https://github.com/codewithcharan/spotify-recommender-project-fastapi

https://github.com/codewithcharan/spotify-recommender-project-fastapi

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• Host: GitHub
• URL: https://github.com/codewithcharan/spotify-recommender-project-fastapi
• Owner: CodeWithCharan
• Created: 2024-02-26T17:00:56.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-02-27T18:10:42.000Z (11 months ago)
• Last Synced: 2024-02-28T18:42:40.635Z (11 months ago)
• Language: Jupyter Notebook
• Size: 24.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Spotify Recommendation System using FastAPI

In this project, I've built an spotify recommendation system to individual preferences. Just type your favorite song name and year, it will give you a list of music recommendations based on your preferences.

To do this, I performed various tasks, including Feature engineering, Exploratory Data Analysis (EDA), fetching data using Spotify Web APIs, Model development and Model serving using FastAPI

## Video Presentation

https://github.com/CodeWithCharan/Spotify-Recommender-Project-FastAPI/assets/106027109/fdf95696-be0c-4442-b771-ee8b0424a0b1

## DATASET

This dataset is taken from : https://www.kaggle.com/datasets/vatsalmavani/spotify-dataset/data


## Acknowledgements

`Thanks to Spotify Web API that makes it easy for developers to fetch data and query Spotify’s catalog for songs`

## Installation

1. Clone the repository:

   ```

   git clone https://github.com/CodeWithCharan/Spotify-Recommender-Project-FastAPI.git

   ```

2. Create a `virtual environment` (optional): [Virtual Environment Set Up](https://github.com/CodeWithCharan/virtual-env-setup)

3. Download `Postman`: 

    - Go to https://www.postman.com/downloads/

    - Choose your desired platform among `Mac`, `Windows` or `Linux`.

    - Click `Download`.

4. Install the required dependencies:

    ```

    pip install -r requirements.txt

    ```

5. Start the `server` in the terminal:

    ```

    uvicorn main:app --reload  

    ```

6. Open `postman` and `Add request`

7. Uvicorn will be running on `localhost:8000`, so paste this URL

8. Select `POST`, `Body`, `raw`, `JSON` and then paste the `sample.json` data in the raw block

## Usage

Now, Enter your favorite `song name` and `year` to get personalized `recommendations`.