1. ML

 import pandas as pd

import numpy as np

from sklearn.preprocessing import StandardScaler

from sklearn.decomposition import PCA

import matplotlib.pyplot as plt

import seaborn as sns

data = pd.read_csv('wine.csv')

X = data.drop('Alcohol' , axis=1)

y = data['Alcohol']

scaler= StandardScaler()

X_scaled = scaler.fit_transform(X)

pca = PCA(n_components=2)

X_pca = pca.fit_transform(X_scaled)

plt.figure(figsize=(10,6))

plt.scatter(X_pca[:,0], X_pca[:,1], c=y, cmap='viridis')

plt.xlabel('principle component 1')

plt.ylabel('principle component 2')

plt.title('PCA: Wine Dataset')

plt.colorbar(label='Wine Class')

plt.show()