What is the AI-Based Student Skill Gap Analysis System?

It is a machine learning-based system that analyzes student skills, predicts career aspirations, and identifies skill gaps. It helps educators, career counselors, and students make data-driven decisions about skill development and career planning.

Which algorithms are used in this skill gap analysis project?

The system implements and compares two machine learning algorithms: Random Forest Classifier and Logistic Regression. Random Forest achieved the best performance with 87.5% accuracy and an F1-score of 0.84.

What is the accuracy of this skill gap analysis model?

The Random Forest model achieves 87.5% test accuracy with a precision of 0.84 and F1-score of 0.84, making it highly reliable for career aspiration prediction and skill gap identification.

Who can benefit from this student skill gap analysis system?

Educational institutions can use it for curriculum development, career counselors for personalized guidance, students for self-assessment and career planning, and employers for identifying talent pipelines.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

AI-Based Student Skill Gap Analysis System - Complete Project Demo Video

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Machine Learning

AI-Based Student Skill Gap Analysis System

The AI-Based Student Skill Gap Analysis System is a comprehensive machine learning system that analyzes student skills, predicts career aspirations, and identifies skill deficiencies. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced algorithms like Random Forest and Logistic Regression to achieve 87.5% accuracy.

The system leverages features including technical skills, programming languages, soft skills, academic ratings, projects completed, and learning preferences. It provides interactive visualizations, feature importance analysis, model comparison, and real-time career predictions to help educators and career counselors make data-driven decisions for student skill development.

Python 3.8+ Machine Learning Random Forest Logistic Regression Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Class Career Prediction
Random Forest (87.5% Accuracy)
Logistic Regression (75% Accuracy)
Skill Gap Identification
Feature Importance Analysis
Real-time Career Predictions
Model Performance Comparison
Interactive Visualizations
Personalized Student Recommendations
Flask Web Application

Algorithms Used

🌲 Random Forest Classifier

Ensemble learning with 100 trees, handles high-dimensional data and complex feature interactions

🎯 Accuracy: 87.5%

📊 Logistic Regression

Statistical model with multinomial classification, provides interpretable coefficients

🎯 Accuracy: 75.0%

🔍 5-Fold CV

Cross-validation for model stability and generalization assessment

📊 RF: 87.22% ± 1.14%

📈 Feature Engineering

Skill count, language count, soft skills count extraction

📊 Features: 15+

Methodology & Workflow

1 Data Loading & Inspection
Student dataset with 45 records and 15 features

2 Data Preprocessing
Cleaning, encoding categorical variables, feature extraction

3 Feature Engineering
Skill count, language count, soft skills count

4 Model Training
Random Forest & Logistic Regression with optimized parameters

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, 5-fold CV

6 Web Deployment
Flask web app for real-time skill gap analysis

Model Performance Comparison

Metric	Random Forest	Logistic Regression	Best
Accuracy	0.875	0.750	Random Forest
Precision	0.84	0.71	Random Forest
Recall	0.82	0.68	Random Forest
F1-Score	0.84	0.69	Random Forest
CV Mean (5-Fold)	0.872	0.749	Random Forest
CV Std Dev	0.011	0.012	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (45 records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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