R Shiny App To Help Buyers Find Houses Based On Preferences
Overview:
So in this project, I worked with a dataset that showed the price of houses based on different characteristics like area, stories, hot water heating, etc. The point of this app is to make it easier for a buyer to find a house based on the buyer’s budget and preferences. I did this through multiple interactive plots and an interactive table that allows the buyer to select different preferences. With the table, the buyer can search for houses based on the minimum and maximum price that the buyer is looking for as well as the column to base this off of.
Findings:
So after playing around with the app, I found out many things. The scatter plot shows that as the area increases, so does the price. This plot does not appear to show much of a connection between the area and the categorical columns as the column values are scattered across different prices for the same category. The bar plot is only really telling the buyer how many houses have a certain category based on the column, so it will help buyers to find out which are most likely going to sell out first. The boxplot does however show a connection between the price and the categorical columns, which is that the more a house offers, then the higher the price will go most likely.
Conclusion:
I can’t provide an exact recommendation since different buyers have different budgets and preferences, but what I do recommend for the buyers is to utilize all of the visualizations and the table to help them out.
Link To The App:
Link To The Code And Dataset:
https://github.com/danielrahman9185/NYCDSA_R_Shiny
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