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Polynomial interpolationΒΆ

The following libraries are imported for the calculation 

import numpy as np
import plotly.graph_objs as go
from plotly.subplots import make_subplots

python code for calculation of the new coordinates of the missing or the null values

Performs polynomial interpolation of given degree for the given data points and x_new, and calculates the error rate.

Parameters: x (array-like): x-coordinates of the data points. y (array-like): y-coordinates of the data points. degree (int): Degree of the polynomial to fit. x_new (array-like): New x-coordinates for which to compute interpolated y values.

Returns: tuple: Interpolated y-values corresponding to x_new, Mean Squared Error (MSE).

def polynomial_interpolation(x, y, degree, x_new):
    # Fit polynomial to data
    coefficients = np.polyfit(x, y, degree)
    polynomial = np.poly1d(coefficients)

    # Compute interpolated y-values for new x-coordinates
    y_new = polynomial(x_new)

    # Calculate mean squared error (MSE)
    y_pred = polynomial(x)
    mse = np.mean((y - y_pred) ** 2)

    return y_new