Diabetes prediction

This project focuses on developing a machine learning model to predict the likelihood of diabetes based on various medical factors. The model analyzes health data patterns and provides predictions to aid early diagnosis and risk assessment.

Features & Technologies Used

1. Data Collection & Preprocessing

Utilized datasets like Pima Indians Diabetes Dataset, which includes features such as glucose level, blood pressure, BMI, and age.

Performed data cleaning, handled missing values, and conducted exploratory data analysis (EDA) using Pandas, Matplotlib, and Seaborn.

2. Machine Learning Algorithms

Implemented various classification algorithms, including:

Logistic Regression

Random Forest

Support Vector Machine (SVM)

XGBoost

Optimized model performance using Hyperparameter Tuning and Cross-Validation.

3. Model Evaluation & Performance Improvement

Assessed model accuracy using Confusion Matrix, Precision, Recall, F1-score, and ROC-AUC Curve.

Compared different models to select the most efficient one in terms of accuracy and reliability.

4. User Interface Development (Optional)

Developed a web-based interface using Flask or Streamlit to allow users to input their medical data and receive predictions.

Real-World Applications

Can assist healthcare professionals in early diabetes diagnosis and risk management.

Can be integrated into mobile health applications for personal health monitoring.

Can be deployed in smart healthcare systems to support medical decision-making.

Skills Gained from the Project

Machine Learning & Data Science (model training and data analysis).

Python & Libraries (Scikit-Learn, NumPy, Pandas, Matplotlib).

Data Preprocessing & Feature Engineering.

Model Deployment using Flask or Streamlit.