define data mining problem this could be defined as the pre-analysis phasewhich starts once the success criteria and the business problem is defined and all the risksassumptions and constraints have been documented this phase involves having detailed technical discussions with your analystsdata scientistsand developers and keeping the business stakeholders in sync the following are the key tasks that are to be undertaken in this phase discuss and document possible machine learning and data mining methods suitable for the solution by assessing possible toolsalgorithmsand techniques develop high-level designs for end-to-end solution architecture record notes on what the end output from the solution will be and how will it integrate with existing business components record success evaluation criteria from data science standpoint simple example could be making sure that predictions are at least accurate project plan this is the final stage under the business understanding phase project plan is generally created consisting of the entire major six phases in the crisp-dm modelestimated timelinesallocated resources and personneland possible risks and contingency plans care is taken to ensure concrete high-level deliverables and success criteria are defined for each phase and iterative phases like modeling are highlighted with annotations like feedback based on smes might need models to be rebuilt and retuned before deployment you should be ready for the next step once you have the following points covered definition of business objectives for the problem success criteria for business and data mining efforts budget allocation and resource planning clearwell-defined machine learning and data mining methodologies to be followedincluding high-level workflows from exploration to deployment detailed project plan with all six phases of the crisp-dm model defined with estimated timelines and risks data understanding the second phase in the crisp-dm process involves taking deep dive into the data available and understanding it in further detail before starting the process of analysis this involves collecting the datadescribing the various attributesperforming some exploratory analysis of the dataand keeping tabs on data quality this phase should not be neglected because bad data or insufficient knowledge about available data can have cascading adverse effects in the later stages in this process data collection this task is undertaken to extractcurateand collect all the necessary data needed for your business objective usually this involves making use of the organizations historical data warehousesdata martsdata lakes and so on an assessment is done based on the existing data available in the organization and if there is any need for additional data this can be obtained from the webi open data sources or it can be obtained from other channels like surveyspurchasesexperiments and simulations detailed documents should keep