Dataset Generator for AI Prototyping

Overview

This project is a flexible and reusable Dataset Generator designed to create synthetic datasets for AI applications. It provides an easy way to generate user data for testing, training, and validating machine learning models. With modular scripts and a testing notebook, this tool enables rapid prototyping of AI workflows without relying on sensitive or proprietary datasets.

Features

• Synthetic Data Generation: Creates datasets with realistic names, emails, ages, and cities using the Faker library.
• Modular Design: Easily extendable scripts to include domain-specific data generation.
• Jupyter Notebook Integration: Provides an interactive way to test and inspect generated data.
• Data Privacy: Uses synthetic data to avoid concerns with real-world data sensitivity.
• Customization: Configure the number of records and data fields as per your requirements.

Project Structure

Directory structure:
└── CodeHive-by-Jay-Dataset-Generator-For-CodeHive/
    ├── README.md
    ├── LICENSE
    ├── main.py
    ├── requirements.txt
    ├── notebooks/
    │   └── generate_and_test.ipynb
    └── scripts/
        ├── generate_images.py
        ├── generate_time_series.py
        └── generate_users.py

Setup Instructions

1. Clone the Repository

$ git clone https://github.com/CodeHive-by-Jay/Dataset-Generator-For-CodeHive
$ cd Dataset-Generator-For-CodeHive

2. Install Dependencies

Ensure you have Python 3.7+ installed. Install the required packages:

$ pip install -r requirements.txt

3. Run the Dataset Generator

Run the main.py script to start the generator:

$ python main.py

How to Use

Generating Data

1. Run the project using main.py. Select the option to generate user data.
2. Specify the number of records you want to create (default: 100).
3. The synthetic dataset will be saved in the data/ directory as synthetic_user_data.csv.

Exploring Data

1. Open the notebooks/generate_and_test.ipynb file in Jupyter Notebook.
2. Execute the notebook to generate and inspect the data.
3. Use the sample data to test machine learning models or preprocessing pipelines.

AI Use Cases

1. Training AI Models

• Train supervised models (e.g., regression, classification) with synthetic data.
• Example: Predict user age based on other features like email domain and city.

2. Data Augmentation

• Combine synthetic data with real-world datasets to improve model generalization.

3. Prototyping AI Applications

• Prototype recommendation engines or chatbots using generated user profiles.

4. NLP Tasks

• Extend the tool to create synthetic text datasets for tasks like spam detection.

5. Federated Learning

• Simulate user data from multiple sources for federated learning experiments.

Customization Options

• Modify generate_users.py to add new fields or customize data types (e.g., phone numbers, addresses).
• Extend the project to include datasets for specific domains (e.g., healthcare, finance).
• Add scripts for generating time-series or categorical data.

Dependencies

• Python 3.7+
• pandas
• faker
• jupyter

Contributing

Feel free to fork this repository and submit pull requests for:

• Adding new features.
• Optimizing code performance.
• Expanding AI use cases.

License

This project is licensed under the MIT License.

Contact

For questions or contributions, please reach out to the developer: