1. Weather By Shavar and Gerlof

2. Significance Predicting the weather creates a huge benefit for a large amount of people. Harsh weather can cause a disaster in the agrarian sector, in case of outdoor events or to civilization in general, like we have seen in Asia. Therefore it’s crucial to be able to predict weather accurately

3. Problem As you’ve seen it can lead to awkward situations if the weather isnt known before it takes place. In our data set the main goal is to predict if outdoor sports games would continue

4. Attributes of our data Outlook Temperature Humidity Windy Play (whether the sports team plays or not) Sunny Overcast rainy

5. Interesting Patterns (by analyzing attributes) Most of the time the temperature is below 72.4 degrees Fahrenheit Almost two times more games are being played then being cancelled. A lot of plays still go on even when its raining!! And even windiness can’t stop players from playing their outdoor sports

6. Classification Acc. We decided to use a decision tree in order to predict if the plays would continue or not The reliability of this was about 50%, just as it is on TV as we all know.

7. Interesting Patterns (by analyzing classifiers) The prediction became more effective by using a more advanced tree. Because of the relatively small amount of attributes, there couldn’t be found any more patterns.

8. In Practice Predicting the weather is used a lot in public already, as we all know on the television, and by analyzing data we should be able to refine our methods.