LocationsCloud Articles

    Supermarket Shopping has become a routine nowadays. The wide range of the items available, low pricing, and ease of shopping will result in a large flow of customers and so there will be a significant increase in the sales income. The sales and  location dataset of a supermarkets  is analyzed in this blog, which spans the years 2017 through 2020. The aim is to determine the company’s strengths and weaknesses, maximize profits, loss reduction, and make future recommendations. This is a comprehensive capstone project that necessitates a working grasp of Python as well as basic statistical principles. This project’s environment comprises of: IDE- JupyterLab File Type- XLS Exploratory Data Analysis Let’s take a closer look at the information in data frame. From the above dataset we can analyze that: 21 Columns 9994 rows/ entries All are Null- values excluding Postal codes Single entry for ‘Country’ Filtering Data Deleting Unnecessary Columns Postal code • Country • R...

    The most common type of theme map is a choropleth map. When geospatial data is linked to some enumeration units, they are extremely effective (Counties, provinces, districts, etc.). The distinct polygons inside a layer of a choropleth map have varying shades of colors signifying a specific property of that polygon (in our case, the number of locations). We’ll make a choropleth map for the United States, with each state’s color graded proportionately to its number of sites. The main steps for developing the Choropleth map are: • Importing the polygon vector map. • Importing the CSV file as a point vecto • Calculating the number of locations inside each state using the ‘Count points in’ tool. • Editing the symbol for getting the Choropleth map • Exporting map as an image or PDF. Importing Polygon Vector Map To create a Choropleth map, we must first obtain a country/region polygon vector map. A state in the United States is represented by each polygon in the vector map. Extra...