Use AutoAI to detect fraud
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Updated
Dec 9, 2022 - Jupyter Notebook
Use AutoAI to detect fraud
Explanatory Model Analysis. Explore, Explain and Examine Predictive Models
Portfolio of course work for my Master's in Data Science.
we proposed a software defect predictive development models using machine learning techniques that can enable the software to continue its projected task.
Machine Learning Introductory Course
Official implementation of the paper *PDE-Driven Spatiotemporal Disentanglement*
Machine learning to predict future number Covid19 Daily Cases (7-day moving average). Long Short Term Memory (LSTM) Predictor and Reinforcement Learning (RL) Prescription with Oxford Dataset
A few-shot learning approach to forecasting the evolution of the brain connectome.
Plotly Dash HTML Python Flask site for user to interact with a trained machine learning model to predict the round-trip cost of flights — based on 9 million 2018 Domestic Flight Prices in the United States.
This repository contains coursework for the Business Analytics Foundations course in the MS Applied Business Analytics program at Boston University.
Built predictive machine learning models to determine whether a given transaction will be fraudulent or not using transactional data
Reproduce Predictive Models of Fire via Deep learning Exploiting Colorific Variation (ICAIIC2019) with Pytorch
PRESS: Predictive State Smoothing in Python (tf.keras)
Course Project for UCSD ECE143: Programming for Data Analytics
This repository contains code for predicting the price of mobile phones using regression models. It includes the implementation of various regression techniques such as Random Forest Regression, Decision Tree Regressor, Linear Regression, and Lasso Regression.
Our Economic Forecasting Model leverages Genetic Algorithms and Random Forests to provide farmers, policymakers, and businesses with cutting-edge insights for informed, profitable decisions in the ever-changing world of agriculture.
An explainable inductive learning model on gene regulatory and toxicogenomic knowledge graph (under development...)
This repository contains code for predicting the price of mobile phones using regression models. It includes the implementation of various regression techniques such as Random Forest Regression, Decision Tree Regressor, Linear Regression, and Lasso Regression.
Supporting code for https://www.biorxiv.org/content/10.1101/796714v4
Trabajo de Fin de Grado del Grado de Ingeniería Informática, realizado en la Escuela Técnica Superior de Ingeniería Informática de la Universidad Politécnica de Madrid.
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