10 Impressive Kaggle Big Data Projects You Need to Check Out

Are you interested in exploring Big Data projects on Kaggle? If yes, then you have come to the right place. Kaggle is a platform for data scientists, machine learning practitioners, and other professionals to explore data, build models, and collaborate with others in the community. In this article, we’ll take a look at 10 impressive Kaggle Big Data projects that can teach you a lot and give you a new perspective on how data can be used.

1. Elo Merchant Category Recommendation

Elo Merchant Category Recommendation is a competition on Kaggle that challenges participants to build a model that predicts customer loyalty based on multiple transactional and behavioral data points. This project is fascinating because it requires contestants to work with highly complex and unstructured data to create an accurate model. The competition saw over 3,000 teams competing and resulted in some extremely impressive results.

2. Santa’s Workshop Tour 2019

This competition is an example of how Big Data can be used in the festive season. Santa’s Workshop Tour is a problem that involves optimizing the delivery route for Santa Claus, taking into account the number of gifts required for each house, the distance between houses, and other constraints. The project is challenging but fun, and it teaches you how to work with optimization problems and advanced data structures.

3. PetFinder.my Adoption Prediction

PetFinder.my is an animal welfare organization that provides assistance to over 100 animal shelters in Malaysia. In this Kaggle competition, participants were asked to build a model that predicts the chances of adopting a pet based on various attributes such as breed, age, color, and gender. This project is unique because it has a social dimension, and it shows how Big Data can be used to improve the outcomes of a social cause.

4. Zillow Prize: Zillow’s Home Value Prediction (Zestimate)

This Kaggle competition is about predicting home values with Zillow’s data. The goal of the contest is to create an algorithm that can predict the selling price of a house better than Zillow’s own Zestimate algorithm. The complexity of this project lies in the fact that real estate prices are influenced by many factors such as location, demographics, and the global economy. The winning teams used a wide variety of techniques such as deep learning, gradient boosting, and stacking.

5. Mercari Price Suggestion Challenge

Mercari is Japan’s largest online marketplace. In the Mercari Price Suggestion Challenge, participants were asked to build a model that suggests the appropriate selling price for a product listed on Mercari’s platform. Participants were provided with attributes such as item description, category, and brand information. This project is interesting because it requires participants to work with raw, unstructured text data and create a model that can make accurate predictions.

6. Google Analytics Customer Revenue Prediction

The Google Analytics Customer Revenue Prediction competition involved predicting customer revenue based on their browsing behavior on an e-commerce website. Participants were provided with raw data on user sessions, events, and transactions. This project is challenging because it encompasses the entire data lifecycle, from data cleaning to model building to deployment.

7. Home Credit Default Risk

Home Credit Default Risk is a Kaggle competition that aims to predict whether a loan applicant will default on a loan. Participants were provided with a wide variety of attributes such as employment information, credit history, and demographic data. This project is fascinating because it requires you to work with imbalanced data, where the number of defaulters is much smaller than non-defaulters.

8. GroupLens: Movie Recommendation

In the GroupLens: Movie Recommendation competition, participants were asked to build a model that predicts a user’s rating of a movie based on their previous ratings. This type of problem is known as collaborative filtering, and it’s a common use case for recommendation systems. Participants had to work with data from over 25 million reviews.

9. Ashrae – Great Energy Predictor III

The Ashrae – Great Energy Predictor III competition challenged participants to create a model that predicts the energy consumption of buildings. This project is interesting because it requires you to work with temporal data and other contextual data such as weather, humidity, and occupancy. The competition saw over 3,900 teams competing and resulted in some extremely impressive results.

10. Porto Seguro’s Safe Driver Prediction

The Porto Seguro’s Safe Driver Prediction competition is about predicting whether a driver will file an insurance claim next year. Participants were provided with a wide variety of attributes such as vehicle information, insurance policy details, and driver demographics. This project is fascinating because it requires you to work with imbalanced data, where the number of claimants is much smaller than non-claimants.

Conclusion

Big Data is transforming the world, and Kaggle is the perfect platform to explore this exciting field. The above-listed projects are just a few examples of what is possible with Big Data. By participating in these projects, you can learn new techniques, improve your skills, and contribute to solving some of the world’s most significant problems. So, go ahead and explore!