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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$\text{'})$

$5.$ Hyperparameter Tuning"

Use GridSearchCV for hyperparameter tuning on each model.

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$)$