Store Sales Prediction Model

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A model designed to predict the future sales of a store based on location characteristics.

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`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── 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.readthedocs.io

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Background

The location of a retail store plays a huge role in it's commercial success. A store location planning team uses various data sources to better understand the potential of candidate locations for new stores in the UK. They need data science help in designing a model that can predict the future sales [normalised_sales] of a store based on location characteristics.

Objective

The objective of this project is to try different data science and machine learning techniques using real world data.

Dataset files:

• train.csv
• test.csv

Columns:

• location_id: id of property location
• normalised_sales: normalised sales value of store
• crime_rate: crime rate in the area (higher means more crime)
• household_size: mean household size in the area
• household_affluency: mean household affluency in the area (higher means more affluent)
• public_transport_dist: index of public transport availability in the area
• proportion_newbuilds: proportion of newly built property in the area
• property_value: average property value in the area
• commercial_property: percentage of commercial properties in the area
• school_proximity: average school proximity in the area
• transport_availability: availability of different transport
• new_store: new Grocery Retail store opened recently
• proportion_nonretail: proportion of non-retail commercial properties in the area
• competitor_density: density of competitor retailers
• proportion_flats: proportion of blocks of flats in the area
• county: county code of the area

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