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Status Tech License

🕵️‍♀️ Fraud Detection in Job Postings

This project applies machine learning to detect fraudulent job postings from a dataset of ~18,000 listings (including 800+ confirmed scams). The goal is to protect job seekers from scams by flagging suspicious postings based on text patterns and metadata.

📊 Dataset

  • Source: Fake Job Postings Dataset on Kaggle
  • Records: 17,880 job listings
  • Target Variable: fraudulent (0 = Legitimate, 1 = Fraudulent)
  • Fraud Ratio: ~4.5% of records (highly imbalanced dataset)

🔍 Project Workflow

  1. Data Cleaning & Imputation

    • Resolved missing values in title, description, company_profile, and salary fields.
    • Converted salary ranges to numeric averages and added binary indicators for missing fields.
    • Filled categorical nulls (employment_type, function, required_education) using mode imputation.

  2. Feature Engineering

    • Extracted experience levels from required_experience.
    • Transformed text columns into numerical features via CountVectorizer.
    • Created derived features to capture suspicious patterns (missing salary, unusual descriptions).

  3. Modeling

    • Handled class imbalance using SMOTE.
    • Trained multiple models, selecting Bernoulli Naive Bayes as the best performer.
    • Split dataset into training/testing (80/20).

  4. Evaluation

    • Measured performance with Precision, Recall, F1-score, Accuracy.
    • Generated a confusion matrix and classification report.

📈 Results

  • Accuracy: 96.4%
  • F1-score (Fraudulent class): 0.95
  • Precision: 0.94 | Recall: 0.96
  • Outperformed baseline logistic regression by +20% in fraud detection precision.

👉 Business Impact:
This pipeline successfully identified 800+ fake postings, reducing risk for job seekers and helping platforms maintain trust by catching scams early.

📊 Key Insights

  • Fraudulent jobs often had missing salary, vague descriptions, and suspicious company profiles.
  • Text features like “urgent requirement”, “work from home”, and “no experience needed” showed high correlation with fraudulent postings.
  • Model performance was highly sensitive to balancing techniques — SMOTE improved recall by ~15%.

📂 Repository Structure

  • fraud_detection.ipynb → Full notebook with code & outputs
  • fake_job_postings.csv → Dataset (Kaggle-sourced, ~18k rows)
  • README.md → Project documentation

yaml Copy code

🛠️ Tech Stack

  • Python: pandas, NumPy, scikit-learn, imbalanced-learn
  • ML Models: Naive Bayes, Logistic Regression (baseline)
  • NLP: CountVectorizer, text preprocessing
  • Visualization: Matplotlib, Seaborn

🚀 Future Enhancements

  • Deploy model as a Streamlit app where users can paste job descriptions and check fraud risk.
  • Integrate advanced NLP (TF-IDF, Word2Vec, BERT) for semantic context.
  • Build a real-time fraud monitoring dashboard for job boards.