Decision Tree, post$-$pruning and cost complexity parameter using sklearn $0\mathrm{.}22[10$ points, Peer Review$]$

We will use a pre$-$processed natural language dataset in the CSV file "spamdata.csv$"$ to classify emails as spam or not. Each row contains the word frequency for $54$ words plus statistics on the longest "run" of captial letters.

Word frequency is given by:

$=/$

Where $$

is the frequency for word $$

$,$

is the number of times word $$

appears in the email, and $$

is the total number of words in the email.

We will use decision trees to classify the emails.

Part A $[5$ points$]$: Complete the function get$\_$spam$\_$dataset to read in values from the dataset and split the data into train and test sets.

My Code:

def get$\_$spam$\_$dataset$($filepath$=$"data$/$spamdata$.$csv$",$ test$\_$split$=0\mathrm{.}1)$:

$\text{'}\text{'}\text{'}$

get$\_$spam$\_$dataset

Loads csv file located at "filepath". Shuffles the data and splits

it so that the you have $(1-$test$\_$split$)*100\%$ training examples and

$($test$\_$split$)*100\%$ testing examples.

Args:

filepath: location of the csv file

test$\_$split: percentage$/100$ of the data should be the testing split

Returns:

X$\_$train, X$\_$test, y$\_$train, y$\_$test, feature$\_$names

Note: feature$\_$names is a list of all column names including isSpam.

$($in that order$)$

first four are np$.$ndarray

$\text{'}\text{'}\text{'}$

# your code here

# Read CSV file

data $=$ pd$.$read$\_$csv$($filepath$,$ header$=$None, delimiter$=\text{'}\text{'})$

# Shuffle the data

data $=$ data.sample$($frac$=1,$ random$\_$state$=42).$reset$\_$index$($drop$=$True$)$

# Extract features and target variable

X $=$ data.iloc$[$:$,$ :$-1].$values

y $=$ data.iloc$[$:$,-1].$values

# Split the data into train and test sets

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=$test$\_$split, random$\_$state$=42)$

# Get feature names

feature$\_$names $=[$f$"$word$\_$freq$\_\{$i$\}"$ for i in range$(1,$ X$.$shape$[1]+1)]$

return X$\_$train, X$\_$test, y$\_$train, y$\_$test, feature$\_$names

# TO$-$DO: import the data set into five variables: X$\_$train, X$\_$test, y$\_$train, y$\_$test, label$\_$names

# Uncomment and edit the line below to complete this task.

test$\_$split $=0\mathrm{.}1$ # default test$\_$split; change it if you'd like; ensure that this variable is used as an argument to your function

# your code here

X$\_$train, X$\_$test, y$\_$train, y$\_$test, label$\_$names $=$ get$\_$spam$\_$dataset$($filepath$=$"data$/$spamdata$.$csv$",$ test$\_$split$=0\mathrm{.}1)$

# X$\_$train, X$\_$test, y$\_$train, y$\_$test, label$\_$names $=$ np$.$arange$(5)$

# Print the shapes of X$\_$train and y$\_$train

print$("$Shape of X$\_$train:", X$\_$train.shape$)$

print$("$Shape of y$\_$train:", y$\_$train.shape$)$

# Print label$\_$names

print$("$Label names:", label$\_$names$)$

its returning wrong answer $,$ can someone help.