Prompt Title: Galaxy Classification using CNN

Sure, here are the steps to download the Galaxy Zoo dataset and preprocess it in Google Colab: 1. Open a new Google Colab notebook and run the following commands to install the necessary packages: ``` !pip install kaggle !pip install numpy !pip install pandas !pip install matplotlib !pip install opencv-python ``` 2. Download the Kaggle API key from your Kaggle account settings page. 3. Upload the Kaggle API key to your Google Drive and mount the drive on Google Colab using the following code: ``` from google.colab import drive drive.mount('/content/drive') ``` 4. Navigate to your Kaggle API key in Google Drive and copy the path to the key file. 5. Set the environment variable for the Kaggle API key by running the following command: ``` import os os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/MyDrive/Kaggle" ``` 6. Go to the Kaggle website and search for the "Galaxy Zoo - The Galaxy Challenge" dataset. Click on the dataset and copy the dataset URL. 7. Run the following command in Google Colab to download the dataset: ``` !kaggle competitions download -c galaxy-zoo-the-galaxy-challenge ``` 8. Unzip the downloaded file using the following command: ``` !unzip galaxy-zoo-the-galaxy-challenge.zip -d galaxy-zoo ``` 9. Preprocess the dataset by resizing the images, normalizing pixel values, and splitting the dataset into training, validation, and test sets. Here's an example code snippet to get you started: ``` import cv2 import numpy as np import pandas as pd from sklearn.model_selection import train_test_split # Read the CSV file containing the image filenames and labels df = pd.read_csv("galaxy-zoo/training_solutions_rev1.csv") # Load the images and preprocess them images = [] for i in range(len(df)): filename = "galaxy-zoo/images_training_rev1/" + str(df["GalaxyID"][i]) + ".jpg" image = cv2.imread(filename) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image = cv2.resize(image, (224, 224)) # Resize the image to 224x224 pixels image = image.astype("float32") / 255.0 # Normalize pixel values between 0 and 1 images.append(image) # Convert the list of images to a numpy array images = np.array(images) # Split the dataset into training, validation, and test sets X_train, X_test, y_train, y_test = train_test_split(images, df.iloc[:, 1:].values, test_size=0.2, random_state=42) X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42) ``` That's it! You now have the Galaxy Zoo dataset preprocessed and ready to be used for machine learning. Note that this is just a starting point, and you'll likely need to make further adjustments to the code depending on the specific CNN model you choose to use.