Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/abinashsahoo007/project-song-recommendation-system
This Project is a Simple Content-Based Song Recommendation System. It suggest similar item to the user based on the content the user provide.
https://github.com/abinashsahoo007/project-song-recommendation-system
correlation cosine-similarity data-mining dbscan-clustering deployment eda heirarchical-clustering k-means-clustering pandas-profiling pca pickle recommender-system statistics streamlit visualization
Last synced: about 2 months ago
JSON representation
This Project is a Simple Content-Based Song Recommendation System. It suggest similar item to the user based on the content the user provide.
- Host: GitHub
- URL: https://github.com/abinashsahoo007/project-song-recommendation-system
- Owner: abinashsahoo007
- Created: 2024-06-30T05:47:55.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-06-30T11:10:48.000Z (6 months ago)
- Last Synced: 2024-07-03T00:35:31.250Z (6 months ago)
- Topics: correlation, cosine-similarity, data-mining, dbscan-clustering, deployment, eda, heirarchical-clustering, k-means-clustering, pandas-profiling, pca, pickle, recommender-system, statistics, streamlit, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 13 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Project-Song-Recommendation-System
Recommendation systems in the present have the main objectives of providing personalized suggestions to users, making their lives easier, and helping them make appropriate decisions. These systems aim to suggest items or resources that are most relevant to the user's interests and needs.
## Project Presentation:
[View the ppt](https://docs.google.com/presentation/d/1EX0RMsQkldZa8Ix7pR0USXYfs5zdl7wo/edit#slide=id.p1)
# Business Problem:
Developing an innovative music recommendation system that effectively predicts and suggests personalized music playlists to users based on their individual preferences, listening history, mood, and contextual factors, thereby enhancing user engagement, satisfaction, and retention within our music streaming platform.
>
The objective of this project is to build a feature of recommendation system to support a music app. As the first phase we need to develop the proof of concept to make the client understand how effective the feature could be.
# Objective:
The primary objectives of this Music Recommendation System project are as follows:
1. **User Personalization:** To create a personalized experience for users by recommending tracks based on their individual tastes and listening habits.
Feature Utilization: To effectively use the features available in the Spotify dataset, such as acoustic properties and metadata, to inform the recommendation algorithms.
2. **Model Accuracy:** To develop a Machine Learning model that accurately predicts user preferences, aiming for high precision and recall in the recommendations.
3. **Scalability:** To ensure the system can handle a large number of users and songs without a decline in performance.
4. **User Engagement:** To increase user engagement by providing relevant song recommendations that would encourage further interaction with the service.
5. **Algorithm Diversity:** To explore and implement different recommendation algorithms and evaluate their effectiveness for this specific application.
6. **Data Analysis:** To perform comprehensive data analysis to understand user behavior and song popularity, which in turn can improve the recommendation engine.
7. **Continuous Learning:** To implement a system that learns over time, improving its recommendations as it gains more data on user preferences.
# Project Flow:
# Model Building using cosine similarity:
Cosine similarity measures the similarity between two vectors of an inner product space. It is measured by the cosine of the angle between two vectors and determines whether two vectors are pointing in roughly the same direction. It is often used to measure document similarity in text analysis and recommendations.
# Final Deployment Page:
## Thank You for Visiting....