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databricks-industry-solutions / product-search
Semantic product search on Databricks
Language:Python 26Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.
Language:Python 20Translating text attributes (like name, address, phone number) into quantifiable numerical representations Training ML models to determine if these numerical labels form a match Scoring the confidence of each match
Language:Python 18Accelerator for customer incentive investment using causal inference techniques
Language:Jupyter Notebook 15Ingest sample retail data, build visualizations to explore past purchase behavior and use machine learning to predict the likelihood of future purchases
Language:Python 13Perform fine-grained forecasting at the store-item level in an efficient manner, leveraging the distributed computational power of the Databricks Lakehouse Platform.
Language:R 11Connect the impact of marketing and your ad spend to sales. Efficiently pinpoint the impact of various revenue-generating marketing activities to understand what works best. Focus on the best-performing channels to optimize media mix and drive revenue.
Language:Python 8Automated Analysis of Product Reviews Using Large Language Models
Language:Jupyter Notebook 8Holiday recipe recommendation app built with Databricks
Language:Jupyter Notebook 7Intermittent Demand Forecasting With Nixtla on Databricks
Language:Python 6- parts-demand-forecasting
Perform demand forecasting at the part level rather than the aggregate level to minimize disruptions in your supply chain and increase sales. Manage material shortages and predict overplanning
Language:Python 5 Zingg fuzzy matching for products using metadata and images
Language:Python 5Get started with our Solution Accelerator for Propensity Scoring to build effective propensity scoring pipelines that: Enable the persistence, discovery and sharing of features across various model training exercises Quickly generate models by leveraging industry best practices Track and analyze the various model iterations generated
Language:Python 5- routing
Get started with our Solution Accelerator for Scalable Route Generation to optimize delivery routes and increase profitability
Language:Python 5 Create advanced customer segments to drive better purchasing predictions based on behaviors. Using sales data, campaigns and promotions systems, this solution helps derive a number of features that capture the behavior of various households. Build useful customer clusters to target with different promos and offers.
Language:Python 5Survival analysis is a collection of statistical methods used to examine and predict the time until an event of interest occurs. In this Solution Accelerator, learn how to use different survival analysis techniques for predicting churn and calculating lifetime value.
Language:Python 5Build a wide-and-deep recommender with collaborative filters that takes advantage of patterns of repeat purchases to suggest both previously purchased and related products.
Language:Python 5Enabling Computer Vision Applications With the Data Lakehouse
Language:Python 4Use machine learning and the Databricks Lakehouse Platform for product matching that can be used by marketplaces and suppliers for various purposes. Resolve differences between product definitions and descriptions and determine which items are likely pairs and which are distinct across disparate data sets.
Language:Python 4Get started with our Solution Accelerator to rapidly ingesting all data sources and types at scale, build highly scalable streaming data pipelines with Delta Live Tables to obtain a real-time view of operation, and leverage real-time insights to tackle your most pressing in-store information needs
Language:Python 4Create fine-grained and viable estimates of buffer stock for raw material, work-in-progress or finished goods inventory items that can be scaled across the supply chain. Free up working capital that would be tied up in inventory and reallocate to more productive uses.
Language:Python 4Identifying Campaign Effectiveness For Forecasting Foot Traffic
Language:Python 3Develop an understanding of how a customer lifetime should progress and examine where in that lifetime journey customers are likely to churn so you can effectively manage retention and reduce your churn rate.
Language:Python 3This Solution Accelerator shows how OOS can be solved with real-time data and analytics by using the Databricks Lakehouse Platform to solve on-shelf availability in real time to increase retail sales. The accelerator can also be used for supply chain solutions.
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Products We Think You Might Like: Generating Personalized Recommendations Using Matrix Factorization
Language:Python 2Build a similarity-based image recommendation system for e-commerce that takes into account the visual similarity of items as an input for making product recommendations.
Language:Python 2Use personalized images to enhance the output of an image generating model
Language:Python 2Clickstream Analytics [Blog] https://www.databricks.com/blog/2023/03/21/using-real-time-propensity-estimation-drive-online-sales.html
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Increase conversion with personalized recommendations: Build a recommender that leverages product affinities to suggest additional items
Language:Python 1Get started with our Solution Accelerator for Order Picking to apply optimization logic to each order to: Avoid unexpected delivery outcomes and assess the impact of small variations on order picking Minimize total store travel time to increase profitability
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Preempt churn with the Databricks Solution Accelerator for predicting subscriber attrition. Learn how to analyze behavioral data to identify subscribers with an increased risk of cancellation. Then use machine learning to quantify the likelihood to churn as well as indicate which factors explain that risk.
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