🎧 Spotify Data Analysis Project

This project explores and analyzes Spotify's top songs dataset from 2023 and previous years, offering insights into trends, artist performances, streaming patterns, and more. Using Python libraries such as Pandas, Matplotlib, and Seaborn, we extract meaningful visualizations and statistical summaries from over 950 songs. 📊 Features

Data Cleaning & Preprocessing
Handled missing values and formatted data for accurate analysis.

Descriptive Statistics
Generated comprehensive statistical summaries including artist count, release years, and playlist/chart appearances.

Exploratory Data Analysis (EDA)

    Most featured artists and their frequency

    Number of songs released per year

    Distribution of stream counts

    Relationships between various features (e.g., artist count vs. streams)

Visualizations
Included histograms, box plots, line plots, count plots, and pair plots to uncover trends and patterns in the data.

📁 Dataset

Source: [Spotify Top Tracks 2023]

Rows: 953 songs

Columns: 24 features including track_name, artist(s)_name, streams, bpm, energy_%, and more.

🛠️ Tools & Libraries

Python

Pandas

NumPy

Matplotlib

Seaborn

📈 Key Insights

Taylor Swift, The Weeknd, and Bad Bunny were among the most frequent artists.

2022 and 2023 saw the highest number of releases in the dataset.

Stream counts vary widely, with several outliers skewing the distribution.

Most songs had a single artist, but collaborative tracks also performed well.

📌 Future Enhancements

Predictive modeling to forecast song popularity

Genre-based analysis