What is the AI-Based Resume Screening System?

It is a machine learning and NLP-based system that automates the resume screening process by analyzing resume text and matching candidates to the most suitable job roles. It helps recruiters save time, reduce bias, and improve candidate-job matching accuracy.

Which algorithms are used in this resume screening 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.

What is the accuracy of this resume screening model?

The Random Forest model achieves 87.5% accuracy with a weighted F1-score of 0.867, making it highly reliable for automated resume screening and candidate shortlisting.

Who can benefit from this AI resume screening system?

HR professionals, recruitment agencies, hiring managers, and organizations of all sizes can benefit from this system to streamline their recruitment process, reduce screening time, and improve candidate-job matching.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, NLTK, Matplotlib, and Seaborn for machine learning and NLP. TF-IDF vectorization is used for text feature extraction. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

AI-Based Resume Screening Using Machine Learning

The AI-Based Resume Screening Using Machine Learning system is a comprehensive machine learning and NLP solution that automates the candidate screening process by analyzing resume content and matching candidates to the most suitable job roles. 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 with TF-IDF vectorization to achieve 87.5% accuracy.

The system leverages NLP techniques to extract meaningful features from resume text, combined with numerical features such as skill count, project count, certification count, and experience years. It provides interactive visualizations, feature importance analysis, model comparison, and real-time resume screening to help recruiters and HR professionals streamline their hiring process.

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

Key Features:

Automated Resume Screening
Random Forest (87.5% Accuracy)
Logistic Regression (84.2% Accuracy)
TF-IDF Text Feature Extraction
Feature Importance Analysis
Real-time Candidate Matching
Model Performance Comparison
Interactive Visualizations
Multi-Class Job Role Prediction
Flask Web Application

Algorithms Used

🌲 Random Forest Classifier

Ensemble learning with 100 trees, handles high-dimensional text features effectively

🎯 Accuracy: 87.5%

📊 Logistic Regression

Linear model with multinomial classification, interpretable coefficients

🎯 Accuracy: 84.2%

📝 TF-IDF Vectorizer

Text feature extraction for resume content, captures term importance

📊 Features: 100+

🔍 5-Fold CV

Cross-validation for model stability and generalization

📊 RF: 0.862 ± 0.041

Methodology & Workflow

1 Data Loading & Inspection
Resume dataset with 100+ samples across 6 job roles

2 Text Preprocessing
Cleaning, removing special characters, standardizing text

3 Feature Engineering
TF-IDF vectorization, skill count, project count, certification 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 resume screening

Model Performance Comparison

Metric	Random Forest	Logistic Regression	Best
Accuracy	0.875	0.842	Random Forest
Precision (Weighted)	0.871	0.838	Random Forest
Recall (Weighted)	0.875	0.842	Random Forest
F1-Score (Weighted)	0.867	0.834	Random Forest
CV Mean (5-Fold)	0.862	0.831	Random Forest
CV Std Dev	0.041	0.052	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (100+ resumes) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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