/Recommender-System

The Recommender-System project is a machine learning-based application designed to predict user preferences and provide personalized recommendations. It leverages various algorithms, such as collaborative filtering and content-based filtering, to analyze user data and suggest relevant items. The project also includes a CI/CD pipeline for automating

Primary LanguagePython

Recommender-System

Overview

The Recommender-System project implements a robust recommendation engine for predicting user preferences. This project serves as a learning path for developing and deploying a recommender system using various data processing and machine learning techniques.

System-Architecture

Tech Stack

  • Python: Core programming language for implementing the recommendation algorithms.
  • Pandas: Data manipulation and analysis library.
  • NumPy: Supports large, multi-dimensional arrays and matrices.
  • SciKit-Learn: Machine learning library for implementing algorithms.
  • Surprise: A Python library for building and analyzing recommender systems.
  • Jupyter Notebook: An open-source web application to create and share documents with live code, equations, visualizations, and narrative text.
  • Flask: Micro web framework used to deploy the model as a web service.
  • Docker: Containerizes the application for consistent deployment.
  • GitHub Actions: Automates CI/CD pipeline for the project.
  • Kubernetes: Orchestrates containerized application deployment.

Output

Here are some examples of the outputs generated by the Recommender-System:

Output Example 1

Output Example 2

Pipeline Flow

  • Data Collection: Gathering and preprocessing data.
  • Model Training: Using collaborative filtering, content-based filtering, or hybrid models.
  • Evaluation: Evaluating the model’s performance using metrics like RMSE or precision.
  • Containerization: Containerizing the application using Docker.
  • CI/CD Pipeline:
    • Code Commit & Push: Code is pushed to the GitHub repository.
    • GitHub Actions Trigger: GitHub Actions triggers the pipeline upon code push.
    • Build & Test: Builds the application and runs tests.
    • Docker Build: Builds a Docker image of the application.
    • Push Docker Image: Pushes the Docker image to a container registry.
    • Deploy to Kubernetes: Deploys the application to a Kubernetes cluster.
    • Monitoring & Logging: Set up Prometheus and Grafana for monitoring and logging the application’s performance.