Exploratory Data Analysis

EDA helps understand data by finding patterns, relationships and unusual observations. This section covers analysis and visualization techniques for exploring datasets.

Introduction

What is Exploratory Data Analysis (EDA)?Importing Libraries and Loading the Dataset Inspecting the Data: shape, info, describe Handling Missing and Duplicate Values

Univariate, Bivariate and Multivariate

Univariate Analysis Bivariate Analysis Multivariate Analysis Comparison and How to Choose

Data Visualization

Introduction to Data Visualization Univariate Plots in Python Bivariate and Multivariate Plots in Python Challenges and Best Practices in Visualization

Correlation Analysis

What is Correlation Analysis?Pearson Correlation Coefficient Other Correlation Coefficients: Spearman, Kendall, Phi, Cramer's V Correlation Analysis in Python: Steps, NumPy, Pandas and Heatmaps Applications, Advantages and Limitations of Correlation

Advanced EDA

Advanced EDA: Overview Descriptive Statistics: Mean, Median, Mode, Std, IQR, Skewness, Kurtosis Visualizing Distributions: Choosing the Right Plot Feature Interactions: Facet Grids and Pair Plots Identifying Outliers and Anomalies Feature Engineering: Log, Polynomial and Interaction Features Dimensionality Reduction: PCA, t-SNE and UMAP

Exploratory Data Analysis (EDA) in Python - Complete Guide | DevNotes