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    Full explanation with code, Check out this video to solve polynomial regression https://youtu.be/vsVarkdQLUk?si=qID-qIA7naVOve9u

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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())
    
    data = [list(map(float, input().split())) for i in range(N)]
    
    data = np.array(data)
    
    X_train, y_train = data[:, :-1], data[:, -1]
    
    T = int(input())
    
    X_test = [list(map(float, input().split())) for i in range(T)]
    
    X_test = np.array(X_test)
    
    degree = 3
    
    poly = PolynomialFeatures(degree)
    
    X_train_poly = poly.fit_transform(X_train)
    X_test_poly = poly.transform(X_test)
    
    model = LinearRegression()
    model.fit(X_train_poly, y_train)
    
    predictions = model.predict(X_test_poly)
    
    for p in predictions:
        print(round(p, 2))
    
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    It’s fascinating to see how they work together to solve complex problems! Sky1 exchange

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