MlboxMLBox is a powerful Automated Machine Learning python library.
Stars: ✭ 1,199 (+202.02%)
AutoTabularAutomatic machine learning for tabular data. ⚡🔥⚡
Stars: ✭ 51 (-87.15%)
fast retrainingShow how to perform fast retraining with LightGBM in different business cases
Stars: ✭ 56 (-85.89%)
HumanOrRobota solution for competition of kaggle `Human or Robot`
Stars: ✭ 16 (-95.97%)
docker-kaggle-ko머신러닝/딥러닝(PyTorch, TensorFlow) 전용 도커입니다. 한글 폰트, 한글 자연어처리 패키지(konlpy), 형태소 분석기, Timezone 등의 설정 등을 추가 하였습니다.
Stars: ✭ 46 (-88.41%)
kaggle-berlinMaterial of the Kaggle Berlin meetup group!
Stars: ✭ 36 (-90.93%)
Apartment-Interest-PredictionPredict people interest in renting specific NYC apartments. The challenge combines structured data, geolocalization, time data, free text and images.
Stars: ✭ 17 (-95.72%)
Auto ml[UNMAINTAINED] Automated machine learning for analytics & production
Stars: ✭ 1,559 (+292.7%)
Mljar SupervisedAutomated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀
Stars: ✭ 961 (+142.07%)
Kaggle-Competition-SberbankTop 1% rankings (22/3270) code sharing for Kaggle competition Sberbank Russian Housing Market: https://www.kaggle.com/c/sberbank-russian-housing-market
Stars: ✭ 31 (-92.19%)
Steppy ToolkitCurated set of transformers that make your work with steppy faster and more effective 🔭
Stars: ✭ 21 (-94.71%)
LightautomlLAMA - automatic model creation framework
Stars: ✭ 196 (-50.63%)
Hyperparameter hunterEasy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Stars: ✭ 648 (+63.22%)
SteppyLightweight, Python library for fast and reproducible experimentation 🔬
Stars: ✭ 119 (-70.03%)
Home Credit Default RiskDefault risk prediction for Home Credit competition - Fast, scalable and maintainable SQL-based feature engineering pipeline
Stars: ✭ 68 (-82.87%)
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 (+3248.36%)
NyaggleCode for Kaggle and Offline Competitions
Stars: ✭ 209 (-47.36%)
Data Science Bowl 2018End-to-end one-class instance segmentation based on U-Net architecture for Data Science Bowl 2018 in Kaggle
Stars: ✭ 56 (-85.89%)
Data-ScienceUsing Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.
Stars: ✭ 15 (-96.22%)
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 (-60.45%)
stackgbm🌳 Stacked Gradient Boosting Machines
Stars: ✭ 24 (-93.95%)
mlforecastScalable machine 🤖 learning for time series forecasting.
Stars: ✭ 96 (-75.82%)
neptune-client📒 Experiment tracking tool and model registry
Stars: ✭ 348 (-12.34%)
JLBoost.jlA 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms
Stars: ✭ 65 (-83.63%)
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 (-78.34%)
datascienvdatascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries
Stars: ✭ 53 (-86.65%)
targets-minimalA minimal example data analysis project with the targets R package
Stars: ✭ 50 (-87.41%)
KaggleKaggle Kernels (Python, R, Jupyter Notebooks)
Stars: ✭ 26 (-93.45%)
recsys2019The complete code and notebooks used for the ACM Recommender Systems Challenge 2019
Stars: ✭ 26 (-93.45%)
fastknnFast k-Nearest Neighbors Classifier for Large Datasets
Stars: ✭ 64 (-83.88%)
autogbt-altAn experimental Python package that reimplements AutoGBT using LightGBM and Optuna.
Stars: ✭ 76 (-80.86%)
kaggle-quora-question-pairsMy solution to Kaggle Quora Question Pairs competition (Top 2%, Private LB log loss 0.13497).
Stars: ✭ 104 (-73.8%)
kaggle-plasticcSolution to Kaggle's PLAsTiCC Astronomical Classification Competition
Stars: ✭ 50 (-87.41%)
featurewizUse advanced feature engineering strategies and select best features from your data set with a single line of code.
Stars: ✭ 229 (-42.32%)
kaggle-codeA repository for some of the code I used in kaggle data science & machine learning tasks.
Stars: ✭ 100 (-74.81%)
HyperGBMA full pipeline AutoML tool for tabular data
Stars: ✭ 172 (-56.68%)
Arch-Data-ScienceArchlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision
Stars: ✭ 92 (-76.83%)