import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report


# Load Iris data
iris = load_iris()
df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
df['species'] = iris.target
df['species_name'] = df['species'].map(dict(zip(range(3), iris.target_names)))

df.head()



print(df.info())
print(df.describe())

# Check class distribution
print(df['species_name'].value_counts())



plt.figure(figsize=(6,4))
sns.countplot(x='species_name', data=df)
plt.title("Count of Each Iris Species")
plt.xlabel("Species")
plt.ylabel("Count")
plt.show()



plt.figure(figsize=(8,6))
sns.heatmap(df.iloc[:, :4].corr(), annot=True, cmap="coolwarm", fmt=".2f")
plt.title("Correlation Heatmap of Iris Features")
plt.show()



sns.pairplot(df, hue='species_name', vars=iris.feature_names[:3])
plt.suptitle("Pairplot of Selected Features", y=1.02)
plt.show()


X = df[iris.feature_names]
y = df['species']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)




# Use multinomial for multi-class classification with softmax
model = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=200)
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)



print("Classification Report:")
print(classification_report(y_test, y_pred, target_names=iris.target_names))



cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(6,4))
sns.heatmap(cm, annot=True, cmap="YlGnBu", fmt='d',
            xticklabels=iris.target_names, yticklabels=iris.target_names)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title("Confusion Matrix Heatmap")
plt.show()





print("Training Accuracy:", model.score(X_train, y_train))
print("Test Accuracy:", model.score(X_test, y_test))