minghao2016 Goto Github PK

-direct-marketing-model-on-gold-customers

Analyze the influence of managers' decisions with the design of analysis on customer's behavior via direct marketing

-pymol

PyMOL tutorial for beginners

10-steps-to-become-a-data-scientist

📢 Ready to learn! you will learn 10 skills as data scientist:📚 Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.

1000_eng_dicts

1000 部英语类词典名录兼词典推荐、选择

1000_genomes_examples

Examples using R and 1000 genomes data

1d-random-walk-and-diffusion

One-dimensional Random Walk and diffusion: simulation and scaling

1day-notebooks

2015

Public material for CS109

2015lab1

2015lab2

2015lab3

2015lab5

Lab 5, the first on Machine Learning!

2016-09-math116

2016-09 Middlebury Intro to Statistical & Data Sciences

2017-new-coder-survey

Open Dataset from freeCodeCamp's 2017 survey of more than 20,000 developers

2018-iflytek-marketing-algorithms-competition-finals-rank1

2018科大讯飞营销算法大赛（冠军方案）

2019-ccf-bdci-car_sales

2019年CCF大数据与计算智能大赛乘用车细分市场销量预测冠军解决方案

2019-ncov

research the 2019-nCov and design the inhibitors with AI

202005-23-building-clinical-decision-support-system-for-cvd

The prediction of cardiovascular disease has great significance for its prevention work. The clinical data information for different patients is difficult to be reasonably represented, also the time interval between events is different, which leads to problems such as difficult prediction. This paper proposes several algorithms based on electronic medical record data to study the risk prediction of cardiovascular disease. The model uses techniques such as neural network to characterize and learn the patient's historical electronic medical record data, which can not only effectively capture the time-series features of the electronic medical record data, but also consider the potential correlation between them, and ultimately improve cardiovascular disease risk prediction performance. The key result is that coronary artery related disease has a prominent influence on the prediction of cardiovascular disease risk.

2d-pharmacophore-search

simple rdkit script

3d_from_dms_extended_data

3dfi

The 3DFI pipeline predicts the 3D structure of proteins and searches for structural homology in the 3D space.

3dinfomax

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

3dstructureclustering

3D structure clustering for PDB, Amber trajectories and Gromacs Trajectories.

6345-proj

6.345 Course Project

a-b-testing-and-beyond

Information and content associated with "A/B Testing and Beyond", a continuing eduction certificate offered by the University of San Francisco's Data Institute.

a-b-testing-with-machine-learning

Implemented an A/B Testing solution with the help of machine learning

a-recsys

A Tensorflow based implicit recommender system

aaltd18

Data augmentation using synthetic data for time series classification with deep residual networks

aane_matlab

Accelerated Attributed Network Embedding, SDM 2017

aane_python

Accelerated Attributed Network Embedding, SDM 2017