Objective:

To analyze and predict the difficulty level of academic courses using historical student performance data, course content, and feedback, helping students and educators make informed decisions.

Why Choose This Project:

• Helps students choose courses that match their skill levels.

• Assists educators in identifying courses that need improvement.

• Integrates machine learning, data analytics, and visualization — highly relevant for data science applications in education.

• Supports personalized learning and adaptive education platforms.

Key Features:

Technology Stack:

• Frontend: HTML, CSS, JavaScript, Bootstrap, Chart.js / D3.js (for dashboards).

• Backend: Python with Flask or Django.

• Machine Learning: scikit-learn, XGBoost, or TensorFlow/Keras.

• NLP: spaCy or NLTK (for analyzing student reviews).

• Database: MySQL / PostgreSQL / MongoDB.

• Optional: Pandas & NumPy for data preprocessing and analysis.

Working Flow:

1. Data Collection

   • Collect historical data including grades, course completion rates, syllabus details, and student feedback.

2. Data Preprocessing

   • Clean and normalize data. Convert textual feedback into numerical features using NLP techniques like sentiment scoring or TF-IDF.

3. Feature Engineering

   • Extract relevant features: number of assignments, prerequisites, lecture hours, review scores, etc.

4. Model Training

   • Train a supervised ML model (e.g., Random Forest, Gradient Boosting, or Neural Network) to predict course difficulty.

5. Model Evaluation

   • Test the model with unseen course data and evaluate metrics such as RMSE, accuracy, or MAE.

6. Dashboard & Visualization

   • Show course difficulty scores, comparisons, and personalized course recommendations on a web-based dashboard.

Main Modules:

1. Data Collection & Preprocessing Module

2. Feature Extraction & Engineering Module

3. Machine Learning & Prediction Module

4. Dashboard & Visualization Module

5. Personalized Recommendation Module

Security Features:

• Secure access to student and course data.

• Proper authentication for administrators and educators.

• Data anonymization to protect student privacy.