Abstract:
Predicting in sporting fields has kept its landmark in Machine Learning by analyzing the
statistics and other indicators to predict a certain result as desired. The result usually shows
the prediction as the user inputs the value to know about their similarity with the
professional figures. We collected the dataset from different sources like FIFA21 and
Kaggle to determine the best dataset for our prediction. It obtains the players’ names and
other indicators to show their capabilities and position where they usually play. The
previous works and hypotheses were analyzed and the implementation of KNN was done
after getting it as the best algorithm among K-Means, SVM, ADASYN which is a custom
model of Linear Regression, and KNN considering parameters like accuracy, precision,
recall, and F1-score. We found 92%, 85%, and 87% accuracy in KNN, ADASYN, and
SVM respectively in finding similarity with the professional players from the user inputs.
K-Means didn't go well with this type of dataset and aim and failed to execute a result.
With the best algorithm found here, KNN, we built a WebApp using the StreamLit
framework and ensured four functions with the essential model. We can 1) find similar
player, 2) find position, 3) know players’ information, and 4) compare your ratings with
any player from the WebApp. We applied these functionalities in all four algorithms and
found KNN giving the best value of each parameter. This WebApp will be beneficial to
dream of being a footballer and also lead allto a better version of their selves along with
storing data in the dataset for futureexperiments.
