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。

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.

Comparative Analysis of Data Protection Mechanisms in Public Clouds

splitting image dataset into train, val, test sets

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

ML model for Crop Detection

Python Preprocessing for Sales Project Notebook

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

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.

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

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

As Tensorflow Kennard-Stone algorithmin uses euclidean distances, the need for an adaptation arrises when dealing with a big vector space that has unknown correlations between its variables, it may improve a lot neural networks performance.

Apply DUPLEX data split to the given dataset and return training and test datasets. REF: Snee, R. D. (1977). Validation of regression models: methods and examples. Technometrics, 19(4), 415-428.

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.

Focus on selecting datasets suitable for a machine learning experiment, with an emphasis on data cleaning, encoding, and transformation steps necessary to prepare the data.

Utilizing Apache Spark & PySpark to analyze a movie dataset. Tasks include data exploration, identifying top-rated movies, training a linear regression model, and experimenting with Airflow.

Final project program DBA mitra Ruangguru X Studi Independen Bersertifikat Kampus Merdeka batch 2

As Tensorflow Kennard-Stone algorithmin uses euclidean distances, the need for an adaptation arrises when dealing with a big vector space that has unknown correlations between its variables, it may improve a lot neural networks performance.

A sample model for predicting the systolic level of an individual by providing the age,cholesterol and blood pressure