DataMimic.io - Realistic Synthetic Data Generation & No-Code EDA Platform

DataMimic.io is a web-based platform empowering data scientists, developers, and QA engineers to generate realistic synthetic datasets and perform no-code Exploratory Data Analysis (EDA). This project addresses critical challenges in data privacy and accessibility by providing a powerful, intuitive interface to create, analyze, and clean tabular data on demand.

Live Demo : Here

---

Key Features

1. Synthetic Data Generation

• Pre-defined Schemas: Generate data for common domains like Medical, Finance, Retail, Education, and Automotive.
• Locality-Based Data: Create realistic data for different regions (US, UK, India, Canada, Australia).
• Data Quality Controls: Fine-tune the dataset with adjustable missing value ratios and data variance.
• AI-Powered Custom Columns: A standout feature that leverages the Google Gemini API to generate entire columns of data based on natural language prompts.
• Flexible Export: Download generated data in CSV, JSON, or Excel formats.

Generator Page Screenshot

*The main generator interface, configured to generate Retail data.*

AI Custom Column Feature Screenshot

*Defining an AI-powered custom column with a simple prompt filed to configure its details.*

2. No-Code EDA & Pre-processing

• Easy Data Upload: Upload your CSV or XLSX files and get an instant, comprehensive data overview.
• Detailed Summary: View total rows/columns, file size, missing value percentages, and detailed column-wise statistics (mean, median, std dev, etc.).
• Powerful Pre-processing Suite: Clean and transform your data with a few clicks:
  • Missing Value Handling: Remove rows/columns or impute with mean, median, or mode.
  • Duplicate Removal: Eliminate duplicate rows.
  • Column Management: Remove specific columns or change data types.
  • Data Scaling: Apply Min-Max Scaling or Standardization (Z-score).
  • Text Cleaning: Standardize text with uppercase, lowercase, or title case.
• Download Processed Data: Export your cleaned dataset, ready for analysis or model training.

EDA Page Screenshot

*The EDA & Pre-processing page after a file has been uploaded, showing the data summary and available operations.*

---

Technical Stack

• Backend:
  • Framework: Flask
  • Data Manipulation: Pandas, NumPy
  • Data Preprocessing: Scikit-learn
  • Synthetic Data: Faker
  • AI Integration: Google Gemini API (via requests)
  • Email: Flask-Mail
• Frontend:
  • HTML5, CSS3, JavaScript (Vanilla JS)
  • Jinja2 Templating
• Deployment:
  • WSGI Server: Gunicorn
  • Hosting: Render.com (Web Service)

---

Project Setup & Local Installation

To run DataMimic.io on your local machine, follow these steps:

1. Prerequisites

• Python 3.9 or higher
• pip and venv

2. Clone the Repository

git clone https://github.com/harsh-kakadiya1/datamimic.io.git
cd datamimic.io

3. Set up a Virtual Environment

# For Windows
python -m venv venv
venv\Scripts\activate

# For macOS/Linux
python3 -m venv venv
source venv/bin/activate

4. Install Dependencies

pip install -r requirements.txt

5. Configure Environment Variables

Create a file named .env in the root of the project directory. This file stores your secret keys and credentials.

SECRET_KEY='a_very_strong_and_random_secret_key'
EMAIL_USER='[email protected]'
EMAIL_PASS='your_gmail_app_password'
GEMINI_API_KEY='your_google_gemini_api_key'

• SECRET_KEY: A long, random string for Flask session security.
• EMAIL_USER / EMAIL_PASS: Your Gmail credentials for the contact form. Use a Google App Password if you have 2-Factor Authentication enabled.
• GEMINI_API_KEY: Your API key from Google AI Studio.

6. Run the Application

flask run

The application will be available at http://127.0.0.1:5000.

Authors

Harsh Kakadiya - GitHub | LinkedIn
Krish Kunjadiya - GitHub | LinkedIn

License

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