Air Quality Index Prediction – IT and Computer Engineering Guide
1. Project Overview
Objective: Build a machine learning model to predict the Air
Quality Index (AQI) based on environmental data.
Scope: Use historical air quality data including pollutant levels, temperature,
and humidity to predict AQI, aiding in environmental monitoring and public
health awareness.
2. Prerequisites
Knowledge: Basics of Python programming, regression
analysis, and data visualization.
Tools: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and XGBoost.
Dataset: Air quality datasets from sources like Kaggle or government
environmental agencies.
3. Project Workflow
- Dataset Collection: Obtain air quality data containing pollutant levels, weather conditions, and AQI.
- Data Preprocessing: Handle missing values, normalize data, and perform feature engineering.
- Exploratory Data Analysis (EDA): Analyze trends, correlations, and distributions of pollutants and AQI.
- Model Development: Use regression models like Linear Regression, Random Forest, or XGBoost to predict AQI.
- Model Evaluation: Evaluate model performance using metrics like Mean Absolute Error (MAE) and R-squared.
- Deployment: Develop a web or mobile app to input parameters and display predicted AQI.
4. Technical Implementation
Step 1: Import Libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from sklearn.metrics import mean_absolute_error, r2_score
import matplotlib.pyplot as plt
import seaborn as sns
Step 2: Load and Preprocess Data
# Load dataset
data = pd.read_csv('air_quality_data.csv')
# Handle missing values
data.fillna(data.mean(), inplace=True)
# Feature engineering
data['Day'] = pd.to_datetime(data['Date']).dt.day
data['Month'] = pd.to_datetime(data['Date']).dt.month
# Select features and target
X = data[['PM2.5', 'PM10', 'NO2', 'Temperature', 'Humidity']]
y = data['AQI']
Step 3: Train the Model
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
# Train XGBoost model
model = XGBRegressor()
model.fit(X_train, y_train)
# Predictions
y_pred = model.predict(X_test)
Step 4: Evaluate the Model
# Calculate metrics
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"Mean Absolute Error (MAE): {mae}")
print(f"R-squared (R2): {r2}")
Step 5: Visualize Predictions
# Plot actual vs predicted AQI
plt.figure(figsize=(10, 6))
plt.scatter(y_test, y_pred, alpha=0.5, color='blue')
plt.xlabel('Actual AQI')
plt.ylabel('Predicted AQI')
plt.title('Actual vs Predicted AQI')
plt.show()
5. Results and Insights
Analyze the performance of the model and interpret its ability to accurately predict AQI. Identify and address any potential weaknesses in the model.
6. Challenges and Mitigation
Data Quality: Ensure accurate and complete data collection
to avoid misleading predictions.
Feature Selection: Select the most relevant features for accurate AQI
prediction.
7. Future Enhancements
Incorporate real-time air quality monitoring data for
dynamic predictions.
Expand the model to predict AQI across different locations and integrate
geospatial data.
8. Conclusion
The Air Quality Index Prediction project highlights the application of machine learning in environmental monitoring, promoting healthier living conditions and awareness.