binomial-distribution
There are 1 repository under binomial-distribution topic.
sharmaroshan / Statistics-for-Data-Science
Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descriptive Analysis, Inferential Analysis, Comparative Analysis, Chi-Square Test, T Test, Z test, AB Testing etc.
Language:Jupyter Notebook 88astralord / RandLib
:rocket: A library designed to facilitate work with probability, statistics and stochastic calculus
Language:C++ 69RF5 / transfusion-asr
Transcribing Speech with Multinomial Diffusion, training code and models.
Language:Python 66gburtini / Probability-Distributions-for-PHP
PHP implementation of statistical probability distributions: normal distribution, beta distribution, gamma distribution and more.
Language:PHP 57SrGrace / 10-Days-of-Statistics-Challenges
10 Days of Statistics Challenges at HackerRank
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nirupamaprv / Practical_Stats
Exercises solved for the Practical Statistics Module of Udacity's DAND: assignments and practice problems
Language:Jupyter Notebook 5caravagnalab / BMix
Binomial and Beta-Binomial mixture models for counts data.
Language:HTML 4rgbstudios / binomial-calc
A Binomial Distribution Calculator with useful charts
Language:HTML 4dineshssdn-867 / Probabilistic-model-for-any-psychiatric-dieaese
This model can be used to find how many genes are involved in psychiatric disease. To verify the model we have taken the example of schizophrenia.
Language:Jupyter Notebook 3gokkayahmet / PRICING-DERIVATIVES-WITH-BINOMIAL-TREE-MODEL
The binomial tree model is a commonly used approach for pricing derivatives, such as options. The basic idea behind the model is to create a tree of possible stock prices over time, based on a set of input parameters
Language:Jupyter Notebook 3nolanbconaway / binoculars
Confidence intervals for binomial proportions.
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- Language:Go 2
nasserkessas / discrete-binomial-distribution-visualiser
An Jupyter notebook experimental discrete binomial distribution grapher
Language:Jupyter Notebook 2R-Aravind / distributions
PyPi package for implementation of Gaussian and Binomial probability distributions.
Language:Python 2- EasyStatistics
Ritvik-Sapra / EasyStatistics
A python package for easy dealing with Binomial and Gaussian distribution
Language:Python 2 sanjushasuresh / GENERATION-OF-RANDOM-SAMPLES
Generation of random samples using R
Language:R 2Sid-149 / 10-Days-of-Statistics
This repository contains the solutions to HackerRank's 10 Days of Statistics.
Language:Python 2th3r3ald0n / randomtennis
Use random sampling to calculate probabililties of winning tennis matches based on constant and alternating probabilities of winning a (service) game.
Language:Jupyter Notebook 2al6133 / Pandemic_Simulation
Simulating a Pandemic in Python from scratch & implemntation of famous SIR method for pandemic simualtion
Language:Jupyter Notebook 1brunogresende / ppca-aedi
RepositĂłrio da disciplina Análise EstatĂstica de Dados e Informações do Mestrado Profissional em Computação Aplicada da UnB.
Language:Jupyter Notebook 1ivanl-exe / rock-paper-scissors-ai
A rock, paper, scissors "AI" which predicts the user's next move from a binomial distribution of the frequency of previous moves; with a CSV player database
Language:Python 1JingShing-Tools / Binomial-Distribution-Simulator
A tool help you generate Binomial Simulation.
Language:Jupyter Notebook 1MarcosVel / Statistical-Data-Analysis
MatĂ©ria de Análise EstatĂstica de Dados da pĂłs-graduação em CiĂŞncia de Dados e Machine Learning
Language:R 1mohaomar495 / Python_projects
PyGauss_Binomial is a Python package that provides functionality for working with probability distributions. It includes classes for the Gaussian and Binomial distributions, along with general distribution calculations and visualizations.
Language:Python 1- Stats-with-Data
mohd-faizy / Stats-with-Data
This repository is a resource for learning and applying statistics in data science. It contains code examples and explanations for many common statistical concepts, from descriptive statistics through regression and time series analysis.
Language:Jupyter Notebook 1 MoinDalvs / Assignment-Basic-Stats-Level1
Assignment Basic Stats
Language:Jupyter Notebook 1nehakalbande / neha_probability_distribution
Gaussian and Binomial distributions Python Package for Machine Learning and Data Science
Language:Python 1- C_Programming_Practicals
pSuchi / C_Programming_Practicals
C Programming Solutions for Real Analysis and Numerical Analysis Problems
Language:C 1 realspal / binomial-experiment-matplotlib
Binomial experiment simulation using Matplotlib.
Language:Python 1rubayat-tithi / Statistical_concept_distribution
This repository has been created to complete an assignment given by datainsightonline.com. This assignment is a part of Data Insight | Data Science Program 2021.
Language:Jupyter Notebook 1sanjaymjoshi / relistats
Computation of Reliability Statistics: Reliability, Confidence, Assurance
Language:Python 1ShaikRiyazSandy / Basic-Statistics_Level-1
Assignment Basic Stats
UdeshikaDissa / Logistic-Regression_Analysis-of-Categorical-Data
Predicting the Likelihood of Diabetes Using Common Signs and Symptoms - About one-third of patients with diabetes do not know that they have diabetes according to the findings published by many diabetes institutes around the world. Detecting and treating diabetes patients at early stages is critical in order to keep them healthy and to ensure their quality of life is not compromised. Early detection will also help to mitigate the risk of serious complications like heart disease & stroke, blindness, limb amputations, and kidney failures as a result of diabetes. The data set consists of signs and symptoms of 516 newly diabetic or would be diabetic patients, who presented at Sylhet Diabetes Hospital in Sylhet, Bangladesh. The data had been collected using the direct questionnaires method at the hospital under the supervisor of Doctors. The Source for the data set is the UCI Machine Learning Repository at, https://archive.ics.uci.edu/ml/datasets/Early+stage+diabetes+risk+prediction+dataset. The data set has 16 descriptive features and one target feature. This study intends to build a logistic regression model to predict the likelihood of having diabetes using common signs and symptoms presented by patients. A successful model will enable early detection of diabetes through signs and symptoms shown by possible patients. This study consists of two phases: 1) Phase I - preprocess and explore the data set in order to make it ready to consume for model development. 2) Phase II - build a logistic regression model to predict the likelihood of having diabetes based on signs and symptoms. The Phase I part has already been completed under previous work/submission and this report intends to cover the work carried out for Phase II. All the activities have been performed in the R package and the report has been compiled using R-Markdown.
Language:HTML 1VaasuCodez / statspykage
StatsPykage is an open source Python package for analysing standard statistical distributions: Gaussian, Binomial, Poisson etc. distributions; hosted on PyPi.org
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