CASE STUDY
Delhi Air Quality Predictive Analytics
Applying exploratory data analysis and machine learning to understand pollution patterns and predict air quality.
ROLE
Data Scientist
PLATFORM
Python · Jupyter Notebook
FOCUS
Machine Learning · Exploratory Data Analysis
LIVE
The Real Problem
Air quality is influenced by multiple environmental factors, making it difficult to identify pollution trends and accurately predict air quality levels. Understanding these patterns is essential for environmental monitoring and informed decision-making.
The Approach
Analyze historical air quality data using exploratory data analysis, statistical visualization, and machine learning to uncover pollution patterns and build a predictive model for Air Quality Index (AQI).
The project combined data cleaning, feature engineering, visualization, and regression modeling to transform raw environmental measurements into meaningful insights and predictive outcomes.
What I Built
- Cleaned and preprocessed air quality data using Python and Pandas
- Performed exploratory data analysis to identify monthly, seasonal, and pollutant trends
- Created statistical visualizations to examine pollutant distributions and relationships
- Developed a Linear Regression model to predict Air Quality Index (AQI) from pollutant measurements
- Evaluated model performance using R², MAE, and RMSE metrics to assess predictive accuracy
What Changed
- Identified seasonal pollution trends and key pollutant relationships affecting air quality
- Demonstrated how environmental data can be transformed into predictive insights through machine learning
- Produced clear visualizations that communicate pollution patterns and model performance
- Delivered a reproducible analytical workflow combining data preparation, exploratory analysis, and predictive modeling