Shraddha Surana 

Global Data Community Lead | Lead Data Scientist, Thoughtworks

Shraddha is a Global Data Community Lead + Lead Data Scientist at Thoughtworks with over a decade of experience in data science & software development across various domains such as Astrophysics; Life sciences; Drug Discovery; Retail & BFSI. She has worked on various projects that involve data intensive computing, price optimization, predicting customer churn, natural language processing & understanding, chat bots, deep learning, GANs & GNNs across use cases. Some of her works involve using machine learning for scientific discoveries in astrophysics to solve problems such as predicting star formation properties of galaxies and radio solar imaging and utilization of distributed datasets to train machine learning models in a distributed fashion. She is an experienced international conference speaker and has published papers in various journals & conference proceedings. She strongly believes in the benefits of mentoring/ guiding and has spent alot of time mentoring industry folks, academicians & students from various countries. An analytical thinker & an effective team lead, her passion is to solve real world problems holistically.
Shraddha Surana profile

Projects by Shraddha Surana

Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

In this Kmeans clustering machine learning project, you will perform topic modelling in order to group customer reviews based on recurring patterns.

Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

In this machine learning project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data.

In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.