ITS 836 DATA SCIENCE AND BIG DATA ANALYSIS
Volk12
ITS836Week11HWClassifierComparison1.pptxSchool of Computer & Information Sciences
ITS 836 Data Science and Big Data Analytics
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Week 11 HW Questions
Question 1: Analyze the data set Social_Network_Ads.csv
# Importing the dataset
dataset = read.csv('Social_Network_Ads.csv')
dataset = dataset[3:5]
# Encoding the target feature as factor
dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1))
ggplot(dataset, aes(x=dataset$Age, y=dataset$EstimatedSalary, color=dataset$Purchased)) + geom_point()
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Improve this plot by:
1a) adding appropriate labels,
Axes titles
1b) Printout your 1st name on all graphs in R.
1c) what does the data say?
Wk11 Question 2: Compare the classifiers
Question 2: using the following classifiers
Naïve Bayes
Logistic Regression
Decision Trees
KNN
Support Vector Machine
Random Forest
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For each classifier show
2a) The classifier boundary for training and test – use ggplot to graph the boundary
2b) Printout your 1st name on all graphs in R
2c) compare all algorithms
2d) compare the confusion Matrix
Wk11 Question 3 Comment on the following table
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Wk11: Question 4: Springleaf Market Response https://www.kaggle.com/c/springleaf-marketing-response
Springleaf offers their customers personal and auto loans.
Direct mail is a way Springleaf's team connects with potential customers
Direct is a fundamental part of Springleaf's marketing strategy.
Springleaf must be sure they are focusing on the right customers
Who are likely to respond and be good candidates for their services.
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Springleaf is asking you to predict
which customers will respond to a direct mail offer.
Question 4a: Clean the data for missing values
Question 4b: Statistically analyze the data
Question 4c: Apply logistic regression with CM
Question 4d: Apply Random Forest
Question 4e: Apply XGBoost
Printout your 1st name on all graphs in R.
Questions?
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