https://github.com/uznetdev/smoking-prediction
This project focuses on analyzing the "Smoking" dataset and building a predictive model for smoking status based on various health metrics. The goal is to identify factors influencing smoking behavior and develop a reliable model for prediction.
https://github.com/uznetdev/smoking-prediction
ai classification data data-science kaggle-competition machine-learning ml roc-auc sklearn smoking
Last synced: about 2 months ago
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This project focuses on analyzing the "Smoking" dataset and building a predictive model for smoking status based on various health metrics. The goal is to identify factors influencing smoking behavior and develop a reliable model for prediction.
- Host: GitHub
- URL: https://github.com/uznetdev/smoking-prediction
- Owner: UznetDev
- License: mit
- Created: 2024-10-27T14:23:56.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-10T06:54:08.000Z (6 months ago)
- Last Synced: 2025-04-05T19:41:53.239Z (about 2 months ago)
- Topics: ai, classification, data, data-science, kaggle-competition, machine-learning, ml, roc-auc, sklearn, smoking
- Language: Jupyter Notebook
- Homepage:
- Size: 28.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# NT-M5-H1 Smoking Prediction Competition
This repository contains a solution to the **NT-M5-H1 Smoking Prediction Competition**, which focuses on predicting smoking status using health-related data. This project employs machine learning models, primarily using Python and various data science libraries, to create accurate predictions based on provided datasets.
## Project Overview
The goal of this project is to predict whether an individual is a smoker based on several health indicators. The dataset includes various attributes related to age, height, weight, cholesterol levels, blood pressure, and other health metrics.
### Key Features
- **Data Analysis and Visualization**: Initial data exploration and visualizations to understand feature distributions and relationships.
- **Feature Engineering**: Creation of new, relevant features to enhance model performance.
- **Modeling**: Implementation of multiple classifiers, including logistic regression, decision trees, ensemble methods, and more.
- **Hyperparameter Tuning**: Finding optimal model parameters using techniques like cross-validation and grid search.
- **Evaluation**: Assessing model performance using metrics such as ROC-AUC and accuracy.
## Repository Structure
```
NT-M5-H1-Smoking-Cmpetition/
├── notebooks/ # Jupyter notebooks for data analysis and modeling
├── data/ # datasets for usage model
├── requirements.txt # List of required libraries and dependencies
├── smoking_model.joblib # Finla model for prediction
└── README.md # Project documentation
```
## Installation
To run this project locally, follow these steps:
1. Clone this repository:
```bash
git clone https://github.com/UznetDev/NT-M5-H1-Smoking-Cmpetition.git
```
2. Navigate to the project directory:
```bash
cd NT-M5-H1-Smoking-Cmpetition
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
### Usage
To load and use the model for prediction, follow these steps:
```python
from joblib import load
# Load the model
model = load('smoking_model.joblib')
# Define input data (X) for prediction
# Example input data for one individual
X_new = [] # Replace with actual input values
# Predict smoking status
prediction = model.predict(X_new)
# Display the result
if prediction[0] == 1:
print("This individual is likely a smoker.")
else:
print("This individual is likely a non-smoker.")
```
### Model Details
The `smoking_model.joblib` file contains a pre-trained model created with `scikit-learn`. You can use the `predict` function to determine smoking status based on input health measurements.
**Note:** The example input (`X_new`) should be replaced with real data according to your project requirements.
## Dataset
The dataset consists of several features related to individuals' health, including:
- **Basic Attributes**: Age, height, weight, waist circumference, eyesight, and hearing levels.
- **Blood Pressure**: Systolic and diastolic blood pressure.
- **Blood Metrics**: Cholesterol, triglyceride, HDL, LDL, and hemoglobin levels.
- **Additional Health Indicators**: Serum creatinine, AST, ALT, Gtp, and urine protein.
- **Target**: A binary column indicating whether an individual is a smoker (1 for smoker, 0 for non-smoker).
### Feature Engineering
The following new features have been engineered to enhance the model's predictive power:
- **BMI** (Body Mass Index)
- **Waist-to-Height Ratio**
- **Cholesterol Ratio** (Total Cholesterol/HDL)
- **Liver Enzyme Ratio** (ALT/AST)
## Models Used
The project explores multiple machine learning algorithms for classification:
- **Linear Models**: Logistic Regression, Ridge Classifier, SGD Classifier
- **Naive Bayes Classifiers**: GaussianNB, MultinomialNB, BernoulliNB
- **Support Vector Machines**: SVC, LinearSVC, NuSVC
- **Decision Trees and Ensembles**: Decision Tree, RandomForest, Gradient Boosting, AdaBoost, Extra Trees
- **Discriminant Analysis**: Linear Discriminant Analysis, Quadratic Discriminant Analysis
### Hyperparameter Tuning
To find the best parameters for the models, this project utilizes optuna and cross-validation methods, specifically focusing on maximizing ROC-AUC scores to improve model performance.
## Evaluation
The models are evaluated based on the following metrics:
- **ROC-AUC Score**: Primary metric to assess model performance in terms of distinguishing between smokers and non-smokers.
## Usage
1. **Exploratory Data Analysis (EDA)**: Start with EDA notebooks in the `notebooks/` folder to understand the dataset and visualize patterns.
2. **Model Training**: Run the scripts or notebooks to train the models and evaluate their performance.
3. **Hyperparameter Tuning**: Use the scripts for hyperparameter tuning to improve model accuracy.
4. **Save nodel**: save model in 'model.joblib.
## Contributing
Contributions are welcome! If you'd like to improve this project, please fork the repository and make a pull request.
1. Fork the repository.
2. Create a new branch for your feature or bug fix:
```bash
git checkout -b feature-name
```
3. Commit your changes:
```bash
git commit -m "Add a new feature"
```
4. Push to your branch:
```bash
git push origin feature-name
```
5. Open a pull request.
## License
This project is licensed under the [MIT](LICENCE) License.
## Contact's
If you have any questions or suggestions, please contact:
- Email: [email protected]
- GitHub Issues: [Issues section](https://github.com/UznetDev/NT-M5-H1-Smoking-Cmpetition/issues)
- GitHub Profile: [UznetDev](https://github.com/UznetDev/)
- Telegram: [UZNet_Dev](https://t.me/UZNet_Dev)
- Linkedin: [Abdurakhmon Niyozaliev](https://www.linkedin.com/in/abdurakhmon-niyozaliyev-%F0%9F%87%B5%F0%9F%87%B8-66545222a/)
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Thank you for your interest in this project. We hope it helps in your journey to understand and predict smoking habits using data science!