🌍 Air Quality Predictor V1.0

🔬 Analyze, visualize, and forecast air pollution (PM2.5) trends using real-world data.

📑 Table of Contents

• Overview
• Features
• Project Structure
• Prerequisites
• Installation
• Usage
• Visualizations
• Technologies
• Contributing

🏷️ Overview

Air pollution impacts millions across the globe. This project leverages real-time data from the WAQI API to analyze, visualize, and predict PM2.5 air quality metrics using modern data science practices.

✨ Features

• 📲 Live Data Collection: Fetches air quality data (PM2.5, temperature, humidity) for any city
• 🧹 Data Cleaning: Handles missing values & normalizes features for robust analysis
• 📊 Exploratory Data Analysis: Intuitive plots to identify trends, outliers & distributions
• 🤖 Machine Learning Model: Linear regression-based PM2.5 prediction
• 📝 Model Evaluation: Key performance metrics and visual inspection
• 💾 Model Saving: Export your trained model for reuse
• 📒 Easy-to-Read Notebooks: All analyses clearly documented in Jupyter Notebooks

🗂️ Project Structure

📁 air-quality-analysis/
├── data_collection.py     # Fetches and stores live air quality data (WAQI API)
├── data_cleaning.py       # Cleans and preprocesses collected data
├── train_model.py         # Trains the machine learning model
├── eda.ipynb              # Jupyter Notebook for Exploratory Data Analysis
├── outputFiles/           # Stores intermediate data (CSV), models (pkl)
└── README.md              # Project documentation (this file!)

⚙️ Prerequisites

• Python 3.12+
• Get your WAQI API Key here
• Recommended: pip package manager

🚀 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/air-quality-analysis.git
   cd air-quality-analysis

2. Install dependencies:

   pip install -r requirements.txt

3. Configure your WAQI API key in :data_collection.py

   API_KEY = "your_api_key_here"

4. Ensure an output directory exists (if not, create one):

   mkdir outputFiles

🏃 Usage

Step 1: Collect live air quality data

python data_collection.py

Step 2: Clean and preprocess gathered data

python data_cleaning.py

Step 3: Explore your dataset visually

jupyter notebook eda.ipynb

Step 4: Train and evaluate the predictive model

python train_model.py

📈 Visualizations

• Distributions: Histogram of PM2.5 levels
• Trends: PM2.5 over time
• Correlations: Scatter plots (PM2.5 vs temp & humidity)
• Outlier Detection: Boxplots
• Model Performance: Actual vs Predicted PM2.5 plot

Example:

🛠️ Technologies

• Python: Data analysis & ML scripting
• Pandas, NumPy: Data manipulation
• Matplotlib, Seaborn: Visualization
• scikit-learn: Machine learning
• Requests: API data fetching
• Jupyter Notebook: Exploratory data analysis

🤝 Contributing

We ❤️ contributions!

1. Fork the repository
2. Create your branch (git checkout -b feature/something)
3. Commit your changes (git commit -m 'Add new feature')
4. Push to the branch (git push origin feature/something)
5. Open a Pull Request