Module 9.3 SVM

gk231192

Jul 3rd, 2025

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# 1. Data Set Up
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, roc_auc_score
# Load Titanic dataset
titanic = sns.load_dataset('titanic')
# Keep only selected columns and drop rows with missing values
df = titanic[['survived', 'sex', 'age', 'fare', 'pclass']].dropna()
df['sex'] = df['sex'].map({'male': 0, 'female': 1})
# Feature matrix and target vector
X = df[['sex', 'age', 'fare', 'pclass']]
y = df['survived']
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Feature scaling
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

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