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mnarulabuilds / repository
Machine Learning Project for classifying Weather into ThunderStorm (0001) , Rainy(0010) , Foggy (0100) , Sunny(1000) and also predict weather features for next one year after training on 20 years data on a neural network This is my first Machine Learning Project. Steps To run the project: Extract the files into a single directory ( say "MyWeatherProject" ) Open Octave(GUI) and go to the above directory Type the following in the COMMAND WINDOW : "YearlyForecast();" (without double quotes) Open the files "PredictionWeather1997.txt" to "PredictionWeather2016.txt" and compare with actual results from "Weather1997.txt" to "Weather2015.txt". Curves are plotted to compare predictions with actual weather conditions and they will be saved in the same directory...
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A modern Python-based Machine Learning project for weather feature prediction and classification. The system uses Neural Networks to classify weather into Thunderstorm, Rainy, Foggy, and Sunny categories, and predicts future weather features (temperatures, humidity, pressure, etc.) based on historical data.
CustomTkinter for easy interaction.Matplotlib.pip install -r requirements.txt
Launch the professional GUI to train models and view predictions:
python desktop_app.py
You can also interact with the core engine directly:
python weather_engine.py
The project includes historical weather data from 1997 to 2015 (WeatherXXXX.txt files) used for training the neural networks.
The original Octave and C++ files have been superseded by this Python migration for better performance, maintainability, and user experience.