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Spotify data analysis github Exploratory Data Analysis (EDA): Identifies key features and patterns in the Spotify dataset. t modelling and feature and permutation importances only: https Begin by familiarizing yourself with the structure of the data set by checking for missing values and data types, performing an initial exploration to understand the different features available. Analyze and visualize popular Spotify tracks, drawing insights on what makes a track popular. pynb is to make data set with song attributes for a playlist. Sign in Product GitHub Copilot. Automate any Data Munging, Exploratory and Statistical Analysis of 174k+ tracks and 10+ audio features of Spotify Data Set with songs released between 1921 and 2021. How we conclude a song to be a hit or a non hit is The Spotify Data Analysis Project is a comprehensive data analysis project that aims to extract insights from a Spotify dataset using SQL. Expand Dataset: Add more rows to the dataset for broader analysis and scalability testing. Implemented multiple regression to identify multivariate predictors of song popularity. - Syed-Sarfaraz-A This project on Spotify Data Analysis Project will teach how to perform exploratory data analysis using Python on music related datasets. Feature Engineering: Leverage Spotify's Rich Dataset for Personalized Music Recommendations: The primary objective of this project is to utilize the extensive dataset provided by Spotify to develop a sophisticated Music Recommendation System. - divya-gh/Spotify_Music_Analysis Contribute to nadont/Spotify-Data-Analysis development by creating an account on GitHub. Feature Engineering: With the rise of Spotify, iTune, Youtube, etc, streaming services have contributed majority of music industry revenues. With a market share of approximately 32%, it has 365 million monthly active users, including 165 million paying subscribers, as of June 2021 WARNING! I have no idea how to stop IFTTTT from logging all of my data into Google Sheets. Collected by Kaggle user and Turkish Data Scientist Yamaç Eren Ay, the data was retrieved and tabulated from the Spotify Web API. The dataset comprises over 170k rows with attributes like acousticness, danceability, energy, and more. Generation of KPI cards and heat maps for data analysis. The Spotify dataset that is used in this project includes audio features from 160k+ songs released The Spotify dataset (titled data. Dashboard Setup: Download or clone this repository to your local machine. Welcome to the Spotify Data Analysis Project! This repository contains code and resources for performing data analysis based on Spotify API data. xlsx: Contains the retrieved Spotify dataset. - RAnush12/Spotify-Data-Analysis Data analysis exploring the relationship between the audio features of a song and how positive or negative its lyrics are, involving sentiment analysis and supervised machine learning; Includes data collection script that scrapes audio feature data from the Spotify API, as well as lyrical data from the LyricWikiAPI Copy these credentials into your spotify_credentials. The days of ranking songs based on how well an album or single sold, or how often it was requested on your local radio station is long gone. - GitHub - gzlupko/Spotify_Data_Analysis: The Spotify Data Analysis Project is a comprehensive data analysis project that aims to extract insights from a Spotify dataset using SQL. ; Spotify Data analysis. This project is aimed at analyzing data from the Spotify platform, utilizing the Spotify API and MongoDB for data extraction, Apache Hadoop for ELT processes, PySpark for transformation, and leveraging Dremio and Power BI for visualization and in-depth data analysis. This project explores a Spotify dataset, focusing on determining the attribute range for popular songs. By examining factors such as play counts, duration, genre preferences, and temporal trends, I aim to gain a deeper understanding of my musical preferences and habits. Cleaned the data before utilising Python's Pandas, NumPy, Matplotlib, and Seaborn to perform exploratory data analysis (EDA) and data visualisation on the Spotify dataset. Contribute to nainaryan/Spotify-Analysis development by creating an account on GitHub. It is the world's largest music streaming service provider and has over 381 million monthly active users, which also includes 172 million paid subscribers. (Mine has been logging songs for literal Contribute to amitkr1111/Spotify-data-analysis development by creating an account on GitHub. Analyzes your Spotify listening data collected through IFTTTT and stored in Google Sheets WARNING! I have no idea how to stop IFTTTT from logging all of my data into Google Sheets. Find and fix vulnerabilities Codespaces Following the completion of a group project for my Fundementals of Data Science class, I wanted to continue my research and analysis of trends among the top streamed songs on Spotify. Creation of HTML visuals for enhanced dashboard presentation. Execution time (E. - Spotify-data-analysis-using-SQL/README. They have an API for developers to explore their music database and get insights into our listening habits. I recomend you to download the repo completely and load the html files in any of your browser. The projects aims to track the most played songs, artists and genres currently using Python and Spotify API - Sandreke/Spotify-Data-Analysis KNN algorithm is applied to the training data set and the results are verified on the test data set. Find and fix vulnerabilities Codespaces Spotify Data Analysis Project. Numerical features provided by spotify are collected and analyzed. About the Project Spotify is a Swedish audio streaming and media services provider founded in April 2006. This project focuses on analyzing and visualizing a dataset containing information about music tracks from Spotify. Using Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Plotly Express, the project explores various aspects of the dataset and presents visualizations to uncover insights and patterns. Class imbalanced issue/Decision tree/ Naive bayes/ Confidence interval for model performance/ ROC, Lift curves /Gain chart - JCHIANG1/Spotify-data-analysis-project Visualize the Data: Use a data visualization tool like Tableau or Power BI to create dashboards based on the query results. We will also use K-means clustering or hierarchical clustering, to group similar songs together and explore the underlying structure of the data. - patrickmcalinden/Spotif You signed in with another tab or window. With the rise of Spotify, iTune, Youtube, etc, streaming services have contributed majority of music industry revenues. Spotify is the world's largest audio streaming platform. Spotify-data-analysis Spotify is one of the most popular audio streaming platforms around the globe. The Spotify 2010 - 2019 Top 100 Songs. Here's a breakdown of what Spotify data analysis entails: Data Sources: Gain familiarity with exploratory data analysis (EDA) techniques to assess a music platform dataset. In this project, we conducted data mining for 200000 tracks extracted Gain familiarity with exploratory data analysis (EDA) techniques to assess a music platform dataset. AvaniPatil, Sushma Akoju, Armana Anand 9/23/2021. In this project, we conducted data mining for 200000 tracks extracted I'm sharing an Exploratory Data Analysis (EDA) and Data Visualization of the data from Spotify using Python - A Data Analysis Project performed in my journey into Data Science. Using data mining techniques and exploratory data analysis, I will delve into the acquired Spotify data to uncover valuable insights about my music consumption patterns. Contribute to Dwipanita/Spotify-Data-Analysis development by creating an account on GitHub. Welcome to the GitHub repository for the Spotify Data Analysis project, part of the AD 699 course. The dataset includes various features such as artist names, track attributes (danceability, energy, loudness, etc. This project analyzes Spotify music data to uncover trends, identify key features influencing track popularity, and predict future hit songs. g. The project involves working with a denormalized dataset, normalizing it, and then performing various SQL queries to extract meaningful insights. Explore dataset structure, columns, and information. Prerequisite: Data Analyst Roadmap ⌛ , Python Lessons 📑 & Python Libraries for Data Science 🗂️ Spotify data analysis. ini file, which will be created automatically (in your home directory) the first time when you try to run the provided Pluto notebook. By conducting an in-depth Exploratory Data Analysis (EDA), we aim to uncover patterns and relationships within the data that can be used to recommend Machine Learning Project on Spotify Data using K-Means Clustering 🎶🤖. Visualize the Data: Use a data visualization tool like Tableau or Power BI to create dashboards based on the query results. (Mine has been logging songs for literal years with no end in sight) I run this in Machine Learning Project on Spotify Data using K-Means Clustering 🎶🤖. Spotify Data Analysis makes use of secondary data from Spotify. Through the Jupyter Notebook environment, this project aims to provide valuable insights into the world of music streaming by analyzing Spotify data. Contribute to aarjun47/Spotify-Data-Analysis development by creating an account on GitHub. About No description, website, or topics provided. Spotify data analysis delves into the vast amount of information generated by the music streaming platform. Contribute to ThanushaSagadevan/Spotify-Data-Analysis-using-Python development by creating an account on GitHub. Find and fix vulnerabilities Codespaces The goal of this Shiny App was to analyze and visualize data from our Spotify accounts. Ensemble methods are extremely good for analyzing multi-feature data with non-linear relationship, plus XGBoost has recently dominated data science field with extreme superiority, so we choose XGBClassifier to train our data, and achieved very excellent accuracy score for both cross-validated and test data. Navigation Menu Toggle navigation. Data analysis - analysed the relationship between the audio features of a song and how positive or negative its lyrics are, involving sentiment analysis and many more factors. It explores metrics like streams, song features (danceability, energy), and album types, identifying trends, top artists, and outliers. Requests: Used for GET requests to Spotify API Flask: Used for mapping routes for UI and API Gunicorn: WSGI HTTP Server for running on Unix Pandas: Used to store the data Matplotlib: Creates plots of data (/data/:dataname) SKLearn: OneClassSVM Algorithm, used I frequently use Spotify and, as I have experience with the Spotify API, I decided to utilize my extensive history data to interact with the API and conduct data analysis. This Jupyter Notebook consists of me importing data -> sanitizing the data -> posing questions -> answering questions. Dataset. This study explores music trends through a detailed analysis of the Spotify dataset to uncover prevailing trends and predict song popularity using machine learning models. Welcome to the "Spotify Music Analysis - 2023" project! Here, we're exploring a carefully compiled dataset showcasing the standout songs of 2023, as documented by Spotify. To improve query performance, we carried out the following optimization process: Initial Query Performance Analysis Using EXPLAIN. The music business has gone through, several technology driven shakeups over the past 30 years. Automate any Spotify Data Analysis Overview. The project involves working with a The dataset is published and available on Kaggle and the data was extracted from the Spotify API using the Python library Spotipy. Used Excel for data cleaning, MySQL for querying, and Power BI for creating interactive visualizations. Conducted in-depth exploratory data analysis on music-related datasets using Python in Jupyter Notebook, leveraging libraries and functions to derive actionable insights and facilitate data-driven decision-making; identified trends and patterns to optimize marketing strategies and increase customer engagement. The dataset encompasses a wide range of attributes for each song, including but not limited to track name, artist name, release date, popularity, duration, and genre. Reload to refresh your session. It combines advanced statistical techniques, machine learning, and exploratory data analysis to deliver actionable insights for artists, producers, and marketers in the music industry. Spotify-Data-Analysis-Project-using-Python The given data appears to be a tabular representation of various music tracks, including information such as the track name, artist name, genre, beats per minute, energy, danceability, loudness, liveness, valence, length, acousticness, speechiness, and Contribute to shromana98/Spotify-Data-Analysis-Project development by creating an account on GitHub. This dataset included metrics for various popular songs throughout the year of 2023 so far, including track name, artist name, artist count, released year, released month, released day, how many spotify playlists it was in, how many spotify charts it was in, streams, This project showcases an in-depth analysis of a Spotify dataset using SQL. Host and manage packages Security. Our objective with this About Exploratory Data Analysis on "Top Songs of Spotify" between 2010-2022. Utilization of secondary data: Leveraged Spotify's datasets to identify patterns and Contribute to abhii264/Spotify-Data-Analysis development by creating an account on GitHub. Initial steps: Conducted data cleaning and exploratory analysis using Python. This document was made to be part of my portfolio as a data analyst. My_Spotify_Data_Analysis The idea behind this project is to analyze my own song-listening data from spotify and explore my music taste from an Analysts point of view. Contribute to fsioni/spotify-data-analysis development by creating an account on GitHub. Feature Engineering: Selects relevant features to build an accurate recommendation model. We initiate our data collection Welcome to the "Spotify_Data_Analysis_2023_SQL_Project"!This project explores a comprehensive dataset of Spotify's most popular tracks of 2023. Select your data source and follow the prompts to load the data. Contribute to jhrcook/spotify-data-analysis development by creating an account on GitHub. By examining a variety of genres and musical elements, this research aims to offer insights into listeners' preferences and the Spotify-Data-Analysis This repository contains code and resources about a study focused on developing a linear regression model using R programming to predict song energy levels based on various audio features. chmedina. where we will be visualising this dataset and creating a exploratary data analysis. Each row in the dataset corresponds to a track, with variables such as the title, artist, and year located in their respective columns. Additionally, we designed a "Stats" panel to Spotify-Data-Analysis--Power-BI This repository contains a Power BI dashboard that analyzes and visualizes the Spotify dataset. GitHub Gist: instantly share code, notes, and snippets. I will use unsupervised learning techniques, such principal component analysis (PCA), to uncover hidden insights in the data that may not be apparent through manual analysis. These songs are shared in the . The primary objective is to visualize patterns and trends in the music I engage with, ultimately aiming to replicate Spotify's yearly wrapped summary of my most listened-to Spotify is a Swedish audio streaming and media services provider founded in April 2006. Spotify is the worlds largest audio streaming application with services available in more than 175 countries. . This project showcases the role that data plays in making decisions advancing research initiatives and even predicting weather patterns. By using this data, analysts can uncover interesting trends and insights about music, listeners, and the music industry as a whole. You signed out in another tab or window. Spotify-Data-Analysis Exploratory data analysis using Python on music related datasets from Kaggle. Spotify_data_analysis_R Objectif du projet Le but de ce projet est d'analyser un jeu de données à l'aide du langage R. ): 0. spotify data-science sql data-visualization audio-analysis data-visualisation data-analytics sql-database tableau music-analysis tableau-desktop sql-schema data-insights tableau-public tableau-dashboards spotify-data streaming-metrics dashboard-design trends-analysis The data used for this analysis was sourced from Spotify's API, which provides information about songs, artists, albums, and user interactions. Use data to identify patterns and relationships between different characteristics. Open the spotify_dashboard. Data analysis - Exploring the relationship between the audio features of a song and how positive or negative its lyrics are, involving sentiment analysisand manyuy more. If you don't want it to log your songs forever, continue at your own risk. Advanced Querying: Dive deeper into query optimization and explore the performance of SQL queries on larger datasets. Data Refresh (For Power BI Service): If you're using Power BI Service, you can set up scheduled data refresh to keep your dashboard up-to-date. You signed in with another tab or window. I used 25% to test data and 75% to train the data. We access the user playlists, tracks and perform EDA on their audio features, using Numpy, Pandas & Matplotlib. The activity will support learners in developing Repo for Spotify Data Analysis using SQL. Find and fix vulnerabilities Codespaces Contribute to arufak/Spotify-Data-Analysis development by creating an account on GitHub. It covers an end-to-end process of normalizing a denormalized dataset, performing SQL queries of varying complexity (easy, medium, and advanced), and optimizing query performance Conducted data cleaning to perform exploratory data analysis (EDA) and data visualization of the Spotify dataset using Python (Pandas, NumPy, Matplotlib and Seaborn). By leveraging data from these platforms, this analysis aims to uncover valuable insights into Select your data source and follow the prompts to load the data. The analysis is performed using SQL and focuses on answering a series of business-critical questions. Implementation of glass morphism backgrounds for aesthetic appeal. Users can upload their datasets and explore various insights through visualizations and metrics. Performed Data analysis to explore the relationship between the audio features of a song and how positive or negative its lyrics are, involving sentiment analysis and many more This Streamlit app enables users to analyze Spotify streaming data interactively. Data Loading and Exploration: Load Spotify dataset Fetching & Statistical Analysis of Spotify Data. Spotify-data-analysis This project sets out to explore the Spotify dataset, a rich collection of music-related information encompassing various tracks, artists, genres, and more. pbix file using Power BI Desktop. ; Spotify Data Analysis Project Report: Python scripts for data preprocessing and analysis. Repo for statistical analysis of spotify tracks. And understanding what makes streaming music popular could hugely impact decision-making for music business. Made use of libraries like Pandas, Numpy, Matplotlib and Seaborn. In the "Blend" panel, we compared various statistics such as listening time, average tempo of songs, and more. Output folder contains Interactive graph that I obtained from my analysis. T. If the data for the current day already exists, the system ensures no duplicate entries are added to maintain data integrity. Additional data was gathered through Spotify's Open-Source Developers Program. As an avid user of Spotify, I have embarked on a project to analyze and gain insights into my listening habits using data mining techniques and exploratory data analysis. In this project conducted in RStudio using the R programming language, we delved into Spotify data to unravel patterns and insights contributing to music's popularity. Contribute to Mahaswe/spotify-data-analysis development by creating an account on GitHub. Objective. pnyb contains the code for analysis of your streaming history. Conclusion This Spotify Data Analysis offers valuable insights into music trends, highlighting the artists and tracks that have captured the hearts of millions. Find and fix vulnerabilities Codespaces Integration of ChatGPT for enriching Spotify data with Python code. Utilizing Power BI's robust visualization capabilities and DAX expressions, the analysis encompasses a range of visualizations, slicers, and KPI calculations to explore The projects aims to track the most played songs, artists and genres currently using Python and Spotify API - Sandreke/Spotify-Data-Analysis My(sushma's) analysis on understanding musical and vocal acoustics and Data visualizations using corrplot library, and Linear regression with result analysis, Generalized Linear model, Random Forest regression, Permutation Importance and Feature importances analysis only w. Data analysis - Exploring the relationship between the audio features of a song and how Analysis and clustering of popular songs and their attributes using Spotify API. Python's spotipy library is used to get data from Spotify. Conducted data cleaning to perform exploratory data analysis (EDA) and data visualization of the Spotify dataset using Python (Pandas, NumPy, Matplotlib and Seaborn). md at main · tmbuthia/Spotify-data-analysis-using-SQL Includes data collection script that scrapes audio feature data from the Spotify API, as well as lyrical data from the LyricWikiAPI. The dashboard provides insights into various aspects of the music streaming service's data, helping users explore trends, patterns, and The Spotify Data Analysis Project: In todays changing world data analysis has become crucial in fields such, as business, research and meteorology. Spotify Data Analysis makes use of secondary data from Spotify. xlsx). Automate any workflow Packages. Key analysis: Explored correlations between a song's audio features and lyrical sentiment through sentiment analysis. The data used was downloaded on Kaggle and was updated on November 25, 2020. This project involves analyzing a dataset from Spotify to uncover insights into music trends, listener preferences, and other interesting patterns. Spotify Data Analysis. Automate any workflow Security. Contribute to mariomm17/spotify_data_analysis development by creating an account on GitHub. Our system takes as input a subset of songs and their associated metadata, including genre, year, artist, rhythm/tempo, and instrumentation and predicts the popularity of the song. Spotify. Find and fix vulnerabilities Actions. Here, l have explored and quantified data about music and drawn valuable insights. Gained actionable insights into music trends, artist performance, and audience preferences. Instant dev About Exploratory Data Analysis on "Top Songs of Spotify" between 2000-2022. I’ve analyzed different metrics like, top 10 songs, top 10 artists etc; and found some interesting results. The activity will support in developing ability to review and interpret a dataset. Instant dev environments GitHub This project explores the audio features of Radiohead's discography using data obtained from Spotify’s API. 2. ipynb: Jupyter notebooks detailing data preprocessing, exploratory data analysis, and visualizations. The analysis of my Spotify streaming data. Explore Spotify data using Python for music trend analysis. This had to be done using R, writing a report in R markdown. ), album types, streaming counts, YouTube views, likes, and comments. Write better code with AI Security. The activity will support in The Spotify Data Analysis Project showcases data's role in diverse fields, using Python and libraries like Pandas,Numpy,Seaborn and Matplotlib, within the Jupyter Notebook environment. This report explores on different data relations which can be formed from the given dataset. If there is no data for the current day, it fetches artist information and top tracks from the Spotify API, adds it to the SQLite database, and creates a CSV file for easy access and analysis. Use #Spotify Data Analysis Case Study. Recommendation System: Recommends songs based on user-input songs using cosine similarity. Use appropriate visualizations such as bar charts, histograms, and scatter plots to uncover trends and This project performs exploratory data analysis (EDA) on a Spotify dataset using SQL. By examining a variety of genres and musical elements, this research aims to offer insights into listeners' preferences and the I'm sharing an Exploratory Data Analysis (EDA) and Data Visualization of the data from Spotify using Python - A Data Analysis Project performed in my journey into Data Science. Sign in Product Actions. Data Refresh (For Power Overview: This Power BI project offers an in-depth analysis of Spotify data, focusing on various measures and KPIs to gain insights into streaming trends, track popularity, and artist performance. This project involves analyzing a Spotify dataset with various attributes about tracks, albums, and artists using SQL. - shubh-Im/Spotify-Data-Analysis-Project-Using-SQL Spotify Data Analysis Project Analyzed Spotify music data to uncover trends in artist popularity, genre distribution, and song characteristics. Automate any You signed in with another tab or window. 17 ms The project utilized a case-study approach using prior built-in music playback data collected from Spotify’s API given by the Professor. Employing statistical techniques and leveraging the power of In this project, a group of six other students and I in the AISC - UC Davis club analyzed a Spotify dataset from 2023. Utilization of Spotify's API to access album cover image URLs. The Spotify dataset that is used in this project includes audio Instantly share code, notes, and snippets. Through meticulous analysis and creative visualizations, we aim to extract meaningful information and showcase the diverse aspects of music that the dataset encapsulates. Data Loading and Exploration: Load Spotify dataset from a CSV file. Spotify is a digital music streaming service that provides users Data Extraction: Uses Spotify to fetch song data from the Spotify Web API. Find and fix vulnerabilities Actions You signed in with another tab or window. Skip to content. csv) consists of 160,000+ tracks from 1921-2020 found in Spotify as of June 2020. Spotify analysis. import pandas as Explore Spotify data using Python for music trend analysis. Host and manage packages Security The "YouTube and Spotify Data Analysis" project is an exploration of user behavior and trends on two of the most popular streaming platforms: YouTube and Spotify. By analyzing key metrics such as energy, valence (emotional positivity), and loudness, we aim to gain insights into the evolution of Radiohead's sound across their albums. By understanding these patterns, artists and marketers can better strategize their releases and promotions to align with listener preferences and behaviors. Visualize genre popularity, user behaviors, and song features impact. This dataset goes beyond the usual song collections, offering a complete look into each track's characteristics, popularity, and visibility across different music platforms. Collaborator: Ruisheng Wang. Project Overview This project involves analyzing a Spotify dataset containing over 20,000 rows to identify trends in streams, audience engagement, and artist performance across platforms. My(sushma's) analysis on understanding musical and vocal acoustics and Data visualizations using corrplot library, and Linear regression with result analysis, Generalized Linear model, Random Forest regression, Permutation Importance and Feature importances analysis only w. Conducted data cleaning to perform exploratory data analysis (EDA) and data visualisation of the Spotify dataset using Python. Credentials are only valid for 1 hour, so they need to be refreshed as shown in the notebook. Utilize Spotify API for data retrieval and Python libraries for ana Contribute to adeebjamal/Spotify-Data-Analysis development by creating an account on GitHub. , spotify. Project Steps: 1. Here, l The analysis of my Spotify streaming data. com A data visualization curriculum of interactive notebooks using Quarto, Plotly, PowerBi and Google Data Studio. This project involves creating a detailed and visually appealing dashboard using Power BI to analyze the most streamed Spotify songs of 2023. Contribute to Bethu02/Spotify_Data_Analysis development by creating an account on GitHub. This GitHub repository houses a comprehensive data analysis project that explores Spotify data using popular Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn. r. Data analysis - Exploring the relationship between the audio features of a song and how positive or negative its lyrics are, involving sentiment analysisand many more. This project showcases an in-depth analysis of a Spotify dataset using SQL. Data analysis: Investigating the link between a song's auditory elements and how favourable or bad its lyrics are, using sentiment analysis and many more techniques. ): 7 ms Planning time (P. Upload your Spotify dataset in Excel format (e. Project Steps: Data Loading and Exploration: Load Spotify dataset from a CSV file. The objective of this project is to analyze the data set of Spotify's top songs for 2023, containing information such as chart position, danceability, release year, energy percentage, "Welcome to our Spotify notebook, where we will be visualising this dataset and creating a exploratary data analysis. It explores music-related We make requests to the Spotify API for data collection, using the free Python libraries, Spotipy and Requests. md: Overview of the project, steps, tools, and libraries used. Contribute to makispl/Spotify-Data-Analysis development by creating an account on GitHub. To see the interactive document published with Quarto, go to this link: https://spotify. We began by analyzing the performance of a query using the EXPLAIN function. The goal is to uncover insights into music performance and trends. csv file. Utilize Spotify API for data retrieval and Python libraries for ana The data, performing an exploratory data analysis and applying linear modelling to the data. After obtaining training and testing data sets, then we will create a separate data frame from testing data set which has values to be compared with actual final values The data, performing an exploratory data analysis and applying linear modelling to the data. Learners will use data to identify patterns and relationships between different characteristics. import pandas as Machine Learning Project on Spotify Data using K-Means Clustering 🎶🤖. Contribute to Kaushaldev15/Spotify-Data-Analysis development by creating an account on GitHub. Cette analyse utilisera plusieurs outils d'informatique décisionnelle (Business Intelligence), tels que la régression linéaire, l'ACP ou la régression logistique. The dashboard provides key insights into streaming trends, popular tracks, and artists, allowing Contribute to Suryatejaindigimilli/Spotify-Data-Analysis development by creating an account on GitHub. The project involves ETL (Extract, Transform, Load) operations using Azure Databricks, data processing with Python, PySpark, and SparkSQL, and final visualizations in PowerBI. t modelling and feature and permutation importances only: https I'm sharing an Exploratory Data Analysis (EDA) and Data Visualization of the data from Spotify using Python - A Data Analysis Project performed in my journey into Data Science. You switched accounts on another tab or window. Automate any Project focus: Analyzing music data insights using Tableau with Spotify datasets. By doing searches with characters(A-Z, 0-9) 1750 songs were collected. ; The query retrieved tracks based on the artist column, and the performance metrics were as follows: . ; README. Find and fix vulnerabilities Codespaces. Spotify_Data_Track_Analysis. You will learn how to analyze, visualize and draw insights with Contribute to ACV24AS/Spotify-data-analysis development by creating an account on GitHub. Objective: Explore and cluster Spotify music data to identify patterns and group similar tracks. It contains information about over a Million songs from over As a freemium service, Spotify implements multitudes of data learning tools and algorithms to leverage its data and create a streamlined experience for its users, unmatched by its competitors. Understand correlations between musical attributes and popularity metrics like streams. Fetching & Statistical Analysis of Spotify Data. - tmbuthia/Spotify-data-analysis-using-SQL Contribute to Arya-05/Spotify-Data-Analysis development by creating an account on GitHub. Toggle navigation. Music Data Analysis and Visualization Project This project focuses on analyzing and visualizing a dataset containing information about music tracks from Spotify. The dataset includes details about each track's attributes, streaming metrics, and cross-platform visibility, offering a rich resource for analyzing music trends. Contribute to vidhya3142/Spotify-Data-Analysis-Pipeline-Using-AWS development by creating an account on GitHub. Contribute to CamilleMagnette/Spotify-Data-Analysis-using-Python development by creating an account on GitHub. Performed data cleaning, visualization, and statistical testing in R on Spotify’s Global Top 50 songs. Contribute to spotify-nlp/spotify-data-analysis development by creating an account on GitHub. jxko sijc sichnkkqz hkg cxvddmxg aywahxa tadys jjus ronkep vbpwq