Use the python$-$based popular industry data mining software

packages and development$/$analysis tools $-$ Panda,

Scikit$-$Learn, Seaborn, etc. Study the related documentation

from the software user guide.

$5.$ On the given dataset, execute any predictive data mining

technique such as decision trees, Bayesian classifiers, neural

networks, k$-$nearest neighbors, ensemble learning, linear

regression etc.

$6.$ You can implement multiple techniques, one followed by another

on the same dataset if you prefer to use this for your analysis.

$7.$ If needed, you should convert the format of your dataset as

needed by the respective technique$($s$).$ If you have already

performed conversion in the assignment on data preprocessing,

you can use the converted data here.

$8.$ If you would like to use the results of your descriptive data

mining techniques and conduct further analysis with predictive

data mining, that is fine as well.

$9.$ You should aim to achieve robustness and generalization in the

mining, e$.$g$.$ by altering seeds in the algorithm.

$10.$ You must modify the concerned parameters, e$.$g$.$ learning rate

and error threshold for neural networks, the value of k for

k$-$nearest neighbors etc. to get good results. Execute at least $3$

different combinations of parameters and present the

experimental results accordingly for at least one technique.

$11.$ Observe the experimental results and draw useful conclusions

from the data.

$12.$ Based on the hypothesis obtained by the learning in the

predictive data mining technique$($s$),$ write a simple program that

uses the learned hypothesis to classify new data, and thus

serves as a prototype mini classification tool.

$13.$ If you have used multiple techniques, you can select the one

that gives greatest accuracy or the one that is most suitable to

your data and domain etc.

$14.$ The program should communicate with the user to give

outputs based on new unseen data. For example, consider that

the learned hypothesis is based on a dataset that uses weather

data to predict if it is okay to play tennis. This can include various

attributes such as $$temperature$,$chance$-$of$-$rain$$ and so forth to

estimate the target $$playing tennis$$ as being $$yes$,$no$$ or

$$maybe$.$ It can be used to manually derive rules such as $$if

temperature $=$ medium and chance$-$of$-$rain $=$ low then play$-$tennis

$=$ yes$$ which can be coded into the program. Thereafter, when a

user inputs new values for parameters such as $$temperature$,$

the program can use these rules to output the classification

target and thereby suggest to the user $$Yes$,$ you can surely play

tennis today$$ or $$No$,$ you should not play tennis today$$ or

$$Maybe$,$ it seems okay to play tennis today$.$ This example can

be modified based on your dataset and domain.

$15.$ Show at least $3$ different runs of such inputs and outputs

based on user interaction. The final goal is to have simple

communication with the user based on the learning done via

predictive data mining for classifying the target attribute. Hence,

please work accordingly based on your respective technique$($s$)$

and application. You can tune this as per the needs of your MS

project, research topics or any other area of interest.