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Unsupervised learning is a type of machine learning where the algorithm is trained on data without explicit supervision. The system tries to learn the patterns and structure from the input data without labeled outputs. Here are 100 tips on working with unsupervised learning:
- Understand Unsupervised Learning: Gain a solid understanding of the principles and goals of unsupervised learning.
- Choose Appropriate Algorithms: Select unsupervised learning algorithms based on the nature of the data and the task at hand (e.g., clustering, dimensionality reduction).
- Data Exploration: Conduct thorough exploratory data analysis to understand the distribution and characteristics of the data.
- Handle Missing Data: Implement strategies to handle missing data appropriately before applying unsupervised algorithms.
- Normalize Data: Normalize or standardize the data to ensure that features are on a similar scale.
- Select Relevant Features: Identify and select features that are most relevant to the unsupervised learning task.
- Choose Distance Metrics: Understand the impact of different distance metrics on clustering algorithms and choose accordingly.
- Preprocess Categorical…
