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Cancer Disease Classification

This document discusses classifying cancer types using machine learning. It loads breast cancer data, splits it into training and test sets, scales the features, and trains logistic regression, decision tree, and random forest models. It evaluates the models on the test set and prints the confusion matrices and test accuracies. Finally, it uses the best model to make predictions on new test data and outputs the results.

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BARATH P
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59 views6 pages

Cancer Disease Classification

This document discusses classifying cancer types using machine learning. It loads breast cancer data, splits it into training and test sets, scales the features, and trains logistic regression, decision tree, and random forest models. It evaluates the models on the test set and prints the confusion matrices and test accuracies. Finally, it uses the best model to make predictions on new test data and outputs the results.

Uploaded by

BARATH P
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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FACULTY INITIATIVE – SLOT-3

BARATH P
II YEAR CSE

CANCER DISEASE CLASSIFICATION


Program and Output:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

#load the data


df = pd.read_csv('/content/data.csv')
df['diagnosis'] = df['diagnosis'].replace({'M':1,'B':0})
df.head()

print("Cancer data set dimensions : {}".format(df.shape))


df.isnull().sum()

df.describe()
X = df.iloc[:, 1:31].values
Y = df.iloc[:, 31].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)

sns.countplot(x ='diagnosis', data = df)


plt.show()
B, M = df['diagnosis'].value_counts()
print('Number of Benign: ',B)
print('Number of Malignant : ',M)

df.corr()
#Visualize the correlation
f,ax = plt.subplots(figsize=(20, 20))
sns.heatmap(df.corr(), annot = True, fmt= '.2f')
#for data scaling
from sklearn.preprocessing import StandardScaler
#for splitting dataset
from sklearn.model_selection import train_test_split
#for fitting SVM model
from sklearn.svm import SVC
#for displaying evaluation metrics
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

#Scale the data (Feature Scaling)


from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)

#Create a function for models


def models(X_train, Y_train):

#Logistic Regression
from sklearn.linear_model import LogisticRegression
log = LogisticRegression()
log.fit(X_train, Y_train)

#Decision Tree
from sklearn.tree import DecisionTreeClassifier
tree = DecisionTreeClassifier()
tree.fit(X_train, Y_train)
#Random Forest
from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier()
forest.fit(X_train, Y_train)

#Print the model accuracy of training data


print('[0]Logistic Regression Training Accuracy : ',log.score(X_train, Y_train))
print('[1]Decision Tree Training Accuracy : ',tree.score(X_train, Y_train))
print('[2]Random Forest Training Accuracy : ',forest.score(X_train, Y_train))

return log, tree, forest

#Getting all the models


model = models(X_train, Y_train)

#test model accuracy on test data using confusion matrix


from sklearn.metrics import confusion_matrix
for i in range (len(model)):
print('Model :',model[i])
cm = confusion_matrix(Y_test,model[i].predict(X_test))
TP = cm[0][0]
FP = cm[0][1]
FN = cm[1][0]
TN = cm[1][1]
print(cm)
print('Testing Accuracy =',(TP + TN)/(TP + FP + FN + TN))
print()
#test our trained model on the test data
test_df = pd.read_csv('/content/data.csv')
test = test_df.drop(['id','diagnosis'],axis = 1)
test.head()
y_pred = pd.DataFrame(model[0].predict(test), columns=['diagnosis'])
final_df = pd.DataFrame({'Id': test_df['id'], 'diagnosis': y_pred['diagnosis']})
final_df

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