KaplanEmrah

KaplanEmrah's Stars

• ageron/handson-ml2

  A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

  Language:Jupyter Notebook28k12.8k

• Niemeyer-Research-Group/OSU-thesis

  Repo for LaTeX-based Oregon State thesis

  Language:TeX69

• jeffheaton/jh-kaggle-util

  Jeff Heaton's Kaggle Utilities

  Language:Python278116

• ageron/handson-ml

  ⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.

  Language:Jupyter Notebook25.2k12.9k

• jsyoon0823/GAIN

  Codebase for Generative Adversarial Imputation Networks (GAIN) - ICML 2018

  Language:Python369152

• fchollet/deep-learning-with-python-notebooks

  Jupyter notebooks for the code samples of the book "Deep Learning with Python"

  Language:Jupyter Notebook18.7k8.7k

• microsoft/NeuronBlocks

  NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

  Language:Python1.4k197

• GeoscienceAustralia/GeodePy

  A toolkit for Geodesy and Surveying in Python

  Language:Python9248

• opengeostat/SmartDHOLES

  Python module for drillhole database management + DJango Web APP

  Language:Jupyter Notebook52

• opengeostat/pygslib

  GSLIB fortran code wrapped into python

  Language:Jupyter Notebook12253

• JasperSnoek/spearmint

  Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012

  Language:Python1.4k335

• jaberg/hyperopt

  Distributed Asynchronous Hyperparameter Optimization in Python

  Language:Python512104

• vvt221/RegressionKriging

  Housing Price Prediction using Regression Krigging

  Language:Jupyter Notebook41

• Lishat/Kriging

  Variogram modelling, fitting and Kriging methods implemented in the field of Geostatistics

  Language:Jupyter Notebook51

• GeostatsGuy/PythonNumericalDemos

  Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.

  Language:Jupyter Notebook1.4k493

• mbrown0294/BostonHousing

  Kaggle Competition - House Prices: Advanced Regression Techniques

  Language:Python1

• tautvydasversockas/BostonHousing_Keras

  Boston housing prices prediction with Keras

  Language:Python1

• bmshahrier/boston-house-price-prediction

  Boston house price prediction.

  Language:Python52

• ss-bhat/BostonHousePricePrediction

  This about predicting boston house price using Ensemble Model - Boosted Decision Tree Regressor and Random Forest Regressor

  Language:Python2

• herpec-j/BostonHousePricesNN

  Implementation of a naive neural network used to predict prices of Boston houses.

  Language:Python2

• Moondancer712/Boston-House-Price-Prediction

  Multi-variables linear regression(Tensorflow)

  Language:Python4

• tensorflow/docs

  TensorFlow documentation

  Language:Jupyter Notebook6.1k5.3k

• awslabs/datawig

  Imputation of missing values in tables.

  Language:JavaScript47869

• arunsank/Regression-Performance-Comparison

  Comparing performance of SVM, Random Forest and ANN models with different tuning techniques

  Language:Python52

• ThomasBrouwer/kmeans_missing

  Implementation of the K-means clustering algorithm, for a dataset in which data points can have missing values for some coordinates.

  Language:Python53

• nvd916/missingValuesPrediction

  CS 4375

  Language:Python1

• sainathGit/DataCleaning_fillMissingValues

  this software uses desision tree to predict the missing class values.

  Language:Python2

• sjtupig/MissingImputer

  use knn, randomforest, xgboost, lightgbm to fill missing values

  Language:Python154

• SebastianRehfeldt/missing-value-aware-feature-selection

  Language:Python3

• JiankaiTu/Decision-Tree-with-continuous-value-and-missing-data

  Language:Python51