Spotify Song Popularity Prediction

Project Overview

This project aims to analyze and predict the popularity of Spotify songs using various song attributes. By leveraging machine learning techniques, we explore how features like tempo, energy, and danceability influence a song's popularity.

Dataset

Name	Description
track_id	The Spotify ID for the track.
artists	The artists' names who performed the track. If there is more than one artist, they are separated by a semicolon (;).
album_name	The album name in which the track appears.
track_name	Name of the track.
popularity	The popularity of a track, a value between 0 and 100. An algorithm calculates it and is mainly based on the total number of plays and recency.
duration_ms	The track length in milliseconds.
explicit	Whether or not the track has explicit lyrics. (true = yes; false = no or unknown).
danceability	Describes how suitable a track is for dancing, based on tempo, rhythm stability, beat strength, and overall regularity. Ranges from 0.0 to 1.0.
energy	A measure from 0.0 to 1.0 representing intensity and activity.
key	The key the track is in, with integers mapping to pitches using standard Pitch Class notation. E.g., 0 = C, 1 = C♯/D♭, etc. -1 indicates no key detected.
loudness	The overall loudness of a track in decibels (dB).
mode	Indicates whether the track is major (1) or minor (0).
speechiness	Detects the presence of spoken words in a track.
acousticness	A measure from 0.0 to 1.0 of whether the track is acoustic.
instrumentalness	Predicts whether a track contains no vocals. Values closer to 1.0 suggest no vocal content.
liveness	Detects the presence of an audience in the recording. Values above 0.8 indicate a live performance.
valence	A measure from 0.0 to 1.0 describing the musical positiveness conveyed by the track.
tempo	The overall estimated tempo of a track in beats per minute (BPM).
time_signature	The time signature (meter) specifies how many beats are in each bar (or measure).
track_genre	The genre to which the track belongs.

Project Objectives

1. Data Cleaning & Preprocessing: Handling missing values, transforming categorical data, and normalizing numerical features.
2. Exploratory Data Analysis (EDA): Understanding patterns, correlations, and distributions of song features.
3. Feature Engineering: Extracting relevant features to improve model performance.
4. Model Training & Evaluation: Implementing and assessing different machine learning models to predict song popularity.

Machine Learning Models Used

• Multiple Linear Regression: Used for predicting numerical popularity scores.
• Decision Trees: A non-linear model for better feature interpretation.
• eXtreme Gradient Boosting (XGBoost): An advanced ensemble learning method for improving accuracy.

Technologies Used

• Programming Language: R
• Libraries: dplyr, ggplot2, caret, xgboost
• Data Processing: dplyr for data manipulation
• Visualization Tools: ggplot2, plotly for graphical analysis

Project Workflow

1. Data Acquisition: Load and inspect the dataset.
2. Data Cleaning & Processing: Handle missing values, normalize numerical variables, and transform categorical features.
3. Exploratory Data Analysis (EDA): Generate descriptive statistics, correlation heatmaps, and distribution plots.
4. Feature Engineering: Select relevant features that impact song popularity.
5. Model Training: Train Multiple Linear Regression, Decision Trees, and XGBoost models.
6. Model Evaluation: Compare model performance using MSE, MAE, and RMSE.
7. Results Interpretation: Draw insights and conclusions from the predictive analysis.