Reproducible Data Analysis: real world products database

Goals:

• map similar sizes into the general size (XXL -> 2XL etc.)
• map colors into the groups (LIGHT GREEN -> GREEN)
• clean brands
• map categories
• extract features from title
• map codes
• find similar products
• group similar products with equal properties across the distibutors.

Working with dataset of 1'561'159 products.

Drop me a message if you want to get raw dataset (all sensitive data removed).

List of all keys:

{ "_id" : "_id", "value" : null }
{ "_id" : "brand", "value" : null }
{ "_id" : "category", "value" : null }
{ "_id" : "code", "value" : null }
{ "_id" : "color", "value" : null }
{ "_id" : "size", "value" : null }
{ "_id" : "source", "value" : null }
{ "_id" : "title", "value" : null }

Example of the record:

{
        "_id" : ObjectId("55fedbc4c702283c66877c14"),
        "code" : "A4N3234",
        "title" : "MARATHON T",
        "color" : "FOREST",
        "source" : "shemeka",
        "size" : "L",
        "category" : "performance",
        "brand" : "a4"
}

Import dataset into the database (mongodb):

mongoimport -d <database_name> -c data --file=products.json

Create indexes (in mongodb console mongo <database_name>):

db.data.createIndex({code: 1})
db.data.createIndex({color: 1});
db.data.createIndex({size: 1});
db.data.createIndex({source: 1});
db.data.createIndex({brand: 1});
db.data.createIndex({category: 1});

NOTE: source original distributor name replaced with alias.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

---

Project based on the cookiecutter data science project template. #cookiecutterdatascience