Spotify EDA Project

Project Overview

The repo contains code for a project in my MSc course, which explains what I have done as part of the programme, which explains my ability in intermediate data analysis and visualization using Python.

This project conducts an exploratory data analysis (EDA) on a Spotify dataset to uncover insights about musical features, trends, and patterns. The analysis leverages Python and libraries like pandas, matplotlib, and seaborn.

Dataset

The dataset includes various musical attributes of songs sourced from Spotify’s Web API, such as acousticness, danceability, energy, and tempo.

Analysis Steps

1. Data Preprocessing: Load and clean the data.
2. Descriptive Statistics: Summarize numerical and categorical features.
3. Data Visualization: Visualize distributions and relationships of features.
4. Correlation Analysis: Examine correlations between musical attributes.
5. Genre Analysis: Explore characteristics of different genres.
6. Trend Analysis: Identify temporal trends in music features.

Key Results

• Distribution Insights: Visualizations of feature distributions like danceability and energy.
• Correlation Findings: Analysis of relationships between different musical attributes.
• Genre Insights: Comparison of musical features across genres.
• Temporal Trends: Observations on how music features have changed over time.

Technical Skills

• Data Manipulation: Efficient data handling with pandas.
• Visualization: Creating visual insights using matplotlib and seaborn.
• Statistical Analysis: Summarizing and interpreting data features.
• Python Programming: Writing clean and efficient Python code.