📊 Advanced Exploratory Data Analysis (EDA) on Synthetic Datasets

Welcome to the repository for Advanced Exploratory Data Analysis (EDA) on synthetic datasets. This project dives deep into data visualization, distribution analysis, correlation checks, and outlier handling techniques, providing a comprehensive walkthrough of modern EDA techniques.

🔍 Project Overview

This project uses a synthetic dataset to demonstrate:

Univariate and multivariate analysis

Visualization techniques using libraries like Matplotlib and Seaborn

Correlation and distribution exploration

Handling of missing data and outliers

Insights derivation for machine learning model readiness

The analysis is fully documented in a Jupyter Notebook for easy readability and reproducibility.

📁 Files

Data.ipynb: Jupyter notebook containing all EDA steps, visualizations, and insights.

📖 Blog Post

For a detailed walkthrough and insights behind the decisions made in this analysis, check out the blog post here: https://medium.com/@sachinsmanoj02/advanced-exploratory-data-analysis-eda-on-synthetic-datasets-d7c82bf78a14