Random Forest for Diabetes Prediction 🍏🍩

Project Overview 🌟

This project aims to predict the likelihood of diabetes in patients using machine learning techniques. By leveraging Random Forest algorithms, the model identifies patterns in key health indicators to provide accurate predictions and support decision-making in diabetes prevention and management. 💡

Key Features 🔑

Data Preparation 🧹

• Dataset: Medical records including health metrics such as glucose levels, BMI, and blood pressure. 🩺
• Cleaning: Removed duplicates, outliers, and anomalous values based on domain knowledge. 🧼
• Normalization: Standardized numerical features for optimal model performance. ⚙️

Exploratory Data Analysis (EDA) 🔍

• Visualized data distributions and relationships using Seaborn and Matplotlib. 📊
• Generated heatmaps to identify correlations between features. 🌡️

Modeling 🤖

• Algorithm: Random Forest Classifier implemented using Scikit-learn. 🌲
• Hyperparameter Tuning: Optimized using GridSearchCV for improved accuracy. 🔧
• Evaluation: Assessed model performance with metrics like accuracy and confusion matrices. 📉

Results 🏆

• The model achieved an accuracy score of 0.81 on the test dataset, demonstrating strong predictive performance. 🎯
• Key features such as glucose levels and BMI were identified as critical indicators for diabetes prediction. 🍩

Repository Structure 🗂️

├── data
│   ├── raw
│   ├── processed
├── models
├── src
└── README.md

Acknowledgments 🙏

This project uses publicly available datasets and tools, and it is inspired by real-world challenges in healthcare data analysis. 🏥

License 📄

This project is licensed under the MIT License. See the LICENSE file for more details. ⚖️