🩺 Diabetes Risk Prediction Report

This interactive report allows healthcare and business users to: - Explore risk factors for diabetes - View model insights - Input data to generate predictions

Understanding the problem

We are trying to predict the risk of diabetes in individuals using information like age, BMI, blood glucose, and lifestyle factors. This can help healthcare professionals focus on high-risk individuals.

A. BASIC SETUP

1. Imports Libraries

2. Load Dataset

📊 Business Overview

This notebook explores customer behavior based on demographic and transactional features. We’ll identify patterns and relationships using charts and ML models to enable actionable business decisions like segmentation, retention strategies, and product targeting.

DATA EXPLORATION

1. Basic EDA (Exploratory Data Analysis)

2. Visual Exploration

3. Correlations

DATA PRE-PROCESSING

1. Encode categorical values

2. Define features and target

3. Scale features

4. Split data

TRAINING

1. Create Model : Logistic Regression and Random Forest

2. Evaluate Model : ROC Curve for the Best Model (Random Forest)

3. Save Model

MAKE INFERENCES

1. Load Model

2. Input widgets

3. Generate Predictions