About You Case Study

This repo contains a full analysis of the sales, promo, and pricing trends of fashion products sold by the European E-commerce company "About You". "About You" is a German fashion online retailer based in Hamburg with an annual revenue just shy of a billion and operations in 24 countries. The insights of this case study are neatly presented in this slide deck

1. The Objective of the Project

This was an open-ended project where the goal was to analyze this dataset and provide an exploratory data analysis EDA using Python. Some of the questions that I attempted to answer were:

• What is the structure of the dataset? What does the data in convey and what is the granularity of the table?
• What are some interesting statistics that could be reported about the dataset?
• How do the top five product groups fare against each other in terms of price points, sales, and promo trends?
• Could we fit an ARIMA model to predict the order volume throughout the year?

2. General Statistics About the Dataset

3. Top Charts Created from the Data

3.1 Price Points by Product Group and Season

3.2 The Number of Orders and Inventory Trends Showing the Seasonal Behavior

3.3 Heatmaps Showing the Peak Sales Periods Per Product Group

3.4 Fitting the ARIMA Model to the Data

2. Usability and Reproducability

• Clone the repo using this command in your terminal git clone https://github.com/omar-elmaria/about_you_case_study.git
• Create a virtual environment by running this command python -m venv venv_about_you_challenge
• Activate the virtual environment by typing this source venv_about_you_challenge/bin/activate if you are on Mac/Linux or source venv_about_you_challenge/Scripts/activate if you are on Windows. You might need to replace the forwardslashes with a backslash if you are on Windows
• Double-check that you are using the correct Python path by typing which python and clicking enter (which python3 on Mac/Linux). It should point to the Python executable in the virtual environment you just created
• Ctrl-Shift-P to view the command palette in VSCode --> Python: Select Interpreter --> Browse to the Python executable in your virtual environment so that the Jupyter notebook uses the correct Python interpreter
• Run this command in the terminal to install the required dependencies pip install -r requirements.txt
• You will likely need to change the path to the dataset stored in the .parquet file in step 1
• Start executing each cell in the Jupyter notebook and the code will run fine