Fake Job Detection System

Project Overview

The Fake Job Detection System is a machine learning project designed to detect fraudulent job postings. By utilizing natural language processing (NLP) techniques and a Random Forest classifier, the system analyzes the textual content of job postings to distinguish between genuine and fake listings.

Project Structure

• Fake_job_detection_code.ipynb: Jupyter Notebook containing the code for data preprocessing, model training, evaluation, and prediction.
• fake_job_postings.csv: Dataset containing job postings information.
• README.md: Project documentation.

Key Features

• Data Preprocessing: Cleaning and preparing text data for analysis.
• Feature Engineering: Combining and transforming text data for model input.
• Modeling: Implementing a Random Forest classifier to detect fraudulent job listings.
• Visualization: Generating word clouds and bar charts to understand data distribution and model results.

Technologies Used

• Python
• pandas
• numpy
• nltk
• scikit-learn
• matplotlib
• seaborn
• Random Forest
• TF-IDF

Data Description

The dataset contains the following features:

• title: The job position title.
• location: The job location.
• department: The department to which the job belongs.
• salary_range: The salary range for the job.
• company_profile: A description of the company.
• description: Detailed job description.
• requirements: Job requirements and qualifications.
• benefits: Additional benefits provided by the company.
• telecommuting: Indicates if telecommuting is allowed (1 for yes, 0 for no).
• has_company_logo: Indicates if the job posting has a company logo (1 for yes, 0 for no).
• has_questions: Indicates if the job posting has screening questions (1 for yes, 0 for no).
• employment_type: Type of employment (full-time, part-time, contract, etc.).
• required_experience: Level of experience required.
• required_education: Minimum education level required.
• industry: Industry sector of the job.
• function: Specific function or area of specialization.
• fraudulent: Indicates if the job posting is fraudulent (1 for yes, 0 for no).

Installation and Usage

1. Clone the repository:

   git clone https://github.com/your-username/Fake-Job-Detection.git
   cd Fake-Job-Detection

2. Install required dependencies: Make sure you have Python and pip installed. Then, run:

   pip install -r requirements.txt

3. Download NLTK resources: In your Jupyter Notebook or Python script, ensure you download the necessary NLTK resources:

   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')

4. Run the Jupyter Notebook: Open Fake_job_detection_code.ipynb in Jupyter Notebook and run the cells to preprocess the data, train the model, and make predictions.

Results

The model achieved an accuracy of [insert accuracy here] in detecting fraudulent job postings. The confusion matrix and classification report provided detailed performance metrics.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

• The dataset used in this project was obtained from [https://www.kaggle.com/datasets/shivamb/real-or-fake-fake-jobposting-prediction].
• Thanks to the contributors of open-source libraries such as pandas, numpy, nltk, scikit-learn, matplotlib, and seaborn.