TIANCHI Purchase Redemption Forecast Challenge

Last update: Aug 26, 2022

Overview

TIANCHI-Purchase-Redemption-Forecast-Challenge

My solution of this Aliyun Tianchi challenge (https://tianchi.aliyun.com/competition/entrance/231573/introduction).

Achieved online score: 132.89, Ranking TOP 5% (431/8452, updated on 2022.1.29)

Introduction：

The challenge focuses on forecasting the daily capital inflow and outflow of the “YuEBao” monetary fund in the future. For the monetary fund, capital inflow means purchase, and capital outflow is redemption.

Datasets and its description are available at the official website of this challenge

Repository Structure:

Notebook for exploratory data analysis
Notebook for feature engineering and modeling
A report(Chinese, English version might be available in the future) for impelemental details and analysis for this project

Note:

All codes are for reference only and shall not be used for commercial purposes without permission. If you have any questions about the code, please contact: [email protected]

Owner

Haorui HE

Software Engineering; Machine Learning; Deep Learning; NLP; Data Science

GitHub Repository

MBTR is a python package for multivariate boosted tree regressors trained in parameter space.

61 Dec 19, 2022

This project used bitcoin, S&P500, and gold to construct an investment portfolio that aimed to minimize risk by minimizing variance.

minvar_invest_portfolio This project used bitcoin, S&P500, and gold to construct an investment portfolio that aimed to minimize risk by minimizing var

1 Jan 06, 2022

WAGMA-SGD is a decentralized asynchronous SGD for distributed deep learning training based on model averaging.

WAGMA-SGD is a decentralized asynchronous SGD based on wait-avoiding group model averaging. The synchronization is relaxed by making the collectives externally-triggerable, namely, a collective can b

6 Jun 18, 2022

Machine Learning approach for quantifying detector distortion fields

DistortionML Machine Learning approach for quantifying detector distortion fields. This project is a feasibility study for training a surrogate model

1 Nov 05, 2021

Distributed Deep learning with Keras & Spark

Elephas: Distributed Deep Learning with Keras & Spark Elephas is an extension of Keras, which allows you to run distributed deep learning models at sc

1.6k Dec 29, 2022

A GitHub action that suggests type annotations for Python using machine learning.

Typilus: Suggest Python Type Annotations A GitHub action that suggests type annotations for Python using machine learning. This action makes suggestio

40 Sep 18, 2022

TorchDrug is a PyTorch-based machine learning toolbox designed for drug discovery

A powerful and flexible machine learning platform for drug discovery

1.1k Jan 08, 2023

Neighbourhood Retrieval (Nearest Neighbours) with Distance Correlation.

Neighbourhood Retrieval with Distance Correlation Assign Pseudo class labels to datapoints in the latent space. NNDC is a slim wrapper around FAISS. N

1 Jan 16, 2022

SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.

SageMaker Python SDK SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the S

1.8k Jan 01, 2023

Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way

Apache Liminals goal is to operationalise the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validat

121 Dec 28, 2022

XManager: A framework for managing machine learning experiments 🧑‍🔬

XManager is a platform for packaging, running and keeping track of machine learning experiments. It currently enables one to launch experiments locally or on Google Cloud Platform (GCP). Interaction

620 Dec 27, 2022

NumPy-based implementation of a multilayer perceptron (MLP)

My own NumPy-based implementation of a multilayer perceptron (MLP). Several of its components can be tuned and played with, such as layer depth and size, hidden and output layer activation functions,

1 Feb 10, 2022

A simple python program that draws a tree for incrementing values using the Collatz Conjecture.

Collatz Conjecture A simple python program that draws a tree for incrementing values using the Collatz Conjecture. Values which can be edited: Length

1 Oct 28, 2021

easyNeuron is a simple way to create powerful machine learning models, analyze data and research cutting-edge AI.

5 Jun 18, 2022

neurodsp is a collection of approaches for applying digital signal processing to neural time series

neurodsp is a collection of approaches for applying digital signal processing to neural time series, including algorithms that have been proposed for the analysis of neural time series. It also inclu

224 Dec 02, 2022

AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

Data Science on AWS - O'Reilly Book Get the book on Amazon.com Book Outline Quick Start Workshop (4-hours) In this quick start hands-on workshop, you

2.8k Jan 03, 2023

Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along with material in the form of Jupyter Notebooks.

Databricks Certification Spark Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along

19 Dec 13, 2022