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Improving the Lead Conversion Rate for Online Course Selection Using Binary Classification Approach

Venkatesh R. Pai, Malini M. Patil


The paper discusses the binary classification problem using a unique data set on online education. ‘Lead Scoring X Online Education’ data set is chosen from Kaggle repository. A lead is a user who is likely to select a course, identified by the company named X based on their analysis of the user activity on the website. The percentage of the registered users who end up selecting a course is referred to as the lead conversion rate. The current lead conversion rate of the company is low. To address this issue, a model is formed that studies the activities of the users who previously ended up selecting a course and identifies the potential leads by predicting whether a new user selects a course or not. This helps the company to target only the potential leads rather than communicating with all the registered users. The methodology is proposed to show that the lead conversion rate is improved using logistic regression, SVM, and Random Forest classification algorithms. From the experiments, it is found that the performance of all the algorithms is quite accurate.


Data Preprocessing, Data Mining, Supervised Learning, Binary Classification.

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