Topic: categorical-features Goto Github
Some thing interesting about categorical-features
Some thing interesting about categorical-features
categorical-features,Feature Importance of categorical variables by converting them into dummy variables (One-hot-encoding) can skewed or hard to interpret results. Here I present a method to get around this problem using H2O.
User: abhmalik
categorical-features,glmdisc Python package: discretization, factor level grouping, interaction discovery for logistic regression
User: adimajo
categorical-features,Kaggle Competition (Encoding categorical variables)
User: alvimahmud-osu
categorical-features,Explore various natural language processing models using Python.
User: ansu-john
categorical-features,Data Science Session: TabNet
User: arfua
categorical-features,Mostl oftenly used Encoding techniques for categorical Varibales are performed here.
User: ashishyadav24092000
Home Page: https://github.com/ashishyadav24092000/Encoding_categorical-variables
categorical-features,In this code handling of the missing values for the categorical features from any dataset is shown.
User: ashishyadav24092000
Home Page: https://github.com/ashishyadav24092000/FE_categorical_missing_values
categorical-features,In this i have performed complete feature engineering that is from handling null values, Categorical features upto performing feature scaling on our test_data and train_data.
User: ashishyadav24092000
Home Page: https://github.com/ashishyadav24092000/MAchineLearning_FeatureEngineering1
categorical-features,Tensorflow implementation of Product-based Neural Networks. An extended version is at https://github.com/Atomu2014/product-nets-distributed.
User: atomu2014
categorical-features,Encode Categorical Features (unmaintained)
User: bfgray3
Home Page: https://CRAN.R-project.org/package=cattonum
categorical-features,A small tutorial to demonstrate the power of CatBoost Algorithm
User: bhattbhavesh91
categorical-features,A mixed attributes predictive algorithm implemented in Python.
User: c4pub
categorical-features,A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Organization: catboost
Home Page: https://catboost.ai
categorical-features,A lightweight library for encoding categorical features in your dataset with robust k-fold target statistics in training with credibility filtering, and custom statistics.
User: circargs
categorical-features,Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.
Organization: cpa-analytics
categorical-features,Medium Post: some techniques useful to deal with missing values of Categorical Features
User: daniele-salerno
categorical-features,Predicting the ideological direction of Supreme Court decisions: ensemble vs. unified case-based model
User: davidmasse
categorical-features,This is project 1 of the Udacity Data Scientist Nanodegree.
User: elenacramer
categorical-features,
User: entron
categorical-features,Using supervised machine learning models to determine credit worthiness
User: gpawlows
categorical-features,clustering with crypto!
User: gpawlows
categorical-features,Laboratory works on Methods of Artificial Intelligence course
User: helga-helga
categorical-features,A python package to compute pairwise Euclidean distances on datasets with categorical features in little time
User: itswajdy
categorical-features,Data Science in the Banking Industry [Volume 1]
User: joviarnandy
categorical-features,Interactive ML Toolset
User: konodyuk
Home Page: https://docs.kts.ai
categorical-features,Supervised Learning Problem. In this categorizing the customers in four groups, as follows: 1- Basic Service 2- E-Service 3- Plus Service 4- Total Service.
User: kunal1198
categorical-features,Multimodal deep learning package that uses both categorical and text-based features in a single deep architecture for regression and binary classification use cases.
User: licesonw
categorical-features,This study creates machine learning models to predict the seriousness of car crashes using 2019 and 2020 crash reports from the publicly accessable database maintained by the Chicago Police Department. A car crash is considered serious if the crash results in an injury or the car is towed due to the crash. Models use categorical features that describe conditions at the time of the crash and crash causes to predict the required target. The current focus is to classify whether a crash results in an injury. All machine learning models are trained, validated, and tested on randomly split 2019 crash reports. The best model (along with all others) are then tested using the full set of 2020 crash reports.
User: maskar
categorical-features,This repository contains a notebook demonstrating a practical implementation of the so-called Entity Embedding for Encoding Categorical Features for Training a Neural Network.
User: mmortazavi
categorical-features,
User: nat-christoforou
categorical-features,Why data analysis? , How to understand the problem, what to do for data analysis, and how clean the data for building Machine Learning models
User: navadeeppasala
categorical-features,A set of tools for machine learning (for the current day, there are active learning utilities and implementations of some stacking-based techniques).
User: nikolay-lysenko
categorical-features,Generic encoding of record types
User: ocramz
categorical-features,Machine Learning (Pyspark-MLlib and Pyspark-Sql)
User: phuocblt
categorical-features,Binary classification, with every feature as categoricals
User: praxitelisk
Home Page: https://www.kaggle.com/praxitelisk/categorical-feature-encoding-challenge-eda-ml
categorical-features,EDA-REGRESSION-CLASSIFICATION-WITH-BALCK-FRIDAY-DATASET
User: rahul221389
categorical-features,Category transformation
User: raynardj
categorical-features,Load monitoring/ load detection is one big breakthrough in tackling the problem of increasing carbon footprint. It helps to provide detailed electricity consumption information in residential households. This project is dedicated to providing a perfect estimate of the usage of the most common appliances in residential buildings.
User: ricardoariasalazar
categorical-features,It predicts the right group of new customers by Segmentation among A, B, C, and D segments using LightGBM Classifier.
User: ritika-0111
categorical-features,log
User: saibharath2
categorical-features,A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
User: serengil
Home Page: https://www.youtube.com/watch?v=Z93qE5eb6eg&list=PLsS_1RYmYQQHp_xZObt76dpacY543GrJD&index=3
categorical-features,Project of a coursework - Categorical Data Analysis (M.Stat Semester 2) under the supervision of Prof. Arindam Chatterjee.,ISID
User: shrayanroy
categorical-features,
User: soum-io
categorical-features,
User: sumansahoo16
categorical-features,A Python framework for deploying recommendation models for form fields.
User: vc1492a
categorical-features,This repository contains notebooks on different topics across - linear algebra, image classification, language models etc.
User: victor7246
categorical-features,Kaggle Categorical Feature Encoding Challenge II, private score 0.78795 (110 place)
User: viktorsapozhok
categorical-features,
User: xman14
categorical-features,Develop a predictive model to understand the LTV of each customer for a DTC meal-kit business.
User: yvetteyyuan
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