import pandas as pd 
# Load the dataset 
df = pd.read_csv('petrol_consumption.csv') 
# Display basic information about the dataset 
print(df.head()) 
print(df.describe()) 
print(df.info()) 
from sklearn.preprocessing import StandardScaler 
# Separate the features and target variable if there's one 
X = df.drop(columns=['Petrol_Consumption'])  # Assuming 'Petrol_Consumption' is the target variable 
y = df['Petrol_Consumption'] 
# Standardize the features 
scaler = StandardScaler() 
X_scaled = scaler.fit_transform(X) 
from sklearn.decomposition import PCA 
# Apply PCA 
pca = PCA(n_components=2) 
X_pca = pca.fit_transform(X_scaled) 
# Check the explained variance 
print("Explained variance by each component:", pca.explained_variance_ratio_) 
print("Cumulative explained variance:", pca.explained_variance_ratio_.cumsum())