Predict Customer Lifetime Value

The goal is to create a method to predict the lifetime value of customers.

Installation

You need to have Python installed on your machine.

MacOS & Linux:

git clone https://github.com/Charles-de-Montigny/predict_customer_lifetime_value.git
cd predict_customer_lifetime_value
bash ./init.sh

Getting Started

Once the installation is completed, proceed as follows to produce a result file:

1- Get the data:

Download the data from this link and unzip the data in the data/raw folder.

data/raw/Online Retail.xlsx

2- From the project root, run the bash script:

cd "to project root"
bash ./run_prediction.sh

3- Retrieve the result file in data/processed/:

data/output/prediction_{YYYY-MM-DD}.csv

Profiling Reports

There is a pandas profiling report available in the reports folder but if you want to run it yourself:

python src/profiling.py trainset
python src/profiling.py testset
python src/profilint.py transac

For more information about the pandas Profiling project: pandas profiling documentation

Project Organization

├── README.md          <- The top-level README for developers using this project.
│
├── run_prediction.sh    <- A standalone script that runs the whole pipeline on the verification set and creates a prediction file.
│
├── config             <- Configuration files for colors and columns to used in analysis.
│
├── data
│   ├── raw            <- The original, immutable data dump.
│   ├── interim        <- Interim steps in data preparation.
│   ├── processed      <- Data sets for modeling.
│   └── output         <- Test tournament with predictions.
│
├── 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`.
│
├── reports            <- Generated analysis as HTML and Notebooks.
│   ├── metrics        <- Data sets for modeling.
│   └── 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.
   │
   ├── make_dataset.py <- Scripts to download or generate data.
   │
   ├── run_eval.py     <- Scripts to evaluate models performance.
   │
   ├── make_prediction.py     <- Scripts to produce final predictions.
   │
   └── profiling.py  <- Scripts to create exploratory and results oriented visualizations.

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