My tags: topic: data preparation

With this tag

• Why Do We Analyze Data?
• The Process of Data Analysis
• CRISP-DM for Data Science
• Big Data: Definition, Characteristics, Evolution, and Business Impact
• The First Step in Knowing Your Data
• IEEE 754 Floating-Point Standard
• Discovering Associations Through Data: From Everyday Patterns to Chicago Taxi Trips (September 2022)
• Taxi Trips – 2022 dataset from the City of Chicago open data portal
• Objective Selection of the Bin Width for a Time Histogram
• Measuring Associations in Data
• Measuring Associations Between Two Continuous Variables
• Correlation Coefficients in Python (Pearson, Spearman, Kendall)
• Karl Pearson
• Harald Cramér
• What Are Statistical Tests?
• Eta Squared (η²): Effect Size in ANOVA
• Understanding Market Baskets and Ideal Customers
• What Can Association Rules Tell Us?
• How Association Rules Are Discovered: Concepts, Scale, Measures, and the Apriori Approach
• Apriori: Frequent Itemsets via the Apriori Algorithm
• association_rules: Generating Association Rules from Frequent Itemsets (mlxtend)
• Cross-Selling
• Stratified Random Sampling
• Linear Congruential Random Number Generator (LCG)
• Partitioning Observations to Train Objective Models
• Putting Similar Observations into Clusters
• Clustering
• Recency, Frequency, and Monetary Value (RFM)
• RFM Analysis
• Creating Segments of Observations for Business Reasons (RFM)
• Least Squares Regression
• Multiple Linear Regression
• Feature Importance in Linear Regression
• Forward Selection: Definition and Core Idea
• Forward Selection and Model Interpretation in Linear Regression
• Understanding Forward and Backward Stepwise Regression
• How Shapley Values Work
• Logistic Regression: Modeling Binary Outcomes via Odds and Log-Odds
• Maximum Likelihood (MLE): Fitting a Distribution to Observed Data
• Assessing Model Fit in Logistic Regression
• Complete and Quasi-Complete Separation in Logistic Regression
• Forward Selection with Nested Models and Deviance Tests
• Interpreting and Assessing a Forward-Selection Logistic Regression Model for College Student Retention
• Motivation of Decision Trees: An Incremental Model of Decision-Making
• The CART Algorithm
• Decision Trees as Piecewise Models and Their Predictive Structure
• How CART Decision Trees Model Interactions
• Cluster Profiling Using Decision Trees
• Using Decision Trees to Explain Clustering Results
• Assessing the Quality of Prediction Models
• Binary Classification Models – Conceptual Framework and Evaluation Metrics
• Nominal Classification Models: Model State and Evaluation Metrics
• Binary Classification Model Evaluation and Threshold Optimization
• Identifying Outliers Using Residuals and Studentized Residuals
• AUC–ROC Curve: Evaluating Classification Model Performance
• Lift Analysis for Direct Mail Campaigns: Concept, Process, and Business Value
• Data Preparation & Analysis