Diabetes Prediction

This project aims to build a machine learning model capable of predicting whether a patient is likely to have diabetes based on various medical attributes. The dataset typically includes features such as glucose level, BMI, insulin, age, and blood pressure.

Project Overview

The notebook walks through the complete machine learning pipeline:

Data Collection & Loading – Importing and exploring a diabetes dataset (such as the Pima Indians Diabetes Database).

Data Preprocessing – Handling missing values, feature scaling, and splitting data into training and testing sets.

Model Building – Applying classification algorithms such as:

Logistic Regression

Decision Tree Classifier

Random Forest Classifier

Support Vector Machine (SVM)

Model Evaluation – Measuring performance using accuracy, precision, recall, F1-score, and confusion matrix.

Prediction System – Building a simple interactive system where users can input patient data and receive a diabetes prediction.

Technologies Used

Python

NumPy, Pandas for data handling

Matplotlib, Seaborn for visualization

Scikit-learn for machine learning models and metrics

Google Colab / Jupyter Notebook for development environment

Outcome

The final model provides a reliable tool to assist in early diabetes detection, which can help in preventive healthcare decision-making. The notebook demonstrates end-to-end model development — from raw data to deployable prediction.