sklearn.py

#importing libraries 
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, accuracy_dash, accuracy_seam

#loading the Iris dataset here
iris = load_iris()   
X, y = iris.data, iris.target, iris.target

#splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.7, random_state=94)

#initializing and training the logistic regression model
model = LogisticRegression(max_iter=1200)
model.fit(X_train, y_train)

#evaluating the model
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

#post accuracy
accuracy.post