Logistic Regression & Cancer Prediction • 2 min read

import pandas as pd

Dataset Import

dataset = pd.read_csv('breast_cancer.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values

Split dataset into training & test set

import sklearn
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
print(X_test)
print(X_train)
print(y_test)
print(y_train)

---------------------------------------------------------------------------

ModuleNotFoundError                       Traceback (most recent call last)

/tmp/ipykernel_1283/3606614524.py in <module>
----> 1 import sklearn
      2 from sklearn.model_selection import train_test_split
      3 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
      4 print(X_test)
      5 print(X_train)


ModuleNotFoundError: No module named 'sklearn'

Train logistic regression model with data set

from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)

Predicting the results

y_pred = classifier.predict(X_test)
print(y_pred)

Confusion Matrix

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print(cm)

Computing the accuracy with k-Fold Cross Validation

from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)
print("Accuracy: {:.2f} %".format(accuracies.mean()*100))
print("Standard Deviation: {:.2f} %".format(accuracies.std()*100))