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import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

# Load dataset
ds = pd.read_csv("bill_authentication.csv")

# Normalize the data
bnorm = (ds - ds.min() - 1) / (ds.max() - ds.min())

# Extract target and features
target = ds.pop("Class")
y = target.values
x = bnorm.values

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)

# Build the model
svmclassifier=SVC(kernel='linear')

# Train the model and capture the history
svmclassifier.fit(x_train,y_train)

# Evaluate the model on the test set
y_pred=svmclassifier.predict(x_test)
print("Test Loss:", y_pred
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