1. Name – Sanket V. Butoliya
UID – U95365115
Major - Business Analytics & Information Systems
Predicting Customer churn using WEKA 3.8

2. Introduction:
Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot
Problem Statement :
A Cellular service provider wants to analyze customer data to predict whether a
customer is going to churn and also identify what are the critical factors that are causing customers to
churn so that preventive actions could be taken based on these factors.
Flow of Presentation :
• Introduction
• Problem Statement
• Dataset Overview
• Data Analysis and Baseline deduction
• ZeroR
• Predictive model
• Decision Tree
• Neural networks
• Naive Bayes
• Ibk (KNN = 50)
• Visualization in Excel
• Conclusion

3. No. Attributes Information Values
1 College? Zero, One
2 Income Numeric
3 Overage Numeric
4 Leftover Numeric
5 House value Numeric
6 Handset price Numeric
7 Avg long calls Numeric
8 Avg duration Numeric
9 Satisfaction Avg, Sat, Unsat, Very_sat, Very_unsat
10 Usage level Avg, high, little, very_high, Very_little
11 Considering change?
No, considering, perhaps, never_thought,
actively_looking_into_it
12 Retained? STAY, LEAVE
Predicted attribute = Customer
Retained?
Number of Instances = 5000
Number of Attributes = 12
Missing Attribute Values = None
Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot

4. Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot

5. Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot
Variables Worth

6. Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot
ZeroR

7. Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot
Decision Tree
60-40 Split 70-30 Split
80-20 Split 90-10 Split

8. Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot
Neural Networks
60-40 Split 70-30 Split
80-20 Split 90-10 Split

9. Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot
Naive Bayes
60-40 Split 70-30 Split
80-20 Split 90-10 Split

10. Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot
Ibk - KNN = 50
60-40 Split 70-30 Split
80-20 Split 90-10 Split

11. Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot
Overage Attribute - Tableau

12. Percentage
Split
Decision
Tree
Neural
networks
Naive Bayes
IBk
KNN = 50
60-40 68.2 62.55 63.4 62.2
70-30 68.8 62.8 64.13 64.2
80-20 70.8 63.8 63.8 63.7
90-10 67.8 63.6 62.4 62.6
Accuracy Table
ZeroR
47.8 %
Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot

13. 40
45
50
55
60
65
70
75
60-40 Split 70-30 Split 80-20 Split 90-10 Split
Accuracy Analysis
Decision Tree Neural networks Naive Bayes Ibk ZeroR
Data set Analysis Weka 3.8 Classification Accuracy Accuracy Plot
Model Comparison - Excel

14. Thank You…