Please solve the following question according to these rules: 1- Write the program code in Python Language 2- Share the program outputs 3- Submitted codes should be well-commented

4- Time Series data are below:

84,7100

84,7300

86,7000

86,3400

88,8100

88,9600

88,3300

88,4600

88,1100

88,0000

86,3800

85,4100

84,6200

83,1500

83,5900

83,2300

83,6600

83,5400

82,9600

81,8300

83,6000

81,1000

84,3700

82,4500

84,5300

84,6400

83,5000

83,7200

84,0300

83,4300

85,0600

84,8400

83,2800

83,7500

82,3100

80,7700

78,7100

78,5700

78,8700

78,1100

77,9500

78,4200

79,3200

78,2100

78,4800

78,9100

79,1600

79,0300

78,1600

77,4700

78,1700

77,7400

76,8700

76,6700

76,9300

76,3500

74,6400

76,6000

76,6500

75,0200

73,4800

72,3800

73,6200

74,2600

75,3000

76,4700

76,9300

74,8500

75,1100

75,5200

77,6400

79,6000

80,9900

80,1600

81,6100

82,2000

82,6800

80,8200

78,1800

77,5900

76,0600

76,1300

77,5000

77,4500

77,8200

75,3700

76,2200

75,6200

75,3700

76,1900

76,7700

76,6500

74,3700

75,4200

74,7100

73,5100

71,4500

71,4700

71,6500

72,3400

Load the data from "TimeSeries.xlsx" into a numpy array, say numdat, and perform the following analysis a. Assign first 150 samples of the "numdat" into a new vector called "numdat_1". b. Assign every (3x+1)* (* = 0,2*) sample into a new vector called "numdat_2". (numdat_2 provides only the values of samples no: 1,4,7...,148). TASK 1: Linear Interpolation C. By using linear and cubic spline interpolation methods of interpolate.interp1d method of Scipy package, estimate the missing sample values of numdat_2 and compare predicted time-series with original time-series (ie. compute the mean squared error (MSE) between true samples and estimated samples). TASK 2: Polynomial Regression d. By using polymonial regression methods polyfit and polyval of Numpy package, represent the characteristics of the numdat_2 time-series with a polynomial. Use fourth and fifth order polynomial regression to estimate the sample values of numdat_2 and compare predicted time-series with original time-series (i.e. compute the mean squared error (MSE) between true samples and estimated samples). (Hint: First model the given samples of numdat_2 by using polyfit method. Once you obtain a polynomial which models the data, evaluate the value of the polynomial for the all samples of numdat_1 using polyval method.) e. Compare the estimation results of TASK 1 and TASK 2. Plot original time-series numdat_1 and the estimated results of TASK 1 and TASK 2. Discuss which method performs better and why