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WRITE PYTHON CODEObjective The objective of this assignment is to practice data preprocessing

WRITE PYTHON CODE**Objective:** The objective of this assignment is to practice data preprocessing techniques for preparing an image dataset for training a Convolutional Neural Network (CNN). You will go through the steps of data collection, splitting, resizing, normalization, and optional data augmentation. **Instructions:** **Data Collection and Organization (5 points):** – Find an image dataset of your choice. It could be a dataset for image classification, object detection, or any other image-based task. – Download and organize the dataset, ensuring it’s correctly labeled and categorized. **Data Splitting (5 points):** – Split the dataset into three subsets: training, validation, and test sets. Use a reasonable split ratio (e.g., 70% for training, 15% for validation, and 15% for testing). **Image Resizing (5 points):** – Choose a CNN architecture that you plan to use for your task. Check the input size expected by the chosen model. – Write a Python script to resize all images in your dataset to match the input size of the chosen CNN model. **Normalization (10 points):** – Implement data normalization. Write Python code to normalize the pixel values of your images. – Ensure that the pixel values are within the range [0, 1] by either dividing by 255 (if the original values are 0-255) or by subtracting the mean and dividing by the standard deviation. **Data Augmentation (Optional, 5 points):** – Implement data augmentation techniques to increase the diversity of your training data. You can include operations such as random rotation, random cropping, and horizontal flipping. – Write Python code to apply these data augmentation techniques to the training images. **Data Batching (5 points):** – Organize your dataset into batches. Define batch sizes suitable for your available computing resources and model. **One-Hot Encoding (for Classification, 5 points):** – If your task is image classification, write Python code to one-hot encode the class labels of your dataset. **Test Your Preprocessed Data (5 points):** – Write Python code to visualize a few images from your preprocessed training data. – Ensure that the images are correctly resized, normalized, and optionally augmented.

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