ValueError X has 10000 features but PCA is expecting 9604 features as input

This error occurs when you try to apply PCA (Principal Component Analysis) on a dataset where the number of features is different from what PCA is expecting. PCA is a dimensionality reduction technique that works by finding the most important features in the data and creating new, smaller features that capture most of the variation in the original data.

In this case, the error message suggests that you have a dataset with 10000 features, but PCA is expecting 9604 features. This could happen if you have previously applied some kind of feature selection or feature extraction technique that reduced the number of features, but you haven't updated the input to PCA accordingly.

To fix this error, you need to make sure that the input to PCA matches the number of features in your dataset. You can check the number of features in your dataset by looking at the shape attribute of the input array. For example, if your input array is called X, you can check the number of features with:

print(X.shape[1])

Once you know the number of features, you can adjust the input to PCA accordingly. If you have applied some kind of feature selection or feature extraction technique, make sure to update the input accordingly. For example, if you applied feature selection and reduced the number of features to 9604, you can create a new input array with:

X_new = X[:, selected_features]

where selected_features is a boolean array indicating which features were selected. You can then apply PCA to X_new:

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_new)

Make sure to update any other parts of your code that rely on the number of features, such as plotting or classification algorithms.