ML | Diagnosing Kaggle Breast Cancer Wisconsin Using KNN

diag | NumPy | Python functions | sin

Michael Zippo 18.07.2021

Implementation of the CNN algorithm for classification.

Code: loading libraries

# doing linear algebra

import numpy as np

# data processing

import pandas as pd

# rendering

import matplotlib.pyplot as plt

Code: Load dataset

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

    
   print   (data.head)

Output:

Code: Load dataset

df.info ()

Output:

 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 texture-fractal_mean 569 non-radius_ null float 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-nulldimension float64 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 concavity_worst nullc points 569 non-null float64 concavity_worst nullc -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

Code: we are dropping columns — "Id" and "Unnamed: 32" as they play no role in forecasting.

df.drop ([ ’Unnamed: 32’ , ’ id’ ], axis = 1 )

print (df.shape)

Exit:

 (569, 31)

Convert diagnostic M and B values to numerical value
M (Malignant) = 1
B (Benign) = 0

       def   diagnosis_value (diagnosis): 
     if   diagnosis   =   =   ’M’  : 
     return   1  
     else  : 
    return   0  
   
   df [  ’diagnosis’  ]   =   df [ ’ diagnosis’  ].   apply   (diagnosis_value)

Code:

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

Exit:

Code :

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

Output:

Code: input and output

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

y = np.array (df [ ’diagnosis’ ])

Code: Separating data for training and testing

       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  )

Code: Using Sklearn

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

Exit:

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

Code: forecast score

knn.score (X_test, y_test)

Exit:

 0.9627659574468085

Code: doing cross validation

neighbors = []

cv_scores = []

from sklearn.model_selection import cross_val_score

# do 10x 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 ())

Code: classification error compared to k

       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)  
   
  # error of misclassification of the graph compared to k  
   plt.figure (figsize   =   (  10  ,   6  )) 
   plt.plot (neighbors, MSE) 
   plt.xlabel (  ’Number of neighbors’  ) 
   plt.ylabel ( ’ Misclassification Error’  ) 
   plt.show ()

Exit :

 The optimal number of neighbors is 13

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ML | Diagnosing Kaggle Breast Cancer Wisconsin Using KNN

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