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Fake Profile Detector

Table of Contents

Introduction

Tanjiro-sama

🕵️‍♂️ The Fake Profile Detector is a machine learning project designed to identify fake user profiles on social media and online platforms. In today's digital landscape, the proliferation of fake profiles poses significant challenges, including misinformation, scams, and cybersecurity threats. This project aims to combat these issues by developing a robust and accurate fake profile detection system.

Features

  • Data Preprocessing: The project includes data cleaning and preprocessing steps to prepare the dataset for model training.

  • Feature Engineering: We employ feature engineering techniques to extract meaningful information from user profiles, enhancing the model's ability to identify fake profiles.

  • Machine Learning Models: Various machine learning algorithms, such as Random Forest and XGBoost, are explored and compared to determine the best-performing model.

  • Evaluation Metrics: The project uses accuracy, precision, recall, F1-score, and ROC AUC to evaluate the model's performance.

Getting Started

Prerequisites

Before you begin, ensure you have met the following requirements:

  • Python 3.7+
  • Pip (Python package manager)

Installation

To set up the project, follow these steps:

  1. Clone the repository:

    git clone https://github.com/your-username/fake-profile-detector.git
cd fake-profile-detector

  2. Install the required packages:

pip install -r requirements.txt

Usage

To use the Fake Profile Detector, follow these steps:

  • Data Preparation: Prepare your dataset with user profiles for fake profile detection.

  • Data Preprocessing: Use the provided data preprocessing scripts to clean and preprocess your dataset.

  • Training: Train the machine learning model using your preprocessed dataset.

  • Evaluation: Evaluate the model's performance using the provided evaluation metrics.

Data

The project uses a dataset containing user profiles with various features, including profile pictures, username characteristics, description length, and more. The dataset is stored in CSV format.

Training and Model

The training process involves selecting and fine-tuning the machine learning model using the training dataset.

Evaluation

The project evaluates the model's performance using various metrics, including accuracy, precision, recall, F1-score, and ROC AUC.

Contributing

Contributions to the Fake Profile Detector project are welcome! To contribute, please follow these guidelines:

  • Fork the repository.
  • Create a new branch for your feature or bug fix.
  • Make your changes and test thoroughly.
  • Submit a pull request with a clear description of your changes.

License

This project is licensed under the MIT License.