title: NameError: name 'predictions' is not defined
body: <p>i am running the below code and getting this error. Please help:</p>
  
  <p>Error: NameError: name 'predictions' is not defined</p>
  
  <p>Code:</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import r2_score
  from sklearn import linear_model
  import statsmodels.api as sm
  import matplotlib.pyplot as plt
  import seaborn as sns; sns.set(color_codes=True)
  import seaborn
  from datetime import date
  from datetime import datetime
  today = date.today()
  sns.set_color_codes("dark")
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import accuracy_score
  
  d3 = today.strftime("%Y%m%d")
  d5 = "S:\\Investment Process\\LCRV_Strategy\\1.VOLS Pack\\Drop\\Main_HY.CDX."+d3+".csv"
  data = df=pd.read_csv(d5, skiprows=3)
  #df.head()
  plt.figure(figsize=(11.5, 8.5))
  
  plt.scatter(
      df['1M 10-50 HY'],
      df['Spread'],
      c='black'
  )
  plt.scatter(x='1M 10-50 HY', y='Spread', data=data.iloc[-1], c='orange')
  plt.xlabel("1M 10-50 HY")
  plt.ylabel("Spread")
  
  plt.plot(
      df['1M 10-50 HY'],
      predictions,
      c='blue',
      linewidth=2
  )
  X = df['1M 10-50 HY'].values.reshape(-1,1)
  y = df['Spread'].values.reshape(-1,1)
  reg = LinearRegression()
  reg.fit(X, y)
  
  print("The linear model is: Y = {:.5} + {:.5}X".format(reg.intercept_[0], reg.coef_[0][0]))
  
  X = df['1M 10-50 HY']
  y = df['Spread']
  #X2 = sm.add_constant(X)
  #est = sm.OLS(y, X2)
  #est2 = est.fit()
  #print(est2.summary())
  plt.show()
  </code></pre>