Idea 1 - Kiva Crowdfunding
Background
Kiva is an online crowd funding platform to extend financial services to the poor of the world. Kiva lenders have provided over 1 billion dollars in loans to over 2 million people around the globe. In order to set the priorities, help inform lenders and understand their target communities, knowing the level of poverty of each borrower is critical. However this requires inference based on a limited set of information for each borrower.
Dataset
Kiva has provided the dataset of loans provided over the last 2 years.
Problem
Estimate welfare levels of borrowers in specific regions, based on economic and demographic characteristics. Part of the challenge is to pair Kiva's data with other data sources because the welfare levels at granular level are needed and not just country level.
Idea 2 - Instacart Market Basket Analysis
Background
Instacart is a grocery ordering and delivery app. After the online order is place, personal shoppers review the order and do the in-store shopping and delivery.
Dataset
Instacart has provided 3 million past orders.
Problem
Predict which previously purchased products will be in user's next order.
Background
The Otto group is one of the world's biggest e-commerce companies spread in many countries. The sell millions of products worlwide every day. Several thousand products are going to be added soon. A consistent analysis of products is crucial.
Dataset
The dataset has 93 features for more than 200,000 products.
Problem
Due to diverse global infrastructure, many identical products get classified differently. The quality of the analysis of the products depend heavily on the ability to cluster similar products. So the main objective is, to build a model that can distinguish between the main product categories.