Kaggle competition project.

Udacity DataNano P4

R portion of Homework 5: This homework assignment featured both logistic regression and tree based regression (simple decision tree, random forest, ect.) After a quick look at the data through the str and summary function I constructed my logrithmic model. The model outputs a value between 0 and 1 however, the data we are predicting is binary, 0…

Using random forest to recognize human activity.

I participated in a Kaggle competition where I predicted the prices of AirBnb rental listings in NYC using supervised machine learning techniques such as linear regression, trees and bootstrap (random forest and boosting).

Predicting which passengers survived the Titanic shipwreck using different ML algorithms.

Implementation of predictive models to determine if the sample tested from a body of water is fit for human consumption. This project includes exploratory analysis, data cleaning, preprocessing, and execution of KNN and Random Forest models for comparison.

Model predicting whether a bank customer will churn or not

Final Project of Data Science for Business at Duke Fuqua (Oct 2022)

Aid Megaline, a mobile carrier, in developing a model that will analyze subscribers' behavior. Once analyzed, subscribers on a legacy plan can be recommended one of Megaline's newer plans: Smart or Ultra.

Predicting Marijuana Use Disorder: A Machine Learning Approach

Diabetes Health Indicator Dataset analysis with R

Revolutionary web page designed to provide accurate taxi fare estimates for your travels

This repository contains the code for a web-based diabetes prediction application using a machine learning model. The application is built using Flask and allows users to input various health parameters to predict the likelihood of diabetes using ensemble voting classifier.

ML model API that predicts heart disease risk using a refined Random Forest model for optimal accuracy.

A classification analysis of data from the Weight Lifting Exercises (WLE) dataset which uses machine learning to predict whether a weightlifting activity was being performed correctly

Predict donors using supervised learning, ensemble methods and data science

A RStudio project that uses Random Forest and Logistic Regression to answer questions about worldwide suicide trends.