---
dataset_info:
  features:
  - name: Age
    dtype: int64
  - name: Gender
    dtype: int64
  - name: Air_Pollution
    dtype: int64
  - name: Alcohol_use
    dtype: int64
  - name: Dust_Allergy
    dtype: int64
  - name: OccuPational_Hazards
    dtype: int64
  - name: Genetic_Risk
    dtype: int64
  - name: chronic_Lung_Disease
    dtype: int64
  - name: Balanced_Diet
    dtype: int64
  - name: Obesity
    dtype: int64
  - name: Smoking
    dtype: int64
  - name: Passive_Smoker
    dtype: int64
  - name: Chest_Pain
    dtype: int64
  - name: Coughing_of_Blood
    dtype: int64
  - name: Fatigue
    dtype: int64
  - name: Weight_Loss
    dtype: int64
  - name: Shortness_of_Breath
    dtype: int64
  - name: Wheezing
    dtype: int64
  - name: Swallowing_Difficulty
    dtype: int64
  - name: Clubbing_of_Finger_Nails
    dtype: int64
  - name: Frequent_Cold
    dtype: int64
  - name: Dry_Cough
    dtype: int64
  - name: Snoring
    dtype: int64
  - name: Level
    dtype:
      class_label:
        names:
          '0': Low
          '1': Medium
          '2': High
  splits:
  - name: train
    num_bytes: 192000
    num_examples: 1000
  - name: test
    num_bytes: 192000
    num_examples: 1000
  download_size: 40754
  dataset_size: 384000
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---




# Lung Cancer Risk Classification Dataset

## Purpose
This dataset is designed for building machine learning models to **predict the risk level of lung cancer** based on patient demographics, lifestyle, and medical history.  
It can be used for **research, model training, and educational purposes** in healthcare AI applications.  
The dataset contains **23 input features** and one target label (`Level`) with three classes:  
- Low (0)  
- Medium (1)  
- High (2)

---

## Dataset Guide

### Features
| Feature Name | Type | Description |
|--------------|------|-------------|
| Age | int | Patient age |
| Gender | int | Gender encoded as 0/1 |
| Air_Pollution | int | Exposure to air pollution |
| Alcohol_use | int | Alcohol consumption indicator |
| Dust_Allergy | int | Presence of dust allergy |
| OccuPational_Hazards | int | Occupational hazards exposure |
| Genetic_Risk | int | Genetic risk factor |
| chronic_Lung_Disease | int | History of chronic lung disease |
| Balanced_Diet | int | Balanced diet indicator |
| Obesity | int | Obesity indicator |
| Smoking | int | Smoking habit indicator |
| Passive_Smoker | int | Exposure to passive smoking |
| Chest_Pain | int | Chest pain indicator |
| Coughing_of_Blood | int | Presence of blood in cough |
| Fatigue | int | Fatigue indicator |
| Weight_Loss | int | Weight loss indicator |
| Shortness_of_Breath | int | Shortness of breath indicator |
| Wheezing | int | Wheezing indicator |
| Swallowing_Difficulty | int | Difficulty swallowing indicator |
| Clubbing_of_Finger_Nails | int | Clubbing of finger nails indicator |
| Frequent_Cold | int | Frequency of colds |
| Dry_Cough | int | Dry cough indicator |
| Snoring | int | Snoring indicator |
| Level | class | Target label: 0=Low, 1=Medium, 2=High |

### Recommended Usage
1. **Load the dataset** using the `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("edcelbogs/lung-cancer-classification")



### Prepare for training in TensorFlow or PyTorch:

#Example Convert to TensorFlow dataset:

tf_dataset = dataset["train"].to_tf_dataset(
    columns=[c for c in dataset["train"].column_names if c != "Level"],
    label_cols="Level",
    shuffle=True,
    batch_size=32
)