xavier homework8

Q1 – Classifier Performance Comparison

Q1a –Analyze the data set Social_Network_Ads.csv and create the plot with correct titles on axes:

Q1b Use the following classifiers

1. Naïve Bayes
2. Logistic Regression
3. Decision Trees
4. KNN
5. Support Vector Machine
6. Random Forest

For each classifier show

• The classifier boundary for training and test
• Printout your 1^st name on all graphs

Q1c Compare the confusion matrix in the following table for the above data set

	TP	TN	FP	FN	Accuracy
Naïve Bayes
Logistic Regression
Decision Trees
KNN
Support Vector Machine
Random Forest

Q2 – Principal Component Analysis

Summarize how the PCA algorithm works using the following link and recreate the code for the IRIS data set.

https://plot.ly/ipython-notebooks/principal-component-analysis/

Q3 Review the material on PCA in the following and visually describe how PCA works (use snapshots)

http://setosa.io/ev/principal-component-analysis/

Q4 LDA Explain how LDA differs from PCA

https://sebastianraschka.com/Articles/2014_python_lda.html

Q5 Compare accuracy of LDA vs PCA techniques using the dimensionality reduction on Wine data. The dataset is attached.

Q5 Dimensionality Reduction – on Wine data.zip

Q1 Classifier Comparison on Social Ads Network.zip

HW Classifier Comparison.docx