This repository provides R scripts for reproducing virtual species generating, modeling species distribution and final figures related with published manuscript.

Visualizing an XGBoost model in R using a sunburst plot (using inTrees)

Course project for 6.869: automatic summarization for neural net interpretability

Sentiment Analysis using Machine Learning

MSc dissertation project, written in using LaTeX.

Will They Pay? A machine learning solution to understand mobile app user payment behavior

To predict the rating of a developer using various data captured during an online test

The project provides explanation of what SHAP is and how it can be used to interpret model. Also contains Notebook with detail on model interpretability method SHAP and code implementation on Heart disease dataset.

Covid Detection via CT Scan Image Analysis

This repository includes a general informations and examples about how to make a machine learning model just a few lines of code in Python using PyCaret package.

Collection of the assignments for Data Science Engineering Methods on National Stock Exchange Dataset and TMNIST dataset

Code for "High-Precision Model-Agnostic Explanations" paper. A follow up to LIME model.

Investigating a neural network response to input parameters using sensitivity analysis techniques.

Identifying Hate Speech in Philippie Election-Related Tweets

erformed a predictive analysis on the customer's Bank Loan Application data to predict loan status. Using python, pandas, scipy, seaborn, AutoML libraries, and machine learning techniques. Used Machine Learning techniques to accurately predict the evaluation scheme if the particular loan will be 'Fully Paid' or 'Charged Off'. This means if Bank …

This repository contains the work in the AI engineer Cognizant virtual training and internship program from forage

Model interpretability for Explainable Artificial Intelligence

An Explainable AI technique introduced in the paper Axiomatic Attribution for Deep Networks

A machine learning project developing classification models to predict COVID-19 diagnosis in paediatric patients.

Interpretability and Fairness in Machine Learning