This repository focuses on Advanced Functionalized Exploratory Data Analysis (EDA), providing a step-by-step guide to uncover patterns, identify relationships, and prepare datasets for further analysis.
Exploratory Data Analysis (EDA) is a critical step in data science that helps to:
- Summarize the main characteristics of datasets.
- Visualize relationships between variables.
- Detect anomalies and patterns.
- Check assumptions and validate statistical techniques.
- Categorical Variables: Distribution and frequency analysis.
- Numerical Variables: Summary statistics and visualizations (histograms, boxplots).
- Target Variable: Correlation and relationships with other variables.
- Correlation Analysis: Identifying highly correlated features.
- Data Cleaning: Handle missing values, remove outliers.
- Descriptive Statistics: Quick summaries of numerical and categorical data.
- Correlation Heatmaps: Visualize feature relationships.
- Automated Functions: Tools for summarizing data and identifying insights.
- Titanic Dataset: Survival analysis based on passenger data.
- NBA Dataset: Performance metrics for NBA players.
- Fraud Detection Dataset: Identifying fraudulent transactions.