Abhishek Thakur

Developed a deep learning algorithm to distinguish between bumblebee and honeybee using images.
The model scored 0.9956 area under roc curve on private set.

Our team ranked 3rd out of 3500+ participants. It was the largest kaggle competition till date.

Recent literature suggests that the demand for women’s health care will grow over 6% by 2020. Given how rapidly the health landscape has been changing over the last 15 years, it’s increasingly important that we understand how these changes affect what care people receive, where they go for it, and how they pay. Through the National Survey of Family Growth, the CDC provides one of the few nationally representative datasets that dives deep into the questions that women face when thinking about… Show more

Recent literature suggests that the demand for women’s health care will grow over 6% by 2020. Given how rapidly the health landscape has been changing over the last 15 years, it’s increasingly important that we understand how these changes affect what care people receive, where they go for it, and how they pay. Through the National Survey of Family Growth, the CDC provides one of the few nationally representative datasets that dives deep into the questions that women face when thinking about their health.

The task was to predict what drives women’s health care decisions in America. Show less

KDD Cup is the annual Data Mining and Knowledge Discovery competition organized by ACM Special Interest Group on Knowledge Discovery and Data Mining, the leading professional organization of data miners.

### Predict which shoppers will become repeat buyers

Ranked 7th out of ~900 participants

The Acquire Valued Shoppers Challenge asked participants to predict which shoppers are most likely to repeat purchase. To aid with algorithmic development, they had provided complete, basket-level, pre-offer shopping history for a large set of shoppers who were targeted for an acquisition campaign. The incentive offered to that shopper and their post-incentive behavior was also… Show more

### Predict which shoppers will become repeat buyers

Ranked 7th out of ~900 participants

The Acquire Valued Shoppers Challenge asked participants to predict which shoppers are most likely to repeat purchase. To aid with algorithmic development, they had provided complete, basket-level, pre-offer shopping history for a large set of shoppers who were targeted for an acquisition campaign. The incentive offered to that shopper and their post-incentive behavior was also provided.

This challenge provided almost 350 million rows of completely anonymised transactional data from over 300,000 shoppers. It was one of the largest problems run on Kaggle to date. Show less

Decode a sequence of pseudorandom numbers

In this competition you are provided a set of tweets related to the weather. The challenge is to analyze the tweet and determine whether it has a positive, negative, or neutral sentiment, whether the weather occurred in the past, present, or future, and what sort of weather the tweet references.

StumbleUpon is a user-curated web content discovery engine that recommends relevant, high quality pages and media to its users, based on their interests. While some pages we recommend, such as news articles or seasonal recipes, are only relevant for a short period of time, others maintain a timeless quality and can be recommended to users long after they are discovered. In other words, pages can either be classified as "ephemeral" or "evergreen". The ratings we get from our community give us… Show more

StumbleUpon is a user-curated web content discovery engine that recommends relevant, high quality pages and media to its users, based on their interests. While some pages we recommend, such as news articles or seasonal recipes, are only relevant for a short period of time, others maintain a timeless quality and can be recommended to users long after they are discovered. In other words, pages can either be classified as "ephemeral" or "evergreen". The ratings we get from our community give us strong signals that a page may no longer be relevant - but what if we could make this distinction ahead of time? A high quality prediction of "ephemeral" or "evergreen" would greatly improve a recommendation system like ours.
Many people know evergreen content when they see it, but can an algorithm make the same determination without human intuition? Your mission is to build a classifier which will evaluate a large set of URLs and label them as either evergreen or ephemeral. Can you out-class(ify) StumbleUpon? Show less

The problem of attributing causes to effects is pervasive in science, medicine, economy and almost every aspects of our everyday life involving human reasoning and decision making. What affects your health? the economy? climate changes? The gold standard to establish causal relationships is to perform randomized controlled experiments. However, experiments are costly while non-experimental "observational" data collected routinely around the world are readily available. Unraveling potential… Show more

The problem of attributing causes to effects is pervasive in science, medicine, economy and almost every aspects of our everyday life involving human reasoning and decision making. What affects your health? the economy? climate changes? The gold standard to establish causal relationships is to perform randomized controlled experiments. However, experiments are costly while non-experimental "observational" data collected routinely around the world are readily available. Unraveling potential cause-effect relationships from such observational data could save a lot of time and effort.
Consider for instance a target variable B, like occurence of "lung cancer" in patients. The goal would be to find whether a factor A, like "smoking", might cause B. The objective of the challenge is to rank pairs of variables {A, B} to prioritize experimental verifications of the conjecture that A causes B.
As is known, "correlation does not mean causation". More generally, observing a statistical dependency between A and B does not imply that A causes B or that B causes A; A and B could be consequences of a common cause. But, is it possible to determine from the joint observation of samples of two variables A and B that A should be a cause of B? Show less

When an employee at any company starts work, they first need to obtain the computer access necessary to fulfill their role. This access may allow an employee to read/manipulate resources through various applications or web portals. It is assumed that employees fulfilling the functions of a given role will access the same or similar resources. It is often the case that employees figure out the access they need as they encounter roadblocks during their daily work (e.g. not able to log into a… Show more

When an employee at any company starts work, they first need to obtain the computer access necessary to fulfill their role. This access may allow an employee to read/manipulate resources through various applications or web portals. It is assumed that employees fulfilling the functions of a given role will access the same or similar resources. It is often the case that employees figure out the access they need as they encounter roadblocks during their daily work (e.g. not able to log into a reporting portal). A knowledgeable supervisor then takes time to manually grant the needed access in order to overcome access obstacles. As employees move throughout a company, this access discovery/recovery cycle wastes a nontrivial amount of time and money.
There is a considerable amount of data regarding an employee’s role within an organization and the resources to which they have access. Given the data related to current employees and their provisioned access, models can be built that automatically determine access privileges as employees enter and leave roles within a company. These auto-access models seek to minimize the human involvement required to grant or revoke employee access.

Objective:
The objective of this competition was to build a model, learned using historical data, that will determine an employee's access needs, such that manual access transactions (grants and revokes) are minimized as the employee's attributes change over time. The model will take an employee's role information and a resource code and will return whether or not access should be granted. Show less