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leadscoring

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Main aim of this case study is to build a 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. 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.

  • Updated Jul 23, 2020
  • Jupyter Notebook

Lead Scoring case study Build a 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. 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.

  • Updated Mar 20, 2021
  • Jupyter Notebook

A CRM built for The Forward Studio (theforwardstudio.co.uk) - My online presence business. It finds businesses that need a stronger digital presence, scores and tracks them through a pipeline from first contact to signed client, and logs payments — all in one tool. This is done by using the google geolocation and places API.

  • Updated Aug 5, 2026
  • Python

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