Customer Segmentation in Marketing with Python Project

Exploring K-Means and Hierarchical Clustering for Effective Marketing Strategies advanced

With Elitsa Kaloyanova

Type: Practice project

Duration: 5 Hours

Case Description

To what extent does our platform’s acquisition channel influence the learning outcomes of our students?
Are there any geographical locations where most of our students discover the platform, specifically through social media platforms like YouTube or Facebook?

You will work on real-world customer data to perform market segmentation—crucial for businesses to understand customer behavior and improve marketing efficiency. The project will involve data preprocessing, exploratory data analysis (EDA), feature engineering, implementation of clustering algorithms, and interpretation of results. You’ll use two popular clustering techniques: k-means and hierarchical clustering.

In the Customer Segmentation in Marketing with Python project, you’ll delve into the diversity of customer behavior and identify distinct segments that could be targeted with personalized marketing strategies.

Project requirements

For this Customer Segmentation in Marketing with Python project, you’ll need Python v.3 and Jupyter Notebook installed. You’ll need to have the following Python libraries installed:

  • pandas 
  • NumPy 
  • Matplotlib 
  • seaborn (optional)
  • scipy
  • sklearn

Project files

You’ll work with data from an onboarding survey of customers, containing information about where they have heard about the platform.
The customer_segmentation_data.csv file contains information about the country of residence, customer lifetime value, and overall engagement.
The Segmentation data legend.xlsx contains additional information about the segmentation data variables. 

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Project content
  • 2 Project files
  • Guided and unguided instructions
  • Part 1: Exploratory Data Analysis
  • Part 2: Model Implementation
  • Part 3: Model Evaluation and Results
  • Part 4: Data Interpretation
  • Quiz
Topics covered
Data Analysis Unsupervised Learning Programming Machine learning