🦟 Malaria Prediction System

📋 Project Overview

A machine learning-based web application for predicting malaria infection risk based on patient symptoms and demographic information.

Live Demo: GitHub Repository: [GitHub Link]

🎯 Key Features

• 🔍 Real-time Prediction: Instant malaria risk assessment based on symptoms
• 📊 Multiple ML Models: Comparison of 6 different algorithms
• 📈 Interactive Dashboard: Comprehensive data visualization
• 📱 Responsive Design: Works on desktop and mobile devices
• 📥 Export Functionality: Download prediction reports as CSV

🏗️ Technology Stack

Component	Technology	Purpose
Frontend	Streamlit	Web interface
Backend	Python 3.13	Core logic
ML Framework	Scikit-learn	Model training
Data Processing	Pandas, NumPy	Data manipulation
Visualization	Matplotlib, Seaborn, Plotly	Charts & graphs
Deployment	Joblib	Model serialization

📁 Project Structure

🔧 Installation & Setup

Prerequisites

• Python 3.8 or higher
• pip package manager
• Git (optional)

Step-by-Step Installation

Method 1: Using Virtual Environment (Recommended)

1. Clone the repository

git clone https://github.com/yourusername/malaria-prediction-system.git cd malaria-prediction-system

2. Create virtual environment

python -m venv .venv

3. Activate virtual environment

On Windows:

.venv\Scripts\activate

On macOS/Linux:

source .venv/bin/activate

4. Install dependencies

pip install -r requirements.txt

5. Run the application

streamlit run app.py

project Link https://malariaprediction-yhr7p7wrw7xpkiwd5fc4yy.streamlit.app/

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