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Breast Cancer Tumor Machine Learning Prediction Using Scikit Learn

Breast Cancer Machine Learning Prediction. Used Scikit Learn for Training, Evaluating, and Prediction. Used Seaborn and Matplotlib for Visualizing.

Run this on Jupyter Notebook

# LIBRARY IMPORTS

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

# DATASET IMPORT

from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()

#VIEW DATA

cancer

cancer.keys()

print(cancer['DESCR'])

print(cancer['target_names'])

print(cancer['target'])

print(cancer['feature_names'])

print(cancer['data'])

cancer['data'].shape

df_cancer = pd.DataFrame(np.c_[cancer['data'], cancer['target']], columns = np.append(cancer['feature_names'], ['target']))

df_cancer.head()

df_cancer.tail()

# VISUALIZE DATA

# SEABORN PAIRPLOT

sns.pairplot(df_cancer, hue = 'target', vars = ['mean radius', 'mean texture', 'mean area', 'mean perimeter', 'mean smoothness'] )

# SEABORN COUNTPLOT

sns.countplot(df_cancer['target'], label = "Count") 

# SEABORN SCATTERPLOT

sns.scatterplot(x = 'mean area', y = 'mean smoothness', hue = 'target', data = df_cancer)

# SEABORN LMPLOT

sns.lmplot('mean area', 'mean smoothness', hue ='target', data = df_cancer, fit_reg=False)

# SEABORN HEATMAP

plt.figure(figsize=(20,10)) 
sns.heatmap(df_cancer.corr(), annot=True) 

# MODEL TRAINING

# DEFINING X and y

X = df_cancer.drop(['target'],axis=1)

X.head()

y = df_cancer['target']

y.head()

# TRAIN TEST SPLIT (20-80)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state=5)

X_train.shape

X_test.shape

y_train.shape

y_test.shape

# IMPORT MODELS

from sklearn.svm import SVC 
from sklearn.metrics import classification_report, confusion_matrix

# TRAIN ON SVC MODEL

svc_model = SVC()
svc_model.fit(X_train, y_train)

# EVALUATING

# PREDICT

y_predict = svc_model.predict(X_test)

cm = confusion_matrix(y_test, y_predict)

sns.heatmap(cm, annot=True)

print(classification_report(y_test, y_predict))
             precision    recall  f1-score   support

        0.0       0.00      0.00      0.00        44
        1.0       0.61      1.00      0.76        70

avg / total       0.38      0.61      0.47       114
# If the result turns out to be terribly off the precision. Like in this case, it is coming out to be 34% only then we need to normalize the data.

# IMPROVING MODEL

X_train.head()

# NORMALIZATION

# TRAIN DATA

min_train = X_train.min()
print(min_train)

max_train = X_train.max()
print(max_train)

range_train = max_train - min_train
print(range_train)

X_train_scaled = (X_train - min_train)/range_train
print(X_train_scaled)

# COMPARING EARLIER TRAIN DATA AND NORMALIZED TRAIN DATA

sns.scatterplot(x = X_train['mean area'], y = X_train['mean smoothness'], hue = y_train)

sns.scatterplot(x = X_train_scaled['mean area'], y = X_train_scaled['mean smoothness'], hue = y_train)

# TEST DATA

min_test = X_test.min()

max_test = X_test.max()

range_test = max_test - min_test

X_test_scaled = (X_test - min_test)/range_test

# TRAIN AND PREDICT

from sklearn.svm import SVC 
from sklearn.metrics import classification_report, confusion_matrix

svc_model = SVC()

svc_model.fit(X_train_scaled, y_train)

y_predict = svc_model.predict(X_test_scaled)

cm = confusion_matrix(y_test, y_predict)

sns.heatmap(cm,annot=True,fmt="d")

print(classification_report(y_test,y_predict))
             precision    recall  f1-score   support

        0.0       0.76      0.97      0.85        39
        1.0       0.98      0.84      0.91        75

avg / total       0.91      0.89      0.89       114
# If the result has improved, the normalization is successful. In this case precision has drastically improved with normalization

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