import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# Load the Iris dataset from sklearn
iris = datasets.load_iris()

# Convert the data into a DataFrame
iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
iris_df['target'] = iris.target

iris_df.shape

iris_df.columns

iris_df.info()

iris_df.describe()

# EDA: Pairplot to visualize relationships between features
sns.pairplot(iris_df, hue='target', palette='viridis', diag_kind='hist')
plt.suptitle("Pairplot of Iris Dataset", y=1.02)
plt.show()



# Other Plots
plt.figure(figsize=(12, 6))

# Boxplot
plt.subplot(1, 2, 1)
sns.boxplot(x='target', y='sepal length (cm)', data=iris_df)
plt.title('Boxplot of Sepal Length by Target')

# Violin Plot
plt.subplot(1, 2, 2)
sns.violinplot(x='target', y='sepal width (cm)', data=iris_df)
plt.title('Violin Plot of Sepal Width by Target')

plt.tight_layout()
plt.show()


# Count Plot (Bar Plot)
plt.figure(figsize=(8, 5))
sns.countplot(x='target', data=iris_df, palette='viridis')
plt.title('Count of Samples by Target')
plt.xlabel('Target (Species)')
plt.ylabel('Count')
plt.xticks(ticks=[0, 1, 2], labels=iris.target_names)
plt.show()


# Correlation Heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(iris_df.corr(), annot=True, cmap='coolwarm', fmt='.2f', linewidths=1, linecolor='white')
plt.title('Correlation Heatmap of Iris Features')
plt.show()


# Separate features (X) and target (y) from the DataFrame
X = iris_df.drop('target', axis=1)  # Features
y = iris_df['target']               # Target (labels)


# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


# Standardize features by removing the mean and scaling to unit variance
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)




# Initialize the Logistic Regression model
model = LogisticRegression()

# Train the model on the training data
model.fit(X_train, y_train)

# Predict on the test data
y_pred = model.predict(X_test)




# Calculate training and testing accuracy
train_accuracy = accuracy_score(y_train, model.predict(X_train))
test_accuracy = accuracy_score(y_test, y_pred)

print("Training Accuracy:", train_accuracy)
print("Testing Accuracy:", test_accuracy)




# Create a confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred)
print("\nConfusion Matrix:")
print(conf_matrix)



# Print classification report
print("\nClassification Report:")
print(classification_report(y_test, y_pred, target_names=iris.target_names))



# Print probabilities of classification for the first few samples in the test set
print("\nProbabilities of Classification:")
probabilities = model.predict_proba(X_test[:5])
for i, prob in enumerate(probabilities):
    print(f"Sample {i+1}: {list(zip(iris.target_names, prob))}")