Exploratory Data Analysis (EDA) is the process of analyzing data sets to summarize their main characteristics — often using visual methods — before applying modeling techniques. EDA helps uncover patterns, detect anomalies, test hypotheses, and check underlying assumptions. This repository includes EDA studies on a variety of datasets, such as ecommerce purchases, salaries, the Titanic dataset, YouTube channels, and more.

• Data_csv_files / – Contains all the CSV datasets used by the notebooks.

• 01_EDA_Ecommerce_Purchases_Dataset.ipynb – Exploratory analysis on consumer purchase data.
• 02_EDA_Salaries Dataset.ipynb – Insights into salary distributions and trends.
• 03_EDA_Adult_dataset.ipynb – Exploring demographic and income data.
• 04_EDA_Titanic_dataset.ipynb – Visualization of survival trends in the Titanic dataset.
• 05_EDA_Googleplaystore_dataset.ipynb – Analysis of app metadata from Google Play Store.
• 06_EDA_Udemy_Dataset.ipynb – Examining trends within Udemy course data.
• 07_EDA_Supermarket_Sales_Dataset.ipynb – Sales and revenue insights from supermarket data.
• 08_EDA_Top_Youtube_Channel_Dataset.ipynb – Exploration of top YouTube channels.
• 09_EDA_IMDB_Movies_Dataset.ipynb – Movie metadata analysis from IMDb.
• 10_Flight_Price_dataset.ipynb – Investigating patterns in flight pricing.

• The notebooks illustrate how to:
• Load, inspect, and clean datasets
• Explore data structure and feature distributions
• Visualize trends with charts (histograms, scatter plots, box plots, etc.)
• Interpret findings and generate insights
• Compare numerical and categorical variables