Hyperparameter Tuning section of the code not working

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

# Import numpy and give it the alias np

import numpy as np

# Load dataset

data $=$ pd$.$read$\_$csv$(\text{'}/$content$/$drive$/$MyDrive$/$Cancer$\_$Data.csv$\text{'})$

# Display basic information

print$($data$.$info$())$

print$($data$.$describe$())$

# Visualize class distribution

sns$.$countplot$($x$=$'diagnosis', data$=$data$)$

plt$.$title$(\text{'}$Distribution of Malignant and Benign Tumors'$)$

plt$.$show$()$

# Handle missing values

# Exclude non$-$numeric columns from mean calculation

numeric$\_$data $=$ data.select$\_$dtypes$($include$=$np$.$number$)$

# Replace infinite values with NaN

numeric$\_$data.replace$([$np$.$inf, $-$np$.$inf$],$ np$.$nan, inplace$=$True$)$

# Calculate mean without infinite values

# Check if there are any columns with all values as NaN after replacing inf

for col in numeric$\_$data.columns:

if numeric$\_$data$[$col$].$isnull$().$all$()$:

# Handle columns with all NaN values $-$ here, we drop the column

numeric$\_$data.drop$($col$,$ axis$=1,$ inplace$=$True$)$

data.drop$($col$,$ axis$=1,$ inplace$=$True$)$

else:

data$[$col$]=$ numeric$\_$data$[$col$].$fillna$($numeric$\_$data$[$col$].$mean$())$

# Encode categorical variables

data$[\text{'}$diagnosis$\text{'}]=$ data$[\text{'}$diagnosis$\text{'}].$map$(\{\text{'}$M$\text{'}$: $1,\text{'}$B$\text{'}$: $0\})$ # M $=$ malignant, B $=$ benign

#Normalize numerical features to ensure they contribute equally to distance calculations in SVC and Random Forest.

from sklearn.preprocessing import StandardScaler

scaler $=$ StandardScaler$()$

features $=$ data.drop$(\text{'}$diagnosis$\text{'},$ axis$=1)$

#Splitting the Data

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

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($features$,$ data$[\text{'}$diagnosis$\text{'}],$ test$\_$size$=0\mathrm{.}2,$ random$\_$state$=42)$

#Fit and transform the scaler on the training data only

X$\_$train$\_$scaled $=$ scaler.fit$\_$transform$($X$\_$train$)$

#Transform the test data using the scaler fit on the training data

X$\_$test$\_$scaled $=$ scaler.transform$($X$\_$test$)$

#Hyperparameter Tuning

from sklearn.model$\_$selection import GridSearchCV

# Define parameter grids

param$\_$grid$\_$svc $=\{\text{'}$C$\text{'}$: $[0\mathrm{.}1,1,10],$ 'kernel': $[\text{'}$linear$\text{'},\text{'}$rbf$\text{'}]\}$

param$\_$grid$\_$rf $=\{\text{'}$n$\_$estimators': $[50,100],$ 'max$\_$depth': $[$None$,10]\}$

# Create GridSearchCV objects

grid$\_$svc $=$ GridSearchCV$($SVC$(),$ param$\_$grid$\_$svc$,$ cv$=5)$

grid$\_$rf $=$ GridSearchCV$($RandomForestClassifier$(),$ param$\_$grid$\_$rf$,$ cv$=5)$

# Fit models with grid search

grid$\_$svc$.$fit$($X$\_$train, y$\_$train$)$

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

#Model Evaluation

#Import the SVC class

from sklearn.svm import SVC

#Create an SVC model

svc$\_$model $=$ SVC$()$

#Train the model

svc$\_$model.fit$($X$\_$train$\_$scaled, y$\_$train$)$

# Import the RandomForestClassifier class

from sklearn.ensemble import RandomForestClassifier

# Create a RandomForestClassifier model

rf$\_$model $=$ RandomForestClassifier$($random$\_$state$=42)$ # Add a random state for reproducibility

# Train the model

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

from sklearn.metrics import accuracy$\_$score, classification$\_$report

# Predictions from each model

svc$\_$pred $=$ svc$\_$model.predict$($X$\_$test$\_$scaled$)$ # Predict on the scaled test data

rf$\_$pred $=$ rf$\_$model.predict$($X$\_$test$\_$scaled$)$ # Predict on the scaled test data

# Evaluate models

print$("$SVC Classification Report:

$",$ classification$\_$report$($y$\_$test, svc$\_$pred$))$

print$("$Random Forest Classification Report:

$",$ classification$\_$report$($y$\_$test, rf$\_$pred$))$