This project has Classification and Clustering done Via kNN and K-Means respectfully. It later tests its efficiency via F1/accuracy/recall/precision for kNN and Davies-Bouldin Index for Clustering. The Data is also visually represented.
For Additional Information please refer to the Documentation.
survivalstan: Survival Models in Stan author: Jacki Novik Overview Library of Stan Models for Survival Analysis Features: Variety of standard survival
House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin
Time series analysis today is an important cornerstone of quantitative science in many disciplines, including natural and life sciences as well as eco
A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.
torque_model The torque model is a spiritual successor to op-smart-torque, which was a project to train a neural network to control a car's steering f
InfiniteBoost Code for a paper InfiniteBoost: building infinite ensembles with gradient descent (arXiv:1706.01109). A. Rogozhnikov, T. Likhomanenko De
The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.
BartPy Introduction BartPy is a pure python implementation of the Bayesian additive regressions trees model of Chipman et al [1]. Reasons to use BART
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A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray What is Analytics Zoo? Analytics Zo
List of Implementations: Currently, the reimplementation of the DeepAR paper(DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
Time-series-momentum Time-series momentum strategy. You can use the data_analysis.py file to find out the best trigger and window for a given asset an
Demand-Forecasting Business Problem A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product.
What's new We have recently released ThunderGBM, a fast GBDT and Random Forest library on GPUs. add scikit-learn interface, see here Overview The miss
The code from the Machine Learning Bookcamp book and a free course based on the book
Regularization and Feature Selection in Least Squares Temporal Difference Learning Description This is Python implementations of Least Angle Regressio
Table of contents Extended Isolation Forest Summary Motivation Isolation Forest Extension The Code Installation Requirements Use Citation Releases Ext
Model Serving Made Easy BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models. Supports multi
Python-ELM v0.3 --- ARCHIVED March 2021 --- This is an implementation of the Extreme Learning Machine [1][2] in Python, based on scikit-learn. From
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