⚽ Soccer Match Outcome Predictor

A machine learning project that predicts the outcome of professional soccer matches
(Home Win / Draw / Away Win) using historical match data and engineered performance metrics.

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📌 Project Overview

This project builds a supervised classification model to predict match results based on:

• Team form (rolling averages)
• Goals scored & conceded
• Home vs Away performance
• Head-to-head statistics
• Engineered rating features (e.g., goal differential, Elo rating)

The objective is to explore feature engineering, model selection, and evaluation in a real-world sports analytics setting.

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🧠 Problem Statement

Given two teams and their historical performance data, predict the match outcome:

• 🟢 Home Win
• 🟡 Draw
• 🔵 Away Win

This is a multi-class classification problem.

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📊 Dataset

The dataset contains:

• Match date
• Home team / Away team
• Full-time goals
• Shots, shots on target
• Possession (if available)
• Rolling performance statistics
• Engineered features

Possible data sources:

• Kaggle soccer datasets
• API-Football

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⚙️ Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib / Seaborn

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🔍 Feature Engineering

Examples of features used:

• Rolling average goals scored (last 5 matches)
• Rolling average goals conceded
• Goal differential
• Home win percentage
• Away win percentage
• Head-to-head win ratio
• Elo rating difference (optional)

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🏗️ Model Pipeline

1. Data Cleaning
2. Feature Engineering
3. Train/Test Split
4. Model Training
5. Cross-Validation
6. Evaluation & Comparison

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📈 Evaluation Metrics

Since this is a multi-class classification problem:

• Accuracy
• Precision
• Recall
• F1 Score
• Confusion Matrix