In this repository, we store the code that we developed in the different competitions that we have participated.

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home. For more info see here.

Whether out at a restaurant or buying tickets to a concert, modern life counts on the convenience of a credit card to make daily purchases. It saves us from carrying large amounts of cash and also can advance a full purchase that can be paid over time. How do card issuers know we’ll pay back what we charge? That’s a complex problem with many existing solutions—and even more potential improvements, to be explored in this competition.

Credit default prediction is central to managing risk in a consumer lending business. Credit default prediction allows lenders to optimize lending decisions, which leads to a better customer experience and sound business economics. Current models exist to help manage risk. But it's possible to create better models that can outperform those currently in use. For more info see here.

This challenge serves as final project for the "How to win a data science competition" Coursera course. In this competition you will work with a challenging time-series dataset consisting of daily sales data, kindly provided by one of the largest Russian software firms - 1C Company.

We are asking you to predict total sales for every product and store in the next month. By solving this competition you will be able to apply and enhance your data science skills. For more info see here.

Kaggle competitions are incredibly fun and rewarding, but they can also be intimidating for people who are relatively new in their data science journey. In the past, we've launched many Playground competitions that are more approachable than our Featured competitions and thus, more beginner-friendly.

The goal of these competitions is to provide a fun and approachable-for-anyone tabular dataset to model. These competitions are a great choice for people looking for something in between the Titanic Getting Started competition and the Featured competitions. If you're an established competitions master or grandmaster, these probably won't be much of a challenge for you; thus, we encourage you to avoid saturating the leaderboard.

Forecasts aren’t just for meteorologists. Governments forecast economic growth. Scientists attempt to predict the future population. And businesses forecast product demand—a common task of professional data scientists. Forecasts are especially relevant to brick-and-mortar grocery stores, which must dance delicately with how much inventory to buy. Predict a little over, and grocers are stuck with overstocked, perishable goods. Guess a little under, and popular items quickly sell out, leading to lost revenue and upset customers. More accurate forecasting, thanks to machine learning, could help ensure retailers please customers by having just enough of the right products at the right time.

Current subjective forecasting methods for retail have little data to back them up and are unlikely to be automated. The problem becomes even more complex as retailers add new locations with unique needs, new products, ever-transitioning seasonal tastes, and unpredictable product marketing. For more info see here

Los bancos juegan un papel crucial en las economías de mercado. Ellos deciden quién puede ser financiado y en qué términos, así como hacer y deshacer grandes inversiones. Para que los mercados y las sociedades funcionen, tanto individuos como compañías necesitan acceso a créditos.

Los algoritmos de credit scoring son los métodos que los bancos tienen para determinar si un préstamo debe ser concedido o no. Los participantes de esta competición deberán mejorar, mediante los últimos avances obtenidos en el área del Machine Learning, un sistema de credit scoring que sea capaz de predecir la probabilidad de que alguien sufra dificultades financieras en los próximos dos años.

El objetivo de esta competición es crear un modelo que las instituciones de crédito puedan utilizar para tratar de tomar las mejores decisiones financieras posibles. For more info see here