43.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error

# Load your dataset from the CSV file
file_path = "C:/Users/wilso/OneDrive/Desktop/uk_renewable_energy.csv"
df = pd.read_csv(file_path)


X = df.drop('CO AQI Value', axis=1)
y = df['Ozone AQI Value']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Create and train the LASSO regression model
alpha_value = 0.01  # Adjust this value to control the strength of regularization
lasso_model = Lasso(alpha=alpha_value)
lasso_model.fit(X_train_scaled, y_train)

# Make predictions on the test set
y_pred = lasso_model.predict(X_test_scaled)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')

# Display the coefficients of the features
coefficients = lasso_model.coef_
print('LASSO Coefficients:')
for feature, coef in zip(X.columns, coefficients):
    print(f'{feature}: {coef}')