Optimising Model performance using automated ML pipeline for predicting purchase propensity @ Fractal Analytics

Jul 2017

24 Mon

25 Tue

26 Wed

27 Thu 08:15 AM – 10:00 PM IST

28 Fri 08:15 AM – 06:25 PM IST

29 Sat

30 Sun

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MLR Convention Centre, Whitefield, Bengaluru,

##Theme and format
The Fifth Elephant 2017 is a four-track conference on:

Data engineering – building pipelines and platforms; exposure to latest open source tools for data mining and real-time analytics.
Application of Machine Learning (ML) in diverse domains such as IOT, payments, e-commerce, education, ecology, government, agriculture, computational biology, social network analysis and emerging markets.
Hands-on tutorials on data mining tools, and ML platforms and techniques.
Off-the-record (OTR) sessions on privacy issues concerning data; building data pipelines; failure stories in ML; interesting problems to solve with data science; and other relevant topics.

The Fifth Elephant is a conference for practitioners, by practitioners.

Talk submissions are now closed.

You must submit the following details along with your proposal, or within 10 days of submission:

Draft slides, mind map or a textual description detailing the structure and content of your talk.
Link to a self-record, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant.
If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and documents showing the full workshop plan.

##About the conference
This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.

We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:

Full-length, 40-minute talks.
Crisp, 15-minute talks.
Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
Hands-on tutorials and workshop sessions of 3-hour and 6-hour duration where participants follow instructors on their laptops.
Off-the-record (OTR) sessions of 60-90 minutes duration.

##Selection Process

Proposals will be filtered and shortlisted by an Editorial Panel.
Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
Proposers are also encouraged to vote and comment on other proposals submitted here.

Selection Process Flowchart

We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.

Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.

There is only one speaker per session. Entry is free for selected speakers.

##Travel grants
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.

##Commitment to Open Source
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.

##Important Dates:

Deadline for submitting proposals: June 10
First draft of the coference schedule: June 20
Tutorial and workshop announcements: June 20
Final conference schedule: July 5
Conference dates: 27-28 July

##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.

Hosted by

The Fifth Elephant

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

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Optimising Model performance using automated ML pipeline for predicting purchase propensity @ Fractal Analytics

Submitted Apr 25, 2017

Section: Full talk for data engineering track Technical level: Advanced

Ensemble learning is the process by which multiple machine-learning models are evaluated and combined to help build a combined model that provides better results. Building these models require experimenting with not just multiple Machine-Learning models, but also with various model-parameters that help build good individual models.

In this talk, we will share how did we built an automated machine-learning pipeline to help evaluate multiple machine learning models and model parameters. The purchase propensity model used multiple ML techniques, ranging from regression techniques to Random-Forest based classifiers and helped build a machine-learning ensemble model over 100’s of millions of transaction data-points. The system that was built provided an ability to scale, both for the various modelling combinations available, and for the size of the datasets involved. We will discuss on how did we employ best practices in Spark during every step of building scalable models.

Outline

Performing Exploratory Data Analysis using Spark.
Discussion on commonly encountered issues during feature engineering.
Discussion over various classification techniques including-
Logistic regression (experimenting with regularization parameters to avoid overfitting)
Random forests
GBM
Addressing technical challenges in performing K-fold cross-validation.
Search for optimal parameters for modeling using Grid-search
Ensemble based approaches (bagging & self-training) using Spark.

Speaker bio

Padma Chitturi is Lead Engineer at Fractal Analytics Pvt Ltd and has over five years of experience in large scale data processing. She has authored the book “Apache Spark for Data Science Cookbook”. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey dean’s work in Google brain) for image classification on the big data platform Spark at Impetus. She works closely with Kafka, Spark, Storm, Cassandra, Hadoop, Deep Learning, Computer Vision and Real-time streaming. She was an open source contributor to Apache Storm.
www.linkedin.com/in/padmachitturi