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: Optimizing SaaS Marketing-Sales Funnel by Extracting the Best Leads with Applied Machine Learning

An end-to-end enterprise-grade example of working a data science problem.

Typescript library to access Faraday's API infrastructure for B2C predictions

Building a end-to-end lead scoring machine learning example with Jupyter, Sagemaker, MLflow, and Booklet.ai.

Trained a model that estimates if a lead is likely to be converted based on lead behavior in historical customer data using ML.

Lead Scoring Case Study using Logistic Regression

A Logistic Regression project

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.

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.

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

Airflow Pipeline for Lead Scoring to Maximize Profit with retraining pipeline and Development experimentation using mlflow

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.

We perform batch inference on lead scoring task using Pyspark.

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

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