The WeatherPrediction class provides a framework for training and evaluating a RandomForestClassifier model to predict weather types based on various meteorological features. It fetches data from a specified CSV file, processes it, trains the model, and allows for predictions on new input data.
This project will detect wheather the weather is sunny, cloudy, snowy or rainy. This project enhanced my ML skills to implement. Here around the accuracy is near 89% which means my data is not overfitting. But then to if anyone have any issue then all the counters and criticizms are welcome but in the form of ADVICE.
- Load weather data from a CSV file.
- Preprocess the data by dropping unnecessary columns.
- Train a RandomForestClassifier on the preprocessed data.
- Evaluate the model's performance using accuracy and classification reports.
- Make predictions based on new input features.
Provide instructions on how to install and set up your project locally. Include any dependencies that need to be installed and any configuration steps.
- Advice all to install following in an "env." folder. And then following steps are there for assistance.
# Clone the repository
git clone https://github.com/PoojanDoshi11/Weather_Detection
# Navigate to the project directory
cd Weather_Detection
# Install dependencies
pip install -r requirements.txt-- Run the Flask app
python app.py-- then follow the provided local host link -- then enter the necessary data and please click on "Detect" -- then enjoy the results