https://github.com/angchekar28/valorant-gameplay-analysis

This project analyzes Valorant gameplay data to understand key factors affecting match outcomes. It compares various machine learning models to predict player performance, rank classification, and match success.
https://github.com/angchekar28/valorant-gameplay-analysis

data-analysis data-science data-visualization exploratory-data-analysis jupyter-notebook machine-learning python

Last synced: 11 days ago
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This project analyzes Valorant gameplay data to understand key factors affecting match outcomes. It compares various machine learning models to predict player performance, rank classification, and match success.

• Host: GitHub
• URL: https://github.com/angchekar28/valorant-gameplay-analysis
• Owner: angchekar28
• License: mit
• Created: 2025-02-04T18:26:05.000Z (16 days ago)
• Default Branch: main
• Last Pushed: 2025-02-04T18:29:32.000Z (16 days ago)
• Last Synced: 2025-02-04T19:35:02.463Z (16 days ago)
• Topics: data-analysis, data-science, data-visualization, exploratory-data-analysis, jupyter-notebook, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 455 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 📌 Valorant Gameplay Analysis & Model Comparison

## 📝 Project Overview

This project analyzes Valorant gameplay data and evaluates different machine learning models to predict player performance. The goal is to understand key factors that influence match outcomes.

## 🎮 Dataset

- **Source:** Valorant match statistics

- **Columns:**

  - `Player ID`, `Agent`, `KDA`, `Win/Loss`, `Map`, `Rank`, `Headshot %`, `Damage per Round`

  - `Rounds Won`, `Rounds Lost`, `Economy`, `Spike Plants`, `Spike Defuses`

## ⚙️ Methodology

1. **Data Preprocessing**

   - Handling missing values

   - Label encoding the output

   - Encoding categorical data (e.g., Agents, Maps)

2. **Exploratory Data Analysis (EDA)**

   - Distribution of player statistics

   - Impact of different agents on performance

   - Correlation between rank and gameplay metrics

3. **Model Implementation**

   - XGBoost Classifer for o/p prediction

   - RandomForestClassifier for o/p prediction

4. **Model Evaluation**

   - Accuracy and precision metrics

   - Confusion Matrix visualization

   - Feature Importance analysis

## 🛠️ Installation & Usage

```bash

# Install dependencies

pip install pandas numpy matplotlib seaborn scikit-learn

# Clone the repository

git clone 

cd 

# Open Jupyter Notebook

jupyter notebook valorant-analysis-models-comparison.ipynb

```

## Conclusion:

- This analysis provides insights into factors affecting Valorant gameplay performance.

- It helps in selecting the best predictive model for ranking and match outcomes.