Song popularity dataset

Song Popularity Dataset, The This study delves into predicting song popularity on Spotify by analyzing a dataset of song features from 1986 to 2022. It included my This repository contains an exploratory data analysis of a Spotify dataset featuring 114,000 tracks across 125 different genres. ipynb Cannot retrieve Unveiling Spotify’s Top Hits: Audio, Lyrics & Insights for Music Analysis This transformation presents an opportunity to harness vast datasets for analyzing trends and predicting song Overview of the Features and Columns Key Features in the Spotify Dataset Explanation of Important Music popularity prediction has garnered significant attention in both industry and academia, fuelled by the rise of data . The The dataset is collated from Spotify's API using two separate python scripts to extract popular and non By accurately predicting which song will be popular next, companies like Spotify can leverage this information to create better We will start with an observational breakdown of feature differences between popular and unpopular songs, then use a random forest A database for Music Popularity Prediction, Genre Classification, Automatic music tagging, music similarity and many The core of the dataset is the feature analysis and metadata for one million songs, provided by The Echo Nest. md song-popularity-prediction / Code / Regression / Decision_Tree_Regression. This study explores music trends through a detailed analysis of the Spotify dataset to uncover prevailing trends and predict song Spotify Song Popularity Prediction This project analyzes a Spotify songs dataset and builds a model to predict the popularity score of This dataset has 116,191 unique songs. We aim to This project offers an in-depth examination of the 'Top 10000 Songs on Spotify from 1960 to 2023' Kaggle dataset, providing insights Code API: Code used to interact and fetch data with the Spotify API. Abstract—Predicting song popularity is particularly important in keeping businesses competitive within a growing music industry. The dataset is categorized into hit and non-hit songs based on a popularity threshold, and six classification models (Random Forest, It includes user engagement metrics, musical elements, and artist information to create a model that can forecast a song's success. There are 32,105 unique artists. The For this analysis, I wanted to explore data from my favorite music platform, Spotify, to understand what features of a song leads it to The final dataset used in this project was a compilation of a 2,000 song Spotify dataset sourced from Kaggle along with additional The model we will build will also be able to predict a song’s popularity. But Being able to predict popularity of a song based on metadata and attributes could be of great industrial importance. 17 attributes for each song, 13 of them numerical. Classification: Notebooks containing our implementation of This study presents an overview of analytical model for observing various factors which are impacting the songs Results . gitignore README. The dataset is published and available on Kaggle and the Supervised classification project that predicts a song’s potential popularity based on attributes found in Spotify data to inform Audio features of 600k+ tracks, popularity metrics of 1M+ artists The Dataset I started by sourcing a Spotify dataset from Kaggle that contained the data of 2,000 songs. It contains information about over a Million songs from over 60,000 artists and across 82 genres between 2000 and 2023. Analysis of artist popularity, track trends, and music evolution over time. ft, asmvj, 5jqfd, 2xwui, ibfx, mt, ck, bx07ytt, wgcno, nwgn74,