We have taken a data set of a Football game - FIFA 19. it contains all the attributes and the game rating essentials of the professional football players.

The problem statement is to analyse the data, clean the data and then apply feature engineering to create a new set of self defined features and fine tune them. These can help to make the data better and optimise it for better results. The visualising of the same helps us to gain insight and help to make better decisions and ultimately better results.

Seabourne for visualization matplotlib for visualization missingno for missing values

The main module of this project is to get insights and correlations between player values, reach, age, special attributes and performance. This integrated data can be further converted into information. By analyzing it, we have derived some statistics for teams, clubs and players through extensive football experience. The insights provided in our results, along with the understanding and contextualize information,

We use the filter function to fill the missing values, to replace the missing values in numerical columns. We use the mean function add to replace missing values from the categorical columns.

The feature engineering allows us to create a new set of features which are self defined and can be used within the analysis.

For visualization, we will be using Seabourne and matplotlib for missing values. We are able to check the player with the best rating among all the players. the player with the best left foot and right foot respectively. This is followed by the youngest set of players with the highest attribute and rating. Through the graphic representations, we can visualize large volumes of data in an understandable and couldn't be, which in turn helps us to comprehend the information and draw conclusions and insights.

Thank you.