metadata
language:
- en
metrics:
- accuracy
- precision
base_model:
- Hemg/logistic_Regression_model
library_name: sklearn
tags:
- Smoker_Classifier_Model
Smoker Classifier Model
Objective:
This machine learning model classifies individuals as smokers or non-smokers based on various features such as age, gender, BMI, number of children, region, and medical charges. The model uses logistic regression for classification and was trained on an insurance dataset.
Dataset Description:
The dataset includes the following features:
- Age: Age of the primary beneficiary.
- Sex: Gender of the insurance contractor (female or male).
- BMI: Body Mass Index, an objective measure of body weight (kg/m²).
- Children: Number of children or dependents covered by health insurance.
- Smoker: Whether the individual is a smoker or not.
- Region: The region where the beneficiary resides in the US (northeast, southeast, southwest, northwest).
- Charges: The individual medical costs billed by the health insurance company.
Model Information:
- Algorithm: Logistic Regression
- Accuracy: 89.89%
- Precision: 95.24%
Preprocessing:
- Missing data handling (e.g., filling missing BMI values).
- Categorical data conversion (e.g., gender and smoker columns converted to numeric).
- Data normalization for better performance.
How to Use:
To use this model, download the model from the Hugging Face repository and load it using the joblib library.
from huggingface_hub import hf_hub_download
import joblib
model_path = hf_hub_download(
repo_id="ShaimaAli1/Smoker_Classifier_Model",
filename="Smoker_Classifier_Model.pkl"
)
model = joblib.load(model_path)