This project was completed during the Lviv Data Science Summer School 2018 (http://cs.ucu.edu.ua/en/summerschool/summer-school-2018/).

1. PREDICTING THE SUCCESS OF RETAIL
ESTABLISHMENTS IN NEW YORK
Authors:
Kateryna Peikova
Mykola Trokhymovych
Taras Sarvas
Maryanna Temnyk
Supervisor:
Anastasios Noulas

2. A beautiful city of New York…

3. … and you are a happy business owner …

4. … where should you place your business?

10. Data & Features
Geographic Features
 Density
 Neighbors entropy
 Competitiveness
Mobility Features
 Area popularity
 Transition density
 Incoming flow
 Transition quality
 Taxi transition density
 Taxi incoming flow
Data:
Foursquare check-ins
New York Yellow taxi

14. Data & Features
Geographic Features
 Density
 Neighbors entropy
 Competitiveness
Mobility Features
 Area popularity
 Transition density
 Incoming flow
 Transition quality
 Taxi transition density
 Taxi incoming flow
Data:
Foursquare check-ins
New York Yellow taxi

15. Foursquare income flow
https://youtu.be/5IkWZJQKbxE

16. Taxi income flow
https://youtu.be/GpQKUg_Zuvo

17. Model & Metrics
Models:
 XGBoost Regressor
 SGDRegressor
 DecisionTreeRegressor
 ElasticNet
 Lasso
 Ridge
Metrics:
 NDCG@10
Learning Type:
Supervised learning
Hyperparameter
Optimization:
 Tree of Parzen
Estimators (TPE)
 RandomizedSearchCV

23. Correlation Heatmap

24. NDCG Metrics
𝐷𝐶𝐺 𝑝 = ෍
𝑖=1
𝑝
𝑟𝑒𝑙𝑖
log2(𝑖 + 1)
𝑛𝐷𝐶𝐺 𝑝 =
𝐷𝐶𝐺 𝑝
𝐼𝐷𝐶𝐺 𝑝

25. Model Performance

26. cross_val_ndcg_mean cross_val_ndcg_std
XGBoost 0.89053 0.0465816
ElasticNet 0.834106 0.0281904
Lasso 0.8328 0.0316315
Ridge 0.825261 0.0300194
DecisionTreeRegressor 0.812228 0.0673468
SGDRegressor 0.650265 0.0238567
Baseline 0.562394 0.0844208
Model Performance

32. And if you ever wonder …

33. … can we do it better?

34. Sure!

35. # то_всьо_прожиться
*everything can be programmed