Machine Learning algorithms in R

Documents on Packages and Concepts of Machine learning

Applied advanced data cleaning and modeling techniques to predict auto insurance claims, focusing on crash probability and claim amounts. Expertise in handling high-dimensional, messy datasets and building interpretable models supports risk assessment and operational efficiency in the insurance domain.

Exploring multiple linear regression methods and techniques while trying to predict the cost of health insurance for a set of individuals using R.

Implementation of Trust Region and Gradient Descent methods for Multinomial Regression

This Git is part of the class of applied regression methods, examples, exercises, etc.

선물 지수와 주가의 상관 관계에 대한 연구와 분석

This project aims to model the demand for shared bikes using various independent variables. The goal is to provide insights to the management team, helping them understand how demand fluctuates with different features

Climate change is a key factor in how extreme weather events affect how ecosystems and species react to these changes in temperatures. University of British Columbia's (UBC) Botanical Garden is interested in improving microclimate information within the garden to understand how areas with shade create respite zones for species. Due to the recent…

Learners from {dbarts} package for mlr3

Surprise Housing House Price Prediction

This is the code used in my masters thesis research.

Used libraries and functions as follows:

Final Project for STA 135 with Dr. Xiucai Ding

Classifying NBA player position based solely on performance statistics

Skillenza Upgrad DataScience Hackathon : Rank #18 in Leaderboard

Using a telecom company's data of services provided to customers and observing how customers use it to predict if they will decide to continue or cease to be a customer of the company.

prediction modelling using mlr on bike data

Data Science Project : A digital media company (similar to Voot, Hotstar, Netflix, etc.) had launched a show. Initially, the show got a good response, but then witnessed a decline in viewership. The company wants to figure out what went wrong.