MoleculeNet benchmark dataset & MolMapNet dataset

A Python toolkit for file processing, text cleaning and data splitting. 文件处理，文本清洗和数据划分的python工具包。

An Exploratory Toolkit for Recommender Systems Datasets and Splits

Data-Splitter is a Python script designed to split a large CSV file containing data into three different formats: JSON, a database table, and another CSV file. The script ensures a random distribution of data across the three output formats based on custom-defined ratios.

Automated leakage-aware splitting of a given PPI dataset into train, validation, and test set

Code used for the analysis described in "Towards mobile music emotion recognition with cEEGrid", studying MER with a range of models, feature extraction, and data splitting techniques. Set up for the DEAP and DAAMEE datasets.

splitting image dataset into train, val, test sets

Reproducible archetype-based train/test splitting and benchmarking for ML data sets

Analyzed customer churn using transaction data. Built ML model to predict lapses. Dataset includes customer status, collection/redemption info, and program tenure. Delivered business presentation outlining modeling approach, findings, and churn reduction strategies.

This project focuses on cleaning and analyzing a loan application dataset to gain insights into the factors influencing loan defaults. Through systematic data cleaning, visualization, and merging with previous application data, it provides a robust foundation for further predictive modeling.

Split a dataset into subsets of specified sizes such that each subset preserves the original label distribution enhanced by stratification on UMAP-based pseudo-labels. This method ensures splits are balanced both by true labels and the data’s underlying manifold structure.

Predicting company bankruptcy using various machine learning models. The dataset is sourced from Kaggle: Company Bankruptcy Prediction.

ML model for Crop Detection

In this project, I have used logistic regression, a supervised machine learning algorithm, to predict whether a person has diabetes or not based on various features such as age, blood pressure, glucose level, body mass index, etc. I have used Python and popular libraries such as Pandas, Scikit-Learn, and Matplotlib to perfom model building

Julia package for "FDR Control via Data Splitting for Testing-after-Clustering (arXiv: 2410.06451)"

Embedding-based method for splitting datasets semantically

Comparative Analysis of Data Protection Mechanisms in Public Clouds

A basic Python script to split a .dat file into individual sample files.

Pattern Recognition - ECE - TTU - Spring 2021

A simple PyTorch-based neural network that classifies student exam outcomes (Pass/Fail) using study hours and previous exam scores. Implements dataset splitting (train/val/test), mini-batch training, and evaluation with configurable hyperparameters.