The specific problem in this project is about the time-series data trend prediction. The specific application scenario is in e-commerce. You are given a real dataset obtained from a real-world e-commerce application where there were 1000 products and 31490 customers (i.e., buyers) who bought these products. Of these 1000 products there are 100 k…
Description of what Sales Operations is
Walmart Stores Sales Forecast using R. (Data from Kaggle)
Using a linear regression method, we build a model to determine the relationship between independent and dependent variable, and then predict the sales. In the process, we will use a statistical point of view for validation.
This depository is Homework 6 for Doing Data Science 6306 Section 401 Tuesdays at 9:30 - 11:00 PM EST, Cohort 2017 Spring semester at SMU -- "DDS-HW6" for short. Authors: Yao Yao, Jason Cessna, Steven Stevenson. This project was submitted through GitHub on RStudio version 1.0.136.
Multilevel Regression analysis of Big Mart Sales dataset which aimed at forecasting sales, seasonality metrics, and recommending strategies to the business retailers by identifying top price elastic products and best performing outlet types.
🎮 A dashboard that visualizes North American and global sales and sales-related data for the video game industry
This project involves comprehensive data analysis and visualization of a retail sales dataset. The primary goal is to derive actionable insights from the data, which includes details about orders, customers, products, and regions.
For this project, I did an analysis on the beverage market in Thailand, in the capacity of a data consultant, so as to recommend to an up-and-coming company on what insights they can derive based on the current state of the market to ensure the success of their budding business
To predict 6 months sales using 4 years time series data of a retail store.
The objective of this project is to detect anomalies within a given dataset and assess their impact on the analysis performance. Our main focus is on developing a predictive model that will enable accurate sales forecasting. By identifying and addressing anomalies within the dataset, we aim to enhance the overall accuracy of the sales prediction
creating an Arima model to forecast weekly sales for different department and store types
Through the use of data visualizations, dashboards simplify complex data sets to provide users with at a glance awareness of current performance
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