Author: Adham Allam

The primary goal of this project is to harness the power of machine learning to build a robust predictive model for disease diagnosis. By analyzing various health attributes, our aim is to accurately classify individuals into diseased or non-diseased categories. This model serves as a valuable tool for healthcare professionals, offering insights into early detection, diagnosis, and prognosis of diseases.

To achieve our objective, we employ a range of powerful tools and techniques, including:

• Python programming language
• Machine learning libraries such as scikit-learn
• Exploratory data analysis (EDA)
• SMOTE for oversampling
• Logistic Regression
• Decision Trees
• Random Forests

Before diving into model building, we meticulously explore our dataset to gain crucial insights into the underlying patterns and relationships. This step involves:

• Data loading
• Summary statistics
• Descriptive statistics
• Analysis of target variables
• Feature correlation examination

We load the dataset containing health attributes and disease status of individuals, preparing it for analysis and model development.

A comprehensive summary of the dataset is provided, including key statistics and characteristics essential for understanding the data's nature.

Detailed descriptive statistics are generated to shed light on various aspects of the dataset, aiding in feature selection and model building.

An in-depth analysis of the target variable is conducted to understand its distribution and significance in the context of disease diagnosis.

We explore the correlation between different features to identify potential relationships and dependencies that can influence the predictive modeling process.

To address class imbalance issues, Synthetic Minority Over-sampling Technique (SMOTE) is applied to create synthetic samples, ensuring a balanced representation of diseased and non-diseased individuals.

The dataset is divided into training and testing sets, facilitating the evaluation of model performance on unseen data.

We implement and evaluate the following machine learning models for disease diagnosis:

• Logistic Regression (accuracy: 72%)
• Decision Tree (accuracy: 86%)
• Random Forest (accuracy: 96%)

Each model undergoes rigorous testing and validation to assess its predictive capabilities and suitability for real-world application.

This README provides an overview of our predictive disease diagnosis model project, outlining our objectives, methodologies, and the tools utilized. Through meticulous analysis and modeling, we aim to contribute to the advancement of healthcare by providing accurate and reliable diagnostic tools.