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----
-library_name: sklearn
-tags:
-- random-forest
-- stroke-prediction
-- sklearn
-pipeline_tag: tabular-classification
-license: mit
----
-
-
-# Stroke Prediction Random Forest Model
-
-This project uses a Random Forest model to predict the risk of strokes based on user input features. The model has been deployed on Hugging Face for seamless integration.
-
-## Features
-- Predicts the likelihood of a stroke based on various health parameters.
-- Fast and efficient model, hosted on Hugging Face.
-
-## Input Features
-The model expects the following inputs:
-- `age`: Patient's age (numeric)
-- `age_group`: Patients age group child(Less than 18 ),Young Adult (18-34 ), Adult (35-59 ), Senior (60 and over )
-- `hypertension`: 1 if the patient has hypertension, else 0
-- `heart_disease`: 1 if the patient has heart disease, else 0
-- `avg_glucose_level`: Average glucose level in the blood
-- `bmi`: Body Mass Index
-- `gender`: Male/Female/Other
-- `ever_married`: Yes/No
-- `work_type`: Type of work (e.g., Private, Self-employed, never_worked)
-- `Residence_type`: Urban/Rural
-- `smoking_status`: Smoking habits (e.g., never smoked, formerly smoked)
-
-## Model Deployment
-The model has been deployed on the [Hugging Face Hub](https://huggingface.co). You can access it via my repo [Random Forest Model for Stroke Prediction](https://huggingface.co/Asiya-Mohammed/random-forest-model).
-