Spotify Data Analysis App

This Streamlit app enables users to analyze Spotify streaming data interactively. Users can upload their datasets and explore various insights through visualizations and metrics.

Features

1. Seasonal Popularity Analysis

   • Visualize the total streams for each month from January to December.

2. Audience Preferences

   • Explore the average audience preferences based on attributes like danceability, energy, and more.

3. Streaming Longevity

   • Analyze the average longevity of tracks in weeks, grouped by genre and artist.

4. Chart Placement Impact

   • Understand how a track's chart position affects its average streams.

5. Emerging Genres

   • Identify emerging genres based on total streams.

6. Top Artists

   • View the top 10 artists by total streams.

7. Genre Popularity

   • Explore the popularity of genres using a pie chart of total streams.

Requirements

• Python 3.8+
• Streamlit
• pandas
• seaborn
• matplotlib
• plotly
• openpyxl (for handling Excel files)

Installation

1. Clone this repository:

   git clone <repository_url>
   cd spotify-data-analysis

2. Install the required packages:

   pip install -r requirements.txt

3. Run the app:

   streamlit run app.py

Usage

1. Upload your Spotify dataset in Excel format (e.g., spotify.xlsx).
2. The app will process and visualize the data, providing insights through:
   • Bar charts
   • Line plots
   • Pie charts
   • Scatter plots
3. Interact with the visualizations and gain insights.

Dataset Requirements

• The dataset should be in Excel format.
• Required columns:
  • date_release: Release date of the track.
  • Streams: Total streams of the track.
  • genre: Genre of the track.
  • artist_name: Name of the artist.
  • Additional attributes for audience preferences (e.g., acousticness, danceability, energy, etc.).