The Spotify Trends 2023 Analysis project aims to explore and analyze the latest trends in music on the Spotify platform for the year 2023. By leveraging data science and machine learning techniques, we seek to uncover insights into user preferences, popular genres, and emerging artists.

The project is organized into the following main components:

• data: Contains the dataset used for analysis.
• notebooks: Jupyter notebooks for data exploration, preprocessing, modeling, and visualization.
• models: Saved machine learning models.
• images: Images and visualizations generated during the analysis.
• src: Source code files.

The key technologies and libraries used in this project include:

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• TensorFlow
• Scikit-learn
• Spotipy (Spotify API wrapper)

The dataset used in this analysis consists of Spotify user listening data for the year 2023. It includes information about track popularity, user preferences, and artist details.

10,000 records

• Handling missing values
• Removing duplicates
• Feature engineering

1. Data Exploration:

   • Understanding the distribution of features
   • Identifying patterns and outliers

2. Data Preprocessing:

   • Cleaning and transforming data for modeling
   • Feature scaling and normalization

3. Modeling:

   • Implementing a recommendation system using collaborative filtering
   • Training machine learning models for genre classification

4. Evaluation:

   • Assessing the performance of the recommendation system
   • Evaluating genre classification models

5. Visualization:

   • Creating interactive visualizations to showcase trends

• Collaborative Filtering with Matrix Factorization

• Convolutional Neural Network (CNN)

The analysis revealed several interesting trends:

• Top genres among Spotify users
• Emerging artists gaining popularity
• User preferences based on geographical location

Visualizations and detailed results can be found in the results directory.

• Incorporate more features for genre classification
• Implement user segmentation for personalized recommendations
• Explore additional data sources for a more comprehensive analysis

Follow these steps to reproduce the analysis:

1. Clone the repository:

   git clone https://github.com/your-username/spotify-trends-2023.git
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

MIT License

Copyright (c) 2023 Pınar

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