This project focuses on optimizing lead conversion strategies for X Education, an e-learning platform. By analyzing lead data and predicting conversion probabilities using machine learning, the project helps prioritize high-potential leads, automate low-priority engagements, and allocate resources efficiently. It is designed for sales and marketing teams to improve decision-making and maximize customer acquisition.
- PPT Presentation: Visual summary of the project approach, insights, and recommendations.
- Brief Summary Report: 500-word document outlining project objectives, methodology, and key learnings.
- Python Files: Source/ Data_cleaning, EDA, Model_building.
- Refer to the PPT for a quick overview of the project and its outcomes.
- Read the Summary Report for a detailed understanding of the approach and learnings.
- Review the Problem-Solution Document to see how each business question was addressed.
- Use the Python scripts to replicate the data preparation, analysis, and modeling processes.
- Data Cleaning and Feature Engineering.
- Exploratory Data Analysis (EDA) for actionable insights.
- Predictive Modeling with machine learning to prioritize leads.
- Resource allocation strategies based on lead scores and probabilities.