You will get PUBG Game Outcome Prediction Using Machine Learning
Project details
Predict PUBG player performance using machine learning! Get an accurate, data-driven model that helps analyze your match stats and improve gameplay strategy.
This project delivers a machine learning solution to predict player performance in PUBG (PlayerUnknown's Battlegrounds) using real game data. I use advanced regression models to forecast metrics like winning percentage, kill ratio, survival time, and match placement based on input data (kills, damage dealt, ride distance, etc.).
Key deliverables include:
Cleaned and processed dataset
A trained regression model (e.g., Linear Regression, XGBoost)
Evaluation report with accuracy metrics
Well-documented Python code (Jupyter Notebook or .py script)
Optional: Deployment using Flask or Streamlit for interactive predictions
Whether you're a gamer, analyst, or developer — this project helps you understand and predict performance trends with real insights.
This project delivers a machine learning solution to predict player performance in PUBG (PlayerUnknown's Battlegrounds) using real game data. I use advanced regression models to forecast metrics like winning percentage, kill ratio, survival time, and match placement based on input data (kills, damage dealt, ride distance, etc.).
Key deliverables include:
Cleaned and processed dataset
A trained regression model (e.g., Linear Regression, XGBoost)
Evaluation report with accuracy metrics
Well-documented Python code (Jupyter Notebook or .py script)
Optional: Deployment using Flask or Streamlit for interactive predictions
Whether you're a gamer, analyst, or developer — this project helps you understand and predict performance trends with real insights.
Machine Learning Tools
Azure Machine Learning, NumPy, pandas, Python, TensorFlowWhat's included
| Service Tiers |
Starter
$10
|
Standard
$20
|
Advanced
$30
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 3 | |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
About Tanisha
AI & Machine Learning | C++, Analytics, Data Structures, Deep Tone
Noida, India - 2:07 am local time
I specialize in:
✅ Sentiment Analysis (NLP)
✅ Web Apps using Streamlit & Flask
✅ Data Cleaning, EDA, and Visualization
✅ Machine Learning Model Building
✅ Python Automation & APIs
🌱 I'm a 3rd-year B.Tech student in Computer Science & Data Science, and I'm building projects like:
🔡 Spell Corrector Web App using NLP (Live Demo)
💬 Tweet Sentiment Analyzer using Logistic Regression
🌾 Sugarcane Yield Prediction (ML + Data Analysis)
Steps for completing your project
After purchasing the project, send requirements so Tanisha can start the project.
Delivery time starts when Tanisha receives requirements from you.
Tanisha works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1: Data Collection and Cleaning
I will review and clean the dataset for missing or invalid values to prepare it for model training