Interesting Use Case - Sports Analytics

Fantastic Computer Vision Use Case

• Person Tracking
• Action Recognition
• Heatmaps

Computed Insights

• Player Tracking
• Speed
• Positioning
• Defense / Pass details
• Analyze the opponents

Keep Thinking!!!

Casual Impact - Paper Read

Paper #1 - Link 

Key Points

• The causal impact of a treatment is The difference between the observed value of the response and the (unobserved) value that would have been obtained under the alternative treatment
• Data #1 -  The first is the time-series behavior of the response itself, prior to the intervention.
• Data #2 - The second is the behavior of other time series that were predictive of the target series prior to the intervention
• This selection is done on the pre-treatment portion. Value for predicting the counterfactual lies in their post-treatment behavior

Summary of Steps Link 

• Fitting a Bayesian structural time series model to observed data 
• Predict with Intervention
• Predict without Intervention 

Paper #2 - Causal Impact Analysis for App Releases in Google Play

• Causal impact analysis uses a control set: a set of unaffected data vectors
• Experiment using different control sets in the study
• The agreement is defined as (YY+NN)/total
• YY indicates a significant change as detected on both datasets
• NN indicates no significant change detected on both datasets

Keep Thinking!!!

Retail Analytics Use cases

Garner Paper Link 

Retail Use Cases

• Product purchase history analysis (POS and transactions)
• Competitive analysis (benchmarking competitors, market share, etc.)
• Multi-channel frequent shopper or loyalty tracking
• Market basket analysis
• Inventory optimization
• Replenishment optimization
• Social media analytics
• Campaign analysis and forecasting
• Promotion optimization
• Price optimization (modeling for pricing elasticity, sales & margins)
• Markdown optimization
• Assortment optimization
• Pricing Intelligence (competitive pricing data)
• Space optimization
• Predictive analytics
• Data visualization
• Multi-channel customer behavioral segmentation
• In-store shopper tracking analytics
• Pricing (based on market data)
• Demand Generation
• Prediction and Forecasting
• Merchandising (product mix & placement)

CX Experience

• Shopper tracking capability
• In-store video analytics
• Mobile devices for associates/ manager
• WiFi for customers

Happy Learning!!!

Good Read - Paper - Frequent Item-set Mining without Ubiquitous Items

 Link 

We use Apriori to find Frequently occurring items.

Example demonstration

• No of Transactions with A - 10
• No of Transactions with B - 50
• No of Transactions with A+B = 5
• Total Transactions - 100

Sup(A) = 10/100 = 0.1

Conf(A-B) = 5/10 = 1/2 = 0.5

Lift = 0.5/0.1 = 5

A->B - > 1, Likelihood

Sup (B) = 50/100 = 1/2 = 0.5

Conf(B-A) = 5/50 = 1/10 = 0.1

Lift = 0.1/0.5 = 1/5 = 0.2

B->A - 0.2 < 1 Less Likelihood

A variation of it without frequently occurring items. The ubiquitousness threshold is similar to the support threshold but, it filters out the items with a frequency higher than the threshold.

Keep Thinking!!!

AI Assisted Automotive Product Development

This Nptel session provides some insights into Automotive Product Development. Link 

Use Case #1 - Text Mining - Understand the edge for Top Selling Cars - Collect data, scrap. Find key topics discussed by customers from reviews, feedback, complaints, What features create the buzz for the product

Key Techniques - Sentiment Analysis, Topic Analysis, Features Vs Sales Correlation. Ranking based on customer feedback.

Sales Numbers vs Features - Decision Tree to map sales numbers vs ranking of features, Classification Algorithm based on available data to map and look at feature impact vs Sales

Insights based on Crash Data Analysis

Use Cases #2 - Classification model for Severity of Injury based on different parameters collected

Use Case #3 - Regression model for mileage prediction

Scarping all product-related content from the web is key to understand

• Evaluating new features in the next version
• Comparing with Nearest Competitor
• Evaluating Sentiment post-sales

Additional use cases

• Material level forecasting models based on Service Patterns / Customer Base
• Seasonality based Product segmentation based on failure rates, criticality
• Perform Cluster level forecasting models
• New Product Launch Aspects and related forecasts on sales/service

A lot of use cases, A lot of innovation!!! 

Happy Learning!!!

Examples Flask vs Fast API

Recently FastAPI I could see more posts/recommendations compared to flask API in a performance context. A basic example of implementation with flask vs Fast API. The format  /syntax, request differences you can spot by comparing them.

CI / CD

DevOps with Github

Makefile

Happy Learning!!!

AI for social cause - Flood Forecasting

I came across this talk in Responsible AI for Social Empowerment (a global virtual summit). Many thanks to Lavanya for sharing the video 

Keynotes

• Scalable and high accuracy flood forecasts

• Providing Alerts, collaborate with local authorities is already taken care
• This initiative is about proactive alerts
• Focus on riverine floods only
• Forecast for water level with these features, variables are
• Hydrologic LSTM time series based models achieved better results

• Inundation model - Simulate behavior across flood level

• Understand areas affected for proactive alerts
• Morphological model - ML + Physics based

• Protect people, public alerts, detailed info about flood extent
• 35 Million warnings past few months

Keep Thinking!!!

Interesting Perspectives

 Perspective #1 - Remembering syntax.

I find it hard sometimes to remember the exact syntax, This below picture summarizes different ways how length is determined for the array. The solution approach is the first step, pseudo-code is the second step, syntax correction is the last step :). 

Perspective #2 - Learning perspectives

Sometimes we evaluate learning interests, passion by asking things we do to keep updated with new skills, technology updates. This perspective when asked to a doctor.

If doctors were interviewed like software developers from r/ProgrammerHumor

If tech interviews were honest from r/ProgrammerHumor

Keep Thinking!!!