Incorporating Weather Into Your Data - Webinar

SEE FARTHER. GO FASTER.
2502 North Rocky Point Dr. Suite 650 | Tampa, FL 33607 | O: 813.968.3238 | www.ccgBI.com
The Power of Predictive
Incorporating Weather into Your Data

Agenda
Introduction
• A brief intro into who we are at Convergence Consulting Group
• What Role Does Weather Play in Our Life
What is our Goal?
• Estimate Restaurant Reservations based on the ‘Actual’ weather
forecast
How do we do it?
• Collect our data
• Build a prediction model
• Visualize our results
Summary Results
Resources

Analytics empowering clients to see farther & go faster
 Vendor Agnostic – Data & analytics focus with leading technology expertise
 Business Value – Improve business processes via analytic solutions
 Partner Approach – Trusted advisor, credible, objective
 Our People – Experienced, passionate, professionals
 Flexible Solutions – Managed Services, Project Delivery, SaaS, BICoE

Core Capabilities
INFORMATION
MANAGEMENT
 Data Integration
 Data Architecture
 Data Governance
 Data Warehousing
STRATEGY &
MANAGEMENT
 Strategic Roadmap
 Rapid Prototyping
 BI&A Center of Excellence
 Cloud Migration
 Tool Rationalization
 Managed Services
BI & ADVANCED
ANALYTICS
 Reporting & Dashboards
 Performance Management
 Descriptive Analytics
 Diagnostic Analytics
 Predictive Analytics
 Prescriptive Analytics

Data Scientist
Architect for over 10 years
MS Predictive Analytics, Northwestern University
SAP Reporting and Analytics Speaker,
SAPInsider and ASUG
Connect with me on LinkedIn or follow me on
twitter @TheAhmedSherif
Meet Your Speaker
Ahmed Sherif
Convergence Consulting Group

Introduction
There are many things that we can predict, weather wasn’t
always one of them.

Introduction
Weather affects our actions and can be costly for business
• Customer foot traffic in stores
• Predict online sales – increase in poor weather
• Power outages - need to close business
• Order fulfillment – shipping, travel

How likely are you to cancel your fine dining restaurant
reservation if it starts to rain?

Case Study - Restaurant
• Analyzing reservation data
• Fine Dining Restaurant
• Multiple restaurants
• Located throughout North America

Objective
We want to estimate what the percentage of a reservation
cancellation is depending on the forecast that day, or even
more specifically, that hour.

What tools are we going to use
Python
• Data Preparation
• Data Analysis
• Predictive Modeling
IBM Cognos Analytics
• Dataviz!

Data Preparation
Pandas is a popular open source analytics library library
providing high-performance, easy-to-use data structures and
data analysis tools for the Python programming

Data Preparation: pandas
Pandas is a fast and efficient Data Frame for data manipulation
and data merging
Reservations
(5,000 From
Restaurant)
Weather
(http://api.weather.com/)
Combined Data
Sources

Data Preparation: pandas
Demo

Data Analysis
We need to get an idea of what’s in our table (dataframe)

Data Analysis
Let’s look at overall Reservation Status

Data Analysis
Let’s look at Status by Weather conditions

Data Analysis
Let’s look at Status by City/Restaurant

Data Analysis: Let’s simplify
Status
• Cancellations = ‘Cancelled’ or ‘No Show’
• Completed = ‘Completed’
Weather
• Nice Weather = ‘Fair’
• Poor Weather <> ‘Fair’

Data Analysis: Simplified

Let’s build our predictive model
Logistic Regression model
• Why Logistic?
• We are only interested in two outcomes
Probability
• Pass/Fail
• Heads/Tail
• Reservation Cancelled/Reservation Kept

Assumptions
We are only focusing on Weather Conditions
Good or Bad Weather Grouping only
Specific weather conditions can provider for a more in depth look
No focus on Wind Speed or other weather predictors
Not focusing on non-weather related predictors
Motto
All Models are wrong, but some are useful!

Survey

Demo

Prediction Scores
Prediction Scores for Cancellation Rates
• 2nd Column is for Positive Cancellation Rate
• Scores stored in arrays

Prediction Scores
Scores can then be formatted into a dataframe and joined to
the the Restaurant Name

Visualize
We can upload our results into Cognos Analytics and Visualize
Further

Visualize
We can do a quick and
dirty visualization just to
confirm if we are seeing
any significant discrepancy
between cancellations for
different cities or
RAIN
SHINE

Visualize

Visualize
Demo

Results
Predict Future Cancellations
We can now predict which restaurants in which cities will likely
face higher cancelations rates based on selected weather
outcomes.
Knowing this will empower restaurant Managers to:
• Accept additional walk-ins
• Deploy marketing efforts
• Adjust staffing
• Limit inventory

Resources
Pandas
http://pandas.pydata.org
Logistic Regression Analysis
https://en.wikipedia.org/wiki/Logistic_regression
Cognos Analytics
https://www.youtube.com/watch?v=PtiD-AUDNBU
45-Video Playlist
National Oceanic and Atmospheric Administration
http://www.ncdc.noaa.gov
Weather API scraper
https://weather.com/

Thank you!
www.ccgBI.com
813.265.3239
asherif@ccgBI.com
Convergence Consulting Group
@ccgbi | @TheAhmedSherif
A copy of the recorded webinar and
presentation will be emailed to you shortly.
Please feel free to email any remaining
questions to Caroline Wright at
cwright@ccgbi.com
Contact us for:
Videos used for Demo!
Python Scripts used for Analysis
Will send you a dropbox link to download

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