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.

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

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

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.

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

A Logistic Regression project

Lead Scoring Case Study using Logistic Regression

The second iteration of Cuana, an E2E customer analytics solution for churn/CLV prediction, segmentation & lead scoring

An Automated Lead Management and CRM do star It If you like It.

Predictive lead scoring for corporate loan data

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

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.

App de Lead Scoring con Inteligencia Artificial para analizar mensajes y detectar intención de compra. Hecha con Python, Streamlit y OpenAI.

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.

🤖 AI-powered lead qualification assistant that identifies high-quality B2B prospects through intelligent 3-step conversational flows. Features real-time lead scoring, Make.com webhook integration, and a beautiful React/TypeScript interface with progress tracking and automated lead processing.

Comprehensive marketing automation workflows covering modern automation strategies, lead nurturing, and customer journey optimization methodologies. Scale marketing efficiency through intelligent automation.

We perform batch inference on lead scoring task using Pyspark.