Segment credit card customers using K-Means in Python [Machine Learning Tutorial]

In this video, we guide you through a real-world data science project using unsupervised machine learning techniques to identify distinct customer groups. You'll step into the shoes of a data scientist at a credit card company, working with real customer data to drive strategic business decisions. What You’ll Learn in This Video: ✔️ How to clean, prepare, and scale data for unsupervised learning ✔️ Techniques for feature engineering and data standardization ✔️ How to determine the optimal number of clusters using the elbow method ✔️ How to implement K-Means clustering with scikit-learn ✔️ Tips to analyze and interpret clusters for real-world business impact Whether you’re aiming to sharpen your machine learning skills or apply data science techniques in the financial industry, this project walkthrough is the perfect opportunity to get hands-on practice with: - Python - pandas - NumPy - Matplotlib & Seaborn - K-Means clustering - Customer segmentation strategy Recommended Prerequisites: - Python Basics for Data Analysis → https://www.dataquest.io/path/python-basics-for-data-analysis/ - Machine Learning in Python → https://www.dataquest.io/path/machine-learning-in-python/ Access the Project: https://www.dataquest.io/projects/guided-project-a-credit-card-customer-segmentation/ Video chapters: Project Brief: 7:04 Load the data: 8:40 Exploratory data analysis (EDA): 11:40 Feature engineering: 23:00 Building the model: 30:20 Analyzing the clusters: 39:40 Q&A: 49:55 #MachineLearning #pythonprojects #kmeans #Python #DataScience #unsupervisedlearning