Dashboard contains interactive visualizations of company's current state built using Javascript. In addition the sales and stock forecast was generated using Facebook Prophet help predict future sales and stock value. A grocery recommendation system was created to help with the user experience. Using k-mean clustering and the Surprise alogorithm, when the user logins the code will make 3 recommendations based on the customer's past orders and suggest 3 products they may also like.
Monthly data from Citi Bike was merged using Python. Two reports were created using Power BI. One report mapped start and end station demonstrating popularity in stations. In addition it included a heat map of hourly days in time versus day of the week to demonstrate peak hours of business. The second report was built by creating new tables in Power BI to retrieve the last time and location of the bike in order to exhibit the ability to build a real-time dashboard of current bike location and how many bikes are in each stations presently. This can help with supply chain as you can view if there are any uneven inventory distributions.
Dashboard contains interactive visualizations to provide snap shot of company's current state and identify areas of improvement. Charts were created using historical retail data but engineered to display real-time data and results to optimize operations and improve overall corporate performance.
The data was pulled from the OpenWeatherMAP API to create a dataset of over 500 cities and it's various weather variables. The main purpose of this project is to analyze how weather factors such as temperature, cloudiness, wind speed and humidity change as you approach or distance from the equator.
