- Predicting if the cancer diagnosis is
- benign or malignant based on several features
- 30 features are used, examples:
- radius (mean of distances from center to points on the perimeter)
- texture (standard deviation of gray-scale values)
- perimeter
- area
- smoothness (local variation in radius lengths)
- compactness (perimeter^2 / area - 1.0)
- concavity (severity of concave portions of the contour)
- concave points (number of concave portions of the contour)
- symmetry
- fractal dimension ("coastline approximation" - 1)
- Datasets are linearly separable using all 30 input features
- Number of Instances: 569
- Class Distribution: 212 Malignant, 357 Benign
- Target class:
- Malignant
- Benign
Dr. Ryan @STEMplicity
Importing the Relevant Libraries¶
In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
Importing the Dataset from SkLearn Library¶
In [2]:
from sklearn.datasets import load_breast_cancer
dataset = load_breast_cancer()
dataset
Out[2]:
In [3]:
dataset.keys()
Out[3]:
In [4]:
print(dataset['DESCR'])
In [5]:
print('Feature Names:\n\n', dataset['feature_names'])
print('\n\nData:\n\n', dataset['data'])
In [6]:
print('Target Names:\n\n', dataset['target_names'])
print('\n\nTarget:\n\n', dataset['target'])
Converting Dataset to Dataframe¶
In [7]:
df = pd.DataFrame(np.c_[dataset['data'], dataset['target']], columns = np.append(dataset['feature_names'], ['target']))
df.head()
Out[7]:
Visualising the Data¶
Pairplot¶
In [8]:
sns.pairplot(df, hue = 'target', vars = ['mean radius', 'mean texture', 'mean area', 'mean perimeter', 'mean smoothness'])
plt.show()
Countplot¶
In [9]:
sns.countplot(df['target'])
plt.show()
Heatmap¶
In [10]:
plt.figure(figsize = (20,10))
corr= df.corr()
matrix = np.triu(corr)
sns.heatmap(corr, annot=True, mask=matrix)
plt.show()
Declaring the Dependent & the Independent Variables¶
In [11]:
X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values
Splitting the Dataset into the Training Set and Test Set¶
In [12]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 5)
Feature Scaling¶
In [13]:
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
Training the Support Vector Machine Model¶
In [14]:
from sklearn.svm import SVC
model = SVC()
model.fit(X_train, y_train)
Out[14]:
Predicting the Test Set Results¶
In [15]:
y_pred = model.predict(X_test)
Confusion Matrix¶
In [16]:
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: {:.2f} %".format(accuracy*100))
sns.heatmap(cm, annot=True , fmt='d')
plt.show()
Classification Report¶
In [17]:
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
k-Fold Cross Validation¶
In [18]:
from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator = model, X = X_train, y = y_train, cv = 10)
print("Accuracy: {:.2f} %".format(accuracies.mean()*100))
print("Standard Deviation: {:.2f} %".format(accuracies.std()*100))
Improving the Model¶
Parameter Optimisation¶
- Applying Grid Search to find the best parameters
In [19]:
from sklearn.model_selection import GridSearchCV
parameters = [
{'C': [0.25, 0.5, 0.75, 1], 'kernel': ['linear']},
{'C': [0.25, 0.5, 0.75, 1], 'kernel': ['rbf'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]},
{'C': [0.25, 0.5, 0.75, 1], 'kernel': ['poly'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]},
{'C': [0.25, 0.5, 0.75, 1], 'kernel': ['sigmoid'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}
]
grid_model = GridSearchCV(estimator = SVC(),
param_grid = parameters,
scoring = 'accuracy',
cv = 10,
n_jobs = -1)
grid_model.fit(X_train, y_train)
y_grid_pred = grid_model.predict(X_test)
best_accuracy = grid_model.best_score_
best_parameters = grid_model.best_params_
print("Best Accuracy: {:.2f} %".format(best_accuracy*100))
print("Best Parameters:", best_parameters)
Confusion Matrix¶
In [20]:
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_grid_pred)
sns.heatmap(cm, annot=True , fmt='d')
plt.show()
k-Fold Cross Validation¶
In [21]:
from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator = grid_model, X = X_train, y = y_train, cv = 10)
print("Accuracy: {:.2f} %".format(accuracies.mean()*100))
print("Standard Deviation: {:.2f} %".format(accuracies.std()*100))