Exploratory Data Analysis of Sales Data with Jupyter Notebook and Power BI

November 9, 2021

For a business to remain competitive it’s critical that it leverages data analysis and AL/ML to improve forecasting and future sales models. This involves analysis of historical sales data, exploring customer demand, analysing the competition, seasonality patterns, the effect of holidays and promotions, and much more.
The objective of this project is twofold. To carry out analysis of the datasets in order to determine the feature characteristics, data reliability, critical details and any weaknesses and/or missing data in the datasets. The second goal is to explore and gain new information and insights. This analysis includes feature engineering allowing data to be transformed into more meaningful information and sales insights.
Python & Jupyter is excellent for EDA and creating & sharing documents that contain live code, equations, visualisations and narrative text. And Power BI provides visual interactive insights.
Of course Data Analysis would only be the first step for any business. This is usually followed by building predictive models for forecasting future sales involving additional steps such as feature selection, model building and finally deployment.