FIFA 23 player research

Keywords: FIFA, player

Background

In FIFA 23 there are so many players from various countries who came to play a football match according to the dataset. there were no null values present in the data and no duplicate rows or information present over there. The data set gave very less errors while predicting. There were some of the columns which were making a very bad impact on the data and gave very bad predictions for those column author discarded from the dataset. While doing the prediction author fell in trouble by seeing the accuracy because of the unwanted columns. So, the author completely remove those columns and some of the columns are duplicated columns so the author deleted those columns as well. So, the author got very less loss in the data.

Aim & Objectives

Aim
To Predict the FIFA 23 player Data Using DL Techniques
Objective
Employ an ANN to predict FIFA 23 player data, offering insights into player performance and attributes.
Develop an ANN model to accurately forecast player ratings, skills, and potential, aiding game strategy decisions.
Evaluate the ANN's ability to capture player trends and dynamics, enhancing realism and engagement in FIFA 23.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

— Preview truncated. The complete document (full chapters, references, diagrams, and appendices) is shared with clients as part of project delivery. —