CS-109A Introduction to Data Science

## RUN THIS CELL TO PROPERLY HIGHLIGHT THE EXERCISES
import requests
from IPython.core.display import HTML
styles = requests.get("https://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/cs109.css").text
HTML(styles)

import sys
import numpy as np
import pylab as pl
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import matplotlib
import sklearn as sk
import sklearn.discriminant_analysis as da
import sklearn.neighbors as knn

#read in the GSS data
gssdata = pd.read_csv("gsspartyid.csv")
gssdata.head()

LDA = da.LinearDiscriminantAnalysis()
X = gssdata[["income","educ","abortion"]]
model_LDA = LDA.fit(X,gssdata['republican'])
print("Specificity is",np.mean(model_LDA.predict(X[gssdata['republican']==0])))
print("Sensitivity is",1-np.mean(model_LDA.predict(X[gssdata['republican']==1])))
print("False positive rate is",np.mean(gssdata['republican'][model_LDA.predict(X)==1]))
print("False negative rate is",1-np.mean(gssdata['republican'][model_LDA.predict(X)==0]))

#read in the first simulation data set
data1 = pd.read_csv("dataset1.csv")
X = data1[['x1','x2']]
data1['x1sq'] = data1['x1']**2
data1['x2sq'] = data1['x2']**2
X2 = data1[['x1','x2','x1sq','x2sq']]
y = data1['y']
n = data1.shape[0]
data1.head()

lda = da.LinearDiscriminantAnalysis()
qda = da.QuadraticDiscriminantAnalysis()
lda.fit(X,y)
qda.fit(X,y)
logit1 = sk.linear_model.LogisticRegression(C = 1000000)
logit2 = sk.linear_model.LogisticRegression(C = 1000000)
logit1.fit(X,y)
logit2.fit(X2,y)

print("Overall misclassification rate of Logit1 is",(1-logit1.score(X,y)))
print("Overall misclassification rate of Logit2 is",(1-logit2.score(X2,y)))
print("Overall misclassification rate of LDA is",(1-lda.score(X,y)))
print("Overall misclassification rate of QDA is",(1-qda.score(X,y)))

knn3=knn.KNeighborsClassifier(3)
knn25=knn.KNeighborsClassifier(25)
knn3.fit(X,y)
knn25.fit(X,y)

#note, the KNNs are not correct :(
print("Overall misclassification rate of kNN3 is",(1-knn3.score(X,y)))
print("Overall misclassification rate of kNN25 is",(1-knn25.score(X,y)))