Kajol Agrawal

As a System Analyst, Played a critical role in making the application Risk Assessment Engine go-live on-premise which has direct impact on core business applications such as Netbanking and Mobile Banking. Also did a POC by setting up Mobile Banking application on a Linux OS complied to run on Mainframes.

Sclipp Lip Reading (Deep Learning)
Lip reading is a technique to understand words or speech by visual interpretation of face, mouth, and lip movement without the involvement of audio, We used machine learning by applying deep learning and neural networks to devise an automated lip-reading system.

Railway Reservation Database (MySQL)
The aim was to design and develop a database maintaining the records of different trains,train status and passengers, this enables user to book, cancel, update the tickets. This was implemented by relational database i.e. MySQL. Entity-Relationship model was made to get the logical view of the system also normalization was done to reduce the redundancy of the data.

Hand Gesture Recognition (Machine Learning)
In this project, a movement of a part of a body that is hand which intends to express an idea or meaning for communication purpose, for this we collected the co-ordinates of hand gestures then used multiple machine learning algorithms to classify them into english alphabets satisfying the low latency constraint.

Quora Questions Similarity (Machine Learning)

In this case study, the aim was of pairing up the duplicate questions from quora. By Identifying which questions asked on Quora are duplicates of questions that have already been asked. It could be useful to instantly provide answers to questions that have already been answered. This was achieved by data pre-processing, feature engineering and applying different algorithms by doing hyper-parameter tuning.

Taxi Demand Prediction (Machine Learning)

In this case study, the aim was to predict demand in respective region for a given time period on basis of expected number of many pickups as accurate as possible expect where the end-user is Taxi-driver. This was achieved by Exploratory data analysis, Data pre-preparation, feature engineering and implementing the model.