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print("\n*****Testing Model Accuracy*****\n")
#Load Test File
test = pd.read_csv("UnsupervisedFootball/TestFootballEvent.csv")
print("\n***** Showing Test Contents *****\n")
print(test.head())
print("\n***** Showing Test Contents *****\n")

totalCorrect = 0
for i in range(len(X)):
    predictedX = np.array(X[i].astype(float))

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#Import Libraries
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt

#Load File To Train KMeans
train = pd.read_csv("UnsupervisedFootball/TrainFootballEvent.csv")

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public class InterestRateSwap
{
    public double Notional { get; set; }
    public DateTime StartDate { get; set; }
    public DateTime MaturityDate { get; set; }
    public double FrequencyInYears { get; set; }
    public List<double> PayDates { get; internal set; }
}

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//Step 1: Implement a contract:  ISwapPricer
public interface ISwapPricer
{
  double PriceSwap(InterestRateSwap interestRateSwap, double currentResetRate, 
                  double fixedRate, IDictionary<double, double> floatingRates);
}

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#Print MSE to see how good the model is
Error = MeanSquaredError(TestData, Predictions)
print('Test Mean Squared Error (smaller the better fit): %.3f' % error)