Data mining is an important process in data analysis that involves discovering useful patterns, relationships, trends, and knowledge from large volumes of data. In modern organizations, huge quantities of data are generated through business transactions, websites, social media, sensors, financial systems, educational institutions, healthcare records, and other digital activities. Merely storing such data is not sufficient. The major challenge is to transform raw data into meaningful information that can support analysis, decision-making, prediction, and planning. Data mining provides systematic techniques for extracting valuable knowledge from large and complex datasets. Data mining can be understood as the process of examining large datasets using statistical, mathematical, and computational methods to identify previously unknown and potentially useful patterns. It is closely associated with data analysis, database systems, statistics, machine learning, and artificial intelligence. The objective is not simply to retrieve individual records but to discover meaningful structures and relationships hidden within the data. For example, a retail organization may analyze transaction data to determine which products are frequently purchased together. A bank may examine customer information to identify unusual transactions or customers likely to default on loans. Similarly, educational institutions can analyze student data to identify factors associated with academic performance. The data mining process generally begins with the collection of relevant data from different sources. Data may come from databases, data warehouses, websites, surveys, transaction systems, sensors, or external sources. Since raw data may contain errors, missing values, duplicate records, and inconsistent formats, it must be prepared before analysis. Data cleaning is therefore an important stage of data mining. Missing values may be treated, incorrect observations may be corrected, duplicate records may be removed, and inconsistent data may be standardized. Data integration is another important step when information is obtained from multiple sources. Different databases may use different structures, names, formats, or coding systems. Integration brings these sources together into a consistent dataset. After integration, data selection is performed to identify the variables and records that are relevant to the particular analytical objective. Data transformation may then be used to convert the selected information into an appropriate form for mining. These stages are important because the quality of the output depends greatly on the quality and suitability of the input data. Several techniques are used in data mining. Classification is one of the major techniques. It involves assigning data objects to predefined categories or classes. For example, a bank can classify loan applicants into low-risk and high-risk groups based on characteristics such as income, credit history, employment, and previous repayment behavior. Classification methods include decision trees, logistic regression, support vector machines, and other machine-learning approaches. Clustering is another important data mining technique. Unlike classification, clustering does not require predefined categories. It groups objects that have similar characteristics. For example, a company may use clustering to divide customers into groups according to purchasing habits, age, income, or preferences. These customer groups can then be analyzed separately for marketing