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.

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.

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

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

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

A machine learning tool that predicts the conversion probability of customer leads using Logistic Regression. Helps businesses prioritize high-value prospects.

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.

Predictive lead scoring for corporate loan data

B2B sales lead quality prediction using XGBoost classifier. Achieves 81.06% ROC AUC and 84.74% recall on 7,420 IT sales leads. Handles class imbalance, high-cardinality categoricals, and missing data through frequency encoding and threshold optimization. Includes statistical analysis, cross-validation, feature importance, and business insights.

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

Comprehensive customer acquisition systems covering modern lead generation, conversion optimization, and customer onboarding methodologies. Build sustainable customer acquisition processes.

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.