Am not too sure with my solution so could help me out
The dataset (CellDNA.csv) contains various measurements (i.e. size, center, etc) from
thousands of bacterium under microscope. The non-zero values in the last column are
the target responses that indicate the bacterium (rows) that are interesting enough for
further study. The 0s in the last column indicate the bacterium (rows) are NOT interesting
candidates for further study. Convert this target dependent variable (last column) to
binary values of either 0s or 1s for your two-class classification.
Write a program using either Python+Keras or MatLab to perform the two-class
classification analysis on this dataset using the neural network approach.
Answer the following questions:
1. Print the configurations (architecture) of all the layers in your neural network,
AND put it in a WORD document.
2. Print the confusion matrix of your classification result, and what is the accuracy
of classification result? The accuracy can be obtained EITHER from training
data, test data, OR both. Please specify which dataset (i.e. training or testing) was
used to calculate the accuracy. Please include your results in the SAME WORD
3. Print the precision, recall, F-score for EACH class. Also, create a ROC curve
for EACH class. Again, please include your results the SAME WORD document.
4. Save your code as “a5.py”, “a5.ipynb”, or “a5.m”