Which algorithms are used in this air quality prediction project?

The system implements and compares two regression algorithms: Lasso (L1 regularization) and Ridge (L2 regularization). Ridge Regression achieved superior performance with 86.4% accuracy and 0.911 ROC-AUC.

What is the accuracy of the air quality prediction model?

The Ridge Regression model achieves 86.4% accuracy with an F1-Score of 0.853 and ROC-AUC of 0.911, demonstrating strong predictive capability for environmental monitoring.

Who can benefit from this air quality prediction system?

Environmental agencies, urban planners, public health officials, researchers, and policymakers can use this system for pollution monitoring, early warning systems, and public health protection.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, and imbalanced-learn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

Intelligent Air Quality Prediction and Environmental Monitoring System - Complete Project Demo Video

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Machine Learning

Intelligent Air Quality Prediction and Environmental Monitoring System

The Intelligent Air Quality Prediction and Environmental Monitoring System is a comprehensive machine learning solution that predicts high CO levels from air quality sensor data. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Lasso and Ridge Regression with leak-free SMOTE, achieving 86.4% accuracy and 0.911 ROC-AUC.

The system utilizes the AirQuality.csv dataset containing 9,357 samples with 14 features including temperature, humidity, and various pollutant concentrations. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time air quality prediction, enabling environmental agencies and public health officials to make informed decisions regarding pollution control measures.

Python 3.8+ Machine Learning Lasso Regression Ridge Regression Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Air Quality Prediction
Ridge Regression (86.4% Accuracy)
Lasso Regression (84.2% Accuracy)
Feature Engineering (pollutant ratios, environmental interactions)
Leak-free SMOTE Cross-Validation
Interactive Visualizations
Real-time CO Level Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

📊 Lasso Regression

L1-regularized linear model with feature selection, alpha=0.01

🎯 Accuracy: 84.2%

📈 Ridge Regression

L2-regularized linear model with stable coefficients, alpha=1.0

🎯 Accuracy: 86.4%

🔧 Feature Engineering

co_nox_ratio, no2_nox_ratio, temp_humidity_ratio, temp_humidity_product

📊 Top Feature: CO(GT)

🔄 SMOTE Oversampling

Synthetic minority oversampling applied leak-free inside CV folds

📊 CV AUC: 0.902

Methodology & Workflow

1 Data Collection
9,357 air quality samples with 14 features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Lasso and Ridge Regression with leak-free SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time air quality prediction

Model Performance Comparison

Metric	Lasso Regression	Ridge Regression	Best
Test Accuracy	84.2%	86.4%	Ridge
Test Precision (Weighted)	83.5%	85.8%	Ridge
Test Recall (Weighted)	84.2%	86.4%	Ridge
Test F1-Score (Weighted)	83.1%	85.3%	Ridge
Test ROC-AUC	89.2%	91.1%	Ridge
CV Mean ROC-AUC	87.9%	90.2%	Ridge

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial AirQuality Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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