This repo contains an R script that algorithmically finds the best distribution that fits several continuous, randomized variables

GEARS a toolbox for Global parameter Estimation with Automated Regularisation via Sampling by Jake Alan Pitt and Julio R. Banga

Johns Hopkins University Bloomberg School of Public Health: Data Science Specialization Program: Regression Models Course: Motor Trend Project repo: date created 61229

Shiny App to visualise and fit a GAM

Labs for the "Statistical Learning and Neural Networks" course @ Polytechnic University of Turin

General RANSAC solver with detailed examples.

Tutorial on Bayesian model fitting with (Py)VBMC.

Develop a data science project using historical sales data to build a regression model that accurately predicts future sales. Preprocess the dataset, conduct exploratory analysis, select relevant features, and employ regression algorithms for model development. Evaluate model performance, optimize hyperparameters, and provide actionable insights.

Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modif…

The projects which involve the use of ML concepts

Multiple linear regression model based on eCommerce customers data in Python language.

Python-based fitting of 1-state MDP Reinforcement Learning algorithms using Global Optimization (Turner Lab, HHMI, 2023)

Statistical Modelling of Swine Flu Outbreak Data

step-by-step walkthrough of model fitting via least squares and staircase validation via negative log likelihood in MATLAB

C++ Tool for Template Fitting for Photometric Redshift Estimation

This MATLAB script fits either a linear or hyperbolic function to time-series data (e.g., growth data).

Welcome to our PC3 backend

This repository hosts a machine learning-based mushroom identification system, utilizing scikit-learn models in a Jupyter notebook. The project analyzes and processes a Kaggle dataset to train a model that classifies mushrooms as edible or poisonous, providing a reliable tool for mushroom enthusiasts and foragers.

Data Analytics on a simple gender classification dataset taken from kaggle. Univariate bivariate and multivariate analysis EDA and model fitting