import numpy as np
from sklearn.linear_model import LinearRegression
 
# Sample data (replace with your actual data)
data = np.array([2.09, 3.60, 1.16, 1.54,1.00, 37.01, 5.09, 10.62, 2.41, 1.46, 1.07, 3.08])
 
# Number of future predictions you want to make
num_predictions = 5
 
# Function to predict the next number and add it to the array
def predict_and_add(data, model, num_predictions):
    for _ in range(num_predictions):
        X = np.arange(len(data)).reshape(-1, 1)
        y = data
 
        # Train the model
        model.fit(X, y)
 
        # Predict the next number
        next_index = len(data)
        next_value = model.predict([[next_index]])
 
        # Add the predicted number to the array
        data = np.append(data, next_value)
 
        # Print the predicted number with two decimal places
        print(f"The predicted next number is: {next_value[0]:.2f}")
 
    return data
 
# Create the linear regression model
model = LinearRegression()
 
# Predict and add the future numbers
updated_data = predict_and_add(data, model, num_predictions)
 
print("Updated data array:", updated_data)

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