Data Con LA 2020 Description Analysts often find themselves lacking the data they want for training a specific model either by being short of records or by these not being rich enough. Exposing some of the common challenges faced when there is not enough data, we will walk through the creative process in training a real-life not-enough-data forecasting demand model where we had to deal with both cases. Using as input just the weekly sales report of 72 restaurants of a renowned fast food franchise we successfully trained a model for choosing the best coordinates to open new restaurants. In the process we faced many common challenges that we addressed in creative and non-orthodox ways that we will be sharing with the audience. Starting with the reduced amount of data that was available for training the model, we complemented the coordinates of the actual points of sales with info from businesses and points of interest that were around them, leveraging on Google Places. Then we translated all that sparse information into more than 300 numeric variables that all together would describe the environment where the restaurants were located based on the kinds of establishments that were around them. We defined our very own formula that we got to name economic concentration index. Finally, we applied some advanced techniques including Ridge Lasso, backward and forward selection, PCA, and Cross Validation to reduce dimensionality and train an surprisingly good linear regression model that would have a residual error fluctuating between 10 and 300 transactions over an average of 2,800 transactions per restaurant per week. With this model we are able to correctly choose 9 out of 10 times the best place to open a new restaurant based only on the given coordinates A and B. *Training without big data * Enrich data with other sources *Creating numeric variables from descriptive sparse information *Modeling with linear regression *Forecasting demand Speaker Luis Valdeavellano, Martinexsa, Data Scientist

1. Machine
Learning over
Small Data:
Generating New Variables
With Predictive Power
Luis Valdeavellano
Data Con LA 2020

2. About Me
Computer Science Engineer
5+ years in Big Data / Data Science
projects in Latin America
Professor at Universidad del Valle de
Guatemala
Cooking / Pizzaiolo / Music /
Photography

3. Creativity
Data Scientist’s main asset
Machine Learning over Small Data

4. Sales: 2361
Sales: 1783
Option A
Option B
Site Location

5. Sales: 2361
Sales: 1783
Option A
Option B
Site Location

8. Define a Roadmap
Formulate Hypothesis
Restaurant sales are
positive affected by
existing in a crowded
place with specific
categories of
businesses and public
spaces.

9. Average distance of
places to centroid
Distance from point of
interest to centroid
vs.

10. Average distance of
places to centroid
Distance from point of
interest to centroid
vs.
ρ =
d =
Economic Concentration
Index

11. Radius-defined ICE
nearest
10
nearest
20
nearest
50
nearest
80
nearest
100
Nearest-defined ICE
200 m 500 m 800 m 1 km 2 km
schools
churches
restaurants
gas stations
336 variables
generated for a single
point of interest
ICE Application over data

12. Overfitting and
Feature Engineering
PCA
Ridge & Lasso
Backward &
Forward
Selection

13. Results
Variable P-value
shopping_mall_count_500m +1.74%
hospital_count_1km +1.45%
ice_800m +1.09%
parking_min_distance - 0.87%
ice_fast_food_500m - 0.62%
bus_station_min_distance - 0.48%
ice_atm_top10 +0.11%
Predicted R2
72.34%
RMSE
382.4

14. Wrap Up
Don’t be afraid of
being creative
Define a
hypothesis
Analyze behavior
by cases
Create your own
function
Don’t overfit your
model
Leverage on existing
algorithms

15. Luis Valdeavellano
lvaldeavellano@martinexsa.com
linkedin.com/in/lvaldeavellano/
ThankYou!