Diabetes is a chronic condition affecting millions of people worldwide. Early prediction and diagnosis are crucial in managing the disease and preventing severe health complications.

In this project, my goal was to develop a predictive model that can accurately forecast the likelihood of a patient developing diabetes based on their medical history and health parameters. I used the PIMA Indian Diabetes Dataset, which includes features such as age, BMI, blood pressure, and glucose levels.

The process involved several steps:

1.	Data Preprocessing: Cleaning the data, handling missing values, and normalizing the features to ensure the model’s effectiveness.
2.	Feature Selection: Identifying the most significant features that contribute to diabetes prediction.
3.	Model Selection: Experimenting with various machine learning algorithms such as Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines.
4.	Model Evaluation: Using metrics like accuracy, precision, recall, and F1-score to evaluate the performance of the models.

After thorough experimentation and tuning, the Random Forest model emerged as the best performer, achieving an accuracy of around 80%. This project taught me the importance of feature selection and the impact of data preprocessing on the model’s performance.