Python neural networks

1. Describe and summarize your dataset by running some codes.
2. Split the dataset using hold out approach on 75:25 split.
3. Run neural networks classifier with any parameters setting on your own choice on the
given dataset.
4. Print the confusion matrix and classification report.
5. Repeat step 2-4 by using K-fold Cross Validation, where K=10.
6. Compare the performance of neural network classifier on hold out and cross
validation. Which method gives the best performance?
7. Explain the confusion matrix for the best method.
8. Comment on whether the neural networks classifier is sufficient to predict the
selected problem for your selected dataset based on the confusion matrix and
classification report.
9. Fine-tune any 2 parameters using GridSearchCV to find the optimal value for the
selected parameters. Print out the best accuracy score.

i wrote some codes in 1,2,3 but i have errors , I need to fixed it