There are 1 repository under goodness-of-fit topic.
Real numbers, data science and chaos: How to fit any dataset with a single parameter
NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.
The collection of exercises I did during Ironhack's Data Science bootcamp.
R package to perform goodness-of-fit tests for capture-recapture models (and various manipulations)
A framework for Benford's Law conformity assessment.
a C++11 library for lattice QCD data analysis
R-squared measure for categorical data goodness-of-fit analysis using the surrogate R-squared
One-sample Kolmogorov-Smirnov goodness-of-fit test.
Tools for evaluating the goodness of fit of a point process model via the time rescaling theorem
Software companion for "Goodness-of-fit tests for the functional linear model based on randomly projected empirical processes"
Perform a chi-square goodness-of-fit test.
Supervised-ML---Simple-Linear-Regression---Newspaper-data. EDA and Visualization, Correlation Analysis, Model Building, Model Testing, Model predictions.
Arknights Headhunting Distribution Analysis.
Supervised-ML---Simple-Linear-Regression---Waist-Circumference-Adipose-Tissue-Data. EDA and data visualization, Correlation Analysis, Model Building, Model Testing, Model Prediction.
Computational statistics project in R on "A Simulative Comparison of Goodness-of-Fit Tests (GOFTs) from an Operational Risk Perspective with Focus on Loss Severity Distributions"
CLV prediction using Regression Analysis of customer invoice data for an online retail store
Detailed implementation of various regression analysis models and concepts on real dataset.
Several goodness-of-fit (GoF) model indexes for Excel
Thompson Sampling equipped with Goodness of Fit test based active change-point detection in Non-Stationary Bandit environment
Companies face the problem that their human resources on whom the company have invested time and money to train them, leave the company voluntarily. It is important for the management to know the variables responsible for employees quitting jobs and also have a prediction that which employees will be quitting their jobs in future. The goal of this project is to design different models for predicting if an employee will stay or leave the company within the next year and analyze the accuracy of the models.
Implementation of various models for contingency table analysis
Hypothesis for Data Science
A predictive model for plane crashes by analysing past aviation data
Stats HW chat bot that solves chi-square problems involving goodness of fit, homogeneity, or independence
Extended diagnostic and visualization tools for Cox proportional hazard models in R
Tests based on Chi-Square distribution using R.
Score-based Hypothesis Testing for Unnormalized Models
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.
Simulation for the Implementation of a Virtual Queuing System in an Amusement Park