Predicting Customer Behaviours

1. PREDICTING CUSTOMER BEHAVIOUR
S
AND MARKET BASKET ANALYSIS
YALIN YENE
R
September 04, 2020

2. Methodology
Results
Conclusions
Introduction
Future Work

3. Introduction Methodology Results Conclusions Future Work Appendix
Business Need: Predicting whether a customer will reorder a product.
Solution:
Using Instacart dataset,
fi
nding customer behaviour by using market
basket analysis techniques and try to predict next order.
Objective and
Goal:
CSV to PostgreSQL.
Join, Groupby, Having etc.
Connect PostgreSQL with Jupyter Notebook
Exploratory Data Analysis.
Building ML Algorithms
Feature Engineering.
Evaluate Model
3

4. 4
–IGD Retail Analysis, 2019
“In 2019, U.S. supermarket and grocery store sales amounted to
about 1.5 trillion U.S. dollars.
Introduction Methodology Results Conclusions Future Work Appendix

5. 5
Introduction Results Conclusions Future Work AppendixMethodology
ProductsDepartments Aisle
Orders
Order
Products
13413421
~3.5M ~34M

6. 6
Introduction Results Conclusions Future Work AppendixMethodology

7. 7
Introduction Methodology Results Conclusions Future Work Appendix
Exploratory Data Analysis

8. 8
Introduction Methodology Results Conclusions Future Work Appendix
Exploratory Data Analysis

9. 9
Introduction Methodology Results Conclusions Future Work Appendix
Exploratory Data Analysis

10. 10
Introduction Methodology Results Conclusions Future Work Appendix
Exploratory Data Analysis

11. 11
Introduction Methodology Results Conclusions Future Work Appendix

12. 12
Introduction Methodology Results Future Work Appendix
Baseline Classi
fi
cation - V1
Product Features -V2
Product total orders
Product average add to cart order
User Features -V3
User total order
User average chart size
User total product
User average days since last order
User - Product - V4
User product average add to cart order
User product order frequency
Conclusions
Category - Dummy Variables -V5
Day of Week - Dummy Variables -V6
Features Importance -V7
Resampled -V8
Smoted - V9
Cross Validation - V10

13. 13
Introduction Methodology Results Future Work Appendix
Baseline Classi
fi
cation - V1- Metrics Comparasion
Conclusions

14. 14
Introduction Methodology Results Future Work Appendix
Baseline Classi
fi
cation - V1- Metrics Comparison
Conclusions

15. 15
Introduction Methodology Results Future Work Appendix
Baseline Classi
fi
cation - V1 - Confusion Matrix
Conclusions

16. 16
Introduction Methodology Results Future Work Appendix
Baseline Classi
fi
cation - V1
Product Features -V2
Product total orders
Product average add to cart order
User Features -V3
User total order
User average chart size
User total product
User average days since last order
User - Product - V4
User product average add to cart order
User product order frequency
Conclusions
Category - Dummy Variables -V5
Day of Week - Dummy Variables -V6
Features Importance -V7
Resampled -V8
Smoted - V9
Cross Validation - V10

17. 17
Introduction Methodology Results Future Work Appendix
Dummy Variables - V5- Metrics Comparasion
Conclusions

18. 18
Introduction Methodology Results Future Work Appendix
Category - Dummy Variables -V5 Confusion Matrix
Conclusions

19. 19
Introduction Methodology Results Future Work Appendix
Features Importance -V7
Conclusions

20. 20
Introduction Methodology Results Future Work Appendix
Resampled -V8
Conclusions

21. 21
Introduction Methodology Results Future Work Appendix
SMOTE -V9
Conclusions

22. 22
Introduction Methodology Results Future Work Appendix
SMOTE -V9
Conclusions
DecisionTree-CVKNN-CV RandomForest-CV

23. 23
Introduction Methodology Results Future Work AppendixConclusions

24. 24
Introduction Methodology Results Future Work AppendixConclusions

25. 25
Introduction Methodology Results Conclusions Future Work Appendix
This project can be extended further by;
• Obtain Detail Data for clustering (customer classi
fi
cation).
• Using cloud & big data platform for implementing ML Algorithms with whole
data

26. THANK YOU
YALIN YENE
R
+44 7786 76155
9
yalinyener@gmail.com
https://github.com/yalinyener
https://medium.com/@yalinyener
https://www.linkedin.com/in/yalinyener