ML-Breast Cancer Project-Copy1.py

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

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import warnings
warnings.filterwarnings('ignore')


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


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df = pd.read_csv("https://raw.githubusercontent.com/ingledarshan/AIML-B2/main/data.csv")df.columns


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df.head()


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df.columns


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df.info()


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df['Unnamed: 32']


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df = df.drop("Unnamed: 32", axis=1)


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df.head()


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df.columns


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df.drop('id', axis=1, inplace=True)
# df = df.drop('id', axis=1)


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df.columns


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type(df.columns)


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l = list(df.columns)
print(l)


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features_mean = l[1:11]

features_se = l[11:21]

features_worst = l[21:]


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print(features_mean)


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print(features_se)


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print(features_worst)


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df.head(2)


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df['diagnosis'].unique()
# M= Malignant, B= Benign


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sns.countplot(df['diagnosis'], label="Count",);


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df['diagnosis'].value_counts()


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df.shape


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df.describe()
# summary of all the numeric columns


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len(df.columns)


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# Correlation Plot
corr = df.corr()
corr


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corr.shape


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plt.figure(figsize=(8,8))
sns.heatmap(corr);


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df.head()


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df['diagnosis'] = df['diagnosis'].map({'M':1, 'B':0})


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df['diagnosis'].unique()


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X = df.drop('diagnosis', axis=1)
X.head()


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y = df['diagnosis']
y.head()


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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)


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df.shape


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X_train.shape


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X_test.shape


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y_train.shape


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y_test.shape


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X_train.head(1)


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


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X_train


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from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, y_train)


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y_pred = lr.predict(X_test)


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y_pred


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y_test


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from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))


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lr_acc = accuracy_score(y_test, y_pred)
print(lr_acc)


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results = pd.DataFrame()
results


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tempResults = pd.DataFrame({'Algorithm':['Logistic Regression Method'], 'Accuracy':[lr_acc]})
results = pd.concat( [results, tempResults] )
results = results[['Algorithm','Accuracy']]
results


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from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)


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y_pred = dtc.predict(X_test)
y_pred


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from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))


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dtc_acc = accuracy_score(y_test, y_pred)
print(dtc_acc)


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tempResults = pd.DataFrame({'Algorithm':['Decision tree Classifier Method'], 'Accuracy':[dtc_acc]})
results = pd.concat( [results, tempResults] )
results = results[['Algorithm','Accuracy']]
results


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from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)


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y_pred = rfc.predict(X_test)
y_pred


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from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))


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rfc_acc = accuracy_score(y_test, y_pred)
print(rfc_acc)


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tempResults = pd.DataFrame({'Algorithm':['Random Forest Classifier Method'], 'Accuracy':[rfc_acc]})
results = pd.concat( [results, tempResults] )
results = results[['Algorithm','Accuracy']]
results


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from sklearn import svm
svc = svm.SVC()
svc.fit(X_train,y_train)


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y_pred = svc.predict(X_test)
y_pred


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from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))


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rfc_acc = accuracy_score(y_test, y_pred)
print(rfc_acc)


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tempResults = pd.DataFrame({'Algorithm':['Random Forest Classifier Method'], 'Accuracy':[rfc_acc]})
results = pd.concat( [results, tempResults] )
results = results[['Algorithm','Accuracy']]
results


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from sklearn import svm
svc = svm.SVC()
svc.fit(X_train,y_train)


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y_pred = svc.predict(X_test)
y_pred


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from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))


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svc_acc = accuracy_score(y_test, y_pred)
print(svc_acc)


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tempResults = pd.DataFrame({'Algorithm':['Support Vector Classifier Method'], 'Accuracy':[svc_acc]})
results = pd.concat( [results, tempResults] )
results = results[['Algorithm','Accuracy']]
results


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