Personalized Music Recommendation Engine

Project Collaboration & Assistance: Music Recommendation Engine (Data Science)
I am looking for assistance or collaboration on building a Music Recommendation Engine in Python.

Workflow & Structure (Step-by-Step):
Weekly Task-Based Approach: The project will not be built all at once. Instead, we will work with a new task or milestone every week, adapting to the specific roadmap and requirements week by week.

What the project involves:
Data Preprocessing: Working with a clean Spotify dataset (via Kaggle) containing track audio features (tempo, energy, danceability, acousticness, etc.).

Recommendation Algorithms: Implementing classic similarity metrics (such as Cosine Similarity or Content-Based/Collaborative Filtering) to recommend similar tracks based on user preferences.

User Interface: Building a clean, interactive web dashboard using Streamlit to search for tracks, view recommendations, and display visual insights.

Requirements & Deliverables:
Clean & Documented Code: Well-structured Python code using standard data science libraries (Pandas, Scikit-learn, etc.).

Research & Resources: Assistance in compiling a list of at least 10 relevant references/resources, adhering to these guidelines:

A brief description of the contribution of each resource to the project.

Project Summary/Report: A short technical report summarizing the model and results.

The goal is a focused, high-quality, and robust Data Science application that runs smoothly and looks great.