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’
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’
Chapter8: Working With Advanced Functions
Section: Chapter Questions
Problem 2.7CP
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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
Supportvector machine classifier with linear kernel
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
9. Train an SVM classifier using the training data, set the kernel to linear and set the regularization
parameter to C= 0.1. Name the classifier clf_linear_firstname.
10. Print out two accuracy score one for the model on the training set i.e. X_train, y_train and the
other on the testing set i.e. X_test, y_test. Record both results in your written response.
11. Generate the accuracy matrix. Record the results in your written response.
Support vector machine classifier with “rbf” kernel
12. Repeat steps 9 to 11, in step 9 change the kernel to “rbf” and do not set any value for C.
Support vector machine classifier with “poly” kernel
13. Repeat steps 9 to 11, in step 9 change the kernel to “poly” and do not set any value for C.
Support vector machine classifier with “sigmoid” kernel
14. Repeat steps 9 to 11, in step 9 change the kernel to “sigmoid” and do not set any value for C.
(Optional: for steps 9 to 14 you can consider a loop)
By now you have the results of four SVM classifiers with different kernels recorded in your written
report. Please examine and write a small paragraph indicating which classifier you would recommend
and why
parameter to C= 0.1. Name the classifier clf_linear_firstname.
10. Print out two accuracy score one for the model on the training set i.e. X_train, y_train and the
other on the testing set i.e. X_test, y_test. Record both results in your written response.
11. Generate the accuracy matrix. Record the results in your written response.
Support vector machine classifier with “rbf” kernel
12. Repeat steps 9 to 11, in step 9 change the kernel to “rbf” and do not set any value for C.
Support vector machine classifier with “poly” kernel
13. Repeat steps 9 to 11, in step 9 change the kernel to “poly” and do not set any value for C.
Support vector machine classifier with “sigmoid” kernel
14. Repeat steps 9 to 11, in step 9 change the kernel to “sigmoid” and do not set any value for C.
(Optional: for steps 9 to 14 you can consider a loop)
By now you have the results of four SVM classifiers with different kernels recorded in your written
report. Please examine and write a small paragraph indicating which classifier you would recommend
and why
note:
python programming
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