The Customer Segmentation project focuses on the segmentation of customers based on their purchase history and behavioral patterns. Companies employing customer segmentation operate under the fact that every customer is different and that their marketing efforts would be better served if they target specific, smaller groups with messages that those consumers would find relevant and lead them to buy something. By harnessing the power of machine learning, this project aims to provide valuable insights for businesses in understanding and catering to their customer segments.
For this project, I utilized an extensive e-commerce transaction dataset that includes detailed customer purchase history data from Kaggle with diverse attributes such as age, gender, interests, and spending habits. This dataset forms the core of the customer segmentation analysis, allowing us to uncover meaningful patterns and segments.
Executed diverse machine learning models, such as Logistic Regression, K-Nearest Neighbors Classifier, and Decision Tree Classifier, to analyze customer behavior and preferences. Augmented insights with a deep learning model for a more nuanced understanding of user segments. Identified and preserved the top-performing model, ensuring optimal accuracy and segmentation efficiency. Improved user engagement through a Streamlit-based, user-friendly interface, facilitating seamless input of customer features for instant insights into distinct customer segments.
To explore this project and its codebase, follow these steps:
- Clone this repository to your local machine.
- Review the project code and documentation to gain insights into the k-means clustering techniques used and AWS integration.
- Feel free to reach out if you have any questions or are interested in collaborating further.
Customer segmentation is a crucial aspect of business strategy, enabling targeted marketing, product recommendations, and personalized experiences. The Customer Segmentation project underscores the power of data-driven insights to drive business growth and customer satisfaction.
Thank you for your interest in this project. I believe that effective customer segmentation can lead to more impactful customer interactions and ultimately, business success.