Lead-Scoring-Case-Study

Lead-Scoring-Case-Study using Logistic Regression in Python

Designed and Developed by

• Project Lead: Vignesh Kumar
• Contributors:
  • Vinod Yadav
  • Ujjwal Verma

Problem Statement

A company named X Education sells online courses to industry professionals. Although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone.

Expectations

1. To Build a logistic regression model to predict whether a lead gets converted or not.
2. Address some more problems presented by the company provided in a separate doc file.

About the Dataset

• The dataset consists of 9000 data points. with various attributes.
• The target variable, is the column ‘Converted’ - wherein 1 means it was converted and 0 means it wasn’t converted.
• Details of the features are provided in the data dictionary.
• Note: Some categorical variables has a level called 'Select' as good as a null value.