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azure-sqldb-spark

This project provides a client library that allows Azure SQL DB or SQL Server to act as an input source or output sink for Spark jobs.

azuredatastudio

Azure Data Studio is a data management tool that enables working with SQL Server, Azure SQL DB and SQL DW from Windows, macOS and Linux.

azuredevopslabs

Learn how you can plan smartly, collaborate better, and ship faster with a set of modern development services with Azure DevOps.

azureml-sdk-for-r

Azure Machine Learning SDK for R

batchsparkscoringpredictivemaintenance

Batch scoring Spark models on Azure Databricks: A predictive maintenance use case

bert

TensorFlow code and pre-trained models for BERT

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

churn

Churn, retention and CLV modeling

ci-cd-pipeline-on-aws

Set up a Continuous Integration and Delivery pipeline for a web application using various AWS services.

classification-predict_failure

Machine Learning to predict device failure

clv_prediction

Predicting Customer Lifetime Value

complete-life-cycle-of-a-data-science-project

Complete-Life-Cycle-of-a-Data-Science-Project

customer-lifetime-prediction-using-python

How you measure the value of your customers? CLV or LTV is a metric that helps you measure the customer's lifetime value to a business. In this kernel, I am sharing the customer lifetime value prediction using BG-NBD, Pareto, NBD & Gamma Model on top of RFM in Python.

customer-lifetime-value

Code and tutorials for calculating and predicting customer-lifetime-value

customer-lifetime-value-prediction

Using data: Customer's invoice file. Introductions: Customer Lifetime Value(CLTV) "Customer Lifetime Value is a monetary value that represents the amount of revenue or profit a customer will give the company over the period of the relationship". CLTV demonstrates the implications of acquiring long-term customers compare to short-term customers. Customer lifetime value (CLV) can help you to answers the most important questions about sales to every company: How to Identify the most profitable customers? How can a company offer the best product and make the most money? How to segment profitable customers? How much budget need to spend to acquire customers? CLTV indicates the total revenue from the customer during the entire relationship. CLTV helps companies to focus on those potential customers who can bring in more revenue in the future. CLTV = ((Average Order Value x Purchase Frequency)/Churn Rate) x Profit margin. Please check the below step for how to calculate CLTV. Algorithm: Step1: Calculate CLTV. Calculate the average order value of customers: Average order value = Total money spent / total number of transactions Calculate Purchase Frequency: Purchase Frequency = Total Number of Orders / Total Number of Customers Calculate Repeat rate and Churn rate: Repeat rate = How many customers have numbers of transactions more than one / total numbers of customers Churn rate = 1 - repeat rate Calculate the profit margin: Profit margin is the commonly used profitability ratio. It represents how much percentage of total sales has earned as the gain. Let's assume our business has approx 5% profit on the total sale. Profit margin = Total money spent on each customer * 0.05 Calculate customer lifetime value: Customer value = (Average Order Value * Purchase Frequency) / Churn rate Customer lifetime value = Customer value * Profit margin Step2: Predictive modelling. Build a regression model for existing customers. Take recent six-month data as independent variables and total revenue over existing time( here taking 2 years) as a dependent variable and build a regression model on this data. Pros and Cons of CLTV: CLTV helps you to design an effective business plan and also provide a chance to scale your business. CLTV draw meaningful customer segments these segment can help you to identify the needs of the different-different segment. Customer Lifetime Value is a tool, not a strategy. CLTV can figure out the most profitable customers, but how you are going to make a profit from them, it depends on your strategy. Generally, CLTV models are confused and misused. Obsession with CLTV may create blinders. Companies only focus on finding the best customer group and focusing on them and repeat the business, but it’s also important to give attention to other customers.

customer-lifetime-value-project

customer-segmentation-and-lifetime-value-prediction

Analyse Customer behavior using RFM and find Customer Lifetime value using Formulas

dailyknowledge

This repository contains the files and resources from my Daily Knowledge hunt

databricks-azureml-mlops

ML Ops demo for Azure Databricks and Azure ML SDK

databricks-intro

An introduction to analyzing data using Spark in Azure Databricks

dbt-cloud-snowflake-demo-template

dbt-codegen

Macros that generate dbt code

dbt-core

dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt-datamocktool

A dbt package for unit testing your SQL analytics models

dbt-external-tables

dbt macros to stage external sources

dbt-redshift

dbt-redshift contains all of the code enabling dbt to work with Amazon Redshift

dbt-snowflake

dbt-snowflake contains all of the code enabling dbt to work with Snowflake

dbt-unit-testing

This dbt package contains macros to support unit testing that can be (re)used across dbt projects.

dbt-utils

Utility functions for dbt projects.

dbt_local_project

Learn with the analytics engineers of dbt Labs how to migrate legacy transformation code into modular dbt data models. Useful if you're porting stored procedures or SQL scripts into your dbt project.