Breast-Cancer-Classification-using-ML
Project 3 Breast Cancer Classification using ML
Dataset Used :
Breast Cancer Wisconsin (Diagnostic) Data Set
Tech Stack used :
- Numpy
- Pandas
- Sklearn
Code
Import Libraries and sklearn Dataset
import numpy as np
import pandas as pd
import sklearn.datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
breast_cancer_dataset=sklearn.datasets.load_breast_cancer()
data_frame=pd.DataFrame(breast_cancer_dataset.data,columns=breast_cancer_dataset.feature_names)
data_frame['label']=breast_cancer_dataset.target
data_frame.head()
Checking missing values
data_frame.isnull().sum()
For checking the Distribution of Target Variable
data_frame['label'].value_counts() #0 for malignant #1 for benign
data_frame.groupby('label').mean()
Seperating features and target
X=data_frame.drop(columns=['label'],axis=1)
Y=data_frame['label']
Splitting into training and test split
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=2)
print(X.shape,X_train.shape,X_test.shape)
Model Training - Logistic Regression
model=LogisticRegression()
model.fit(X_train,Y_train)
Model evaluation (training and test data)
X_train_prediction=model.predict(X_train)
training_data_accuracy=accuracy_score(Y_train,X_train_prediction)
print("Accuracy on training data ",training_data_accuracy)
_test_prediction=model.predict(X_test)
test_data_accuracy=accuracy_score(Y_test,X_test_prediction)
print("Accuracy on test data ",test_data_accuracy)
Buidling a predictive system
input_array=(20.29,14.34,135.1,1297,0.1003,0.1328,0.198,0.1043,0.1809,0.05883,0.7572,0.7813,5.438,94.44,0.01149,0.02461,0.05688,0.01885,0.01756,0.005115,22.54,16.67,152.2,1575,0.1374,0.205,0.4,0.1625,0.2364,0.07678);
inputtonumpy=np.asarray(input_array)
input_reshaped=inputtonumpy.reshape(1,-1)
prediction =model.predict(input_reshaped)
print(prediction)
if(prediction[0]==1):
print("Breast cancer is benign")
else:
print("Breast cancer is malignant")
