🔬 Diabetes Disease Prediction with Machine Learning

Welcome to the Diabetes Disease Prediction Project! This repository demonstrates the use of machine learning models, specifically ensemble methods, to predict diabetes based on medical and demographic data.

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🚀 Project Overview

This project focuses on developing accurate and reliable machine learning models to predict diabetes status (positive or negative) using the Diabetes Prediction Dataset. The dataset includes key medical indicators such as age, BMI, hypertension, heart disease, and blood glucose levels.

The goal is to assist healthcare professionals in identifying at-risk individuals and personalizing treatment strategies. By utilizing ensemble methods, the project ensures robust and efficient predictions.

Explore the detailed implementation on Kaggle:
Diabetes Disease Prediction using Ensemble Method

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📊 Dataset

The dataset used in this project is sourced from Kaggle:
Diabetes Prediction Dataset

Dataset Features:

• Age: Age of the patient.
• Gender: Male or Female.
• Body Mass Index (BMI): A measure of body fat based on height and weight.
• Hypertension: Presence or absence of high blood pressure.
• Heart Disease: Presence or absence of heart-related conditions.
• Smoking History: Patient's smoking status.
• HbA1c Level: Average blood glucose level over the past 3 months.
• Blood Glucose Level: Current blood glucose level.

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🔧 Technologies Used

• Python: For data preprocessing, model training, and evaluation.
• Scikit-learn & XGBoost: For implementing ensemble methods like Random Forest, Gradient Boosting, etc.
• Pandas & NumPy: For data manipulation and feature engineering.
• Matplotlib & Seaborn: For data visualization and exploratory data analysis.
• Streamlit: For creating an interactive dashboard for real-time predictions.

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📂 Project Files

• app.py: A Streamlit-based dashboard for interactive visualization and real-time diabetes prediction.
• diabetes_prediction_model.ipynb: Jupyter Notebook containing the step-by-step implementation of machine learning models.
• data_preprocessing.py: Python script for data cleaning, feature engineering, and preprocessing.
• requirements.txt: List of dependencies required to run the project.
• README.md: Comprehensive project documentation.

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🛠️ Installation

To set up and run the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/diabetes-prediction-model.git  
   cd diabetes-prediction-model