GitHub / shaiasi / Lead_Scoring_Logistic_Regression-Project
This case study involves helping X Education, an education company, improve its lead conversion rate by building a logistic regression model to assign lead scores. The aim is to identify potential leads with the highest chances of converting to paying customers and handling future problems to achieve a target conversion rate of 80%.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shaiasi%2FLead_Scoring_Logistic_Regression-Project
PURL: pkg:github/shaiasi/Lead_Scoring_Logistic_Regression-Project
Stars: 0
Forks: 0
Open issues: 0
License: None
Language: Jupyter Notebook
Size: 3.49 MB
Dependencies parsed at: Pending
Created at: about 2 years ago
Updated at: about 2 years ago
Pushed at: about 2 years ago
Last synced at: about 2 years ago
Topics: data-analysis, data-science, lead-generation, lead-scoring-case-study, machine-learning-algorithms, outlier-detection