Technologies Used:

• Backend: PHP / Java / Node.js

• Frontend: HTML, CSS, Bootstrap, JavaScript

• ML Tools: Python, Scikit-learn, Pandas, NumPy

• APIs: OpenWeatherMap API / NOAA data

• Database: MySQL / MongoDB

• Visualization: Chart.js, Google Charts

Project Objective:

To develop a web-based system that accurately forecasts weather conditions like temperature, humidity, rainfall, and air pressure using historical data, APIs, and machine learning models. The app displays real-time and predictive weather insights for users.

Key Features:

1. Real-Time Weather Updates:
   Fetch current weather data using public APIs and display it in a user-friendly format.

2. Weather Forecasting using ML:
   Train ML models on historical weather data to predict weather for the next 1 to 7 days.

3. City/Location-Based Search:
   Users can check the weather for any city or region using geolocation or manual input.

4. Graphical Weather Visualization:
   Line graphs and charts for temperature trends, humidity levels, and rainfall forecasts.

5. Weather Alerts Module:
   Notify users about extreme weather conditions like storms, heatwaves, or heavy rain.

6. Admin Panel:
   Admins can upload additional datasets, verify forecast accuracy, and manage city info.

Data Sources & Inputs:

• Real-time data via OpenWeatherMap API

• Historical weather datasets from:

  • Kaggle (e.g., 10 years of weather data)

  • NOAA (National Oceanic and Atmospheric Administration)

• Features:

  • Date

  • Temperature (min/max)

  • Humidity

  • Wind Speed

  • Pressure

  • Rainfall

  • Weather condition (clear, cloudy, storm, etc.)

How It Works:

1. Data Collection:
   Historical data is collected and cleaned for ML training.

2. Model Training:
   Regression models (like Linear Regression, LSTM, or Random Forest) predict weather values.

3. API Integration:
   Real-time weather API data is fetched and merged with ML outputs.

4. Frontend Interface:
   Responsive web interface displays both real-time and forecasted data with charts.

5. Admin Analysis:
   Historical accuracy comparison and system performance tracking through admin tools.