evaluate-models

Python client for Kolena's machine learning testing platform

In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)

A collection of handy ML and data visualization and validation tools. Go ahead and train, evaluate and validate your ML models and data with minimal effort.

In Machine Learning Project is "predict whether a person will be diagnosed diabetes or not" - Using K Nearest Neighbor Aldorithm.

This repository includes the model code for saving and loading neural network model trained

In this assignment, I have built and evaluate several machine-learning models to predict credit risk using free data from LendingClub. Credit risk is an inherently imbalanced classification problem (the number of good loans is much larger than the number of at-risk loans), so I needed to employ different techniques for training and evaluating models with imbalanced classes. You will see use of the imbalanced-learn and Scikit-learn libraries to build and evaluate models using the two following techniques: Resampling and Ensemble Learning.