The goal of the project is to find out how factors such as Area, the number of bathrooms, Location, and House type will influence the price of properties in London. After that, they will then use this information to identify the most affordable places to stay given a set of conditions and the final goal is to answer the question "Does the number of bathroom affects the price of the property?"
Describe the data used or collected.
The dataset they use is called "Housing Prices in LONDON, which contains 11 variables in total. This dataset comprises of various house listings in London and neighbouring region.
Describe how the research question will be answered, e.g. what approaches / methods will be used.
To answer the question they have plotted a smooth graph with two factors: the price of the property and the number of bathrooms in each property. Then they got a very rough answer " Yes " because there was an increasing slope shown in the graph. However, they still found that so many confounding variables. Furthermore, the resulting graph seems did not fit the data well. As they said they will continue to work with these data to solve problems and reach their final goal well.
Is there anything that is unclear from the proposal?
Although they directly used the ggplot to make a graph, the proposal does not include specific statistical methods.
Provide constructive feedback on how the team might be able to improve their project.
Perhaps they can add more factors because just one response factor and one explanatory factor might lead to an oversimple project.
What aspect of this project are you most interested in and would like to see highlighted in the presentation.
I am looking forward to seeing how they will get rid of those confounding variables.
Provide constructive feedback on any issues with file and/or code organization.
Good files and code organization.
(Optional) Any further comments or feedback?
Overall it is a very interesting topic. hope they can perfectly complete it.
The goal of the project is to find out how factors such as Area, the number of bathrooms, Location, and House type will influence the price of properties in London. After that, they will then use this information to identify the most affordable places to stay given a set of conditions and the final goal is to answer the question "Does the number of bathroom affects the price of the property?"
The dataset they use is called "Housing Prices in LONDON, which contains 11 variables in total. This dataset comprises of various house listings in London and neighbouring region.
To answer the question they have plotted a smooth graph with two factors: the price of the property and the number of bathrooms in each property. Then they got a very rough answer " Yes " because there was an increasing slope shown in the graph. However, they still found that so many confounding variables. Furthermore, the resulting graph seems did not fit the data well. As they said they will continue to work with these data to solve problems and reach their final goal well.
Although they directly used the ggplot to make a graph, the proposal does not include specific statistical methods.
Perhaps they can add more factors because just one response factor and one explanatory factor might lead to an oversimple project.
I am looking forward to seeing how they will get rid of those confounding variables.
Good files and code organization.
Overall it is a very interesting topic. hope they can perfectly complete it.