Welcome to the data exploration journey of understanding customer behavior and enhancing sales forecasting for a UK-based company specializing in unique all-occasion gifts. Our goal is to unlock valuable insights from customer data and historical sales, laying the foundation for effective customer segmentation and improved sales predictions.
-
Understand the Data:
- Dive deep into the provided Online Retail II dataset to comprehend its intricacies and features.
-
Exploratory Data Analysis (EDA):
- Perform comprehensive exploratory data analysis to uncover hidden patterns, trends, and anomalies within the dataset.
-
Data Preparation:
- Preprocess and prepare the data for subsequent analyses, ensuring its suitability for modeling.
-
Customer Segmentation:
- Utilize advanced segmentation techniques to categorize customers based on their behavior, preferences, and historical interactions.
-
Forecasting Models:
- Develop and implement tailored forecast models for each customer segment, aiming for accurate sales predictions.
-
Results Presentation:
- Present the findings, insights, and actionable recommendations in a clear and concise manner.
The heart of our exploration lies in the Online Retail II dataset, offering a real-world snapshot of online retail transactions. The primary data elements include:
online_retail_II.xlsx
This comprehensive table captures records for all created orders, boasting 1,067,371 rows and 8 columns. With a size of 44.55MB, it serves as a rich source of information for our analysis.
| Data Element | Type | Description |
|---|---|---|
| Invoice | object | Invoice number, uniquely assigned to each transaction. If starting with ‘c’, it signifies a cancellation. |
| StockCode | object | Unique product (item) code assigned to each distinct product. |
| Description | object | Descriptive name of the product (item). |
| Quantity | int64 | Quantities of each product (item) per transaction. |
| InvoiceDate | datetime | Date and time of the invoice generation. |
| Price | float64 | Unit price of the product in pounds (£). |
| Customer ID | int64 | Unique customer identifier with a 5-digit integral number. |
| Country | object | Country name where the customer resides. |
.
├── data
│ ├── 2009-2010.csv
│ └── 2010-2011.csv
├── models
│ └── t2v
├── notebooks
│ ├── ds4a_retail_challenge.ipynb
│ ├── gensim_lda.py
│ └── utils.py
├── README.md
└── requirements.txt
- https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/
- https://github.com/nicodv/kmodes
- https://towardsdatascience.com/understanding-topic-coherence-measures-4aa41339634c
- https://medium.com/@thomas.shawcarveth/market-segmentation-and-predicting-marketing-success-with-data-science-f48c99e3b4e1
- https://www.geeksforgeeks.org/rfm-analysis-analysis-using-python/
- https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/
https://github.com/evansphillips/retail-customer-segmentation-forecasting
Leave a Reply