https://github.com/datkanber/advanced-eda
Step-by-step guide for advanced Exploratory Data Analysis (EDA) to uncover patterns and prepare data.
https://github.com/datkanber/advanced-eda
data-science exploratory
Last synced: about 1 year ago
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Step-by-step guide for advanced Exploratory Data Analysis (EDA) to uncover patterns and prepare data.
- Host: GitHub
- URL: https://github.com/datkanber/advanced-eda
- Owner: datkanber
- Created: 2024-12-05T16:24:12.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-08T21:35:59.000Z (over 1 year ago)
- Last Synced: 2024-12-08T22:27:32.981Z (over 1 year ago)
- Topics: data-science, exploratory
- Language: Jupyter Notebook
- Homepage:
- Size: 722 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Advanced Functional Exploratory Data Analysis
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.
## 📖 What is EDA?
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.
### 🔍 Key Analysis Areas:
1. **Categorical Variables**: Distribution and frequency analysis.
2. **Numerical Variables**: Summary statistics and visualizations (histograms, boxplots).
3. **Target Variable**: Correlation and relationships with other variables.
4. **Correlation Analysis**: Identifying highly correlated features.
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## 🚀 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.
---
## 📊 Dataset Examples
- **Titanic Dataset**: Survival analysis based on passenger data.
- **NBA Dataset**: Performance metrics for NBA players.
- **Fraud Detection Dataset**: Identifying fraudulent transactions.