Exploratory Data Analysis of Geolocation Data using Machine Learning

Geographical location is the process of identifying an object or person's location with the help of technology. It uses various methods, such as GPS (Uses satellite signals to pinpoint a device's location globally, with an accuracy of 5–10 meters outdoors) Wi-Fi location (Relies on nearby Wi-Fi signals to estimate location, achieving 5–30 meters' accuracy, especially in urban and indoor settings) and IP positioning, to determine the latitude and length of a device or individual. Common uses of geolocation include navigation and location services, social media, electronic commerce, fleet management, and emergency services. The exploration analysis of geolocation data is an important aspect of gaining insights into the patterns and trends within the data. The problem arises when people move from one place to another and need to find the right place. The existing system includes hostels and apartments for rent, and it has purchased and sold options. It also does not recommend restaurants, gyms, etc. based on users' preferences. Previous research lacks the accuracy of the real recommendations. An algorithm called K- means clustering is used to classify certain data elements with certain properties. However, it has a drawback when the radii of two circular groups with the same average center diverge. Another is Hierarchical clustering. Actually, it is a very useful technique in exploratory analysis of geolocation data, since it discovers hierarchical relationships and structures within the spatial data set. The technique designs a hierarchy of clusters from bottom- up, which is known as agglomerative, or top-down, that is known as divisive. Agglomerative clustering is more commonly used.