Data story behind Food delivery Apps

Since I use food delivery apps heavily. Both swiggy, ubereats. My views and reflections of data story/measures/ machine learning use cases from these applications

My observations based on application use. Ubereats highlights below activities based on historical data collected

• Previously ordered restaurants 
• Previously ordered items highlighted
• Review based listings 
• Projecting estimated delivery times

I personally face challenges while trying to shift to a low-calorie diet as recommendations are more tuned for past orders.

• Recommending a similar item every day from other restaurants based on historical data
• No option to set preferences for the coming week - Balanced diet customized to need /preferences based on user choices for a week 
• Fold quality issues exist no matter how good review ratings are

I have worked on real-time systems, reporting and moved to AI. Now we have all tools to query data in motion, historical data and future data forecast. This view provides a complete end to end perspective to understand data, numbers. Some of below metrics/ measure overlap across transactions/ historical data / ai

Key Metrics / Measures

• Average Order delivery time at different times (Morning / Lunch / Evening / Holidays / Weekends)
• Average Order Order pickup time at different times
• Order acceptance rate
• Clicks/ conversions 
• A/B experiment and conversions 
• Payments type vs orders
• Average menu browsing time
• Frequently searched items across days / restaurants / seasons
• Predict order delays using Traffic data
• Peak seller's 
• Top customers 
• Weekday trends
• Top trends based on seasonality 

Data science use cases

• Forecast on volumes of items based on historical data 
• OCR, Recommendation at User Level / Sold together items
• Deep learning for automated food classification, tagging
• Segmenting customers based on Age / Gender / Veg / Non-Veg / Cusine Choices and providing recommendations
• Forecast Order Volumes and assign Delivery partners based on Projected numbers to reduce other delays

Tech Talk - 

Everything that is measurable can be managed, monitored, improved. There have to be more quality aspects to be integrated as we risk ourselves trusting rating for better quality. Hope quality bar keeps improving and story evolves into another version customizing based on personal diet plans and choices. Happy finding the data story behind these food delivery businesses Apps !!!

Day #245 - Exploratory Data Analysis Goals

• Find Insights, Turn it into Why-questions
• Seek Surprises sudden peaks, lows, Harness it into How Questions
• Plot data in different dimensions Month / Year / Sales, Find Insights in every perspective

Learn the story behind the numbers!!!

Happy Mastering DL!!!

Innovation Session Notes

Today was interesting. Attended Innovation Session. Very inspiring, motivational session. Thanks Akshay Cherian.

• Communicating to brain
• Communicating with emotion

Different types of Questions

• Know How
• Know When
• Know What
• Know Why

Great Learnings / Habits

• Listening at different levels
• Insights learnt
• Find Insights, Turn it into question
• Seek Surpises, Harness it
• Insights + States of Flow loop on each other
• We can perform when it keeps us exciting and not overwhelming
• Creativity is presence of constraints
• Create disproportionate value
• Solve by doing, Comfortable with failure
• It will hurt a bit if you are doing something meaningful

Books

Geography of Genius
States of Flow Assessment

Steps

• List, Cluster, Reorder
• Start Challenge vs Idea
• Turn Top barriers into Questions
• Rate Idea Valuable / Simple
• Turn Ideas into Steps
• Review Ideas
• Create and Share output

Fresher Tips

• Work for free until they see value

Challenges / Problems / Opportunities

• Redefinition of problem 
• Think from Celebration
• Pissed of is better than passion
• Treat it as a Games
• Breakthrough tools
• Breakthrough environment
• Don't define the problem by Single word
• Meeting for Evolution not Evaluation of ideas
• Reduce perceived Risk of Sharing

Futuristic

• Don't operate from crisis to crisis
• Anticipate and prepare for next-gen risks
• Plan a budget for time and money
• Manage time and invest time differently
• Learn a combination of skills

Key Lessons

• Like how we pay Monthly Bills, How much did you budget your time and money for learning, upskill or coding. I felt slap on face with this question.
• If someone is in a role where you want to be. Look at his previous role and see what he has done to move to that role
• If someone is successful see the pattern, practice they follow don't put it as luck, motivation. Emulate them

Sharing my Earlier Work

Why it fails

Will definitely include this game for our team building. 😂

The same outcome with communication when channel down the hierarchy. pic.twitter.com/MgEWsEGzP7

— Alvin Foo (@alvinfoo) November 23, 2020

Happy Learning!!!

Day #244 - Data Annotation Guidelines

• Quality of Images and capturing significant traits like styles / shapes / colors
• Object to annotate captured from nearest possible view (Best Possible Angle)
• Impact of poor background light / night and too far images. Discard low quality / poor miniature of objects (Occurs in edges of image)
• Handling partial objects
• When Annotating multiple objects the class imbalance factors between them, Fix before training. Analyzing Number of Objects, Occurrences - Distribution for Sampling balances
• Check for Data set impacts for Daylight / Night and annotate / train / build model accordingly

Guideline - The object under training occupies the center spot / nearest closest better view to know the side / front or reasonably good amount of features like color / styles

Good data / quality data is as important than the model / approach we take.

Happy Mastering DL!!!

Day #243 - Retail Analytics Opportunities

Notes from Recently Attended Session
Instore Retail

• Store Managers
• Associates usage
• Online / Offline Users

Customer Experience

• Optimize layout
• Improve promotion effectiveness
• Shopper Journey Outcome

Store Performance

• Segmentation 
• Forecasting

Store Managers

• People Person
• Genuine interest for customers, supervising, nurture ideas
• People Skills, Sales Skills, Management Skills

Store Manager

• Stocking
• Delegate Activity
• Customer Service
• Sales projections
• Readiness

As Required

• Forecasting
• Staffing
• Hiring
• Associate Performance

Challenges

• Difficult Customers
• Personal issues
• Customer Expectations
• Not get yelled at

Sale

• Conversation that ends in a transaction
• Know better, focus on training people
• Product knowledge

Apple Selling Philosophy

• A - Approach (Welcome Approach)
• P - Probe - Needs
• P - Provide Solutions
• L - Listen Concerns / Issues
• E - End with farewell / Invitation

Power Hours - Most Sale time / peak hours

• Sales Split by hour
• Sales Split by Week
• Labour vs Traffic Approach
• %% of sales at that Hour

Types of Retailers

• Malls - Retailers - Pantaloon / Shoppers Shop
• Individual Stores - Kirana Stores
• Online Sales - Amazon / Flipkart
• Chain of Stores - Pothys / Chennai Silks (Have own sourcing units) - Brand Conscious

Power Centers

• Maximum Value Proposition
• Competitive prices
• Volume & competitive price

Factors to Setup Stores

• Demographics
• Income levels
• Spending group
• Frequency of spending
• Family Area
• Single People
• Average basket size
• Average shopper duration time
• Cultural aspects

Models / Recommendations

• Model for income group
• Model for domain
• Model for age group

Happy Retailing!!!

Artificial Intelligence (AI) Podcast - Lex Fridman

Excellent talk with deep tech conversations, principles and thought process. Some of the questions, interesting lines I liked from the podcast

• AI Assisted driving for a safer and better world
• Dream of Autopilot - Autonomy revolution
• Design Choices - Instrument Cluster, Display, Sensor suites
• Display - Health check on vehicles perception of reality
• Inputs - Camera, Radar, Ultrasonics, GPS
• Information rendered into vector space with lane lines, traffic lights
• Vector space re-rendered on display for people to understand the system
• Considered parts / Uncertainties - Road Segmentation, Vehicle detection, object detection other techniques underlying
• Debug Views - Augmented Vision with boxes, labels, Visualizer vector space representation from all sensors
• Technical Aspects, Neural Network, Data, Hardware to allocate resources
• Data - Vast amounts (12 ultrasonic sensors, GPS, IMU), 400K cars on the road
• The massive inflow of data
• Full self-driving computer development in progress
• Cameras at FULL frame rate, FULL Resolution 
• Driving - Learn from Edge Cases
• Autopilot disengagements - Aspects / Ideas
• Take over for convenience / Optimal spline for traversing the intersection
• Navigate complex intersection
• Lane change based /freeway/highway interchange 
• Automatically overtake slow cars
• Exit freeway
• Full Self Driving Computer in Production
• Tesla is Appreciating Asset
• Navigate Parking Lots
• Metric (Incidents per mile)
• Assess the probability of a crash, injury, permanent injury, death
• Video of faces/body
• Moving from Elevator support to Automatic Elevator
• Body Pose, Cognitive Load
• Camera-based driver monitoring
• More reliable than human then Driver Monitoring won't help much
• Operational Design Domain
• Instrument Cluster Display, Capabilities
• Neural Net - Basic Bunch of Matrix Math
• Learn both on valid and invalid data
• What is a car
• What is definitely not a car
• Key ideas for Artificial General Intelligence
• Tesla Goal - World's best Self Driving Vehicle
• AI will convince to fall in love with it

Happy Mastering DL!!!

Day #242 - Working with labelmg

Download labelmg from link

Key things 

• Open Directory, Create Bounding Box, Label the object, Save the bounding box
• XML will be generated with the coordinates

Happy Mastering DL!!!

Day #241 - Tensorflow on CPU - Object Detection

Finally was able to train Custom Object Detection

Notes on Custom Object Detection (Notes - Link )
Step #1 - Define Inputs - Specify files in TFRecord file format
Step #2 - Configure Train_config. Key Values are

• Model parameter initialization.
• Input preprocessing.
• SGD parameters.

Step #3 - fine_tune_checkpoint should provide a path to the pre-existing checkpoint, To speed up the training process, it is recommended that users re-use the feature extractor parameters from a pre-existing image classification or object detection checkpoint
Step #4 - SGD - hyperparameters for gradient descent
Step #5 - Evaluator Config)

To get reasonable mAP@IoU scores for object detection API:

1. Try varying the Intersection over Union (IoU) threshold, e.g 0.2-0.5 and see if you get an increase in average precision. You would have to modify matching_iou_threshold parameter in object_detection/utils/object_detection_evaluation.py

2. Try different evaluator classes (the default one is EVAL_DEFAULT_METRIC = 'pascal_voc_detection_metrics'). If you are training on Open Image Dataset it makes sense to use open_images_V2_detection_metrics

3. Check your eval config file and increase the number of examples used in the evaluation set, e.g.

eval_config: {
  num_examples: 20000
  num_visualizations: 16
  min_score_threshold: 0.2
  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
  max_evals: 1
}

4. Train the object detector for more iterations
5. Check current mAP against reported metrics (e.g. COCO mAP@IoU=0.5)

Step by Step: Build Your Custom Real-Time Object Detector  - Link
Detectron2 Train a Instance Segmentation Model
Installing the Tensorflow Object Detection API

For custom object training BMW has shared their opensource framework. It is a packaged version of the complete object detection setup. (Yolo / TensorFlow this is good set of tools)

https://github.com/BMW-InnovationLab

I haven't experimented with it. This is a good place to leverage the setup as common tool.  This was released few months back. I am working in my windows setup for a while.

Happy Mastering DL!!!

Day #240 - Setting up Tensorflow GPU on windows 10

The post is based on the session link . I have made few changes and updates on the same

Happy Mastering DL!!!

Day #239 - Home Depot Retail Data Science Cases

Key Lessons

• 45% percent of online orders picked up from stores
• Data Science for better search, recommendation, personalization
• Product search - similar product search with images
• Personalization - bought together, sold together
• Weather, Seasonality, Trends
• Segmentation by product, division
• Crowd behavior iin-store(Retain-store level analytics)
• Relevancy of Search Engine

Happy Mastering DL!!!