1. Data Preparation: Data preparation is the first step in the data mining process and involves cleansing, transforming, and formatting data into a format that is suitable for mining.
2. Data Exploration: Data exploration is the process of searching through data to uncover patterns and relationships. This can include looking for trends, correlations, patterns, or outliers in the data.
3. Data Modeling: Data modeling is the process of building and testing models based on the data. This can include tasks such as regression analysis, cluster analysis, or decision tree analysis.
4. Data Evaluation: Data evaluation is the process of evaluating the accuracy and usefulness of the models created. Different techniques such as cross-validation or A/B testing can be used to evaluate the models.
5. Deployment: The final stage of data mining is deployment. This includes deploying the models into production and making them available to the users. It also includes monitoring the models and making changes or adjustments as needed.
2. Data Exploration: Data exploration is the process of searching through data to uncover patterns and relationships. This can include looking for trends, correlations, patterns, or outliers in the data.
3. Data Modeling: Data modeling is the process of building and testing models based on the data. This can include tasks such as regression analysis, cluster analysis, or decision tree analysis.
4. Data Evaluation: Data evaluation is the process of evaluating the accuracy and usefulness of the models created. Different techniques such as cross-validation or A/B testing can be used to evaluate the models.
5. Deployment: The final stage of data mining is deployment. This includes deploying the models into production and making them available to the users. It also includes monitoring the models and making changes or adjustments as needed.