Phase 1: Business Understanding

The ecommerce company that our group will be studying and researching is Play-Asia. Play-Asia is well known for their ecommerce website that offers many services ranging from video games, consoles, accessories, and other related merchandise. The Covid-19 pandemic has caused a lot of companies to go bankrupt as they were not able to cope with the challenges faced. However, as Play-Asia is an ecommerce company, they were able to gain more traction as they ship internationally. Gamers around the world could enjoy purchasing games in the comfort of their own home We believe that with the proper skillset and strategies put into data analytics, it could play a vital role in maintaining a company’s stability especially while in the use of machine learning. We were able to obtain a well-suited dataset that will allow us to conduct an Exploratory Data Analysis (EDA) to gain valuable insights into the following business questions:

1. What are the years that have the most and least sales? Why does that happen?

2. Which video games generate the highest sales profits? Why does that happen?

3. Is there a correlation between year and sales?

In addition to collecting data, our team will use the dataset to create an effective machine learning model. With the help of this model, we will be able to forecast future sales using data from the past. We will carefully assess this model's accuracy. If the model is successful, we intend to add more features and implement it in real-time. Employees and management will both have timely information at their disposal as a result, enabling them to accurately assess and plan stock budgets. We want to give decision-makers a useful tool that enables them to make wise stock management decisions by implementing this improved model in real-time. The model will enable more precise forecasting thanks to its predictive powers and extra features, which will help to optimize resource allocation and maintain a smooth supply chain.

  Phase 2: Data Understanding and EDA

During this phase, we will be diving into the data using language tools. The language tools that we will be using are Scala and Apache Spark SQL analytics engine to generate the analysis. Extract Transform Load (ETL)

1. Extract:

Data is extracted from different sources such as databases, files, APIs, or web scraping depending on the diverse systems with varying formats and structures of the data.

2. Transform:

Once data is extracted, it will undergo transformation operations to ensure consistency, quality, and compatibility which involves cleaning the data, removing duplicates, handling missing values, and applying data validation rules. The data may also be standardized, aggregated, or enriched with additional information during this stage.

3. Load After the data has been extracted and transformed, it will be loaded into a target destination such as a data warehouse, data mart, or database. The loading process involves mapping the transformed data to the appropriate structure and schema of the target system.