"Find Nearest Place To Target Sales" is a solo personal practice project aimed at identifying the nearest targeted branch that is eligible to do sales at a low cost. The primary objective of this project is to use data and analytics to help businesses optimize their sales operations and reduce their costs.
The project uses the geopy module to find the longitude and latitude of the location, which is then used to calculate the distance between the targeted branch and the nearest eligible branch. The K-Nearest Neighbor (KNN) algorithm is used to identify the nearest branches, and the Euclidean Distance formula is used to calculate the distance between them.
The formula for Euclidean Distance is as follows: d = sqrt((x2-x1)^2+(y2-y1)^2+(z2-z1)^2), where A = (x1, y1, z1) and B = (x2, y2, z2) are the coordinates of the two locations.
Using these techniques, we have developed a robust and efficient system that can quickly identify the nearest eligible branch for sales operations, helping businesses optimize their operations and reduce their costs. This system is particularly useful for businesses that operate in multiple locations and need to optimize their sales operations across different regions.
The project is implemented using Python, and the KNN algorithm and Euclidean Distance formula are used to analyze the data and identify the nearest branches. We have also used various visualization tools to present the data in a meaningful and actionable way.
Overall, the "Find Nearest Place To Target Sales" project is an excellent example of using data and analytics to optimize business operations and reduce costs. The use of advanced techniques such as the KNN algorithm and Euclidean Distance formula, along with the geopy module, has enabled us to develop a robust and efficient system that can help businesses make informed decisions about their sales operations.