Upload app.py

app.py

@@ -0,0 +1,73 @@
+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)