Weather Conditions Classification

This project focuses on classifying different weather conditions from image data (or relevant weather-condition inputs) using machine learning techniques. It achieves a high classification accuracy of 97%. It uses the Multi-class Weather Dataset to train and test a model that can identify among multiple weather classes (e.g. cloudy, sunny, foggy, rainy etc.).

Goal

- To build a model capable of distinguishing multiple weather condition classes with high accuracy.

- To demonstrate that classification of weather conditions from images/data using modern machine learning (probably deep learning) can reach strong performance (97 %).

Approach

- Data collection & preprocessing

- Use the Multi-class Weather Dataset.

- Clean and prepare the data: probably resizing images, normalization, splitting into training/validation/test sets.

Model development

- Set up a classification model (likely a convolutional neural network (CNN) or a deep learning architecture suitable for image classification).

- Configure the model to handle multiple classes.

Training

- Train the model on the prepared dataset, tuning hyperparameters (learning rate, batch size, number of epochs etc.).

- Possibly use augmentation or regularization to avoid overfitting, given high accuracy.

Evaluation

- Measure performance using accuracy, but likely also other relevant metrics (precision, recall, confusion matrix) to understand where misclassifications happen.

- Obtain result: accuracy ≈ 97% on classifying weather conditions.

Outcomes

- The model achieves very high accuracy (97%), meaning it correctly classifies weather condition images/data in most cases.

- Demonstrates that multi-class weather condition classification is feasible with high reliability using this dataset and model architecture.

- Offers a baseline or benchmark for further work in weather condition detection or automated image-based weather understanding.