Exploratory Data Analysis (EDA) is akin to detective work for data, employing visual tools to uncover patterns and insights. It transforms raw numbers into a compelling narrative, unveiling surprises and secrets within the dataset. Think of it as an investigative journey into the core of your data, revealing hidden truths along the way.

In this Data set we will focus on EDA and Visualization on Forbes Data 2022 and Visualization will be done by Library Called as Plotly

Conducted a comprehensive data visualization analysis using PowerBI on IPL data to determine the optimal candidate for the Impact Player role. This analysis focuses on evaluating various match situations to identify which player, among the potential impact players, is most likely to create a significant impact when needed.

This repository showcases a machine learning solution for predicting passenger survival on the Titanic using Scikit-Learn and Flask. The project demonstrates the use of a Scikit-Learn pipeline for efficient model training and preprocessing, and wraps the trained model in a Flask application to provide easy-to-use predictions via a web interface.