Make sure you have downloaeded and extracted all 4 of the dataset Files
This Jupyter notebook implements a machine learning-based image classification model to classify images of skies. It demonstrates the process of loading image data, extracting features using the img2vec library, training a Random Forest model for classification, and making predictions. The model achieves an accuracy of 96%.
Key Features: Image loading using the PIL library. Feature extraction from images using img2vec. Use of a Random Forest model for image classification. Prediction of sky image classes with visual outputs to verify model accuracy. Achieved 96% accuracy on the test set.
Confusion matrix :
Installation: Ensure you have the following packages installed:
-Scikit-learn (for the Random Forest implementation)
-img2vec (for feature extraction)
-PIL (Python Imaging Library)
-NumPy ( for reshapping the images )
-Matplotlib ( for visualizing data)
How to Use: Clone the repository and navigate to the notebook file. Run all cells to load the dataset of sky images, extract features using img2vec, train the Random Forest model, and view predictions. Check the displayed images and classification results to verify the predictions.