ExtraaLearn Lead Conversion Prediction

Developed a machine learning model to predict lead conversion for ExtraaLearn, identifying key factors that drive customer acquisition in the EdTech industry.

Project Overview

This machine learning project focuses on predicting lead conversion for ExtraaLearn, an EdTech startup offering cutting-edge technology programs. As the data scientist, I developed a model to identify high-potential leads and uncover key factors driving conversions.

Objectives

  • Build an ML model to predict lead conversion likelihood
  • Identify crucial factors in the conversion process
  • Create profiles of leads with high conversion potential

Methodology

  • Data Exploration: Analyzed lead attributes and interaction data
  • Feature Engineering: Created relevant features to enhance model performance
  • Model Development: Implemented and compared multiple ML algorithms
  • Model Evaluation: Used metrics like accuracy, precision, and recall
  • Feature Importance: Identified top predictors of lead conversion

Technologies Used

  • Python (Pandas, Scikit-learn, Matplotlib)
  • Jupyter Notebooks
  • Machine Learning: Random Forest, Gradient Boosting

View Project on GitHub

Explore the full analysis, including code, visualizations, and detailed findings, in the GitHub repository.