Machine Learning Projects learning and Practicing

This repo is for copula based analysis on bivariate as well as multivariate data sets in ecology and related fields. For details and citation we refer to this publication: Ghosh et al., Advances in Ecological Research, vol 62,pp 409, 2020

An R package for regularized weight based SCA and PCA

This Project is about to identify if a person's back pain is abnormal or normal using collected physical spine details/data.

Detecting Damped Lyman-alpha Absorbers (DLAs) with Gaussian Processes

ML4SCI hackathon NMR spin challenge winning project. Training machine learning models for multi-target regression problem.

Predicting compressive strength of concrete using machine learning models with featurization and Hyper parameter tuning

SARIMAX model for forecast traffic volume

Linear Regression Models on Montesinho Forest Fire

Predicting the price of a football player using Machine Learning Algorithms

It calculates the accuracy score and confusion matrix for a logistic regression model. The dataset is about coupon used or not in an apparel store known as Simmons .

Annual Income Prediction Using Machine Learning

A dredge function to select the best models through an exhaustive combination of parameters.

Multiple Linear Regression, Subset selection and Model Regularisation to determine which soil nutrients are most important in determining grain yield.

A project to predict the average prices of Avocado in USA.

The aim of this project is to predict fraudulent credit card transactions using machine learning models.

Predicting whether or not a cell has cancer.

Determine a Prototype from a number of runs of Latent Dirichlet Allocation.

This project uses supervised machine learning techniques with multiple regression models to predict CO2 emissions in Canada, it includes data cleaning, encoding, analyzing and visualization to identify patterns, resulting in a model that can make accurate predictions.

This has been a machine learning quest to classify cancer types using gene expression data, utilizing powerful tools and techniques to preprocess, train and evaluate models. The ultimate goal, to save lives through early diagnosis with high accuracy and precision.