Diabetes Prediction

📌 Project Overview

This project aims to predict the likelihood of a patient having diabetes based on health parameters. By utilizing machine learning models, it provides insights into potential diabetes risks and assists in early diagnosis.

🚀 Features

• Data preprocessing and exploratory data analysis (EDA)
• Feature selection and engineering
• Machine learning model development
• Model evaluation and optimization

🛠 Tech Stack

• Python
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn
• Jupyter Notebook

📂 Dataset

The dataset includes various medical parameters such as:

• Age
• Blood Pressure
• Glucose Levels
• BMI
• Insulin Levels
• Diabetes Pedigree Function

📊 Machine Learning Models Used

• Logistic Regression
• Random Forest Classifier
• Support Vector Machine (SVM)
• XGBoost

🔥 Results

The models are evaluated based on accuracy, precision, recall, and AUC-ROC score. The best-performing model provides reliable predictions for diabetes risk.

📁 Repository Structure

📂 Diabetes-Prediction
👉 📂 data (Dataset & processed data)
👉 📂 notebooks (Jupyter Notebooks)
👉 📂 models (Trained models)
👉 📂 images (Code and Results Screenshots)
👉 📄 README.md (Project documentation)

🖼 Code and Results

Include images of code and results in the images folder. Example:

💜 How to Run the Project

1. Clone the repository:

   git clone https://github.com/rohitinu6/Diabetes-Prediction.git

2. Navigate to the project folder:

   cd Diabetes-Prediction

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Jupyter Notebook or Python scripts to train and test models.

📞 Links

• GitHub Repository: Diabetes Prediction
• Portfolio: Rohit Dubey
• GitHub Profile: rohitinu6
• LinkedIn: Rohit Dubey
• Twitter/X: @rohitdubey003

🗝 Tags

Machine Learning Diabetes Prediction Health Analytics Data Science Python EDA

📝 License

This project is licensed under the MIT License.

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💡 For any queries or collaboration opportunities, feel free to connect! 🚀