🚀 CourseFinder – AI Course Recommendation & Skill Gap Analyzer 🎓📈

CourseFinder is an AI-powered full-stack web application that helps learners discover the right online courses and identify missing skills required to achieve their career goals.

It intelligently combines Course Recommendation and Skill Gap Analysis using Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML).

The platform integrates courses from Udemy and Coursera, ensuring platform diversity, personalized learning, and career-oriented recommendations.

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🌟 Why CourseFinder?

🔹 Learners feel overwhelmed by thousands of online courses 🔹 Existing platforms rely mainly on keyword search or popularity 🔹 Most systems ignore career goals and skill gaps

👉 CourseFinder bridges this gap by recommending courses that truly help users level up their skills and careers.

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🎯 Project Objectives

• ✅ Build an intelligent AI-based course recommendation system
• ✅ Identify skill gaps based on career goals
• ✅ Integrate multiple learning platforms (Udemy + Coursera)
• ✅ Provide explainable and transparent recommendations
• ✅ Deliver a clean, intuitive, and user-friendly interface

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✨ Key Features

🔍 AI Course Recommendation System

• 🧠 NLP-based recommendations using TF-IDF Vectorization
• 📐 Similarity measurement using Cosine Similarity
• 🎓 Courses from Udemy and Coursera
• 🎚️ Filter by difficulty (Beginner / Intermediate / Advanced)
• ⚖️ Platform-balanced recommendations
• 🔗 Clickable course cards redirect to official platforms
• 🧩 Explainable AI – shows why a course is recommended

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🧠 Next Level Up – Skill Gap Analysis

• 🎯 Career-oriented learning paths (Data Scientist, ML Engineer, etc.)
• 📊 Compares user skills vs job-required skills
• 🔍 Identifies missing skills (Skill Gaps)
• 📚 Recommends courses to bridge the gap
• 📈 Clear insights for career planning

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👤 User Management

• 🔐 User Authentication (Register / Login)
• 👤 Profile Management
• ⭐ Bookmark favorite courses
• 📊 Personalized Dashboard

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📊 Visualization & Analytics

• 📈 Course difficulty distribution charts
• 📊 Interactive dashboards using Chart.js
• 🧠 Skill-gap insights with visual analytics

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🏗️ Project Structure

Course-Finder/
│
├── backend/
│   ├── app.py                  # Flask application entry point
│   ├── recommender.py          # Course recommendation logic
│   ├── auth.py                 # Authentication logic
│   ├── database.py             # Database handling
│   ├── bookmarks.py            # Bookmark feature
│   ├── dataset/
│   │   └── combined_course_recommendation_dataset.csv
│   ├── skill_gap_model/
│   │   ├── skill_gap_merged_unique_only.csv
│   │   └── train.py
│   ├── vectorizer.joblib       # TF-IDF model
│   ├── job_skill_data.joblib   # Skill-gap data
│   └── requirements.txt
│
├── templates/
│   ├── homepage.html
│   ├── dashboard.html
│   ├── course.html
│   ├── skillgap.html
│   ├── login.html
│   ├── register.html
│   └── profile.html
│
├── coursefinder.db             # SQLite Database
└── README.md

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🛠️ Technologies Used

🔙 Backend

• 🐍 Python
• 🌐 Flask
• 📊 Pandas, NumPy
• 🤖 Scikit-learn

🎨 Frontend

• 🧱 HTML
• 🎨 CSS
• ⚡ JavaScript
• 📊 Chart.js

🧠 AI / Machine Learning

• TF-IDF Vectorization
• Cosine Similarity
• Hybrid Recommendation Logic
• Skill Gap Analysis

🗄️ Database

• SQLite

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⚙️ How the System Works

🔍 Course Recommendation Flow

1. User enters a keyword or topic
2. NLP processes course descriptions and skills
3. Similarity scores are calculated
4. Relevant courses are ranked and displayed

🧠 Skill Gap Analysis Flow

1. User selects a target career role
2. Required skills for the role are identified
3. User’s existing skills are compared
4. Courses are recommended to fill missing skills

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🧩 System Architecture

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▶️ How to Run the Project

🧩 Step 1: Clone the Repository

git clone https://github.com/your-username/Course-Finder.git
cd Course-Finder

🧩 Step 2: Install Dependencies

pip install -r requirements.txt

🧩 Step 3: Run the Application

python app.py

🌍 Step 4: Open in Browser

http://127.0.0.1:5000/

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🎓 Use Cases

• 🎯 Students exploring career paths
• 🎯 Professionals upskilling or reskilling
• 🎯 Personalized learning recommendations
• 🎯 Career-oriented skill development

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🧠 Academic & Industry Relevance

This project demonstrates how AI, NLP, and ML can be applied to build a hybrid, explainable, and goal-oriented recommendation system, aligning academic learning with real-world career requirements.

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👨‍💻 Author

Om 🎓 AI / ML Project – Course Recommendation & Skill Gap Analysis