As stated by the team, the goal of the project is to discover whether or not number of bathrooms affects the price of a property in London.
Describe the data used or collected.
The data used comes from Kaggle and shows housing prices in London as well as a number of factors such as number of bedrooms, bathrooms, location, house type and area in sq. ft. The dataset in the project has 11 variables which are all descriptive of a property listing in London and its features.
Describe how the research question will be answered, e.g. what approaches / methods will be used.
The research question will be answered by plotting a regression line of price of a home as number of bathrooms increases, as well as using the other variables found in the dataset to address any confounding variables and eliminate other factors which may contribute to a price rise.
Is there anything that is unclear from the proposal?
The exact methodology of removing the confounding variables - will there be a way to mathematically account for any impact they may have on the data or will it simply be noted and the extent of their effects be measured? One goal is to determine the strength of the relationship between number of bathrooms and price of properties. Is there a threshold for what will be considered a strong relationship?
Provide constructive feedback on how the team might be able to improve their project.
Rather than using geom_point to map your data under you regression line, It may be more valuable to use geom_hex, or another plotting device which would allow you to see density rather than individual points. As it stands now, it isn't very clear on how many properties are actually being shown at a given bathroom number level.
What aspect of this project are you most interested in and would like to see highlighted in the presentation.
I would like to see whether or not it can be discovered that there is a clear relationship between number of bathrooms and property value that isn't intrinsically linked to square footage.
Provide constructive feedback on any issues with file and/or code organization.
The file and code organization look well organized. The in-code notes are a very helpful way of distinguishing what is going on.
(Optional) Any further comments or feedback?
Looks very organized and succinct. The only thing I would clarify is the exact methodology for honing in on how number of toilets specifically effects price.
As stated by the team, the goal of the project is to discover whether or not number of bathrooms affects the price of a property in London.
The data used comes from Kaggle and shows housing prices in London as well as a number of factors such as number of bedrooms, bathrooms, location, house type and area in sq. ft. The dataset in the project has 11 variables which are all descriptive of a property listing in London and its features.
The research question will be answered by plotting a regression line of price of a home as number of bathrooms increases, as well as using the other variables found in the dataset to address any confounding variables and eliminate other factors which may contribute to a price rise.
The exact methodology of removing the confounding variables - will there be a way to mathematically account for any impact they may have on the data or will it simply be noted and the extent of their effects be measured? One goal is to determine the strength of the relationship between number of bathrooms and price of properties. Is there a threshold for what will be considered a strong relationship?
Rather than using geom_point to map your data under you regression line, It may be more valuable to use geom_hex, or another plotting device which would allow you to see density rather than individual points. As it stands now, it isn't very clear on how many properties are actually being shown at a given bathroom number level.
I would like to see whether or not it can be discovered that there is a clear relationship between number of bathrooms and property value that isn't intrinsically linked to square footage.
The file and code organization look well organized. The in-code notes are a very helpful way of distinguishing what is going on.
Looks very organized and succinct. The only thing I would clarify is the exact methodology for honing in on how number of toilets specifically effects price.