🎓Uma série de coisas acerca do curso de LEIC do ISEL. Links para trabalhos finais de curso e outros

Fix holes and customize the map

The dataset covers the Indian Premier League (IPL) with details on matches (date, teams, venue, results), player stats (runs, wickets), team stats (wins, losses), season summaries, and umpire info. The EDA reveals patterns and insights, highlighting dominant teams, star players, and trends across seasons.

A repository for scraping IPL Hawkeye data

This PHP and MySQL-based project is designed to manage a cricket tournament with user and admin functionalities. It allows users to sign up, view tournament scorecards and enables admins to manage matches.

Latest Dataset of IPL (Indian Premier League) matches, including ball-by-ball data in CSV format and match data in JSON format.

This is the auction portal used during the IPL Auction Simulation DreamTeam!

Dive into the heart of IPL with this PowerBI project for in-depth analysis of IPL (Indian Premier League) data, providing interactive visualizations, player performance insights, and match statistics. Uncover trends, compare teams, and explore historical data to gain a comprehensive understanding of the IPL journey.

This repository contains a comprehensive analysis of the Indian Premier League (IPL) from 2008 to 2024. Leveraging the latest dataset, it offers insights into match statistics, player performances, team comparisons, and trends over the years.

Welcome to the "IPL Wining Predictor" project! This machine learning model, built using predicts the probability of a team winning an IPL match based on the current match situation.

The objective of this project is to create a comprehensive analysis report of Virat Kohli's cricket career spanning from 2008 to 2022.

🏏 IPL Win Predictor is a machine learning application that predicts the probability of winning for IPL teams based on current match conditions.

This project uses Power BI to analyze IPL cricket data, featuring dashboards with insights on batting averages, strike rates, and player roles. It identifies the top 11 players and includes navigable pages focused on specific roles like Anchors, Finishers, and All-Rounders.

APACHE SPARK: Data Analysis, Transformation, and Visualisation with PySpark, IPL Data Analysis

This project involves a comprehensive analysis of the IPL 2022 Auction. The goal was to gain insights into the auction dynamics, player characteristics, and spending patterns of different teams.

Used streamlit and to present data while manipulating SQL queries from OLAP database cloud server

This is a data analysis project about IPL T20 dataset about players and teams

This dashboard gives different types of insights of IPL from season 2008 to 2024.

Welcome to the "IPL Win Predictor" project! This machine learning model, built using logistic regression, predicts the probability of a team winning an IPL match based on the current match situation.

A flask based application Predicting score by analysis data(ipl ball by ball 2008 to 2024)