data-analyst

Exploratory data analysis with data quality checks identifies patterns and data issues using descriptive statistics and charting. It validates missing values, outliers, data types, and scaling before modeling to ensure dataset reliability.

The CRISP-DM workflow structures machine learning projects through business understanding, data understanding, preparation, modeling, evaluation, and deployment. It defines goals and success criteria upfront, then executes a full lifecycle to turn raw datasets into validated outcomes.

Choosing statistical and ML methods requires matching techniques to your inference or prediction goals. The process compares algorithms, selects appropriate statistical models, and validates results using metrics with cross-validation design to ensure measurable outcomes.

Yes, customer churn analysis uses this structured analytics workflow to identify key drivers, quantify impact, and propose next actions. It applies statistical modeling and ML evaluation to churn data to produce decision-ready insights based on measurable outcomes.

Evaluating machine learning models with cross-validation requires metric-based validation design. It compares algorithm performance using appropriate evaluation metrics, validates statistical soundness, and produces an executive summary with recommended actions for deployment guidance.

No, cleaned data is not required beforehand. The data analysis workflow includes data preparation steps that handle missing values, outliers, data types, and scaling as part of the CRISP-DM lifecycle before applying statistical and ML methods.