Introduction : 

What Is Pancreatic Cancer?

Pancreatic cancer is a type of cancer that starts in the pancreas. Cancer starts when cells in the body begin to grow out of control. 
Pancreatic cancer is usually not found until advanced stages because it is hard to detect. Signs of pancreatic cancer include jaundice and weight loss. Risk factors include having diabetes and exposure to certain chemicals. Specific treatment depends on the size and location of the tumor, and whether or not it has spread to other areas of the body.

What are the symptoms of pancreatic cancer?

cancer_df.drop(['sample_id','sample_origin','benign_sample_diagnosis','stage','patient_cohort'],axis=1,inplace=True)

cancer_df.head()

Handle Missing Values :

cancer_df.isnull().sum()

Inference : 

plasma and REG1A contain null values.

# 1 - no pancreatic disease

# 2 - benign

# 3 - pancreatic cancer

plt.figure(figsize=(8,5))

sns.countplot(x='diagnosis',data=cancer_df,palette='Set2');

plt.title("Countplot for Target variable",{'fontsize':20});​

Data Visualization :

sns.countplot(x='sex',data=cancer_df,hue='diagnosis',palette='Set1');

plt.title("Countplot for sex variable based on dignosis",{'fontsize':20});

Inference :

fig, axes = plt.subplots(2, 3, figsize=(12, 8), sharey=True)

sns.boxplot(ax=axes[0][0],x="plasma_CA19_9", data=cancer_df,palette='dark')

sns.boxplot(ax=axes[0][1],x="creatinine", data=cancer_df,palette='dark')

sns.boxplot(ax=axes[0][2],x="LYVE1", data=cancer_df,palette='dark')

sns.boxplot(ax=axes[1][0],x="REG1B", data=cancer_df,palette='dark')

sns.boxplot(ax=axes[1][1],x="TFF1", data=cancer_df,palette='dark')

sns.boxplot(ax=axes[1][2],x="REG1A", data=cancer_df,palette='dark');

sns.boxplot(x='age',data=cancer_df,palette='bright');

plt.title("Distibution of feature age",{'fontsize':20})​

Split Data into dependent and independent feature :

X = cancer_df.drop('diagnosis',axis=1)

y = cancer_df.diagnosis

for i in X.columns:

    if X[i].dtype == 'O':

        print(f"{i} : ",X[i].unique())



encoder1=LabelEncoder()

X.sex = encoder1.fit_transform(X.sex)



def fun(df):

    if df==1:

        return 0

    if df==2:

        return 1

    else:

        return 2

y = y.apply(fun)

Split data int two subsets: training data and testing data :

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=11)

print(X_train.shape,X_test.shape)

models = list()

accuracy = list()

Hyperparameter Tuning :
1] Random Forest :

rf  = RandomForestClassifier(random_state=43)

param_grid = { 

    'n_estimators': [200, 500],

    'max_features': ['auto', 'sqrt', 'log2'],

    'max_depth' : [4,5,6,7,8],

    'criterion' :['gini', 'entropy']

}

CV_rfc = GridSearchCV(estimator=rf, param_grid=param_grid, cv= 5)

CV_rfc.fit(X_train, y_train)

CV_rfc.best_params_

new_rf = RandomForestClassifier(criterion='entropy',max_depth=8,n_estimators=240)

new_rf.fit(X_train,y_train)

models.append("Random Forest")

accuracy.append(abs(new_rf.score(X_test,y_test)))

Classification Report :

ypred1 = new_rf.predict(X_test)

print("Classification Report for Random Forest")

print(classification_report(y_test,ypred1))

2] Support Vector Machine :

param_grid = {'C': [0.1, 1, 10, 100, 1000],

              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],

              'kernel': ['rbf']}

 

grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3)

grid.fit(X_train,y_train)

grid.best_params_



#Output:{'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}

s = SVC(C=10,gamma=0.0001,kernel='rbf')

s.fit(X_train,y_train)

models.append("SVM")

accuracy.append(abs(s.score(X_test,y_test)))



ypred2 = s.predict(X_test)

print("Classification Report SVM")

print(classification_report(y_test,ypred2))

3] Logistic Regression :

logreg=LogisticRegression(max_iter=3500,penalty='l2')

logreg.fit(X_train,y_train)

models.append("Logistic Regression")

accuracy.append(abs(logreg.score(X_test,y_test)))



ypred3 = logreg.predict(X_test)

print("Classification Report Logistic Regression")

print(classification_report(y_test,ypred3))

4] Dicision Tree :

dt = DecisionTreeClassifier()

dt.fit(X_train,y_train)

models.append("Decision Tree")

accuracy.append(abs(dt.score(X_test,y_test)))



ypred4 = dt.predict(X_test)

print("Classification Report Dicision Tree")

print(classification_report(y_test,ypred4))

Accuracy of Models :

plt.figure(figsize=(8,6))

plt.bar(models,accuracy,color=['red','deepskyblue','orange','blue']);

plt.title("Accuracy of models",{'fontsize':20})

Thank You ):