Machine Learning
  • Visualizing Data
  • Characterizing Data
  • Introduction to Machine Learning (ML)
    • Why Use Machine Learning?
  • Linear Regression
    • Guide with the Boston Housing Dataset
    • Case Study Using the California Housing Dataset
  • Multiple Linerar Regression
    • Multiple Linear Regression Case Study: Predicting House Prices in California
  • Polynomial Regression
  • Logistic Regression
  • Decision Tree (CART)
  • Ensemble Learning
  • Random Forest
  • XGBoost
  • K Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Naive Bayes Classifier (NBC)
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  • Introduction

Characterizing Data

Introduction

Before analyzing data, we need to understand its characteristics. Descriptive statistics help summarize and describe important features of a dataset.

In this guide, we will cover:

  • Measures of Central Tendency (Mean, Median, Mode)

  • Measures of Dispersion (Range, Variance, Standard Deviation)

  • Measure of Association (Correlation, Covariance)

  • Skewness (Understanding the shape of distributions)

  • Probability Basics (Fundamental probability concepts)

  • Common Probability Distributions (Uniform, Binomial, and Standard Normal)

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