Build a Complete EDA Web App with Streamlit & Python | Automated Exploratory Data Analysis

Are you tired of writing the same boilerplate code for every new dataset? In this tutorial, we’ll build a powerful, reusable web app using Streamlit that automates the entire Exploratory Data Analysis (EDA) process. What you’ll learn to build: Dataset Overviews: Instantly view shapes, info, and descriptive statistics. Data Cleansing: Find and impute missing values, remove duplicates, and detect outliers. Visual Discovery: Generate univariate plots, bivariate analyses, and interactive correlation heatmaps. Feature Engineering: Perform log transformations, binning, and one-hot encoding directly through the UI. Export Tools: Save your modified datasets as CSVs and download your plots as PNGs. Whether you're a data scientist looking to speed up your workflow or a developer building a portfolio project, this step-by-step walkthrough (including a full code review) will give you the tools to analyze any dataset with just a few clicks. Don't forget to like and subscribe for more Data Science and Streamlit tutorials! Link to the code (Github repo): https://github.com/mktaop/eda_with_streamlit #streamlit #webapp #python #datascience #machinelearning #ml #howto #analytics #dataanalytics #dataanalysis #datacleaning #datavisualization