- Prediction of diabetes based on patient health data.
- Utilizes machine learning algorithms for high accuracy.
- Includes data preprocessing steps to handle missing values and scale data.
- Provides evaluation metrics to assess model performance.
This project aims to predict the likelihood of diabetes in patients using a machine learning model developed in Python. By analyzing various health metrics such as glucose levels, blood pressure, BMI, and other relevant features, the model can assess the risk of diabetes with high accuracy. The dataset used for training the model is the PIMA Indian Diabetes Dataset, a well-known dataset in the medical community. The project involves data preprocessing steps to handle missing values and scale the data appropriately, ensuring that the machine learning algorithms can perform optimally. The model employs multiple machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machine (SVM), to determine the best approach for predicting diabetes. The performance of each algorithm is evaluated using metrics such as accuracy, precision, recall, and F1-score. The project also includes a comprehensive Jupyter Notebook for interactive exploration and visualization of the dataset and the model's results. This diabetes prediction model serves as a valuable tool for healthcare professionals to identify high-risk patients and take preventive measures, ultimately contributing to better health outcomes.