title: Python error. ImportError: No module named feature_format
body: <p>I am learning Regression from the Udacity course <code>"Intro to Machine Learning"</code> and I am going through the mini-project in it. </p>
  
  <p>I copied the code of the project, wrote the regression part, as a part of code and tried running it on Jupyter Notebook in Python 2. Unfortunately, I got the following error.</p>
  
  <pre><code>    ImportError                               Traceback (most recent call last)
      &lt;ipython-input-8-433cee6c5e25&gt; in &lt;module&gt;()
           15 import pickle
           16 sys.path.append("../tools/")
      ---&gt; 17 from feature_format import featureFormat, targetFeatureSplit
           18 dictionary = pickle.load( open("../final_project/final_project_dataset_modified.pkl", "r") )
           19 
  
      ImportError: No module named feature_format
  </code></pre>
  
  <p>The code on which I am working is as follows:
  Loads up/formats a modified version of the datasetn(why modified?  we've removed some trouble pointsnthat you'll find yourself in the outliers mini-project).nDraws a little scatterplot of the training/testing datanYou fill in the regression code where indicated:</p>
  
  <pre><code>    #!/usr/bin/python
      import sys
      import pickle
      sys.path.append("../tools/")
      from feature_format import featureFormat, targetFeatureSplit
      dictionary = pickle.load( open("../final_project/final_project_dataset_modified.pkl", "r") )
  
      ### list the features you want to look at--first item in the 
      ### list will be the "target" feature
      features_list = ["bonus", "salary"]
      data = featureFormat( dictionary, features_list, remove_any_zeroes=True)
      target, features = targetFeatureSplit( data )
  
      ### training-testing split needed in regression, just like classification
      from sklearn.cross_validation import train_test_split
      feature_train, feature_test, target_train, target_test = train_test_split(features, target, test_size=0.5, random_state=42)
      train_color = "b"
      test_color = "r"
  
  
      ### Your regression goes here!
      ### Please name it reg, so that the plotting code below picks it up and 
      ### plots it correctly. Don't forget to change the test_color above from "b" to
      ### "r" to differentiate training points from test points.
      from sklearn import linear_model
      reg = linear_model.LinearRegression()
      reg.fit(feature_train,target_train)
  
  
      ### draw the scatterplot, with color-coded training and testing points
      import matplotlib.pyplot as plt
      for feature, target in zip(feature_test, target_test):
          plt.scatter( feature, target, color=test_color ) 
      for feature, target in zip(feature_train, target_train):
          plt.scatter( feature, target, color=train_color ) 
  
      ### labels for the legend
      plt.scatter(feature_test[0], target_test[0], color=test_color, label="test")
      plt.scatter(feature_test[0], target_test[0], color=train_color, label="train")
  
      ### draw the regression line, once it's coded
      try:
          plt.plot( feature_test, reg.predict(feature_test) )
      except NameError:
          pass
      plt.xlabel(features_list[1])
      plt.ylabel(features_list[0])
      plt.legend()
      plt.show()
  </code></pre>