As a student interested in Data Science and Machine Learning, I often faced the same problem:

I needed datasets to test ideas, algorithms, and projects, but finding the right dataset wasn't always easy.

Sometimes I needed:

• A dataset with specific correlations
• A dataset generated from a formula
• Missing values following MCAR, MAR, or MNAR patterns
• Time-series data for experimentation
• Multiple datasets that could be merged and compared

Most existing libraries solved only one part of the problem.

So I decided to build my own Python package:

Introducing GOSEIDATASET

GOSEIDATASET is a Python library designed for:

✅ Synthetic Dataset Generation

✅ Missing Value Simulation

✅ Time Series Generation

✅ Dataset Merging

✅ Supervised Learning Utilities

Installation

pip install goseidataset

Random Dataset Generation

Generating a dataset is straightforward:

from goseidataset import DatasetGenerator

dg = DatasetGenerator()

df = dg.generate_random(
 n_rows=100,
 constraints={
 "sleep": [4, 10],
 "revision": [0, 8],
 "session": ["Morning", "Evening"]
 }
)

print(df.head())

Generate Correlated Data

Need a dataset where features have predefined relationships?

df = dg.generate_correlated(
 n_rows=1000,
 target="marks",
 correlations={
 "hours": 0.8,
 "stress": -0.5
 },
 constraints={
 "marks": [0, 100],
 "hours": [0, 12],
 "stress": [0, 100]
 }
)

Formula-Based Dataset Generation

Generate data from mathematical relationships:

df = dg.generate_formula(
 n_rows=500,
 formula="hours*10 + revision*5",
 constraints={
 "hours": [1, 10],
 "revision": [0, 5],
 "marks": [0, 125]
 },
 target="marks"
)

Missing Value Simulation

One of the main goals of the package was to help test imputation techniques.

Supported methods include:

• MCAR
• MAR
• MNAR
• Random Missing
• Consecutive Missing
• Block Missing
• Correlation-Based Missing

Example:

from goseidataset import MissingValueGenerator

mv = MissingValueGenerator(df)

result = mv.mcar(
 column="marks",
 percentage=20
)

Time Series Generation

Generate timestamp-based features easily:

from goseidataset import TimeSeriesGenerator

ts = TimeSeriesGenerator(df)

result = ts.timestamp_series()

Supervised Learning Utilities

The package also includes utilities for:

• Dataset comparison
• Weighted ensemble learning
• Dataset merging
• Missing value imputation

Example:

from goseidataset import Supervised_learning

sl = Supervised_learning(
 dataset_a,
 dataset_b,
 target="Retention"
)

result = sl.compare_models(model)

What I Learned Building This Project

Building this package taught me much more than writing Python code.

I learned about:

• Package structure
• API design
• Documentation
• Testing
• PyPI packaging
• Dependency management
• Versioning
• Real-world debugging

One of the biggest lessons was that writing the code is only part of the work. Making a package easy for others to install, understand, and use is equally important.

Future Improvements

Planned features include:

• Classification dataset generators
• Advanced time-series simulation
• More missing-value mechanisms
• Better visualization utilities
• Additional machine learning helpers

Feedback Welcome

This is my first published Python package, and I'd love to hear feedback from the community.

PyPI:
https://pypi.org/project/goseidataset/

GitHub:
https://github.com/GITTY5678/Gosei-dataset

Thanks for reading!