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Module 4 Final Project

Introduction

In this lesson, we'll review all the guidelines and specifications for the final project for Module 4.

Objectives

  • Understand all required aspects of the Final Project for Module 4
  • Understand all required deliverables
  • Understand what constitutes a successful project

Final Project Summary

Congratulations! You've made it through another intense module, and now you're ready to show off your newfound skills using Logistic Regression!

The Project

For this project, you're going to use Kaggle's "Telco Customer Churn" dataset and create a classification model. You'll start by identifying the dichotomous problem and then you'll use everything you've learned about Data Science and Logistic Regression thus far to preprocess and explore the data and then build and interpret a classification model that uses Logistic Regression to answer your chosen question.

REMINDER: This dataset has imbalanced classes and numerous categorical features. Please make sure to address these concerns in your presentations/notebooks.

Dataset: https://www.kaggle.com/blastchar/telco-customer-churn

The Deliverables

  1. A Jupyter Notebook containing any code you've written for this project. This work will need to be pushed to your GitHub repository in order to submit your project.

  2. A Blog Post.

  3. An "Executive Summary" Presentation that gives a brief overview of your problem/dataset, and each step of the OSEMN/CRISP-DM process.

  4. Level Up: An organized README.md file in the GitHub repository that describes the contents of the repository. This file should be the source of information for navigating through the repository.

Jupyter Notebook Must-Haves

For this project, your Jupyter Notebook should meet the following specifications:

Organization/Code Cleanliness

  • The notebook should be well organized, easy to follow, and code is commented where appropriate.
    • Level Up: The notebook contains well-formatted, professional looking markdown cells explaining any substantial code. All functions have docstrings that act as professional-quality documentation.
  • The notebook is written to technical audiences with a way to both understand your approach and reproduce your results. The target audience for this deliverable is other data scientists looking to validate your findings.

Process, Methodology, and Findings

  • Your notebook should contain a clear record of your process and methodology for exploring and preprocessing your data, building and tuning a model, and interpreting your results.
  • We recommend you use the OSEMN process to help organize your thoughts and stay on track.

Blog Post Must-Haves

Refer back to the Blogging Guidelines for the technical requirements and blog ideas.