Analysis and Findings as of Finals

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Mid-Term	Finals

Derived Variables

As from the Data Exploration phase up till Mid-Terms, our team has realized that there are numerous factors that will possibly each of the different events. Therefore, our team has decided to put forward the factors, as shown in the table below, which will be used to assist us in building predictive models to explain the variations in the demands or tickets sold for each event.

No.	Factors	Description
1	Concurrent Events	Our team is hypothesizing that the events, which started at the same time period will make people buy less tickets. There might be a possibility that if there are more concurrent events ongoing, the demand may be split across the concurrent events.
2	Day of Week	The day of the week will also potentially affect the demands for each event, as events which are held in the weekdays may be attract less customers than events held over the weekends.
3	Event Name	As from our Exploratory Analysis, we realized that each of the events perform differently, and there are a multitude of factors which affects its variations in demands.
4	Time Period	Our team chose to divide 24-hours in a single day to 3-hours time block (e.g., 12AM to 2:59AM as Early Midnight & 3AM to 5:59AM as Late Midnight), as different customers might prefer events due to their lifestyle.
5	Month	The month factor is important as there might be a possibility that certain months, such as the holiday or festive season, which will potentially attract more customers to buy tickets for the different events.

With all the identified factors, our team prepared the data to be in a suitable data structure to be fed into our predictive model in the Model Calibration and Validation phase.

Model Calibration & Validation

1. Regression Model