Plotting 3D surface plot for non-linear multivariate regression with 5 variables

[khwajaosama]

I am new to Python. I am trying to plot surface plots to show my model fit. I have developed a multivariate polynomial model using sklearn library. When I try to plot surface plot of the model prediction, the dimensions of meshgrid do not match with the expected dimension of the model.

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import pandas as pd df = pd.read_excel("Data.xlsx") # The file contains 6 columns and 46 rows factors = ['a', 'b', 'c', 'd', 'e'] # These are the names of independent variables target = df['f'] # Independent variable var = df[factors]   import numpy as np from sklearn.preprocessing import PolynomialFeatures from sklearn import linear_model   poly = PolynomialFeatures(degree = 2) Var = poly.fit_transform(var) clf = linear_model.LinearRegression() clf.fit(Var,target) modelPredictions = clf.predict(Var)   # testing accuracy absError = modelPredictions - target SE = np.square(absError) # squared errors MSE = np.mean(SE) # mean squared errors RMSE = np.sqrt(MSE) # Root Mean Squared Error, RMSE Rsquared = 1.0 - (np.var(absError) / np.var(target)) print('RMSE:', RMSE) print('R-squared:', Rsquared)   # plotting surface plots from mpl_toolkits import mplot3d import matplotlib.pyplot as plt X4 = np.linspace(0,6,100) X5 = np.linspace(50,70,100) X1 = np.ones((100,100))*1200 X2 = np.ones((100,100))*10 X3 = np.ones((100,100))*75 X,Y = np.meshgrid(X4,X5)   Z = clf.predict((X1,X2,X3,X,Y)) fig = plt.figure(figsize=(6,6)) ax = fig.add_subplot(111, projection='3d')   ax.plot_trisurf(X, Y, Z)

It gives me an error

Error:
Found array with dim 3. Estimator expected <= 2.

for command "Z = clf.predict((X1,X2,X3,X,Y))"