Prepup is a free, open-source package for data preprocessing in terminal
Project description
💻 Prepup: Interactive Data Preprocessing Toolkit
🚀 Quick Overview
Prepup is a powerful, user-friendly data preprocessing tool designed to simplify and streamline your data analysis workflow directly from the terminal. Whether you're a data scientist, analyst, or researcher, Prepup provides an intuitive interface for exploring, cleaning, and preparing your datasets.
✨ Features
Interactive Mode
- 📊 Load datasets from various formats (CSV, Excel, Parquet)
- 🔍 Comprehensive data inspection
- 📈 Advanced data exploration
- 🧹 Missing value handling
- 📊 Feature visualization
- 🤖 Automatic Machine Learning (AutoML) model selection
Key Functionalities
- Data Loading
- Feature Inspection
- Correlation Analysis
- Distribution Checking
- Outlier Detection
- Missing Value Imputation
- Feature Standardization
- Automatic Model Training
🛠 Installation
Using pip
pip install prepup-linux
From Source
git clone https://github.com/sudhanshumukherjeexx/prepup-linux.git
cd prepup-linux
pip install .
💻 Usage
Interactive Mode
prepup
Loading a Specific Dataset
prepup path/to/your/dataset.csv
Main Menu Options
- Load Dataset
- Inspect Data
- Explore Data
- Visualize Data
- Impute Missing Values
- Standardize Features
- Export Data
- AutoML (Train & Evaluate Models)
🎮 Interactive Workflow Example
-
Launch Prepup
prepup -
Load Your Dataset: Choose option 1 and enter your dataset path
-
Inspect Data: Use option 2 to explore features, data types, and missing values
-
Preprocess: Impute missing values | Standardize features
-
Analyze: Visualize data distributions | Perform correlation analysis | Run AutoML for model selection
🤖 AutoML Capabilities
- Supports both Classification and Regression tasks
- Evaluates multiple machine learning algorithms
- Provides performance metrics
- Saves results to CSV
📦 Dependencies
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
- and more (see requirements.txt)
🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
📋 License
Distributed under the MIT License. See LICENSE for more information.
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