import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import joblib

# Load dataset
data = pd.read_csv('Iris.csv')
data.drop(columns=['Id'], inplace=True)

# Prepare features and target
X = data.drop(columns=['Species'])
y = data['Species']

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Save the model
joblib.dump(model, 'model.pkl')