what is wrong with my code and correct it : # Import necessary libraries

import re

import pandas as pd

import string

import numpy as np

from nltk$.$corpus import stopwords

from nltk$.$tokenize import word$\_$tokenize

from nltk$.$stem.porter import PorterStemmer

from nltk$.$stem import WordNetLemmatizer # Move this import statement up

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.ensemble import RandomForestClassifier

from sklearn.pipeline import Pipeline

from sklearn.feature$\_$extraction.text import TfidfVectorizer

from gensim.models import Word$2$Vec

from sklearn.preprocessing import LabelEncoder

def datacleaning$($text$)$:

if isinstance$($text$,$ str$)$:

tokens $=$ word$\_$tokenize$($text$)$

# Normalization

normalized$\_$tokens $=[$token$.$lower$()$ for token in tokens$]$

# Noise removal $($remove non$-$alphabetic characters$)$

cleaned$\_$tokens $=[$re$.$sub$($r$\text{'}[^$a$-$zA$-$Z$]\text{'},\text{'}\text{'},$ token$)$ for token in normalized$\_$tokens$]$

# Stemming

stemmer $=$ PorterStemmer$()$

stemmed$\_$tokens $=[$stemmer$.$stem$($token$)$ for token in cleaned$\_$tokens$]$

# Lemmatization

lemmatizer $=$ WordNetLemmatizer$()$

lemmatized$\_$tokens $=[$lemmatizer$.$lemmatize$($token$)$ for token in cleaned$\_$tokens$]$

# Stop$-$word removal

stop$\_$words $=$ set$($stopwords$.$words$(\text{'}$english$\text{'}))$

filtered$\_$tokens $=[$token for token in lemmatized$\_$tokens if token not in stop$\_$words$]$

# Join the tokens into a string

result$\_$text $=\text{'}\text{'}.$join$($filtered$\_$tokens$)$

return result$\_$text

def train$\_$word$2$vec$\_$model$($sentences$)$:

model $=$ Word$2$Vec$($sentences$,$ vector$\_$size$=100,$ window$=5,$ min$\_$count$=1,$ workers$=4)$

return model

def get$\_$word$\_$embeddings$($text$,$ model$)$:

words $=$ word$\_$tokenize$($text$)$

embeddings $=[$model$.$wv$[$word$]$ for word in words if word in model.wv$]$

if not embeddings:

# If no valid word found in the model, return zeros

return $[0\mathrm{.}0]*$ model.vector$\_$size

# Flatten the list of vectors into a $1$D array

flat$\_$embedding $=[$val for sublist in embeddings for val in sublist$]$

return flat$\_$embedding

def data$\_$cleaning$\_$pipeline$($df$\_$path, df$\_$body, df$\_$title$)$:

df $=$ pd$.$read$\_$csv$($df$\_$path$)$

# Drop rows where 'issue$\_$body' or 'issue$\_$label' is null

df $=$ df$.$dropna$($subset$=[$df$\_$body, 'issue$\_$label'$])$

X $=$ df$[[$df$\_$body, df$\_$title$]]$

y $=$ df$["$issue$\_$label"$]$

# Convert categorical labels to numeric using LabelEncoder

label$\_$encoder $=$ LabelEncoder$()$

y $=$ label$\_$encoder.fit$\_$transform$($y$)$

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}3,$ random$\_$state$=42)$

# Apply data cleaning to X$\_$train and X$\_$test

X$\_$train$[$df$\_$body$]=$ X$\_$train$[$df$\_$body$].$apply$($lambda text: datacleaning$($text$))$

X$\_$train$[$df$\_$title$]=$ X$\_$train$[$df$\_$title$].$apply$($lambda text: datacleaning$($text$))$

X$\_$test$[$df$\_$body$]=$ X$\_$test$[$df$\_$body$].$apply$($lambda text: datacleaning$($text$))$

X$\_$test$[$df$\_$title$]=$ X$\_$test$[$df$\_$title$].$apply$($lambda text: datacleaning$($text$))$

# Train Word$2$Vec model on the training data

sentences $=[$word$\_$tokenize$($text$)$ for text in X$\_$train$[$df$\_$body$]]$

word$2$vec$\_$model $=$ train$\_$word$2$vec$\_$model$($sentences$)$

# Apply Word$2$Vec embeddings to X$\_$train

X$\_$train$[$df$\_$body$]=$ X$\_$train$[$df$\_$body$].$apply$($lambda text: get$\_$word$\_$embeddings$($text$,$ word$2$vec$\_$model$))$

X$\_$train$[$df$\_$title$]=$ X$\_$train$[$df$\_$title$].$apply$($lambda text: get$\_$word$\_$embeddings$($text$,$ word$2$vec$\_$model$))$

# Apply Word$2$Vec embeddings to X$\_$test

X$\_$test$[$df$\_$body$]=$ X$\_$test$[$df$\_$body$].$apply$($lambda text: get$\_$word$\_$embeddings$($text$,$ word$2$vec$\_$model$))$

X$\_$test$[$df$\_$title$]=$ X$\_$test$[$df$\_$title$].$apply$($lambda text: get$\_$word$\_$embeddings$($text$,$ word$2$vec$\_$model$))$

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

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ data$\_$cleaning$\_$pipeline$(\text{'}$C:$/$Users$/$lynda$/$Documents$/$Winter$/$Principals of AI engneering$/$sample$1/$sample$1.$csv$\text{'},$

'issue$\_$body',

'issue$\_$title'$)$

# X$\_$train.info$()$

# y$\_$train.info$()$

# Rest of your code...

print$("$Shapes after preprocessing:"$)$

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

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

print$("$X$\_$test shape:", X$\_$test.shape$)$

print$("$y$\_$test shape:", y$\_$test.shape$)$

print$("$Types after preprocessing:"$)$

print$("$X$\_$train type:", type$($X$\_$train$))$

print$("$X$\_$test type:", type$($X$\_$test$))$

print$("$y$\_$train type:", type$($y$\_$train$))$

print$("$y$\_$test type:", type$($y$\_$test$))$from sklearn.experimental import enable$\_$halving$\_$search$\_$cv

from sklearn.model$\_$selection import GridSearchCV

from sklearn.model$\_$selection import RandomizedSearchCV

from sklearn.ensemble import RandomForestClassifier

from sklearn.model$\_$selection import HalvingGridSearchCV

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.metrics import accuracy$\_$score

import pandas as pd

import numpy as np

rf $=$ RandomForestClassifier$()$

rf$.$fit$($X$\_$train, y$\_$train$)$

y$\_$pred $=$ rf$.$predict$($X$\_$test$)$

# Evaluate the model

accuracy $=$ accuracy$\_$score$($y$\_$test, y$\_$pred$)$

print$("$Accuracy:$",$ accuracy$)$