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Competition - Dance Party Songs
Which songs are most suitable for a dancing party?
📖 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.
💾 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.
| Column | Description |
|---|---|
track_id | The Spotify ID number of the track. |
artists | Names of the artists who performed the track, separated by a ; if there's more than one. |
album_name | The name of the album that includes the track. |
track_name | The name of the track. |
popularity | Numerical 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_ms | The length of the track, measured in milliseconds. |
explicit | Indicates whether the track contains explicit lyrics. true means it does, false means it does not or it's unknown. |
danceability | A 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. |
energy | A 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. |
key | The key the track is in. Integers map to pitches using standard Pitch class notation. E.g.0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1. |
loudness | The overall loudness, measured in decibels (dB). |
mode | The modality of a track, represented as 1 for major and 0 for minor. |
speechiness | Measures 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. |
acousticness | A confidence measure ranges from 0.0 to 1.0, with 1.0 representing the highest confidence that the track is acoustic. |
instrumentalness | Instrumentalness 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. |
liveness | A measure of the probability that the track was performed live. Scores above 0.8 indicate a high likelihood of the track being live. |
valence | A score from 0.0 to 1.0 representing the track's positiveness. High scores suggest a more positive or happier track. |
tempo | The track's estimated tempo, measured in beats per minute (BPM). |
time_signature | An 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_genre | The genre of the track. |
Source (data has been modified)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_scorespotify = pd.read_csv('data/spotify.csv')
spotify.head()# Checking for missing values
spotify.isnull().sum()rows_with_missing_values = spotify[spotify.isnull().any(axis=1)]
print(rows_with_missing_values)spotify = spotify.dropna()
spotify.isnull().sum().sum()# Checking unique values
spotify.nunique()# Checking summary statistics
spotify.describe()# Checking data types
spotify.info()# Plot song durations
plt.figure(figsize=(10, 6))
plt.hist(spotify['duration_ms'] / 60000, bins=30, color='skyblue', edgecolor='black')
plt.title('Distribution of Song Durations')
plt.xlabel('Duration (minutes)')
plt.ylabel('Frequency')
plt.grid(True)
plt.show()# Drop songs longer than 5 minutes
spotify = spotify[spotify['duration_ms'] <= 300000] # 300000 ms = 5 minutes
# Drop songs shorter than 2 minutes
spotify = spotify[spotify['duration_ms'] >= 120000] # 120000 ms = 2 minutes# Create a box plot for song durations
plt.figure(figsize=(8, 6))
plt.boxplot(spotify['duration_ms'] / 60000, vert=False)
plt.title('Box Plot of Song Durations')
plt.xlabel('Duration (minutes)')
plt.ylabel('Song')
plt.show()# Descriptive statistics
audio_features = spotify.drop(['track_id', 'artists', 'album_name', 'track_name', 'explicit', 'track_genre'], axis=1)
statistics = audio_features.describe()
print(statistics)