Data Science Web Driven Development

Using Open Source Technologies

Description

The job recommendation system is a web application designed to provide personalized job recommendations to users based on their preferences and behavior. It employs various machine learning algorithms, including collaborative filtering and natural language processing (NLP), to suggest relevant job listings.

Machine Learning: Developing algorithms and statistical models that enable computers to perform tasks without explicit instructions, but rather by learning patterns from data.

Data Science: Extracting insights and knowledge from data through various techniques and tools, including ML, statistics, and domain-specific expertise.

Supervised Machine Learning Models Used

1. Collaborative Filtering: Recommends jobs based on user interactions and preferences.
2. Natural Language Processing (NLP): Analyzes job descriptions and user profiles to identify similar jobs by text classification.
3. K Nearest Neighbor (KNN)

Technologies Used

• Python/Django: Backend development framework.
• JavaScript/HTML/CSS: Frontend development.
• Pandas, NumPy: Data manipulation and analysis.
• Scikit-learn: Machine learning library.
• SQLite/PostgreSQL: Database management.
• Bootstrap/Crispy Forms: Frontend styling.
• TfidfVectorizer: Feature extraction for NLP.
• NearestNeighbors: Model for finding similar items.
• Linear_kernel: Computes cosine similarity.
• Django Forms: User input handling.
• Django Authentication: User authentication.

Functionality

The app allows users to:

• Browse job listings.
• View detailed job descriptions.
• Receive personalized job recommendations based on their profile and past interactions.
• Explore similar jobs based on job category and description.
• Apply for jobs directly through the platform.

Real-World Application

In real-world scenarios, this app can serve as a valuable tool for job seekers by:

• Providing tailored job suggestions, saving time in the job search process.
• Enhancing user experience on job platforms.
• Improving job matching accuracy between candidates and employers.
• Increasing user engagement and retention on job portals.

Libraries Used

• Pandas
• NumPy
• Scikit-learn
• Django
• TfidfVectorizer
• NearestNeighbors
• Linear_kernel
• Django Forms
• Django Authentication

Features

• User registration and authentication.
• Job browsing and detailed job view.
• Personalized job recommendations.
• Similar job suggestions based on NLP analysis.
• Application for jobs directly through the platform.
• User profile management.

Future Improvements

• Integration with external job APIs for real-time data.
• Enhanced NLP techniques for better job similarity analysis.
• Incorporation of user feedback for improved recommendations.
• Implementation of advanced recommendation algorithms for better performance.

Video Series

1. Data Science Web Development Job Recommendation System Python Django Streamlit Part 1
   Watch on YouTube
2. Data Science Web Development Job Recommendation System Python Django Streamlit [Admin Theme] Part 2
   Watch on YouTube
3. Data Science Web Development Python Django Streamlit Model Training and Prediction Part 3
   Watch on YouTube
4. Data Science Web Development Python Django Streamlit [View, URL and Template Rendering] Part 4
   Watch on YouTube
5. Data Science Web Development Python Django Streamlit How to generate 10000 Fake Dataset CSV Part 5
   Watch on YouTube
6. Data Science Web Development Theory. Project Overview Episode 6
   Watch on YouTube