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Data Analysis & Interpretation 4.4: K-Means Cluster Analysis

Week 4

This week’s assignment involves running a k-means cluster analysis. Cluster analysis is an unsupervised machine learning method that partitions the observations in a data set into a smaller set of clusters where each observation belongs to only one cluster. 1,180 more words

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Data Analysis & Interpretation 4.3: Predicting Employed Rate with Lasso Regression

Week 3

Run a lasso regression analysis using k-fold cross validation to identify a subset of predictors from a larger pool of predictor variables that best predicts a quantitative response variable. 729 more words

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Data Analysis & Interpretation 4.2: Predicting County Poverty Group with Random Forest

Week 2

Run a Random Forest.

You will need to perform a random forest analysis to evaluate the importance of a series of explanatory variables in predicting a binary, categorical response variable. 458 more words

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Data Analysis & Interpretation 4.1: Predicting County Poverty Group with Decision Tree

Course 4: Machine Learning for Data Analysis

This course focuses on various machine learning algorithms: Decision Trees, Random Forests, Lasso Regression, and K-Mean Cluster Analysis. 861 more words

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Data Analysis & Interpretation 3.4

Week 4

This week’s assignment is to test a logistic regression model.

Summarize in a few sentences 1) what you found, making sure you discuss the results for the associations between all of your explanatory variables and your response variable. 1,709 more words

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Data Analysis & Interpretation 3.3

Week 3

This week’s assignment is to test a multiple regression model.

Discuss the results for the associations between all of your explanatory variables and your response variable. 1,446 more words

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Data Analysis & Interpretation 3.1

Describe 1) your sample, 2) the data collection procedure, and 3) a measures section describing your variables and how you managed them to address your own research question. 428 more words

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