Introduction
Unsupervised learning is a type of machine learning where data is not labeled. The goal is to discover patterns and insights from the data without any guidance.
Steps Involved
- Data Collection: Gather data that is relevant to your problem.
- Data Exploration: Understand the data by analyzing its distribution and identifying patterns.
- Feature Extraction: Select the most important features from the data for analysis.
- Model Selection: Choose an unsupervised learning algorithm that fits your data and objectives.
- Model Training: Apply the algorithm to the data to generate a model.
- Model Evaluation: Assess the performance of the model and make adjustments if necessary.
- Interpretation: Extract insights from the model and communicate them to stakeholders.
Key Concepts
- Unsupervised Learning: Machine learning without labeled data.
- Clustering: Grouping data points into similar clusters based on their attributes.
- Association: Discovering relationships between items in the data.
Common Algorithms
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Apriori Algorithm
Data Science Example
Scenario: Identify customer segments from customer data.
Unsupervised Learning Concepts:
- Clustering: Group customers into clusters based on their demographics, purchase history, etc.
- Association: Discover relationships between products purchased or services used.
Steps:
- Collect customer data including age, gender, location, purchase history, and website interactions.
- Explore the data to identify patterns and relationships.
- Extract features that are relevant to identifying customer segments (e.g., age, purchase frequency, average order value).
- Use a clustering algorithm (e.g., K-Means) to group customers into distinct segments.
- Interpret the clusters to understand the characteristics of each segment and target them with tailored marketing campaigns.