Spotify EDA Project

This project is an Exploratory Data Analysis (EDA) of the Spotify Songs dataset, which is a publicly available dataset containing information on various songs available on the Spotify platform. The objective of the project is to perform data analysis and visualization on the dataset to gain insights into the popularity and characteristics of various songs on Spotify. To achieve this, the project was implemented using Python programming language and popular data analysis libraries such as pandas, matplotlib, seaborn, and plotly. The analysis involved loading and preprocessing the dataset, performing EDA on various features such as popularity, duration, and audio features, and visualizing the data using various charts and plots.

Through the project, several key insights were gained into the popularity and trends of songs on Spotify. For instance, we found that the most popular songs tend to be relatively short in duration, have a high energy level, and relation between tempo and danceability. We also found that there is a positive correlation between a song's popularity and its tempo, valence, and instrumentalness. The methods used in the project included data cleaning and preprocessing, exploratory data analysis, data visualization, and statistical analysis. Some of the popular visualization techniques used in the project include bar charts, line charts, scatter plots, heatmaps, and boxplot.

Key Learnings:

• Loading and preprocessing data using pandas
• Performing EDA on a large dataset
• Visualizing data using matplotlib, seaborn, and plotly
• Gaining insights into the popularity and trends of songs on Spotify

Overall, this project provided an excellent opportunity to learn and apply various data analysis and visualization techniques using real-world data. By completing this project, I have gained valuable skills that can be applied to a wide range of data analysis tasks. Information such as code, visuals and more can be accessed from here Spotify EDA.