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An indie-pop dance party playlist
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  • Report
  • Which songs are most suitable for a dancing party?

    šŸ“– Background

    It's that vibrant time of year again - Summer has arrived (for those of us in the Northern Hemisphere at least)! There's an energy in the air that inspires us to get up and move. In sync with this exuberance, your company has decided to host a dance party to celebrate. And you, with your unique blend of creativity and analytical expertise, have been entrusted with the crucial task of curating a dance-themed playlist that will set the perfect mood for this electrifying night. The question then arises - How can you identify the songs that would make the attendees dance their hearts out? This is where your coding skills come into play.

    šŸ’¾ The Data

    You have assembled information on more than 125 genres of Spotify music tracks in a file called spotify.csv, with each genre containing approximately 1000 tracks. All tracks, from all time, have been taken into account without any time period limitations. However, the data collection was concluded in October 2022. Each row represents a track that has some audio features associated with it.

    track_idThe Spotify ID number of the track.
    artistsNames of the artists who performed the track, separated by a ; if there's more than one.
    album_nameThe name of the album that includes the track.
    track_nameThe name of the track.
    popularityNumerical value ranges from 0 to 100, with 100 being the highest popularity. This is calculated based on the number of times the track has been played recently, with more recent plays contributing more to the score. Duplicate tracks are scored independently.
    duration_msThe length of the track, measured in milliseconds.
    explicitIndicates whether the track contains explicit lyrics. true means it does, false means it does not or it's unknown.
    danceabilityA score ranges between 0.0 and 1.0 that represents the track's suitability for dancing. This is calculated by algorithm and is determined by factors like tempo, rhythm stability, beat strength, and regularity.
    energyA score ranges between 0.0 and 1.0 indicating the track's intensity and activity level. Energetic tracks tend to be fast, loud, and noisy.
    keyThe key the track is in. Integers map to pitches using standard Pitch class notation. E.g.0 = C, 1 = Cā™Æ/Dā™­, 2 = D, and so on. If no key was detected, the value is -1.
    loudnessThe overall loudness, measured in decibels (dB).
    modeThe modality of a track, represented as 1 for major and 0 for minor.
    speechinessMeasures the amount of spoken words in a track. A value close to 1.0 denotes speech-based content, while 0.33 to 0.66 indicates a mix of speech and music like rap. Values below 0.33 are usually music and non-speech tracks.
    acousticnessA confidence measure ranges from 0.0 to 1.0, with 1.0 representing the highest confidence that the track is acoustic.
    instrumentalnessInstrumentalness estimates the likelihood of a track being instrumental. Non-lyrical sounds such as "ooh" and "aah" are considered instrumental, whereas rap or spoken word tracks are classified as "vocal". A value closer to 1.0 indicates a higher probability that the track lacks vocal content.
    livenessA measure of the probability that the track was performed live. Scores above 0.8 indicate a high likelihood of the track being live.
    valenceA score from 0.0 to 1.0 representing the track's positiveness. High scores suggest a more positive or happier track.
    tempoThe track's estimated tempo, measured in beats per minute (BPM).
    time_signatureAn estimate of the track's time signature (meter), which is a notational convention to specify how many beats are in each bar (or measure). The time signature ranges from 3 to 7 indicating time signatures of 3/4, to 7/4.
    track_genreThe genre of the track.

    Source (data has been modified)

    šŸ’Ŗ Challenge

    Your task is to devise an analytically-backed, dance-themed playlist for the company's summer party. Your choices must be justified with a comprehensive report explaining your methodology and reasoning. Below are some suggestions on how you might want to start curating the playlist:

    • Use descriptive statistics and data visualization techniques to explore the audio features and understand their relationships.
    • Develop and apply a machine learning model that predicts a song's danceability.
    • Interpret the model outcomes and utilize your data-driven insights to curate your ultimate dance party playlist of the top 50 songs according to your model.


    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    import as px
    from sklearn.cluster import KMeans
    from sklearn.preprocessing import StandardScaler
    spotify = pd.read_csv('data/spotify.csv')
    # key variables
    top = 20 # Top-n list
    TARGET_GENRE = 'indie-pop'


    df = spotify.copy()

    Drop duplicates

    original_shape = df.shape[0]
    df = df.drop_duplicates().reset_index(drop = True)
    print(f"{original_shape - df.shape[0]} rows dropped")

    Drop nulls

    original_shape = df.shape[0]
    df = df.dropna().reset_index(drop = True)
    print(f"{original_shape - df.shape[0]} rows dropped")