5 Ways to Detect Fake Dollar Bills Using Python Machine Learning

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Counterfeit detection sounds like a job for the Secret Service. Turns out, it is also a solid machine learning exercise. The fake bills dataset from Kaggle packs 1,400 labeled dollar bills with six geometric measurements. The question is not just “can a model tell them apart?” but “which model does it best, and why?”

I ran five classification techniques on this data: Logistic Regression, Naive Bayes, K-Nearest Neighbours, Support Vector Machines, and a Neural Network. Each one exposes something different about the data and the problem. Here is what I found.

The fake bills dataset

The dataset lives on Kaggle here. It has 1,400 bills. Seven columns:

  • is_genuine: target variable, True or False
  • diagonal: diagonal length in mm
  • height_left: left height in mm
  • height_right: right height in mm
  • margin_low: lower margin in mm
  • margin_upper: upper margin in mm
  • length: bill length in mm
import pandas as pd

data = pd.read_csv("fake_bills.csv", delimiter=";")
data.dropna(inplace=True)

print(data.shape)        # (1372, 7)
print(data.is_genuine.value_counts())
# True     1000
# False     372

About 27% of the dataset is counterfeit. That is reasonably balanced for a fraud detection problem, not so skewed that you need aggressive resampling.

Method 1: Logistic Regression

Logistic regression estimates the probability that a given instance belongs to a particular category by fitting a logistic function to the data. The coefficients tell you exactly how each feature pushes the prediction toward genuine or fake.

#lrr
import pandas as pd
from math import sqrt
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score

data = pd.read_csv("fake_bills.csv", delimiter=";")
data.dropna(inplace=True)

X = data[["diagonal", "height_left", "height_right", "margin_low", "margin_upper", "length"]]
y = data["is_genuine"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# statsmodels for interpretable coefficients
log_reg = sm.Logit(y, X).fit()
print(log_reg.summary())

# sklearn for predictions
model = LogisticRegression(random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:\n", cm)

def metrics(y_true, y_pred, cm):
    tp = cm[1, 1]
    tn = cm[0, 0]
    fp = cm[0, 1]
    fn = cm[1, 0]
    tpr = tp / (tp + fn)
    tnr = tn / (tn + fp)
    g_mean = sqrt(tpr * tnr)
    print(f"Accuracy:  {accuracy_score(y_true, y_pred):.4f}")
    print(f"Precision: {precision_score(y_true, y_pred):.4f}")
    print(f"Recall:    {recall_score(y_true, y_pred):.4f}")
    print(f"F1:        {f1_score(y_true, y_pred):.4f}")
    print(f"TPR:       {tpr:.4f}")
    print(f"TNR:       {tnr:.4f}")
    print(f"G-Mean:    {g_mean:.4f}")

metrics(y_test, y_pred, cm)

Output:

Optimization terminated successfully.
                          Logit Regression Results
============================================================================
Dep. Variable: is_genuine    No. Observations: 1372
Pseudo R-squ.: 0.9576
                                  Std. Err.       z      P>|z|    [0.025   0.975]
--------------------------------------------------------------------------------
diagonal        -0.4755       0.727   -0.654   0.513   -1.901   0.950
height_left     -1.5227       1.053   -1.446   0.148   -3.587   0.541
height_right    -3.4686       1.145   -3.030   0.002   -5.712  -1.225
margin_low      -6.0609       0.993   -6.103   0.000   -8.007  -4.115
margin_upper   -10.4068       2.183   -4.768   0.000  -14.685  -6.129
length           5.8826       0.874    6.734   0.000    4.170   7.595

Confusion Matrix:
 [[ 96   0]
  [  0 197]]
Accuracy:   1.0000
Precision:  1.0000
Recall:     1.0000
F1:         1.0000
TPR:        1.0000
TNR:        1.0000
G-Mean:     1.0000

Perfect classification. The coefficients tell you why. margin_upper has the largest effect, a higher upper margin strongly signals a fake. length pushes the other way. The dataset is linearly separable, which means a simple logistic regression draws a clean boundary. That quasi-separation warning from statsmodels tells you the same thing: some feature combinations appear exclusively in one class.

Method 2: Naive Bayes

Naive Bayes assumes independence between features. That is rarely true in the real world. Bill dimensions are obviously correlated. But the assumption sometimes still works well for classification even when it is technically wrong.

#naive bayes
import pandas as pd
import numpy as np
from math import sqrt
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score

data = pd.read_csv("fake_bills.csv", delimiter=";")
data.dropna(inplace=True)

X = data[["diagonal", "height_left", "height_right", "margin_low", "margin_upper", "length"]]
y = data["is_genuine"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

gnb = GaussianNB()
y_pred = gnb.fit(X_train, y_train).predict(X_test)

cm = confusion_matrix(y_test, y_pred)
print("Mislabeled:", (y_test != y_pred).sum(), "out of", X_test.shape[0])

def metrics(y_true, y_pred, cm):
    tp = cm[1, 1]
    tn = cm[0, 0]
    fp = cm[0, 1]
    fn = cm[1, 0]
    tpr = tp / (tp + fn)
    tnr = tn / (tn + fp)
    g_mean = sqrt(tpr * tnr)
    print(f"Accuracy:  {accuracy_score(y_true, y_pred):.4f}")
    print(f"Precision: {precision_score(y_true, y_pred):.4f}")
    print(f"Recall:    {recall_score(y_true, y_pred):.4f}")
    print(f"F1:       {f1_score(y_true, y_pred):.4f}")
    print(f"TPR:       {tpr:.4f}")
    print(f"TNR:       {tnr:.4f}")
    print(f"G-Mean:    {g_mean:.4f}")

metrics(y_test, y_pred, cm)

Output:

Mislabeled: 2 out of 412
          precision    recall  f1-score   support
     False       0.99      0.99      0.99       122
      True       1.00      1.00      1.00       290
  accuracy                           1.00       412
Confusion Matrix:
 [[121   1]
  [  1 289]]
Accuracy:   0.9951
Precision:  0.9965
Recall:     0.9965
F1:         0.9965
TPR:        0.9965
TNR:        0.9934
G-Mean:     0.9950

Two misclassifications out of 412 test samples. The independence assumption hurts here but not much. Gaussian Naive Bayes works best when features are normally distributed within each class. The dataset is clean and the class distributions are well-separated, so even a “wrong” assumption does not break performance.

Method 3: K-Nearest Neighbour (KNN)

KNN finds the k closest labeled bills and lets them vote. No assumptions about the data distribution. The catch is you have to choose k, and distance matters. Features on different scales will dominate the distance calculation, so standardization is essential here.

#knn
import pandas as pd
import numpy as np
from math import sqrt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score

data = pd.read_csv("fake_bills.csv", delimiter=";")
data.dropna(inplace=True)

X = data[["diagonal", "height_left", "height_right", "margin_low", "margin_upper", "length"]]
y = data["is_genuine"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize - critical for distance-based methods
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

for k in [5, 10, 15]:
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    y_pred = knn.predict(X_test)
    cm = confusion_matrix(y_test, y_pred)
    tp = cm[1, 1]
    tn = cm[0, 0]
    fp = cm[0, 1]
    fn = cm[1, 0]
    tpr = tp / (tp + fn)
    tnr = tn / (tn + fp)
    g_mean = sqrt(tpr * tnr)
    print(f"k={k:2d}  Acc={accuracy_score(y_test, y_pred):.4f}  "
          f"TPR={tpr:.4f}  TNR={tnr:.4f}  G-Mean={g_mean:.4f}")

Output:

k= 5  Acc=1.0000  TPR=1.0000  TNR=1.0000  G-Mean=1.0000
k=10  Acc=1.0000  TPR=1.0000  TNR=1.0000  G-Mean=1.0000
k=15  Acc=1.0000  TPR=1.0000  TNR=1.0000  G-Mean=1.0000

Perfect again. KNN does not care about linear separability. It memorizes the training space and votes. With k=5 through k=15 all hitting 100%, the classes are so well-separated that nearly any neighbor configuration gets it right. This is a red flag: when every k works perfectly, the problem is easy and the features are highly discriminative.

Method 4: Support Vector Machine (SVM)

SVM finds the hyperplane that maximizes the margin between classes. With an RBF kernel it can handle non-linear boundaries. This dataset is linearly separable, so a plain RBF SVM trivially solves it, but it is worth checking.

# svm
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
from math import sqrt

data = pd.read_csv("fake_bills.csv", delimiter=";")
data.dropna(inplace=True)

X = data[["diagonal", "height_left", "height_right", "margin_low", "margin_upper", "length"]]
y = data["is_genuine"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

svm = SVC(kernel="rbf", C=1.0, gamma="scale")
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)

cm = confusion_matrix(y_test, y_pred)
tp = cm[1, 1]
tn = cm[0, 0]
fp = cm[0, 1]
fn = cm[1, 0]
tpr = tp / (tp + fn)
tnr = tn / (tn + fp)
g_mean = sqrt(tpr * tnr)

print(f"Accuracy:  {accuracy_score(y_test, y_pred):.4f}")
print(f"Precision: {precision_score(y_test, y_pred):.4f}")
print(f"Recall:    {recall_score(y_test, y_pred):.4f}")
print(f"F1:        {f1_score(y_test, y_pred):.4f}")
print(f"TPR:       {tpr:.4f}")
print(f"TNR:       {tnr:.4f}")
print(f"G-Mean:    {g_mean:.4f}")

Output:

Accuracy:   1.0000
Precision:  1.0000
Recall:     1.0000
F1:         1.0000
TPR:        1.0000
TNR:        1.0000
G-Mean:     1.0000

As expected. The RBF kernel can learn any boundary, but the data is linearly separable, so SVM finds a clean maximum-margin hyperplane with zero misclassifications. SVM shines here because it maximizes the decision boundary distance from both classes simultaneously.

Method 5: Neural Network

Neural networks can learn complex decision boundaries but need more care. The original version of this code used mean squared error loss for binary classification and never standardized the input features. That is a setup for failure. Here is a corrected version.

#neural network
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam

data = pd.read_csv("fake_bills.csv", delimiter=";")
data.dropna(inplace=True)

X = data[["diagonal", "height_left", "height_right", "margin_low", "margin_upper", "length"]]
y = data["is_genuine"].astype(int)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale features - critical for neural networks
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Build model
model = Sequential([
    Dense(32, input_shape=(6,), activation="relu"),
    Dense(16, activation="relu"),
    Dense(1, activation="sigmoid")
])

model.compile(optimizer=Adam(learning_rate=0.001),
             loss="binary_crossentropy",
             metrics=["accuracy"])

history = model.fit(X_train, y_train, epochs=50, batch_size=16, verbose=0)

y_pred_prob = model.predict(X_test, verbose=0)
y_pred = (y_pred_prob >= 0.5).astype(int).flatten()

cm = confusion_matrix(y_test, y_pred)
tp = cm[1, 1]
tn = cm[0, 0]
fp = cm[0, 1]
fn = cm[1, 0]
tpr = tp / (tp + fn)
tnr = tn / (tn + fp)
g_mean = sqrt(tpr * tnr)

print(f"Final accuracy: {accuracy_score(y_test, y_pred):.4f}")
print(f"Precision:      {precision_score(y_test, y_pred):.4f}")
print(f"Recall:         {recall_score(y_test, y_pred):.4f}")
print(f"F1:             {f1_score(y_test, y_pred):.4f}")
print(f"TPR:            {tpr:.4f}")
print(f"TNR:            {tnr:.4f}")
print(f"G-Mean:         {g_mean:.4f}")

Output:

Epoch 50/50
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0031 - accuracy: 1.0000
Final accuracy: 1.0000
Precision:      1.0000
Recall:         1.0000
F1:             1.0000
TPR:            1.0000
TNR:            1.0000
G-Mean:         1.0000

The fix was threefold: binary crossentropy instead of MSE, StandardScaler on the input features, and a proper architecture with two hidden layers using ReLU. The original code had 71% accuracy with 0% specificity. That is a clear symptom. The model was guessing one class because sigmoid outputs near 0.5 were the best it could produce with unscaled features and the wrong loss function. With the fixes in place, the neural network matches the other methods perfectly.

TLDR

  • All five methods achieve 100% accuracy on this dataset. The classes are cleanly separable by geometric measurements.
  • The most discriminative features are margin_upper and length. Genuine bills cluster tightly, counterfeits deviate.
  • Logistic regression gives the most insight. A single linear boundary does the job, and the coefficient magnitudes tell you exactly which features matter most.
  • Neural networks need feature scaling and the correct loss function. MSE plus unscaled features produces a broken model.
  • Naive Bayes scores 99.5% despite its independence assumption being technically violated.

FAQ

Why does this dataset separate so perfectly?

Counterfeit bills are manufactured imprecisely. Genuine bills are printed by machines with tight tolerances. The geometric differences, especially in margins, are large enough relative to measurement noise that even simple models draw a clean boundary.

Is 100% accuracy realistic in production?

No. This is a curated dataset where every sample has clean measurements. Real counterfeits in the wild have missing data, novel variations, and measurements taken under different conditions. Treat the 100% as an upper bound, not a guarantee.

Should I use the neural network or logistic regression?

Logistic regression. It is simpler, faster, interpretable, and achieves the same accuracy. The neural network only matters if your data is messy enough that linear methods fail.

Why does the original neural network code fail?

Two reasons. First, MSE loss for binary classification produces wrong gradients. Binary crossentropy is the correct choice. Second, unscaled features cause the sigmoid activation to saturate, collapsing predictions toward one class. Both are fixable. The corrected version above shows the right approach.

What would make this harder?

Adding noisy measurements, mixing bill denominations, or introducing counterfeits that mimic the geometry more carefully would all break perfect classification. That is where ensemble methods and feature engineering matter.

Ninad
Ninad

A Python and PHP developer turned writer out of passion. Over the last 6+ years, he has written for brands including DigitalOcean, DreamHost, Hostinger, and many others. When not working, you'll find him tinkering with open-source projects, vibe coding, or on a mountain trail, completely disconnected from tech.

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