Alumni Recommendation System using Cosine Similarity

The Alumni Recommendation System using Cosine Similarity is an innovative project designed to enhance alumni engagement and networking through advanced data analytics techniques. By leveraging the cosine similarity algorithm, this system analyzes alumni profiles to identify similarities based on skills, interests, and career paths.

Overview

The project involves several key components:

1. Data Collection: Gathering alumni profiles and relevant data points such as skills, interests, and career trajectories.
2. Cosine Similarity Analysis: Employing the cosine similarity algorithm to quantify the similarity between alumni profiles.
3. Recommendation Engine: Generating personalized recommendations for alumni connections based on similarity scores.
4. User Feedback Integration: Incorporating user preferences and feedback to refine recommendations and improve accuracy over time.

Motivation

Alumni engagement is vital for educational institutions and organizations to foster a strong sense of community and support among former students. Traditional methods of alumni networking often lack personalization and effectiveness. This project aims to address this challenge by leveraging data analytics to facilitate more meaningful and relevant connections among alumni.

Features

• Cosine Similarity Algorithm: Analyzing alumni profiles to identify similarities and recommend connections.
• Personalized Recommendations: Providing tailored recommendations based on individual skills, interests, and career paths.
• Continuous Refinement: Incorporating user feedback to improve the accuracy and relevance of recommendations over time.

Usage

To use this project, follow these steps:

1. Data Input: Input alumni profiles and relevant data points into the system.
2. Run Cosine Similarity Analysis: Execute the cosine similarity algorithm to calculate similarity scores.
3. Generate Recommendations: Use the recommendation engine to generate personalized connections for alumni.
4. Feedback Integration: Incorporate user feedback to continuously refine and improve the recommendation engine.

Installation

Clone the repository and install any necessary dependencies:

git clone https://github.com/yourusername/Alumni-Recommendation-System.git
cd Alumni-Recommendation-System
pip install -r requirements.txt