Building a leading scoring model with logistic regression to help the Dunder Mifflin sales team. 📊

An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.

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.

X Education needs a machine learning model which will increase their lead conversion beyond 80%. To solve this problem logistic regression model is developed which predicts a lead conversions up to 90% accuracy.

Scoring Leads for an Ed-Tech company to enable higher leads conversion

Predicting lead conversion rates using a Logistic Regression model for an EdTech company.

Develop data products aimed at improving business results on a continuous and regular basis, using supervised and unsupervised machine learning methodology.

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.

X Education needs a machine learning model which will increase their lead conversion beyond 80%. To solve this problem logistic regression model is developed which predicts a lead conversions up to 90% accuracy.