How to use

!rm -rf data_science
!git clone https://github.com/GLI-Lab/data-science.git data_science

Data

Overview

Dataset	Type	Primary Task	Target Variable
Amtrak.csv	Time series	Regression	Ridership
BostonHousing.csv	Tabular	Regression	MEDV
california_housing.csv	Tabular	Regression	median_house_value
titanic.csv	Tabular	Classification	Survived
california_cities.csv	Tabular	Regression	population

Source & Loader

https://www.kaggle.com/datasets?sort=votes

# titanic
import seaborn as sns
titanic = sns.load_dataset("titanic")
titanic.to_csv("./data/titanic.csv", index=False)

# California Housing Prices
https://www.kaggle.com/datasets/camnugent/california-housing-prices

# Credit Card Fraud Detection
https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

# MUTAG
import torch_geometric.transforms as T
from torch_geometric.datasets import TUDataset
dataset = TUDataset(
    root='./data', name='MUTAG', use_node_attr=False, cleaned=True, transform=T.Compose([
        T.LocalDegreeProfile(),
    ])
)