import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (
accuracy_score,
precision_score,
recall_score,
confusion_matrix,
)
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
data = pd.read_csv("C:\\Users\\Downloads\\DS\\Iris_Dataset.csv")
X = data.drop(columns=["Id", "Species"])
y = data["Species"]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
logreg_model = LogisticRegression()
logreg_model.fit(X_train, y_train)
y_pred_logreg = logreg_model.predict(X_test)
accuracy_logreg = accuracy_score(y_test, y_pred_logreg) * 100
precision_logreg = precision_score(y_test, y_pred_logreg, average="weighted") * 100
recall_logreg = recall_score(y_test, y_pred_logreg, average="weighted") * 100
print("Logistic Regression Model Performance:")
print("Accuracy:", accuracy_logreg)
print("Precision:", precision_logreg)
print("Recall:", recall_logreg)
conf_matrix_logreg = confusion_matrix(y_test, y_pred_logreg)
print("Confusion Matrix:")
print(conf_matrix_logreg)
dt_model = DecisionTreeClassifier()
dt_model.fit(X_train, y_train)
plt.figure(figsize=(12, 8))
plot_tree(
dt_model,
filled=True,
feature_names=X.columns,
class_names=data["Species"].unique()
)
plt.show()
