https://github.com/mariam-zaidi/lead_scoring_case_study

Leads Scoring_Logistic regression model
https://github.com/mariam-zaidi/lead_scoring_case_study

logistic-regression matplotlib numpy pandas seaborn sklearn

Last synced: 6 days ago
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Leads Scoring_Logistic regression model

• Host: GitHub
• URL: https://github.com/mariam-zaidi/lead_scoring_case_study
• Owner: Mariam-Zaidi
• Created: 2023-01-03T15:22:40.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-09-16T03:17:29.000Z (2 months ago)
• Last Synced: 2024-09-16T04:43:17.132Z (2 months ago)
• Topics: logistic-regression, matplotlib, numpy, pandas, seaborn, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 2.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Lead_Scoring_Case_study

Build a logistic regression model to assign a lead score that can be used by an education company to target potential leads and to assign a lead score between 0 and 100 to each of the leads that the company can use to target potential leads. A higher score would mean that the lead is hot, i.e. is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted.

The Business aim is to identify and acquire these potential leads called “Hot Leads”. The typical lead conversion rate at X Education is around 30%. If we help them successfully identify these Hot Leads then their lead conversion rate should go upto 80%, as their sales team will be able to focus more on communicating with Hot Leads.