We perform batch inference on lead scoring task using Pyspark.
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Updated
Jul 11, 2021 - Jupyter Notebook
We perform batch inference on lead scoring task using Pyspark.
Lead Scoring is such a powerful metric when it comes to quantifying the lead & it is nowadays used by every CRM. In this repository, we are going to take a look at the UpGrad lead scoring case study and see how can we solve this problem through several supervised machine learning models.
Lead Scoring Analysis and Segmentation. A lead scoring analysis is conducted for an online teaching company with a low client conversion rate. The goals are to reverse this trend by using a machine learning model based on available company data and to categorize customers with an effective segmentation.
Lead scoring is a pivotal metric for assessing leads and has become a standard in contemporary CRM systems. Within this repository, we delve into how the lead scoring strategy helps solve customer conversion problem, exploring the application of various supervised machine learning models
Lead-Scoring-Case-Study
Predict the lead score for who is most likely to convert into a paying customer.
Fixed few things of https://github.com/PredictionIO/template-scala-parallel-leadscoring so you can run locally
Request a quote is designed for small business owners to receive inquiry or quote requests from customers.
Logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads
Lead scoring is an effective lead prioritization method used to rank prospects based on the likelihood of converting them to customers. This repository aimed to develop an automatic lead scoring through logistic regression technique. Stepwise selection approach is used to identify and select important variables for the model.
Predictive lead scoring for corporate loan data
X Education has appointed you to help them select the most promising leads, i.e. the leads that are most likely to convert into paying customers.
Portfolio project: Machine learning automation project for online educational company. Lead scoring and segmentation models.
Airflow Pipeline for Lead Scoring to Maximize Profit with retraining pipeline and Development experimentation using mlflow
A Logistic Regression project
Lead Scoring Case Study using Logistic Regression
Trained a model that estimates if a lead is likely to be converted based on lead behavior in historical customer data using ML.
In this project, I leverage machine learning models including Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and LightGBM to predict customer lead scoring. I apply WOE and SHAP for feature selection and use Optuna for hyperparameter turning, aiming to identify potential lead customers effectively.
Building a end-to-end lead scoring machine learning example with Jupyter, Sagemaker, MLflow, and Booklet.ai.
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