Kaggle HumpbackCode for 3rd place solution in Kaggle Humpback Whale Identification Challenge.
Stars: ✭ 135 (-46.64%)
Dog Breeds ClassificationSet of scripts and data for reproducing dog breed classification model training, analysis, and inference.
Stars: ✭ 105 (-58.5%)
Data Analysis主要是爬虫与数据分析项目总结,外加建模与机器学习,模型的评估。
Stars: ✭ 142 (-43.87%)
LightgbmA fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Stars: ✭ 13,293 (+5154.15%)
Ml Dl ScriptsThe repository provides usefull python scripts for ML and data analysis
Stars: ✭ 119 (-52.96%)
CryptoCryptocurrency Historical Market Data R Package
Stars: ✭ 112 (-55.73%)
Deeptoxictop 1% solution to toxic comment classification challenge on Kaggle.
Stars: ✭ 180 (-28.85%)
Pytorch zooA collection of useful modules and utilities (especially helpful for kaggling) not available in Pytorch
Stars: ✭ 84 (-66.8%)
MlboxMLBox is a powerful Automated Machine Learning python library.
Stars: ✭ 1,199 (+373.91%)
Data Science Bowl 2018End-to-end one-class instance segmentation based on U-Net architecture for Data Science Bowl 2018 in Kaggle
Stars: ✭ 56 (-77.87%)
Ml Fraud DetectionCredit card fraud detection through logistic regression, k-means, and deep learning.
Stars: ✭ 117 (-53.75%)
Kaggle HousepricesKaggle Kernel for House Prices competition https://www.kaggle.com/massquantity/all-you-need-is-pca-lb-0-11421-top-4
Stars: ✭ 113 (-55.34%)
Kaggle NdsbCode for National Data Science Bowl. 10th place.
Stars: ✭ 45 (-82.21%)
Kaggle Freesound Audio Tagging8th place solution (on Kaggle) to the Freesound General-Purpose Audio Tagging Challenge (DCASE 2018 - Task 2)
Stars: ✭ 111 (-56.13%)
Girls In Ai免费学代码系列:小白python入门、数据分析data analyst、机器学习machine learning、深度学习deep learning、kaggle实战
Stars: ✭ 2,309 (+812.65%)
PinsPin, Discover and Share Resources
Stars: ✭ 149 (-41.11%)
SegmentationTensorflow implementation : U-net and FCN with global convolution
Stars: ✭ 101 (-60.08%)
NyaggleCode for Kaggle and Offline Competitions
Stars: ✭ 209 (-17.39%)
D2l EnInteractive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.
Stars: ✭ 11,837 (+4578.66%)
ChefboostA Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python
Stars: ✭ 176 (-30.43%)
Kaggle CompetitionsThere are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.
Stars: ✭ 86 (-66.01%)
Kaggle Crowdflower1st Place Solution for CrowdFlower Product Search Results Relevance Competition on Kaggle.
Stars: ✭ 1,708 (+575.1%)
Facial Expression RecognitionClassify each facial image into one of the seven facial emotion categories considered using CNN based on https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge
Stars: ✭ 82 (-67.59%)
KaggleCode for Kaggle Competitions
Stars: ✭ 128 (-49.41%)
Allstate capstoneAllstate Kaggle Competition ML Capstone Project
Stars: ✭ 72 (-71.54%)
Home Credit Default RiskDefault risk prediction for Home Credit competition - Fast, scalable and maintainable SQL-based feature engineering pipeline
Stars: ✭ 68 (-73.12%)
LightautomlLAMA - automatic model creation framework
Stars: ✭ 196 (-22.53%)
Fraud DetectionCredit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook
Stars: ✭ 58 (-77.08%)
Kaggle HpaCode for 3rd place solution in Kaggle Human Protein Atlas Image Classification Challenge.
Stars: ✭ 226 (-10.67%)
Machine Learning Workflow With PythonThis is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation
Stars: ✭ 157 (-37.94%)
Ds bowl 2018Kaggle Data Science Bowl 2018
Stars: ✭ 116 (-54.15%)