https://github.com/rkirlew/league-of-legends-champion-classifier-using-k-nearest-neighbors-knn-

This project leverages machine learning to classify champions in the popular game League of Legends based on their attributes. The model predicts a champion's class (e.g., Assassin, Tank, Mage) using features such as Base HP, Base Mana, Difficulty, Role, and Range Type
https://github.com/rkirlew/league-of-legends-champion-classifier-using-k-nearest-neighbors-knn-

classification k-nearest-neighbours league-of-legends league-of-legends-champions machine-learning

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This project leverages machine learning to classify champions in the popular game League of Legends based on their attributes. The model predicts a champion's class (e.g., Assassin, Tank, Mage) using features such as Base HP, Base Mana, Difficulty, Role, and Range Type

• Host: GitHub
• URL: https://github.com/rkirlew/league-of-legends-champion-classifier-using-k-nearest-neighbors-knn-
• Owner: RKirlew
• License: gpl-3.0
• Created: 2025-01-08T02:13:37.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-01-09T22:19:58.000Z (4 months ago)
• Last Synced: 2025-01-18T21:19:26.368Z (3 months ago)
• Topics: classification, k-nearest-neighbours, league-of-legends, league-of-legends-champions, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 30.3 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# League-of-Legends-Champion-Classifier-using-K-Nearest-Neighbors-KNN-

This project leverages machine learning to classify champions in the popular game League of Legends based on their attributes. The model predicts a champion's class (e.g., Assassin, Tank, Mage) using features such as Base HP, Base Mana, Difficulty, Role, and Range Type

![image](https://github.com/user-attachments/assets/af6df1b0-9c84-43d0-853a-eba2899465c2)

# League of Legends Champion Classifier using K-Nearest Neighbors (KNN)

This project leverages machine learning to classify champions in the popular game **League of Legends** based on their attributes. The model predicts a champion's class (e.g., Assassin, Tank, Mage) using features such as Base HP, Base Mana, Difficulty, Role, and Range Type. This project showcases a complete ML pipeline, including data preprocessing, feature engineering, and model evaluation.

---

## Objective

To predict the **class** of a League of Legends champion based on their attributes using a **K-Nearest Neighbors (KNN)** model.

---

## Dataset

The dataset contains attributes of League of Legends champions, including:

- **Base HP**: Champion's health at the start of the game.

- **Base Mana**: Champion's mana at the start of the game.

- **Difficulty**: Level of complexity to play the champion (e.g., Beginner, Intermediate, Advanced).

- **Role**: Primary role of the champion (e.g., Top, Mid, Jungle, Support, ADC).

- **Range type**: Whether the champion attacks at range or melee.

- **Class**: The target variable, representing the champion's class (e.g., Assassin, Mage, Tank).

---

## Methodology

### 1. **Data Preprocessing**

- **Encoding Categorical Data**: Categorical features (`Difficulty`, `Role`, `Range type`) were converted to numerical values using `LabelEncoder`.

- **Feature Scaling**: Numerical features (`Base HP`, `Base Mana`, `Difficulty`) were normalized using `StandardScaler` to ensure uniformity and improve model performance.

### 2. **Model Training**

- Split the data into training and testing sets using an 80-20 ratio.

- Trained a KNN classifier with `k=4` to predict the champion class.

### 3. **Prediction Pipeline**

- A user-friendly prediction pipeline was built to accept champion attributes, process the input, and return the predicted class as output.

---

### Acknowledgments

This project uses the **League of Legends Dataset** created by [Gabriel](https://www.kaggle.com/gabkgonzales) and [Marwan](https://www.kaggle.com/marwant1), available on [Kaggle](https://www.kaggle.com/datasets/gabkgonzales/league-of-legends-dataset). 

A big thank you to them for providing this excellent dataset for the community to explore and analyze.

## Implementation

### Data Preprocessing

```python

from sklearn.preprocessing import LabelEncoder, StandardScaler

#Encode the categorical variables

le = LabelEncoder()

df['Difficulty'] = le.fit_transform(df['Difficulty'])

df['Role'] = le.fit_transform(df['Role'])

df['Range type'] = le.fit_transform(df['Range type'])

df['Classes'] = le.fit_transform(df['Classes'])

#Normalize the numerical features

scaler = StandardScaler()

normalized_features = scaler.fit_transform(df[['Base HP', 'Base Mana', 'Difficulty']])