.. _sphx_tag_topic-data-preparation:

My tags: topic: data preparation
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.. toctree::
    :maxdepth: 1
    :caption: With this tag

    ../learn/data_preparation_and_analysis/01-why-do-we-analyze-data.rst
    ../learn/data_preparation_and_analysis/02-the-process-of-data-analysis.rst
    ../learn/data_preparation_and_analysis/03-crisp-dm-for-data-science.rst
    ../learn/data_preparation_and_analysis/04-big-data-definition-characteristics-evolution-and-business-impact.rst
    ../learn/data_preparation_and_analysis/05-the-first-step-in-knowing-your-data.rst
    ../learn/data_preparation_and_analysis/06-ieee-754-floating-point-standard.rst
    ../learn/data_preparation_and_analysis/07-discovering-associations-through-data-from-everyday-patterns-to-chicago-taxi-trips-september-2022.rst
    ../learn/data_preparation_and_analysis/08-taxi-trips-2022-dataset-from-the-city-of-chicago-open-data-portal.rst
    ../learn/data_preparation_and_analysis/09-objective-selection-of-the-bin-width-for-a-time-histogram.rst
    ../learn/data_preparation_and_analysis/10-measuring-associations-in-data.rst
    ../learn/data_preparation_and_analysis/11-measuring-associations-between-two-continuous-variables.rst
    ../learn/data_preparation_and_analysis/12-correlation-coefficients-in-python-pearson-spearman-kendall.rst
    ../learn/data_preparation_and_analysis/13-karl-pearson.rst
    ../learn/data_preparation_and_analysis/14-harald-cramer.rst
    ../learn/data_preparation_and_analysis/15-what-are-statistical-tests.rst
    ../learn/data_preparation_and_analysis/16-eta-squared-2-effect-size-in-anova.rst
    ../learn/data_preparation_and_analysis/17-understanding-market-baskets-and-ideal-customers.rst
    ../learn/data_preparation_and_analysis/18-what-can-association-rules-tell-us.rst
    ../learn/data_preparation_and_analysis/19-how-association-rules-are-discovered-concepts-scale-measures-and-the-apriori-approach.rst
    ../learn/data_preparation_and_analysis/20-apriori-frequent-itemsets-via-the-apriori-algorithm.rst
    ../learn/data_preparation_and_analysis/21-association-rules-generating-association-rules-from-frequent-itemsets-mlxtend.rst
    ../learn/data_preparation_and_analysis/22-cross-selling.rst
    ../learn/data_preparation_and_analysis/23-stratified-random-sampling.rst
    ../learn/data_preparation_and_analysis/24-linear-congruential-random-number-generator-lcg.rst
    ../learn/data_preparation_and_analysis/25-partitioning-observations-to-train-objective-models.rst
    ../learn/data_preparation_and_analysis/26-putting-similar-observations-into-clusters.rst
    ../learn/data_preparation_and_analysis/27-clustering.rst
    ../learn/data_preparation_and_analysis/28-recency-frequency-and-monetary-value-rfm.rst
    ../learn/data_preparation_and_analysis/29-rfm-analysis.rst
    ../learn/data_preparation_and_analysis/30-creating-segments-of-observations-for-business-reasons-rfm.rst
    ../learn/data_preparation_and_analysis/31-least-squares-regression.rst
    ../learn/data_preparation_and_analysis/32-multiple-linear-regression.rst
    ../learn/data_preparation_and_analysis/33-feature-importance-in-linear-regression.rst
    ../learn/data_preparation_and_analysis/34-forward-selection-definition-and-core-idea.rst
    ../learn/data_preparation_and_analysis/35-forward-selection-and-model-interpretation-in-linear-regression.rst
    ../learn/data_preparation_and_analysis/36-understanding-forward-and-backward-stepwise-regression.rst
    ../learn/data_preparation_and_analysis/37-how-shapley-values-work.rst
    ../learn/data_preparation_and_analysis/38-logistic-regression-modeling-binary-outcomes-via-odds-and-log-odds.rst
    ../learn/data_preparation_and_analysis/39-maximum-likelihood-mle-fitting-a-distribution-to-observed-data.rst
    ../learn/data_preparation_and_analysis/40-assessing-model-fit-in-logistic-regression.rst
    ../learn/data_preparation_and_analysis/41-complete-and-quasi-complete-separation-in-logistic-regression.rst
    ../learn/data_preparation_and_analysis/42-forward-selection-with-nested-models-and-deviance-tests.rst
    ../learn/data_preparation_and_analysis/43-interpreting-and-assessing-a-forward-selection-logistic-regression-model-for-college-student-retention.rst
    ../learn/data_preparation_and_analysis/44-motivation-of-decision-trees-an-incremental-model-of-decision-making.rst
    ../learn/data_preparation_and_analysis/45-the-cart-algorithm.rst
    ../learn/data_preparation_and_analysis/46-decision-trees-as-piecewise-models-and-their-predictive-structure.rst
    ../learn/data_preparation_and_analysis/47-how-cart-decision-trees-model-interactions.rst
    ../learn/data_preparation_and_analysis/48-cluster-profiling-using-decision-trees.rst
    ../learn/data_preparation_and_analysis/49-using-decision-trees-to-explain-clustering-results.rst
    ../learn/data_preparation_and_analysis/50-assessing-the-quality-of-prediction-models.rst
    ../learn/data_preparation_and_analysis/51-binary-classification-models-conceptual-framework-and-evaluation-metrics.rst
    ../learn/data_preparation_and_analysis/52-nominal-classification-models-model-state-and-evaluation-metrics.rst
    ../learn/data_preparation_and_analysis/53-binary-classification-model-evaluation-and-threshold-optimization.rst
    ../learn/data_preparation_and_analysis/54-identifying-outliers-using-residuals-and-studentized-residuals.rst
    ../learn/data_preparation_and_analysis/55-auc-roc-curve-evaluating-classification-model-performance.rst
    ../learn/data_preparation_and_analysis/56-lift-analysis-for-direct-mail-campaigns-concept-process-and-business-value.rst
    ../learn/data_preparation_and_analysis/index.rst