Domain:

Artificial Intelligence (AI) & Machine Learning (ML)
Sub-Domains: Data Science, Natural Language Processing (optional), Recommender Systems

 Overview:

This system is designed to help students or job seekers identify the most suitable career paths based on their skills, academic background, interests, and preferences. By using machine learning algorithms, the system analyzes user data and maps it to relevant careers or roles in the job market.

Purpose and Importance:

• Problem Addressed: Many students struggle with career decisions due to lack of guidance or awareness about available options.

• Solution: This system acts as a virtual career counselor using AI to suggest personalized and data-backed career options.

• Impact: Helps reduce career mismatches and boosts confidence in career planning.

   Technology Stack:

• Frontend: HTML/CSS, React or Bootstrap (optional UI)

• Backend: Flask or Django (Python)

• ML Algorithms: K-Nearest Neighbors (KNN), Decision Tree, or a simple Neural Network

• Database: SQLite or MongoDB

• Libraries: Pandas, Scikit-learn, NumPy, Matplotlib (for analysis), NLTK (if using NLP)

• Optional APIs: LinkedIn Jobs API, Coursera/EdX APIs for learning path suggestions

Key Features:

1. User Profile Input:

   • Education level

   • Subjects of interest

   • Technical and soft skills

   • Hobbies or extracurriculars

2. Career Prediction Engine:

   • ML model trained on datasets (e.g., job listings, academic performance data)

   • Recommends 3–5 career paths ranked by suitability

3. Learning Path Suggestion:

   • Suggests online courses or certifications to prepare for the recommended careers

4. Dashboard:

   • Visualization of career match score

   • Trends in related job markets (optional)

5. Resume Analysis (Advanced Optional):

   • NLP model to scan uploaded resume and make recommendations accordingly

Data Sources:

• Public career datasets (e.g., Kaggle, CareerBuilder)

• Job portal data (scraped or via APIs)

• Career role descriptions from O*NET or LinkedIn

Machine Learning Workflow:

1. Data Collection: Gather student profiles and career outcomes

2. Preprocessing: Clean, normalize, and encode data

3. Model Selection: Use classification or clustering algorithms

4. Training & Testing: Train the model on 70% of the data, test on the remaining

5. Evaluation: Use accuracy, precision, and recall metrics

6. Deployment: Use Flask/Django to deploy the model as a web app