incorporating weather into your data - webinar

S E E F A R T HER. G O F A S TER. 2502 North Rocky Point Dr. Suite 650 | Tampa, FL 33607 | O: 813.968.3238 | www.ccgBI.com

The Power of PredictiveIncorporating Weather into Your Data


AgendaIntroduction

• 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, objectiveOur 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 ScientistArchitect for over 10 yearsMS Predictive Analytics, Northwestern UniversitySAP Reporting and Analytics Speaker, SAPInsider and ASUGConnect with me on LinkedIn or follow me on twitter @TheAhmedSherif

Meet Your Speaker

Ahmed SherifConvergence Consulting Group


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


IntroductionWeather 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


ObjectiveWe 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 usePython

• Data Preparation• Data Analysis• Predictive Modeling

IBM Cognos Analytics• Dataviz!


Data PreparationPandas 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: pandasPandas 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 AnalysisWe need to get an idea of what’s in our table (dataframe)


Data AnalysisLet’s look at overall Reservation Status


Data AnalysisLet’s look at Status by Weather conditions


Data AnalysisLet’s look at Status by City/Restaurant


Data Analysis: Let’s simplifyStatus

• Cancellations = ‘Cancelled’ or ‘No Show’• Completed = ‘Completed’

Weather• Nice Weather = ‘Fair’• Poor Weather <> ‘Fair’


Data Analysis: Simplified


Let’s build our predictive modelLogistic Regression model

• Why Logistic?• We are only interested in two outcomes

Probability• Pass/Fail• Heads/Tail• Reservation Cancelled/Reservation Kept


Let’s build our predictive modelAssumptions

We are only focusing on Weather ConditionsGood or Bad Weather Grouping onlySpecific weather conditions can provider for a more in depth lookNo focus on Wind Speed or other weather predictors

Not focusing on non-weather related predictors

MottoAll Models are wrong, but some are useful!


Survey


Let’s build our predictive model

Demo


Prediction ScoresPrediction Scores for Cancellation Rates

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


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


VisualizeWe 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

RAINSHINE


Visualize


Visualize

Demo


ResultsPredict 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


ResourcesPandas

http://pandas.pydata.org

Logistic Regression Analysishttps://en.wikipedia.org/wiki/Logistic_regression

Cognos Analyticshttps://www.youtube.com/watch?v=PtiD-AUDNBU45-Video Playlist

National Oceanic and Atmospheric Administrationhttp://www.ncdc.noaa.gov

Weather API scraperhttps://weather.com/

http://pandas.pydata.org/

https://en.wikipedia.org/wiki/Logistic_regression

https://www.youtube.com/watch?v=PtiD-AUDNBU

http://www.ncdc.noaa.gov/

https://weather.com/

Thank you!

www.ccgBI.com

813.265.3239

[email protected]

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 [email protected]

Contact us for: Videos used for Demo!Python Scripts used for AnalysisWill send you a dropbox link to download

http://www.ccgbi.com/

http://www.ccgbi.com/

mailto:[email protected]

incorporating weather into your data - webinar

Technology