The Fifa-Dataset-SQL-Data-Analysis project uses SQL to explore FIFA player data, analyzing performance and attributes to extract meaningful insights on player statistics, trends, and patterns in football.

Problem Statement The ultimate Soccer database for data analysis and machine learning The dataset comes in the form of an SQL database and contains statistics of about 25,000 football matches, from the top football league of 11 European Countries. It covers seasons from 2008 to 2016 and contains match statistics (i.e: scores, corners, fouls etc...) as well as the team formations, with player names and a pair of coordinates to indicate their position on the pitch. +25,000 matches +10,000 players 11 European Countries with their lead championship Seasons 2008 to 2016 Players and Teams' attributes* sourced from EA Sports' FIFA video game series, including the weekly updates Team line up with squad formation (X, Y coordinates) Betting odds from up to 10 providers Detailed match events (goal types, possession, corner, cross, fouls, cards etc...) for +10,000 matches The dataset also has a set of about 35 statistics for each player, derived from EA Sports' FIFA video games. It is not just the stats that come with a new version of the game but also the weekly updates. So for instance if a player has performed poorly over a period of time and his stats get impacted in FIFA, you would normally see the same in the dataset. Object predict the overall rating of soccer player based on their attributes such as 'crossing', 'finishing etc.

This project focuses on analyzing the FIFA World Cup 2022 dataset, aiming to uncover valuable insights and trends. It involves data cleaning, Excel formula creation, SQL analysis, Power BI visualization, and personal growth in data analysis and visualization techniques.

30daysduckdbchallenge Phase 1

This project involves analyzing a FIFA dataset using SQL. The goal is to extract meaningful insights from the dataset through various SQL queries.

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This project explores a FIFA player dataset using SQL queries to uncover hidden insights about player attributes, performance metrics, and trends in the beautiful game.

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The Fifa-Dataset-SQL-Data-Analysis project uses SQL to explore FIFA player data, analyzing performance and attributes to extract meaningful insights on player statistics, trends, and patterns in football.

This project demonstrates my SQL and data analysis skills using the FIFA 26 (FC 26) player dataset. The dataset includes detailed information about football players, their clubs, leagues, and stats.

An end-to-end data analysis project on the FIFA World Cup dataset. The project covers data cleaning in MS Excel, exploratory data analysis (EDA) using Python, analytical queries in SQL, and interactive dashboard creation in Power BI to uncover insights on matches, goals, teams, and tournaments.

Exploratory Data Analysis (EDA) is performed on FIFA World Cup dataset using SQL. The queries were executed on Oracle Database and has python code to insert data into database. The output of queries are captured using Spool files.