import sklearn
from sklearn import datasets
import numpy as np

iris = datasets.load_iris()
digits = datasets.load_digits()

from sklearn.datasets import load_iris
iris_data = load_iris()

print(iris_data.data[0]) # Feature values for first sample
print(iris_data.target[0]) # Target value for first sample


# The imputer replaces missing values with the mean

from sklearn.impute import SimpleImputer

imputer = SimpleImputer(strategy='mean')  
imputed_data = imputer.fit_transform(iris_data.data)

# Feature Scaling

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaled_data = scaler.fit_transform(iris_data.data)


# Visualizing the Data
 
import matplotlib.pyplot as plt
plt.scatter(iris_data.data[:, 0], iris_data.data[:, 1], c=iris_data.target)
plt.xlabel('Sepal Length')
plt.ylabel('Sepal Width')
plt.show()


# Training a Simple Model
 
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(scaled_data, iris_data.target)