我有一個多項式回歸圖，我正在嘗試使用下一個 X 值找到預測值 y。import numpy as npimport matplotlib.pyplot as pltfrom sklearn.linear_model import LinearRegressionimport jsonimport matplotlib.pyplot as pltfrom sklearn.pipeline import make_pipelinefrom sklearn.preprocessing import PolynomialFeatureswith open('/Users/aus10/Desktop/PGA/Data_Cleanup/Combined_Player_Stats.json') as json_file:    players_data = json.load(json_file)for obj in players_data:    obj['Scrambling_List'] = [i for i in obj['Scrambling_List'] if i]for obj in players_data:    def create_2d_lst(lst):        try:            if len(lst) < 1:                return [0, 0]            else:                return [[i, j] for i, j in enumerate(lst)]        except:                pass    try:             scrambling = create_2d_lst(obj['Scrambling_List'])        total_putts_GIR = create_2d_lst(obj['Total_Putts_GIR_List'])        SG_Putting = create_2d_lst(obj['SG_Putting_List'])    except Exception:        pass    data = scrambling    X = np.array(data)[:,0].reshape(-1,1)    y = np.array(data)[:,1].reshape(-1,1)    poly_reg = PolynomialFeatures(degree=4)    X_poly = poly_reg.fit_transform(X)    pol_reg = LinearRegression()    pol_reg.fit(X_poly, y)    predicted_y = poly_reg.fit_transform(X)    m = pol_reg.coef_    c = pol_reg.intercept_    prediction_value = (len(X) + 1)    prediction = pol_reg.predict(poly_reg.fit_transform([[prediction_value]]))    def viz_polymonial():        plt.scatter(X, y, color='red')        plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color='blue')        plt.plot(prediction, marker='x', color='green')        plt.title('Projected Scrambling Percentage')        plt.xlabel('Tournaments')        plt.ylabel('Scrambling Percentage')        plt.show()        return    viz_polymonial()    當我使用 時prediction = prediction_value = (len(X) + 1)， prediction = pol_reg.predict(poly_reg.fit_transform([[prediction_value]]))我應該獲得 的下一個值X，但它返回 0，而它應該是 len(X) + 1。我需要為Xset 獲取正確的值作為預測值。我不確定為什么它為零，因為當我打印預測值時，我得到了正確的值