Logistic Regression

Breast Cancer Wisconsin

Source

Attribute Information:

1) ID number
2) Diagnosis (M = malignant, B = benign)

3-32)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1)

Importing the Relevant Libraries

import numpy as np
import pandas as pd

Importing the Dataset

url = "https://DataScienceSchools.github.io//Machine_Learning/Sklearn/Case_Study/Classification/BreastCancer.csv"

dataset = pd.read_csv(url)

dataset.head()

	Sample code number	Clump Thickness	Uniformity of Cell Size	Uniformity of Cell Shape	Marginal Adhesion	Single Epithelial Cell Size	Bare Nuclei	Bland Chromatin	Normal Nucleoli	Mitoses	Class
0	1000025	5	1	1	1	2	1	3	1	1	2
1	1002945	5	4	4	5	7	10	3	2	1	2
2	1015425	3	1	1	1	2	2	3	1	1	2
3	1016277	6	8	8	1	3	4	3	7	1	2
4	1017023	4	1	1	3	2	1	3	1	1	2

Declaring the Dependent & the Independent Variables

X = dataset.iloc[:, :-1].values

y = dataset.iloc[:, -1].values

Splitting the dataset into the Training set and Test set

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

Feature Scaling

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)

Training the Logistic Regression Model

from sklearn.linear_model import LogisticRegression

model = LogisticRegression(random_state = 0)

model.fit(X_train, y_train)

LogisticRegression(random_state=0)

Predicting the Test Set Results

y_pred = model.predict(X_test)

Confusion Matrix

from sklearn.metrics import confusion_matrix, accuracy_score

cm = confusion_matrix(y_test, y_pred)

accuracy = accuracy_score(y_test, y_pred)

print("Accuracy is: ", accuracy, "\n\n Confusion Matrix:\n\n ", cm)

Accuracy is:  0.9473684210526315 

 Confusion Matrix:

  [[103   4]
 [  5  59]]