Data Science project using different classification algorithms
The goal of the project is to build a model to help a university predict which of its students are likely to attrite from the Univerisity. However, the dataset could be used to build a model to predict student retention by switching the target variable in the dataset.
Exploratory Data Analysis
Data Visualisation: determine relationships between independent variables (linear and non-linear relationships)
Aggregating Majors into custom 'Departments' to narrow down the independent variables, easiuer to deal with the categorical variable and notice their effect.
Bagging Decisiontree Classififer:
Score: 82.35%
Random Forrest Classifier:
Score: 80.79%
AdaBoost Classifier:
Score: 82.25%
GradientBoost Classifier:
Score: 84.79%
Hypertuning is essential to improving accuracy.
Second term performance appears to bear the most weight on a students' decision to leave or stay at the univerisity. DIstance from home is the second most relevant factor in a student leaving the Univerity.
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