The document analyzes emails from the Enron bankruptcy scandal using predictive analytics. It cleans and pre-processes a dataset of 158 users' emails, creates bags of words and extracts frequent words. It then classifies the sentiments and applies a CART model to train and test datasets, checking the accuracy with confusion matrices, ROC curves and AUC scores. The CART model depicts key terms like "California" and "Gas" and achieves 84.04% accuracy, outperforming the baseline model. The ROC shows tradeoffs between sensitivity and specificity, and AUC indicates the model can differentiate responsive from non-responsive emails 83.57% of the time.

1. ENRON EMAIL TEXT
ANALYTICS
AUTHOR : RADHIKA KINI

2. BACKGROUND : ENRON
• United states company based in Texas was into production and distribution of
energy
• Major Bankruptcy scandal surfaced in 2000 which lead to the dissolution of
accounting firm Arthur Anderson
• Synonymous to Fraudulency in Finance Sector
• Played a key role in energy crisis

3. ENRON DATA SET
• Repository of emails exchanged between 158 users
• Contents of the dataset
1. Email: contents of the email
2. Reponsive: was the email related to energy?
• Use of predictive analytics(CART Model) to understand the behavior of the emails.
• Capture the sentiments of the data set

4. STEPS INVOLVED
1. Cleaning the data
2. Pre-processing
3. Bag of words creation
4. Extracting the frequent words
5. Classifying the sentiments
6. Applying the CART Model on the training and test data sets
7. Checking for accuracy (Confusion Matrix, ROC, AUC)

5. GRAPHS
• The 20 Frequent words of the
data set
• The sentiments of the Corpus
• CART Tree

6. GRAPHS
• Responsiveness of the Test Data
set
• Graph indicating the actual
accuracy to the model accuracy
• ROCR Curve of the CART Model

7. PARAMETERS
• Confusion Matrix (CART Model)
• Accuracy_Model : 84.04%
• Actual_Accuracy: 83.67
• AUC : 83.57

8. CONCLUSION
• The CART model depicts the terms such as California, Gas, Bid, Jeff to be decision
points as they are heavily involved terms during the scandal
• The accuracy of the CART model outperforms the accuracy of the baseline model
• The ROC is a tradeoff between sensitivity and specificity. From the graph we can
clearly see that
• AUC means that the model can differentiate between a randomly selected
responsive and non-responsive document 83.57% of the time.

9. THANK YOU