Data Analytics Using Python Notebook
Budget: $10 – $65 USD
The banks are having a bit of trouble with debt at the moment. They have lent lots of money to people who promised to pay it back, and then didn’t. In the future, they would like to avoid lending to the kind of person who won’t pay back the loan, and that is where you come in. We have got some data from a bank describing 2000 of its loan customers. The data also tells us whether or not each customer repaid the loan. The question is simple – Can we predict who will repay the loans and who won’t?
Your assignment is to answer that question using data mining techniques and produce a system that would be able to tell the bank how likely it is that a new customer would pay back a loan. You can use any software of your choice (for example, Weka or scikit learn in Python) and you will not be required to submit any code, just a report.
You should employ best practice for both the project management and the machine learning aspects of the project. The data you need for the project is also available on MS team (loans.csv). You must hand in a report covering the following:
Business understanding/ problem statements (15 Marks)
Summarise the project by describing the task you are given, the data you received and the requirements of the finished system. Define any terminology that you will use in the report (for example, model, variable,etc.). Describe the project methodology you will use.
Data Summary (15 marks)
List the variables that you found in the file provided by the company. For each one, say whether it is nominal or numeric, continuous or discrete and whether or not it should be considered for building the machine learning. Explain your decisions.
Data Preparation (25 marks)
Describe what you did with the data prior to the modelling process (data cleaning). Show histograms of the one example variable before and after any pre-processing that you carried out. If you corrected any mis-typed entries in the data, report what you changed. Carry out descriptive analysis and explain each what they represent, use as many graph as possible and provide the descriptions
Machine learning Modelling (35 marks)
Give a detailed technical description of the techniques and the way the models are represented, which machine learning you use and why. Include one diagram showing the structure of each type of model that you build. Describe what hyper parameters may be changed and what effect this has. If you varied the hyper parameters of a model, show how this impacted on the results. Describe how you split the data for training, validation and testing purposes. Be methodical and record each result. Explain the test and pay attention to overfitting and underlining. Show an ROC curve for the decision as to whether or not a loan will be repaid and describe what the curve shows.
Report compilation and submission (10)
Students should work in group of 6, and one of the group’s members should submit the group report on eLearning. The report cover should be attached, and the report topics should be the same as the one on the cover
There is a 6000 maximum word limit on the report and marks will be deducted at a rate of 10 for every 1000 words over you go. You do not need to submit the models that you built, just the report. You can assume that the client has a good technical understanding of data mining and statistics, so do not shy away from technical terms in your report. Where you use them, however, explain what they mean in plain language too. To maximise your mark, make sure you follow the instructions above and include everything that is asked for in the report.
Your assignment is to answer that question using data mining techniques and produce a system that would be able to tell the bank how likely it is that a new customer would pay back a loan. You can use any software of your choice (for example, Weka or scikit learn in Python) and you will not be required to submit any code, just a report.
You should employ best practice for both the project management and the machine learning aspects of the project. The data you need for the project is also available on MS team (loans.csv). You must hand in a report covering the following:
Business understanding/ problem statements (15 Marks)
Summarise the project by describing the task you are given, the data you received and the requirements of the finished system. Define any terminology that you will use in the report (for example, model, variable,etc.). Describe the project methodology you will use.
Data Summary (15 marks)
List the variables that you found in the file provided by the company. For each one, say whether it is nominal or numeric, continuous or discrete and whether or not it should be considered for building the machine learning. Explain your decisions.
Data Preparation (25 marks)
Describe what you did with the data prior to the modelling process (data cleaning). Show histograms of the one example variable before and after any pre-processing that you carried out. If you corrected any mis-typed entries in the data, report what you changed. Carry out descriptive analysis and explain each what they represent, use as many graph as possible and provide the descriptions
Machine learning Modelling (35 marks)
Give a detailed technical description of the techniques and the way the models are represented, which machine learning you use and why. Include one diagram showing the structure of each type of model that you build. Describe what hyper parameters may be changed and what effect this has. If you varied the hyper parameters of a model, show how this impacted on the results. Describe how you split the data for training, validation and testing purposes. Be methodical and record each result. Explain the test and pay attention to overfitting and underlining. Show an ROC curve for the decision as to whether or not a loan will be repaid and describe what the curve shows.
Report compilation and submission (10)
Students should work in group of 6, and one of the group’s members should submit the group report on eLearning. The report cover should be attached, and the report topics should be the same as the one on the cover
There is a 6000 maximum word limit on the report and marks will be deducted at a rate of 10 for every 1000 words over you go. You do not need to submit the models that you built, just the report. You can assume that the client has a good technical understanding of data mining and statistics, so do not shy away from technical terms in your report. Where you use them, however, explain what they mean in plain language too. To maximise your mark, make sure you follow the instructions above and include everything that is asked for in the report.