Project Overview
This project aims to analyze customer spending trends using K-Means clustering, a popular unsupervised machine learning algorithm. By clustering customers based on their annual income and spending score, the goal is to gain insights into different customer segments to inform marketing strategies and business decisions.
Data Source
The dataset used in this project is "Mall_Customers.csv", which contains information about customers' annual income and spending score.
Requirements
Python 3.x Libraries: Matplotlib Pandas Scikit-learn
How to Run Clone or download the project repository. Ensure you have Python installed on your system. Install the required libraries using pip: pip install matplotlib pandas scikit-learn Open a terminal or command prompt. Navigate to the project directory. Run the script "SpendTrend_Analytics_KMeans_Clustering.py" using Python: python SpendTrend_Analytics_KMeans_Clustering.py
Methodology The script follows these main steps:
Load the dataset using Pandas. Prepare the data by selecting relevant features (annual income and spending score). Determine the optimal number of clusters using the Elbow Method. Perform K-Means clustering with the optimal number of clusters. Visualize the clusters and centroids to gain insights into customer segments.
Output
The script generates a plot showing clusters of customers based on their annual income and spending score. Each cluster is represented by a different color, with centroids marked in black.
Contact For any questions or suggestions, please contact: Name: Kushagra Taneja Email: [email protected]