Binary classification task where the student must classify people into those that earn above £50k and those that earn below £50k. Classifier must be built using Scikit-learn, choice of classification method is up to the student.
Binary classification task where the student must classify people into those that earn above £50k and those that earn below £50k
Classifier must be built using Scikit-learn, choice of classification method is up to the student. Seeing as labelled data is provided then any supervised method provided in Scikit-learn would make sense, for example perceptron or SVM.
A train and test set are provided. Features pertain to individuals life such as age, education, hours worked per week. The train set also provides labelled data for each person on whether they are >50k or <50k
Code must be accompanied by a two page report:
The report must contain the following sections.
1. Approach (10 marks)
Present a high-level description and explanation of the machine learning approach (e.g., logistic regression, multi-layer perceptron, support vector machine) you have used. You should give details of the way that the approach works and note any underlying theory and important assumptions on which it depends.
2. Methods (30 marks)
Explain in detail how you have gone about training and testing your classifier, including data pre-processing. Some discussion of the nature of the training data and any issues that arise from that should be include d here. You should explain what data was used for training and how. You should also explain how performance of the classifier was assessed.
Use graphs and/or tables to illustrate your results, e.g.:
Show how the choice of hyper-parameters affect performance
Use graphs to show changing performance for different training sets.
Present confusion matrices and other relevant measures of performance.
In the discussion, critically evaluate the work and discuss any limitations or problems. If you think that there might be ways of getting better performance, then explain how. If you feel that you could have done a better job of evaluation, then explain how. What lessons have been learn ed?