🌍 Air Pollution Prediction using Machine Learning

📌 Overview

This project predicts real-time air pollution levels using Random Forest. It fetches data , preprocesses it, and provides insights into harmful gases like NO, PM10, NO3, and SO2. The system is designed to handle large datasets efficiently and provide accurate air quality predictions.

🚀 Features

✅ Real-time Air Quality Prediction using machine learning models.
✅ Data Collection & Preprocessing to handle missing values and outliers.
✅ Random Forest for accurate classification.
✅ Exploratory Data Analysis (EDA) and visualization for insights.
✅ Scalable & Efficient to handle large datasets.

🛠️ Technologies Used

• Programming Language: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn
• Machine Learning Algorithms: Random Forest
• Data Clustering: Cosine Similarity

📜 Installation & Usage

1️⃣ Install Dependencies

Ensure you have Python installed, then run:

pip install pandas numpy matplotlib seaborn scikit-learn

2️⃣ Run the Prediction Model

python AirQualityPredictor.py

3️⃣ View Results

• The system will process the dataset, train models, and display air quality predictions.
• Visualizations of pollutant levels will be generated.

📊 Data Processing Steps

1. Data Collection: Fetching pollution data from CPCB.
2. Preprocessing: Handling missing values, outliers, and normalizing data.
3. Feature Engineering: Extracting meaningful features from pollutant data.
4. Model Training: Applying Random Forest, Logistic Regression, and Decision Trees.
5. Evaluation: Using precision, recall, and confusion matrices to measure accuracy.

🏗️ Future Enhancements

🔹 Expand Dataset to include global pollution data.
🔹 User Interface (UI) for real-time data visualization.
🔹 Deep Learning Models (LSTM) for time-series forecasting.

🤝 Contributing

Pull requests are welcome! If you’d like to contribute, feel free to fork this repository and submit a PR.

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📌 Stay informed, breathe clean! 🌱