Machine-Learning

In this project, we designed Machine Learning models such as Linear models, K-Nearest Neighbors (KNN), Support Vector Machine(SVM), XGBoost, Gradient Boosted tree, and Neural Network. Performed Regression and Classification Analysis on a de-identified dataset from a manufacturing company using R programming.

Responsibilities:

1. Performed Exploratory Data Analysis (EDA) on data generated from computer simulations by a manufacturing company.
2. Trained, evaluated, and tuned 15 different Machine Learning models of various complexity from a simple linear model to XGboost, Neural Network, SVM, and KNN for both Regression and Classification analysis.
3. Evaluated and chose the best model based on their performance using metrics such as RMSE and ROC and identified the important variables that influence the output.

Instruction:

1. The files include rmd, HTML, and one ppt files.
2. The rmd files are run in R studio to generate HTML files.
3. Since the data set is not included, you won't be able to run the rmd files.
4. You review the result in the HTML files for each rmd file.