University of Utah IS 6482 - Data Mining - Taken: Spring 2020

Linear Regression, Logistic Regression, KNN, SVM, Naive Bayes

KNN implementation with R to predict wine quality

AutoValuate: A machine learning-driven tool for classifying used car prices as high or low, enabling smarter decisions in the car resale market.

This case serves as an illustration how data science can help analytical chemistry, in-field analysis and ecology. An additional point to be stressed is the reality of the subject case. The best practice for data scientists always consists in facing difficulties present in real cases – data cleaning, preparation, analysis of the data logic, stra…

Finding Donors for CharityML

Error clustering

This project uses machine learning to classify breast cancer tumors as malignant or benign using the Breast Cancer Wisconsin (Diagnostic) Dataset.

PROJECTS from Data Science and Analytics, MSc Program 2016-2017 | Hira Fatima

Introduction to Machine Learning course - Spring 2021 - Supervised and Unsupervised Learning, KNN Classification Models, Naive-Bayes Classifier, Regression Analysis, K-Means and DBSCAN Clustering Analysis, Association Rules and PCA, Confusion Matrix, Normalization, Dummy Variables.

Some nice code scripts in R depectinve various ML models

This project includes implementation of supervised machine learning algorithms in R language.

Implemented and compared Random Forest, Decision Tree, KNN, SVM, and Logistic Regression outcomes with a confusion matrix. Concluded that Random Forest achieved the highest accuracy of 85% to predict the loan status for investors.

Comparison of several different models for identification of refugee locations following the 2010 Haiti Earthquake.

The Prediction and Classification of Wine Quality

This project analyzes to two datasets to help make better decisions for our client Budweiser. This is done as a Midterm Project for DS6306 at Southern Methodist University.

Healthcare

In this study we seek to predict employee attrition with KNN clustering and Naive Bayes, and to predict employee salary using multiple linear regression

DSCI 552 - Machine Learning for Data Science (Spring 2023) | Graduate Level Course taught by Prof. Ke-Thia Yao at USC | Credits - 4

We will be looking at Tele-Marketing Data and attempt to predict whether the tele-marketing call will be successful or not.