Symphony - END to END Data Science and Machine Learning Project

Symphony is a full-stack Data Science project which utilizes Machine Learining, API Integration, frontend and Backend development to create an interactive Webapp for songs and playlist recommendations. Additionaly, a Tableau dashboard is embedded in the webapp is dockerised and a CI/CD pipeline has been setup as well. The Webapp is deployed on Amazon Web.

Technologies Used in this project

,

• Python and it's Analytical and Visualisation libraries such as Pandas, Numpy, Matplotlib etc.
• Machine Learning Libraries such as Scikit-learn.
• Spotify API
• Tableau for in-depth genre Analysis Dashboard.
• Flask for server side and API development.
• Docker and Amazon Web Services(AWS) for CI/CD pipleline.
• Javascript, React, HTML, CSS for frontend Development.

Methods Used

• Inferential Statistics
• k-means Clustering
• Data Visualization
• Classification
• Exploratory Data Analysis

Project Description

The ml model, EDA jupyter notebook and flask server are in the ML model and backend folder. The react and Java Script frontend Website is in the client folder.

Here is a screen recording of the working Webapp:

Symphony-made.by.Chaitanya.-.Google.Chrome.2023-07-07.20-24-35.mp4