Load & check the data: 1. Load the data into a pandas dataframe named data_firstname where first name is you name. 2. Carryout some initial investigations: a. Check the names and types of columns. b....

Load & check the data:
1. Load the data into a pandas dataframe named data_firstname where first name is you name.
2. Carryout some initial investigations:
a. Check the names and types of columns.
b. Check the missing values.
c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)
d. In you written response write a paragraph explaining your findings about each column.
Pre-process and visualize the data
3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’
4. Fill any missing data with the median of the column.
5. Drop the ID column
6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add them
to your written response explaining what are the key insights and findings from the plots.
7. Separate the features from the class.
8. Split your data into train 80% train and 20% test, use the last two digits of your student number
for the seed.
Build Classification Models
Support vector machine classifier with linear kernel

breast cancer problem : I have already answered 1 to 3. Please provide solution from 4.