A step-by-step guide on how to build a lead scoring model using PyCaret and increase the ROI on marketing campaigns.
Introduction
Leads are the driving force of many businesses today. With the advancement of subscription-based business models particularly in the start-up space, the ability to convert leads into paying customers is key to survival. In simple terms, a “lead” represents a potential customer interested in buying your product/service.
Normally when you acquire the lead, either through a third party service or by running a marketing campaign yourself, it typically includes information like:
- Name and contact details of the lead
- Lead attributes (demographic, social, customer preferences)
- Source of origin (Facebook Ad, Website landing page, third party, etc.)
- Time spent on the website…
A step-by-step guide on how to predict customer churn the right way using PyCaret that actually optimizes the business objective and improves ROI
Introduction
Customer retention is one of the primary KPI for companies with a subscription-based business model. Competition is tough particularly in the SaaS market where customers are free to choose from plenty of providers. One bad experience and customer may just move to the competitor resulting in customer churn.
What is Customer Churn?
Customer churn is the percentage of customers that stopped using your company’s product or service during a certain time frame. One of the ways to calculate a churn rate is to divide the number of customers lost during a given time interval by the number of active customers at the beginning of…
A step-by-step tutorial on how to convert ML models trained using PyCaret to ONNX for high-performance scoring (CPU or GPU)
Introduction
In this tutorial, I will show you how you can train machine learning models using PyCaret — an open-source, low-code machine learning library in Python—and convert them in ONNX format for deployment on an edge device or any other non-Python environment. For example, you can train machine learning models using PyCaret in Python and deploy them in R, Java, or C. The Learning Goals of this tutorial are:
👉 What is PyCaret and how to get started?
👉 What are different types of model formats (pickle, onnx, pmml, etc.)
👉 What is ONNX (pronounced as ONEX) and what are its…
A step-by-step tutorial on training and deploying machine learning models in Alteryx Designer using PyCaret
Introduction
In this tutorial, I will show you how you can train and deploy machine learning pipelines in a very popular ETL tool Alteryx using PyCaret — an open-source, low-code machine learning library in Python. The Learning Goals of this tutorial are:
👉 What is PyCaret and how to get started?
👉 What is Alteryx Designer and how to set it up?
👉 Train end-to-end machine learning pipeline in Alteryx Designer including data preparation such as missing value imputation, one-hot-encoding, scaling, transformations, etc.
👉 Deploy trained pipeline and generate inference during ETL.
PyCaret
PyCaret is an open-source, low-code machine learning library and…
How to get started with PyCaret for all your machine learning projects
PyCaret
PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. Its ease of use, simplicity, and ability to quickly and efficiently build and deploy end-to-end machine learning pipelines will amaze you.
PyCaret is an alternate low-code library that can replace hundreds of lines of code with few lines only. This makes the experiment cycle exponentially fast and efficient.
PyCaret is simple and easy to use. All the operations performed in PyCaret are sequentially stored in a Pipeline that…
MACHINE LEARNING | PYCARET | KNIME ANALYTICS PLATFORM
A step-by-step guide on training and scoring machine learning models in KNIME using PyCaret
PyCaret
PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. Its ease of use, simplicity, and ability to quickly and efficiently build and deploy end-to-end machine learning pipelines will amaze you.
PyCaret is an alternate low-code library that can replace hundreds of lines of code with few lines only. This makes the experiment cycle exponentially fast and efficient.
PyCaret is simple and easy to use. All the operations performed in PyCaret are sequentially stored in a Pipeline that is fully automated for deployment. Whether it’s imputing missing values, one-hot-encoding, transforming…
A beginner-friendly, step-by-step tutorial on integrating MLOps in your Machine Learning experiments using PyCaret
PyCaret
PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is known for its ease of use, simplicity, and ability to quickly and efficiently build and deploy end-to-end ML prototypes.
PyCaret is an alternate low-code library that can replace hundreds of code lines with few lines only. This makes the experiment cycle exponentially fast and efficient.
A step-by-step, beginner-friendly tutorial on how to write and train custom machine learning models in PyCaret
PyCaret
PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to quickly and efficiently build and deploy end-to-end ML prototypes.
PyCaret is an alternate low-code library that can replace hundreds of code lines with few lines only. This makes the experiment cycle exponentially fast and efficient.
PyCaret is simple and easy to use. All the operations performed in PyCaret are sequentially stored in a Pipeline that is fully automated for deployment. …
A step-by-step, beginner-friendly tutorial on how to build an end-to-end Machine Learning Pipeline with PyCaret and deploy it as an API.
👉 Introduction
This is a step-by-step, beginner-friendly tutorial on how to build an end-to-end Machine Learning Pipeline with PyCaret and deploy it in production as a web API using FastAPI.
Learning Goals of this Tutorial
- Build an end-to-end machine learning pipeline using PyCaret
- What is a deployment and why do we deploy machine learning models
- Develop an API using FastAPI to generate predictions on unseen data
- Use Python to send a request to API for generating predictions programmatically.
This tutorial will cover the entire machine learning life cycle at a high level which is broken down into the following sections:
Hands-on Tutorials
A step-by-step tutorial on unsupervised anomaly detection for time series data using PyCaret
👉 Introduction
This is a step-by-step, beginner-friendly tutorial on detecting anomalies in time series data using PyCaret’s Unsupervised Anomaly Detection Module.
Learning Goals of this Tutorial
- What is Anomaly Detection? Types of Anomaly Detection.
- Anomaly Detection use-case in business.
- Training and evaluating anomaly detection model using PyCaret.
- Label anomalies and analyze the results.
👉 PyCaret
PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently.
PyCaret is an alternate low-code library that can be used to replace…
Moez Ali
Data Scientist, Founder & Author of PyCaret
