🚀 AI Resume Ranker and Test Evaluator (Complete hiring process)

Intelligent Resume Ranking System Powered by Machine Learning & Big Data log of Test Evaluation using Apache Spark 🤖✨ 💾💻⭐

Features • Installation • Quick Start • Usage • Results

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##🤝 Team Members

• 🎓 Manikesh Kumar , 23BDS032
• 🎓 Amarjeet Raj , 23BDS006
• 🎓 Ojas Jogdand , 23BDS039

📋 Table of Contents

• ✨ Features
• 📦 Prerequisites
• 🔧 Installation
• 🚀 Quick Start
• 📖 Usage Guide
• 📊 Project Structure
• 🎯 Expected Outcomes
• 🔍 Cheat Detection
• 📝 Examples
• 🤝 Contributing
• 📄 License

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✨ Features

• 🎯 Resume Ranking: Automatically rank resumes based on job description match
• 🧠 ML-Powered Matching: Uses machine learning to extract and compare features
• ⚡ Distributed Processing: Leverages Apache Spark for large-scale data processing
• 🔎 Cheat Detection: Identify suspicious quiz attempts with statistical analysis
• 📊 Data Analytics: Comprehensive analytics on website logs and user behavior
• 🎓 Interactive Labs: Jupyter Notebook labs for learning Apache Spark fundamentals
• 💾 CSV Export: Export ranked results and analytics to CSV format

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📦 Prerequisites

Before you begin, ensure you have the following installed:

• 🐍 Python >= 3.8
• ☕ Java Development Kit (JDK) >= 8
• 📦 pip (Python package manager)
• 💻 Git

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🔧 Installation

Step 1️⃣: Clone the Repository

git clone https://github.com/DataScience-ArtificialIntelligence/Resume_Screening_Test_Evaluation.git
cd AI-Resume-Ranker

Step 2️⃣: Create a Virtual Environment

bash

On Windows

python -m venv venv
venv\Scripts\activate

On macOS/Linux

python3 -m venv venv
source venv/bin/activate

Step 3️⃣: Install Dependencies

pip install -r requirements.txt

Step 4️⃣: Install PySpark & Additional Tools

pip install pyspark findspark

Step 5️⃣: Verify Installation

python -c "import pyspark; print(f'PySpark {pyspark.__version__} installed successfully! ✅')"

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🚀 Quick Start

Using the Resume Ranker 📄

bash python main.py
--resumes ./resumes
--jd job_description.txt
--top 5
--output output/ranked_resumes.csv

Evaluating Test Responses 📝

bash python evaluate_tests.py
--responses ./responses
--shortlist output/ranked_resumes.csv
--out output/test_results
--min_mcq 8
--min_code 300

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📖 Usage Guide

1. Prepare Your Data 📁

Create the following folder structure:

AI-Resume-Ranker/ ├── resumes/ # Place resume files here (.pdf, .docx, .txt) ├── responses/ # JSON files of candidate test responses ├── output/ # Generated output files ├── job_description.txt # Job description file └── ws-logs_filtered.csv # Website logs for analysis

2. Prepare Job Description 🎯

Create a job_description.txt file with the role details:

Role: Machine Learning Engineer

Responsibilities:

• Develop and deploy ML models
• Build data pipelines

Required: Python, Machine Learning, SQL Preferred: TensorFlow, Docker, Kubernetes

3. Run Resume Ranking 🏃

bash python main.py --resumes ./resumes --jd job_description.txt --top 10

4. Evaluate Test Responses ✅

bash python evaluate_tests.py
--responses ./responses
--shortlist output/ranked_resumes.csv
--out output/test_results

5. Analyze Website Logs 📊

Open resume.ipynb in Jupyter Notebook for interactive Spark analytics:

bash jupyter notebook resume.ipynb

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📊 Project Structure

AI-Resume-Ranker/ │ ├── 📄 main.py # Main resume ranking script ├── 📄 evaluate_tests.py # Test evaluation script ├── 📓 resume.ipynb # Interactive Spark lab notebook │ ├── 📁 utils/ │ ├── extract_text.py # Extract text from PDFs/DOCX │ ├── extract_features.py # Feature extraction from resumes │ ├── ranker.py # Ranking algorithm │ └── test_evaluator.py # Test evaluation logic │ ├── 📁 resumes/ # Resume files (input) ├── 📁 responses/ # Test response files (input) ├── 📁 output/ # Output results │ ├── 📄 requirements.txt # Python dependencies ├── 📄 job_description.txt # Job description template ├── 📄 ws-logs_filtered.csv # Website logs data │ ├── 📄 README.md # This file └── 📄 LICENSE # License file

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🎯 Expected Outcomes

Resume Ranking Output 📊

A CSV file (ranked_resumes.csv) with:

• ✅ Candidate name and file path
• ✅ Overall ranking score (0-100)
• ✅ Skill match percentage
• ✅ Experience level match
• ✅ Ranking position

Example Output:

name,file_path,score,rank john_doe,./resumes/john_doe.pdf,95.5,1 jane_smith,./resumes/jane_smith.docx,87.3,2 bob_wilson,./resumes/bob_wilson.pdf,76.2,3

Test Evaluation Output ✅

Three CSV files:

1. selected_candidates.csv - 🎉 Candidates who passed
2. rejected_candidates.csv - ❌ Candidates who were rejected
3. all_ranked_candidates.csv - 📋 Complete ranking with scores

Cheat Detection Report 🚨

Identifies suspicious patterns:

• ⏱ Unusually fast completion times
• 📊 Statistical anomalies (< 1/5 of average time)
• 👥 User behavior analysis
• 📈 Time spent on each problem

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🔍 Cheat Detection (Bonus) 🕵

The resume.ipynb notebook includes advanced cheat detection analytics:

python

Detect cheaters based on response time anomalies

cheaters = identify_cheaters(quiz_logs, threshold=0.2)

Detects:

• 🏃 Suspiciously fast quiz completions
• 📝 Inadequate problem-solving time
• 🎯 Statistically improbable answer patterns
• 🔗 Collaborative behavior indicators

Output Includes:

• List of flagged users
• Detailed timeline analysis
• Early bird detectors
• Fastest solvers per problem

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📝 Examples

Example 1: Ranking Resumes 🎯

bash

Basic usage

python main.py --resumes ./resumes --jd job_description.txt

Advanced usage with custom settings

python main.py
--resumes /path/to/resumes
--jd /path/to/job_desc.txt
--top 15
--output results/my_rankings.csv

Example 2: Evaluating Candidates 📝

bash python evaluate_tests.py
--responses ./responses
--shortlist output/ranked_resumes.csv
--out output/final_results
--min_mcq 5
--min_code 200

Example 3: Running Spark Analytics 🔥

Launch Jupyter and execute cells in resume.ipynb:

python from pyspark import SparkContext sc = SparkContext("local", "Analytics")

Load and analyze quiz logs

logs_rdd = sc.textFile("ws-logs_filtered.csv")

... perform analysis

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🎓 Learning Resources

PySpark Lab Features 📚

The included Jupyter notebook (resume.ipynb) teaches:

1. RDD Operations 🎯

   • Creating RDDs from lists and files
   • Map, filter, flatMap transformations
   • Reduce and aggregation operations

2. Optimizations ⚡

   • Lazy evaluation
   • Caching and persistence
   • Checkpointing
   • Lineage tracking

3. Spark UI 🖥

   • Job monitoring
   • Stage analysis
   • Storage management

4. Real-World Analytics 📊

   • Quiz log analysis
   • Cheat detection algorithms
   • Performance metrics

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🔐 Requirements

Python Packages:

scikit-learn # Machine learning pandas # Data manipulation PyPDF2 # PDF processing python-docx # DOCX processing numpy # Numerical computing pyspark # Distributed computing findspark # Spark initialization

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🤝 Contributing

We welcome contributions! 🎉

1. Fork the repository 🍴
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request 🔔

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🐛 Troubleshooting

❌ PySpark not found?

bash pip install pyspark --upgrade python -m pip install findspark

❌ Java not installed?

• Download JDK from oracle.com
• Set JAVA_HOME environment variable

❌ Permission denied on Linux/Mac?

bash chmod +x main.py evaluate_tests.py

❌ Module import errors?

bash pip install -r requirements.txt --force-reinstall

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📧 Contact & Support

• 📧 Email: 23bds032@iiitdwd.ac.in
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details. 📜

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Made with ❤ by [Manikesh Kumar, Amarjeet Raj, Ojas Jogdand]

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