Python module for data science and machine learning users.

Overview

dsnk-distributions package

dsnk distribution is a Python module for data science and machine learning that was created with the goal of reducing calculation complexity.

Files

  • Gaussian distribution for calculating and visualizing a Gaussian distribution. calculation and representation of mean value, and standard deviation of the distribution. Output a histogram of the instance variable data using matplotlib pyplot library.
  • Binomial distribution for calculating and visualizing a Binomial distribution. Inherit functionality from Gaussian distribution with addition of represnting probability of an event occuring n (int) number of trials.

  • Generic distribution class for calculating and visualizing a probability distribution. It performs its functions with help of Binomial distribution and Gaussian distribution.

User installation

The easiest way to install dsnk_distribution is using pip

    pip install dsnk_distribution

or conda:

    conda install dsnk_distribution
Source code

You can check the latest sources with the command:

git clone https://github.com/Emmanuel262/dnsk_distribution.git

Development

The project began after learning the fundamentals of data science and machine learning, and volunteers will soon be needed to help enhance the project's performance and efficiency, as the tasks to be improved will be posted soon.

Owner
Emmanuel ASIFIWE
Emmanuel ASIFIWE
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Auto_TS: Auto_TimeSeries Automatically build multiple Time Series models using a Single Line of Code. Now updated with Dask. Auto_timeseries is a comp

AutoViz and Auto_ViML 519 Jan 03, 2023
This project has Classification and Clustering done Via kNN and K-Means respectfully

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. T

Mohammad Ali Mustafa 0 Jan 20, 2022
A logistic regression model for health insurance purchasing prediction

Logistic_Regression_Model A logistic regression model for health insurance purchasing prediction This code is using these packages, so please make sur

ShawnWang 1 Nov 29, 2021
Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort

Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort

2.3k Jan 04, 2023
Covid-polygraph - a set of Machine Learning-driven fact-checking tools

Covid-polygraph, a set of Machine Learning-driven fact-checking tools that aim to address the issue of misleading information related to COVID-19.

1 Apr 22, 2022
MLflow App Using React, Hooks, RabbitMQ, FastAPI Server, Celery, Microservices

Katana ML Skipper This is a simple and flexible ML workflow engine. It helps to orchestrate events across a set of microservices and create executable

Tom Xu 8 Nov 17, 2022
NumPy-based implementation of a multilayer perceptron (MLP)

My own NumPy-based implementation of a multilayer perceptron (MLP). Several of its components can be tuned and played with, such as layer depth and size, hidden and output layer activation functions,

1 Feb 10, 2022
moDel Agnostic Language for Exploration and eXplanation

moDel Agnostic Language for Exploration and eXplanation Overview Unverified black box model is the path to the failure. Opaqueness leads to distrust.

Model Oriented 1.2k Jan 04, 2023
Bayesian optimization in JAX

Bayesian optimization in JAX

Predictive Intelligence Lab 26 May 11, 2022
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Distributed (Deep) Machine Learning Community 23.6k Jan 03, 2023
An open-source library of algorithms to analyse time series in GPU and CPU.

An open-source library of algorithms to analyse time series in GPU and CPU.

Shapelets 216 Dec 30, 2022
ML Kaggle Titanic Problem using LogisticRegrission

-ML-Kaggle-Titanic-Problem-using-LogisticRegrission here you will find the solution for the titanic problem on kaggle with comments and step by step c

Mahmoud Nasser Abdulhamed 3 Oct 23, 2022
Pandas DataFrames and Series as Interactive Tables in Jupyter

Pandas DataFrames and Series as Interactive Tables in Jupyter Star Turn pandas DataFrames and Series into interactive datatables in both your notebook

Marc Wouts 364 Jan 04, 2023
Dragonfly is an open source python library for scalable Bayesian optimisation.

Dragonfly is an open source python library for scalable Bayesian optimisation. Bayesian optimisation is used for optimising black-box functions whose

744 Jan 02, 2023
Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along with material in the form of Jupyter Notebooks.

Databricks Certification Spark Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along

19 Dec 13, 2022
pywFM is a Python wrapper for Steffen Rendle's factorization machines library libFM

pywFM pywFM is a Python wrapper for Steffen Rendle's libFM. libFM is a Factorization Machine library: Factorization machines (FM) are a generic approa

João Ferreira Loff 251 Sep 23, 2022
Optimal Randomized Canonical Correlation Analysis

ORCCA Optimal Randomized Canonical Correlation Analysis This project is for the python version of ORCCA algorithm. It depends on Numpy for matrix calc

Yinsong Wang 1 Nov 21, 2021
Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.

SDK: Overview of the Kubeflow pipelines service Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on

Kubeflow 3.1k Jan 06, 2023
Lightweight Machine Learning Experiment Logging 📖

Simple logging of statistics, model checkpoints, plots and other objects for your Machine Learning Experiments (MLE). Furthermore, the MLELogger comes with smooth multi-seed result aggregation and co

Robert Lange 65 Dec 08, 2022
BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models.

Model Serving Made Easy BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models. Supports multi

BentoML 4.4k Jan 04, 2023