Data Mining Techniques

1. Classification:
   • Assigns items to predefined categories or classes. Common algorithms include decision trees, support vector machines, and neural networks.
2. Clustering:
   • Groups similar items together based on their features. K-means and hierarchical clustering are popular methods.
3. Association Rule Learning:
   • Finds relationships between variables in large datasets. A common example is market basket analysis, which identifies items frequently bought together.
4. Regression:
   • Predicts a continuous value based on input features. Linear regression and logistic regression are commonly used techniques.
5. Anomaly Detection:
   • Identifies outliers or unusual data points that do not fit the expected pattern. Techniques include statistical methods and machine learning algorithms.
6. Dimensionality Reduction:
   • Reduces the number of variables under consideration. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are examples.

ETL Process

1. Extract:
   • Data is collected from various sources, such as databases, files, and APIs. This step gathers the raw data needed for analysis.
2. Transform:
   • The extracted data is cleaned and transformed into a suitable format. This involves removing duplicates, handling missing values, and converting data types. It may also include aggregating data or creating new features.
3. Load:
   • The transformed data is loaded into a data warehouse or database. This step makes the data available for analysis and reporting.

Together, these techniques and processes enable effective data analysis, helping to uncover patterns, relationships, and insights from large datasets.