🌍 Python package of VTK-based algorithms to analyze geoscientific data and models

Stochastic gradient descent with model building

PyR@TE 3

A targeted resource for mastering Scikit-Learn, featuring practice problems, code examples, and interview-focused machine learning concepts in Python. Covers model building, evaluation, and preprocessing techniques to excel in data science interviews.

A neural network model builder, leveraging a neuro-symbolic interface.

Tree-level completions of LNV operators for neutrino-mass model building

Tool demonstrating building credit risk models

This project carefully studies the customer reviews of a airline company, around 10,000+ reviews are collected through webscrapping and and by sentiment analysis captured the expierence of the customers. And based on that designed a Machine learning algorithm which is a random forest classifier to predict customers who are likely to book seats.

Web application for logistic regression made using Streamlit.

The fraud identification models were build using Python Scikit-learn machine-learning module.

Successfully trained a machine learning model which can predict whether a given transaction is fraud or not.

"Welcome to the HR Employee Promotion Prediction project! This repository contains the code and resources for a machine learning project that focuses on predicting employee promotions. By analyzing various employee attributes, this project aims to provide valuable insights for HR decision-making and talent recognition within organizations.

This dataset contains information about drug classification based on patient general information and its diagnosis. Machine learning model is needed in order to predict the outcome of the drugs type that might be suitable for the patient.

Web application for linear regression made using Streamlit.

This project is to develop a machine learning model and deploy it as a user-friendly web application that predicts the resale prices of flats in Singapore.

A Python program that predicts NBA champions using neural networks built with TensorFlow in Python, leveraging historical team performance data, such as shooting stats and other metrics, with a binary target indicating championship wins.

Built and tested 6 supervised machine learning algorithm to develop a predictive classification model to classify 13000+ projects as success or failure.

Computing model catalogues for hadronic axion models

The modelsandbox package and its core Model class allow for users to build intricate, multi-level, highly parameterized mathematical models without needing extensive knowledge of Python to design complex classes and analysis structures.