N2KAI.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Step 1: Data Collection and Preprocessing
n2k_data = pd.read_csv('extracted_features.csv')  # Assuming you have the extracted features in a CSV file

# Step 2: Data Preparation
# Split the data into features (X) and labels (y)
X = n2k_data.drop('impact', axis=1)
y = n2k_data['impact']

# 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)

# Step 3: Model Training
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Step 4: Model Evaluation
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

# Step 5: Generate 'new_renewable_energy_data.csv' (Sample Data)
new_data = pd.DataFrame(columns=X.columns)  # Create an empty DataFrame with the same columns as the features

# Generate a sample row of data for the new renewable energy development
sample_row = {
    'Urban fabric': 1,
    'Industrial, commercial and military units': 0,
    'Road networks and associated land': 1,
    'Railways and associated land': 0,
    'Port areas and associated land': 0,
    'Airports and associated land': 0,
    # ... Add the rest of the features here
}

# Append the sample row to the new_data DataFrame
new_data = new_data.append(sample_row, ignore_index=True)

# Save the new_data to 'new_renewable_energy_data.csv'
new_data.to_csv('new_renewable_energy_data.csv', index=False)

# Step 6: Predicting the Impact of Renewable Energy Developments
predictions = model.predict(new_data)

# Step 7: Generate 'trade_off_data.csv' (Sample Data)
trade_off_data = pd.DataFrame(columns=['Factor 1', 'Factor 2', 'Trade-off Score'])

# Generate sample trade-off data
sample_data = [
    {'Factor 1': 'Environmental Impact', 'Factor 2': 'Economic Viability', 'Trade-off Score': 0.75},
    {'Factor 1': 'Social Acceptance', 'Factor 2': 'Biodiversity Conservation', 'Trade-off Score': 0.6},
    # Add more sample data rows as needed
]

# Append the sample data to the trade_off_data DataFrame
trade_off_data = trade_off_data.append(sample_data, ignore_index=True)

# Save the trade_off_data to 'trade_off_data.csv'
trade_off_data.to_csv('trade_off_data.csv', index=False)

# Step 8: Trade-off Evaluation
# Perform analysis using AI techniques to evaluate trade-offs
# Example: Calculate the weighted average trade-off score
weights = [0.5, 0.5]  # Example weights for the two factors
trade_off_data['Weighted Score'] = trade_off_data['Trade-off Score'] * weights
weighted_average = trade_off_data['Weighted Score'].sum()

print("Weighted Average Trade-off Score:", weighted_average)

# Step 9: Further Analysis and Decision-Making
# Based on the predictions and trade-off evaluation, further analysis and decision-making processes can be implemented
# Example: If the predicted impact is high and the trade-off score