Description
Naive Bayes – Cancer Tumor Classification
For this part, you will focus on a cancer dataset that comprises of 569 rows and 32 columns and
perform Naive Bayes Classification.
What do you need to do?
1. Load the dataset from ‘cancer.csv’ into a pandas DataFrame and print it along with its shape.
‘diagnosis’ is the target variable.
2. Print the column names and the data type of each column.
3. Plot the ‘Radius Mean’ VS ‘Texture Mean’ along with the classes represented as colors or
shapes. Is the data linearly separable?
4. Perform encoding on the target variable (here label encoding will suffice).
5. Divide the data into X and Y, where X is the set of features and Y is the target variable.
6. Split the data into train and test data. Choose a split size of 70 – 30.
7. Given the nature of the data and its features, choose which Naive Bayes is the most suitable.
Mention this in your report along with why you make your choice.
You may use the Naive Bayes from sklearn.
8. Once you have trained your model, evaluate the model performance by printing the performance
matrix.
9. Write a report with screenshots of your results and the final results for step 8.
10.Submit your code as an .ipynb file and a pdf file reporting your findings.
