🧠 Alzheimer’s Disease Prediction System

📌 Overview

This project focuses on predictive analysis of Alzheimer’s disease using machine learning techniques. The goal is to enable early detection and risk assessment by analyzing patient data and identifying patterns that indicate the likelihood of the disease.

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🎯 Objective

To develop a reliable and efficient model that predicts Alzheimer’s disease at an early stage, helping in timely diagnosis and improved healthcare outcomes.

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🚀 Features

• Data preprocessing and cleaning
• Exploratory Data Analysis (EDA)
• Feature selection and engineering
• Multiple machine learning models
• Model evaluation (Accuracy, Precision, Recall, F1-score)
• Interactive web application for predictions

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🤖 Machine Learning Model

Models Used:

• Logistic Regression
• Random Forest
• Support Vector Machine (SVM)

Workflow:

1. Data Collection
2. Data Cleaning & Preprocessing
3. Feature Selection
4. Model Training
5. Model Evaluation
6. Prediction

The best-performing model is selected based on evaluation metrics and used in the web application.

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🌐 Web Application

Key Functionalities:

• Input patient data through a simple interface
• Real-time prediction of Alzheimer’s risk
• Displays prediction results clearly

Tech Used:

• Frontend: HTML, CSS, JavaScript
• Backend: Python (Flask / Django)
• ML Integration: Pickle / Joblib

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib / Seaborn
• Flask / Django