Iris_Classification

#Task #2
#classification of iris flowers into different species based on their sepal and petal measurements.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.preprocessing import LabelEncoder

 # Load the Iris dataset
iris = sns.load_dataset('iris')

rows = iris.head(10)
print(rows)
iris.shape
rows.describe()
# Visualize the whole dataset based on species
sns.set(style="ticks")
sns.pairplot(iris, hue="species", palette="Set2")
X = iris.drop('species', axis=1)
y = iris['species']
# Split the data into training and testing sets (e.g., 80% for training and 20% for testing)
X_train, X_test, y_train, y_test = train_test_split(X, y , test_size=0.2, random_state=42)

# Create and train a Random Forest classifier
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = rf_model.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")

# Display a classification report
print(classification_report(y_test, y_pred))

# Display a confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(conf_matrix)

# Visualize feature importance
feature_names = iris.columns
importance = rf_model.feature_importances_
sorted_indices = np.argsort(importance)[::-1]
sorted_feature_names = feature_names[sorted_indices]
sorted_importance = importance[sorted_indices]

plt.figure(figsize=(10, 6))
sns.barplot(x=sorted_importance, y=sorted_feature_names)
plt.title("Feature Importance")
plt.xlabel("Importance")
plt.ylabel("Features")
plt.xticks(rotation=45)  
plt.show()
X_new = np.array([
    [8.2, 9.1, 4.4, 2],
    [4.9, 2.2, 3.8, 1.1],
    [5.3, 2.5, 4.6, 1.9]
])
#Prediction of the species from the input vector
prediction = rf_model.predict(X_new)
print("Prediction of Species: {}".format(prediction))