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The objective of this assessment is to build data science models that yield valuable insights

Home Page: https://marketing-campaign-app.streamlit.app/

Python 49.61% Cython 2.01% C 0.61% JavaScript 13.68% Shell 0.01% HTML 31.96% Jinja 0.03% Vue 0.01% CSS 0.23% C++ 1.77% Batchfile 0.01% XSLT 0.05% Roff 0.01% Jupyter Notebook 0.01% Lua 0.01% Makefile 0.01% Fortran 0.02% Forth 0.01% Smarty 0.01% CMake 0.01%
classification-model marketing streamlit-webapp

marketing-campaign-streamlit's Introduction

Classification models for marketing campaigns

Overview:

A telecommunication company recently launched a marketing campaign to promote the adoption of their new subscription plan among customers. The company seeks assistance in gaining a comprehensive understanding of its customers and identifying the customer segments that display the highest responsiveness to marketing campaigns. The response variable, subscribed, indicates whether the client subscribed to a new plan, which was the objective of the campaign.

Repository Structure:

  • csv/: Contains the dataset files.
  • app/: Contains the main Streamlit app.
  • REPORT: Contains the Exploratory Data Analysis (EDA) report, the final report for the project in Word format and Jupyter notebooks compressed in RAR format.
  • README.md: Overview of the project (this file).
  • Remaining folders for supporting functions in the main Streamlit app.

Instructions:

  1. Ensure you have Python and Jupyter Notebook installed on your system.
  2. Clone this repository to your local machine.

For more information, please refer to the web app presentation of the project using the following link:

Marketing Campaign Web App

Acknowledgments

This project is based on the dataset consolidated from census data in the USA and its documentation provided for educational purposes. (Dataset is from the Master of Data Science and Innovation course of University of Technology Sydney, and it is the asset of TD School)

marketing-campaign-streamlit's People

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