HDB_Resale_Prices VS awesome-gradient-boosting-papers

Compare HDB_Resale_Prices vs awesome-gradient-boosting-papers and see what are their differences.

Our great sponsors
  • WorkOS - The modern identity platform for B2B SaaS
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • SaaSHub - Software Alternatives and Reviews
HDB_Resale_Prices awesome-gradient-boosting-papers
1 1
21 980
- -
5.4 3.7
4 months ago about 1 month ago
Python Python
MIT License Creative Commons Zero v1.0 Universal
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

HDB_Resale_Prices

Posts with mentions or reviews of HDB_Resale_Prices. We have used some of these posts to build our list of alternatives and similar projects.

awesome-gradient-boosting-papers

Posts with mentions or reviews of awesome-gradient-boosting-papers. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing HDB_Resale_Prices and awesome-gradient-boosting-papers you can also consider the following projects:

Unredactor - In this project we are tryinbg to create unredactor. Unredactor will take a redacted document and the redacted flag as input, inreturn it will give the most likely candidates to fill in redacted location. In this project we are only considered about unredacting names only. The data that we are considering is imdb data set with many review files. These files are used to buils corpora for finding tfidf score. Few files are used to train and in these files names are redacted and written into redacted folder. These redacted files are used for testing and different classification models are built to predict the probabilies of each class. Top 5 classes i.e names similar to the test features are written at the end of text in unreddacted foleder.

SharpLearning - Machine learning for C# .Net

awesome-fraud-detection-papers - A curated list of data mining papers about fraud detection.

Intrusion-Detection-System-Using-Machine-Learning - Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

stidler - Error support for **idlesteam.com**