Delhi Air Quality Prediction

This repository contains a Jupyter Notebook that analyzes and predicts air quality in Delhi using historical data. The project leverages data visualization and preprocessing techniques to explore patterns in air pollutants and build predictive insights.

Project Overview

Sections Covered:

1. Importing Libraries: Loading necessary Python libraries.
2. EDA (Exploratory Data Analysis): Investigating the dataset structure and initial insights.
3. Distribution of Dataset: Exploring data distributions.
4. Formatting the Date Column: Converting and handling date values for time-series analysis.
5. Visualizing PM2.5 Levels Across Weekdays and Seasons: Seasonal and weekly trends in particulate matter (PM2.5) concentrations.
6. Observations: Key findings from visualizations and analysis.
7. Monthly Distribution of Pollutants: Monthly trends of air pollutants.

Libraries Used

• numpy: For numerical computations.
• pandas: For data manipulation and analysis.
• matplotlib.pyplot: For data visualization.
• seaborn: For enhanced statistical visualizations.
• warnings: To manage and suppress warnings.

Dataset

• File: delhi_aqi.csv
• Description: Historical data on air quality in Delhi, including various pollutants and timestamps.

Instructions to Run

1. Clone the repository:

   git clone https://github.com/B1tW1z/DelhiAQI-Analysis.git

2. Install the required libraries:

   pip install numpy pandas matplotlib seaborn

3. Open the Jupyter Notebook:

   jupyter notebook delhi-air-quality.ipynb

4. Ensure the dataset delhi_aqi.csv is in the same directory as the notebook.
5. Run the cells sequentially to reproduce the analysis.

Key Insights

• Seasonal and weekly trends of PM2.5 levels.
• Monthly variations in air pollutant concentrations.
• Observations that can inform policy decisions and public health measures.

Future Work

• Implementing machine learning models to predict air quality indices (AQI).
• Extending analysis to include meteorological data for enhanced predictions.

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