Skip to content

Latest commit

 

History

History
20 lines (14 loc) · 1.39 KB

README.md

File metadata and controls

20 lines (14 loc) · 1.39 KB
Raw

Python Machine Learning & APIs Course

==================================================================================

Project Overview

This repository contains all the code and documentation for the Email Lead Scoring Project, an initiative to integrate advanced data science techniques into practical business solutions for email marketing. The project was completed as part of the Python for Machine Learning and APIs course by Business Science University and involves the development of predictive models for identifying potential leads who are likely to make purchases, and a user-friendly application for easy interaction with these models.

Objectives

  • To develop a machine learning model capable of scoring email subscribers based on their likelihood to make a purchase.
  • To conduct a comprehensive ROI analysis for optimizing lead targeting strategies.
  • To deploy a Streamlit application that enables easy access to lead scoring tools for decision-makers.
  • To integrate the models into business processes through API endpoints developed using FastAPI.

Technology Stack

  • Machine Learning & Data Analysis: PyCaret, MLflow
  • API Development: FastAPI
  • Front-End Application: Streamlit
  • Data Visualization and Reporting: Python (various libraries)