https://github.com/percival33/machine-learning-engineering
Uni project about enhancing fictional music streaming service, by developing machine learning models to generate popular playlists
https://github.com/percival33/machine-learning-engineering
data-analysis data-science machine-learning python
Last synced: 11 months ago
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Uni project about enhancing fictional music streaming service, by developing machine learning models to generate popular playlists
- Host: GitHub
- URL: https://github.com/percival33/machine-learning-engineering
- Owner: Percival33
- License: mit
- Archived: true
- Created: 2023-11-19T14:32:23.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-23T23:48:19.000Z (about 2 years ago)
- Last Synced: 2025-03-16T20:13:54.484Z (about 1 year ago)
- Topics: data-analysis, data-science, machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 2.34 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Machine Learning Engineering
## Team
- Miłosz Mizak
- Marcin Jarczewski
## Project Overview
This project is a part of the Machine Learning Engineering at Warsaw University of Technology course aimed at developing a practical solution for "Pozytywka," an online music streaming service. As analysts, we step into the role of tackling a vaguely described task, requiring us to specify details for implementation. The challenge involves understanding the problem, analyzing data, and sometimes negotiating with management (tutor) to ensure the models are production-ready and future-proof for subsequent versions.
### Data Collection
"Pozytywka" collects data crucial for this project, including:
- A list of available artists and music tracks
- A user database
- User session history
- Technical information regarding the caching level of individual tracks
## Task
Extend the "Pozytywka" service by generating popular playlists – sets of matching songs tailored to capture the interest of a broad audience. This initiative aims to enhance user engagement by offering compilations based on the most popular music genres, updated weekly with 10 to 20 songs each.
## Project Phases
### Stage 1
- Define the business problem, modeling tasks, assumptions, and success criteria.
- Analyze the provided data to assess sufficiency for task realization, identifying any gaps or requirements for additional data.
Report available [here](https://github.com/Percival33/Machine-Learning-Engineering/blob/main/reports/JarczewskiMizak_05wariant02_etap1.ipynb) (in Polish).
### Stage 2
1. **Model Development:**
- Develop a baseline model (the simplest possible for the given task).
- Develop an advanced target model.
- Report detailing the model building process and comparing results.
2. **Application Implementation:**
- Implement an application (as a microservice) that:
- Serves predictions using the developed model.
- Conducts an A/B experiment comparing both models and collects data for later quality assessment.
3. **Demonstration Materials:**
- Provide materials showing the implementation is functional.
Report available [here](https://github.com/Percival33/Machine-Learning-Engineering/blob/main/reports/JarczewskiMizak_05_wariant02_etap2.ipynb) (in Polish).
Project based on the cookiecutter data science project template. #cookiecutterdatascience