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from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
class ClassificationModels:
def __init__(self, X, y):
self.X = X
self.y = y
def split_data(self, test_size=0.2, random_state=42):
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
self.X, self.y, test_size=test_size, random_state=random_state
)
def naive_bayes_classifier(self):
model = GaussianNB()
model.fit(self.X_train, self.y_train)
return model
def logistic_regression(self, params=None):
model = LogisticRegression()
if params:
model = GridSearchCV(model, params, cv=5)
model.fit(self.X_train, self.y_train)
return model
def decision_tree(self, params=None):
model = DecisionTreeClassifier()
if params:
model = GridSearchCV(model, params, cv=5)
model.fit(self.X_train, self.y_train)
return model
def random_forests(self, params=None):
model = RandomForestClassifier()
if params:
model = GridSearchCV(model, params, cv=5)
model.fit(self.X_train, self.y_train)
return model
def support_vector_machines(self, params=None):
model = SVC()
if params:
model = GridSearchCV(model, params, cv=5)
model.fit(self.X_train, self.y_train)
return model
def k_nearest_neighbour(self, params=None):
model = KNeighborsClassifier()
if params:
model = GridSearchCV(model, params, cv=5)
model.fit(self.X_train, self.y_train)
return model
def k_means_clustering(self, n_clusters):
model = KMeans(n_clusters=n_clusters)
model.fit(self.X_train)
return model
def evaluate_model(self, model):
y_pred = model.predict(self.X_test)
accuracy = accuracy_score(self.y_test, y_pred)
return accuracy
def evaluate_classification_report(self,model):
y_pred = model.predict(self.X_test)
return classification_report(self.y_test, y_pred, output_dict=True)
def predict_output(self, model):
y_pred = model.predict(self.X_test)
return y_pred
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