diff --git a/predictions_FB.py b/predictions_FB.py
new file mode 100644
index 0000000..6a0c75f
--- /dev/null
+++ b/predictions_FB.py
@@ -0,0 +1,98 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from fbprophet import Prophet
+import yfinance as yf
+import math
+import os
+import sys
+from sklearn.metrics import mean_squared_error
+
+
+class suppress_stdout_stderr(object):
+    '''
+    Used to supress the verbose output of the inner workings of the FB prophet model
+    '''
+    def __init__(self):
+        self.null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)]
+        self.save_fds = (os.dup(1), os.dup(2))
+
+    def __enter__(self):
+        os.dup2(self.null_fds[0], 1)
+        os.dup2(self.null_fds[1], 2)
+
+    def __exit__(self, *_):
+        os.dup2(self.save_fds[0], 1)
+        os.dup2(self.save_fds[1], 2)
+        os.close(self.null_fds[0])
+        os.close(self.null_fds[1])
+
+def fb_error(df):
+    train_length = math.floor(0.1*len(df))
+    train = df[:-train_length]
+    test = df[-train_length:]
+    error_check = Prophet(daily_seasonality = True) 
+    with suppress_stdout_stderr():
+        error_check.fit(train) 
+    future = error_check.make_future_dataframe(periods=train_length) 
+    prediction = error_check.predict(future)
+    test_predict = prediction[-train_length:]
+    test['predict'] = test_predict['trend']
+    test['error'] =((test['y'] - test['predict'])/test['y'])*100
+    mean_err = round(test['error'].mean(),3)
+    return mean_err
+
+
+def fb_predict(df, plot: bool=False, days: int=1, verbose: bool=True, error= False):
+    '''
+    :param dfi - A dataframe with just the column of close price named as Close and date as index 
+    :param plot - Whether you want to plot the resulyts or not( Default is False)
+    :param days - Number of days into the future you want to predict (Dafault is 1)
+    :param verbose - If you want the output of the model workings 
+
+    :type dfi - pandas.dataframe
+    :type plot - Boolean Value
+    :type days - Integer
+    :type verbose - Boolean Value
+
+    :return mean_err - The mean percentage error in calculations
+    :return pred - A pandas datafrmae with predictions, upper and lower bounds
+
+    :rtype mean_err - float
+    :rtype pred - pandas.dataframe
+    '''
+    pd.options.mode.chained_assignment = None
+    arr = [i for i in range(len(df))]
+    ar = df.index
+    df.index= arr
+    df['ds'] = ar
+    df = df[['ds', 'Close']]
+    df.rename(columns = {"Close":'y'}, inplace = True)
+
+    if error:
+        mean_err = fb_error(df)
+
+    predictions = Prophet(daily_seasonality = True) 
+    if not verbose:
+        with suppress_stdout_stderr():
+            predictions.fit(df) 
+    future = predictions.make_future_dataframe(periods=days) 
+    prediction = predictions.predict(future)
+    pred = pd.DataFrame(prediction[-days:][['ds','trend', 'yhat_upper', 'yhat_lower']])
+    pred.rename(columns = {'ds' : 'Date', 'trend':'Guess', 'yhat_lower':'Lower Bound', 'yhat_upper':'Upper Bound'}, inplace = True)
+    pred.set_index(np.arange(1,len(pred)+1), inplace = True)
+    if plot:
+        predictions.plot(prediction)
+        plt.title("Prediction of the AZPN Stock Price using the Prophet")
+        plt.xlabel("Date")
+        plt.ylabel("Close Stock Price")
+        plt.show()
+    if error:
+        return mean_err, pred
+    else:
+        return pred
+
+
+
+
+