import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report, accuracy_score



# 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


# Exploratory Data Analysis (EDA)
print(iris_df.describe())  # Summary statistics
print(iris_df.head())      # View first few rows



# Visualization (pairplot for all features)
import seaborn as sns
# 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()



# 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)




# Create the SVM model
clf = SVC(kernel='linear')  # Experiment with different kernels (e.g., 'rbf')

# Train the model
clf.fit(X_train, y_train)

# Make predictions on the testing set
y_pred = clf.predict(X_test)



# Evaluate model performance
print(classification_report(y_test, y_pred))
print("Training Accuracy:", accuracy_score(y_train, clf.predict(X_train)))
print("Testing Accuracy:", accuracy_score(y_test, y_pred))




from sklearn.model_selection import GridSearchCV
# Define a parameter grid to explore
param_grid = {'kernel': ['linear', 'rbf'],
              'C': [0.01, 0.1, 1, 10, 100]}
# Create the GridSearchCV object
grid_search = GridSearchCV(SVC(), param_grid, cv=5)  # 5-fold cross-validation
# Fit the grid search to the training data
grid_search.fit(X_train, y_train)
# Get the best model and its parameters
best_model = grid_search.best_estimator_
best_params = grid_search.best_params_
print(best_params)
# Use the best model for prediction and evaluation
y_pred = best_model.predict(X_test)
print(classification_report(y_test, y_pred))
print("Testing Accuracy:", accuracy_score(y_test, y_pred))