## Main.Assignments History

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#### Final Project (Due Dec 2)

#### Final Project (Due Dec 15)

#### Assignment 3 (Due Oct 14)

#### Assignment 3 (Due Oct 12)

#### Assignment 4 (Due Nov 18)

#### Assignment 4 (Due Oct 26)

Classification using decision tree/random forest

#### Final Project (Due Dec 2)

#### Assignment 1 (Due Sept. 9)

#### Assignment 1 (Due Sept. 14)

#### Assignment 2 (Due Sept. 23)

#### Assignment 2 (Due Sept. 28)

#### Assignment 5 (Due Nov 14)

#### Assignment 5 (Due Dec 2)

#### Assignment 4 (Due Nov 4)

#### Assignment 4 (Due Nov 18)

#### Assignment 5 (Due Nov 14)

#### Assignment 5 (Due Nov 14)

#### Assignment 5 (Due Nov 4)

#### Assignment 5 (Due Nov 14)

#### Assignment 4 (Due Oct 21)

#### Assignment 4 (Due Nov 4)

Clustering and frequent itemset mining

Clustering

#### Assignment 3 (Due Oct 9)

#### Assignment 3 (Due Oct 14)

#### Assignment 4 (Due Oct 21)

Frequent itemset mining

#### Assignment 5 (Due Nov 4)

#### Assignment 5 (Due Nov 4)

#### Assignment 5 (Due Nov 30)

#### Assignment 5 (Due Nov 4)

#### Assignment 2 (Due Sept. 20)

#### Assignment 2 (Due Sept. 23)

#### Assignment 3 (Due Oct 10)

#### Assignment 3 (Due Oct 9)

#### Assignment 4 (Due Nov 15)

#### Assignment 4 (Due Oct 21)

#### Assignment 1 (Due Sept. 5)

#### Assignment 1 (Due Sept. 9)

#### Assignment 5

#### Assignment 5 (Due Nov 30)

#### Assignment 4

#### Assignment 4 (Due Nov 15)

Frequent itemset mining Homework4.pdf

#### Assignment 3

#### Assignment 3 (Due Oct 10)

Clustering and frequent itemset mining

#### Assignment 2

Get familiar with Weka and KNN classifier

#### Assignment 2 (Due Sept. 20)

Classification using decision tree/random forest

#### Assignment 2

#### Assignment 3

#### Assignment 4

#### Assignment 5

#### Assignment 1 Due Sept. 5

#### Assignment 1 (Due Sept. 5)

#### Assignment 1

#### Assignment 1 Due Sept. 5

Assignment 1: The Suspect Prediction Problem

Assignment Due: September 16, 12:00PM.

Download the assignment data and requirement here assignment1.zip

The police department of Los Angeles gave you a set of data to screen potential suspects. You must predict whether an individual is a suspect or not based on his/her feature information. The prediction should be reported as a probability score between 0 and 1. In this assignment, you will design a K-NN classifier to accomplish this job.

#### Assignment 2

Assignment 2: The Insurance Policy Customer Identification Problem

Assignment Due: September 30, 11:59pm.

Download the assignment requirement here assignment2.pdf

A Dutch insurance company wants to build a classifier system to determine whether a customer will buy its caravan insurance policy. They also want to have an explanation why.

Information about customers consists of 86 variables and includes product usage data and socio-demographic data derived from zip area codes. The data was supplied by the Dutch data mining company Sentient Machine Research and is based on a real world business problem. The training set contains over 5000 descriptions of customers, including the information of whether or not they have a caravan insurance policy. A test set contains 4000 customers.

You are expected to build three classifiers for this problem and compare their performances. You will use the Weka data mining package. So no programming needed. Just Play the data and have fun.

#### Assignment 3

Hierarchical Clustering in Microarray Data Mining

Assignment Due: October 14, 11:59pm.

Clustering is a fundamental data analysis tool in many scientific research areas. In this experiment, you are expected to apply hierarchical clustering to the Microarray data mining in bioinformatics. You will apply hierarchical clustering techniques for discovering functionally related genes of given biological processes. Basically, you will do similar analysis as published in the famous paper by Michael B. Eisen et al., the most cited paper of data mining in bioinformatics, with more than 6300 citations since 1998.

Download your assignment here: assignment3.zip

#### Assignment 4

Dimensionality Reduction

Assignment Due: November 30th, 23:59:59pm

Download your assignment here: assignment4.zip