https://github.com/anshutrivedi2166/project-1

PLAYER RATING ANALYSIS
https://github.com/anshutrivedi2166/project-1

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
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PLAYER RATING ANALYSIS

• Host: GitHub
• URL: https://github.com/anshutrivedi2166/project-1
• Owner: ANSHUTRIVEDI2166
• Created: 2024-12-19T09:56:47.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-03-16T13:45:44.000Z (7 months ago)
• Last Synced: 2025-06-01T21:47:37.372Z (5 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

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README

          
# project-1

repository for our 4th sem project

#Project: Player Rating Analysis

This project focuses on Player Rating Analysis using Python and machine learning techniques. It involves analyzing player performance based on key statistics such as position, team, nationality, goals, and assists The dataset is processed using techniques like OneHotEncoding and Standard Scaling to handle categorical and numerical data

#Key Steps:

# Data preprocessing using ColumnTransformer

# Training machine learning models – Support Vector Regressor (SVR), Random Forest Regressor, and XGBoost Regressor

# Custom weighted logic – Different weights are assigned based on player position (e.g., a defender scoring a goal contributes more to the rating than a striker scoring a goal)

# Performance evaluation – Comparing actual and predicted player ratings to assess model accuracy

#Skills:

Python,

Machine Learning,

Data Preprocessing,

Feature Engineering,

Ensemble Models,

Model Evaluation,

#Summary:

The project aims to identify patterns in player performance and improve prediction accuracy through data-driven insights The use of ensemble models like Random Forest and XGBoost helps enhance predictive power and robustness