ANLY482 AY2017-18T2 Group02 Findings & Insights

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DATA PREPARATION

Data cleaning

Out of the 182,080 entries in train.csv, 3 columns had missing data. In teacher_prefix, there were 4 missing points, while in project_essay_3 and project_essay_4, there were 175706 missing entries. For teacher_prefix, the empty entries were replaced with Unknown. For the missing project essays, we understood from the project brief that submissions after May 17 2016 only required submissions to submit project_essay_1 and project_essay_2. The change in submission format also meant that projects submitted before and after May 17 could not be compared on the same basis. Noting that 175,706 entries were submitted after, and only 6374 entries were submitted before, we decided to remove the 6374 entries submitted before May 17 such that our analysis would be uniform. We were left with 175,706 complete project entries.

Identification of Response Variable

In our dataset, our response variable is whether the project is approved or not. This is represented in the column project_is_approved, which can hold values of either 0 (not approved) or 1 (approved). We changed the column data type into a nominal data type. A simple distribution of the response variable shows us that projects are 84.69% approved and 15.31% failed. Hence, our investigative efforts will be into why certain projects fail and to engineer features that are representative of failed projects.