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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 | |