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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import load_model
model = load_model("auto_mpg_model.h5")
imputer = SimpleImputer(strategy='median')
scaler = StandardScaler()
def predict_mpg(data):
input_data = np.array(data).reshape(1, -1)
input_scaled = scaler.fit_transform(input_data)
prediction = model.predict(input_scaled)
return prediction[0][0]
input_data = [4, 121, 110, 2800, 15.4, 81, 3]
predicted_mpg = predict_mpg(input_data)
print("Predicted MPG:", predicted_mpg)
auto_mpg_data_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"
column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration', 'Model Year', 'Origin']
data = pd.read_csv(auto_mpg_data_url, names=column_names, na_values='?', comment='\t', sep=' ', skipinitialspace=True)
data = data.dropna()
X = data.drop('MPG', axis=1)
y = data['MPG']
imputer = SimpleImputer(strategy='median')
X_imputed = imputer.fit_transform(X)
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_imputed)
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
model = Sequential([
Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
Dense(32, activation='relu'),
Dense(1)
])
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2, verbose=2)
model.save("auto_mpg_model.h5")
model = load_model("auto_mpg_model.h5")
imputer = SimpleImputer(strategy='median')
scaler = StandardScaler()
def predict_mpg(data):
input_data = np.array(data).reshape(1, -1)
input_scaled = scaler.fit_transform(input_data)
prediction = model.predict(input_scaled)
return prediction[0][0]
input_data = input()
predicted_mpg = predict_mpg(input_data)
print("Predicted MPG:", predicted_mpg) |