Weather Prediction

Introduction

This is a simple weather forecasting app built with Streamlit that allows users to:

  • Enter the name of a city to get today's weather details.
  • Predict tomorrow's temperature based on historical data using an XGBoost machine learning model.

The app fetches real-time weather data using the OpenWeather API and uses machine learning techniques to predict future temperatures.

Features

  • Real-time weather data: Fetches current weather details such as temperature, humidity, pressure, wind speed, and weather condition.
  • Temperature prediction: Uses machine learning (XGBoost) to predict the next day's temperature based on historical weather data.
  • User-friendly interface: Allows easy input of city names and provides clear output for current and predicted weather conditions.

Colab Link for Model Here

Prerequisites

Before running the app, ensure you have the following:

  • Python 3.9+
  • OpenWeather API Key (You can get one by signing up at OpenWeather)

Installation

  1. Install the required dependencies: 
    pip install -r requirements.txt

How to Run

  1. Run the Streamlit app:
    streamlit run streamlit_app.py
  2. Open the app in your browser at http://localhost:8501.

Usage

  1. Enter the city name in the input field.
  2. Click Get Weather Forecast.
  3. The app will display:
    • Today's weather data: temperature, humidity, pressure, wind speed, and weather condition.
    • Tomorrow’s predicted temperature using the trained XGBoost model.

Code Structure

  • streamlit_app.py: Main Python file for the Streamlit app.
  • requirements.txt: List of Python dependencies.
  • README.md: Documentation for the project.

How It Works

  1. Fetching Weather Data: The app uses the OpenWeather API to fetch weather data for the next 5 days (including today).
  2. Training the Model: The app extracts relevant features (humidity, pressure, wind speed, and weather condition) from the weather data and uses these to train the XGBoost regression model.
  3. Predicting Tomorrow's Temperature: The last available day's weather data is used to predict the temperature for the next day.
  4. Displaying Results: The current day's weather is displayed, and the predicted temperature for the next day is calculated and shown on the app interface.

Example

If you input "London" and press Get Weather Forecast, the app will display today's weather and the predicted temperature for tomorrow:

Today's Weather in London:
- Temperature: 12°C
- Humidity: 65%
- Pressure: 1012 hPa
- Wind Speed: 3.5 m/s
- Weather Condition: Clouds

Predicted Temperature for Tomorrow: 14.32°C

Technologies Used

  • Streamlit: For creating the user interface.
  • OpenWeather API: For fetching real-time weather data.
  • XGBoost: A powerful machine learning algorithm for predicting tomorrow's temperature.
  • Pandas: For data manipulation and preparation.
  • Scikit-learn: For preprocessing and scaling data.
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