Bengaluru Air Quality Analysis 🌍📊

This project focuses on analysing the air quality of Bengaluru using Python and statistical techniques.

The analysis uses AQI data along with major air pollutants such as PM2.5, PM10, Carbon Monoxide, Nitrogen Dioxide, Sulphur Dioxide, Ozone, and Dust.

🔍 What I Analysed

• Descriptive statistics of AQI
• Distribution of major air pollutants
• AQI trends over time
• Yearly and monthly AQI patterns
• AQI category distribution
• Correlation between pollutants and AQI
• PM2.5 and AQI relationship
• Hypothesis testing
  • Shapiro-Wilk Test
  • One-Sample t-Test
  • Independent Samples t-Test
  • Chi-Square Test
• Multiple Linear Regression
• Actual vs Predicted AQI

📊 Key Findings

• January recorded the highest average AQI.
• July recorded the lowest average AQI.
• The Moderate category occurred most frequently.
• PM2.5 showed the strongest relationship with AQI.
• The multiple linear regression model achieved an R² of approximately 0.93.

🛠️ Tools & Technologies

• Python
• Pandas
• NumPy
• SciPy
• Matplotlib
• Statistical Analysis
• Data Visualization
• Hypothesis Testing
• Linear Regression

🎓 Project Context

This project was completed as part of the Python Programming for Statistics course during my M.Sc. Statistics programme at CHRIST (Deemed to be University).

The project helped me apply statistical concepts and Python programming techniques to a real-world environmental dataset.