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Gracie Han

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Trees and Rules KJ8.1-8.3
We used a few simulated dataset to demonstrate the nuts and bolts of a few trees and rules machine learning algorithm, random forest, cforest, gbm, cubist. We also looked into details the issues of correlated variables' impact on model, the impact of different bagging and learning rates, and the variable granularity.
Trees and Rules
Using the chemical manufacturing process database (K&J 7.5), applies a few tree based system to decide the optimal model and compare them
7.5Version1
Non Linear Regression KJ7.5Chemical Manufacture
This project uses data imputation, data splitting, and pre-processing steps , and train several nonlinear regression models, on the K&J 7.5 chemical manufacturing data set. The models used included KNN, SVM, MARS, NN. Models performance were done.
K&J7.2
Friedman (1991) introduced several benchmark data sets create by simulation. This document uses a few models to evaluate it (KNN, MARS,SVM,MARS)
Permeability Prediction
PLS model tuning is used to determine the permeability of potential drug compounds.
LinearRegression Chemical Manufacturing Process
Using the data of chemical manufacturing process, the objective is to understand the relationship between biological measurements of the raw materials (predictors), measurements of the manufacturing process (predictors), and the response of product yield (outcome).
ATM
Power Usage
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ARIMA Model, HA8.1-8.7
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Movie Recommend via SVD IBCF UBCF
Research Question:How can I recommend the top 10 movies to certain people (users), based on their history of preference, or on the experience of similar people like them, or some other mechanism?Q Content based filtering, User based filtering, and SVD.
MovieLenseCollaborativeFilteringvsSVD
Content based filtering, User based filtering, and SVD are performed on the movielense data within the recommenderlab package. Their performance is also compared.
Jokes Recomders
This project compares different recommendation systems for certain users, on the jokes they might like. The comparisons are based on users (UBCF) and items (IBCF). Different normalization mechanisms are also compared.
MarketBasketAnalysis 2
Singular Value Decomposition
This is the singular value decomposition methods for the recommendation system, using movie lense dataset
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Khansari1
HM1 Ferrari
OralProjectFinal
Oral
Document
606 Lab7
Assignment 6 606
Lab6 DATA606
606 Hmwk5
606 Lab5
Week9 API
606 Lab4b Hui Han
Lab4a Hui Gracie Han
606 Chap4 Assignment Hui Han
Assignment 3 606
Lab3-606
Create SQL Database Version3
DB Creation SQLite fm CSV-Version2
Proj3_607
Technical Skills ONET
Tech Skills ONET Occupation
Week7 607
Read Json, HTML, XML data into R
Project2 607 Doc Do Not Travel
Assignment3 AirlineData
Project1 607
Tournament Data
Week3 606 Exercise
Week3 607 Regular Expression
HomeWork Chap1 - IntroStat
Hui (Gracie) Han Solution
LAB0 DATA606 Part2
606 Lab0 Part1
Week2 Solution
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