ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross Validation

# performing linear algebra
import numpy as np 

# data processing
import pandas as pd

# visualisation
import matplotlib.pyplot as plt

df = pd.read_csv("..\\breast-cancer-wisconsin-data\\data.csv")

print (data.head)

df.info()

RangeIndex: 569 entries, 0 to 568
Data columns (total 33 columns):
id                         569 non-null int64
diagnosis                  569 non-null object
radius_mean                569 non-null float64
texture_mean               569 non-null float64
perimeter_mean             569 non-null float64
area_mean                  569 non-null float64
smoothness_mean            569 non-null float64
compactness_mean           569 non-null float64
concavity_mean             569 non-null float64
concave points_mean        569 non-null float64
symmetry_mean              569 non-null float64
fractal_dimension_mean     569 non-null float64
radius_se                  569 non-null float64
texture_se                 569 non-null float64
perimeter_se               569 non-null float64
area_se                    569 non-null float64
smoothness_se              569 non-null float64
compactness_se             569 non-null float64
concavity_se               569 non-null float64
concave points_se          569 non-null float64
symmetry_se                569 non-null float64
fractal_dimension_se       569 non-null float64
radius_worst               569 non-null float64
texture_worst              569 non-null float64
perimeter_worst            569 non-null float64
area_worst                 569 non-null float64
smoothness_worst           569 non-null float64
compactness_worst          569 non-null float64
concavity_worst            569 non-null float64
concave points_worst       569 non-null float64
symmetry_worst             569 non-null float64
fractal_dimension_worst    569 non-null float64
Unnamed: 32                0 non-null float64
dtypes: float64(31), int64(1), object(1)
memory usage: 146.8+ KB

df.drop(['Unnamed: 32', 'id'], axis = 1)
print(df.shape)

(569, 31)

def diagnosis_value(diagnosis):
    if diagnosis == 'M':
        return 1
    else:
        return 0

df['diagnosis'] = df['diagnosis'].apply(diagnosis_value)

sns.lmplot(x = 'radius_mean', y = 'texture_mean', hue = 'diagnosis', data = df)

sns.lmplot(x ='smoothness_mean', y = 'compactness_mean', 
           data = df, hue = 'diagnosis')

X = np.array(df.iloc[:, 1:])
y = np.array(df['diagnosis'])

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size = 0.33, random_state = 42)

knn = KNeighborsClassifier(n_neighbors = 13)
knn.fit(X_train, y_train)

KNeighborsClassifier(algorithm='auto', leaf_size=30, 
             metric='minkowski', metric_params=None, 
             n_jobs=None, n_neighbors=13, p=2, 
             weights='uniform')

knn.score(X_test, y_test)

0.9627659574468085

neighbors = []
cv_scores = []

from sklearn.model_selection import cross_val_score
# perform 10 fold cross validation
for k in range(1, 51, 2):
    neighbors.append(k)
    knn = KNeighborsClassifier(n_neighbors = k)
    scores = cross_val_score(
        knn, X_train, y_train, cv = 10, scoring = 'accuracy')
    cv_scores.append(scores.mean())

MSE = [1-x for x in cv_scores]

# determining the best k
optimal_k = neighbors[MSE.index(min(MSE))]
print('The optimal number of neighbors is % d ' % optimal_k)

# plot misclassification error versus k
plt.figure(figsize = (10, 6))
plt.plot(neighbors, MSE)
plt.xlabel('Number of neighbors')
plt.ylabel('Misclassification Error')
plt.show()

The optimal number of neighbors is 13