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    import numpy as np 
    from sklearn.preprocessing import PolynomialFeatures 
    from sklearn.linear_model import LinearRegression
    
    f, n = map(int, input().strip().split())
        
    x_observations = [];
    y_amount = []
    for _ in range(n):
        l = list(map(float, input().strip().split()))
        x_observations.append(l[0:-1])
        y_amount.append(l[-1])
    
    poly_features = PolynomialFeatures(degree=3)
    X_poly = poly_features.fit_transform(np.array(x_observations))
    
    model = LinearRegression()
    model.fit(X_poly, np.array(y_amount))
    
    #result
    t = int(input().strip())
    for _ in range(t):
        l = poly_features.fit_transform(np.array(list(map(float, input().strip().split()))).reshape(1, -1))
        print( round(( model.predict(l) )[0],2))
    
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    # Enter your code here. Read input from STDIN. Print output to STDOUT
    import numpy as np 
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.linear_model import LinearRegression
    
    
    
    F,N=map(int,input().split())
    train_data= []
    for i in range(N):
        rows=list(map(float,input().split()))
        train_data.append(rows)
    T = int(input())
    test_data=[list(map(float,input().split())) for _ in range(T)]
    
    X_train=[]
    y_train=[]
    for row in train_data:
        X_train.append(row[:F]) 
        y_train.append(row[F]) 
    
    X_train = np.array(X_train)  # Convert to numpy array for model input
    y_train = np.array(y_train)
    X_test=np.array(test_data)
    #print(X_test)
    
    poly=PolynomialFeatures(degree=3)
    X_train_poly=poly.fit_transform(X_train)
    X_test_poly=poly.transform(X_test)
    
    model=LinearRegression()
    model.fit(X_train_poly,y_train)
    y_pred=model.predict(X_test_poly)
    
    for pred in y_pred:
        print(pred)