Predict what a customer will buy next based on purchase history using automated feature engineering
Switch branches/tags
Nothing to show
Clone or download
Latest commit 1eb3d02 Dec 11, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.gitignore add tutorial Dec 12, 2017
LICENSE Initial commit Dec 12, 2017
README.md Update README.md Dec 11, 2018
Tutorial.ipynb Handled missing data (#9) Aug 29, 2018
process_data.py add tutorial Dec 12, 2017
requirements.txt upgrade dash version to 0.18.2 (#8) Aug 17, 2018
utils.py Update utils for python3 Jul 12, 2018

README.md

Predicting a customer's next purchase using automated feature engineering

Featuretools

As customers use your product, they leave behind a trail of behaviors that indicate how they will act in the future. Through automated feature engineering we can identify the predictive patterns in granular customer behavioral data that can be used to improve the customer's experience and generate additional revenue for your business.

In this tutorial, we show how Featuretools can be used to perform feature engineering on a multi-table dataset of 3 million online grocery orders provided by Instacart to train an accurate machine learning model to predict what product a customer buys next.

Note: If you are running this notebook yourself, refer to the read me on Github for instructions to download the Instacart dataset

Highlights

  • We automatically generate 150+ features using Deep Feature Synthesis and select the 20 most important features for predictive modeling
  • We build a pipeline that it can be reused for numerous prediction problems (you can try this yourself!)
  • We quickly develop a model on a subset of the data and validate on the entire dataset in a scalable manner using Dask.

Read the tutorial

Link to notebook: Tutorial

Running the tutorial

  1. Clone the repo
git clone https://github.com/Featuretools/predict_next_purchase.git
  1. Install the requirements
pip install -r requirements.txt
  1. Download the data

You can download the data directly from Instacart here.

After downloading the data save the CSVs to a directory called data in the root of this repository. Then run the following command in your terminal from the root of this repo.

>> python process_data.py
 70%|██████████████████████████▌           | 145/207 [07:43<03:18,  3.20s/it]

Expect this command to take up to 20 minutes to run as it prepares the data for the tutorial notebook

Feature Labs

Featuretools

Featuretools is an open source project created by Feature Labs. To see the other open source projects we're working on visit Feature Labs Open Source. If building impactful data science pipelines is important to you or your business, please get in touch.

Contact

Any questions can be directed to help@featurelabs.com