Lemonade consumption forecast Some guys, decided to make some money, by selling lemonade in different public places. Each guy has a fix spot on which she sell the lemonade. In every morning, they go to a lemonade maker warehouse and buy the stock for the entire day, based on their forecasts. At any moment of the day, they can ask for an instant delivery, which will cost more and we don't wana this to happen. Also, if they are buying more than it was needed for that day, they will make smaller profit.
The thing is that every guy knows the places of their business. They begin to see patterns in customers habits, the consume based the day of the week, the weather, the events from the area, etc. All of those variables, the forecasts and real consumptions data are stored in a database.
Better formulations of the problem are welcome.
My wish is to implement a software approach which will help lemonade sellers for a better forecast their lemonade needs. Do you have any idea about which machine learning method can help me in forecasting lemonade sellers needs?
 A: Straightforward approach
This is a kind of regression problem, where you need to predict numerical value. However, simple linear regression is not applicable, since not all of your features are numerical (whether, day of week, etc.). It is impossible to make them numerical (you can't assign number 1 to Monday and number 2 to Tuesday, because Tuesday is not Monday + 1, but rather another independent day). So you need to first split your data into several parts based on categorical features (like day of week) and then apply linear regression to each part. MD5' algorithm does exactly this - it first builds decision tree and then assign distinct regression model to each leave. 
More intelligent analysis
However, previous approach doesn't concern details of the problem itself, including consumer details. More intelligent analysis includes detailed view of each available feature and extraction of more features (which may be combinations of other features - see polynomial regression). If you have not very big number of consumers, you may predict probability of selling for each particular user and then just count those, who will likely buy lemonade on particular day. If you have lots of users, use clustering to perform market segmentation and learn probabilities or regression models for each segment.
Also try to properly categorize local event types, count their participants, compute distance from selling point, etc. Just learn your features and build appropriate model. 
