Credit Card Fraud Detection Project: Guide to Building a Machine Learning Model

A credit card fraud detection project involves building a system to identify fraudulent credit card transactions in real-time. Fraudulent activities are a major concern for financial institutions, often leading to financial losses. 

This credit card fraud detection project using machine learning aims to use algorithms to detect anomalies in transaction patterns and prevent fraud. Fraud detection matters because it helps prevent significant financial losses and ensures the security of online transactions, safeguarding both businesses and consumers.

In this guide, you'll learn to build a powerful fraud detection model with machine learning, enhancing security in financial systems.

Stay ahead in data science, and artificial intelligence with our latest AI news covering real-time breakthroughs and innovations.

How Does Credit Card Fraud Work? Key Steps and Insights

Understanding how credit card fraud works is crucial in building an effective credit card fraud detection project. Fraudsters exploit vulnerabilities in credit card systems to steal sensitive information and make unauthorized transactions. 

Here’s a breakdown of the key steps involved in credit card fraud:

• Information Theft
  The first step in most fraud cases is the theft of credit card details. Fraudsters can gain access to cardholder information through various means, such as phishing attacks, data breaches, or skimming devices placed on ATMs or point-of-sale terminals.
• Test Transactions
  Once the information is stolen, fraudsters often perform small test transactions to ensure the card is active and that the fraud will not be immediately detected. These transactions might seem insignificant but are crucial for verifying card details.
• Large Unauthorized Purchases
  After confirming the card details are valid, fraudsters proceed to make larger, unauthorized purchases. These transactions often target high-value goods or services, which can be easily resold for profit.
• Detection and Reporting
  The final step is the detection of the fraudulent activity. This is where systems like the credit card fraud detection project using machine learning come into play, identifying anomalies in transaction patterns and flagging suspicious activities for further investigation.

To build an effective fraud detection system, mastering machine learning is crucial. Learn how to leverage algorithms for detecting fraud with our Machine Learning Courses.

Now that you understand how fraud works, let’s explore the key steps involved in building an effective credit card fraud detection project.

What Are the Steps Involved in Building a Credit Card Fraud Detection Project?

Building a credit card fraud detection project using machine learning involves several steps, each crucial for creating an effective fraud detection system. 

We’ll focus on two primary methods of credit card fraud detection: supervised learning and unsupervised learning. 

Unsupervised Learning

Unsupervised learning algorithms work without labeled data, making them ideal for identifying anomalies in transaction data where fraud labels might not be available. These models detect outliers, or transactions that deviate significantly from normal patterns, which could indicate fraudulent activity.

Here are some common unsupervised learning algorithms:

• Isolation Forest:
  This algorithm isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values. It works well in detecting anomalies, especially when the data has a high-dimensional structure.
• One-Class SVM (Support Vector Machine):
  A popular technique for anomaly detection, One-Class SVM learns a boundary around the "normal" data points, allowing it to flag any point outside of this boundary as a potential outlier.
• Local Outlier Factor (LOF):
  LOF detects anomalies by measuring the local density deviation of a data point compared to its neighbors. It is effective in identifying points that are significantly different from the surrounding data.

Advantages of Unsupervised Learning for Fraud Detection:

• No need for labeled data: It can be particularly useful in scenarios where labeled data is scarce or unavailable.
• Identifying new fraud patterns: Since it doesn’t rely on predefined labels, it can detect novel or evolving fraud patterns.

Supervised Learning

On the other hand, supervised learning algorithms require labeled data for training, meaning that past transactions are marked as either fraudulent or legitimate. These algorithms learn the patterns from the labeled dataset and can then predict whether future transactions are fraudulent or not.

Common supervised learning algorithms for credit card fraud detection include:

• Random Forest:
  Random Forest algorithm is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy. It works well for large datasets and can handle both categorical and numerical data.
• XGBoost:
  Known for its speed and efficiency, XGBoost (Extreme Gradient Boosting) is a powerful machine learning algorithm that is often used for classification tasks, including fraud detection. It uses boosting to correct errors made by previous models and provides highly accurate predictions.
• K-Nearest Neighbors (KNN):
  KNN classifies transactions based on the majority label of their nearest neighbors. It’s a simple algorithm that can be very effective for fraud detection, especially when the data has clear clusters of normal and fraudulent transactions.
• Neural Networks:
  Deep learning models, such as neural networks, can learn complex patterns in data, making them ideal for credit card fraud detection where the relationships between features are nonlinear and intricate.

Advantages of Supervised Learning for Fraud Detection:

• Predictive accuracy: These algorithms generally provide more accurate results when enough labeled data is available.
• Easier integration: Supervised learning models are often easier to integrate into existing systems where labeled historical data exists.

Also Read: Difference Between Supervised and Unsupervised Learning

Now that we've covered the methods, let’s dive into the technical steps, starting wit importing the necessary packages for your credit card fraud detection project.

Import Packages

Before you can start building your credit card fraud detection project using machine learning, you need to import the necessary Python packages. 

Let’s start by importing the required libraries. 

# Importing basic libraries
import pandas as pd               # For data manipulation
import numpy as np                # For numerical computations
import matplotlib.pyplot as plt    # For visualization
import seaborn as sns            # For advanced plotting


# Machine Learning Libraries
from sklearn.model_selection import train_test_split   # For splitting data into train and test sets
from sklearn.preprocessing import StandardScaler        # For feature scaling
from sklearn.ensemble import RandomForestClassifier     # For implementing Random Forest algorithm
from sklearn.metrics import confusion_matrix, classification_report  

# For model evaluation
from sklearn.decomposition import PCA                    # For dimensionality reduction (if needed)


# Importing the dataset
df = pd.read_csv('creditcard.csv')   # Load your dataset (adjust the path as necessary)

Explanation:

• import pandas as pd: Pandas is used for data manipulation and handling. It’s excellent for working with data frames and processing data in table format (e.g., CSV files).
• import numpy as np: NumPy is essential for performing numerical operations on arrays, which is useful when working with large datasets or complex mathematical calculations.
• import matplotlib.pyplot as plt & import seaborn as sns: These libraries are used for data visualization. Matplotlib allows for basic plots like line charts and histograms, while Seaborn provides advanced plotting options, making it easier to visualize complex data.
• from sklearn.model_selection import train_test_split: This function helps you split your dataset into training and testing sets, an essential step in machine learning to evaluate your model’s performance.
• from sklearn.preprocessing import StandardScaler: StandardScaler helps scale the features (i.e., normalize them), ensuring that all the features contribute equally to the model.
• from sklearn.ensemble import RandomForestClassifier: This is the machine learning algorithm we will use to detect fraud. Random Forest is a powerful, ensemble-based algorithm that is particularly effective for classification tasks.
• from sklearn.metrics import confusion_matrix, classification_report: These are used for evaluating your model's performance by generating metrics such as accuracy, precision, recall, and the confusion matrix.
• from sklearn.decomposition import PCA: Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of the data, which can be helpful when working with very high-dimensional datasets.

Ensure PCA component selection is justified by checking the retained variance before applying it.

Let's move on to identifying and handling any errors in the dataset.

Look for Errors

When working on a credit card fraud detection project, it’s essential to clean the dataset and ensure that it’s free from errors before you proceed with building the model. This step involves checking for missing values, duplicate entries, and any inconsistencies in the data that may affect your model's accuracy. 

Let's start by loading and inspecting the dataset for potential issues. You can download the dataset here.

# Loading the dataset
import pandas as pd
# Load the dataset from a CSV file
df = pd.read_csv('creditcard.csv')

# Display the first few rows of the dataset
print(df.head())

# Check for missing values in the dataset
print("\nMissing values in each column:")
print(df.isnull().sum())

# Check for duplicate rows
print("\nDuplicate rows in the dataset:", df.duplicated().sum())
# Check the data types of the columns
print("\nData types of each column:")

print(df.dtypes)

Explanation:

1. df = pd.read_csv('creditcard.csv'): This loads the dataset into a Pandas DataFrame, making it easier to manipulate and analyze.
2. df.head(): This shows the first few rows of the dataset to give you an initial look at its structure and columns.
3. df.isnull().sum(): This checks for any missing values in each column. Missing values in your data could cause issues during training, so they need to be handled (either by filling them or removing them).
4. df.duplicated().sum(): This checks for any duplicate rows in the dataset. Duplicate records could distort the training process, leading to misleading results.
5. df.dtypes: This shows the data types of each column in the dataset. Ensuring the correct data type is crucial for the proper functioning of machine learning algorithms.

Expected Output: 

 Time     V1     V2   V3    ...   Amount  Class
0    0.0  -1.359807  1.191857  -0.028568 ...   149.62     0
1    0.0  -1.191857  1.191857   0.107264 ...    2.69      0
2    1.0  -1.359807  1.191857  -0.023146 ...   378.66     0
...

Missing values in each column:
Time       0
V1         0
V2         0
...
Amount     0
Class      0
dtype: int64

Duplicate rows in the dataset: 0

Data types of each column:
Time      float64
V1        float64
V2        float64
...
Amount    float64
Class       int64
dtype: object

Explanation of the Output:

• Missing values: The output shows that there are no missing values in any of the columns, which is a good sign for your data quality.
• Duplicate rows: In this case, the dataset has no duplicate rows, meaning the data is unique and does not require cleaning in that regard.
• Data types: The data types are all correct for numerical columns like Time, V1, V2, and others. However, the Class column, which contains labels indicating fraudulent or non-fraudulent transactions, is of type int64, which is correct for classification tasks.

Next, we’ll move on to visualizing the data to uncover any trends and patterns.

Visualization

Once you have cleaned the dataset and checked for errors, the next step in your credit card fraud detection project is visualizing the data. 

Visualizations can help you understand the distribution of transaction amounts, the balance between fraudulent and non-fraudulent transactions, and the correlations between features.  

Here, we'll use matplotlib and seaborn to create the visualizations. The visualizations we will create include:

1. A histogram for the distribution of transaction amounts.
2. A count plot to show the distribution of fraud and non-fraud labels.
3. A correlation heatmap to visualize relationships between the features.

Code Example: 

# Set the style for the plots
sns.set(style="whitegrid")
# Load the dataset
df = pd.read_csv('creditcard.csv')

# Visualizing the distribution of transaction amounts
plt.figure(figsize=(10,6))
sns.histplot(df['Amount'], bins=50, color='blue', kde=True)
plt.title('Distribution of Transaction Amounts')
plt.xlabel('Amount')
plt.ylabel('Frequency')
plt.show()

# Visualizing the class distribution (fraud vs. non-fraud)
plt.figure(figsize=(6,6))
sns.countplot(x='Class', data=df, palette='Set1')
plt.title('Class Distribution (0: Non-Fraud, 1: Fraud)')
plt.xlabel('Class (0: Non-Fraud, 1: Fraud)')
plt.ylabel('Count')
plt.show()

# Visualizing the correlation heatmap for the first few columns
plt.figure(figsize=(12,8))
sns.heatmap(df.iloc[:,1:11].corr(), annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Heatmap of First 10 Features')
plt.show()

Output:

Explanation of the Code:

1. Transaction Amount Distribution:
   • sns.histplot(df['Amount'], bins=50, color='blue', kde=True): This creates a histogram to show the distribution of transaction amounts in the Amount column. The bins=50 argument divides the data into 50 intervals, and kde=True adds a Kernel Density Estimate to smooth the distribution.
   • The plt.xlabel('Amount') and plt.ylabel('Frequency') set the labels for the x and y axes, respectively, to make the graph more readable.
2. Class Distribution (Fraud vs. Non-Fraud):
   • sns.countplot(x='Class', data=df, palette='Set1'): This count plot shows the distribution of the Class column, where 0 represents non-fraudulent transactions and 1 represents fraudulent transactions. The palette='Set1' argument sets a color scheme for the plot.
   • plt.xlabel('Class (0: Non-Fraud, 1: Fraud)') and plt.ylabel('Count') set the labels for the x and y axes, making it clear what the plot represents.
3. Correlation Heatmap:
   • sns.heatmap(df.iloc[:,1:11].corr(), annot=True, cmap='coolwarm', fmt='.2f'): This generates a heatmap to show the correlation between the first 10 features in the dataset (excluding Time and Amount). The annot=True argument annotates each cell with the correlation coefficient, while cmap='coolwarm' defines the color scheme.
   • plt.title('Correlation Heatmap of First 10 Features') adds a title to the heatmap for context.

Also Read: Bar Chart vs. Histogram: Which is Right for Your Data?

Now that we've visualized the data, let's move on to splitting the dataset for training and testing.

Splitting the Dataset

Before building a credit card fraud detection project using machine learning, you need to split your dataset into training and testing sets. This is an essential step because you want to train your model on one portion of the data and test its performance on unseen data to evaluate its generalization ability. The typical split is 70% for training and 30% for testing, though this can vary.

In this section, we’ll use scikit-learn’s train_test_split function to divide our dataset into these two parts.  

# Split the dataset into features (X) and target (y)
X = df.drop(columns=['Class'])  # Features: Drop the 'Class' column
y = df['Class']  # Target: 'Class' column
# Split the dataset into 70% training and 30% testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Checking the shapes of the resulting sets
print(f"Training data shape: {X_train.shape}")
print(f"Testing data shape: {X_test.shape}")

Explanation of the Code:

1. X = df.drop(columns=['Class']): This line separates the features from the target variable. The features are all columns except Class, which indicates whether a transaction is fraudulent or not.
2. y = df['Class']: This line assigns the Class column as the target variable, which we are trying to predict (fraud or non-fraud).
3. train_test_split(X, y, test_size=0.3, random_state=42): This function splits the data into training and testing sets. test_size=0.3 indicates that 30% of the data will be used for testing, while the remaining 70% will be used for training the model. The random_state=42 ensures the split is reproducible (i.e., it will be the same every time you run the code).
4. print(f"Training data shape: {X_train.shape}"): This checks the shape (number of rows and columns) of the training data to verify the split.

Expected Output: 

Training data shape: (199364, 30)
Testing data shape: (85495, 30)

Explanation of the Output:

• The training set contains 199,364 rows and 30 features, while the testing set contains 85,495 rows and 30 features.
• The 30 features represent the different attributes of the transactions, such as time, V1, V2, etc., while the target variable (Class) is separated out.

Let's move on to calculating the mean and covariance matrix to understand the relationships between the features better.

Calculate Mean and Covariance Matrix

Before training a machine learning model, it's important to understand the underlying structure of the dataset. The mean helps in understanding the central tendency of the data, while the covariance matrix reveals how the features are related to each other.

The covariance matrix is especially important in detecting fraud, as it helps identify which features vary together, which might indicate fraud patterns.

# Calculate the mean of each feature
mean_values = X_train.mean()
print("Mean of each feature:\n", mean_values)
# Calculate the covariance matrix of the features
cov_matrix = X_train.cov()
print("\nCovariance Matrix:\n", cov_matrix)

Explanation of the Code:

1. mean_values = X_train.mean(): This line calculates the mean of each feature in the training dataset. The mean helps you understand the average value for each feature, which is useful for detecting anomalies.
2. cov_matrix = X_train.cov(): This calculates the covariance matrix for the features in the training dataset. Covariance measures how two variables change together. A high covariance indicates that the variables are highly correlated, which could be an indicator of related features in fraud detection.

Expected Output: 

Mean of each feature:
 Time      2.456345
 V1        0.008345
 V2       -0.006529
 ...
 Amount    88.158639
 dtype: float64

Covariance Matrix:
             Time          V1          V2       ...
 Time      0.000000  -0.000123   0.000037    ...
 V1       -0.000123   0.005231  -0.004928    ...
 V2        0.000037  -0.004928   0.004755    ...
 ...

Explanation of the Output:

• Mean of each feature: The output shows the average values for each feature in the training data. For instance, the average value of Time is around 2.46, and Amount is 88.16.
• Covariance Matrix: The covariance matrix shows the relationships between pairs of features. For example, the covariance between V1 and V2 indicates how these two features change together. Positive covariance values suggest they move in the same direction, while negative values indicate opposite directions.

Now that we've prepared the data, let's add the final touches before moving on to building the model.

Add the Final Touches

Before we can build the credit card fraud detection project using machine learning, it's essential to apply some final preprocessing steps to ensure the data is in the best shape possible.  

In this section, we’ll focus on:

• Scaling the features so they are on the same scale, which is crucial for many machine learning algorithms.
• Ensuring the dataset is in the correct format to be fed into the model. 

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)  # Fit and transform the training data
X_test_scaled = scaler.transform(X_test)        # Transform the test data using the same scaler


# Check the first few rows of the scaled data
print("Scaled Training Data:\n", X_train_scaled[:5])

# Ensure the target variable 'Class' is in the correct format (binary)
y_train = y_train.astype('int')
y_test = y_test.astype('int')

Explanation of the Code:

1. StandardScaler(): This is used to scale the features. Many machine learning models, especially those based on distance (like KNN), work better when the features are on the same scale. StandardScaler standardizes the features by removing the mean and scaling to unit variance.
2. scaler.fit_transform(X_train): This line scales the training data. The fit_transform method first calculates the mean and standard deviation for each feature and then scales the data accordingly.
3. scaler.transform(X_test): We use transform here on the test data to ensure it is scaled based on the training data's parameters (mean and standard deviation). This avoids data leakage.
4. y_train.astype('int'): This ensures that the target variable (y_train and y_test) is in the correct integer format, as many machine learning algorithms expect the target to be numeric.

Expected Output: 

Scaled Training Data:
 [[ 0.18359516 -0.12304172  0.25795785 ...]
 [ 0.62347355  0.45922689 -0.74609777 ...]
 [-1.45573817 -0.78963902  1.25933658 ...]
 ...

Explanation of the Output:

• The scaled training data should now have zero mean and unit variance for each feature. The X_train_scaled array contains the scaled values of the training dataset.
• The target variable y_train and y_test are now ready for classification tasks.

Also Read: 16 Best Neural Network Project Ideas & Topics for Beginners [2025]

Now that the data is prepared, let’s dive into the machine learning techniques that will power your credit card fraud detection project.

Machine Learning Techniques for Detecting Credit Card Fraud

When building a credit card fraud detection project using machine learning, selecting the right algorithms is crucial. 

Below, you’ll look at several popular algorithms and their benefits, helping you choose the best approach for your project.

This section also covers how to evaluate the results of your credit card fraud detection model to ensure it’s working effectively.

1. Decision Trees

Decision trees are a simple yet powerful algorithm for classification tasks. They work by splitting the data into branches based on feature values, making decisions based on questions that lead to either fraud or non-fraud classification.

Benefits:

• Interpretable: Easy to understand and interpret.
• Efficient: Works well for both categorical and continuous data.
• Handles missing values: Can handle datasets with missing or incomplete data.

2. Random Forest

Random Forest is an ensemble method that combines multiple decision trees to improve the model’s accuracy and stability. Each tree is trained on a random subset of the data, and the final prediction is made by averaging the results from all the trees.

Benefits:

• Improved Accuracy: By averaging multiple decision trees, it reduces overfitting and improves predictive accuracy.
• Handles imbalanced datasets: Works well with datasets where fraudulent transactions are rare. Address class imbalance in Random Forest by using class weighting to improve fraud detection.
• Less prone to overfitting: Due to ensemble learning, it is less likely to overfit the data.

3. Anomaly Detection

Anomaly detection algorithms focus on identifying unusual patterns in the data. For fraud detection, anomaly detection models aim to find transactions that deviate significantly from typical patterns, which are likely to be fraudulent.

Benefits:

• Works well with imbalanced data: Because anomalies (fraudulent transactions) are much less frequent, anomaly detection works well with this type of dataset.
• No need for labeled data: Can detect fraud even without prior knowledge of what constitutes fraudulent behavior.

Also Read: Advanced Techniques in Anomaly Detection: Applications and Tools

4. Neural Networks

Neural networks, especially deep learning models, are capable of capturing complex patterns in data by mimicking the human brain's structure. These models can learn non-linear relationships between features and are suitable for large, complex datasets.

Benefits:

• High Predictive Power: Ideal for detecting intricate patterns in the data.
• Adaptable: Can be applied to large-scale datasets and continuously improve with more data.
• Powerful in detecting complex fraud: Works well when there are subtle or complex patterns in the data that simpler models might miss.

Also Read: Understanding 8 Types of Neural Networks in AI & Application

5. Ensemble Methods

Ensemble methods combine multiple models to impro0ve prediction accuracy. By aggregating the predictions from different models, these methods often produce better results than individual algorithms.

Benefits:

• Higher Accuracy: Combines the strengths of different algorithms to improve performance.
• Versatile: Works with a variety of base models (e.g., decision trees, random forests, etc.).
• Reduces Overfitting: By using multiple models, ensemble methods can prevent overfitting and produce more robust predictions.

Once you’ve trained your model, it's important to evaluate its performance to ensure it's accurate and reliable. 

Evaluating the Results of a Credit Card Fraud Detection Model

Common metrics for evaluating fraud detection models include:

• Accuracy: The proportion of correct predictions. However, for imbalanced datasets (fraud detection), accuracy may not be the best metric.
• Precision: The proportion of positive predictions that are actually correct. This is especially important when you want to minimize false positives (non-fraudulent transactions incorrectly marked as fraudulent).
• Recall: The proportion of actual positives that were correctly identified. High recall ensures that most fraudulent transactions are caught.
• F1-Score: The harmonic mean of precision and recall, providing a balance between the two metrics.
• Confusion Matrix: A table that helps visualize the performance of your model, showing the true positives, false positives, true negatives, and false negatives.

Code Example (Model Evaluation): 

from sklearn.metrics import classification_report, confusion_matrix

# Assuming 'y_test' are the true labels and 'y_pred' are the predicted labels from the model
y_pred = model.predict(X_test)  # Replace 'model' with your trained model
# Print classification report for precision, recall, and F1-score
print(classification_report(y_test, y_pred))
# Display confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:\n", conf_matrix)

Expected Output: 

precision    recall  f1-score   support
     0       0.99      0.98      0.98     85299
     1       0.13      0.27      0.17       196

    accuracy                           0.97     85495
   macro avg       0.56      0.63      0.58     85495
weighted avg       0.97      0.97      0.97     85495

Confusion Matrix:
 [[85210    89]
  [ 143   53]]

Explanation:

• Classification Report: The precision, recall, and f1-score for each class (fraud and non-fraud) are displayed. Notice that recall is low for class 1 (fraud), which indicates that not all fraudulent transactions are being detected.
• Confusion Matrix: The matrix provides a breakdown of true positives, false positives, true negatives, and false negatives. In this case, the model has correctly identified 53 fraudulent transactions but has also missed many (false negatives).

Also Read: Convolutional Neural Networks: Ultimate Guide for Beginners in 2024

Next, let’s explore how to visualize and preprocess the data to prepare it for building an effective fraud detection model.

How to Visualize and Preprocess Data for a Fraud Detection Project?

Data preprocessing and visualization are essential steps in preparing your dataset for building a credit card fraud detection project using machine learning. Proper data cleaning ensures that your model learns from clean, structured data, while visualization helps to identify patterns and anomalies that are important for fraud detection. 

1. Heatmaps

A heatmap provides a graphical representation of the correlation between different features in the dataset. By visualizing correlations, we can identify which features are related to each other, which is particularly useful in detecting fraudulent transactions. 

# Calculate the correlation matrix
correlation_matrix = df.corr()
# Plot the heatmap
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Heatmap of Features')
plt.show()

Expected Output:

The heatmap will show correlations between the features, where strong correlations may reveal relationships between features that could be important for detecting fraud. For example, a high correlation between V1 and V2 suggests that these features are closely related, which could inform feature selection or engineering.

2. Handling Missing Values

Missing values are a common issue in real-world datasets, and it’s crucial to handle them before feeding the data into a machine learning model. Common approaches to handle missing values include filling them with the mean or removing rows with missing values.

Code Example: 

# Checking for missing values
missing_values = df.isnull().sum()
# Filling missing values with the median of each column
df = df.fillna(df.median())

Explanation:

• df.isnull().sum() checks for missing values in the dataset.
• df.fillna(df.median()) fills any missing values with the median of the respective columns. Using the median prevents bias that could be introduced by using the mean in case of outliers.

3. Distribution Analysis

Visualizing the distribution of transaction amounts helps us understand the scale and spread of the data. This is important because most machine learning algorithms work better when the data is distributed normally or uniformly.

Code Example: 

# Plotting the distribution of transaction amounts
plt.figure(figsize=(10, 6))
sns.histplot(df['Amount'], bins=50, color='blue', kde=True)
plt.title('Distribution of Transaction Amounts')
plt.xlabel('Transaction Amount')
plt.ylabel('Frequency')
plt.show()

Expected Output:

This histogram will display the distribution of the Amount column. You will likely see that the majority of transactions are small, but there will be a few large transactions. Understanding this distribution helps you handle outliers and decide if any transformation or feature engineering is needed.

4. Scaling and Normalization

Feature scaling is essential for many machine learning algorithms that rely on distance-based measures, like KNN or SVM. Scaling ensures that all features contribute equally to the model and prevents certain features from dominating due to differences in their units.

Code Example: 

from sklearn.preprocessing import StandardScaler
# Initialize the StandardScaler
scaler = StandardScaler()
# Scale the features (excluding the target column)
X_scaled = scaler.fit_transform(df.drop(columns=['Class']))
# Convert scaled features back to DataFrame
df_scaled = pd.DataFrame(X_scaled, columns=df.columns[:-1])

Explanation:

• StandardScaler() standardizes the data by removing the mean and scaling it to unit variance.
• fit_transform() applies scaling to the feature columns. This step is crucial for ensuring that the model doesn’t give more importance to larger values in any one feature.
• df.drop(columns=['Class']) removes the target variable (Class) from the feature set before scaling.

The more you practice visualizing, preprocessing, and analyzing data using machine learning, the more confident you'll become in building and optimizing your models.

How upGrad Can Help You Master Machine Learning?

To truly excel in credit card fraud detection projects using machine learning, mastering key programming skills and techniques is essential. 

upGrad offers specialized courses that strengthen your programming foundation in languages like Python, as well as core topics in data science and machine learning, all of which are vital to successfully building and optimizing fraud detection models.

Here's a selection of courses to help you level up:

• Learn Basic Python Programming  
• Post Graduate Certificate in Data Science & AI (Executive) 
• Post Graduate Certificate in Machine Learning and Deep Learning (Executive) 
• Fundamentals of Deep Learning and Neural Networks  

You can also get personalized career counseling with upGrad to guide your career path, or visit your nearest upGrad center and start hands-on training today! 

Expand your expertise with the best resources available. Browse the programs below to find your ideal fit in Best Machine Learning and AI Courses Online.

Best Machine Learning and AI Courses Online

Discover in-demand Machine Learning skills to expand your expertise. Explore the programs below to find the perfect fit for your goals.

In-demand Machine Learning Skills

Discover popular AI and ML blogs and free courses to deepen your expertise. Explore the programs below to find your perfect fit.

Popular AI and ML Blogs & Free Courses

Reference Link:
https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud/code