app_local_rag/predict.py at main · anaskex/app_local_rag · GitHub

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from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler

######################################################## LogisticRegression

from sklearn.linear_model import LogisticRegression

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)

model = LogisticRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print(f"\n✅ LR Accuracy: {accuracy:.2%}")

######################################################## naive_bayes

from sklearn.naive_bayes import GaussianNB

model = GaussianNB()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print(f"\n✅ NB Accuracy: {accuracy:.2%}")

######################################################## SVC

from sklearn.svm import SVC

# Scale for KNN and SVM
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

model = SVC(kernel="rbf")
model.fit(X_train_scaled, y_train)
y_pred = model.predict(X_test_scaled)

accuracy = accuracy_score(y_test, y_pred)
print(f"\n✅ SVM Accuracy: {accuracy:.2%}")

######################################################## DecisionTreeClassifier

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print(f"\n✅ Tree Accuracy: {accuracy:.2%}")

######################################################## KNeighborsClassifier

from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train_scaled, y_train)
y_pred = model.predict(X_test_scaled)

accuracy = accuracy_score(y_test, y_pred)
print(f"\n✅ KNN Accuracy: {accuracy:.2%}")

######################################################## RandomForestClassifier

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print(f"\n✅ RF Accuracy: {accuracy:.2%}")

######################################################## AdaBoostClassifier

from sklearn.ensemble import AdaBoostClassifier

model = AdaBoostClassifier(n_estimators=50)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print(f"\n✅ ADA Accuracy: {accuracy:.2%}")