Precision, Recall, F1 Score Python implementation

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# loading the csv data to a Pandas DataFrame
heart_data = pd.read_csv('heart.csv')

# print first 5 rows of the dataset
heart_data.head()

# print last 5 rows of the dataset
heart_data.tail()

# number of rows and columns in the dataset
heart_data.shape

# getting some info about the data
heart_data.info()

# checking for missing values
heart_data.isnull().sum()

# statistical measures about the data
heart_data.describe()

# checking the distribution of Target Variable
heart_data['target'].value_counts()

X = heart_data.drop(columns='target', axis=1)
Y = heart_data['target']

print(X)

print(Y)

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)

print(X.shape, X_train.shape, X_test.shape)

model = LogisticRegression(max_iter=1000)

# training the LogisticRegression model with Training data
model.fit(X_train, Y_train)

from sklearn.metrics import accuracy_score

# accuracy on training data
X_train_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(Y_train, X_train_prediction)
print(training_data_accuracy)

print('Accuracy on Training data : ', round(training_data_accuracy*100, 2), '%')

# accuracy on test data
X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(Y_test, X_test_prediction)
print(test_data_accuracy)

print('Accuracy on Test data : ', round(test_data_accuracy*100, 2), '%')

from sklearn.metrics import precision_score

# precision for training data predictions
precision_train = precision_score(Y_train, X_train_prediction)
print('Training data Precision =', precision_train)

# precision for test data predictions
precision_test = precision_score(Y_test, X_test_prediction)
print('Test data Precision =', precision_test)

from sklearn.metrics import recall_score

# recall for training data predictions
recall_train = recall_score(Y_train, X_train_prediction)
print('Training data Recall =', recall_train)

# recall for test data predictions
recall_test = recall_score(Y_test, X_test_prediction)
print('Test data Recall =', recall_test)

from sklearn.metrics import f1_score

# F1 score for training data predictions
f1_score_train = f1_score(Y_train, X_train_prediction)
print('Training data F1 Score =', f1_score_train)

# F1 Score for test data predictions
f1_score_test = recall_score(Y_test, X_test_prediction)
print('Test data F1 Score =', f1_score_test)

def precision_recall_f1_score(true_labels, pred_labels):

  precision_value = precision_score(true_labels, pred_labels)
  recall_value = recall_score(true_labels, pred_labels)
  f1_score_value = f1_score(true_labels, pred_labels)

  print('Precision =',precision_value)
  print('Recall =',recall_value)
  print('F1 Score =',f1_score_value)

# classification metrics for training data
precision_recall_f1_score(Y_train, X_train_prediction)

# classification metrics for test data
precision_recall_f1_score(Y_test, X_test_prediction)