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ranked_recommendation

ranked_recommendation_Logo.png

Usage

  • Basic Usage: Run ranked_recommendation.exe or use ranked_recommendation_main.ipynb
  • Configuration: Modify pyproject.toml to add or remove packages.

Data

  • Sources: The dataset is “The Instacart Online Grocery Shopping Dataset 2017”, Accessed from https://www.instacart.com/datasets/grocery-shopping-2017 on 12/3/2023.

The dataset “The Instacart Online Grocery Shopping Dataset 2017” is an anonymized dataset contains over 3 million grocery orders from more than 200,000 Instacart users. For each user, Instacart provided between 4 and 100 of their orders, with the sequence of products purchased in each order. However, given the restriction of the assignment of 10MB. I have shrinked and modified the size of the dataset.

  • Structure: Table of key features

Example

Input data format

products
    product_id
    product_name

order_products
    order_id
    product_id
    add_to_cart_order
    reordered

Result ✅

  • Findings:

  • Based on both rankings markov chains model and from the initial counts of sales they shown to be similar. I was expecting the ranking of products to be different, however given that this is smaller dataset the results might change with the extended dataset.

  • Visualizations:

  • Results

Directory Structure

.
├── docs <- markdown files for mkdocs
│   └── img <- assets
├── notebooks <- jupyter notebooks for exploratory analysis and explanation
└── src - scripts for processing data eg. transformations, dataset merges etc.
│   ├── data <- loading, saving and modelling your data
│   ├── features <- feature engineering
│   ├── model <- algorithms and models
│   ├── plots <- plots
│   └── utils <- api and other
├── LICENSE <- License
├── mkdocs.yml <- config for mkdocs
├── pyproject.yml <- config project
└── README.md <- README file of the package

Contributing

To contribute create a PR a use conventional commits

fix: <description>
feat: <description>
docs: <description>
refactor: <description>

License

The project is licensed under the MIT License.

I hope this is helpful!