Data infrastructure consultant for scaling startups.

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Lucid Lessons • Peerlist

A classic problem in the field of pattern recognition is that of handwritten digit recognition. Suppose that you have an image of a digit submitted by a user via a scanner, a tablet, or other digital devices. The goal is to develop a model that can correctly identify the digit (between 0-9) written in an image.

Classifying handwritten digits based on pixel

A 360 degree  near-real time performance analytics dashboard connected via APIs of GA, SimilarWeb, BigQuery, Socialnsider, Meltwater, Search console to churn out smart reports on the go.

Digital Footprint for a Leisure, tourism and 

A large company named XYZ, employs, at any given point of time, around 4000 employees. However, every year, around 15% of its employees leave the company and need to be replaced with the talent pool available in the job market. The management believes that this level of attrition (employees leaving, either on their own or because they got fired) is bad for the company, because of the following reasons - The former employees’ projects get delayed, which makes it difficult to meet timelines, resulting in a reputation loss among consumers and partners A sizeable department has to be maintained, for the purposes of recruiting new talent More often than not, the new employees have to be trained for the job and/or given time to acclimatise themselves to the company Hence, the management has contracted an HR analytics firm to understand what factors they should focus on, in order to curb attrition. In other words, they want to know what changes they should make to their workplace, in order to get most of their employees to stay. Also, they want to know which of these variables is most important and needs to be addressed right away.

HR analytics predictive modelling to prevent 

This company is the largest online loan marketplace, facilitating personal loans, business loans, and financing of medical procedures. Borrowers can easily access lower interest rate loans through a fast online interface. Like most other lending companies, lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). The credit loss is the amount of money lost by the lender when the borrower refuses to pay or runs away with the money owed. In other words, borrowers who default cause the largest amount of loss to the lenders. In this case, the customers labelled as 'charged-off' are the 'defaulters'. If one is able to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Identification of such applicants using EDA is the aim of this case study. In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default.  The company can utilise this knowledge for its portfolio and risk assessment.

Hypothetical consumer lending finance comapny

A web-app that compares user's inputs such as name, height, weight, diet with that of dinosaurs' species and appends random facts of dinos across 9 tiles using pure Javascript

Infographic web app

model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for the management to understand the pricing dynamics of a new market.

New Car launch price - significant variables 

“Global Mart” is an online store super giant having worldwide operations. It takes orders and delivers across the globe and deals with all the major product categories - consumer, corporate & home office. Now as a sales/operations manager, you want to finalise the plan for the next 6 months.  So, you want to forecast the sales and the demand for the next 6 months, that would help you manage the revenue and inventory accordingly. The store caters to 7 different market segments and in 3 major categories. You want to forecast at this granular level, so you subset your data into 21 (7*3) buckets before analysing these data. But not all of these 21 market buckets are important from the store’s point of view. So you need to find out 2 most profitable (and consistent) segment from these 21 and forecast the sales and demand for these segments.

Retail Giant Sales forecasting using Timeseri

R-pi, Git web calling, Python, Unix, AWS Lambda, Amazon API GATEWAY, Amazon S3, DynamoDB, Linux CLI tools, Linux text editor, MQTT with Python Description: Designed a streaming pipeline using services mentioned above and below that capture data-points from my open source board to DOM. Cloud formations for a predefined template. Connecting DHT-11(Temp sensor) to Raspberry Pi(Debian based OS) to catch readings and push to an EC2 using AWS IoT boilerplate, Green Grass and MQTT

Server-less IoT Web-App

Sparks funds case study

15k events/second flowing in from fluentD and Kafka across 5 Million Weekly active users and tracing out customer journeys on real time dashboard with Elasticsearch and Kibana. Monitoring via prometheus and Grafana.

Streaming Kafka pipelines

The aim of analysis is to identify the root cause of the problem (i.e. cancellation and non-availability of cars) and recommend ways to improve the situation. As a result of your analysis, you should be able to present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them.