This is a project based on the FIFA World Cup 2019 and Analyzes the Performance and Efficiency of Teams, Players, Countries and other related things using Data Analysis and Data Visualizations

Visualization of FIFA 19 database

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This is a Jupyter Notebook( iPython Notebook) with Data Analysis (EDA) on FIFA WC Qatar 2022 match data.

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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.

The Motive of this project is to analyze FIFA World Cup Data,we have the data from 1930 to 2014. The data we have is separated by Matches ,Players and Cup Holders since 1930 to 2014. Lot of Analysis has been done with nice Visualizations to understand and to get insights from data

A complete end to end data engineering project on GCP

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About The Motive of this project is to analyze FIFA World Cup Data,we have the data from 1930 to 2014. The data we have is separated by Matches ,Players and Cup Holders since 1930 to 2014. Lot of Analysis has been done with nice Visualizations to understand and to get insights from data.

Analysis for the Best Possible Sports Team with a Specific Focus on Soccer

Data Analysis on Fifa 18 Dataset in Python. Project includes visualizations and Machine learning for prediction purposes

FIFA-2019 Exploratory Data Analysis and Visualization

Big Domty is a powerful code developed for conducting comprehensive player analysis within the context of FIFA 22, the popular football simulation video game. This code utilizes advanced data processing and statistical techniques to extract valuable insights and provide meaningful information about individual players.

The objective of this data science project is to conduct sentiment analysis on tweets related to the FIFA World Cup 2022. By analyzing the sentiment of tweets, the project aims to gain insights into the public's perception, emotions, and opinions surrounding the event.

final assignment for R

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In this project, a predictive model has been developed to visualize FIFA 20 data. The exploration includes the application of unsupervised learning techniques, specifically Principal Component Analysis (PCA) for dimensionality reduction and clustering analysis.

This project analyzes the FIFA 2021 dataset to explore player statistics, team performance, and match outcomes. Using Python and libraries like pandas and Matplotlib, it provides insights into player ratings, team strengths, and trends in football data. The analysis showcases data science techniques applied to sports analytics.

Cricket prediction can be viewed as one of the objectives of sports analyt- ics, which aims at helping decision makers to gain competitive advantage. Data Analysis is becoming more common specially in Sports. Using data analysis results has become familiar in sports organization such as Interna- tional Cricket Council (ICC), International Federation of Association Foot- ball (FIFA), GrandSlam of the International Tennis Federation.The difculty of this task depends on many factors, like the availability of data for the past events, the ability to gather data for future events, the knowledge needed to interpret gathered data, and others. In sports like Cricket and Football predicting the results become more difcult due to the changing nature of the Game. People focus on sports results,which is an important aspect, and the result of the game has become the focus and concentration of sports game. Various techniques for modelling a cricket match exist that yield dif- ferent result prediction algorithms.The modelling can be put under the four generic categories which are empirical models,dynamic systems,statistical techniques,articial intelligence(including expert systems.In the articial intel- ligence category, there are several approaches that focus on Bayesian network modelling. The Matrix factorization technique became very popular in the eld of multimedia content recommender systems where it showed good scal- ability and predictive accuracy. The idea behind using the latent features in our case is to be able to build a successful model. The outcome of this project will be a system which will predict the results of the upcoming matches.

Data Analysis: Web scraping FIFA's World Cup statistics [Python, BeautifulSoup, Requests, Pandas]

In-Depth EDA and Data Visualization of the FIFA 21 dataset with the help of matplotlib, seaborn and plotly.

A data analysis project using FIFA 23 dataset with pandas and visualization tools

Data analysis and visualisation for all players in FIFA 19!

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Exploratory data analysis on FIFA 2019 data

Using this dataset to analyse various features of players. Using analysis to create dream team of top 11 players with different formations.

Data Analysis of FIFA 19 players using Python libraries like Numpy, Pandas, Matplotlib and Seaborn

FIFA 19 Player Data Analysis and Visualization

This is a Machine Learning project to analyze FIFA 21 data and create clusters of players based on their features.