Data Analysis Algorithms Pdf -

: Random Forest (handles mixed data types, nonlinear relationships, feature importance)

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. data analysis algorithms pdf

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This post serves as a complete guide to the most important algorithms used in data analysis today. You can for offline reference. What Are Data Analysis Algorithms? A data analysis algorithm is a step-by-step set of rules or calculations designed to process data, extract insights, or make predictions. These algorithms fall into four broad categories: Want the PDF version

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.