Merge pull request #756 from Varunshiyam/Fixes-#754

Fixes #754 Air quality prediction

Prediction Models/Air_Quality_prediction/Readme.md

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+# Air Quality Prediction Model using LSTM
+
+This repository contains an LSTM-based model for predicting air quality levels. The model analyzes historical air quality data to forecast future levels, which is valuable for planning interventions and informing the public about expected pollution levels.
+
+## Table of Contents
+
+- [Problem Statement](#problem-statement)
+- [Project Overview](#project-overview)
+- [Model Architecture](#model-architecture)
+- [Dataset](#dataset)
+- [Preprocessing](#preprocessing)
+- [Training and Evaluation](#training-and-evaluation)
+- [Results](#results)
+- [Usage](#usage)
+- [Future Work](#future-work)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Problem Statement
+
+Air quality significantly impacts public health, and accurate forecasting allows for timely interventions. However, due to various influencing factors, air quality prediction requires complex modeling. This project leverages an LSTM model to predict future air quality levels based on historical data, addressing the need for a reliable forecasting tool.
+
+## Project Overview
+
+This project applies a Long Short-Term Memory (LSTM) model to:
+1. Learn from temporal patterns in historical air quality data.
+2. Forecast future air quality levels, assisting in monitoring and policy-making.
+
+The model provides insights into future pollution levels, helping to mitigate adverse effects on the environment and public health.
+
+## Model Architecture
+
+The LSTM model architecture consists of:
+- **Input Layer**: Processes sequential data from historical air quality measurements.
+- **LSTM Layers**: Captures long-term dependencies in data, essential for accurate forecasting.
+- **Dense Layer**: Outputs the forecasted air quality level.
+
+## Dataset
+
+The dataset includes time-series air quality measurements with features such as pollutant concentration, temperature, humidity, and other meteorological indicators. Each data point represents an air quality measurement over time.
+
+## Preprocessing
+
+Data preprocessing steps:
+1. **Normalization**: Scales features to ensure consistency.
+2. **Sequence Generation**: Creates sequences of historical data to train the LSTM model.
+3. **Splitting**: Divides data into training, validation, and test sets.
+
+## Training and Evaluation
+
+- **Training**: The model is trained on historical air quality data, optimizing for Mean Squared Error (MSE).
+- **Evaluation**: Model performance is evaluated using metrics such as MSE and MAE to assess prediction accuracy.
+
+## Results
+
+The model achieves competitive results in predicting air quality, accurately capturing patterns over time. Performance metrics include:
+- **MSE**: Measures average squared difference between predicted and actual values.
+- **MAE**: Measures average absolute difference between predicted and actual values.
+