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CIFAR_10_KNN.ipynb

# CIFAR-10 Dataset Exploration and KNN Classification This Jupyter Notebook documents the journey of exploring the CIFAR-10 dataset and implementing a K-Nearest Neighbors (KNN) classifier for image classification tasks. CIFAR-10 is a popular dataset consisting of 60,000 32x32 color images across 10 different classes, with 6,000 images per class. ## Overview ### 1. Dataset Loading and Exploration - Load the CIFAR-10 dataset. - Understand its structure and content. - Visualize sample images from different classes. ### 2. Data Preprocessing - Prepare the dataset for training and testing. - Normalize the data. - Split the dataset into training and testing sets. ### 3. Baseline Model - Implement a basic KNN classifier as a starting point. - Evaluate the baseline model's performance and accuracy. ### 4. Hyperparameter Tuning - Experiment with different hyperparameters for KNN. - Document the results and performance improvements. ### 5. Data Augmentation - Apply data augmentation techniques. - Observe the impact on model accuracy. ### 6. Model Evaluation - Evaluate the final KNN classifier's performance on the test dataset. - Report the achieved accuracy. ## How to Use 1. Clone the Repository: https://github.com/tejash6895/CIFAR-10_KNN/tree/main 2. cd cifar-10-knn-exploration 2. Set Up the Environment: - Install required Python packages: ``` pip install opencv-python matplotlib numpy scikit-learn ``` 3. Specify the Dataset Path: - Replace `/path/to/cifar-10-python.tar.gz` in the code with the actual path to your CIFAR-10 dataset. 4. Run the Code: - Run the Jupyter Notebook cells for loading, preprocessing, training, and evaluating the KNN classifier. 5. Experiment and Reflect: - Experiment with hyperparameters and data augmentation to improve model accuracy. ## License This project is licensed under the MIT License - see the [LICENSE](https://github.com/tejash6895/CIFAR-10_KNN/blob/master/LICENSE) file for details.

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