Analyzing a dataset with millions of rows requires a systematic approach to handle the data’s volume and complexity effectively. Start by understanding the dataset’s structure and defining your analysis objectives. Begin with data preprocessing: clean the data by handling missing values, outliers, and errors, and normalize it to ensure consistency. For initial exploration, consider using a representative sample to speed up processing.

Next, perform Exploratory Data Analysis (EDA) by creating visualizations and calculating descriptive statistics to identify patterns, trends, and anomalies. Proceed with feature engineering by selecting relevant features and transforming them to enhance model performance.

To handle large data efficiently, process it in chunks to avoid memory overload and utilize parallel processing frameworks like Dask or Apache Spark. When it comes to modeling, choose scalable algorithms suitable for large datasets, such as decision trees or gradient boosting. Train models on a subset of the data, evaluate performance, and then scale up to the full dataset. Use cross-validation and hold-out test sets to ensure robust model evaluation.

Optimize model performance through hyperparameter tuning and leverage cloud services for distributed computing. Finally, interpret the results, translating them into actionable insights, and communicate findings through clear reports and visualizations tailored to your audience.