TASK 2 of Machine Learning Internship Internship at Prodigy Infotech: Customer Segmentation using K-Means Clustering
Customer segmentation is the process of grouping a company's customers using shared characteristics so that the company can cater to each group effectively and appropriately. Machine learning models can process customer data and discover patterns difficult to spot through intuition and manual examination of data.
For this task, developed a K-means clustering algorithm to group customers of a retail store based on their purchase history. Leveraging Python and essential libraries like pandas, scikit-learn, and matplotlib, engineered a robust solution to segment customers and gain insights into their shopping behavior.
Here's a detailed overview of the accomplishment:
• Data Preparation: Ingested customer data from a CSV file containing features such as age, annual income, spending score etc. This ensured seamless integration for analysis and modeling.
• Data Preprocessing: Performed data preprocessing tasks to standardize the features, making them suitable for clustering analysis.
•Clustering Algorithm Implementation: Developed a K-means clustering algorithm, utilizing the scikit-learn library to train the model and predict cluster labels for each customer.
• Data Visualization: Visualized the clustering results using plots, showcasing the distinct clusters formed based on customers' annual income and spending scores.
• Insights Generation: By assigning cluster labels to customers, gained valuable insights into customer segmentation, enabling targeted marketing strategies and personalized customer experience.