a.txt

import pandas as pd
import numpy as np
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Load the concrete data
url = 'https://cocl.us/concrete_data'
data = pd.read_csv(url)

# Prepare the data
X = data.drop('Strength', axis=1)  # Features
y = data['Strength']              # Target variable (concrete strength)

# --- A. Baseline Model ---

# Function to build and evaluate the baseline model
def build_and_evaluate_model():
    model = Sequential()
    model.add(Dense(10, activation='relu', input_shape=(X.shape[1],)))
    model.add(Dense(1, activation='linear'))  # Output layer for regression
    model.compile(optimizer='adam', loss='mean_squared_error')

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
    model.fit(X_train, y_train, epochs=50, verbose=0)  # Suppress training output for clarity
    y_pred = model.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    return mse

# Repeat the experiment 50 times
mse_values = [build_and_evaluate_model() for _ in range(50)]

# Report the results
mean_mse = np.mean(mse_values)
std_dev_mse = np.std(mse_values)
print(f"Mean MSE: {mean_mse:.2f}")
print(f"Standard Deviation of MSE: {std_dev_mse:.2f}")