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Assignment 1

The document contains a series of Python functions that perform operations on a cancer dataset using the K-Nearest Neighbors algorithm. It includes functions for data preparation, model training, predictions, and accuracy evaluation. Additionally, there is an optional plotting function to visualize training and test accuracies for malignant and benign cells.

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ajieiengq
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7 views2 pages

Assignment 1

The document contains a series of Python functions that perform operations on a cancer dataset using the K-Nearest Neighbors algorithm. It includes functions for data preparation, model training, predictions, and accuracy evaluation. Additionally, there is an optional plotting function to visualize training and test accuracies for malignant and benign cells.

Uploaded by

ajieiengq
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as TXT, PDF, TXT or read online on Scribd
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Question 1

def answer_one():
return pd.DataFrame(data=np.c_[cancer.data, cancer.target],
columns=list(cancer.feature_names) + ['target'])

Question 2
def answer_two():
cancerdf = answer_one()
df = cancerdf['target'].value_counts()
df.index = ['benign', 'malignant']
return df

Question 3
def answer_three():
cancerdf = answer_one()
X = cancerdf.iloc[:, 0:30]
y = cancerdf['target']
return X, y

Question 4
from sklearn.model_selection import train_test_split

def answer_four():
X, y = answer_three()
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
return X_train, X_test, y_train, y_test

Question 5
from sklearn.neighbors import KNeighborsClassifier

def answer_five():
X_train, X_test, y_train, y_test = answer_four()
knn = KNeighborsClassifier(n_neighbors = 1)
knn.fit(X_train, y_train)
return knn

Question 6
def answer_six():
cancerdf = answer_one()
means = cancerdf.mean()[:-1].values.reshape(1, -1)
knn = answer_five()
return knn.predict(means)

Question 7
def answer_seven():
X_train, X_test, y_train, y_test = answer_four()
knn = answer_five()
return knn.predict(X_test)

Question 8
def answer_eight():
X_train, X_test, y_train, y_test = answer_four()
knn = answer_five()
return knn.score(X_test, y_test)

Optional Plot
def accuracy_plot():
import matplotlib.pyplot as plt

%matplotlib notebook

X_train, X_test, y_train, y_test = answer_four()

mal_train_X = X_train[y_train==0]
mal_train_y = y_train[y_train==0]
ben_train_X = X_train[y_train==1]
ben_train_y = y_train[y_train==1]

mal_test_X = X_test[y_test==0]
mal_test_y = y_test[y_test==0]
ben_test_X = X_test[y_test==1]
ben_test_y = y_test[y_test==1]

knn = answer_five()

scores = [knn.score(mal_train_X, mal_train_y), knn.score(ben_train_X,


ben_train_y),
knn.score(mal_test_X, mal_test_y), knn.score(ben_test_X, ben_test_y)]

plt.figure()

bars = plt.bar(np.arange(4), scores,


color=['#4c72b0','#4c72b0','#55a868','#55a868'])

for bar in bars:


height = bar.get_height()
plt.gca().text(bar.get_x() + bar.get_width()/2, height*.90, '{0:.
{1}f}'.format(height, 2),
ha='center', color='w', fontsize=11)

plt.tick_params(top='off', bottom='off', left='off', right='off',


labelleft='off', labelbottom='on')

for spine in plt.gca().spines.values():


spine.set_visible(False)

plt.xticks([0,1,2,3], ['Malignant\nTraining', 'Benign\nTraining', 'Malignant\


nTest', 'Benign\nTest'], alpha=0.8);
plt.title('Training and Test Accuracies for Malignant and Benign Cells',
alpha=0.8)

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