Abstract
In our daily life, we have websites like 99acres, Magicbricks, etc., which help us to find rooms or flats on rent in any city, but they do not give option to find accommodation according to our preferences that is food, budget, and accommodation. In this model, we will help the students to find best area in any city by classifying their choices such as food, budget. First, we will gather the datasets; then, we will clean the datasets according to our needs. After that we have our data, we need to understand it. A best way to understand data is by visualizing the data via graphs. To visualize the data, graphs help us to show more precise information which makes it easy to scan information in order to understand. After visualizing the data, we will run K-Means Clustering which will help by grouping the location. Find best K value for our population. From the Foursquare API, get all the geolocational data to find these people accommodation! Finally, we will run K-Means Clustering on data to plot the final results on the map.
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Shamini, P.B., Trivedi, S., Shriram, K.S., Rishi, R.R.S., Sabarish, D.S. (2023). Exploratory Spatial Data Analysis (ESDA) Based on Geolocational Area. In: Subhashini, N., Ezra, M.A.G., Liaw, SK. (eds) Futuristic Communication and Network Technologies. Lecture Notes in Electrical Engineering, vol 966. Springer, Singapore. https://doi.org/10.1007/978-981-19-8338-2_13
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DOI: https://doi.org/10.1007/978-981-19-8338-2_13
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