python based simple Tools for Neural network training for image classifier, (dir based image converter, Normalization, Blur Augmentation, Multiple Model Evaluate )

Jupyter notebook and "Streamlit" python scripts for identifying features that can predict employee turn over rates at 250 senior care centers across the US. Combines multiple repetition of Lasso regression and linear regression. Integrates U.S. census data, employee salary, and employee tenure with data on employee satisfaction and engagement to…

Centralize messages and elevate user experience with custom exception handling and data annotation validation. Simplify your messaging system effortlessly.

The Newbie Club's Proposals and Standardizations

Project templates and scaffolding for Intent Solutions. Standardized directory structures, configs, and boilerplate for new projects.

Towards a standardizing apparel data

Predicting the best efficacy of legit and fraudulent transactions for people across the world to help business in increasing their revenue and for customers to have hassle-free smooth transactions.

Standardization attempt and Proof of Concept of cross tracking site services save file/exported list for media entries in JSON and YAML

Student WIL work on the ABS data modeled into the Azure Synapse.

This project predicts employee salaries based on factors like gender, department, experience, and location, providing insights and accurate salary predictions through statistical and machine learning methods.

Identify which tolerance standard and class or grade a dimension and its tolerance belongs to.

📊 Predict intern performance using machine learning to guide mentorship, enhance training, and improve outcomes based on data-driven insights.

This project is a Streamlit-based web application that predicts the species of an Iris flower using a trained Machine Learning model. The user inputs flower measurements (sepal length, sepal width, petal length, petal width), which are scaled and passed to a pre-trained model to predict the Iris class.

Minimum Information About Particle Tracking Experiments (MIAPTE) guidelines

In this case, we train our model with several medical informations such as the blood glucose level, insulin level of patients along with whether the person has diabetes or not so this act as labels whether that person is diabetic or non-diabetic so this will be label for this case.

This repo evaluates Logistic Regression, Random Forest, and Support Vector Machine models for predicting stroke risk. Implemented in Python, the project includes data pre-processing, model training, and performance metric calculations

Customer churn is a significant issue for big business companies. Companies are attempting to create methods for predicting customer churn to get a direct impact on getting more revenues, particularly in telecom companies.

In this project we have performed all types of feature transfromation on the titanic dataset and we have seen the usage of qqplot to check whether a feature is normal/gaussian distributed or not.

This SQL project cleans a 2022 layoffs dataset by removing duplicates, standardizing values, fixing dates, and eliminating unnecessary data for analysis.

DRT: A New Toolbox for the Standard EEG Data Structure in Large-scale EEG Applications. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001709