Career Recommendation System Using ML & Flask – Personalized Roadmaps for Students and Professionals

#machinelearning #flask #python #career

Introduction
Selecting the most suitable profession is among the most important decisions for both working professionals and students. Given the huge number of job positions, sectors, and educational qualifications, career guidance seems intimidating and perplexing. To mitigate this difficulty, I created a Career Recommendation System with Flask (Python) and a standardized JSON dataset to assist users in getting personalized career recommendations based on their interests and education. The objective is to provide an intelligent, inclusive, and interactive facility that can automatically create career roadmaps to facilitate easier planning of professional careers. The facility enables users to navigate various professions via a minimalist web interface and generates step-by-step directions for selected occupations. Whether you're a college graduate, high school student, or career changer, this tool provides you with a launching point and a direction all driven by AI concepts and lightweight web technologies.

Key Features
The Career Recommendation System is designed to make career decision-making easier through a simple, swift, and smart interface. One feature that differentiates it from others is the dynamic career path generation — users can pick their current level of education, and the system automatically trims down applicable career paths. For every chosen path, it creates a step-by-step guide, taking users from where they are now to what they want to be. The application features an easy-to-use and interactive user interface that allows users to select from dropdown menus without getting perplexed. With Flask powering the backend, it delivers quick response times as well as undisturbed data delivery. Having a JSON-based database allows the system to be easily maintained and scaled with additional career fields or steps. The software is light, deployable on any low-level server, and has minimal setup, so it's perfectly suitable for application in schools, colleges, or career sites. Each feature is created with simplicity, speed, and ease of use.

Novelty / Uniqueness
In contrast to static career guidance resources, this tool implements a dynamic and interactive career planning model. It doesn't merely post job titles—it builds real-time customized career maps based on the user's level of education. Through the use of a well-organized dataset, it allows the application to make smart decisions and react in real time to the inputs of the user. Another distinct feature is its emphasis on vocational learning—implying skill-based courses related to relevant fields of study along with career tracks, prompting users to make actionable steps towards employability. The real-time creation of career trajectories provides an engaging component that questionnaire-based or paper-based tools cannot offer. Further, the design of the system supports modular growth, i.e., additional categories, roles, and skills can be incorporated without redesigning the backend. It also facilitates future integration of AI/ML models to enable smarter predictions. This combination of tangible usefulness, smart recommendations, and future-proofed architecture sets it apart from traditional guidance apps.

Tech Stack & Setup
The Career Recommendation System is implemented using Core Python and Flask for handling backend routes and logic. Flask was used for its light weight, flexibility, and speed, making it suitable for small to medium web applications. Frontend is implemented using HTML and CSS and is minimalistic and user-friendly. Data storage and career logic are implemented using a JSON file, and it serves as a mock database, making the application portable and easy to scale.

Prerequisites:
Python 3+
Flask (pip install flask)
A modern web browser

Setup Instructions:

Step 1: Clone the repository
git clone: https://github.com/GudepuRakshitha/Career-Recommendation-System-by-using-ML-flask-API

Step 2: Navigate to the project directory
cd career-recommendation-system

Step 3: Install Flask
pip install flask

Step 4: Run the Flask app
python app.py
Once running, open your browser and navigate to:
http://127.0.0.1:5000/
to begin exploring career options.

Project Structure

career-recommendation-system/
│
├── app.py # Main Flask backend code
├── career_data.json # JSON database of career choices & roadmaps
├── templates/
│ └── index.html # User interface for interaction
└── static/ # (Optional) Images and CSS

This organization maintains a tidy separation between frontend and backend. app.py deals with all routing logic, career_data.json contains dynamic data, and the templates directory is where the UI lives. You can readily grow the project by introducing additional categories to the JSON file or adding more to the frontend via Bootstrap or JavaScript.

Sample Output Images
[(https://drive.google.com/file/d/1loRui7R6nPXeBl4IrbZYYxPKqBKiBFzc/view?usp=sharing)]

Future Improvements

Integration of Machine Learning: Apply user data and interests to forecast best career options.
Database Migration: Move from JSON to SQL/NoSQL for improved performance and scalability.
Real-time Trends: Incorporate APIs to represent today's job market needs and skills.
AI Chatbot: Incorporate an interactive chatbot for voice/text career guidance.
These updates will make the system more intelligent, scalable, and industry specific.

Author Info & Links