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    My solution in python3:

    from sklearn import linear_model
    
    first_input=input()
    m = int(first_input.split()[0])
    n = int(first_input.split()[1])
    
    x_s = []
    y_s = []
    for i in range(n):
        x_n_y = input().split()      
        x_s.append([float(x_n_y[j]) for j in range(m)])
        y_s.append(float(x_n_y[-1]))
    
    lm = linear_model.LinearRegression()
    lm.fit(x_s, y_s)
    a = lm.intercept_
    b = lm.coef_
    b_s = [b[k] for k in range(m)]
    
    q = int(input())
    for l in range(q):
        tests = input().split()
        summed = a 
        for i in range(m):
            summed += b_s[i]*float(tests[i])
        print(summed)