Store Sales Prediction Model
==============================
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
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