Create Your Own Job Recommendation Web App with Python & Cosine Similarity

Job Recommendation System Using Cosine Similarity | Streamlit Web App

In this video, I’ll walk you through the process of creating a Job Recommendation System using Cosine Similarity and building an interactive Streamlit web app to present it. We’ll cover everything from data preprocessing, feature engineering, model selection, and implementation.

What’s Covered:
Introduction: A brief overview of the job recommendation system and the problem we are solving.
Data Preprocessing: I’ll show how to clean and prepare the data for analysis.
Feature Engineering: Learn how to extract key features from job descriptions and user profiles to build meaningful recommendations.
Cosine Similarity: We dive into the mathematics behind Cosine Similarity to calculate the similarity between job listings and users.
Model Selection: I explain why we chose Cosine Similarity for this recommendation task and how it works with text data.
Building the Streamlit App: I demonstrate how to convert the recommendation system into a functional web app using Streamlit.
Tools & Libraries:
Cosine Similarity for measuring the similarity between job descriptions.
Streamlit for creating the interactive web app.
Python Libraries like Pandas, Scikit-learn, and Numpy for data manipulation and model building.
By the end of this video, you'll have a fully functioning job recommendation web app that can suggest jobs based on user input. Whether you're looking to improve your data science skills or build a practical project, this tutorial is perfect for you.

Project link - github.com/ritikbh193/Job-Recommendations-website/…

If you enjoyed the video or have any questions, feel free to leave a comment below!