To build a system that categorizes leads based on the likelihood of their purchasing CodePro’s course. This system will help remove the inefficiency caused by junk leads in the sales process. CodePro is an EdTech startup that had a phenomenal seed A funding round. It used the money to increase its brand awareness. As the marketing spend increased, it got several leads from different sources. Although it had spent significant money on acquiring customers, it had to be profitable in the long run to sustain the business. The main objectives of lead scoring are as follows: Remove Junk by categorising leads on the basis of propensity to purchase Gain insights to streamline lead conversion and address improper targeting We have chosen L2AC (Leads to Application Completion) as our business metric, as choosing L2P (Leads to Payment) will aggressively drop the leads. A lead is generated when any person visits CodePro’s website and enters their contact details on the platform. A junk lead is generated when a person who shares their contact details has no interest in the product/service. Having junk leads in the pipeline creates significant inefficiency in the sales process. we are creating three different pipelines for our use case. Data Pipeline Training Pipeline Inference Pipeline Our system metrics for the ML model are : AUC score >75% Precision > 65% Recall > 75% Tools used- Airflow,Mlflow,Pycaret,Pandas profiling