An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)

Last update: May 24, 2022

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Overview

Rugby score prediction

An end-to-end machine learning web app to predict rugby scores

Overview

An demo project to provide a high-level overview of the key steps needed in going from raw data to a live deployed machine learning app.

What is covered

Data wrangling with Pandas & data storage with SQLite
Machine learning (Neural Network) with Keras
Web app with Flask (and a bit of CSS & HTML)
App deployment with Docker and Heroku

Owner

Business consultant, data scientist, Chartered Management Accountant (CIMA)

GitHub Repository

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Code for Fold2Seq paper from ICML 2021

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An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)

Related tags

Overview

Rugby score prediction

Overview

What is covered

Owner

Algorithmic Trading using RNN

NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset

Controlling the MicriSpotAI robot from scratch

This is a vision-based 3d model manipulation and control UI

Header-only library for using Keras models in C++.

Acute ischemic stroke dataset

PolyGlot, a fuzzing framework for language processors

Benchmarking Pipeline for Prediction of Protein-Protein Interactions

An improvement of FasterGICP: Acceptance-rejection Sampling based 3D Lidar Odometry

Cross-Modal Contrastive Learning for Text-to-Image Generation

This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML)

Code for Fold2Seq paper from ICML 2021

Code for ICML 2021 paper: How could Neural Networks understand Programs?

[ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen

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Embeddinghub is a database built for machine learning embeddings.

SAS: Self-Augmentation Strategy for Language Model Pre-training

LERP : Label-dependent and event-guided interpretable disease risk prediction using EHRs

Our implementation used for the MICCAI 2021 FLARE Challenge titled 'Efficient Multi-Organ Segmentation Using SpatialConfiguartion-Net with Low GPU Memory Requirements'.