Disease Outbreak Prediction Project - Complete Overview

This project aims to predict disease outbreaks based on environmental, demographic, and healthcare-related factors. It leverages machine learning models and clustering techniques to categorize risk levels and make predictions.

🔹 Technologies Used & Their Purpose The project involves multiple technologies and libraries, each serving a specific function:

1️⃣ Python 🐍 (Core Programming Language) Used for data processing, analysis, and machine learning. Provides flexibility, scalability, and a rich ecosystem for AI/ML applications.

2️⃣ Data Handling & Processing Technology Purpose Pandas (import pandas as pd) Data manipulation, handling missing values, encoding categorical variables. NumPy (import numpy as np) Efficient numerical operations, mathematical computations. Matplotlib & Seaborn (import matplotlib.pyplot as plt, import seaborn as sns) Data visualization (correlation heatmaps, feature importance).

3️⃣ Data Preprocessing & Feature Engineering Technology Purpose Scikit-Learn (sklearn.preprocessing) Standardizing features using StandardScaler() for normalization. LabelEncoder Converts categorical variables into numerical format.

4️⃣ Machine Learning & Clustering Technology Purpose Scikit-Learn (sklearn.ensemble) Uses RandomForestClassifier() to make predictions. Scikit-Learn (sklearn.cluster) Uses KMeans & DBSCAN for clustering outbreak risk levels. K-Means Clustering (KMeans(n_clusters=3))

Categorizes regions into low, medium, and high risk based on data patterns. Works well for large datasets with clear cluster separation. DBSCAN Clustering (DBSCAN(eps=0.5, min_samples=5)) Used for smaller datasets with noisy data. Identifies outliers in risk categorization.

5️⃣ Model Training & Evaluation Technology Purpose Train-Test Split Splits the dataset into training and testing sets (80-20). Random Forest Classifier (RandomForestClassifier()) Predicts outbreak risks based on historical data. SMOTE & Random Undersampling (imbalanced-learn) Balances the dataset by handling class imbalances. Performance Metrics (sklearn.metrics) Evaluates model accuracy, F1-score, ROC-AUC score. accuracy_score() → Measures how often predictions are correct. f1_score() → Handles class imbalance by considering both precision and recall. roc_auc_score() → Evaluates multi-class classification performance.

6️⃣ Visualization & Insights Technology Purpose Matplotlib & Seaborn Plots feature importance, correlation heatmaps. Color-coded Heatmap Visualizes relationships between variables. Feature Importance Analysis Identifies the most impactful factors in outbreak prediction.

📌 Project Workflow - Step by Step

1️⃣ Data Collection & Preprocessing ✔ Loads dataset from Google Drive (CSV format). ✔ Handles missing values, categorical encoding, and feature scaling.

2️⃣ Clustering (Risk Categorization) ✔ K-Means / DBSCAN clusters regions into different outbreak risk levels.

3️⃣ Machine Learning Model Training ✔ Splits dataset into training and testing sets. ✔ Applies SMOTE & Random Undersampling to balance classes. ✔ Trains a Random Forest Classifier for outbreak risk prediction.

4️⃣ Model Evaluation ✔ Calculates Accuracy, F1-Score, ROC-AUC Score. ✔ Displays feature importance for insights.

5️⃣ Visualization & Analysis ✔ Heatmap for feature correlation. ✔ Color-coded risk categorization. ✔ Feature importance ranking to understand key outbreak drivers.

🔹 Deployment & Execution Platforms Platform Purpose Google Colab Cloud-based execution with no setup required. VS Code (Mac) Local execution with GitHub version control. GitHub Code storage, version control, and collaboration. 📌 Summary This project is a data-driven, AI-powered solution to predict disease outbreaks. It integrates machine learning, data visualization, and clustering techniques to provide actionable insights for public health management.

📌 Disease Outbreak Prediction - README.md 📝 Project Overview This project predicts disease outbreak risks using machine learning models and clustering techniques. It analyzes environmental, demographic, and healthcare factors to classify regions based on outbreak probabilities.

Technology Purpose Python - Core programming language Pandas & NumPy - Data manipulation & numerical processing Matplotlib & Seaborn - Data visualization (heatmaps, feature importance) Scikit-Learn - Machine learning (classification, clustering) Imbalanced-Learn - Handling class imbalance (SMOTE & undersampling) Google Colab & VS Code - Development & execution environments GitHub - Version control & project collaboration

Demographics: Population density, age distribution Healthcare: Hospital availability, vaccination rates Environmental: Air quality, temperature, humidity Historical Outbreaks: Past disease incidents

💾 Dataset Link: Click Here