https://github.com/anshutrivedi2166/player-rating-analysis-football

PLAYER RATING ANALYSIS
https://github.com/anshutrivedi2166/player-rating-analysis-football

4th-semester-project data-preprocessing data-science data-visualization machine-learning model-evaluation python pythonprojects random-forest sports-analytics sports-data svm xbgoost

Last synced: 4 months ago
JSON representation

PLAYER RATING ANALYSIS

• Host: GitHub
• URL: https://github.com/anshutrivedi2166/player-rating-analysis-football
• Owner: ANSHUTRIVEDI2166
• Created: 2024-12-19T09:56:47.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-07-01T07:53:04.000Z (4 months ago)
• Last Synced: 2025-07-01T08:39:19.860Z (4 months ago)
• Topics: 4th-semester-project, data-preprocessing, data-science, data-visualization, machine-learning, model-evaluation, python, pythonprojects, random-forest, sports-analytics, sports-data, svm, xbgoost
• Language: Jupyter Notebook
• Homepage:
• Size: 208 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# ⚽ Project: Player Rating Analysis

Welcome to our 4th semester machine learning project — **Player Rating Analysis**.  

This project aims to predict and analyze player ratings using statistical features and machine learning models, with custom logic to reflect the real impact of player positions.

## 📊 Objective

To build a system that can analyze and predict player performance ratings based on various match and player-related statistics like:

- Goals

- Assists

- Position

- Nationality

- Club

The ratings are influenced by **custom position-based weight logic** that adds realism and fairness to the rating mechanism.

---

## 🧠 Key Features

- ⚙️ **Data Preprocessing**: 

  - Categorical encoding using `OneHotEncoder`

  - Feature scaling with `StandardScaler`

  - Combined using `ColumnTransformer`

- 🔁 **Model Training**:

  - Support Vector Regressor (SVR)

  - Random Forest Regressor

  - XGBoost Regressor

- 🧮 **Custom Logic**:

  - Dynamic weight assignment depending on player roles

  - For example:

    - A defender scoring a goal has **higher impact** than a striker

    - Assists by midfielders are **weighed more** than those by forwards

- 📈 **Model Evaluation**:

  - Compare predicted vs. actual ratings

  - Measure performance using metrics like MAE, RMSE, and R² Score

---

## 🧰 Tech Stack & Tools

- **Language**: Python 🐍

- **Libraries**:

  - `pandas`, `numpy` – Data processing

  - `scikit-learn` – Preprocessing, SVR, evaluation

  - `xgboost` – XGBoost Regressor

  - `matplotlib`, `seaborn` – Visualization

---

## 📂 Folder Structure