
" danceability | \n",
" acousticness | \n",
" energy | \n",
" instrumentalness | \n",
" liveness | \n",
" loudness | \n",
" speechiness | \n",
" valence | \n",
" tempo | \n",
" time_signature | \n",
" lyrics | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" BLOOD. | \n",
" DAMN. | \n",
" Kendrick Lamar | \n",
" 2017-04-14 | \n",
" 118066 | \n",
" 61 | \n",
" 0.357 | \n",
" 0.14200 | \n",
" 0.238 | \n",
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" 0.265 | \n",
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" 156.907 | \n",
" 4 | \n",
" is it wickedness?\\nis it weakness?\\nyou decide... | \n",
"
\n",
" \n",
" 1 | \n",
" DNA. | \n",
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" Kendrick Lamar | \n",
" 2017-04-14 | \n",
" 185946 | \n",
" 78 | \n",
" 0.638 | \n",
" 0.00454 | \n",
" 0.523 | \n",
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" 0.0842 | \n",
" -6.664 | \n",
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" 0.422 | \n",
" 139.913 | \n",
" 4 | \n",
" i got, i got, i got, i got—\\nloyalty, got roya... | \n",
"
\n",
" \n",
" 2 | \n",
" YAH. | \n",
" DAMN. | \n",
" Kendrick Lamar | \n",
" 2017-04-14 | \n",
" 160293 | \n",
" 64 | \n",
" 0.670 | \n",
" 0.57600 | \n",
" 0.700 | \n",
" 0.000005 | \n",
" 0.2260 | \n",
" -7.893 | \n",
" 0.196 | \n",
" 0.648 | \n",
" 69.986 | \n",
" 4 | \n",
" new shit, new kung fu kenny\\n\\ni got so many t... | \n",
"
\n",
" \n",
" 3 | \n",
" ELEMENT. | \n",
" DAMN. | \n",
" Kendrick Lamar | \n",
" 2017-04-14 | \n",
" 208733 | \n",
" 71 | \n",
" 0.748 | \n",
" 0.20400 | \n",
" 0.705 | \n",
" 0.000000 | \n",
" 0.2460 | \n",
" -4.547 | \n",
" 0.485 | \n",
" 0.483 | \n",
" 189.891 | \n",
" 4 | \n",
" new kung fu kenny\\nain't nobody prayin' for me... | \n",
"
\n",
" \n",
" 4 | \n",
" FEEL. | \n",
" DAMN. | \n",
" Kendrick Lamar | \n",
" 2017-04-14 | \n",
" 214826 | \n",
" 64 | \n",
" 0.746 | \n",
" 0.13700 | \n",
" 0.798 | \n",
" 0.000000 | \n",
" 0.1390 | \n",
" -8.382 | \n",
" 0.349 | \n",
" 0.553 | \n",
" 109.968 | \n",
" 4 | \n",
" ain't nobody prayin' for me\\n(ain't nobody pra... | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" name album artist release_date length popularity \\\n",
"0 BLOOD. DAMN. Kendrick Lamar 2017-04-14 118066 61 \n",
"1 DNA. DAMN. Kendrick Lamar 2017-04-14 185946 78 \n",
"2 YAH. DAMN. Kendrick Lamar 2017-04-14 160293 64 \n",
"3 ELEMENT. DAMN. Kendrick Lamar 2017-04-14 208733 71 \n",
"4 FEEL. DAMN. Kendrick Lamar 2017-04-14 214826 64 \n",
"\n",
" danceability acousticness energy instrumentalness liveness loudness \\\n",
"0 0.357 0.14200 0.238 0.085900 0.5500 -16.780 \n",
"1 0.638 0.00454 0.523 0.000000 0.0842 -6.664 \n",
"2 0.670 0.57600 0.700 0.000005 0.2260 -7.893 \n",
"3 0.748 0.20400 0.705 0.000000 0.2460 -4.547 \n",
"4 0.746 0.13700 0.798 0.000000 0.1390 -8.382 \n",
"\n",
" speechiness valence tempo time_signature \\\n",
"0 0.265 0.494 156.907 4 \n",
"1 0.357 0.422 139.913 4 \n",
"2 0.196 0.648 69.986 4 \n",
"3 0.485 0.483 189.891 4 \n",
"4 0.349 0.553 109.968 4 \n",
"\n",
" lyrics \n",
"0 is it wickedness?\\nis it weakness?\\nyou decide... \n",
"1 i got, i got, i got, i got—\\nloyalty, got roya... \n",
"2 new shit, new kung fu kenny\\n\\ni got so many t... \n",
"3 new kung fu kenny\\nain't nobody prayin' for me... \n",
"4 ain't nobody prayin' for me\\n(ain't nobody pra... "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lyrics_text = []\n",
"for lyric in lyrics:\n",
" lyrics_text.append(lyric.lyrics.lower())\n",
" \n",
"df_kendrick['lyrics'] = lyrics_text\n",
"df_kendrick.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quantifying Lyrical Sentiment."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to quantify lyrical sentiment I will use the [NRC Emotion Lexicon](https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm). This dataset assigns different emotions or sentiments to english words. For example, the word *abandon* is mapped to the emotions of *fear* and *sadness*. My strategy is to compute the proportions for each of the sentiments. I recognize that this is a very limited approach to lyric analysis but at the same time I believe that it feels like a good starting point given the current constraints."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create a function that given a song's lyrics and a emotion computes the number of words in the song associated to the emotion."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"nrc = pd.read_table(\"NRC-Emotion-Lexicon-Wordlevel-v0.92.txt\", header=None, names=['word','emotion','dummy'])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def count_emotion(song_lyrics, emotion):\n",
" nrc_emotion = nrc[nrc['emotion'] == emotion]\n",
" nrc_emotion = nrc_emotion[nrc_emotion['dummy'] == 1]\n",
" nrc_emotion = nrc_emotion.reset_index(drop=True)\n",
" sum_emotion = 0\n",
" for word in nrc_emotion['word']:\n",
" sum_emotion = sum_emotion + song_lyrics.count(word) \n",
" return sum_emotion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we create a function that computes the number of words associated to each emotion in a song and computes its proportions."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def emotion_prop(song_lyrics):\n",
" emotion_proportions = []\n",
" for emotion in nrc['emotion'].unique():\n",
" emotion_proportions.append(count_emotion(song_lyrics, emotion))\n",
" array = np.array(emotion_proportions)\n",
" return array/array.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we append these proportions for each song in the dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"for i in range(len(nrc['emotion'].unique())):\n",
" df_kendrick[nrc['emotion'].unique()[i]+\"_index\"] = df_kendrick.apply(lambda row: emotion_prop(row['lyrics'])[i], axis=1)\n",
" df_kendrick.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As desired. We are ready to begin visualizing Kendrick's discography!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First I wanted to see what type of lyrical and musical features dominate - in average - in each Kendrick album. To do so, I decided to produce an interactive radar plot with all albums overlayed in top of each other. This plot might look messy at first, but one can select (or unselect) each album by clicking over its legend or plot. In this way, we have a clear yet compact visualization."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"lyrical_variables = nrc['emotion'].unique()+\"_index\"\n",
"musical_variables = [\"danceability\", \"acousticness\", \"energy\", \"instrumentalness\", \"liveness\",\"speechiness\", \"valence\"]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def radar_discography_plot(df,x,musical,export=False):\n",
" artist = df['artist'][0] \n",
" df = df[::-1]\n",
" df = df.groupby('album',as_index=False,sort=False).mean()\n",
" df = df.drop(['length','popularity','loudness','tempo','time_signature'], axis = 1)\n",
" df_long = pd.melt(df, id_vars='album', value_vars=df.columns.values[1:])\n",
" if musical == True:\n",
" fig = px.line_polar(df_long, r=\"value\", theta=\"variable\", color=\"album\", line_close=True, range_r=[0,1],\n",
" color_discrete_sequence=px.colors.qualitative.Bold)\n",
" else:\n",
" fig = px.line_polar(df_long, r=\"value\", theta=\"variable\", color=\"album\", line_close=True, range_r=[0,0.3],\n",
" color_discrete_sequence=px.colors.qualitative.Bold)\n",
" if musical == True:\n",
" fig.update_layout(title = {'text':\"Musical Features of \" + artist + \"s' Discography\",'y':0.98,'x':x,'xanchor': 'center','yanchor': 'top'})\n",
" else:\n",
" fig.update_layout(title = {'text':\"Lyrical Features of \" + artist + \"s' Discography\",'y':0.98,'x':x,'xanchor': 'center','yanchor': 'top'})\n",
" fig.show() \n",
" if export == True:\n",
" fig.write_html(artist+\".html\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First let's visualize the musical features of each of his albums."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
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"Overall, I would argue that these visualizations adequately represent Kendrick's music. For example, _untitled unmastered_ stands out for its live instrumentation and somewhat subdued tone. On the other hand, Kendrick is characterized for his intertwined story telling: _To Pimp a Butterfly_ references _good kid, m.A.A.d city_ while _DAMN._ references its two predecessors. It is to be expected that his lyrics have pretty similar features across his discography. One final point of interest is the predominance of both _negative_ and _positive_ lyrics. While seemingly contradictory, duality has been a staple in Kendrick's music. For example, consider, _u_ and _i_ from _To Pimp a Butterfly_ : members of the Genius community have noticed the the duality between both tracks: \"_u_ acts as a complete contrast to its lead single _i_, an anthem of peace, positivity, and prosperity starting with self-love\". \n",
"\n",
"I hope this first part of the project incentivizes the reader to explore the discography of their favorite artist!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PART II: Predicting musical success.\n",
"\n",
"While it was fun to look at Kendrick's discography, such data will not allow me to answer the main question that drives this research: what are the determinants of popularity in music? This is because the Spotify API does not allow us to count the number of times a song has been streamed. The closer feature to this would be a track's _popularity_ , however, there are two issues: the algorithm that drives this variable is not know and the variable appears like a better indicator of current popularity - i.e what is trending - rather than all-time success. Thus I decided to use a dataset from Wikipedia that contains information about the [Top 100 most streamed songs of all time](https://en.wikipedia.org/wiki/List_of_most-streamed_songs_on_Spotify#100_most-streamed_songs). I will use once again similar methods to obtain both musical and lyrical features from each song."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" artist | \n",
" album | \n",
" streams | \n",
" release_date | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" \"Shape of You\" | \n",
" Ed Sheeran | \n",
" ÷ | \n",
" 2476 | \n",
" 6 January 2017 | \n",
"
\n",
" \n",
" 1 | \n",
" \"Rockstar\" | \n",
" Post Malone featuring 21 Savage | \n",
" Beerbongs & Bentleys | \n",
" 1882 | \n",
" 15 September 2017 | \n",
"
\n",
" \n",
" 2 | \n",
" \"One Dance\" | \n",
" Drake featuring Wizkid and Kyla | \n",
" Views | \n",
" 1840 | \n",
" 5 April 2016 | \n",
"
\n",
" \n",
" 3 | \n",
" \"Closer\" | \n",
" The Chainsmokers featuring Halsey | \n",
" Collage | \n",
" 1757 | \n",
" 29 July 2016 | \n",
"
\n",
" \n",
" 4 | \n",
" \"Thinking Out Loud\" | \n",
" Ed Sheeran | \n",
" × | \n",
" 1521 | \n",
" 20 June 2014 | \n",
"
\n",
" \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" 95 | \n",
" \"Moonlight\" | \n",
" XXXTentacion | \n",
" ? | \n",
" 951 | \n",
" 14 August 2018 | \n",
"
\n",
" \n",
" 96 | \n",
" \"Work\" | \n",
" Rihanna featuring Drake | \n",
" Anti | \n",
" 949 | \n",
" 27 January 2016 | \n",
"
\n",
" \n",
" 97 | \n",
" \"Lovely\" | \n",
" Billie Eilish and Khalid | \n",
" 13 Reasons Why: Season 2 (A Netflix Original S... | \n",
" 952 | \n",
" 19 April 2018 | \n",
"
\n",
" \n",
" 98 | \n",
" \"There's Nothing Holdin' Me Back\" | \n",
" Shawn Mendes | \n",
" Illuminate | \n",
" 945 | \n",
" 20 April 2017 | \n",
"
\n",
" \n",
" 99 | \n",
" \"Me Rehúso\" | \n",
" Danny Ocean | \n",
" 54+1 | \n",
" 938 | \n",
" 16 September 2016 | \n",
"
\n",
" \n",
"
\n",
"
100 rows × 5 columns
\n",
"
"
],
"text/plain": [
" name artist \\\n",
"0 \"Shape of You\" Ed Sheeran \n",
"1 \"Rockstar\" Post Malone featuring 21 Savage \n",
"2 \"One Dance\" Drake featuring Wizkid and Kyla \n",
"3 \"Closer\" The Chainsmokers featuring Halsey \n",
"4 \"Thinking Out Loud\" Ed Sheeran \n",
".. ... ... \n",
"95 \"Moonlight\" XXXTentacion \n",
"96 \"Work\" Rihanna featuring Drake \n",
"97 \"Lovely\" Billie Eilish and Khalid \n",
"98 \"There's Nothing Holdin' Me Back\" Shawn Mendes \n",
"99 \"Me Rehúso\" Danny Ocean \n",
"\n",
" album streams \\\n",
"0 ÷ 2476 \n",
"1 Beerbongs & Bentleys 1882 \n",
"2 Views 1840 \n",
"3 Collage 1757 \n",
"4 × 1521 \n",
".. ... ... \n",
"95 ? 951 \n",
"96 Anti 949 \n",
"97 13 Reasons Why: Season 2 (A Netflix Original S... 952 \n",
"98 Illuminate 945 \n",
"99 54+1 938 \n",
"\n",
" release_date \n",
"0 6 January 2017 \n",
"1 15 September 2017 \n",
"2 5 April 2016 \n",
"3 29 July 2016 \n",
"4 20 June 2014 \n",
".. ... \n",
"95 14 August 2018 \n",
"96 27 January 2016 \n",
"97 19 April 2018 \n",
"98 20 April 2017 \n",
"99 16 September 2016 \n",
"\n",
"[100 rows x 5 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_wiki = pd.read_html(\"https://en.wikipedia.org/wiki/List_of_most-streamed_songs_on_Spotify#100_most-streamed_songs\")[0]\n",
"df_wiki = df_wiki.drop(100)\n",
"df_wiki = df_wiki.drop('Rank',axis=1)\n",
"df_wiki.columns = ['name', 'artist','album','streams','release_date']\n",
"df_wiki"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First I standarize the data such that we can find its musical features in Spotify first. I write a few functions that will help me for that task."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def fix_name(string):\n",
" return string.replace('\"','')\n",
"\n",
"def fix_featuring(string):\n",
" sep = ' featuring'\n",
" rest = string.split(sep, 1)[0]\n",
" return rest\n",
"\n",
"def fix_and(string):\n",
" sep = ' and'\n",
" rest = string.split(sep, 1)[0]\n",
" return rest\n",
"\n",
"def fix_apostrophe(string):\n",
" return(string.replace(\"'\",\"\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are ready to standarize the data."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"df_wiki['name'] = df_wiki['name'].apply(fix_name)\n",
"df_wiki['name'] = df_wiki['name'].apply(fix_apostrophe)\n",
"df_wiki['artist'] = df_wiki['artist'].apply(fix_featuring)\n",
"df_wiki['artist'] = df_wiki['artist'].apply(fix_and)\n",
"df_wiki.at[78, 'name'] = \"I Don't Wanna Live Forever\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we obtain the id's for every track"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"ids = []\n",
"for i in range(len(df_wiki['artist'])):\n",
" try:\n",
" artist= df_wiki['artist'][i]\n",
" track= df_wiki['name'][i]\n",
" track_id = sp.search(q='artist:' + artist + ' track:' + track, type='track')\n",
" ids.append(track_id['tracks']['items'][0]['id'])\n",
" except:\n",
" print(i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we create the dataframe."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" album | \n",
" artist | \n",
" release_date | \n",
" length | \n",
" popularity | \n",
" danceability | \n",
" acousticness | \n",
" energy | \n",
" instrumentalness | \n",
" liveness | \n",
" loudness | \n",
" speechiness | \n",
" valence | \n",
" tempo | \n",
" time_signature | \n",
" streams | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Shape of You | \n",
" ÷ (Deluxe) | \n",
" Ed Sheeran | \n",
" 2017-03-03 | \n",
" 233712 | \n",
" 86 | \n",
" 0.825 | \n",
" 0.58100 | \n",
" 0.652 | \n",
" 0.00000 | \n",
" 0.0931 | \n",
" -3.183 | \n",
" 0.0802 | \n",
" 0.931 | \n",
" 95.977 | \n",
" 4 | \n",
" 2476 | \n",
"
\n",
" \n",
" 1 | \n",
" rockstar (feat. 21 Savage) | \n",
" beerbongs & bentleys | \n",
" Post Malone | \n",
" 2018-04-27 | \n",
" 218146 | \n",
" 88 | \n",
" 0.585 | \n",
" 0.12400 | \n",
" 0.520 | \n",
" 0.00007 | \n",
" 0.1310 | \n",
" -6.136 | \n",
" 0.0712 | \n",
" 0.129 | \n",
" 159.801 | \n",
" 4 | \n",
" 1882 | \n",
"
\n",
" \n",
" 2 | \n",
" One Dance | \n",
" Views | \n",
" Drake | \n",
" 2016-05-06 | \n",
" 173986 | \n",
" 82 | \n",
" 0.792 | \n",
" 0.00776 | \n",
" 0.625 | \n",
" 0.00188 | \n",
" 0.3290 | \n",
" -5.609 | \n",
" 0.0536 | \n",
" 0.370 | \n",
" 103.967 | \n",
" 4 | \n",
" 1840 | \n",
"
\n",
" \n",
" 3 | \n",
" Closer | \n",
" Closer | \n",
" The Chainsmokers | \n",
" 2016-07-29 | \n",
" 244960 | \n",
" 85 | \n",
" 0.748 | \n",
" 0.41400 | \n",
" 0.524 | \n",
" 0.00000 | \n",
" 0.1110 | \n",
" -5.599 | \n",
" 0.0338 | \n",
" 0.661 | \n",
" 95.010 | \n",
" 4 | \n",
" 1757 | \n",
"
\n",
" \n",
" 4 | \n",
" Thinking out Loud | \n",
" x (Deluxe Edition) | \n",
" Ed Sheeran | \n",
" 2014-06-21 | \n",
" 281560 | \n",
" 83 | \n",
" 0.781 | \n",
" 0.47400 | \n",
" 0.445 | \n",
" 0.00000 | \n",
" 0.1840 | \n",
" -6.061 | \n",
" 0.0295 | \n",
" 0.591 | \n",
" 78.998 | \n",
" 4 | \n",
" 1521 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" name album artist \\\n",
"0 Shape of You ÷ (Deluxe) Ed Sheeran \n",
"1 rockstar (feat. 21 Savage) beerbongs & bentleys Post Malone \n",
"2 One Dance Views Drake \n",
"3 Closer Closer The Chainsmokers \n",
"4 Thinking out Loud x (Deluxe Edition) Ed Sheeran \n",
"\n",
" release_date length popularity danceability acousticness energy \\\n",
"0 2017-03-03 233712 86 0.825 0.58100 0.652 \n",
"1 2018-04-27 218146 88 0.585 0.12400 0.520 \n",
"2 2016-05-06 173986 82 0.792 0.00776 0.625 \n",
"3 2016-07-29 244960 85 0.748 0.41400 0.524 \n",
"4 2014-06-21 281560 83 0.781 0.47400 0.445 \n",
"\n",
" instrumentalness liveness loudness speechiness valence tempo \\\n",
"0 0.00000 0.0931 -3.183 0.0802 0.931 95.977 \n",
"1 0.00007 0.1310 -6.136 0.0712 0.129 159.801 \n",
"2 0.00188 0.3290 -5.609 0.0536 0.370 103.967 \n",
"3 0.00000 0.1110 -5.599 0.0338 0.661 95.010 \n",
"4 0.00000 0.1840 -6.061 0.0295 0.591 78.998 \n",
"\n",
" time_signature streams \n",
"0 4 2476 \n",
"1 4 1882 \n",
"2 4 1840 \n",
"3 4 1757 \n",
"4 4 1521 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features = get_tracks_features(ids)\n",
"tracks_features_to_csv(features, \"top100\")\n",
"df_top = pd.read_csv(\"top100.csv\",index_col=[0])\n",
"df_top['streams'] = df_wiki['streams']\n",
"df_top.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have the musical features with the number of streams. Now, let's add the lyrics! However, before adding them it is better to standarize the naming of songs across the current database and the Genius API since the wrapper for the latter is unreliable. This way, we minimize the chance of error."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"def fix_featuring2(string):\n",
" sep = ' (feat'\n",
" rest = string.split(sep, 1)[0]\n",
" return rest\n",
"\n",
"def fix_with(string):\n",
" sep = ' (with'\n",
" rest = string.split(sep, 1)[0]\n",
" return rest"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"df_top['name'] = df_top['name'].apply(fix_featuring2)\n",
"df_top['name'] = df_top['name'].apply(fix_with)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"df_top.at[6, 'name'] = \"Sunflower\"\n",
"df_top.at[6, 'artist'] = \"Post Malone\"\n",
"df_top.at[20, 'artist'] = \"Justin Bieber\"\n",
"df_top.at[35, 'name'] = \"Bohemian Rhapsody\"\n",
"df_top.at[35, 'artist'] = \"Queen\"\n",
"df_top.at[49, 'artist'] = \"Justin Bieber\"\n",
"df_top.at[54, 'name'] = \"Can't Stop the Feeling\"\n",
"df_top.at[54, 'artist'] = \"Justin Timberlake\"\n",
"df_top.at[78, 'name'] = \"I Don't Wanna Live Forever\"\n",
"df_top.at[86, 'artist'] = \"J Balvin\"\n",
"# some addition fixes\n",
"df_top.at[65, 'release_date'] = '2015-03-10'\n",
"df_top.at[20, 'release_date'] = '2015-10-22'"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Searching for \"Shape of You\" by Ed Sheeran...\n",
"Done.\n",
"Searching for \"rockstar\" by Post Malone...\n",
"Done.\n",
"Searching for \"One Dance\" by Drake...\n",
"Done.\n",
"Searching for \"Closer\" by The Chainsmokers...\n",
"Done.\n",
"Searching for \"Thinking out Loud\" by Ed Sheeran...\n",
"Done.\n",
"Searching for \"God's Plan\" by Drake...\n",
"Done.\n",
"Searching for \"Sunflower\" by Post Malone...\n",
"Done.\n",
"Searching for \"Dance Monkey\" by Tones And I...\n",
"Done.\n",
"Searching for \"Havana\" by Camila Cabello...\n",
"Done.\n",
"Searching for \"Perfect\" by Ed Sheeran...\n",
"Done.\n",
"Searching for \"Say You Won't Let Go\" by James Arthur...\n",
"Done.\n",
"Searching for \"Love Yourself\" by Justin Bieber...\n",
"Done.\n",
"Searching for \"Señorita\" by Shawn Mendes...\n",
"Done.\n",
"Searching for \"Photograph\" by Ed Sheeran...\n",
"Done.\n",
"Searching for \"Lean On\" by Major Lazer...\n",
"Done.\n",
"Searching for \"Despacito - Remix\" by Luis Fonsi...\n",
"Done.\n",
"Searching for \"Believer\" by Imagine Dragons...\n",
"Done.\n",
"Searching for \"Starboy\" by The Weeknd...\n",
"Done.\n",
"Searching for \"New Rules\" by Dua Lipa...\n",
"Done.\n",
"Searching for \"bad guy\" by Billie Eilish...\n",
"Done.\n",
"Searching for \"Sorry\" by Justin Bieber...\n",
"Done.\n",
"Searching for \"Don't Let Me Down\" by The Chainsmokers...\n",
"Done.\n",
"Searching for \"Something Just Like This\" by The Chainsmokers...\n",
"Done.\n",
"Searching for \"Thunder\" by Imagine Dragons...\n",
"Done.\n",
"Searching for \"SAD!\" by XXXTENTACION...\n",
"Done.\n",
"Searching for \"I Took A Pill In Ibiza - Seeb Remix\" by Mike Posner...\n",
"Done.\n",
"Searching for \"XO Tour Llif3\" by Lil Uzi Vert...\n",
"Done.\n",
"Searching for \"HUMBLE.\" by Kendrick Lamar...\n",
"Done.\n",
"Searching for \"Let Me Love You\" by DJ Snake...\n",
"Done.\n",
"Searching for \"Faded\" by Alan Walker...\n",
"Done.\n",
"Searching for \"Better Now\" by Post Malone...\n",
"Done.\n",
"Searching for \"Lucid Dreams\" by Juice WRLD...\n",
"Done.\n",
"Searching for \"Stressed Out\" by Twenty One Pilots...\n",
"Done.\n",
"Searching for \"Congratulations\" by Post Malone...\n",
"Done.\n",
"Searching for \"All of Me\" by John Legend...\n",
"Done.\n",
"Searching for \"Bohemian Rhapsody\" by Queen...\n",
"Done.\n",
"Searching for \"Someone You Loved\" by Lewis Capaldi...\n",
"Done.\n",
"Searching for \"Happier\" by Marshmello...\n",
"Done.\n",
"Searching for \"Treat You Better\" by Shawn Mendes...\n",
"Done.\n",
"Searching for \"Take Me to Church\" by Hozier...\n",
"Done.\n",
"Searching for \"Unforgettable\" by French Montana...\n",
"Done.\n",
"Searching for \"Cheap Thrills\" by Sia...\n",
"Done.\n",
"Searching for \"Uptown Funk\" by Mark Ronson...\n",
"Done.\n",
"Searching for \"Stay With Me\" by Sam Smith...\n",
"Done.\n",
"Searching for \"7 rings\" by Ariana Grande...\n",
"Done.\n",
"Searching for \"Let Her Go\" by Passenger...\n",
"Done.\n",
"Searching for \"Shallow\" by Lady Gaga...\n",
"Done.\n",
"Searching for \"Despacito - Remix\" by Luis Fonsi...\n",
"Done.\n",
"Searching for \"Cold Water\" by Major Lazer...\n",
"Done.\n",
"Searching for \"What Do You Mean?\" by Justin Bieber...\n",
"Done.\n",
"Searching for \"Jocelyn Flores\" by XXXTENTACION...\n",
"Done.\n",
"Searching for \"7 Years\" by Lukas Graham...\n",
"Done.\n",
"Searching for \"Girls Like You\" by Maroon 5...\n",
"Done.\n",
"Searching for \"thank u, next\" by Ariana Grande...\n",
"Done.\n",
"Searching for \"Can't Stop the Feeling\" by Justin Timberlake...\n",
"Done.\n",
"Searching for \"Too Good At Goodbyes\" by Sam Smith...\n",
"Done.\n",
"Searching for \"Stitches\" by Shawn Mendes...\n",
"Done.\n",
"Searching for \"That's What I Like\" by Bruno Mars...\n",
"Done.\n",
"Searching for \"Cheerleader - Felix Jaehn Remix Radio Edit\" by OMI...\n",
"Done.\n",
"Searching for \"Wake Me Up\" by Avicii...\n",
"Done.\n",
"Searching for \"I Like It\" by Cardi B...\n",
"Done.\n",
"Searching for \"Psycho\" by Post Malone...\n",
"Done.\n",
"Searching for \"This Is What You Came For\" by Calvin Harris...\n",
"Done.\n",
"Searching for \"Heathens\" by Twenty One Pilots...\n",
"Done.\n",
"Searching for \"Can't Feel My Face\" by The Weeknd...\n",
"Done.\n",
"Searching for \"See You Again\" by Wiz Khalifa...\n",
"Done.\n",
"Searching for \"Hello\" by Adele...\n",
"Done.\n",
"Searching for \"Radioactive\" by Imagine Dragons...\n",
"Done.\n",
"Searching for \"I Fall Apart\" by Post Malone...\n",
"Done.\n",
"Searching for \"Work from Home\" by Fifth Harmony...\n",
"Done.\n",
"Searching for \"Attention\" by Charlie Puth...\n",
"Done.\n",
"Searching for \"Counting Stars\" by OneRepublic...\n",
"Done.\n",
"Searching for \"In My Feelings\" by Drake...\n",
"Done.\n",
"Searching for \"Without Me\" by Halsey...\n",
"Done.\n",
"Searching for \"I Don't Care\" by Ed Sheeran...\n",
"Done.\n",
"Searching for \"Can't Hold Us - feat. Ray Dalton\" by Macklemore & Ryan Lewis...\n",
"Done.\n",
"Searching for \"One Kiss\" by Calvin Harris...\n",
"Done.\n",
"Searching for \"SICKO MODE\" by Travis Scott...\n",
"Done.\n",
"Searching for \"I Don't Wanna Live Forever\" by Taylor Swift...\n",
"Done.\n",
"Searching for \"Chandelier\" by Sia...\n",
"Done.\n",
"Searching for \"Rockabye\" by Clean Bandit...\n",
"Done.\n",
"Searching for \"Taki Taki\" by DJ Snake...\n",
"Done.\n",
"Searching for \"The Hills\" by The Weeknd...\n",
"Done.\n",
"Searching for \"Eastside\" by benny blanco...\n",
"Done.\n",
"Searching for \"Sugar\" by Maroon 5...\n",
"Done.\n",
"Searching for \"We Don't Talk Anymore\" by Charlie Puth...\n",
"Done.\n",
"Searching for \"Mi Gente\" by J Balvin...\n",
"Done.\n",
"Searching for \"IDGAF\" by Dua Lipa...\n",
"Done.\n",
"Searching for \"I'm the One\" by DJ Khaled...\n",
"Done.\n",
"Searching for \"I Like Me Better\" by Lauv...\n",
"Done.\n",
"Searching for \"Demons\" by Imagine Dragons...\n",
"Done.\n",
"Searching for \"It Ain’t Me\" by Kygo...\n",
"Done.\n",
"Searching for \"Riptide\" by Vance Joy...\n",
"Done.\n",
"Searching for \"Ride\" by Twenty One Pilots...\n",
"Done.\n",
"Searching for \"Old Town Road - Remix\" by Lil Nas X...\n",
"Done.\n",
"Searching for \"Moonlight\" by XXXTENTACION...\n",
"Done.\n",
"Searching for \"Work\" by Rihanna...\n",
"Done.\n",
"Searching for \"lovely\" by Billie Eilish...\n",
"Done.\n",
"Searching for \"There's Nothing Holdin' Me Back\" by Shawn Mendes...\n",
"Done.\n",
"Searching for \"Me Rehúso\" by Danny Ocean...\n",
"Done.\n"
]
}
],
"source": [
"lyrics = []\n",
"for i in range(len(df_top['name'])):\n",
" lyrics.append(genius.search_song(df_top['name'][i], df_top['artist'][i]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are still some errors despite the standarization. We solve these by detecting the null values and replacing by correct values"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"while None in lyrics:\n",
" none_index = [i for i in range(len(lyrics)) if lyrics[i] == None] \n",
" for i in none_index:\n",
" lyrics[i] = genius.search_song(df_top['name'][i], df_top['artist'][i])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we append the lyrics into the dataframe and operate to get its emotional features."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"lyrics_text = []\n",
"for lyric in lyrics:\n",
" lyrics_text.append(lyric.lyrics.lower())\n",
"df_top['lyrics'] = lyrics_text"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we use the NRC Lexicon to obtain the lyrical features of each song:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"for i in range(len(nrc['emotion'].unique())):\n",
" df_top[nrc['emotion'].unique()[i]+\"_index\"] = df_top.apply(lambda row: emotion_prop(row['lyrics'])[i], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we transform the release date into a date type and obtain the number of days since release as a measure of the song's longevity. First, we create a few helper functions."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"def string_to_date(string):\n",
" return date(*map(int, string.split('-')))\n",
"\n",
"df_top['release_date'] = df_top['release_date'].apply(string_to_date)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"def days_since_release(date):\n",
" x = date.today() - date\n",
" return x.days\n",
"\n",
"df_top['days_since_release'] = df_top['release_date'].apply(days_since_release)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we apply some transformations on a few variables in order to minimize the condition number of the linear regression."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"df_top['streams'] = df_top['streams'].apply(lambda x: float(x))\n",
"df_top['loudness'] = (df_top['loudness']-df_top['loudness'].min())/(df_top['loudness'].max()-df_top['loudness'].min())\n",
"df_top['length'] = (df_top['length']-df_top['length'].min())/(df_top['length'].max()-df_top['length'].min())\n",
"df_top['popularity'] = (df_top['popularity']-df_top['popularity'].min())/(df_top['popularity'].max()-df_top['popularity'].min())\n",
"df_top['days_since_release'] = (df_top['days_since_release']-df_top['days_since_release'].min())/(df_top['days_since_release'].max()-df_top['days_since_release'].min())\n",
"df_top['tempo'] = (df_top['tempo']-df_top['tempo'].min())/(df_top['tempo'].max()-df_top['tempo'].min())\n",
"df_top['streams_scaled'] = (df_top['streams']-df_top['streams'].min())/(df_top['streams'].max()-df_top['streams'].min())"
]
},
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"cell_type": "code",
"execution_count": 37,
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"name": "stdout",
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"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: streams_scaled R-squared: 0.239\n",
"Model: OLS Adj. R-squared: 0.047\n",
"Method: Least Squares F-statistic: 1.244\n",
"Date: Mon, 20 Apr 2020 Prob (F-statistic): 0.243\n",
"Time: 18:04:44 Log-Likelihood: 60.175\n",
"No. Observations: 100 AIC: -78.35\n",
"Df Residuals: 79 BIC: -23.64\n",
"Df Model: 20 \n",
"Covariance Type: nonrobust \n",
"======================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"--------------------------------------------------------------------------------------\n",
"const -0.1094 0.531 -0.206 0.837 -1.166 0.947\n",
"length -0.0853 0.128 -0.664 0.508 -0.341 0.170\n",
"danceability 0.0269 0.152 0.176 0.860 -0.276 0.330\n",
"acousticness -0.0050 0.085 -0.059 0.953 -0.174 0.164\n",
"energy -0.4499 0.189 -2.382 0.020 -0.826 -0.074\n",
"instrumentalness 1.4024 0.826 1.697 0.094 -0.242 3.047\n",
"liveness 0.3604 0.172 2.099 0.039 0.019 0.702\n",
"loudness 0.2562 0.126 2.040 0.045 0.006 0.506\n",
"speechiness 0.1976 0.221 0.893 0.375 -0.243 0.638\n",
"valence 0.0854 0.102 0.840 0.403 -0.117 0.288\n",
"tempo 0.0330 0.076 0.433 0.666 -0.119 0.185\n",
"anger_index 0.3789 0.855 0.443 0.659 -1.322 2.080\n",
"disgust_index -0.0465 0.728 -0.064 0.949 -1.495 1.402\n",
"fear_index -0.4628 0.705 -0.656 0.513 -1.866 0.940\n",
"joy_index 0.9893 0.770 1.284 0.203 -0.544 2.523\n",
"negative_index -0.1660 0.618 -0.269 0.789 -1.396 1.064\n",
"positive_index 0.3695 0.589 0.627 0.533 -0.804 1.543\n",
"sadness_index 1.1241 0.686 1.639 0.105 -0.241 2.489\n",
"surprise_index -0.0660 1.087 -0.061 0.952 -2.230 2.098\n",
"trust_index 0.3652 0.691 0.529 0.599 -1.010 1.741\n",
"days_since_release 0.1396 0.192 0.729 0.468 -0.242 0.521\n",
"==============================================================================\n",
"Omnibus: 55.827 Durbin-Watson: 0.543\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 227.537\n",
"Skew: 1.857 Prob(JB): 3.90e-50\n",
"Kurtosis: 9.389 Cond. No. 215.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"#we drop non-relevant variables and anticipation_index due to multicollinearity\n",
"X = df_top.drop([\"streams_scaled\",\"name\", \"album\", \"artist\",\"release_date\",\n",
" \"popularity\",\"time_signature\",\"streams\",\"lyrics\",\"anticipation_index\"], axis=1).copy()\n",
"X2 = sm.add_constant(X)\n",
"y = df_top['streams_scaled']\n",
"lr_model = linear_model.LinearRegression()\n",
"lr_model.fit(X, y)\n",
"print(sm.OLS(y, X2).fit().summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are ready to go! Let's visualize the relationship between the musical and lyrical features of a song and its popularity. "
]
},
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"fig = make_subplots(rows=4, cols=2,shared_yaxes=True,\n",
" vertical_spacing = 0.05,\n",
" horizontal_spacing = 0.025, \n",
" y_title = \"Scaled streams\" )\n",
"for i in range(4):\n",
" for j in range(2):\n",
" fig_trend = px.scatter(df_top, x=columns_music[2*i+j], y=\"streams_scaled\",trendline = \"ols\",hover_name=\"name\")\n",
" fig.add_trace(fig_trend.data[0], row=i+1, col=j+1)\n",
" fig.add_trace(fig_trend.data[1], row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_music[2*i+j]], y=lr_model.predict(X),\n",
" mode=\"markers\",marker=dict(color='Orange'),\n",
" hovertemplate = \"Full OLS model prediction
\" + \n",
" \"streams: %{y}
\" + columns_music[2*i+j] +\": %{x}
\"),\n",
" row=i+1, col=j+1)\n",
" results = px.get_trendline_results(fig_trend)\n",
" fig.update_xaxes(title_text=columns_music[2*i+j].capitalize(),row=i+1, col=j+1)\n",
"fig.update_layout(height=2000, width=900, title= {'text': \"Musical Features\", 'x':0.5},showlegend=False)\n",
"fig.update_traces(marker_size=4)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try to analyze lyrical features next!"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"scrolled": false
},
"outputs": [
{
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"text": "Joy index"
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" \n",
"
\n",
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"columns_emotion = (df_top.columns)[18:]\n",
"fig = make_subplots(rows=5, cols=2,shared_yaxes=True,\n",
" vertical_spacing = 0.05,\n",
" horizontal_spacing = 0.025, \n",
" y_title = \"Scaled streams\" )\n",
"for i in range(5):\n",
" for j in range(2):\n",
" fig_trend = px.scatter(df_top, x=columns_emotion[2*i+j], y=\"streams_scaled\",trendline = \"ols\",hover_name=\"name\")\n",
" fig.add_trace(fig_trend.data[0], row=i+1, col=j+1)\n",
" fig.add_trace(fig_trend.data[1], row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_emotion[2*i+j]], y=lr_model.predict(X),\n",
" mode=\"markers\",marker=dict(color='Orange'),\n",
" hovertemplate = \"Full OLS model prediction
\" + \n",
" \"streams: %{y}
\" + columns_emotion[2*i+j] +\": %{x}
\"),\n",
" row=i+1, col=j+1)\n",
" results = px.get_trendline_results(fig_trend)\n",
" fig.update_xaxes(title_text=(columns_emotion[2*i+j].capitalize()).replace(\"_\",\" \"),row=i+1, col=j+1)\n",
"fig.update_layout(height=2000, width=1000, title= {'text': \"Lyrical Features\", 'x':0.5}, showlegend = False)\n",
"fig.update_traces(marker_size=4)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model seems to perform quite OK. Still, I am a bit worried some variables are not bringing anything to the table. I will try to refine the model in the next section."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Refining the model\n",
"\n",
"Let's implement a Lasso regression and compare its MSE to our previous linear model."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear Regression MSE: 0.017573399462309323\n",
"Lasso Regression MSE: 0.023106569591163435\n"
]
}
],
"source": [
"lasso_model = linear_model.Lasso()\n",
"lasso_model.fit(X, y)\n",
"print(\"Linear Regression MSE:\", metrics.mean_squared_error(y, lr_model.predict(X)))\n",
"print(\"Lasso Regression MSE:\", metrics.mean_squared_error(y, lasso_model.predict(X)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interestingly, it seems that our Linear model manages to outperform our Lasso models. Let's visualize their different predictions as before."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"scrolled": false
},
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"fig = make_subplots(rows=4, cols=2,shared_yaxes=True,\n",
" vertical_spacing = 0.05,\n",
" horizontal_spacing = 0.025, \n",
" y_title = \"Scaled streams\" )\n",
"for i in range(4):\n",
" for j in range(2):\n",
" fig_trend = px.scatter(df_top, x=columns_music[2*i+j], y=\"streams_scaled\",trendline = \"ols\",hover_name=\"name\")\n",
" fig.add_trace(fig_trend.data[0], row=i+1, col=j+1)\n",
" fig.add_trace(fig_trend.data[1], row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_music[2*i+j]], y=lr_model.predict(X),\n",
" mode=\"markers\",marker=dict(color='Orange'),\n",
" hovertemplate = \"Full OLS model prediction
\" + \n",
" \"streams: %{y}
\" + columns_emotion[2*i+j] +\": %{x}
\"),\n",
" row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_music[2*i+j]], y=lasso_model.predict(X),\n",
" marker=dict(color='Red'),\n",
" hovertemplate = \"Lasso model prediction
\" + \n",
" \"streams: %{y}
\" + columns_music[2*i+j] +\": %{x}
\"),\n",
" row=i+1, col=j+1)\n",
" results = px.get_trendline_results(fig_trend)\n",
" fig.update_xaxes(title_text=(columns_music[2*i+j].capitalize()).replace(\"_\",\" \"),row=i+1, col=j+1)\n",
"fig.update_layout(height=2000, width=1000, title= {'text': \"Musical Features\", 'x':0.5}, showlegend = False)\n",
"fig.update_traces(marker_size=4)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
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},
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"fig = make_subplots(rows=5, cols=2,shared_yaxes=True,\n",
" vertical_spacing = 0.05,\n",
" horizontal_spacing = 0.025, \n",
" y_title = \"Scaled streams\" )\n",
"for i in range(5):\n",
" for j in range(2):\n",
" fig_trend = px.scatter(df_top, x=columns_emotion[2*i+j], y=\"streams_scaled\",trendline = \"ols\",hover_name=\"name\")\n",
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" mode=\"markers\",marker=dict(color='Orange'),\n",
" hovertemplate = \"Full OLS model prediction
\" + \n",
" \"streams: %{y}
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\"),\n",
" row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_emotion[2*i+j]], y=lasso_model.predict(X),\n",
" marker=dict(color='Red'),\n",
" hovertemplate = \"Lasso model prediction
\" + \n",
" \"streams: %{y}
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\"),\n",
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" fig.update_xaxes(title_text=(columns_emotion[2*i+j].capitalize()).replace(\"_\",\" \"),row=i+1, col=j+1)\n",
"fig.update_layout(height=2000, width=1000, title= {'text': \"Lyrical Features\", 'x':0.5}, showlegend = False)\n",
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"fig.show()"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Something fishy is going on with the lasso regression... It looks like an horizontal line! Let's extract the coefficients of both model and compare them."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" lasso linreg\n",
"length 0.0 -0.085312\n",
"danceability 0.0 0.026892\n",
"acousticness 0.0 -0.005017\n",
"energy -0.0 -0.449886\n",
"instrumentalness 0.0 1.402413\n",
"liveness 0.0 0.360372\n",
"loudness 0.0 0.256238\n",
"speechiness 0.0 0.197584\n",
"valence 0.0 0.085369\n",
"tempo -0.0 0.032987\n",
"anger_index -0.0 0.378939\n",
"disgust_index -0.0 -0.046521\n",
"fear_index -0.0 -0.462835\n",
"joy_index 0.0 0.989285\n",
"negative_index -0.0 -0.165996\n",
"positive_index 0.0 0.369506\n",
"sadness_index -0.0 1.124084\n",
"surprise_index 0.0 -0.065989\n",
"trust_index 0.0 0.365189\n",
"days_since_release -0.0 0.139555\n"
]
}
],
"source": [
"lasso_model = linear_model.Lasso()\n",
"lasso_model.fit(X, y)\n",
"lasso_coefs = pd.Series(dict(zip(list(X), lasso_model.coef_)))\n",
"lr_coefs = pd.Series(dict(zip(list(X), lr_model.coef_)))\n",
"coefs = pd.DataFrame(dict(lasso=lasso_coefs, linreg=lr_coefs))\n",
"print(coefs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our suspicions are confirmed: all Lasso coefficients are 0. That's a pretty weird result. This means that our Lasso regression reduces to the simplest of estimators: sample mean of the dependent variable. Let's splice the model between testing and training data and compare their MSE to check whether our linear model still beats the Lasso model."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear Model: {'mse_train': 0.011462616788246201, 'mse_test': 0.044234680031768454}\n",
"Lasso Regression: {'mse_train': 0.014931725291319514, 'mse_test': 0.04784550215519792}\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25)\n",
"\n",
"def fit_and_report_mses(mod, X_train, X_test, y_train, y_test):\n",
" mod.fit(X_train, y_train)\n",
" return dict(\n",
" mse_train=metrics.mean_squared_error(y_train, mod.predict(X_train)),\n",
" mse_test=metrics.mean_squared_error(y_test, mod.predict(X_test))\n",
" )\n",
"\n",
"print(\"Linear Model:\", fit_and_report_mses(linear_model.LinearRegression(), X_train, X_test, y_train, y_test))\n",
"print(\"Lasso Regression:\", fit_and_report_mses(linear_model.Lasso(), X_train, X_test, y_train, y_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even if the linear model performs better on the training stage, Lasso (and hence, the naive estimator) beats our linear model. That's a quite worrying fact, since it means that our variables have very limited predicting capabilites. I will try one last model: a neural network model as implemented during the lecture notes. Let's see if we can beat both other models. First, I use it on the whole dataset."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear Regression MSE: 0.017573399462309323\n",
"Lasso Regression MSE: 0.023106569591163435\n",
"NN Regression MSE: 0.007307496202566639\n"
]
}
],
"source": [
"# do not forget to scale the model!\n",
"nn_scaled_model = pipeline.make_pipeline(\n",
" preprocessing.StandardScaler(),\n",
" neural_network.MLPRegressor((30, 20)) \n",
")\n",
"\n",
"nn_scaled_model.fit(X, y)\n",
"\n",
"print(\"Linear Regression MSE:\", metrics.mean_squared_error(y, lr_model.predict(X)))\n",
"print(\"Lasso Regression MSE:\", metrics.mean_squared_error(y, lasso_model.predict(X)))\n",
"print(\"NN Regression MSE:\", metrics.mean_squared_error(y, nn_scaled_model.predict(X)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cool! This model seems to outperform both previous models by a very good margin. Let's visualize the predictions."
]
},
{
"cell_type": "code",
"execution_count": 47,
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"fig = make_subplots(rows=4, cols=2,shared_yaxes=True,\n",
" vertical_spacing = 0.05,\n",
" horizontal_spacing = 0.025, \n",
" y_title = \"Scaled streams\" )\n",
"for i in range(4):\n",
" for j in range(2):\n",
" fig_trend = px.scatter(df_top, x=columns_music[2*i+j], y=\"streams_scaled\",trendline = \"ols\",hover_name=\"name\")\n",
" fig.add_trace(fig_trend.data[0], row=i+1, col=j+1)\n",
" fig.add_trace(fig_trend.data[1], row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_music[2*i+j]], y=lr_model.predict(X),\n",
" mode=\"markers\",marker=dict(color='Orange'),\n",
" hovertemplate = \"Full OLS model prediction
\" + \n",
" \"streams: %{y}
\" + columns_emotion[2*i+j] +\": %{x}
\"),\n",
" row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_music[2*i+j]], y=lasso_model.predict(X),\n",
" marker=dict(color='Red'),\n",
" hovertemplate = \"Lasso model prediction
\" + \n",
" \"streams: %{y}
\" + columns_emotion[2*i+j] +\": %{x}
\"),\n",
" row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_music[2*i+j]], y=nn_scaled_model.predict(X),\n",
" mode=\"markers\",marker=dict(color='Purple'),\n",
" hovertemplate = \"Neural Network model prediction
\" + \n",
" \"streams: %{y}
\" + columns_emotion[2*i+j] +\": %{x}
\"),\n",
" row=i+1, col=j+1)\n",
" results = px.get_trendline_results(fig_trend)\n",
" fig.update_xaxes(title_text=(columns_emotion[2*i+j].capitalize()).replace(\"_\",\" \"),row=i+1, col=j+1)\n",
"fig.update_layout(height=2000, width=1000, title= {'text': \"Musical Features\", 'x':0.5}, showlegend = False)\n",
"fig.update_traces(marker_size=4)\n",
"fig.show()"
]
},
{
"cell_type": "code",
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\n",
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"source": [
"fig = make_subplots(rows=5, cols=2,shared_yaxes=True,\n",
" vertical_spacing = 0.05,\n",
" horizontal_spacing = 0.025, \n",
" y_title = \"Scaled streams\" )\n",
"for i in range(5):\n",
" for j in range(2):\n",
" fig_trend = px.scatter(df_top, x=columns_emotion[2*i+j], y=\"streams_scaled\",trendline = \"ols\",hover_name=\"name\")\n",
" fig.add_trace(fig_trend.data[0], row=i+1, col=j+1)\n",
" fig.add_trace(fig_trend.data[1], row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_emotion[2*i+j]], y=lr_model.predict(X),\n",
" mode=\"markers\",marker=dict(color='Orange'),\n",
" hovertemplate = \"Full OLS model prediction
\" + \n",
" \"streams: %{y}
\" + columns_emotion[2*i+j] +\": %{x}
\"),\n",
" row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_emotion[2*i+j]], y=lasso_model.predict(X),\n",
" marker=dict(color='Red'),\n",
" hovertemplate = \"Lasso model prediction
\" + \n",
" \"streams: %{y}
\" + columns_emotion[2*i+j] +\": %{x}
\"),\n",
" row=i+1, col=j+1)\n",
" fig.add_trace(go.Scatter(x = df_top[columns_emotion[2*i+j]], y=nn_scaled_model.predict(X),\n",
" mode=\"markers\",marker=dict(color='Purple'),\n",
" hovertemplate = \"Neural Network model prediction
\" + \n",
" \"streams: %{y}
\" + columns_emotion[2*i+j] +\": %{x}
\"),\n",
" row=i+1, col=j+1)\n",
" results = px.get_trendline_results(fig_trend)\n",
" fig.update_xaxes(title_text=(columns_emotion[2*i+j].capitalize()).replace(\"_\",\" \"),row=i+1, col=j+1)\n",
"fig.update_layout(height=2000, width=1000, title= {'text': \"Lyrical Features\", 'x':0.5}, showlegend = False)\n",
"fig.update_traces(marker_size=4)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let's divide split the dataset between a training/testing part and hope that our NN model beats the naive estimator."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear Regression: {'mse_train': 0.02048448685532459, 'mse_test': 0.013027925914433454}\n",
"Lasso Regression: {'mse_train': 0.0272749774766269, 'mse_test': 0.010809195586301959}\n",
"NN Model: {'mse_train': 0.0035198720881701186, 'mse_test': 0.05458352120520873}\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25)\n",
"\n",
"def fit_and_report_mses(mod, X_train, X_test, y_train, y_test):\n",
" mod.fit(X_train, y_train)\n",
" return dict(\n",
" mse_train=metrics.mean_squared_error(y_train, mod.predict(X_train)),\n",
" mse_test=metrics.mean_squared_error(y_test, mod.predict(X_test))\n",
" )\n",
"\n",
"print(\"Linear Regression:\", fit_and_report_mses(linear_model.LinearRegression(), X_train, X_test, y_train, y_test))\n",
"print(\"Lasso Regression:\", fit_and_report_mses(linear_model.Lasso(), X_train, X_test, y_train, y_test))\n",
"print(\"NN Model:\", fit_and_report_mses(nn_scaled_model, X_train, X_test, y_train, y_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sadly, none of our models were able to beat the naive estimator in the testing phase.\n",
"\n",
"## Concluding remarks\n",
"\n",
"I would like to continue refining these models as most likely there is an issue of overspecification. However, the project is already quite long so I feel this is a good place to end it. Visually, it seems to me that musical features are not very good indicators of popularity while lyrical features have stronger predictive capabilities: people tend to like positive and happy music, at least when it comes to its lyrical content. However, the triumph of the naive estimator is a quite dissapointing result, so further research on this topic would focus on which regressors should be included in the model and which regressors should be droped.\n",
"\n",
"Besides these conclusions, my main objective with this project was to lay tge groundwork for future data analysis of music. That is, to create a somewhat structured way to gather this data and quantify its features. Still, given that the Spotify API does not gives us a way to access the number of times a song has been streamed, alternative ways to measure this variable should be explored. Otherwise conducting further research will prove difficult."
]
}
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