Data Analysis Algorithms Pdf «HIGH-QUALITY • Checklist»

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Data has labels? ├─ Yes → Supervised │ ├─ Output continuous? → Regression (Linear, Random Forest) │ └─ Output categorical? → Classification (Logistic, Decision Tree, Naïve Bayes) └─ No → Unsupervised ├─ Want groups? → Clustering (K-Means, DBSCAN) └─ Want fewer features? → Dimensionality reduction (PCA) | Algorithm | Training Speed | Interpretability | Memory Use | Handles Nonlinearity | |-----------|---------------|------------------|------------|----------------------| | Linear Regression | Fast | High | Low | No | | Logistic Regression | Fast | High | Low | No (without kernels) | | Decision Tree | Medium | High | Medium | Yes | | Random Forest | Medium | Medium | High | Yes | | K-Means | Fast | Medium | Low | No | | PCA | Medium | Low | Medium | No | | Gradient Boosting | Slow | Low | High | Yes | Practical Example: Customer Churn Prediction Problem : A telecom company wants to predict which customers will cancel their subscription. data analysis algorithms pdf

Introduction Algorithms are the hidden engines behind every data analysis project. From cleaning messy datasets to predicting future trends, understanding core data analysis algorithms is essential for data scientists, analysts, and decision-makers. → Print it, annotate it, and keep it on your desk