f1 score complete code

# Import Libraries
import pandas as pd
from sklearn.preprocessing import OrdinalEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.ensemble import RandomForestClassifier

#Dataset
columns=['class','age','menopause','tumor_size','inv_nodes','node_caps',
         'deg_malig','breast','breast_quad','irradiat']
df=pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer/breast-cancer.data",names=columns)

# Feature Engineering
ordinalencoder=OrdinalEncoder()
categorical_data=df.select_dtypes(include=['object'])
transformed_data=ordinalencoder.fit_transform(categorical_data)
df[categorical_data.columns]=transformed_data

# Data Splitting
y=df.pop('class')
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.1, random_state=42)

# Model Implementation
clf = RandomForestClassifier()
clf = clf.fit(X_train, y_train)
predictions=clf.predict(X_test)
print(f1_score(predictions,y_test))