Mastering FIFA 21 Dataset Analysis: A Comprehensive Guide

FIFA 21 Data Analysis. In today’s data-driven world, the ability to effectively clean and analyze datasets is crucial. This blog post will delve into the world of sports analytics, focusing on the popular FIFA 21 dataset. We’ll guide you through the process of loading, cleaning, and analyzing this dataset to identify valuable players, enhancing your skills in data science and machine learning.

Loading and Cleaning the FIFA 21 Dataset

Before diving into data analysis, the first step is to obtain and prepare your dataset. You can download the full FIFA 21 Dataset from Kaggle FIFA Player Stats Database. This dataset includes comprehensive player statistics, which are crucial for our analysis.

Initial Data Loading

Using Python and the Pandas library, load the dataset into a DataFrame. This is a critical step to ensure that all subsequent data manipulations are based on accurate and complete data.

import pandas as pd

# Load the data
df = pd.read_csv('./FIFA21_official_data.csv')

Data Cleaning Process

After loading the data, it’s essential to check for and address any issues such as missing values or incorrect data types, which could affect the accuracy of your analysis.

# Check for missing values
print(df.isnull().sum())

# Fill numeric columns with the mean
for col in df.select_dtypes(include='number').columns:
    df[col].fillna(df[col].mean(), inplace=True)

# Fill categorical columns with the mode
for col in df.select_dtypes(include='object').columns:
    df[col].fillna(df[col].mode()[0], inplace=True)

This approach helps maintain the integrity of the dataset by ensuring that no essential data is lost. Once the dataset is clean, save the cleaned data for further analysis.

# Save the cleaned dataset
df.to_csv('./FIFA21_cleaned_data.csv', index=False)

Building and Training the Model

With a clean dataset, the next step is to build a model that can predict whether a player is valuable. This involves selecting the right features, training a model, and evaluating its performance.

Feature Selection and Preparation

Identify features that are likely to influence a player’s value, such as ‘Age’, ‘Potential’, and ‘International Reputation’. Avoid using the ‘Overall’ rating directly, as it could bias the model.

ml_features = ['Age', 'Potential', 'International Reputation', 'Weak Foot', 'Skill Moves']

Model Training

Using the RandomForest algorithm, train the model on the selected features. This method is effective for handling complex datasets with multiple features influencing the outcome.

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score

# Split data and train the model
X_train, X_test, y_train, y_test = train_test_split(df[ml_features], df['Valuable Player'], test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

Model Evaluation

After training, evaluate the model using the test data to understand its accuracy and reliability.

# Make predictions and evaluate the model
y_pred = model.predict(X_test)
ml_accuracy = accuracy_score(y_test, y_pred)
ml_f1_score = f1_score(y_test, y_pred)

print("Accuracy:", ml_accuracy)
print("F1 Score:", ml_f1_score)

Conclusion

FIFA 21 Data Analysis dataset provides valuable insights into what attributes contribute to a player’s value. This process not only enhances your understanding of data science techniques but also applies these methods to real-world datasets, providing a practical framework for future projects.

For further exploration and a deeper dive into the FIFA 21 dataset, consider visiting the detailed guide and tutorials available on Kaggle. Here, you can find additional resources and community projects to enhance your learning and understanding of sports analytics.