Using data from Taarifa and the Tanzanian Ministry of Water, can you predict which pumps are functional, which need some repairs, and which don't work at all? This is an intermediate-level practice competition. Predict one of these three classes based on a number of variables about what kind of pump is operating, when it was installed, and how it is managed. A smart understanding of which waterpoints will fail can improve maintenance operations and ensure that clean, potable water is available to communities across Tanzania.

In this project we will attempt to predict if customers are satisfied or dissatisfied for one of the biggest banks - Santander. The main goal is to identify unhappy customers so that the bank can take steps to improve their situation. This is very needed since dissatisfied customers rarely voice their complaints and thus can churn without the possibility of retention.

In this project we will try to get data on a particular topic from Twitter. We will use this collection of tweets to estimate the moods of the tweets (using Natural Language Processing) and also map their locations. We will just barely scratch the surface on what is possible to do with the Twitter API. In future sessions we might also show who the most important people (influencers) are on this topic for example.

Opening a restaurant is no easy feat. There might be a lot of different variables involved in whether such an enterprise will eventually prove to be a success or a failure. Here the revenue is predicted based on a number of input variables.

Predicting sales in Rossmann shops across Germany.

Investigating energy consumption in US households.

By using Kaggle data we can visualise the number of crimes committed in different parts of the city.

Analysing student GRE and GPA scores, and using that to predict admission.