Apartment-Interest-PredictionPredict people interest in renting specific NYC apartments. The challenge combines structured data, geolocalization, time data, free text and images.
Stars: ✭ 17 (-77.63%)
LightautomlLAMA - automatic model creation framework
Stars: ✭ 196 (+157.89%)
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 (+17390.79%)
MlboxMLBox is a powerful Automated Machine Learning python library.
Stars: ✭ 1,199 (+1477.63%)
Auto ml[UNMAINTAINED] Automated machine learning for analytics & production
Stars: ✭ 1,559 (+1951.32%)
Machinejs[UNMAINTAINED] Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml
Stars: ✭ 412 (+442.11%)
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 (-59.21%)
Awesome Decision Tree PapersA collection of research papers on decision, classification and regression trees with implementations.
Stars: ✭ 1,908 (+2410.53%)
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 (+106.58%)
HyperGBMA full pipeline AutoML tool for tabular data
Stars: ✭ 172 (+126.32%)
TpotA Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Stars: ✭ 8,378 (+10923.68%)
Mljar SupervisedAutomated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀
Stars: ✭ 961 (+1164.47%)
HumanOrRobota solution for competition of kaggle `Human or Robot`
Stars: ✭ 16 (-78.95%)
lleavesCompiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
Stars: ✭ 132 (+73.68%)
HungabungaHungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!
Stars: ✭ 614 (+707.89%)
KagglerCode for Kaggle Data Science Competitions
Stars: ✭ 614 (+707.89%)
ForecastingTime Series Forecasting Best Practices & Examples
Stars: ✭ 2,123 (+2693.42%)
kaggle-plasticcSolution to Kaggle's PLAsTiCC Astronomical Classification Competition
Stars: ✭ 50 (-34.21%)
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 (+131.58%)
docker-kaggle-ko머신러닝/딥러닝(PyTorch, TensorFlow) 전용 도커입니다. 한글 폰트, 한글 자연어처리 패키지(konlpy), 형태소 분석기, Timezone 등의 설정 등을 추가 하였습니다.
Stars: ✭ 46 (-39.47%)
AutoTabularAutomatic machine learning for tabular data. ⚡🔥⚡
Stars: ✭ 51 (-32.89%)
AutodlAutomated Deep Learning without ANY human intervention. 1'st Solution for AutoDL [email protected]
Stars: ✭ 854 (+1023.68%)
fast retrainingShow how to perform fast retraining with LightGBM in different business cases
Stars: ✭ 56 (-26.32%)
stackgbm🌳 Stacked Gradient Boosting Machines
Stars: ✭ 24 (-68.42%)
maggyDistribution transparent Machine Learning experiments on Apache Spark
Stars: ✭ 83 (+9.21%)
LightGBM.jlLightGBM.jl provides a high-performance Julia interface for Microsoft's LightGBM.
Stars: ✭ 40 (-47.37%)
handson-ml도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
Stars: ✭ 285 (+275%)
PracticalMachineLearningA collection of ML related stuff including notebooks, codes and a curated list of various useful resources such as books and softwares. Almost everything mentioned here is free (as speech not free food) or open-source.
Stars: ✭ 60 (-21.05%)
data-science-learning📊 All of courses, assignments, exercises, mini-projects and books that I've done so far in the process of learning by myself Machine Learning and Data Science.
Stars: ✭ 32 (-57.89%)
kaggleKaggle solutions
Stars: ✭ 17 (-77.63%)
ml-workflow-automationPython Machine Learning (ML) project that demonstrates the archetypal ML workflow within a Jupyter notebook, with automated model deployment as a RESTful service on Kubernetes.
Stars: ✭ 44 (-42.11%)
deep autovimlBuild tensorflow keras model pipelines in a single line of code. Now with mlflow tracking. Created by Ram Seshadri. Collaborators welcome. Permission granted upon request.
Stars: ✭ 98 (+28.95%)
tsfusePython package for automatically constructing features from multiple time series
Stars: ✭ 33 (-56.58%)
BossNAS(ICCV 2021) BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
Stars: ✭ 125 (+64.47%)
kaggle-camera-model-identificationCode for reproducing 2nd place solution for Kaggle competition IEEE's Signal Processing Society - Camera Model Identification
Stars: ✭ 64 (-15.79%)
PyData-Pseudolabelling-KeynoteAccompanying notebook and sources to "A Guide to Pseudolabelling: How to get a Kaggle medal with only one model" (Dec. 2020 PyData Boston-Cambridge Keynote)
Stars: ✭ 23 (-69.74%)
clara-train-examplesExample notebooks demonstrating how to use Clara Train to build Medical Imaging Deep Learning models
Stars: ✭ 80 (+5.26%)
mlforecastScalable machine 🤖 learning for time series forecasting.
Stars: ✭ 96 (+26.32%)
Data-Science-ProjectsData Science projects on various problem statements and datasets using Data Analysis, Machine Learning Algorithms, Deep Learning Algorithms, Natural Language Processing, Business Intelligence concepts by Python
Stars: ✭ 28 (-63.16%)
Algorithmml & dl & kaggle
Stars: ✭ 24 (-68.42%)