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FEAST Feature Store Example- Learn to use FEAST Feature Store to manage, store, and discover features for customer churn prediction machine learning project.
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Business Overview
Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production. Feast is able to serve feature data to models from a low-latency online store (for real-time prediction) or from an offline store (for scale-out batch scoring or model training). Some of the problems solved by feast are
Consistent access to data in modeling
Deploying new features into production
Point-in-time correct data for models
Reusability of the features across the project
One of the main problems is the consistency of the data between training and production. Feast helps to achieve this in a very simple way. In this project, we used customer churn prediction problem to get hands-on training in Feast.
Reference: docs.feast.dev
Aim
To predict Customer churn using a Feature store Feast
Data Description
The dataset used is customer churn data with 8 features. The dataset contains information about 891 customers.
Features:
Created_at
Customer_id
Churned
Category
Sex
Age
Order gmv
Credit type
Tech Stack
Libraries: feast, pandas, sklearn, flask, pickle
Approach
Feast installation and setup
Offline store and Retrieval
Online store and Retrieval
Training data creation using feast
Model training
Random Forest
Gradient Boosting
Real time predictions using Feast
Interactive model deployment using Feast
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