Welcome to this project where I use K-means clustering to unlock valuable insights
from marketing data to better understand this company's customers.
(Skills used: Python, Matplotlib, Pandas, Numpy, Scikit-learn)
Primary Goals
This project aims to uncover distinct customer segments and gain a deeper insight into this company’s target audience through both the use of visual exploratory data analysis and the K-means machine learning algorithm.
The Dataset
This dataset contains comprehensive marketing and customer data for a mock company. Some key attributes include year of birth, education level, marital status, income, and number of children among others. The dataset offers a rich foundation for exploring customer segmentation, behavior patterns, and demographic insights. It serves as an ideal resource for demonstrating advanced analytical techniques such as K-means clustering.
Jupyter Notebook
This Jupyter Notebook encompasses the entire workflow for the project, featuring all the visuals and code necessary for a comprehensive analysis. The primary sections include inspecting the data to understand its structure and contents, formatting and cleaning the data to ensure accuracy and consistency, visualizing and exploring the data to uncover initial patterns and insights, and finally, building and fine-tuning the K-means clustering model.
K-Means Clustering
K-means clustering is an unsupervised machine learning algorithm used to partition a dataset into distinct groups or clusters based on how similar the data is. By assigning each data point to one of the clusters, the model helps identify patterns and groupings within the dataset. In this particular application, K-means clustering can reveal distinct customer segments within the marketing data, enabling the company to tailor marketing strategies to different customer profiles and enhance overall customer engagement.
Takeaways
The project yielded valuable insights by identifying distinct customer segments through K-means clustering, which enables more targeted and effective marketing strategies. With the newly formed clusters, we can now ask very specific questions to better understand what differentiates each group, such as demographic factors, purchasing behavior, and lifestyle preferences. Ultimately, these insights can help narrow the scope of the broader goal building a successful marketing campaign