GeeseTools 0.2.1

Modular and extensible data preprocessing library

🪿🪿GeeseTools

• Fast & Flexible Data Analysis Toolkit

Welcome to GeeseTools – a lightweight and modular toolkit designed for quick data preprocessing, model building, evaluation, and visualizations. Perfect for quick experiments and rapid prototyping in machine learning workflows!

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Features

• Clean and preprocess your datasets effortlessly with datapreprocessor
• Quickly train and evaluate models with utils
• Auto-generate plots for insights and performance metrics
• Minimal setup, beginner-friendly, and fully extensible

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Module Structure

📦 GeeseTools/
├── 📂 data/                            
│   └──📄 heart.csv             # Default Dataset            
│    
├── 📁 DataPreProcessor       
│   └──📜 DataPreProcessor.py   # Main Script
│
└── 📁 utils                    
    ├──📜 train_models.py       # Model training,
    ├──📜 evaluation.py         # Model evaluation
    └──📜 plot.py               # Evaluation visualization

Installation

pip install GeeseTools

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📚 How to Use

1. Import the modules

from datapreprocessor import datapreprocessor as dpp
from utils import train_model as tm
from utils import evaluate_model as eval
from utils import plot

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2. Preprocess your data

# Creating object for DataPreProcessor Class
obj = dpp(pd.read_csv("heart.csv"), target="diagnosis")

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3. Train a model

model, task_type, history = tm.train_model(X_train, y_train)

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4. Evaluate the model

metric, y_pred = eval.evaluate_model(model, X_test, y_test, task_type)

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5. Plot results

plot.plot_model_outputs(y_test, y_pred) # for Classification problem
or
plot.plot_model_outputs(history) # for Regression problem

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Example Notebook

Check out DataAnalysis.ipynb for a full example pipeline from preprocessing to visualization.

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Dependencies

• scipy pandas ipython
• seaborn openpyxl
• matplotlib scikit-learn
• imbalanced-learn

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Contributing

Feel free to fork and improve! PRs are welcome for new features, improvements, or bug fixes.

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Contact

Made with ❤️ by Abhijeet
LinkedIn | GitHub

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License

MIT © Abhijeet You're free to use, modify, and distribute this project with proper attribution.

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