You have to devise an Object Oriented solution for this problem, i$.$e$.$ you MUST create a CLASS, and

solve the problem calling the class methods.

The train dataset contains data regarding a train crash. It has ~$891$ records. However, all records are not

complete, i$.$e$.$ some have missing data. In this data set, our outcome of interest is the survived column,

on whether an individual survived $(1)$ or died $(0)$ after the Train crash.

$1.$ Clean the data and remove any unwanted records. How many records do you have now?

$(20$ points$)$

$2.$ The train picked most passengers from which station $?$

$(20$ points$)$

$3.$ Do some basic data exploration $($e$.$g$.$ using commands as head$(),$ info$(),$ describe$(),$ nunique$(),$ etc$).$

Which variables will you NOT select?

$(20$ points$)$

$4.$ Are there any outliers in the data? If yes, treat them.

$(15$ points$)$

$5.$ Partition the data into a training set $($with $70\%$ of the observations$),$ and testing set $($with $30\%$ of the

observations$)$ using the random state of $12345$ for cross validation. $(15$ points$)$

$6.$ On the partitioned data, build the best KNN model. Show the accuracy numbers. $($Hint: What is the

best value of k$?$ How do you decide the $$best k$?)$

$(30$ points$)$

$7.$ On the partitioned data, build the best logistic regression model. Show the accuracy numbers.

$(30$ points$)$

$8.$ On the partitioned data, build the decision tree. Show the accuracy numbers. What tree depth did

you choose, i$.$e$.$ which one is ideal and why? $(30$ points$)$

$9.$ Based on the results of k$-$nearest neighbor, and logistic regression, what is the best model to classify

the data? Provide explanation to support your argument. $(20$ points$)$

$10.$ Show some interesting graphs of the data, i$.$e$.,$ that can describe the original data survived pclass sex age sibsp parch fare station. By using TrainData excell data bsurvived pclass sex age sibsp parch fare station.