Category Archives for "Kaggle"

Mar 04

12 Amazing Marketing and Sales Challenges in Kaggle

By ganpati | Kaggle

Everything around us is moving towards data: big data, mobile data, performance data, campaign data, product data, and even data about how we track our data. And it’s a one of its kind opportunity for marketers with a knack for data to seize moment. Kaggle is one of the best platforms to showcase your accumen in analyzing data to the world. Here are some amazing marketing and sales challenges in Kaggle that allows you to work with close to real data and find out for yourself how you can make the most of analytics in marketing and sales.

Search Relevancy: Connect Customers to the Right Products

Also Read 8 Amazing Banking and Insurance Challenges in Kaggle

Search relevancy is an implicit measure many retailers use to gauge how quickly they can get customers to the right products. Currently, human raters evaluate the impact of potential changes to their search algorithms, which is a slow and subjective process. With this challenge, by removing or minimizing human input in search relevance evaluation, Home Depot hopes to increase the number of iterations their team can perform on the current search algorithms.
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Can You Predict the Policy an Insurance Customer Will End Up Choosing

As a customer shops an insurance policy, he/she will receive a number of quotes with different coverage options before purchasing a plan. If the eventual purchase can be predicted sooner in the shopping window, the quoting process is shortened and the issuer is less likely to lose the customer’s business. Can you predict what policy they will end up choosing?
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Predict the Daily Sales for a Retail Store

Reliable sales forecasts enable store managers to create effective staff schedules that increase productivity and motivation. By helping Rossmann create a robust prediction model, you will help store managers stay focused on what’s most important to them: their customers and their teams! In their first Kaggle competition, Rossmann is challenging you to predict 6 weeks of daily sales for 1,115 stores located across Germany.
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Predicting Annual Restaurant Sales for a Region

New restaurant sites take large investments of time and capital to get up and running. When the wrong location for a restaurant brand is chosen, the site closes within 18 months and operating losses are incurred. Finding a mathematical model to increase the effectiveness of investments in new restaurant sites would allow TFI to invest more in other important business areas, like sustainability, innovation, and training for new employees. Using demographic, real estate, and commercial data, this competition challenges you to predict the annual restaurant sales of 100,000 regional locations.
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Predicting Monthly Online Sales of an Online Product

The objective of the competition is to help build as good a model as possible to predict monthly online sales of a product. Imagine the products are online self-help programs following an initial advertising campaign.
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How Successful A Product Launche Will Be

Launch is often evident within the first few weeks of sales. This competition asks you to predict how successful each of a number of product launches will be 26 weeks after the launch, based only on information up to the 13th week after the launch.
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Helping Walmart Better Predict Sales of Weather-Sensitive Products

Intuitively, we may expect an uptick in the sales of umbrellas before a big thunderstorm, but it’s difficult for replenishment managers to correctly predict the level of inventory needed to avoid being out-of-stock or overstock during and after that storm. Walmart relies on a variety of vendor tools to predict sales around extreme weather events, but it’s an ad-hoc and time-consuming process that lacks a systematic measure of effectiveness. Helping Walmart better predict sales of weather-sensitive products will keep valued customers out of the rain.
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Project the Sales for Each Department in Each Walmart Store

In this challenge you are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and one must project the sales for each department in each store. To add to the challenge, selected holiday markdown events are included in the dataset. These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact.
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Which Web Pages Served by StumbleUpon are Sponsore

Online media companies rely more and more on paid advertising to keep their lights on and their content engines humming. “Native advertising” is a popular alternative to the unsightly banner ads and infuriating pop-ups of Internet Advertising 1.0. Native ads mimic the core content of the site they’re advertising on, ideally avoiding any interruption of the user’s experience. In this challenge can you predict which web pages served by StumbleUpon are sponsored
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Can You Predict if a Visitor will Click on a Given Ad

Display advertising is a billion dollar effort and one of the central uses of machine learning on the Internet. However, its data and methods are usually kept under lock and key. In this research competition, CriteoLabs is sharing a week’s worth of data for you to develop models predicting ad click-through rate (CTR). Given a user and the page he is visiting, what is the probability that he will click on a given ad?
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Where Will New Airbnb Guests Book Their First Travel Experience?

New users on Airbnb can book a place to stay in 34,000+ cities across 190+ countries. By accurately predicting where a new user will book their first travel experience, Airbnb can share more personalized content with their community, decrease the average time to first booking, and better forecast demand.
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Which Shoppers are Most Likely to Repeat Purchase

The Acquire Valued Shoppers Challenge asks participants to predict which shoppers are most likely to repeat purchase. To aid with algorithmic development, you are provided with 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 is also provided.
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Maximize sales and minimize returns of bakery goods

Planning a celebration is a balancing act of preparing just enough food to go around without being stuck eating the same leftovers for the next week. The key is anticipating how many guests will come. Grupo Bimbo must weigh similar considerations as it strives to meet daily consumer demand for fresh bakery products on the shelves of over 1 million stores along its 45,000 routes across Mexico.

In this competition, Grupo Bimbo invites Kagglers to develop a model to accurately forecast inventory demand based on historical sales data. Doing so will make sure consumers of its over 100 bakery products aren’t staring at empty shelves, while also reducing the amount spent on refunds to store owners with surplus product unfit for sale.

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TalkingData Mobile User Demographics

TalkingData, China’s largest third-party mobile data platform, understands that everyday choices and behaviors paint a picture of who we are and what we value. Currently, TalkingData is seeking to leverage behavioral data from more than 70% of the 500 million mobile devices active daily in China to help its clients better understand and interact with their audiences.

In this competition, Kagglers are challenged to build a model predicting users’ demographic characteristics based on their app usage, geolocation, and mobile device properties. Doing so will help millions of developers and brand advertisers around the world pursue data-driven marketing efforts which are relevant to their users and catered to their preferences.

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Dec 31

8 Amazing Banking and Insurance Challenges in Kaggle

By ganpati | Kaggle

Here are some amazing competitions in Kaggle that allows you to work with close to real data and find out for yourself what happens in the actual industry. A brief description from the competition page is provided here, and if it interests you, click on the following link to visit the competitions. You may also join one of our teams here.

Also Read 12 Amazing Marketing and Sales Challenges in Kaggle

1. Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. Visit the Competition Page

2. This competition asks you to determine whether a loan will default, as well as the loss incurred if it does default. Unlike traditional finance-based approaches to this problem, where one distinguishes between good or bad counterparties in a binary way, it seeks to anticipate and incorporate both the default and the severity of the losses that result. Visit the Competition Page

3. Improve credit risk models by predicting the probability of default on a consumer credit product in the next 18 months. Visit the Competition Page

4. Deep understanding of different risk factors helps predict the likelihood and cost of insurance claims. The goal of this competition is to better predict Bodily Injury Liability Insurance claim payments based on the characteristics of the insured customer’s vehicle. Visit the Competition Page

5. An important part of succeeding as an insurance company is having a good understanding of which of the company’s current customers will be with the company into the future. The goal of this competition is to predict which current customers will still be with the company in 6 months, given many of the customer’s characteristics. Visit the
Competition Page

6. Using an anonymized database of information on customer and sales activity, including property and coverage information, Homesite is challenging you to predict which customers will purchase a given quote. Accurately predicting conversion would help Homesite better understand the impact of proposed pricing changes and maintain an ideal portfolio of customer segments. Visit the Competition Page

7. Within the business insurance industry, fire losses account for a significant portion of total property losses. High severity and low frequency, fire losses are inherently volatile, which makes modeling them difficult. In this challenge, your task is to predict the target, a transformed ratio of loss to total insured value, using the provided information. This will enable more accurate identification of each policyholder’s risk exposure and the ability to tailor the insurance coverage for their specific operation. Visit the
Competition Page

8. In a one-click shopping world with on-demand everything, the life insurance application process is antiquated. Customers provide extensive information to identify risk classification and eligibility, including scheduling medical exams, a process that takes an average of 30 days.

The result? People are turned off. That’s why only 40% of U.S. households own individual life insurance. Prudential wants to make it quicker and less labor intensive for new and existing customers to get a quote while maintaining privacy boundaries. Visit the Competition Page