This project focuses on performing Exploratory Data Analysis (EDA) on my Spotify streaming history data. The analysis aims to uncover patterns, trends, and insights into my listening habits over different years.

The project is organized into the following sections:

1. Data Loading:

   • The Spotify streaming history data is loaded from separate datasets representing different years.

2. Data Cleaning:

   • Missing values are handled appropriately.
   • Timestamps are converted to datetime objects.
   • Anomalies in the data are explored and cleaned.

3. Data Exploration:

   • Distribution analysis of streaming durations, platforms, and countries.
   • Identification of top tracks, artists, and albums.
   • Analysis of streaming habits over time.

4. Visualization:

   • Creation of visualizations using Matplotlib and Seaborn to represent findings.

5. Summary and Insights:

   • Key insights and observations drawn from the analysis.

• /main.ipynb: Jupyter notebook containing the code for each step of the analysis.
• /my_spotify_data: Folder containing the Spotify streaming history datasets for different years.
• /readme.md: This file provides an overview of the project.
• /final_report.md: This file offers a comprehensive analysis report.