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README.md
Air Quality Index (AQI) Prediction using Machine Learning
Prerequisites
Python 3.x
Flask
Flask-Cors
Pickle
Install Dependencies:
pip install Flask flask-cors pickle
Project Overview
This project aims to develop a machine learning model capable of predicting Air Quality Index (AQI) values based on historical meteorological and air quality data. The model will utilize a combination of data preprocessing, feature engineering, and machine learning algorithms to accurately forecast AQI levels.
Data
The dataset used for this project should contain the following information:
- Meteorological Data:
- Temperature
- Humidity
- Wind speed
- Atmospheric pressure
- Rainfall
- Air Quality Data:
Data Preprocessing
- Handling Missing Values:
- Identify and handle missing values using techniques like imputation or removal.
- Feature Engineering:
- Create new features that might be relevant for prediction, such as:
- Time-based features (e.g., hour, day of week, season)
- Meteorological indices (e.g., relative humidity)
- Data Splitting:
- Divide the dataset into training and testing sets for model training and evaluation.
Model Development
- Algorithm Selection:
- Experiment with various machine learning algorithms, including:
- Linear Regression
- Decision Trees
- Random Forest
- Support Vector Regression
- Neural Networks
- Model Training:
- Train the selected model on the training dataset.
- Model Evaluation:
- Evaluate the model's performance using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).
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