This is a capstone project to predict the compression strength based on the mixture properties by selecting several machine learning model: Ordinary Least Square, Neural Network, Decision Tree, and Random Forest. This problem was originally proposed by Prof. I-Cheng Yeh, Department of Information Management Chung-Hua University, Hsin Chu, Taiwan in 2007. It is related to his research in 1998 about how to predict compression strength in a concrete structure.

This is a learn by building project to classify whether kyphosis is present or absent using Neural Network Model Analysis. Kyphosis is a medical condition that causes a forward curving of the back. Kyphosis is an exaggerated, forward rounding of the back. It can occur at any age but is most common in older women.

This is a learn by building project to perform data forecasting and analysis of dataset of U.S. finished motor gasoline products supplied from The U.S. Energy Information Administration (EIA). Data set consists of weekly data from February 1991 to July 2005, analyzed by using STLM and TBATS forecasting method.

This is a learn by building project to perform data clustering and principle component analysis of Spotify dataset using K-Means Clustering & Principle Component Analysis method.

This is a learn by building project to predict the chance of survive of Titanic's passenger using Naive Bayes, Decision Tree & Random Forest Analysis method.

This is a learn by building project to predict the chances of students getting admission for Masters Program in a university based on several academic performance measurement using Logistic Regression & K-Nearest Neighbor Analysis method.

This is a learn by building project to predict the chances of students getting admission for Masters Program in a university based on several academic performance measurement using Ordinary Least Square (OLS) multiple linear regression method.

Mental Health in Tech Industry

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