makePrediction.py

# Function which will be called to make a prediction
def makePrediction(model,example_to_predict):
    # One-hot encode the example to match the data it was trained on.
    encoded_x = encodeSingleElement(x,example_to_predict)
    y_pred = model.predict(encoded_x.reshape(1, -1))
    
    return y_pred

# Load model with best rMse and make prediction
fileName = "results/" + "bestRegressionModel_" + str(LineaReggressionMetrics.ROOT_MEAN_SQUARED_ERROR.name) + ".sav"
bestRegression = joblib.load(fileName)

#Example ICO
#Format - price_usd,price_btc,total_supply,market_cap_usd,available_supply,usd_raised,eth_price_launch,btc_price_launch,ico_duration,month,day,country
example_x = np.array([1.71456,0.00019931,1000000000,905793616,528295082,24000000,297.63,3420.4,7,8,9,182])

y_pred = makePrediction(bestRegression,example_x)
print("Predicted value of example ICO after six months: ",y_pred )