⚽ FIFA World Cup Prediction Matrix

An AI-powered FIFA World Cup Prediction System that predicts football match outcomes using Machine Learning and historical FIFA World Cup data (2010–2022).

The project performs data preprocessing, feature engineering, model training, and interactive match prediction through a Streamlit web application.

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📌 Project Overview

This project analyzes historical FIFA World Cup matches and predicts the probable winner of future matches using machine learning algorithms.

The application allows users to:

• Select Home Team and Away Team
• Predict Match Winner
• View Winning Probabilities
• Analyze Historical Performance
• Compare Different Machine Learning Models

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🚀 Features

• Interactive Streamlit Dashboard
• Historical Match Analysis
• Feature Engineering Pipeline
• Machine Learning-based Winner Prediction
• Probability Score for Both Teams
• Model Performance Comparison
• Easy-to-use User Interface

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🛠️ Tech Stack

• Python
• Streamlit
• Pandas
• NumPy
• Scikit-Learn
• Matplotlib
• Plotly

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📂 Project Structure

FIFA-World-Cup-Prediction/
│
├── app.py                    # Streamlit Application
├── main.py                   # Project Execution
├── train_model.py            # Model Training
├── feature_engineering.py    # Feature Engineering
├── eda.py                    # Exploratory Data Analysis
├── data_audit.py             # Data Quality Checks
├── requirements.txt
├── README.md
│
├── RESUKTSSS.xlsx            # Historical FIFA Dataset
├── SC.xlsx                   # Supporting Dataset

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📊 Machine Learning Workflow

1. Data Collection
2. Data Cleaning
3. Exploratory Data Analysis
4. Feature Engineering
5. Model Training
6. Model Evaluation
7. Winner Prediction
8. Streamlit Deployment

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🤖 Machine Learning Models Used

• Logistic Regression
• Random Forest Classifier
• Gradient Boosting Classifier

The models were evaluated using classification metrics to determine the best-performing algorithm for predicting match outcomes.

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📈 Dataset

The project uses historical FIFA World Cup match data containing:

• Home Team
• Away Team
• Match Results
• Goals Scored
• Historical Team Statistics
• Engineered Performance Features

Datasets included:

• RESUKTSSS.xlsx
• SC.xlsx

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▶️ Installation

Clone the repository

git clone https://github.com/yourusername/FIFA-World-Cup-Prediction.git

Go to the project folder

cd FIFA-World-Cup-Prediction

Install dependencies

pip install -r requirements.txt

Run the application

streamlit run app.py

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🎯 Future Improvements

• Live FIFA API Integration
• Player-Level Statistics
• Team Ranking Updates
• Deep Learning Models
• Explainable AI (SHAP)
• World Cup Bracket Prediction

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👩‍💻 Author

Swathi Reddy

B-Tech Computer Science Engineering (Data Science) Bennett University

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