BREAST CANCER.py

#!/usr/bin/env python
# coding: utf-8

# BREAST CANCER CLASSIFICATION
# 
# Predicting if the cancer is malignant or benign. 
# 30 features.
# 

# # IMPORTING DATA

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


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from sklearn.datasets import load_breast_cancer


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cancer = load_breast_cancer()


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cancer


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cancer.keys()


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print(cancer['DESCR'])


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print(cancer['target'])


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print(cancer['target_names'])


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print(cancer['feature_names'])


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cancer['data'].shape


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


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df_cancer.head()


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# # VISUALIZING DATA

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


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sns.countplot(df_cancer['target'])


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


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


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# # MODEL TRAINING

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


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X


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y = df_cancer['target']


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y


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from sklearn.model_selection import train_test_split


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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=5)


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X_train


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X_test


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y_train


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y_test


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from sklearn.svm import SVC


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from sklearn.metrics import classification_report, confusion_matrix


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svc_model = SVC()


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svc_model.fit(X_train, y_train)


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# # EVALUATING THE MODEL

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y_predict = svc_model.predict(X_test)


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y_predict


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cm = confusion_matrix(y_test, y_predict)


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sns.heatmap(cm, annot = True)


# # IMPROVING THE MODEL

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min_train = X_train.min()


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range_train = (X_train - min_train).max()


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X_train_scaled = (X_train - min_train)/range_train


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


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min_test = X_test.min()
range_test = (X_test - min_test).max()
X_test_scaled = (X_test - min_test)/range_test


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svc_model.fit(X_train_scaled, y_train)


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y_predict = svc_model.predict(X_test_scaled)


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cm = confusion_matrix(y_test, y_predict)


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sns.heatmap(cm, annot = True)


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print(classification_report(y_test, y_predict))


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# # IMPROVING MODEL PART 2

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param_grid = {'C':[0.1,1,10,100], 'gamma': [1, 0.1, 0.01, 0.001 ], 'kernel': ['rbf'] }


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from sklearn.model_selection import GridSearchCV


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grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 4)


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grid.fit(X_train_scaled, y_train)


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grid.best_params_


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grid_predictions = grid.predict(X_test_scaled)


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cm = confusion_matrix(y_test, grid_predictions)


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sns.heatmap(cm, annot=True)


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print(classification_report(y_test, grid_predictions))


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